RERUN
Claim 20

Does temperature explain how much water forests lose after a wildfire?

Authors' own code

The study said temperature explains about 15% of how much water northern forests lose after a fire. The Lab retraced it: ✓ same result.

Whose code is this?

The lead author's original script from 2008, run unchanged on the published data. Nobody rewrote it.

See the code

Wildfires are reshaping northern forests, and how much water those forests lose matters for climate models.

What this result means

We test-ran this code once while building the Lab, so we've already seen one result. Its official run came after forecasting closed.

We test one thing: does this code and data give this number? That's not the same as the science being right. The forecast price never changes the verdict.

See the proof
0.1495share explained, the paper says. Counts if between 0.149 and 0.15.
Where the number comes fromMultiple R-Squared: 0.1495The author's 2008 output file, line 251, for ET ~ Tavg

The exact claim as posted

Temperature explains about 15% of yearly forest water loss after wildfires

Bond-Lamberty et al. (Global Change Biology, 2009) studied water loss from Canadian boreal forests after fire. Their regression of yearly evapotranspiration on average temperature has an R-squared of 0.1495, as recorded in the author's 2008 output file. This re-runs the author's own original script, unchanged, on the published data.

RERUNCLAIM #0020

Does temperature explain how much water forests lose after a wildfire?

Measured in
share explained
Paper says
0.1495
Counts if
0.149 to 0.15
Lab got
0.1495
VerdictNumbers check out
a942a4e8eee4390f46aeNov 09, 2026

This result is final

Paper said 0.1495. We got 0.1495.

The code and data produced the same result. That doesn't prove the study's method is right.

This slip is the Lab's .

✓ Verified on NEAR. See the contract's history

See the full receipt

  1. People forecast
  2. Forecasting ends
  3. The Lab retraces it
  4. Verdict posted, open to challenge
  5. Winners collect

What we didn't check

  • Whether the sample was fair or large enough.
  • Whether test data leaked into training or tuning.
  • Whether the result holds on new data.
  • Whether the study's method was the right one for its question.

Reading how the odds moved…

The evidence

See the full receipt

How much evidence is behind this?

Each step is a tougher test. Only the first one decides who wins the market.

  1. 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. 2. –

    Stress test: does the number depend on luck?

    Fetching the extra runs…

  3. 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…

What the Lab retraces

Source
https://doi.org/10.5281/zenodo.4617894
B. Bond-Lamberty, S. D. Peckham, S. T. Gower, B. E. Ewers, MIT
Code, at this exact version
https://github.com/bpbond/ten-years-paper33
commit c28b8a2e3149f7d90f2dc0179f9c976785e762b1
Commands
r4.4
$ Rscript --vanilla ET.r > rerun_output.txt
Where the result is read
rerun_output.txt using regex ET ~ Tavg[\s\S]*?Multiple R-squared:\s+([0-9.]+)
Data files
None
Limits
1 CPU, 1 GB, 5 minutes
a942a4e8eee4390f46ae932c48d15ccd0c83ebc0a39eab408c50e054088f6986
Show the claim exactly as stored on the blockchain
{"artifact":{"commit":"c28b8a2e3149f7d90f2dc0179f9c976785e762b1","data":[],"entrypoint":"Rscript --vanilla ET.r > rerun_output.txt","output":{"extractor":{"expr":"ET ~ Tavg[\\s\\S]*?Multiple R-squared:\\s+([0-9.]+)","type":"regex"},"path":"rerun_output.txt"},"repo_url":"https://github.com/bpbond/ten-years-paper33","runtime":"r4.4","setup":[]},"metric":{"comparison":"within_tolerance","name":"R-squared of evapotranspiration on average temperature","reported_value":"0.1495","tolerance":{"type":"abs","value":"0.0005"}},"private_data":null,"resources":{"cpu":1,"max_minutes":5,"mem_gb":1},"source":{"authors":"B. Bond-Lamberty, S. D. Peckham, S. T. Gower, B. E. Ewers","doi":"10.5281/zenodo.4617894","license":"MIT","type":"paper","url":"https://doi.org/10.5281/zenodo.4617894"},"statement":"Bond-Lamberty et al. (Global Change Biology, 2009) studied water loss from Canadian boreal forests after fire. Their regression of yearly evapotranspiration on average temperature has an R-squared of 0.1495, as recorded in the author's 2008 output file. This re-runs the author's own original script, unchanged, on the published data.","title":"Temperature explains about 15% of yearly forest water loss after wildfires","version":1}

Community notes

Notes are people's own words, kept on the blockchain. They never change a verdict, and the Lab's AI never reads them. Posting costs a little NEAR for storage, and what isn't used comes back.