Unknown Engine · Practice run

Your experiment report

Ordinary adaptive reference — disclosed development example (self-reported; unverified)

Completed replay reconstructed successfully. This verifies the recorded simulation and scoring; it does not certify the participant or establish a global rank.

Brier loss · lower is better0.000000Better than neutral 0.5 forecasts
Brier skill · not accuracy1.000000Relative to neutral Brier loss 0.25
Target experiments used4 / 46 source + 4 target = 10 total units

1 - Brier loss / 0.25. This is relative forecast loss, NOT accuracy: 1 means zero loss, 0 ties constant 0.5, and negative values are worse than that neutral reference.

Declared experiment units; excludes computation, model tokens, money, and development effort. Six-experiment referee-generated donor fixture when donor information is supplied; zero in the none arm. Not measured participant discovery effort.

Comparison on your world

Post-completion deterministic reference on the identical physical world, with the same initial public information, information arm, and target experiment budget. Computation is not equalized or measured; the policy is not asserted to be optimal.

ParticipantBrier lossTarget unitsSource unitsTotal units
Your run0.0000004610
Finite Bayesian reference0.000000268
Neutral 0.5 forecast0.250000———

Analytical neutral baseline; no comparator run or cost comparison.

Your Brier loss is tied relative to the finite Bayesian reference. Difference (your loss minus reference loss): 0.000000. Negative differences favor your run. Differences within 1e-12 are reported as tied.

Reference experiment sequence

  1. {"kind": "probe", "x": 0.5}
  2. {"kind": "probe", "x": -2.0}

Stopping reason: zero_forecast_uncertainty.

Your twelve forecasts

Physical eventProbabilityOutcomeSquared loss
q@-1.5>=-21.00000010.000000
q@-1.5>=01.00000010.000000
q@-1.5>=21.00000010.000000
q@-0.5>=-21.00000010.000000
q@-0.5>=01.00000010.000000
q@-0.5>=21.00000010.000000
q@0.5>=-21.00000010.000000
q@0.5>=01.00000010.000000
q@0.5>=20.00000000.000000
q@1.5>=-21.00000010.000000
q@1.5>=00.00000000.000000
q@1.5>=20.00000000.000000

Run and verification details

Run ID
ue-c805f5ec4856982aec1dff9e00fc11017b3d9a295cd0c2a607cb4a8a46b099b2
Protocol
unknown-engine/transfer-0.5-alpha.1
Family
finite-cubic-affine-sensor/1
Information arm
method
Experiment budget
4
Canonical replay SHA-256
c805f5ec4856982aec1dff9e00fc11017b3d9a295cd0c2a607cb4a8a46b099b2
World configuration SHA-256
cc4e21333e2a88ee5ca3a418eda0f75958cffc2b28cbc700a789bae63d90af5c
Installed package version
Unavailable in this source checkout

UTF-8 JSON; sorted keys; separators comma and colon; ensure_ascii=true; no NaN/Infinity. SHA-256 identifies these canonical replay bytes, not pretty-printed file bytes.

Implementation SHA-256

transfer.py
0deab2756e88d1c3441f6c7643c07522c320d21fdec28a67af26e68eb2f177ae
transfer_matched.py
bf1313530e40460ff443781146d5b2961e09a6abb57ad72f279e0fef2dd8dcd0
transfer_report.py
9497bfda5b1f9d964aaabbc95bff5a9a1cf413381fea199e0f063868c0dc3477

What this report establishes