Receipt for run 1: the Lab's permanent record of what it did
Does a 1986 figure for a forest pest's timing hold up?
Does a 1986 figure for a forest pest's timing hold up?
- Measured in
- model setting
- Paper says
- 1.559
- Counts if
- 1.48105 to 1.63695
- Lab got
- no number
An independent rebuild got a different number, which could be the rebuild's doing and not the paper's.
Why: rebuild mismatch
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 23
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 test couldn't be run fairly, for example because the data changed. Every share pays back half.
- 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 entrypoint: Runs the script's own lines up to the model fit and skips the table formatting after it, which needs kableExtra.
--- entrypoint [ENV by human] Runs the script's own lines up to the model fit and skips the table formatting after it, which needs kableExtra.
The full record (technical)
✓ Verified on NEAR. See the contract's history
- Outcome
- INVALID
- 19ef61085d1bfae1011de7c8c5006c243a97c56f2066c7bbea8d1179e60c8a0f
- Commit
- 98329d447ef025c260879f28316015fc0b24fb08
- Data fingerprints
- None
- Software versions fingerprint
- 60d624664ae19947f5a95fe874b81eca20d75867520df460adb87173f87742c8
- Runtime
- 3s, exit code 0
- Started, finished
- 11/7/2026, 5:23:40 AM, 11/7/2026, 5:24:43 AM
- Lab
- rerun-lab-43f5f2.testnet
- Lab code fingerprint
- 49fabb2e2f141c064ca032b253ce0f7e4f8054a781e5b3d64ee0d8a238372802
- AI used
- z-ai/glm-5.3-flash
- d2fb686a1002e801fceaa1a8e914c42cfab76f380d4db3b345002265db3722f3
- Signed AI replies
- 3dc299501086469e8fab5619e5599fd1
- Log fingerprint
- bd0190e16029dfe5b115470ea5a68517cd7520c993ea22eb3b9faec94a5bb507
- Write-up fingerprint
- 4e215ec866dd79380fa0d8f756a6eff70276e81c817a59b22d2e5b7cb294a4d5
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