Receipt for run 2: the Lab's permanent record of what it did
A logistic model predicts trial response with 93% accuracy
A logistic model predicts trial response with 93% accuracy
- Paper says
- 0.93
- Counts if
- 0.91 to 0.95
- Lab got
- 0.93
Paper said 0.93. We got 0.93.
The code and data produced the same result. That doesn't prove the study's method is right.
Check this yourself
You don't have to trust us or the . With Docker and Node installed, this retraces the claim on your own computer and compares the number:
git clone <this repository> && cd rerun && pnpm install && pnpm sandbox:build pnpm verify-claim 16
How much evidence is behind this?
Each step is a tougher test. Only the first one decides who wins the market.
- 1. ✓
The Lab's retrace of the code and data named in the claim
The Lab retraced the code and data and got a number inside the claim's range. YES wins.
- 2. –
Stress test: does the number depend on luck?
Fetching the extra runs…
- 3. –
Rebuilt from the description (experimental)
Fetching the AI's rebuild…
Coming next: a data-leakage check, for the same rows in training and test data, and a maths check on the statistics a paper reports. Neither has run on this claim.
Even all three steps can't prove the science is true. They show how well this result stands up to checking.
Verified
Your browser just re-checked these itself, so you don't have to trust this site.
Checking…
The write-up
The write-up isn't available right now. Its fingerprint is below.
The full record (technical)
✓ Verified on NEAR. See the contract's history
- Outcome
- REPRODUCED
- 5082cac3ebc8904e3559bca9af1c2d8f4c68df3c4565be2c5547e61f4ae3bbd8
- Commit
- 563c58831162400e57dc0129eb2d2744fb3a1368
- Data fingerprints
- be35dcd48bdcc0222a5c4d5ef769adb4a58e77c1914af80d8aa457ad742f9b7d
- Software versions fingerprint
- 8dab88328537f15c219ff04f4c55b0150f9867347067f4b5868ed8b0769249af
- Runtime
- 3s, exit code 0
- Started, finished
- 11/4/2026, 12:35:34 PM, 11/4/2026, 12:37:19 PM
- Lab
- rerun-lab-43f5f2.testnet
- Lab code fingerprint
- 49fabb2e2f141c064ca032b253ce0f7e4f8054a781e5b3d64ee0d8a238372802
- AI used
- Qwen/Qwen3.8-27B
- e64fd688cd1c7010833d26ef300906801628beff4715e99e01a20efb24e67454
- Signed AI replies
- bc1304b76b6d4514bf55b2fecf1a6390, f4da8a2d9aed40068cc6f4c4aa087f41
- Log fingerprint
- d703a3caeb7c8920890384d9dd62da26793a4f1eaee9ffc5e6ec84fe3aa12357
- Write-up fingerprint
- e7ca3dca6fa32bf2c064b1749749ff30e24285bbd6100b44a28ca82d369a22a0
Show the end of the run log
accuracy=0.93