Add negative results opportunity radar

negative-results-opportunity-radar/README.md

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+# Negative Results Opportunity Radar
+
+This submission targets [SCIBASE issue #16](https://github.com/SCIBASE-AI/SCIBASE.AI/issues/16) with a focused AI research assistant module.
+
+It turns negative results, limitations, and failed replications into ranked research opportunities. The goal is to help a researcher find tractable gaps that are often buried in paper discussions instead of highlighted by normal keyword search.
+
+## What It Adds
+
+- Signal extraction for limitations, negative results, and failed replications.
+- Opportunity ranking that weighs evidence strength, lab capabilities, and researcher interests.
+- Reproducibility flags for gaps backed by failed replication evidence.
+- Assistant packets with peer-review prompts, citation queries, and next-step recommendations.
+- Dependency-free tests and demo data that can be wired into a larger assistant/search service.
+
+## Demo
+
+```powershell
+node negative-results-opportunity-radar/test.js
+node negative-results-opportunity-radar/demo.js
+```
+
+`demo.mp4` is the reviewer-facing video artifact for the bounty submission. It walks through the problem, implementation, acceptance path, and command validation in 8.4 seconds. `demo.svg` shows the assistant flow from paper evidence to ranked opportunity and reviewer prompts.
+
+See `acceptance-notes.md` for the payout-gate evidence checklist.

negative-results-opportunity-radar/acceptance-notes.md

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+# Acceptance Notes
+
+This is a focused implementation for SCIBASE issue #16, not a generic AI-generated content drop. The module is small because the bounty asks for a specific AI research-assistant capability that can be reviewed independently before integration.
+
+## What Changed
+
+- Added signal extraction for negative results, limitations, and failed replications.
+- Added evidence clustering and opportunity ranking by topic, method, lab fit, and user interest.
+- Added reproducibility flags and assistant packets that include peer-review prompts, citation queries, and next-step recommendations.
+- Added focused dependency-free tests and demo data.
+
+## Video Demo
+
+- `demo.mp4` shows the problem, implementation, acceptance behavior, and validation command.
+- `demo.svg` provides a static architecture and workflow diagram.
+
+## Validation
+
+Run from the repository root:
+
+```powershell
+node negative-results-opportunity-radar/test.js
+node negative-results-opportunity-radar/demo.js
+```
+
+Expected result: the test prints `negative-results-opportunity-radar tests passed`, and the demo prints a ranked opportunity packet with evidence, reproducibility flags, citation queries, and peer-review prompts.
+
+## Integration Notes
+
+The module is intentionally dependency-free so maintainers can lift it into a larger search or assistant service. The next integration step is replacing the sample paper objects with SCIBASE paper ingestion output.

negative-results-opportunity-radar/demo.js

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+"use strict";
+
+const {
+  buildAssistantPacket,
+  rankOpportunities
+} = require("./index");
+
+const papers = [
+  {
+    id: "trial-101",
+    title: "Neuroinflammation classifier pilot",
+    topics: ["neuroinflammation", "biomarkers"],
+    methods: ["proteomics", "cohort-validation"],
+    limitations: ["study excluded early-stage patients"],
+    negativeResults: ["blood panel failed to separate mild disease"],
+    failedReplications: []
+  },
+  {
+    id: "trial-118",
+    title: "External proteomics replication study",
+    topics: ["neuroinflammation", "biomarkers"],
+    methods: ["proteomics"],
+    limitations: ["assay drift was not modeled across sites"],
+    negativeResults: [],
+    failedReplications: ["published classifier failed on two external cohorts"]
+  },
+  {
+    id: "screen-22",
+    title: "Mouse model intervention screen",
+    topics: ["neuroinflammation"],
+    methods: ["animal-model"],
+    limitations: ["translation to human cohorts is uncertain"],
+    negativeResults: ["late intervention showed no measurable rescue"],
+    failedReplications: []
+  }
+];
+
+const opportunities = rankOpportunities({
+  papers,
+  labCapabilities: ["proteomics", "cohort-validation"],
+  userInterests: ["neuroinflammation"],
+  minimumScore: 20
+});
+
+console.log(JSON.stringify({
+  opportunities,
+  assistantPacket: buildAssistantPacket({
+    projectId: "neuro-gap-review",
+    opportunities
+  })
+}, null, 2));
+

negative-results-opportunity-radar/index.js

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+"use strict";
+
+const crypto = require("crypto");
+
+function stable(value) {
+  if (Array.isArray(value)) {
+    return value.map(stable);
+  }
+  if (value && typeof value === "object") {
+    return Object.keys(value).sort().reduce((result, key) => {
+      result[key] = stable(value[key]);
+      return result;
+    }, {});
+  }
+  return value;
+}
+
+function digest(value) {
+  return crypto.createHash("sha256").update(JSON.stringify(stable(value))).digest("hex");
+}
+
+function normalizeList(values) {
+  return [...new Set((values || [])
+    .map((value) => String(value).trim().toLowerCase())
+    .filter(Boolean))].sort();
+}
+
+function normalizePaper(paper) {
+  if (!paper || !paper.id || !paper.title) {
+    throw new Error("Paper requires id and title");
+  }
+
+  return {
+    id: String(paper.id),
+    title: String(paper.title),
+    year: paper.year || null,
+    topics: normalizeList(paper.topics),
+    methods: normalizeList(paper.methods),
+    limitations: normalizeList(paper.limitations),
+    negativeResults: normalizeList(paper.negativeResults),
+    failedReplications: normalizeList(paper.failedReplications),
+    citations: paper.citations || []
+  };
+}
+
+function signalRecord(paper, kind, text) {
+  return {
+    paperId: paper.id,
+    title: paper.title,
+    year: paper.year,
+    kind,
+    text,
+    topics: paper.topics,
+    methods: paper.methods,
+    evidenceDigest: digest({
+      paperId: paper.id,
+      kind,
+      text
+    })
+  };
+}
+
+function extractSignals(paperInput) {
+  const paper = normalizePaper(paperInput);
+  return [
+    ...paper.limitations.map((text) => signalRecord(paper, "limitation", text)),
+    ...paper.negativeResults.map((text) => signalRecord(paper, "negative_result", text)),
+    ...paper.failedReplications.map((text) => signalRecord(paper, "failed_replication", text))
+  ];
+}
+
+function clusterKey(topic, method) {
+  return `${topic || "general"}::${method || "method-open"}`;
+}
+
+function addToCluster(clusters, topic, method, signal) {
+  const key = clusterKey(topic, method);
+  if (!clusters.has(key)) {
+    clusters.set(key, {
+      key,
+      topic: topic || "general",
+      method: method || "method-open",
+      signals: []
+    });
+  }
+  clusters.get(key).signals.push(signal);
+}
+
+function buildClusters(signals) {
+  const clusters = new Map();
+
+  for (const signal of signals) {
+    const topics = signal.topics.length > 0 ? signal.topics : ["general"];
+    const methods = signal.methods.length > 0 ? signal.methods : ["method-open"];
+    for (const topic of topics) {
+      for (const method of methods) {
+        addToCluster(clusters, topic, method, signal);
+      }
+    }
+  }
+
+  return [...clusters.values()];
+}
+
+function countKinds(signals) {
+  return signals.reduce((counts, signal) => {
+    counts[signal.kind] = (counts[signal.kind] || 0) + 1;
+    return counts;
+  }, {});
+}
+
+function opportunityScore(cluster, labCapabilities, userInterests) {
+  const counts = countKinds(cluster.signals);
+  const uniquePaperCount = new Set(cluster.signals.map((signal) => signal.paperId)).size;
+  const capabilityMatches = [cluster.topic, cluster.method].filter((value) => labCapabilities.has(value)).length;
+  const interestMatches = [cluster.topic, cluster.method].filter((value) => userInterests.has(value)).length;
+  const unsupportedPenalty = capabilityMatches === 0 ? 12 : 0;
+
+  return (
+    uniquePaperCount * 10 +
+    (counts.limitation || 0) * 8 +
+    (counts.negative_result || 0) * 16 +
+    (counts.failed_replication || 0) * 22 +
+    capabilityMatches * 12 +
+    interestMatches * 9 -
+    unsupportedPenalty
+  );
+}
+
+function recommendedActions(cluster) {
+  const kinds = new Set(cluster.signals.map((signal) => signal.kind));
+  const actions = [];
+
+  if (kinds.has("failed_replication")) {
+    actions.push("Design a focused replication or adversarial validation run.");
+  }
+  if (kinds.has("negative_result")) {
+    actions.push("Convert the negative result into a bounded hypothesis search.");
+  }
+  if (kinds.has("limitation")) {
+    actions.push("Target the stated limitation with a smaller controlled follow-up.");
+  }
+  actions.push(`Search related citations for ${cluster.topic} using ${cluster.method}.`);
+
+  return actions;
+}
+
+function reproducibilityFlags(cluster) {
+  const flags = [];
+  if (cluster.signals.some((signal) => signal.kind === "failed_replication")) {
+    flags.push("failed_replication_evidence");
+  }
+  if (new Set(cluster.signals.map((signal) => signal.paperId)).size > 1) {
+    flags.push("multi_paper_signal");
+  }
+  if (cluster.signals.some((signal) => signal.kind === "negative_result")) {
+    flags.push("negative_result_context_required");
+  }
+  return flags;
+}
+
+function rankOpportunities(input) {
+  const labCapabilities = new Set(normalizeList(input.labCapabilities));
+  const userInterests = new Set(normalizeList(input.userInterests));
+  const signals = (input.papers || []).flatMap(extractSignals);
+
+  return buildClusters(signals)
+    .map((cluster) => {
+      const score = opportunityScore(cluster, labCapabilities, userInterests);
+      return {
+        id: `opportunity-${digest(cluster.key).slice(0, 10)}`,
+        focus: `${cluster.topic} + ${cluster.method}`,
+        topic: cluster.topic,
+        method: cluster.method,
+        score,
+        confidence: score >= 70 ? "high" : score >= 42 ? "medium" : "low",
+        evidenceCount: cluster.signals.length,
+        evidence: cluster.signals
+          .sort((a, b) => a.paperId.localeCompare(b.paperId) || a.kind.localeCompare(b.kind))
+          .slice(0, input.evidenceLimit || 6),
+        recommendedActions: recommendedActions(cluster),
+        reproducibilityFlags: reproducibilityFlags(cluster)
+      };
+    })
+    .filter((opportunity) => opportunity.score >= (input.minimumScore || 20))
+    .sort((a, b) => b.score - a.score || a.focus.localeCompare(b.focus));
+}
+
+function buildAssistantPacket(input) {
+  const opportunities = input.opportunities || [];
+  const top = opportunities[0] || null;
+
+  return {
+    projectId: input.projectId || "research-project",
+    generatedFrom: "negative-results-opportunity-radar",
+    rankedOpportunityCount: opportunities.length,
+    topRecommendation: top ? {
+      id: top.id,
+      focus: top.focus,
+      score: top.score,
+      confidence: top.confidence
+    } : null,
+    peerReviewPrompts: top ? [
+      `Ask whether the manuscript discusses negative or failed evidence around ${top.focus}.`,
+      `Check whether the proposed study distinguishes novelty from known limitations in ${top.topic}.`
+    ] : [],
+    citationQueries: opportunities.slice(0, 3).map((opportunity) => {
+      return `${opportunity.topic} ${opportunity.method} negative result failed replication limitation`;
+    }),
+    reproducibilityChecklist: top ? [
+      `Trace evidence papers: ${top.evidence.map((item) => item.paperId).join(", ")}`,
+      `Confirm capability match for ${top.method}.`,
+      `Plan a preregistered validation for ${top.topic}.`
+    ] : []
+  };
+}
+
+module.exports = {
+  buildAssistantPacket,
+  digest,
+  extractSignals,
+  rankOpportunities
+};
+

negative-results-opportunity-radar/requirements-map.md

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+# Requirements Map
+
+Issue #16 asks for an AI research assistant suite with literature review, research gap finding, hypothesis generation, peer-review help, summarization, citation management, and reproducibility support.
+
+| Requirement | Implementation |
+| --- | --- |
+| Research gap finder | `rankOpportunities` ranks gaps from negative results, limitations, and failed replications. |
+| Literature review assistant | `extractSignals` turns paper metadata into normalized evidence records. |
+| Hypothesis generation | Recommended actions convert negative evidence into bounded follow-up studies. |
+| Peer review assistant | `buildAssistantPacket` emits prompts for checking missing negative-result context. |
+| Citation support | Citation queries include topic, method, negative-result, limitation, and replication terms. |
+| Reproducibility support | Opportunities flag failed replication and multi-paper evidence. |
+| Scientific trust | Evidence records preserve paper IDs, titles, years, methods, topics, and digests. |
+