FireALT dataset: estimated active layer thickness for paired burned unburned sites measured from 2001-2023
Bibliographic record
Abstract
As the northern high latitude permafrost zone experiences accelerated warming, permafrost has become vulnerable to widespread thaw. Simultaneously, wildfire activity across northern boreal forests and Arctic/subarctic tundra regions impacts permafrost stability through the combustion of insulating organic matter, vegetation, and post-fire changes in albedo. Despite the importance of wildfire-permafrost interactions, little research has been conducted to understand differences across ecosystem types. To address this knowledge gap, we solicited observations of active layer thickness from paired burned and unburned sites across the northern high-latitude permafrost region. We compiled 52,466 permafrost thaw depth observations from 18 contributors collected as part of individual field studies. As thaw depths were taken at various times throughout the season, we estimated end-of-season active layer thickness for each measurement using the square root of additional air thawing degree days between the day of measurement and the date of the end of the thawing season at each site. The FireALT dataset includes 48,669 ALT estimates with 32 attributes across 9,446 plots and 157 burned/unburned pairs spanning Canada, Russia, and the United States. The data were collected between 2001 and 2023 and span fire events from 1900 to 2022. Time since fire ranges from zero to 114 years. The FireALT dataset addresses a key challenge: the ability to assess the impacts of wildfire on ALT when measurements are taken at various times throughout the thaw season depending on the time of field campaigns (typically June through August) by estimating ALT at the end of the season maximum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".