Why KELLY can be stronger here
KELLY is designed to win by being scoreable: every forecast should connect to source evidence, causal assumptions, falsifiers, settlement, calibration, replay, and memory lineage.
why it mattersA forecast platform is only as good as its ability to be wrong in public, adjust from reality, and improve the next forecast without hiding the chain.
sector edgeShows air-traffic density, velocity pressure, water-level context, and disruption pressure.
best claim boundaryKELLY should be called better only where its scorecards, calibration, replay receipts, and source coverage outperform alternatives. This page is built to make that measurable.
Where the proof lives
proofSource plan: the approved feed rows on this page
proofForecast method: the doctrine timeline on this page
proofAuthority evidence: Authority Index and replay receipts
proofScore evidence: settlement and calibration records
proofSystem evidence: Brain Fleet, generality evidence, and advanced proof labs
Professional layout pattern
The lab layout follows successful product patterns: fast first read, watchable objects, citations beside claims, source freshness, score history, and direct proof links.
patternCitation-forward answer pattern: put sources and proof next to claims.
patternForecasting track-record pattern: keep probability, resolution rule, score, and history together.
patternWatchlist pattern: let users inspect a saved set without writing ground truth.
patternAlert/status pattern: show what changed, what is stale, and what KELLY is watching.
Research references
TradingViewCharts, alerts, screeners, replay, multi-chart layouts, and fast command/search patterns make market work feel immediate. MetaculusForecast questions need clear resolution rules, track records, scoring, calibration, and aggregation history. Windy / Windy.appWeather users need maps, layers, local points, alerts, history, model comparison, and fast location context. PerplexityAnswer users expect citations beside claims, follow-up context, and checkable source trails.
KELLY's specialized forecast method
Traffic/flow density, velocity pressure, water/port context, anomaly detection, settlement, memory update.
1Approved source intake only
2Tag source, freshness, domain, and authority
3The Book of Why: ask what caused the change, what would have happened otherwise, and what evidence can falsify the causal story
4Thinking in Systems: map feedback loops, delays, stocks, flows, constraints, and downstream effects
5Thinking, Fast and Slow: separate fast pattern recognition from slower deliberate verification
6Superforecasting: start with base rates, update incrementally, keep probabilities explicit, and score the forecast later
7Thinking in Bets: treat every forecast as a wager against reality with a clear settlement rule
8Build causal pressure nodes and forecast cones
9Separate KELLY forecast from user forecast
10Track falsifiers, uncertainty, and competing explanations
11Close against reality
12Score calibration and error direction
13Apply continuous micro-adjustments after settlement to improve future accuracy
14Create replayable memory evidence when eligible
What this lab is for
Showing dense public movement data and anomaly learning.
safe claimShows air-traffic density, velocity pressure, water-level context, and disruption pressure.
boundaryNo routing instruction, no aviation safety instruction, and no guarantee of delays.
Live proof status
proof score42 / early
visible cells0
readiness score0%
sources wired2/3
public writesblocked
Proof links
Every transport view should show coverage, source freshness, flow pressure, and later reality closure.
Approved source plan
| source | status | cost | role |
|---|
OpenSky Network
opensky |
available |
free_limited |
OAuth2 client credentials (recommended): set OPENSKY_CLIENT_ID and OPENSKY_CLIENT_SECRET from opensky-network.org/my-opensky/account. Bearer tokens expire in ~30 minutes; leave OPENSKY_ACCESS_TOKEN empty unless refreshing often. Anonymous fallback works with no secrets. |
NOAA CO-OPS Water Levels
noaa-water |
wired |
free_no_key |
Water-level/tide observations when a station is configured. |
OpenAI
openai |
wired |
paid_optional |
Only for narrative phrasing. The proof engine does not require an LLM. |
User questions this lab can inspect
| question lane | plain question | node |
|---|
| Air density |
Is air traffic density unusual? |
TRANSPORT:AIR_TRAFFIC_DENSITY |
| Speed pressure |
Is movement slowing or changing? |
TRANSPORT:VELOCITY_PRESSURE |
| Coverage |
Is there enough coverage? |
TRANSPORT:COVERAGE |
| Port water |
Is water-level context changing? |
TRANSPORT:WATER_LEVEL |
| Overall pressure |
Is transport pressure rising? |
TRANSPORT:RISK |
Default focuses
| focus | meaning | context |
|---|
| New York region |
air and harbor flow |
40.7128, -74.006 |
| Los Angeles region |
air and port flow |
34.0522, -118.2437 |
| Chicago region |
air flow |
41.8781, -87.6298 |
| Atlanta region |
air flow |
33.749, -84.388 |
| Seattle region |
air and port flow |
47.6062, -122.3321 |
Causal pressure map
TRANSPORT:AIR_TRAFFIC_DENSITYTRANSPORT:VELOCITY_PRESSURETRANSPORT:COVERAGETRANSPORT:WATER_LEVELTRANSPORT:RISK
causal linkTRANSPORT:AIR_TRAFFIC_DENSITY -> TRANSPORT:RISK
causal linkTRANSPORT:VELOCITY_PRESSURE -> TRANSPORT:RISK
causal linkTRANSPORT:COVERAGE -> TRANSPORT:RISK
causal linkTRANSPORT:WATER_LEVEL -> TRANSPORT:RISK
Boundary receipt
{
"readOnly": true,
"publicUserWriteAllowed": false,
"noAdviceSubstitute": "No routing instruction, no aviation safety instruction, and no guarantee of delays.",
"noClaimOfPerfectPrediction": true,
"proofRequiredForEveryForecast": true
}