Proof layer
The data layer is the proof behind private investment thesis review.

Use this page to inspect the resolved corpus, calibration record, model forecasts, and named resolution sources behind Crene's private thesis review workflow.

Crene Data: Calibration Corpus

Proof layer · JUN 2026

The proof layer behind Crene thesis reviews.

Events, thesis maps, factors, and scenarios. Binary events carry four-model probability histories, named resolution sources, and Brier scores once resolved. Thesis maps, factors, and scenarios extend the same scoring discipline into structured decomposition: 3 live thesis maps, 3 anchor factors with 347 active drivers, and 400 pathways across 4 active scenarios decomposed into 266 scored components.
active binary forecasts

Across all categories. Repolled daily.

resolved questions

Closed against named sources. Binary outcomes are Brier scored.

broad corpus Brier

Lower is better. Broad resolved corpus, rounded. See methodology for leakage-controlled and macro-only subsets.

directional accuracy

Consensus direction vs outcome.

Assumption structure

Raw count tells you coverage. Structure tells you whether that coverage is actually diversified.

Crene separates the number of questions from the independence of the assumptions underneath them. A scenario, factor, or cluster can track many components while still depending on a small number of shared drivers. Question density across domains is the first read on that structure. As the observation record deepens, Crene estimates how many effectively independent assumptions sit underneath each object.

Question density is a structural read, not a correlation estimate. It shows how tightly each object's questions cluster by domain. Crene does not publish exact effective-independence or covariance figures until the observation record is deep enough to estimate them reliably. The same rule applies here as everywhere else on the page: no number is promoted before the record can support it.

Corpus reconciliation

The data page uses the broad corpus view. Total resolved questions refers to the full Crene-native resolved set currently in the database. The headline broad corpus Brier is rounded and differs from the leakage-controlled benchmark and macro-only subset discussed on the methodology page. Model leaderboard sample sizes are per-model rows, so they may differ from the consensus benchmark.

What is in the corpus
Multi model forecasts

Independent probability estimates from four production models (Claude, GPT, Gemini, Grok) for binary forecast objects. No model sees another model output.

Events

Binary outcome questions across macro releases (CPI, NFP, PMI), central bank decisions, commodities, crypto, policy, and market events. Each resolved against a named source.

Thesis Maps

3 live thesis maps decomposing regime-level binary questions into 309 active assumptions: Warsh Fed rate path (100), AI economic realization (109), and AI content dominance (100). Each assumption is independently forecast and tracked through resolution.

Factors

3 anchor factors with 347 active drivers. Continuous state variables (S&P 500, US 10Y Treasury yield, US GDP attributable to AI) are forecast as probabilistic distributions with daily driver refreshes.

Scenarios

400 pathways across 4 active scenarios: AI Labor Transition, Empire by Default, European Rearmament, and India as the Third Growth Pole. 266 components spanning demographics, AI, labor, defense, currency, fiscal, trade, governance, and market structure.

Belief trajectories

Consensus probability is snapshotted daily for binary forecast objects. The full evolution of the forecast is preserved through resolution.

Per event Brier scoring

Each model is scored on resolved binary events. Per model performance is measured continuously, not curated. Continuous factors and long-horizon scenario structures require different calibration metrics as outcomes accrue.

Continuously growing

New investment thesis maps, factors, and scenario components expand the corpus alongside macro, policy, market, and AI-related event flow. Resolutions compound the calibration signal.

How resolution works

Every outcome is tied to a verifiable source. Crene resolves binary events against a tiered source allowlist. Resolution is automated, but the source for each outcome is named and auditable.

Tier A
Primary

Government statistical agencies, central banks, regulators. SEC filings, BLS releases, Fed statements, BEA, BoJ, ECB.

Tier B
Authoritative secondary

Major news wires reporting primary releases. Reuters, Bloomberg, AP, agency wire confirmations.

Tier C
Corroborating

Used only to confirm Tier A or B. No event resolves on a single Tier C source.

Why this matters

Events, thesis maps, factors, and scenarios form the evidence layer behind Crene's thesis review workflow. Four model surfaces feed one resolved-event calibration record. Structural maps remain separate from scored evidence until outcomes accrue.

Access

Proof layer access. Resolved event archive, per-event 4-model breakdown, calibration history, and thesis map decompositions behind the private thesis review workflow. Use the form below.

Marketplaces. Crene is listed on Neudata, Eagle Alpha, and Monda for institutional procurement.

API. Programmatic access to the proof layer behind live and resolved thesis objects. API documentation.

Data | Crene