{"ok":true,"schema":"sb.signals.v1","asOf":"2026-10-16","generatedAt":"2026-10-16T23:28:45.296361+00:00","generated_by":"Signal Bureau — signals for systems (https://sigb.ai/machine/)","methodology":{"attention_edge":"Measured association: flagged markets repriced materially at 1.47x the rate of matched unflagged controls (95% CI 1.32-1.63, shock-days excluded, controls reweighted to the flagged cohort; n=2852 flagged vs 119840 controls, window 2026-03-29 to 2026-10-16, as of 2026-10-16). An attention-leads-movement association — never a directional claim. Methodology: \"Does the Attention Signal Predict Repricing — Flagged vs Control\". Aggregate regression summary (per-stratum bundle is roadmap): https://sigb.ai/api/record-data","not_a_claim":"No directional or trading claim. The measurement says flagged markets MOVE more often than controls — it says nothing about which way. Informational only; never investment or betting advice.","trajectory":"Each signal reports momentum on two horizons that can legitimately disagree. trajectory.direction is a REGIME verdict (latest coverage vs the 3-week baseline, sustained over 7 days); trajectory.baselineRelativePct is the number it is based on. trajectory.deltaPct is the honest day-over-day tick. A name well above its baseline reads 'rising' even on a down day — so direction:'rising' with a negative deltaPct is not a contradiction, it is a name still elevated that dipped today. Read direction for the regime, deltaPct for today.","provenance":"Every signal carries an explicit evidenceStatus: 'receipted' signals include source-attributed evidence (name + URL) you should check before acting; 'unreceipted' signals report the cross-domain acceleration measurement without attributable article receipts in this build — treat those as leads, not sourced claims. Filter on evidenceStatus === 'receipted' for fully attributable rows. Evidence rows carry TWO named date axes and you should read the one your use needs: capturedAt is the day we could have observed the document and is never later than this record's generation day, while publishedAt is the source's own declared date and MAY be in the future — an award's period-of-performance start or a journal's ahead-of-print issue date is a real publication stamp, not a sighting. `date` is the long-standing key and carries the capture axis, so it equals capturedAt; where a source declared only a forward date there is no capture to report, both read null and capturedAtWithheld states the reason rather than substituting a clamped date, which would be a fabricated observation. Market-question rows additionally carry marketDeadline (parsed from the question), deadlinePassed (computed at serve time), and pricedAt (the freshest bus price stamp) — a market whose deadline has passed is a historical capture of the arc, never current expectation. lastSeen under `observed` obeys the same bound: it is the newest day we actually saw the subject, never a forward source date. detectedAt is the entity's latest-sweep detection date (renamed from firstFlagged 2026-07-21, which over-promised).","clusters":"Some rows carry clusterClass: true with a clusterNote — the name is an umbrella term (AI above all) spanning many unrelated stories, so its verticalCount and signalCount overstate any single thesis; normalize by baseline before treating breadth as signal. Labeling only: cluster rows are not re-ranked or suppressed. Fields absent means the name reads as a single story thread.","coverage":"When populated, the coverage object names three DIFFERENT vertical counts that must never be read as one number: registered (every vertical in the coverage registry, including new ones in burn-in), activelyScraped (verticals producing articles on the current daily scrape cycle), and spannedByFlaggedEntities (verticals the currently flagged entities draw evidence from — moves with the news). When the committed snapshot predates the coverage object, the field is served as an explicit null with coverageUnavailableReason stating why; counts are never fabricated. nextBuildAt and buildCadence, when present, name the next scheduled data build and the schedule it rides — a quiet gap before nextBuildAt is scheduled rest, not staleness.","desks":"The desks object serves each desk's evidence roster (graph subjects whose home domains match the desk's beat, with story counts from the committed corpus) and typedLinks — connections between two desk subjects where the dome's relational parser typed the claim (acquisition, meeting-visit, sanction-trade, ceasefire-deal, treaty-agreement/exit) in at least 2 independent headlines naming BOTH parties. together is the pair's co-coverage count; verbs are the typed assertions with their counts. An empty typedLinks means no verb has accrued for that desk's pairs yet — the roster is still evidence.","counts":"Each signal carries counts.attention.current (scope tracked-coverage: documents = article signals the tracked feeds carried, verticals = the coverage verticals they came from, both on the row's detection day; domains is the same count under its old name, kept permanently) and counts.evidence.total (scope retained-corpus: documents deduped by url and distinct publishers, over firstSeen..lastSeen). Every count names its unit, window and scope; a window that cannot be determined reads {state:'cannot-measure', reason}, never a guessed date. The two scopes answer different questions — never compare or add them. signalCount and verticalCount are deprecated aliases of counts.attention.current.documents.count and .verticals.count. Each row's verticals lists its coverage verticals by name (domains is the same list under its old name), and why restates the detection sentence as coverage verticals across subject areas. Feed rows are compact: each count inherits its namespace's window and scope, and the per-row prose lives here (get_entity serves the full per-count form with definitions). observed.homeCategories lists the subject's home beats by id, where 'auto-vertical' (legacy homeDomains value 'auto') is the auto-vertical scrape layer, Uncategorised auto-scraped feeds; home beats are not the counts.attention.current.verticals count.","identity":"slug is a discovery input and may change; canonicalId (sb:e/...) is the durable identity — the same id the node door serves (traverse, GET /api/v1/node/e/{hex}). addresses names the page and the node-door URL (the MCP calls are get_entity {name: label} and traverse {id: canonicalId}); aliases are the curated shorthand that resolves to it. canonicalId is null with canonicalIdState when the entity graph does not carry the subject.","licensing":"Feed outputs are licensed for evaluation, individual use, and live consumption. Systematic bulk collection over time, redistribution as a dataset, or use to train/calibrate models or competing signal products requires a commercial license — https://sigb.ai/terms/ (section 4a)."},"count":338,"desks":{"ai-compute":{"subjects":[{"name":"accenture","stories":165},{"name":"techcrunch disrupt","stories":131},{"name":"millennium prize","stories":128},{"name":"gpt-6 luna","stories":83},{"name":"devday","stories":82},{"name":"openai devday","stories":41},{"name":"mllms","stories":27},{"name":"multimodal large language","stories":26}],"typedLinks":[],"basis":"roster: graph subjects whose home domains match the desk's beat, by story count; typedLinks: dome-parsed relational claims where BOTH parties are desk subjects"},"defense":{"subjects":[{"name":"pentagon","stories":2125},{"name":"navy","stories":1765},{"name":"u.s. military","stories":836},{"name":"lockheed martin","stories":606},{"name":"hegseth","stories":486},{"name":"cold war","stories":485},{"name":"f-35","stories":454},{"name":"pete hegseth","stories":445},{"name":"u.s. navy","stories":418},{"name":"u.s. army","stories":350},{"name":"northrop grumman","stories":341},{"name":"u.s. air force","stories":327}],"typedLinks":[],"basis":"roster: graph subjects whose home domains match the desk's beat, by story count; typedLinks: dome-parsed relational claims where BOTH parties are desk subjects"},"energy-climate":{"subjects":[{"name":"oil","stories":9032},{"name":"inflation","stories":5282},{"name":"interest rates","stories":4100},{"name":"housing","stories":3902},{"name":"hormuz","stories":3634},{"name":"renewable energy","stories":2467},{"name":"federal reserve","stories":2465},{"name":"oil prices","stories":2332},{"name":"cpi","stories":1525},{"name":"natural gas","stories":1523},{"name":"lng","stories":1121},{"name":"oman","stories":1106}],"typedLinks":[{"a":"oman","b":"hormuz","together":640,"verbs":{"meeting-visit":2}}],"basis":"roster: graph subjects whose home domains match the desk's beat, by story count; typedLinks: dome-parsed relational claims where BOTH parties are desk subjects"},"geopolitics":{"subjects":[{"name":"india","stories":11163},{"name":"united states","stories":10371},{"name":"europe","stories":9544},{"name":"iran","stories":9368},{"name":"france","stories":7617},{"name":"european union","stories":7404},{"name":"russia","stories":6601},{"name":"congress","stories":6371},{"name":"germany","stories":6314},{"name":"israel","stories":6170},{"name":"africa","stories":6094},{"name":"south korea","stories":6037}],"typedLinks":[{"a":"iran","b":"israel","together":2712,"verbs":{"meeting-visit":2}},{"a":"russia","b":"iran","together":2027,"verbs":{"sanction-trade":17,"meeting-visit":6}},{"a":"european union","b":"germany","together":1733,"verbs":{"sanction-trade":3}},{"a":"france","b":"european union","together":1693,"verbs":{"sanction-trade":4}},{"a":"india","b":"congress","together":1691,"verbs":{"meeting-visit":3}},{"a":"european union","b":"russia","together":1509,"verbs":{"sanction-trade":24}},{"a":"france","b":"russia","together":1219,"verbs":{"sanction-trade":3}},{"a":"russia","b":"germany","together":1155,"verbs":{"meeting-visit":6,"sanction-trade":3}}],"basis":"roster: graph subjects whose home domains match the desk's beat, by story count; typedLinks: dome-parsed relational claims where BOTH parties are desk subjects"},"health-bio":{"subjects":[{"name":"colorectal cancer","stories":1752},{"name":"immunotherapy","stories":586},{"name":"remains unclear","stories":553},{"name":"kras","stories":429},{"name":"risk factors","stories":401},{"name":"lung cancer","stories":347},{"name":"adverse events","stories":290},{"name":"javascript","stories":281},{"name":"ctdna","stories":231},{"name":"hispanic","stories":188},{"name":"egfr","stories":165},{"name":"precision medicine","stories":154}],"typedLinks":[],"basis":"roster: graph subjects whose home domains match the desk's beat, by story count; typedLinks: dome-parsed relational claims where BOTH parties are desk subjects"},"space-economy":{"subjects":[{"name":"international space station","stories":263},{"name":"low-earth orbit","stories":140},{"name":"watkins","stories":124},{"name":"force station","stories":113},{"name":"transporter-18","stories":59},{"name":"roman space telescope","stories":51}],"typedLinks":[],"basis":"roster: graph subjects whose home domains match the desk's beat, by story count; typedLinks: dome-parsed relational claims where BOTH parties are desk subjects"}},"receiptedCount":326,"unreceiptedCount":12,"receiptsState":{"state":"read"},"totalFlagged":338,"nextBuildAt":"2026-10-17T12:50:00+00:00","buildCadence":"8 scheduled data builds daily between 12:50 and 23:05 UTC; the market panel is sampled every 2 hours around the clock. A quiet gap between builds is scheduled rest, not staleness.","coverage":{"registered":143,"activelyScraped":143,"spannedByFlaggedEntities":116,"definitions":{"registered":"every vertical in the coverage registry, including new ones in burn-in","activelyScraped":"verticals on the live scrape schedule today","spannedByFlaggedEntities":"verticals the currently-flagged entities draw evidence from — moves with the news"}},"signals":[{"name":"ai","label":"AI","slug":"ai","url":"https://sigb.ai/entity/ai/","trajectory":{"direction":"rising","directionBasis":"regime: latest vs 3-week baseline, sustained over 7 days","baselineRelativePct":38,"deltaPct":-3,"deltaHorizon":"day-over-day","trackedDays":159},"signalCount":1490,"verticalCount":126,"verticals":["3d-printing","africa","african-economy","agriculture-food","ai-academic-integrity","ai-advancements-general","ai-for-small-business","ai-inference-infrastructure","ai-policy","ai-safety","anti-corruption","antitrust"],"domains":["3d-printing","africa","african-economy","agriculture-food","ai-academic-integrity","ai-advancements-general","ai-for-small-business","ai-inference-infrastructure","ai-policy","ai-safety","anti-corruption","antitrust"],"canonicalId":"sb:e/0ac5c51b855dbe127ecdc905","aliases":[],"addresses":{"page":"https://sigb.ai/entity/ai/","rest":"https://sigb.ai/api/v1/node/e/0ac5c51b855dbe127ecdc905"},"counts":{"attention":{"current":{"scope":"tracked-coverage","window":{"start":"2026-10-16","end":"2026-10-16"},"documents":{"count":1490,"unit":"documents"},"verticals":{"count":126,"unit":"verticals"},"domains":{"count":126,"unit":"domains","aliasOf":"verticals"}}},"evidence":{"total":{"scope":"retained-corpus","window":{"start":"2026-03-03","end":"2026-10-15"},"documents":{"count":25652,"unit":"documents"},"publishers":{"count":1134,"unit":"publishers"}}}},"baselineMultiple":null,"detectedAt":"2026-10-16","why":"In 126 coverage verticals across 108 subject areas (additive_manufacturing, aerospace_defense, africa +105 more)","desks":["ai-compute"],"clusterClass":true,"cluster":"ai","clusterNote":"An umbrella of many stories, not one story — spread across 126 verticals; raw counts overstate it.","evidence":[{"title":"Seoul stocks open sharply lower on lingering concerns over AI, US inflation","source":"Korea Times","url":"https://www.koreatimes.co.kr/economy/20261015/seoul-stocks-open-sharply-lower-on-lingering-concerns-over-ai-us-inflation?utm_source=rss","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"},{"title":"Seoul stocks extend losses as AI, US inflation concerns persist","source":"Korea Times","url":"https://www.koreatimes.co.kr/economy/20261015/seoul-stocks-extend-losses-as-ai-us-inflation-concerns-persist?utm_source=rss","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"},{"title":"Seoul stocks extend losses as AI, U.S. inflation concerns persist","source":"Yonhap English","url":"https://en.yna.co.kr/view/AEN20261015005000320","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"},{"title":"Seoul shares crash over 7% on chip rout amid AI, inflation woes","source":"The korea Herald News Rss","url":"https://www.koreaherald.com/article/10904056","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"},{"title":"ScaleFlux bets its controller chips can break AI’s memory bottleneck","source":"SiliconANGLE","url":"https://siliconangle.com/2026/10/14/liquid-cooling-aside-ai-memory-storage-bottleneck-aidatapipelineforum/","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"},{"title":"Samsung Puts AI Inside Base Stations, Expands Global 6G Alliances","source":"Seoul Economic Daily","url":"https://en.sedaily.com/finance/2026/10/15/samsung-puts-ai-inside-base-stations-expands-global-6g","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"},{"title":"Ricursive Intelligence is betting on AI-designed chips to help solve the compute crisis","source":"TechCrunch","url":"https://techcrunch.com/2026/10/14/ricursive-intelligence-is-betting-on-ai-designed-chips-to-help-solve-the-compute-crisis/","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"},{"title":"Out-of-box Hyderabad: Chips for a smarter, greener AI future","source":"newindianexpress","url":"https://www.newindianexpress.com/cities/hyderabad/2026/Oct/15/out-of-box-hyderabad-chips-for-a-smarter-greener-ai-future","date":"2026-10-15","capturedAt":"2026-10-15","publishedAt":"2026-10-15"}],"observed":{"stories":25652,"outlets":1134,"activeDays":213,"firstSeen":"2026-03-03","lastSeen":"2026-10-15","homeDomains":[],"homeCategories":[],"assertions":{},"movesWith":[{"name":"openai","together":4393},{"name":"china","together":3745},{"name":"anthropic","together":3674},{"name":"google","together":3525},{"name":"ai agents","together":2752},{"name":"buy","together":2607},{"name":"nvidia","together":2556},{"name":"trump","together":2442},{"name":"united states","together":2325},{"name":"europe","together":2187}],"provenance":"curated","basis":"counted from the committed corpus (newsletter verticals + auto-verticals + article bodies), deduped by url; market questions excluded — they are expectation, not reporting"},"ask":"AI: what does the tracked record show, and what would change the read?","evidenceStatus":"receipted","evidenceCount":8},{"name":"school district","label":"School District","slug":"school-district","url":"https://sigb.ai/entity/school-district/","trajectory":{"direction":"steady","directionBasis":"day-over-day (history too thin for a baseline)","baselineRelativePct":null,"deltaPct":11,"deltaHorizon":"day-over-day","trackedDays":2},"signalCount":20,"verticalCount":14,"verticals":["ai-for-small-business","ai-inference-infrastructure","construction","education-workforce","frontier-model-race","intelligence-surveillance","longevity-therapeutics","media-entertainment","mental-health","sacramento-california-business","semiconductor-equipment","semiconductors"],"domains":["ai-for-small-business","ai-inference-infrastructure","construction","education-workforce","frontier-model-race","intelligence-surveillance","longevity-therapeutics","media-entertainment","mental-health","sacramento-california-business","semiconductor-equipment","semiconductors"],"canonicalId":"sb:e/804247facce6268c1e455a42","aliases":[],"addresses":{"page":"https://sigb.ai/entity/school-district/","rest":"https://sigb.ai/api/v1/node/e/804247facce6268c1e455a42"},"counts":{"attention":{"current":{"scope":"tracked-coverage","window":{"start":"2026-10-16","end":"2026-10-16"},"documents":{"count":20,"unit":"documents"},"verticals":{"count":14,"unit":"verticals"},"domains":{"count":14,"unit":"domains","aliasOf":"verticals"}}},"evidence":{"total":{"scope":"retained-corpus","window":{"start":"2026-09-03","end":"2026-10-14"},"documents":{"count":158,"unit":"documents"},"publishers":{"count":78,"unit":"publishers"}}}},"baselineMultiple":7.8,"detectedAt":"2026-10-16","why":"In 14 coverage verticals across 13 subject areas (artificial_intelligence, auto, business +10 more)","desks":[],"evidence":[{"title":"Frontline Education breach exposes school district employee data","source":"BleepingComputer","url":"https://www.bleepingcomputer.com/news/security/frontline-education-data-breach-impacts-school-district-employees/","date":"2026-10-03","capturedAt":"2026-10-03","publishedAt":"2026-10-03"},{"title":"Boxlight expands STEM partnership with Louisiana school district","source":"Engineering.com","url":"https://www.engineering.com/boxlight-expands-stem-partnership-with-louisiana-school-district/","date":"2026-09-28","capturedAt":"2026-09-28","publishedAt":"2026-09-28"},{"title":"Microsoft’s move in the AI school debate: controls over how student data gets used","source":"Fortune","url":"https://fortune.com/2026/09/09/microsoft-school-district-ai-standard-enforceable-safety/","date":"2026-09-10","capturedAt":"2026-09-10","publishedAt":"2026-09-10"},{"title":"How a Colorado School District Is Fighting Trump’s Anti-Transgender Push","source":"NYT","url":"https://www.nytimes.com/2026/09/07/us/politics/jefferson-county-trump-transgender-lawsuit.html","date":"2026-09-07","capturedAt":"2026-09-07","publishedAt":"2026-09-07"}],"observed":{"stories":158,"outlets":78,"activeDays":40,"firstSeen":"2026-09-03","lastSeen":"2026-10-14","homeDomains":["business"],"homeCategories":["business"],"assertions":{},"movesWith":[{"name":"sacramento","together":24},{"name":"ai","together":40},{"name":"texas","together":29},{"name":"housing","together":25},{"name":"k-12","together":25},{"name":"united states","together":24},{"name":"trump","together":21},{"name":"los angeles","together":20},{"name":"english","together":18},{"name":"executive director","together":17}],"provenance":"observed","basis":"counted from the committed corpus (newsletter verticals + auto-verticals + article bodies), deduped by url; 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