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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Metis: governed tacit memory for AI agents</title>
<style>
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--tint:#F2F5FA; --soft:#F5F5F5; --line:#e4e7ee; --white:#fff;
--green:#2E7D32;
--grad:linear-gradient(120deg,#EA4700 0%,#FF9900 100%);
--mono:"Cascadia Mono",ui-monospace,SFMono-Regular,Menlo,Consolas,monospace;
--sans:"Halyard Display","Helvetica Neue",Arial,Helvetica,sans-serif;
}
*{box-sizing:border-box}
html{scroll-behavior:smooth}
body{margin:0;font-family:var(--sans);color:var(--ink);background:var(--white);line-height:1.6}
a{color:var(--ember);text-decoration:none}
a:hover{text-decoration:underline}
.wrap{max-width:1060px;margin:0 auto;padding:0 22px}
/* nav */
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border-bottom:1px solid var(--line)}
nav .wrap{display:flex;align-items:center;gap:18px;height:62px}
nav img{height:30px}
nav .links{margin-left:auto;display:flex;gap:18px;flex-wrap:wrap}
nav .links a{color:var(--ink);font-size:14px;font-weight:600}
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.btn:hover{text-decoration:none;transform:translateY(-1px)}
section{padding:56px 0;border-bottom:1px solid var(--line)}
section.tintbg{background:var(--tint)}
h2{font-size:30px;margin:0 0 8px;letter-spacing:-.5px}
h2 .em{color:var(--ember)}
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h3{font-size:18px;margin:0 0 6px}
.grid{display:grid;gap:18px}
.g3{grid-template-columns:repeat(3,1fr)}
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@media(max-width:820px){.g3,.g2{grid-template-columns:1fr}.hero h1{font-size:38px}}
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.card p{margin:6px 0 0;color:var(--muted);font-size:14.5px}
figure{margin:18px 0;text-align:center}
figure img,.diagram{max-width:100%;width:100%;height:auto;border:1px solid var(--line);border-radius:12px;background:#fff}
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border-left:4px solid var(--ember);font-family:var(--mono);font-size:13.5px;line-height:1.55}
pre .c{color:#9aa0aa}
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th{background:var(--amber);color:#fff;text-align:left;padding:11px 14px;font-size:13px}
td{padding:11px 14px;border-top:1px solid var(--line);vertical-align:top}
tr:nth-child(even) td{background:var(--soft)}
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.ex{border:1px solid var(--line);border-radius:12px;overflow:hidden;background:#fff}
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.ex .head h3{margin:0 0 8px}
.ex .body{padding:18px 20px}
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ol.timeline{list-style:none;counter-reset:s;padding:0;margin:8px 0 0;
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@media(max-width:820px){ol.timeline{grid-template-columns:1fr}}
ol.timeline li{counter-increment:s;position:relative;padding:10px 12px 10px 46px;background:#fff;
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display:flex;align-items:center;justify-content:center}
footer{padding:40px 0;color:var(--muted);font-size:14px}
footer .wrap{display:flex;gap:18px;flex-wrap:wrap;align-items:center}
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</style>
</head>
<body>
<nav><div class="wrap">
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alt="Brightbeam">
<div class="links">
<a href="#what">What it is</a>
<a href="#loop">Capture loop</a>
<a href="#layers">Authority</a>
<a href="#gate">Gate</a>
<a href="#taxonomy">Taxonomy</a>
<a href="#chap">CHAP</a>
<a href="#examples">Examples</a>
<a href="demo.html">Interactive demo</a>
</div>
</div></nav>
<header class="hero"><div class="wrap">
<span class="pill">The fourth stratum, made practical</span>
<h1>Metis</h1>
<p class="lead">Capture how expert work actually gets done, govern it, and serve it to AI agents
as memory they are allowed to use. Local-first, fully auditable, built on CHAP.</p>
<div class="btns">
<a class="btn primary" href="demo.html">Open the interactive demo</a>
<a class="btn ghost" href="#start">Quickstart</a>
<a class="btn ghost" href="https://github.com/BrightbeamAI/metis">GitHub</a>
</div>
</div></header>
<section id="what"><div class="wrap">
<h2>What <span class="em">Metis</span> does</h2>
<p class="sub">A technician hears a machine sounds wrong before any warning light. An analyst senses
a sample looks off before a test confirms it. That know-how never reaches an SOP. Metis captures
it as a governed, inspectable fragment, and only lets an agent use it under the conditions where it holds.</p>
<div class="grid g3">
<div class="card"><div class="ic">1</div><h3>Capture</h3>
<p>Observe a work event, ask one bounded question, and confirm with the worker. The result is a
partial fragment tied to its provenance, conditions, and consent. Never treated as fact.</p></div>
<div class="card"><div class="ic">2</div><h3>Govern</h3>
<p>A Mission Group reviews each fragment and decides what role it may play: evidence only,
advisory cue, or controlled instruction. Every decision is human and recorded.</p></div>
<div class="card"><div class="ic">3</div><h3>Serve to agents</h3>
<p>Validated fragments become tacit memory an agent retrieves through a condition-aware gate,
alongside procedural, semantic, and episodic memory, always with use constraints attached.</p></div>
</div>
</div></section>
<section class="tintbg" id="gap"><div class="wrap">
<h2>The gap we capture</h2>
<p class="sub">The most useful tacit knowledge lives in the gap between work-as-imagined (the written
procedure) and work-as-done (what experienced people actually do). That gap is a signal worth a
question, not proof of a better rule.</p>
<div class="grid g2">
<div class="card"><h3 style="color:var(--muted)">Work-as-imagined</h3>
<p style="font-size:15px">"Reduce load only when the alarm threshold is crossed." (SOP-17)</p></div>
<div class="card" style="border-color:var(--ember)"><h3 style="color:var(--ember)">Work-as-done</h3>
<p style="font-size:15px">"Reduce throughput earlier when high load coincides with low-frequency
vibration and a dull acoustic cue." (an experienced operator)</p></div>
</div>
</div></section>
<section id="loop"><div class="wrap">
<h2>The capture loop</h2>
<p class="sub">Observe, Infer, Whisper, Confirm, Remember. Inference produces a hypothesis, never
trusted knowledge. The whisper is short and never asks a worker to justify their performance. Each
step is a recorded CHAP event.</p>
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<text x="28" y="38" font-size="20" font-weight="bold" fill="#282829">The capture loop</text>
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</figure>
</div></section>
<section class="tintbg" id="layers"><div class="wrap">
<h2>Three authority layers</h2>
<p class="sub">Promotion is about what a fragment is allowed to do, decided by people, not by a model.</p>
<div class="layers">
<div class="layer ev"><span class="tag">EVIDENCE</span>
<div><b>Learning and review only.</b><p>Raw observations, confirmations, early hypotheses, and
rejected fragments. Never used for an operational decision and never visible to an agent.</p></div></div>
<div class="layer ad"><span class="tag">ADVISORY</span>
<div><b>Conditional decision support.</b><p>Tier-2 validated. Retrievable only when conditions
match, presented as situated guidance with provenance and use constraints, never a universal rule.</p></div></div>
<div class="layer co"><span class="tag">CONTROLLED</span>
<div><b>Formally incorporated.</b><p>Requires change-control metadata and exact condition matching.
Becomes controlled instruction only when policy allows, with full audit lineage preserved.</p></div></div>
</div>
</div></section>
<section id="memory"><div class="wrap">
<h2>Four memory stores, one agent context</h2>
<p class="sub">Procedural says what is prescribed. Semantic gives facts. Episodic gives past cases.
Tacit gives validated situated guidance, and it is the only store reached through a gate.</p>
<figure><svg class="diagram" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 920 330" font-family="Arial, Helvetica, sans-serif" role="img" aria-label="Memory broker and the four memory stores">
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<text x="28" y="38" font-size="20" font-weight="bold" fill="#282829">Four memory stores, one agent context</text>
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<text x="44" y="123" font-size="11" fill="#5A5A5A">SOPs, checklists, policies</text>
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<text x="405" y="160" font-size="14" font-weight="bold" fill="#FFFFFF" text-anchor="middle">Memory</text>
<text x="405" y="180" font-size="14" font-weight="bold" fill="#FFFFFF" text-anchor="middle">Broker</text>
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<text x="660" y="169" font-size="14" font-weight="bold" fill="#282829" text-anchor="middle">Context</text>
<text x="660" y="194" font-size="11" fill="#5A5A5A" text-anchor="middle">procedural + semantic</text>
<text x="660" y="210" font-size="11" fill="#5A5A5A" text-anchor="middle">+ episodic + tacit</text>
<text x="660" y="226" font-size="11" fill="#5A5A5A" text-anchor="middle">+ use constraints</text>
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<text x="845" y="176" font-size="13" font-weight="bold" fill="#282829" text-anchor="middle">AI</text>
<text x="845" y="192" font-size="13" font-weight="bold" fill="#282829" text-anchor="middle">agent</text>
</svg>
</figure>
</div></section>
<section class="tintbg" id="gate"><div class="wrap">
<h2>The retrieval gate is a governance check</h2>
<p class="sub">This is not semantic similarity. The gate asks whether a fragment is allowed to be used
here, and it fails closed when a required condition is unknown. Every attempt is recorded.</p>
<figure><svg class="diagram" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 920 360" font-family="Arial, Helvetica, sans-serif" role="img" aria-label="Condition-aware retrieval gate">
<defs>
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<path d="M0 0 L10 5 L0 10 z" fill="#5A5A5A"/>
</marker>
</defs>
<rect width="920" height="360" fill="#F2F5FA"/>
<text x="28" y="38" font-size="20" font-weight="bold" fill="#282829">The retrieval gate is a governance check, not similarity search</text>
<text x="28" y="60" font-size="13" fill="#5A5A5A">A fragment is eligible only when every check passes. The first failing check is the recorded reason.</text>
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<text x="44" y="140" font-size="11" fill="#5A5A5A">site, area, line</text>
<text x="44" y="158" font-size="11" fill="#5A5A5A">equipment family / id</text>
<text x="44" y="176" font-size="11" fill="#5A5A5A">operating mode, shift</text>
<text x="44" y="194" font-size="11" fill="#5A5A5A">role, risk class</text>
<text x="44" y="212" font-size="11" fill="#5A5A5A">trigger context</text>
<text x="44" y="242" font-size="11" fill="#5A5A5A" font-style="italic">fails closed if a</text>
<text x="44" y="258" font-size="11" fill="#5A5A5A" font-style="italic">required value is missing</text>
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<text x="415" y="116" font-size="14" font-weight="bold" fill="#EA4700" text-anchor="middle">Gate checks (in order)</text>
<g font-size="12" fill="#282829">
<text x="270" y="142">1. revocation status active</text>
<text x="270" y="162">2. consent permits use</text>
<text x="270" y="182">3. endogenous reviewed</text>
<text x="270" y="202">4. authority is advisory / controlled</text>
<text x="270" y="222">5. Tier-2 validated</text>
<text x="270" y="242">6. review date not expired</text>
<text x="270" y="262">7. role authorised, risk not high</text>
<text x="270" y="282">8. conditions match, no exclusions</text>
<text x="270" y="302">9. controlled needs exact match</text>
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<text x="762" y="178" font-size="11" fill="#5A5A5A" text-anchor="middle">+ retrieval decision artefact</text>
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<text x="762" y="236" font-size="14" font-weight="bold" fill="#EA4700" text-anchor="middle">Blocked, with reason</text>
<text x="762" y="258" font-size="11" fill="#5A5A5A" text-anchor="middle">evidence_layer_not_authorised</text>
<text x="762" y="276" font-size="11" fill="#5A5A5A" text-anchor="middle">conditions_do_not_match</text>
<text x="762" y="294" font-size="11" fill="#5A5A5A" text-anchor="middle">consent_withdrawn, revoked ...</text>
<text x="762" y="312" font-size="11" fill="#5A5A5A" text-anchor="middle">recorded in the audit chain</text>
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</svg>
</figure>
</div></section>
<section id="chap"><div class="wrap">
<h2>Runs on <span class="em">CHAP</span></h2>
<p class="sub">Metis does not invent a protocol. It runs on CHAP, the Collaborative Human-Agent
Protocol, so capture, review, retrieval, and revocation are structured collaboration events on a
append-only, hash-linked evidence chain. Metis runs on the official chap-coordinator Python
reference implementation; the adapter drives a real Coordinator rather than re-implementing the protocol. Repo:
<a href="https://github.com/BrightbeamAI/chap">github.com/BrightbeamAI/chap</a>.</p>
<table>
<tr><th>Metis concept</th><th>CHAP concept</th></tr>
<tr><td>Capture Cell</td><td>Workspace with human, agent, service, and group participants</td></tr>
<tr><td>Operator / Whisperer / Mission Group</td><td>Human / agent / group participant</td></tr>
<tr><td>Tacit fragment, memory object, agent context</td><td>Artefacts of kind <code class="inline">tacit.*</code> with a schema reference</td></tr>
<tr><td>Whisper, operator confirmation</td><td><code class="inline">whisper.ask</code> / <code class="inline">whisper.answer</code></td></tr>
<tr><td>Mission Group review</td><td><code class="inline">review.request</code> and <code class="inline">decide.*</code></td></tr>
<tr><td>Retrieval decision</td><td>Routing decision artefact</td></tr>
<tr><td>Revocation, supersession</td><td><code class="inline">control.*</code> events plus records</td></tr>
<tr><td>Audit trail</td><td>Hash-linked (JCS) evidence chain; Ed25519 signing via the security-signed/1.0 profile</td></tr>
</table>
</div></section>
<section id="taxonomy"><div class="wrap">
<h2>A working <span class="em">taxonomy</span> of tacit knowledge</h2>
<p class="sub">Tacit knowledge is not one thing. Metis uses 17 categories across 6 domains, from
the paper. The category sets how a fragment is captured and what evidence it needs before it can
influence work. It is never a claim that a fragment is true.</p>
<div class="grid g3"><div class="card"><h3>Procedural and embodied</h3><div style="margin-top:8px"><span class="chip k">K1 Procedural</span><span class="chip k">K2 Embodied</span><span class="chip k">K3 Rhythmic</span></div></div><div class="card"><h3>Material and equipment</h3><div style="margin-top:8px"><span class="chip k">K4 Equipment-specific</span><span class="chip k">K5 Material</span><span class="chip k">K6 Tool-extended</span></div></div><div class="card"><h3>Perceptual and aesthetic</h3><div style="margin-top:8px"><span class="chip k">K7 Sensory</span><span class="chip k">K8 Aesthetic</span></div></div><div class="card"><h3>Inferential</h3><div style="margin-top:8px"><span class="chip k">K9 Heuristic</span><span class="chip k">K10 Diagnostic</span><span class="chip k">K11 Anticipatory</span></div></div><div class="card"><h3>Meta-cognitive and affective</h3><div style="margin-top:8px"><span class="chip k">K12 Meta-cognitive</span><span class="chip k">K13 Affective-regulatory</span></div></div><div class="card"><h3>Social and normative</h3><div style="margin-top:8px"><span class="chip k">K14 Collaborative</span><span class="chip k">K15 Cultural-narrative</span><span class="chip k">K16 Judgemental-ethical</span><span class="chip k">K17 Strategic</span></div></div></div>
</div></section>
<section class="tintbg" id="notwhat"><div class="wrap">
<h2>What Metis is <span class="em">not</span></h2>
<div class="grid g2">
<div class="card"><h3>Not a new protocol</h3><p>It runs on CHAP and adds no parallel protocol.</p></div>
<div class="card"><h3>Not worker surveillance</h3><p>It records no audio, video, biometrics, screenshots, or keystrokes. Capture is consented and worker-visible.</p></div>
<div class="card"><h3>Not a vector database</h3><p>Retrieval is a condition-aware governance check, not semantic similarity.</p></div>
<div class="card"><h3>Not autonomous</h3><p>Humans make every governance decision. A local model only ever drafts, and fragments are never treated as fact.</p></div>
</div>
</div></section>
<section class="tintbg" id="examples"><div class="wrap">
<h2>Examples and demo walkthroughs</h2>
<p class="sub">Three runnable synthetic examples. Each runs locally with deterministic fixtures and
produces a replayable evidence chain. Run any of them with <code class="inline">metis demo <scenario></code>, or step through them in the <a href="demo.html">interactive demo</a>.</p>
<div class="grid" style="grid-template-columns:1fr;gap:16px">
<div class="ex">
<div class="head">
<h3>1. Manufacturing pump vibration</h3>
<span class="chip k">K7 sensory</span><span class="chip k">K10 diagnostic</span><span class="chip k">K4 equipment</span>
<p style="color:var(--muted);font-size:14px;margin:8px 0 0">An operator reduces throughput early
on a dull acoustic cue and low-frequency vibration, before the alarm. It becomes an advisory cue,
retrievable only for that pump family and operating mode.</p>
</div>
<div class="body">
<pre><span class="c"># full local walkthrough, then probe the gate</span>
metis demo manufacturing-pump-vibration
metis retrieve --context examples/manufacturing_pump_vibration/context_matching.json
metis retrieve --context examples/manufacturing_pump_vibration/context_non_matching.json</pre>
<div class="out">
<span class="chip ok">matching context: 1 eligible</span>
<span class="chip no">gear pump, low load: blocked, conditions_do_not_match</span>
</div>
<ol class="timeline">
<li>Workspace created</li><li>Participants added</li>
<li>Procedural, semantic, episodic memory loaded</li><li>Local model status checked</li>
<li>Observation loaded (the gap)</li><li>Candidate inferred (hypothesis only)</li>
<li>Whisper asked, operator confirmed (Tier-1)</li><li>Evidence-layer fragment stored</li>
<li>Mission Group Tier-2 review</li><li>Promoted to Advisory</li>
<li>Tacit memory object created</li><li>Retrieval allowed under matching context</li>
<li>Retrieval blocked under non-matching context</li><li>Agent memory context built</li>
<li>Evidence chain exported and verified</li>
</ol>
</div>
</div>
<div class="ex">
<div class="head">
<h3>2. Batch quality visual inspection</h3>
<span class="chip k">K8 aesthetic</span><span class="chip k">K7 sensory</span>
<p style="color:var(--muted);font-size:14px;margin:8px 0 0">A specialist flags a batch as "looks
off" before the lab confirms drift. Weak evidence keeps it advisory, with a constraint to confirm
against annotated exemplars. It never becomes a universal rule.</p>
</div>
<div class="body">
<pre>metis demo batch-quality-visual-inspection
metis memory query --context examples/batch_quality_visual_inspection/context_matching.json</pre>
<div class="out">
<span class="chip ok">advisory cue, requires exemplar confirmation</span>
<span class="chip no">not converted to a rule</span>
</div>
</div>
</div>
<div class="ex">
<div class="head">
<h3>3. Shift handover gap</h3>
<span class="chip k">K14 collaborative</span><span class="chip k">K12 meta-cognitive</span>
<p style="color:var(--muted);font-size:14px;margin:8px 0 0">A team lead senses a handover is
incomplete even though the form is filled. It becomes a checklist prompt that asks a human to
confirm open threads verbally, and never auto-closes the handover.</p>
</div>
<div class="body">
<pre>metis demo shift-handover-gap
metis memory query --context examples/shift_handover_gap/context_matching.json</pre>
<div class="out">
<span class="chip ok">checklist prompt for a human</span>
<span class="chip no">no automation</span>
</div>
</div>
</div>
</div>
</div></section>
<section id="start"><div class="wrap">
<h2>Quickstart</h2>
<p class="sub">No cloud, no GPU. The demo and tests run without any model using deterministic fixtures.</p>
<div class="grid g2">
<div>
<h3>Install and run</h3>
<pre>git clone https://github.com/BrightbeamAI/metis
cd metis
pip install -e .
metis demo manufacturing-pump-vibration</pre>
<h3>Optional local model</h3>
<pre><span class="c"># install Ollama from https://ollama.com</span>
ollama pull gemma4
metis config set model.name gemma4
metis model check</pre>
</div>
<div>
<h3>Use it from Python</h3>
<pre>from metis import MetisEngine
from metis.conditions.context import TacitContext
from metis.consent.model import ConsentRecord, ConsentStatus
eng = MetisEngine()
eng.join_default_participants()
res = eng.capture_observation(
{"observation_id":"OBS-1",
"work_as_done":"Reduce throughput early on a dull acoustic cue.",
"context": TacitContext(equipment_family="centrifugal_pump",
operating_mode="high_load")},
consent=ConsentRecord(consent_status=ConsentStatus.granted),
category="K7_sensory")
eng.tier2_review(res.fragment.fragment_id, "promoted_to_advisory",
summary="advisory cue only")
ctx = TacitContext(equipment_family="centrifugal_pump",
operating_mode="high_load", risk_class="moderate")
for e in eng.agent_context("tsk_42", ctx).tacit_memory:
print(e.content, e.use_constraints)</pre>
</div>
</div>
<div class="note" style="margin-top:18px"><b>Ethical use.</b> Metis captures fragments of human
work. It records no audio, video, biometrics, screenshots, or keystrokes. Fragments are never treated
as fact, evidence-layer fragments never reach an agent, and the audit chain is append-only. Production
use needs worker consultation, legal review, and domain validation.</div>
</div></section>
<footer><div class="wrap">
<img 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xo7MAX3R4TvLfPmzduTvVJ7Dhgw4EFoKP+YGkRYwL+2eNBJ7UW0CvVQfsXTOrs6Zao63LhWdCOdhLhorTk/dYqh8nThVpOXQCGDfJ2QU+Fq6eVuQm3WEQaWKX2ZRDSgUZbBXNqKbrQBFZbvpnYpBImIm2JO5yF2BT/Kt44QyGUYodUNhrCJrjQKbeIT3k1qV0Oeyi+JINgZgbCttD5omrO+SWdWZhg+zPv9739/AgZ7D4K7OVJA8ApRD24YDkzVsZzey+hIJp911llHMNzcjyMaPmblrDRha8jT8v/lZLDGFsFhnhgNc1avXr0+cHqdMMJBY3c1XCOAxOiNxSXf9MLdwV/4rkAdvJW3GlUYXEy8zz//vE+++RYoPiRLdWDH7lFM5rXTZGcIuGY5F7ovZYXqZJa/HyCNPW4gRPJGRzFjfaAksOJdwiFMDzOxveMRRxzxFiQ3eDj10eiMSgCKQ4EwTFaInFMnnnjiEpj7K7VFHqndvnAROmLsGrbQz3ciqQH7J/CFVJgA0DVDls8+++wD5pG+ZNa80oWPp3BRayZOjK0EmmGX84xXF1S4/2Rr84nuv//+27KEPRR6opzk1k5ULWhVcTSxqx944IG7wUrLw2bJW2GFwzIbkhu+811DWeKcn7OA37k0wmyAJZ9moUDRNRRbqtdee00Tk23VzngCmZMGoHNaFmPR+QlpbAOxoJrrXYaWtWyNNdaYCYMLP6nnvrjRgGXPEmP5WcM2xQsscyEKBVlBzTR85Vfx3//+90CWwLsgJOTvm4XS8Kg8scrKyscvuuiiy/iWMNE4utkc+UuYGYHWbEiUMs6LAk0lUFLXXnttF3rrfo4w8edu0KchatPgAjaufaifPEVvjCGoFmWNPsKSrbGLgdlDJGn6KBIq5BqD3j2g48Y8FglPGlIO6x9lln8ZAuUqhjvLSKRJ6WYvpAs/W47SuwVToGgCxTIjbw2rUgiT4ZyxOYBvManvSoMmPJW2qqrqZYYXOmPUMHULYKwUZ7dEseLtAk5mtSQIJ82Wo8WkOP5ysaLbcihtsZwLnwQ2QAOgeZUEiujJE5gtuEah9+vPP//8m0RU5OAEgdBKgb9VCuQ1h5KLKT2IKIEVPeaYY3pjmXqik94yqlfvJ9Vb1qtLOA39DtIqLl7pXtQji+J7qUGSSy3LcKtgNt5XOdJQvfDPRCbBKWKyHgwTNzNtQ3+rTsvQpDZnIhY5IVMU/6VihRFHxxBWYwX9MGmX8qgebD01FI9Sut8gBfISKNAnyv4MGc3oeIEgZ4zYxMljx47d+Omnn76WbfLrwKS0Q+/JQcLMvARDnTIMwm6+7LLLXiUDvJt3A5kaHHiITrUIk700JyEtSngGEYB0OqE8wSnmHxNPjdMcEUB5it1Qo3/961/bcUbLFuAgFG1+vkJNRGYSfDanzj2hBHIqdxPgWpdZM/1HI9NyuOgj2lg6NQYb0Uz1nMIgr97p/Y0B+ktKG9goMgsiBqPX04x7romyFMulq2KgdAQH745ZtmxZbxEZ5znEElw99JRxlpZf3XPPPa8mD8Vtltl98HQ3PuGRZB/M6i+//PIR4GhOE8PPzxnGRODEseX4mvcnKhuRC8Gwfnm6/WswZOvA8HIgAq1OKgQMd8DNTMaC64cYH84Rng6+bpi/qm/q15QHe5dzVllllbnQKYpGJ093veddZjpbzbFFgKmDqlbFfkYWpDmHm3ln1IIThBEoIoipARGI4cgYjKXmIljqEZ9K0VCljDmGrgiTfry16asX/to0ZJjWiw5iXh7t7I1h+KaNZ3/GqOkr4qZnE73SNZGfyhh755132qJpXcaOWg13cp3TIsM3ip6IcwXHywzdFnNsQDllLPqKCdVkBDZCudfbb7/dA+EnlTCQVDLAA7cEm9CMrQcwCrq7OzDzZg6kPrcX7xXDqck4zaYY4FsszDACRcgbqiN1Wz/33HMnBVWCQ0iNyzVeqaVhBfaSgiXG52a5zzfZZJOTHn744bfJr7l7SatJyQ5iTXbUXsGB2nsIL5zCAnszxVFD/d3vfjeJe2aCWzTACugMbmgblcCEtHXaRxB80Z+nGoHy0eOPP97o1kXZGw3DC19wrFMrvAIb7meGJw1PHphSdLB8FBjx1xQYVqCky6z5g6DKhaE0YamTjtXTaSs6XoFMFmVD2Ls0vFNg6CmkIUlRmMeWwXcYBZ7CWfND7Th1rYrjF4YxXBvL/M+G4GQ1kyBhYgQhcGIoCW8xIftfZUraXENERSuEM42ZOvqdgIWho8rMU8twZ7biO47P/OuAtGpAFTyFbvxCTDvWC0pHB1+RquQKRIG8BQpDHRmohXZUmmePBXMYGDS8f6IBnL/tttvquk95FpoZ07iCi8482ZL7gK5B24ppvOtyUfxlor4KDVI2HD2Z2OzmDBnMubdOXM/yEGbwVktEQ6ju2bPnpVibqkxFK48Ld/tp6Ic21U0elDdn3gh+CYHljP+/d4DkrR06dRxl6/vm3GBwMTRc6cBq9Iu6EO0rOAD8RvK5TwAhsW+n0OgMSwAaRYG8BUqjcnMltryOafhmXMC9i66iuPTSSzU8UC+UsyG4QOX1yeFHfdh528cvkQSdhms4HUepxmXHwn6CxIBSPD00ANlzvMAu42cJMNqcGhyuaGUyCLj+samyjetn0KcRlOCrM0fM3c2NwRPa9uDZEoFi8hQ9GuuoA2m86gieAZYqpsno2Fjcf4vpm02gWGL/8MMPGz700EO3MN+wH5O557Hf510aoHqgvHtKCzPHG/Apv3kNqdY6gV8twRxbKTx4AluGGqEeJqVjzAVN5ajFM0mjBip4oYYexC2YCykU0g2T+NUI2cb2+iqrjsvUKljByoKAlsWutgE0Fr+C4VQC5E8B0xX7BzdJiHblJlkV2oEjAibtjCNXzWUUCzcxvuYNvB5zkJMapB6ctJNAYSIKCX9pNUzEfsPxeaddeOGF7+EtGAXZ7q888nH06DlxBl46DvFbcwBTwToXym3Q1bsQTz5lL8VtXgoUq9GmSwVDBWoaMHMcdVY9UHLp0qU9OX/jTlZ7TgOAafhpQE34ISGSS5CoXEIJQaLTyOMc2Dydk90OwIjvKfmrPHo3h+PYTVm7msacI38tcesM3Hacn9teccOUPQdMO0Q0b+A1+p0rv1J4y6FA3r0Sar1RPWkwnqUQA1nH+FeRjNBy4hPs3ePLX4EIlza6yW6LLbZY9Morr9wgv+ZsnLYsmW9wMnt1VD569/e33377sUwuf8TvQAGaCafAvw3xORvUfexDYBaUQ3YorZmA7g6pvyWy6uznSgxM/csO1HBK9ViMUohtGfpJ0/5NuXwFis4YXSyh4qdWS/Wnx5NBVWso2Z63ISgE1rKrtxQihsJwmnyTtlLB1aTnHHLIITPuvPPOp3VBle6UKVbl51vjKiO4xDCvnsdK0f333XffRVtttZVWSZpNK3HKoMYhK81v9BuaOt7+L8oinMsx3qvce++9tbubojWfduWPaeFD4OP0rQeFhq7JZAxBCw22xcMLK1DEmVE2kK3gJPez0R5mQyxP4YDtRYrb/8pnzJjRhWsS+rH3ZX0YeyMOzF0NGHz6M7nCYGYJJJmadsMu5Qwmal9GmCzG3z9hE5MZDWAJvdtzI0aMuJlJZK0+pFemmhlPCQddizoHWkqA5+whiSNB3QqBsj7vx/S7oeSkzhqctqF5NiYd59bexhGTn8NvBT2ek44mxZaTtTiL+HBoor1vnm2lMbi31LRhBIoYzvR2EKqWi5pe4Ei+TymQH5HEVArTo4bWjkvQ14a4o7ir5hiYvS1+xnkRWvzs5KeGMezAAw/cl6MA78JfF2Y1WgMAhj1a0uAgjQjYOeeS6MnNzW3Yl/yLlZzbP/jgA/Xm6oJU3hahPYk+KgvM/CU4ztVpcSov+PnOCdGTigdi0HhL0rbjexlvkuUtwLUhTiftASKiutI7UMDQkGXT5MdHSl8UJ9woo/bc3Emn9bJoRkaBuIZERGURnBjbLgbTuY5mXlAEMbBF15BwfrHRcjakzJI5BJK3iOT12DC7wrH8wQcfnPrJJ5+czQFFx2D0ZfTAHExr4Kqxo9nsc/HFF2vCMG9chYiHU6PTUqQeXWClKKbCPeKmvYSvJpBpBJ0RJlMJ0CFEOdOlATTdRxwtcjETsx8hBHMKBsql2xopWrI/2x76gKaY3lcABRTDNBYy1LyEjWZgWZjut+jp/LZxm/yNDZSuiSgUX1n8TbmgfRs73LcBv4W36U7CFhTiS3uwQiRsMqtVpJgXuZdzYjecPXv2CRAbcN6VqXx4VDEp4m7BYcm92VPzluNncQibv40nvLVn6MWTTz75Qnrvcm7Ra//EE0+cw7EEUvcBb+SD8s10CtOSchK7mX05BnIu2tYpRCrapF4mAnn8TjFEXM6eqzdJswOPV3nqgRPj07h6kW6nkSNH6riFvA1JJEiuu+66DzfffPOTUfN909PQTN7YvXScMGHC6QhoHZHZLM49RHPqPyetQiDqbh+FgBciy5YTJS+BAtE1v9GYBp1AW7mK0+NHAmcNTVz5OFMRMF9SDIdJ/FDiybZDeTd4167wh4m+Pvvss18BlOCUb7fddrUcOn0P2lBbNSxHqCgrtyNpeuiQojGM44qJF7gn+AkJGcKs0HSnaZZvcNGJdykE3xtoHpC4VmN4X1yIrzLHoHMMwbo7Ef/Js1Rlwp9X7qGPjcNBWjNIc41vZq4AtM5u4HcCXhIoRti7gpv6s64nCVHWIMSC6ByU7tcUVmh1L4s2YjbnUaOLYvj1JfMQ/6Vhm4vRsxK4PCyjok1swOSscG2MxDdp1XiAY2El/vOf/0xmY9y1WrlCgPn2rGpcehA6UXrzdmhbNxx33HGbCxb+OSc/XcUq2qfFkQyiY8aM+R8rUNOlEdg68MqYNNY7xfByY/bM7IKHbo7Le6JS+ZNWtMj1SIDltSfMIll6t2wKFF2gZBRfDBflNrrPNW7XkxHu+ZN5mzXuvvtuMWma+z0jhvCE6QVD+UqzkIq0Eq3pQlZuniPIGNgFgVGjkSaD3cZqd91113jU9h6CJX89QWmLHeYSHInTTjtN5/Fq2GO0rhy4EZxKsSLXitsO/3TooYd2IlnevOHkr/1LuZ4Ew01f4S2cS+6XSYG8maYQxczB3CYLmNJsCiNuhPmKdixryr/RAsUDf51mv4R7g07FxuYrhFzOnln48yQRKhvRcP/OJK1ZuVKD8oDfpF4ObSXYahjOPcxKwwJWXgLxcsqDMhNLMuzZlKs/Tie9tBQ9zSokm5R4pcwaTYGmFihGI8FOpY+GPHrClIDhSIrNg2Gi5hXHaSxqbLH/+7//m7bjjjueztLnMhpWzmVg0gr3BMOxvblQ62y+ddyflqBbQgNUmVJodS+yqvYOgk5CMqc2iOalMiU5guA4DPUO4Fu/zf4m3iVXokBOCoRq0DmhBERQA7OPoqFprM5GwM013EGg+M49kMYcIyBNhTmXBYztTcMPyCrvIGkUzqOhT2rixIn3sdx6Ed9uuvj27mqATHrGMfs4YZdddtmLdJrk9i0TYUV3Ko/NhO+fOJ/3MvBcBr3T/jbc6y0hD+3LOFzqGi5aO5w4mutIl0l16ZUuXz+MJAsCJ998S/GLSwF3w8mZkxo5jGCWdMVYuZ4MgMorzp21xzBWXxMmV3AopuLwn1n77ruvGXPbPDNgN+qnYOIM/HvuueefLC2/TgOUnyY0fXFUOpWD8rTllr5LMfgbCCJ4myVm33SNQjZEYlBWgTSvU3bzzTe/jEXoo+AZR/MKM2+h60i1GtYeTfLirbfeejRZSvvSalF6OMh3XnNGmfGZlA0l4EIUtxSlBVEgL4GitiXGAP98H/VwsfXXX39ntJOjYG5dtptmTh96iOFMo2TI8z++7URqzuGIDzxfbzVAp1wprvBYutdee53BoUOznbmHXIyvxEkOPF4Dq8trWQ7tSkbC0Te/JgyQAFnJwd8XMZcyFzxVDznLI/xEEIRKO66QvXrw4MFX77fffj3xllAxgkVxrMPPt7CZYfyWBhTjRgQlz4WLzaL0/oVQIC+BojIxcSkmDSNQxDhK0hqjtEoY6GSM1G7DnL4jfkYoZDKbIrudhA75LWCi8F38JVBk3erLvO60+X5LqPAkGfYkb7nllin06heSl27KCuzVVUYeswTO0YuboeFcSt66w1m0LQquYcumMhE3zkrU9M022+wi5ocSGmril6shG7wRKMY6mG0Tf8RQ7hnmVcaMHz9eq1oa2rnLJ6Gq+aOsh3huTcbyW9unnnqqD8NFbSAtuV8RBcIYtpnuW2VGTS1jM9WWqNFrYAjm2Vhg2AgTlVGOQOw0derUSozGBsPYQzEGWxsBITVcoDzTKkDOadzmmxWYJ1ie/ZwfGoM02KjNAAvxj55YQks9+QS0lT7Ym5ymRkjjso3BE4oMyHDJ6dOnH4LwXEgjPBs4uuypbmxX17g90xbTE5rJ0C3JcOxWjoXYmuMh98HPLHOLzji/urBCpUyrP9Rp/zfffPNGyvchR3W+yArSc+wEf5P039ky8g4SVJ2J3xuBtDl3Bo2AfzbhMde6ks4Ph2KSpgS7CBQII1CUralwDLoqHnvssQvpXcSknujApOaaTuK2gmE68ZiG6AgTwcnFPGJyA5setYbhzoMYnmn/T6Cm4IlMwz2FQPJvf/vblaeccspGM2bM+IMaFXhZrcsTMkLFDOVodEexI/tZznPRTmSj+XgmaAJPcKZKjOBYwUTrWTfeeGNf6mYwuIrOuerCYChNUeUnTQQtbD0814MPDsBw7hvm1KayneJztiPMYWg0nzjmcCeEcFuMEbt88cUXq7EM3RfNtB91uRp59+TRHpomKH0pi6amQFiBYvGKYnthexXr5/kWt8KIGg4oXP/MnEkuRhL3Kz4MqX03b3J49RR+m16etKZx8LvYTvjW7r777vOvuOKKv7DD+mmM67rjJwTsPFIWDoSBojkkqoLNkONZhh45efJk7Y0xZcpK0AQeDk7KP/r3v//9C4TAkWgYE9G41lD2oBsKC+pStNdkrRKkuF5EVwZ0gx/WRwtNvPfeezXsuTIbLgWQuGauBPhlpNFFZ9JwNS9j+hbFwYUSaHVRS/9/CRQIVOO9CoDGYY5szPWmp9KNgWn4YlyenAxEHAmTFD3fj1zLcDErPIvAw3A9TBmO+70Qz8NP+Th5Jdmv8yH3Bl0DTjLNl8TwLYPCeCQ4EzSyvgiVq2+77TadhBaYLg/UGhTVKY+EchR7m7fZjXw4k7TfUB4zvAwJNF0GlVH1K41F0sFx0s5kCFehx/nWNmbFSSguAuU3eYpZSPr+KqKlG3yxSgNzhRIk5G96PhquGVpwRus/7r333qdt43YaeLHQzIIL3hIcwinBeav/5JqJR5kj0TUZ7u35WenkQQPSHEyCFa1t0QrO5VvnjKjH9hVGhBfdQUNjb4PB27M77LDDkR06dJiOn255NBpgGAQyy8BvCZdyOphWCAw95c5jfiuMOKJH2glGJpx0YMaH8HO8mqQzyci+9DNPChRdoOTCB34x2oD4RpqJmI/Dnv86c+bMy1lxyZW8aOEWLzIQI//E1RhHM1x4A/xEs5zzOaj26p1TaCnH9O3b9yTS1GtU/G4up/Ikubpk8p/+9Kdd0b5ep6y6OlXCxmoszdp4XbQXShprqVOy9LMCRmEl18Io0GwCJYNpdAWFTmCv5qSr01hZuRw6rcCYLXTPWWS6Rln6/oHbDU9hKPYTPbplbt9sVT6ctJXI3LlzT+ackK2I3Oym+cLLQTrB+SdfjB49emRlZeUdlKmWSVMFmXmizPpx0hT7ZYeaJh9pq3zoOtvvoKMOANdvI/hMhNK/FkeBZhEoMIhdGhYDSZBEO3bsOI3rM47iHNqroJJWdVqEMAE94WgYGxuTt7AcPQehssJhdtujZ1UswkRFM+FY0nZgqfTyM888sx8RJVSahe4WSZWJb6OpXHbZZT+wGnM457ucSLk+Q7BrDkjOCh6brGhv8krDVr4INz06w3gxZ+E8fPrpp2/D9ST/Jky7mFsEX6QRLn3Uo4AY++farBdU8B9ikgQMq+GCmFWMG2PS9RuY5pazzjprX8zXJ+AvhjHMLObiu9mdIxxkA5Ngz84EbHEmUBbhb5jbD0+lc4RHivmUDe+///5LsM2RYZ/XfIrqIWx5ARtyecaHehZn3urxk5wLc9uf//znPdk3dTdzKwuEo+IgODVnVNRGDHzDF5BU2MbY0LiS4eUz2PMcywrb6PPPP19nGJfcL4ACZbL1gDfFMGIs1WhYpg5TPDUSY23JnIIal5n1h0lTbMJ7G0HyEEOcR1kJmUm8asXl0Zg5LA5aDaplQlBjbDV4v0Zp/IHbCoGmeGHhExWg4IPjFdWy6GK8zmYItBm/1+N3tRoCbxPX7x9xdDvibgiki5kfOp546XkYyqCGmwQ3rSQJEODqT+AKB9R+zXUoXOVptBNMB4jqP6qGy3MkE7YDMGQ7mPmfg1kWXlVxRGfNCTHJKlODevkDR1GsszDTnh60Ueei8oqeWi0yfMGZNPMwZJyEtnrPlClTZB0tw0ADjzyyhJrwd9FJvFADjjnrl3RabdLm0yyYthCNfAsX8bHKKHzSPOSGK/zBQ3WappU7vFDfwBcu5pB1F70MeOHg8JVp/+Cd5suG5K/xqQpreqmGAPBLA+JqFcYuAfhJlil/4JTx6ZzY9l/M6adg3PQpE4MLSa+8xTSWcSxD+oF2+2uFQo8Vin4VAypm93INxDXdoBtImG8RHhdlojh2/PHHL2DX9BngfxuV0VHCEhcIRmnBc+X3338/CiH6JZrKxSRIl1WV7gyjLB2y4JHeNEQ17qzARng4ZbMQlmM78z4/PmZP0w0IlAMo697sVepNvNbgqHIobq0apZiR70CaCj6PrOP0NsZ/pNE8lJh3MUZx7zDcfRiemHz99dd/hQZo+dE0eKUnXpYTTd2eiqc88POMb+M68DSksngLTmAamzbobfEEL6OBqnHi+Fl3FrPebufEt2YSuZnInTj3t6EN82ISJKaNKb9MHARGNNOjrRnEsTTJnYNHjDJm+s+BmXWqvDHH9ojTIC8YTcRciPr8w8CBA7+lYF/DMMuYaBU8MYqZ2KQARiJSUD69GccPAeLXXnDBBQ9ihfm5iCYYnhQDALhoFamMZxrppA3l7Rz8xCHR22+//amqqqrRzI+sTdmq1cuqUvyAKi2aVEwTn6RZiraifSzL8U9ec801X3IG7KkK94Oh9JTBaCi8V+iaDNL75ueHh5+/4Ktcwkdx+I4hML/g80I2CF6OxjCUzY/bc5jUVtCwF+GdwbcDS8QaGhkNDT8lTTdy+TthZp4MhtUxCgtJN58hzTTOFn51xIgRU9gWME1ZKjFOfKG5EsDVFxgm1PUvI87SE0444TzSdFBCVzTPT/hTB2uJFwrK98oMmImrrrrqC2yRTJ364UNdR+Abcx0s34voaL8XPp4I5++pdrWESffxGCHKKNN0WOCSrh8LkjwNscV8a6+99ivCQfHkbeOEfQtQVgZhE+cTz8FZzCIJmB6XNxRxd975lqEhhHLnp2/yVDnyJriFk4lDc5TB4uJ+u+vDwUlDHDGnLWs7jP3WwEZoAKb8vZlsXpNG0R1B0ZmOqZy36KIhhYzZangthk+/Y/f2t+x6nsO5Np9zmPV04sznkRNcpdFTT5Bk0ojwQNdSaGiRbAn4NDUOEihGU7BEKNJb4q5QkrceiuBvGLieZ/CPguHSgLzTmEEPo5nJw6l0lSMfl89cUz5wPeO6+ET1qE5IjxUyrfi2BzHZciieyqg5BDuvYTsvywuKW8j6yJeXi0bDllKnTr256wqSB7pG0cRWcGAOjQ0UcfPtbcLmmS/sfOMH4VFIWEH5tLQwym2FhiqWn0bLFS9l8pMETmYcFcf4mY8i8obgl1x+FGgsT2cyQH65l2L/5ijgCI90ud0dRdgwd5o0oNLHr4ICJYHyq6jG5i9EpjBxY1QSIG5qlL5LFChRoESBEgVKFChRoESBEgWalgKlIU/T0ruUWxEo0NiJxCKg5AnSPSxsycNAN54qSD64lgSKZ9WXPFsqBRxmt6tMFs1GLXVaIMV8O3i725u+WxzeHniKLOlVuVw00ppf1t6MXIlChotgZtmQtwzZrM2Csbtw/w4JzzMa+FtjOc/wkmdRKGAM0IoCOQdQH4ZXqtBMnyOLJgl2laPF4O3glJdGkkmsYhq2WWksQSJDJvUqavw1VpioAPYb/wY5YAiuzatBMEqJ8qZAk/eslldGjRrVh+tYRvLb7n2KcNbMUxdddFF6R3JjeSpvagQkEN4K5gL6jblh8kh+m31YmP6Xs7P7IXag/4fgtOV4AKiiBjl4xg477LAtOVj8EDIz7RRL51Zsk7iL84Jfwi+n8CsbMmTIDUTUuaeyaixIwwRWNVvQF3Bs4hw2SM3ipPRPFy1a9M211167iMu+7N4JQ1gVpOEMkIou/NPWY5PVS0eiAK3UaI9jsSlGyRWHAvFUlGMeywdv83fUzJegtOoubfFbnDzTUM05MsOGDTueA7GPUb7ajyPHRsveCJSTiVnNz5bGAOrwyrg5YRs2Wf4RfpfThrwo93X3O/LIIz/iJoJviNPg/TNpCjXuQ/c3deKqlJMRKLuBo8UnynUy8wD9Fg9tLPiAqzIuxt6JSuhFRFVEQQQKcIx74403zBv4Cc6Inc4msFfZyPfk0KFD3+VuH21uM0cWCPkGMcLwSLz68/c3aL1i6S4rOIy9oMjXFaH0300BGmvr2mQ01b3/g5EpaJzDm0Z6wx9qlMmxY8f24UiFAxAk2h2robOwU9hOHCdxBbc2zuHbbN5sED8JWuGdESiA7Qfegq6d8eYKWzStrbjudSf8bsVP9041lXAWHmmnvPmRvO+++7b67LPPJA/MHSuKQFg5e7L6cFZRKzZz5txUq0uctM/C7rtQ4QvlpHqY+RIh/O233w5g6/4A8juMDWXT2SL//D777HPblVdeqXMvtN27YUKlVSSRRKvkZItaJEpJphSq9jzgQF5OG4mWU1t2L45HrKJ4iS8TNL7j2MG7KhsQEzoIW0JDPKZL5F588cU9iXMdfCS1qVkapl/JOQaiHQdFrUfDBL26OUt9SCjSgE9hx/9T7E7+2i99E/inyL8jO8nPYCe4udgN9NSWzBBH7fbCCy/sxH1cOgso0OnOFEknJda7kAIlnbEqHWeYEKJGObynL0Ogvlz5uSMa0tVsjdewS5LbMIIYJZ046GMKgSORQySj00LClwRKELkaHRaNqYeASZqmvTpMLeZJcHtkby4M2xthknmLow4Iiupkt4MOOugujunUaXMtydVyxGZ7jqfQzZmmITjtAXkSq6Ut6HZKnekxnvLqfCIzFdBUBSBPtfkUw7Gd6PQ3Ysc4TbTWnJNEmFEIENhrzZkzR/cwzc6FlwGWK1Jjwy1iIijfUvd08leK4c+anLVxCae33cNYsif5GIGmOGHztBIwdIKwgEvxPCnQlHR2OhZlGePMmEPpPXV6nDomg4bDS2qYurJkMOfM7EIY3ilz2jbfzerAw3TW3HqwFhfFdQNP4WPagD5oA3EacISpgSPeeuutVSVMVCaFNaFT593uiSee0ISxFAzbBs0EmXDmPJUOq622WiXxNJJQmXydbY++EQoYYAlptY8oBJVwidG77Pnwww9P4gKqdcnPFqipCVvAopZANZYCalhO40oy1FmDuRP14jpnJeu2S9tRMc4/nDgVPE09JPMrrng5ygLFAL1xmaqdeFwn31WeffbZo5zG2iR876JvbNddd92bzn0IwkMCLS0T+BbShpYc6KUD1hWWDuc7ywUGZsUujIdhFCErJ/WK7wRq1RAOSn6Y8WZvvEV8K3hMvNK/3xYFXPVfzil/hzNn0hvB4UkENQI1BoYPmx5wwAG6DF4Ts4E9qSegwntKOERZLt7Q4fd6PK0yiv8RKOVvv/322EmTJvUlPlHTwrTwGNVlYIWWVsg6MUo4Au2vnTQmF91N3kJGH999991ghm6SFzatCc/8l7dAUYZhHjKqR7zMjN2/wVmqlA6bXptrNC7g9PWO/M7qidxpsr4Z3Tt+epeeItDAEjWL9gX2oO6tdhK78847Oy9YsGAneEM3EKqnsfVcL1ep6uKZZ599djRvjfd5peHUi9uEPxJ//etfI8w/DFSe4GOOy7T5Cz/HP8KQqB/XxI7it+YvTBuz8Qr9dsGPHnzwwcPIe4imIchHdHdnx09DwwjL3n0R1hLS3lLdSZW3QHHnluNbxKqHnV98B2mjqbCUtve0adPGKC7+4XoZ5VJ6moQGas6cNh3YS6nuGuMcoaE8othobMlB2YPhBfGDFTRZ4AkTLyc5t3ijkSNHbq+0zpMVtyk8hCsu0a9fv86salYpTw3vVQ63U1nlh8AsQ3DuecMNN6zKb8P3vItGZ2BL+LZnfuQQ8m4FTlaIufNM05tJ5SouuusMvoGTxnkLFGbZzYHDQW9N5IhQvC2Sbhp6flNAHcKsOZUoY+ETkeyrqULCENVhbzchPPMoeTaKAtIBzXpd3kzTsGxj1113XQWNTAxvhIV4KggUKnuMBtIZ/jmQeJpL8dVoguA0NszhWdMYEYYSJl2YfM2cPzHZKK7zpGi062I5ux0B8jP3IjUWF6/0go1/6pxzztls5syZu/NbTnl6RY8Kd9plZ84FrnLi+bJAXsMKNXAmZ97j5PYl5JwJVNhECWvDeHdVGGEtKlhXLggZSeZcDV7h5koMepk1X331VTHF5U4+npURGQ5VYq1qE9Gaau7jk+TMlQdRSq5hFECaYO3DDCJ3GxV33RheMQz/3HPPDZ4+ffr2/JYLrFuXsMGYt3qL7bffvu8zzzzzoU3nCm9Y8fNIpbxwpgwff/zxQNqAHSp4atyKr0cdKtrMYRjo/RtDvZ/yyDLfqLHx48eXY19yFMK6LfjJKjazPadhgpvaX4yhm+Z4XuPxrYswAkWFNeomd+ss5xKoY+++++5PAeopzrjHtxVGMF2Z6FmbSbLDdbmVkIFYRuDwHeg0SUUBo5yovj+FvJHIi3mbAojo9RJPiSTKxnW6pjpaNinO1BZT1FEGoPXj1EtQ+tFYCtQuXxbHpGw6wtxbyDc2g7r0Yu4kdid/chg+5ySreMThjxR3CHXHAO4gYJzLoz0pgWp6XZaF++/wq+FDDO42oEMtB7ovvRRfjwz2MHvfEtuskcS/Db+C26UAU0ItgWay06xZs3a2didBpXfwizEVsSHx7uDxFSgKmM2zBo8I4CWl0gKFO3aWcFHX5hBJ96j4OcEUrBT3pLRDqu3O6s2VLEt1h7B+aer564IkNJ15SNHdDz/8cFnSGnhZAsWkMsImFjHX/dQDU/pRLApM1MRccQQ3zCseTHEP9DCsqCfDM20QKmE6PpXWNGJJFm4f/GqrrbYawaa2L/gZjvEKTC8JhK5duz5Cxyr7GLM3hrd/Y6zrDKNciPcetinbcEfOIm+ebziiDn3Lq6qqHmH1aUcgaQThqTnZXMBBwjDerVu3p9FqdslFTwkUEVyJTMPNeMskXuEpBMoiNhMO4junc6Raa97l48aN25NrJhdJApPQwOLtlZfxQ5jUMF5bzKzyGNKbTWGClzPTUoRfPAWoZw2PW2EMdhu9p24btFdw+PJLJi/BZ+Z4BYY95zrwpCE0iSM/o20471Xh+4/AJzTu4n2elbvtttsxDu5enXzeZRE+5513npl6oKPfA9uYRdA3LG3NrY909J8Ap6tTNt/2GFqg0MgXsVV8kAUY9LYlJk4rnvKNN95Y8yGaqE0LKP32eqiAGt25zC7HM0grIhhCWJil96+PAtRxuiGimW7ALXpfazLQiz+C/JweXQ0giXbwKfdmayd9k/GPUw7lF7/11luH0AnPQUDkVQ4JoC5durwOjG4OPN/Gmw8nOLDactXrI9BWbS9n525prVEDdTL30UcfXR84vm0yL+lHZUWY8DIz56q4oEcFVQFw2qGYRAW9C1XuK/nLKW3dV/Z/pZMaxsRuW0KN0AmKnw2h5PNLooDDJ5YfWl922WVHM8xZDR4Q/+RVFGApvlkMYB6lLwJlHDDUcPIDlFeu3pG5ulWW353BSfmHdsRPYRuy0aBBg/5IorzaaK5MuLlxY9rVdtBWhBJNLN0Dk4KS5rHaPfbYY+s46TzjFxRZzxzqPGPsqPy2V69eH4lDcAFRTZAkUQItZTm/hKPpvXIlKoX/cikAs4opYpgL9ESY/AGGVy+oAoVi+MySO1afUQwlD2auQPvEAFenBWXGLeRv+Fb46kkyudqHFct2Eoxh81B6lZtFjHKsU/eaOHFiV9IaDUv4h4Vj49k0vDVP0g6YB7Hnqa3ycHANBVNlIE0bdiRrpce3TppKoESYIFsBQj9SCFtW3zfqagQVq5ph0vdByPsCKAX8oijgYuw4KvUOOo4ABjYaLjyTm2G8S6sGkESj7sN2jv2JogbVUFjeOQT7tkYT6ItwNNp2cNSfQ8HZlFvlR8PaGG1taxvq0Mn+DPVWGkvDbbbZZgMWSfZzaKt8QsFQJNLENDmO+b0ECmCjUh+zADSZQHn66adlaNRFyOVywpY4SwcPHvwZ76ZkglyolcKLR4HYWWed1YVjLQ5xsvBk2LDZi4WcRlDB8uhepGuntGoEtoGFhRU2nuA6sNWu2jDx2Uudo3CpY+lQkIhqGqo5poEVoiMuueSSjqRsTFuNczxBDAF1OE9X4NshWKi2Jdwpl9k2AE3XBJd2+q3SOOXVZ9rN5ksZ+E0cpSdR81nlEXSbGe84q0Mbcjblt5ZYTp6ScJmPmVEmr7dIJ0Km4ei75H59FKCetU8nvssuu4yjAWri3o8X07zCxGWaL6FI2j/zW/BkP8Viwi7koe0doRpRQ6gs2M5TxpJ3H7b8f66VKjVgpxH74pmJt36LDpQzwW7gQ4GrYU/Y5fM0+qQxq6Scdre1phCAJ5sctffQuFj8mVxO9ujRYxpmI2sIFx4rQNP5NUbqpYG4P2wmesuft5bsoGvZHkjHHg5hFeRZscQzBUY7eZo4MmoLNEF251f6TjO0ZeyivFV5BXaJm266qTW96DirjgNfDO/p4CH1+Foatj2tXzzBSDJnIBP+/TjIS1pyoM2FJ6D8PZPsHO7GNoDQtlceWcDOKR3XEMPI81hw1wKFBG2+zrSzl1566WiGf2Y7AgA8214QYHCRxpcCxiocjNbDL27BBYqTkRDWY8atzCxvw6lUf1KY1FAnTD/rORhEp2+V06N8x5b1CU6gIWy9iNk/bH6ldx3di0oHMXp2FTTMB1jaMp/kgKGd2csyhF7QnsnhlYcm62UgFlljjTVu4pBnGT3qwGfPhiY84SfBSTHsGckhTevxHWp/mODm68S/ekgXxUK8F/t4OpK/0bCFc57OdAbQo4ZhzwYc0biXYONCtVmVXXFJU3v88ccPB5+d6KyRT+aExrxQAY7iC552RXd54YUXzEFXrvKm4eWlQgmgQ7A0gIAPVWRrJmO3Z7b7KnqJTqS3VPVlFphDw6PxGN/MIL2YLdBsOvpXI7hCETkA11JQPhQ4r0E9ZVYOsINp7LzbokqP410ujqfO1Qiz4uOh+LKCXURPeTOHJs9+4IEHNpHdhhqujwNcNIGldgcmJMcQ500e8YuMugruwFttqozhTj/yM3MnTlm8eD5n/kyEUrx49Pnnnxd9HiGB2YpCmTwJZAEqHKd/bRG8R0DWjjy+RLLpfN4GdwlnTcyyYLIW8TSUsgI0nSwvgaJU66yzToSj9qV++bkyVNfuTLBtjiDZn30V21H5Mm6zBKhHWKfg6mVU2DiFv/X222//h4ATFihMFGdpbJ/BtTXLB6biiVr21cOJZn1dQSVXUAqgbPKXrF5e1ubeTV9tc0Bqtio0F2P7oSBeVHKe1CGHHLIrfLIVsHz37BBmQOmNYdZ9bLr7gnmBiWxWPYGhdC/h4bBYPf5SIhpCTL0zS577sSx947nnnvsecdV7N7SBGVx8/sXmzZun+YWBDs6G7/mdhZdP+kxvzZ0kWH4etu66646h3P8kQiDe7rxGjBixExPdO+InzU6C1FRbZia5fou+Ko8ejO76E7+1A6teGw0jUIyqowwZE7Zlb8TtXImxlJ+eBCJOBcKhK5W4KkSQykc5vIWJYCpIyEozgTne4DyIsysrK808CmEEBUjifVPx5e/2OCC+fNlRnHCdgFSeOCmfkms8BTijOhmvSZVVR3sdxTUaX0WGG+b0HG4E5aZ6dcLF3G05l2MUPV87elDDtD+zS10s8YDSSBNh5WQlk/uPEVL9r3/962t2Iz/yyiuvHEeYVkUsXAd8Or0BgG1KZ44aPQyBcny9CIX9kcLEvT2NuF9mORqSjcougUjZdHPEAeB/N+e9/ARsI1Qy24fo5NBL0w2tOZFtFB16J2BoYpjgNO0bgo7Th0TXfvnll9sw+pDRaj0XRqCkE4CTrFeHpD08PhAM0jYkKHRWZk7pLEag94hj1jv5+uuvP3rnnXf+AbCexMrKbiJSbe/qcm7n6YgYlwoLxbJilTwKQAG11GiMu3iTCQ7jWZYX3wRkH2f3+gDG5cPgLVtzhmm90qilMKfwKYJEFz4ZQUZjeZD9MqPpyDp4pZEfvChDsTLSprCj2I3016HdTMPf9NhqaH5pG+BfyxxDB3bd9wG+KUvdKxCSb5ltWtEHLW4ownBLBMoTguaHN2lMdWmbDPM426gdOn6eSAhOULhNZOOggfVhFasjAiXrhoGGMEYg8UFeM9PCwRxlBxIWn8y3CWApaz4q1INYNJ7JexGRKF9IVXQ4AkTnYCZrIskEY1c0ssxMSr8LQwHD8alYMk4fwTVbjaKzw8DqQcthzj1g+u58+zK1GJmOR1prFO34KU5BEyOr04mfeuqpH3J04mQ0lX0Eg0eAPPGDL5NoKb2uvvrqQxEo5xC1nrqutI1x5KsyRdCwq9jqvwodq7niIxdMMTxx/MqvYKkdOi8ljmD54z/+8Y8XmGhdRpos/BUPp3atc2HGMHfUCTxIVnfjhA8unvRyxwWm/ZlCWK4JPFnwzrCe9i0JnZcDsDD2fdzAFI/fnsiKSgrbdNNN72AG+lQ2ci3kt+kxnHRuUN7fU5wUSlV6mowGjHzS3OVdMeF80U56c/7HuDCxYRfaRWwuRl7/Ir6EifgnwWrgYrTb+2VZLa3G8SgAABuTSURBVJYS7+Cy8JMfLo5AiXHM6D6XXnrpajbf0PxmE/i/DReCz7rgaucF/WMTAl61TEg/7YWzTSj8aMCCnURw7sCJdNvxbcpj49g3noqXOu6444ZwnswoffsIEyOklC+T3F8zLzWF7wR4ZwkpB7Zpr8SRoGo3YMAAzaNoYtYIUSdOo6zvLIwGvUHEpGONfT8Iej1zJ+vgIUYxdieEewoiz8wEqvQ0HQ08KyG8J3Ur2ySZco9krqEnPKqJNt/6lnaiBtW/f//nUPfVKyquFRoJNI5XaMQziecLQ/B5NEGrpc9Kjlr8AzBMT07+FhZejXJRFiQinBmyjvLSE+TI1wxbuO/7ctrAh/pNg9Zch1cyA4zGXE4ex3CLn9/owggUzlM5HM1Pw0A/2kpCa7ohyr1YE7mE/m7op3OI/ASKwQncJHQixOuDh4RJyxAoBjv+wVRrMi9z6CmnnPIflos1WaYZchHF9DZ6l9yvjgK1XG7elft2RsOgdljg2/qII1fDIckToIQZT/Pb9LD8TjGWn8dwebK+1UjgH88WadNo4yBC5eArrrhCp+MbXuNdCJe44447YjNmzNAuY6stecFVWUEzFUEQzqusrPyAYdwEcFeZ6gKyUxktRQ2ezY5bsut3V+ImWDY3DVpl5jF2JwjYjVkN2ou4focnGVojXOPkuWL+/PkTe/bsqVMYQ1njCm9dLYwxYj1hIpQLScxsEoTwoXI155LATmU1lvWu6tOnzwUIGElWT6YIAbIUpQVTQA1ADRvLzwPQTqrs3IhPfRsBIQFAo3uZidfXiae5OWmytsMRDydZer4X/x/VKBXm5dTopOkof4YOWz/55JNbEg/v+mq7V9owfsBNcERHJzSUtRWfvMw4KzMt8aSJGDwZanxCY17CTvxJvN+jKdg26VkODdmA2wqBcjR4t2fIV0+A4leBtn808WR3kpm1+a38caJthJXV/2Lj8iHazHf4rSCdn+YjbcpoeOQhLWwAO6GladbLxCKvDJrNSVJSeAmWJOPbU1Bt737vvfdWoQDgXjdf02zIlTIuCAVsPaoBsJT5O4a6hwNYt0f6MrDClQ7GT8oylis1VuJnhImQEn/wJGDsKKsf7xDnefGSI6QUxcuJp+TizN+cKJN/r0j5+gkej4zu+vO2p9yr5WZ1jIrn4C6bmk/APcI5zfNYDr+JuSBTLqVTnEw8SKs2q4vxttxuu+0OzIiTGjdu3O8xqNsdOkjzy9IgBE/5Q6MUCyIL2dF/I+1tGe3tO4TL16RRxp7DLtIprbQaaSi9OaKyG3GNQBFMwS6KQCETbd7REQQ1vO0xc5ZQyjfLCSEKI+mbYuZ/l5122ukS/NpmECwrHR4yZKEk9FrquVTA0lMMGjATS/tWDSDjvSoipF8Upt8bG6V+xBczZjU4wVG980h4ROm932Gc/1qddx0DK451CCl91g4fPvxOpYPhPWE68SWkZOhWy2rFphhpbkMaras2ti0ozyjDOM0FAjIbTyd/+1Icfc90PGoPO+ywyQyBZoG/Ly6kMUNEBHFrVkYPBG+zgVb+wGnL+c1jmWeRHZiF74D/+eXEjWHT8ypa2lOExDGaq8GodBZhRnsC7s8JMr4QRhpKtUV4VhFP9DRap6IF9Q4ZYAyVkuwC/oryZhm02MhCiHmRGAzTnu/WxG3LWE7CQi6oogVC8czEFPsXRg0bNuwDkP0n/lLrDJPZfMx7OP9TMWQtk1tJmenXCy39KDAFZDcoWsM1nj2fX3ZiOidM7w6sPhxEPZfz2DkDr6QmjdoWjDuJuYnviZTWTtwJ4DNjYQujv8Sw4U166E3xM8LIlbc7iR2OtMZQ8wjiPENg4GRkvcTeP9QCU/T06wJPGne9oUBmEspuhAaaxheEmU6XQ8jmsGfnCYYfx1JuotTFyUyrMgFedimb4YZyw8Rz+MU4g7kPw8gRrqyFk6V9GgzCVNsbqjkn5hbeS0lbjpZUwwbC6cy97JCO6PMh2vLE6fglPJ/nSecRRqDYSo9iQLQMNWvvBx98cJpPXsabTV4x7hfpiPTrw63uWzA3shMSdQhCRrsdwT/NYFlgQNSEE7/VZ599dgI2Bo+df/75sxVR6RSeTjQFBjuw4xsrYmXdmL+vVqk8OS6doPTRKApEY6mVK5e3jrdKfhF5EVIPzxtajI2iQ+lw1pcwwdlOIs2QgujwQAQBEaEDm7fJJptowlWdmOGNejygBHVOtwwu5WDqO+G9TeVFHvXgOvHMi7C4GhaNdytsWba+/PLLTaMkUEz2M4+5EwV/i/Vi33zzjYY8mtz0g2H8FcwZzQvpOOcoT/scddRRt7I9YG/4XzuV5V+vDKQDvTr8tIuayeXDOIXtNVaJqjHa2x+BsqoTroZE8nrOlA36l6HxvXjzzTc/wZPuHGjfuiGgXgKvH+Bl2iLCR1esZmUyG08RQxI1XTDXt6SRws2p91SumcHmd06nwisS786kO4WeZoZTWK98rJ/JS3lS4SnO1Tye9Dovw1cNzIlIKUIBKeDfGQRlQv1pjqEte8GuFw+ofv14wQnTZEiKqxueJF171T+PGkkWx1s/xeFUsv4Yv33u5CGeNvzE2/JX+o0WkGBYnmJIdRtpdUODJ/ygcinMpuPdnXmJaeJbvD3ztWWTBrLWWmt9uPfee/clnSY3BUdljOvEf/2k/Ha6II2z/HkE2xzEjcBdyJLvVscee+xaTAhruGTaq/Jx4tZLC8xaCVImsfclL5VXq6rGngRhvKfSODDqpXPDItzcKoAQe560pl4IT7u8BEqe12gIUcsIFSeffPImSOUvQcggq0oHi6zHRfRUZWXle6hmq6vQaYxLH78oClB3hnGZF9ta16M49e7J8JYfYPoaxviL99lnnwMcHspiXEsEwde3eISnDZ3QeeIxv0alqDaMBpZkMnIu9z+ZoYpgWXgWftDbiS/cYqyubEb55iKk/Dpnky+4mc6SM38exkxfJ9unBYry+vOf/7wuR3j8oHj8tE9WO5FwIDw1cODAh4ClDbUSQsZP3+5H5QWeEQTrrbfek3fddVdH8k6PUFSOqqqq9aG5rNWV1jdfhZNPinLOJt0qSmsfwsxFX0rsR4R6Gko+AkXAbUa8JQlbYx15nOvkqHqFVnT3I6YA8cSIESO2J21aOAluyf1yKEDdlTP8bc2Kws1grR7Qj9ds/ZvelwnKd0grhtVu9VyPNAzFab3jjjtuQs89X7xDfmn+Vd7uR4JNDQ3hlcS4TFe2VPDkpQkTX41JAiWO8BvLsGEJc4Z+moXJT3mqQXJNzFWk66C0PLZRShtvxdzIP4SfhIAb58xvB/+VNO7l+s4Md/8WPGknwB7p5JkuK7/L99xzz0q0pqlqd8TVPIsvPPKrldDjZICNSWsEKu9oWkKRcUGdkBdAvZURn6rcBGfL3o0UP5Jx3CBXmKJmOcLNZBuIa0PiC4qAnyqr5H4hFFDdq87oddfjkK291ZCYNMwatriLI6YnXYzzTp5iPkF38PZgvkD84+tgcAMTXokwJ/I9qze6lGpz3wR1AUbAMCcQI4+DmNR84L777ptNkBpSPs7wOhasfeDrtpTPN73TFow9B/MnyssshUMjgkw7kQCJMtSbwDLy/sTp7grLwklpyE8T3KbzzoogYD+3xSja2NvsA3q5ztu0L4JNW42iQS7BPmcWYeboBWDz6eskqCug84DRo0e/78Qym4h8UxQqwBYIBCXAfmKM+BoTtevCXMw91Rp1zy8vFQqi9sfUOEaBA0voB6Pk36wUUO8la9YxMH0Xej91CIF1TgMxw1tWTHaGwYc5fBOqEPBahJPza9kUt5YaGU7Zewowx58k0RR2FYMQKlrhuEn54aeGndMprSKRphUWp+aUe779hueKa/JDiyFa6kt+6+5lA8O+8Y9huDaVYcy9nPVyguDjFN+3HITVxfL4r3TSOhDUKxEoDx944IELycuUz+Yp+EccccRKdmLPxk+ZeUD62UudAnEq2FfUF18ROo2bpKQ8/NTQtMqIGhn6KlLgZTkQMGb19ARnqxeiV5F0FuZZjwoqdU9vNiI9xxkTukskkBGzMix5NBsFqCsxnFGFOXpxHTSHr1TnICRey6pvLz+nEZjxujSbfB7xjRdMLz/iSr1PsUF1KltAVhXexMvpnDKqnBqyrMIk5euUMT2sAUA9HMjDlJ04tWwVWMBk6uZOWsEwDdJ5G5jO0G0e5TZt0KbPhBvitxliMTJ4DfjmNsLMwjl4VDD/dIpoYZ8A2IZmDPHuJ62ko8E/FOEyM2/k7xRSV7P2IlogKIXrQfLrAGPFDU4QCK0U2JQUEEM69aXl3EPQRHtSl/JL92S58FF8tIwGOSfvXFmYcLLBmCley5aP/hi77aG0OD8twwtm9LrrrtMVMd2U1iuC2w/aKL8fmcuQbU0956JbEq38YzSZl/FzC5t68cP8kCBHOK8kvzuI/yNPVh2QhYR9NUPLWcQXWNFA7yznoAPImAxY1yKCrigxQrFJBQrjYUnoOKbXuh9VY7/AShPiepickxVfVsFKHi2TAqpbPcKO1YRVsc3QJKCpb+rTm0t9iuLAEay8Hpu/D9hMb83rqC3EJk+ePI63Goh4NaxLsUepO43R2o4EpnNoseiYY47JOqBICV1lXsHu6lsRPgulpQQC9QmE3hLIZbShqc8888w9Au886RTKz9KLUcj3jByWOvVkNJV0xJ8/TB1Kg0TgdeHEPVnrpoXUbH4I2aINeRyEpf5GOYJ/EEh8B4H88rMFloprhjxI1okscWkGntPCSq6lU8BV3+UsUR7Mqp7mCUx9i8HBP13HLekbvqxVY8JM4SBXGdINxYvuNh77WvYirRnuOGXKKqfTSDV8SdHAnyZtGyd9ukHbPFz+7bD+nUxbyLni40VLDa/kzwqONhMqn6yFGFdeZQz5BnXv3n0O+fkOs2wdCjbDxHlspRgKDLNS1ZQaigrS8YILLriQdzekptE++A50IC/T62ns02iQhA4EXgosOAXEnGowAI5hfboK1tFjZZnJb+Ov8IJnWgCAwhk8Ze/eFhP0wwGpHe/G5cBZbag1Pfu6DOsMTzvlzyoncMy2EhprBCO06aRboQwU30mjn26Hd3TpQQcddDlpV+qHOzDoW1F5dM6udmq/+vvf//5+4mvPTdBkcwpbse+IMw/twzcvlUNOE7NY6nbl4i8NH4ygzJJWJqbPPwFCCluVzCdW2lsI2SGNdkdWYIl3Eqez7eKol4KTjpz5IYIQLq1GQf/jUQ+X5R6oG+vGQ5vvZkEoeeRDgY+JPJV9L+fVaRpZSak3qqxu/uHxxx/fmvtwfu9EUkBW/BbmYcYHDF2GsEVgMKr8q+CXC2nxuMz4+zg8q/i+jVE9P0+CzXgziWc6dOhiGiO/00505IdoWQ4dX+X5D3ugdqWhawiTUxEQrYEhl+JIkFtZtrcGa+k8PD5SXP+xEsH6E21UwX7lMP7kEWMjYoSnkriamA3cOi6A9RzIRVBxtDITVCARQuEidBkmwR3YkzOIreKncTDMCBFDhc3hiGLiaD1+JnuDzDo3+WcJleHXHcsCQrxNqqJVHdDFOSCXghtFgTXLf4zuu9qGi86rs5/wg6VlzwrqTdpJOfUm7TKIZwRH9efHwAovqoPfQLPuCEkESnv2x2gL87s8K8R3TrgX46aOPvroTvTUfR2eDcRTeSAUlmPkJw3FOPy84NrgJFeRrmTS9yba0PY09JzDfhe8KCYaz++xxx66hVMCxrMcrvgUIbWc50uVRT+UzCLifts00mTY6tCbMO3Ty+/0ch0/xwakPbDwU4ZW+3Dno2+pjuVoMp0ZJ1axQrMpJ1htiiCRumUKlJkg8zfxVHjjzRkXT7799tvf8sOjYKyvv7P+6ET18r1qU8labDBJVpjzTjNxKv3mJgPqJF5bU7Z0SKtLIg+knk/tmzZhT5PH4YtaBMrQTz75ZIR6VKuRpiN5fKi+1XPX8bBHhEZ4Cbbggocfz1ropmHDqxEEysjdd9/9TsqhO3w8eM8miSSee+65jsTvTbo6qZQOyv4QfXiqsamaQajJT/BxnkIFf9GkbLfddvsvNjlPqf3ZuEqXnYNRaww8zQmxrHsX58vOJ15WZ5yZ1oFXSyc+m8OsdKC375kqSksm5mFYu/a9997bBvuWFWGGPGlVVbuFKdDlAuTnFKYK0aNvkFRUXmYFMFeFKo1JoLQIpAUUTjPT0oq8pGs8Mm92v3j1shHVOq3DZMS/kisOBajOstpENDn/+wmRjb0EvMlW9VfOuRwnUeeyG8rJyMRRGg03cvIH8fJ2Dg8a3uLbl3kVJr7DJdmavzq7lkezIvk/NjTKXwZ59RzxpXVFOSRpbRq7LFpNYptfvch1P7ikoVZpfthiiy2+JHrOeUEH3wRa0BKseG9ixWw7NChN5nqWRygoDOEcZTJ86mWXXfYY+RncBQtn2lcmbk4+wj/K9prPMfTTsQYVQXWifAhPMZ3RDxP8LgiU+WEESr28NfEUxtEzqWCGYLxzqbtpkMRVoYyKrJlwlvGkdhojmnQk+zGcj3gkVVu315BDhVgLt2Gld8EpAHmTtVzCZw6z8oCuelYj+dvf/rYZPfbmWgVAWw3iMa1cqKeJYRYgy9D7+C3jx4JWIzjpiNFuWHWOQcvW3hnT8D2KYBqjwnER5iz2Z3v/P8ePH/+p45eJl8qmXfj9lQDcUYK8tSDB49GZDXFpAKRTgzX08sLDwy/B5OcrpP2AMgwjH48opufWkErtJVFVVXUf5/Au1Il2HEKlQmXi7wmD+3xmcezIMuK39YyAp+ihMN4p7HfWQAB15+enQZWt+A1yIGIqhsQ2U8HxrUSbidLhkqhqUaz6PjvzzDMv4vdK7FeMlpRFkCmEjpQmBJ2YKOe/BFcoohGv5PKkABwZk8gO6B3EX60RDofR8XRGUHhz/c/5qsK1ETCy4YYb/pOJx9v4LfDheq2f4QR9iR+UTzmNcQC96fbwkRp2QDEi5h4g1P5uXOI+irTn8PjhFQWmDhoKdOQnrQGSJGNswLPzJ4JpOl2/xOJ5nG07S5h7uZNb+zbETzefe2l0EtJxtJMZzLvcAVxdNeIH3ss/dfDBB8/6y1/+IoHXwyuC9VN75YmyzaECk/7e+L8WRFSbLu83iKQlvYsYOeGAm3Z+RphlnT9ixIjTuDCdBYVIFLP7rFlwFzBJL6oFoust4peeotAA1vZUAVXHtp5PO+20oTTEP8DUeP18NKCrvuyn6TEVhyXUL/DUxKEafyGFic1LDXI5O93vhscS8FhgI1YiBCLtP55EoOzPVac9SFdry2iB8k4yPCjjhLiBhMF2ZhjiCq7/CQxTZk64/6x+SKhfok0SjX0i85PTgCVhIr96jrJJS4qA120Mq+YSKC0op3aiOMQ1DwsvP1KWHxB+9WBn/iCO4KptRhBgfZRXUQRKZsYBv9MFlZrGE0PSfb3RRhuNQU17XOmEcED6UlAzU0A85aCgdwX33fyR4UVPDY3FpK7wTExtukhlZeUEtBMxv1R1XdhVsAeYElA67S26yy67/Bst5WNwMyfnkw/evk7IS6OoYneujMLSbUVl0oOrxb6jC3MtAwRF2gf+vgAJMMZfrIqoo7QNOCi+CSMf206U5zwExTVCDE0+rQHib9oK3nHgy27rdhJL6IRuP8rHZMhqDYdOfaGyqF06flkvIUMcuQha2npoRK3SRMqKXTwPQxwHedWoOaMBxOOMRWdTQaM51l+9lSRfaGIUD90S5CAKqB6duoztt99+vXSmKfMmQUmMoHHS6RjEBTyTSaBEZuI9MHGegW78MBBbxLkn9woE/GY0Cj9wSqchBe/YzJkzd0dQVikujcdKISNU2OS3Nn4dHa3HhnmB1YSs4C1hzkETsqKbb2P1AkB8MwczZsyYh9AiPsgUjE6Zokwi384Zsboj3NDaC1aAn2RClGHZDODJZiaoDUqYiA4RJov7NIVAscLD4m8loPkNwgkQ1kapFZj7TuIci50nTZr0MoH14tnEpXfLpAAMpZ4wxsTcbgiU1dVY+G0anA/GanhEi0YZ3r5HB6Iziota52QlgaXO62GEgK6LMEOBHPlq/1GS1c11Lr74Yt2d7O6ATcPDNsScHSJeJtzTkbdpB2p48PnXXBUzj283LM90Xp7cZ6QJ1iUMaW4gT82XKJpoJ6UlxorT/6qqqh6UJ/k2ZPhohCLGcNOVXrgLlpejDKaOiRNhVWxNdir3aFChvIB7+FlEjCRGaEh4yE+z4Zr00rJWNVrJi6yxH4mEO+iMM874hHCpvUFzJj9nNZzPIPn5c8zSV3EpEB07dmxX7I1GO9monm39Z+WshqUGiMpezXDnESLIHNHUeRADZwHK3yPGDt4vnTytpuQJRY1FuOjNqkoZu5BHYjKhTYNuQZTCEngQfhrKeMJxPM15JMCLcMTBF8zlqKFLCOftODPXCAno9yjDkvclRAREyEJPWbredeutt36DV0Nbhqk7DrH+Atj6ziUjopq3ofztEWb9Fdn0FrwL7QxcMtK4UgTXE0OIrKRCX+NyoQuRgrsz9tqV+ZL7yFwS3kjyfBCBanUDI32IhKWnaDRIaWEe+iZZ7XTqSC1OK4VJrGL3YS5hsL7F5PjbOE7U+i/xAp3KNO7gVd0X3Tn4iMdqmKebiMGXhFiQFmWGN0pHeXTI14bjxo37A2kMH/MWp5W9++676zH0iDLM8xUQwCBqnWOj3zQ24EkoBNLHxvd5p1jW/RFYV9OeAG9chDLNxF5Fe3bSNwT4pA/yNoIdGmnIsyzHsr+FI1qk2FYzpIzxq0xttfGohreI8nPpbfT83xKYNaiLS9jYt5BZ/G9RM79AvZ2KuvcOe3pmX3vttfMAK4M1m599h89tCgJov7bV1E6C2l8JAFVS/nDC5/hbjkmVxpK11dVtysug94uQYrihtW4CbMfxjgfDSyvhoaDGIs43l2zJOIuxvoyuZMUpfhFTNoVLMsR6n4lLWcDqiEhNAAfOp9CwZP3dGvP6/TkCYDL8q0anVR8tjfdAy16ulRXFw3mVQRp5kuXVtgifT4lgGqBXxFx+wBetlE8tRy8+y5DrM/LuR95JtIpH0BR1pahxime/w74d2JoPW0CbncndPx2ArXbqKQCFD6tO1azstdf1pGUk3InI5c5wJGy+OeOhJiZgtKWoQ9WM92pPOukkqbcipBhOvZp6izQTNaTwlDFRsc0pl6WWLrqlvKLOiA6YJVdEClQvTcU79lr968goxtfn1WUEU1VzX9OYsDykDaY0riiHOs+l3tM8UES0zfDGga/GuIhh9oH87iCchQ/8Gpg9jTZKg9XdxRJA4mG5RTTq3RWGYFHD8oWjMOaX4tjofEX64FnrOtie/2070Rs37/TTT59ExHGU4wfu9LmLb8H2bPyeAD08ga06WX7iiSeOlBAkn8A6smXr3bv30v8H/7qs7PFEMrwAAAAASUVORK5CYII=" alt="Brightbeam" style="height:26px">
<span>Metis, a reference toolkit for governed tacit fragment capture.</span>
<a href="https://github.com/BrightbeamAI/metis">GitHub</a>
<a href="https://github.com/BrightbeamAI/chap">CHAP</a>
<a href="README.md">Docs</a>
<span style="margin-left:auto">Apache-2.0</span>
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