ABoVE: Wildfire Date of Burning within Fire Scars across Alaska and Canada, 2001-2019
Bibliographic record
Abstract
This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".