Receipt for run 1: the Lab's permanent record of what it did
Does a 1991 model get the timing of a forest pest right?
Does a 1991 model get the timing of a forest pest right?
- Measured in
- model setting
- Paper says
- -0.0457
- Counts if
- -0.046157 to -0.045243
- Lab got
- -0.045599
Paper said -0.0457. We got -0.045599.
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 22
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.
Setup changes, and who made them
- Human setup: Installs the packages the script needs by hand, skipping the repository's renv setup.
--- setup [DEPENDENCY by human] Installs the packages the script needs by hand, skipping the repository's renv setup.
The full record (technical)
✓ Verified on NEAR. See the contract's history
- Outcome
- REPRODUCED
- c4a9b60c770317e5f7ec3aacbac1513bd2f133958bb226d29762fa2a39ae86d1
- Commit
- 98329d447ef025c260879f28316015fc0b24fb08
- Data fingerprints
- None
- Software versions fingerprint
- 60d624664ae19947f5a95fe874b81eca20d75867520df460adb87173f87742c8
- Runtime
- 6s, exit code 0
- Started, finished
- 11/7/2026, 5:20:50 AM, 11/7/2026, 5:23:34 AM
- Lab
- rerun-lab-43f5f2.testnet
- Lab code fingerprint
- 49fabb2e2f141c064ca032b253ce0f7e4f8054a781e5b3d64ee0d8a238372802
- AI used
- z-ai/glm-5.3-flash
- 68c20277ec5979c120f4ce4bef4858a08e503a83ead4a9a32145d4b8ed2a945c
- Signed AI replies
- 706786ccfceb4d719661902d1754b76e
- Log fingerprint
- 6ca774c01dce1bfd4fe8d88e3f028bd9eea04ffe6d13ff39faabd7e16756af9e
- Write-up fingerprint
- eb216203b33d5654040c570eca9ba169eb493fcb3ec1bfbe213c860b293bf81f
Show the end of the run log
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
Warning messages:
1: glm.fit: fitted probabilities numerically 0 or 1 occurred
2: glm.fit: fitted probabilities numerically 0 or 1 occurred