Sealed Before the Event: A Publicly Verifiable, Bitcoin-Anchored Forecast Record of 92 Graded Outcomes, with a Source-Agnostic Protocol for Scoring Unexplainable Forecast Sources
Background Public prediction records are hard to evaluate: posts can be edited or deleted, and even honest records rarely separate anteriority (did this exact claim exist unchanged before the event?) from skill (do the claims carry predictive information?). A motivating case: a public forecast naming mass-casualty attacks on public gatherings in Moscow in a March–April 2024 window was published and server-timestamped on 4 September 2023 — ~200 days before the Crocus City Hall attack and 185 days before a comparable official alert. Methods Every forecast was published verbatim before its window; each text was canonicalised, SHA-256-hashed into a manifest, and anchored to the Bitcoin blockchain via OpenTimestamps — the anchor fixes integrity (the bytes unchanged since the anchoring block), while anteriority rests on platform-issued timestamps, pre-event public distribution, and third-party archives (§2.1.2); a four-level rubric (HIT/NEAR/PARTIAL/MISS) was fixed at seal time and misses retained. We formalise a five-step source-agnostic scoring protocol for sources whose generative mechanism is unavailable or unexplained: demand cryptographic anteriority; freeze the claim verbatim; grade the stream, never the anecdote; set weight by calibration, not theory; and let the frozen denominator filter uninformative sources. Results Across 92 graded forecasts (73 HIT, 6 NEAR, 4 PARTIAL, 9 MISS) the self-assigned aggregate Brier is 0.0958 (launch subset 0.036; strategic warning 0.116). On this small, high-confidence sample a base-rate baseline ties the aggregate Brier — conceded up front. A pre-specified clustered luck test — correlated calls collapsed into independent events, strict all-HIT scoring, luck prior floored at 0.5 per event — yields 51 of 68 strict successes (exact binomial p = 2.2 × 10 −5 ; break-even luck prior ≈ 0.65). A five-way calibration-integrity suite is reported with sample-size ceilings. All statistics recompute from public artifacts with zero-dependency tooling. Conclusions The contribution is the protocol — belief-independent evaluation of unexplainable forecast sources — not validation of the disclosed generative method (Vedic jyotish, treated as a black box). Applications to warning analysis and war-game parameterization are outlined; the limitations — self-grading, sample size, operator selection — are load-bearing.