The Role of Humans Determining Fire Regimes: The AnthropoFire Project
Bibliographic record
Abstract
Fire regimes can be defined by the extent of the burned area, size and intensity of fires, fire seasonal length, time of burning and/or annual variability. All these properties are not only controlled by the climate, as humans also play a crucial role in the distribution and characteristics of fires at the regional and global scale. The AnthropoFire project aims to identify the main human drivers of fire occurrence, and assess how these drivers should be included into fire models and fire risk assessment systems. As part of this task, annual maps of burned area have been generated from time series of Landsat images covering the period 1984–2020 using Google Earth Engine (GEE) over three regions (Bolivia, Spain–Portugal, and Canada) characterized by different fire regimes. For each of these regions, several physical and socio-economic variables such as those related to climate (i.e., temperature, precipitation, drought), vegetation, land use, distance to roads, human settlements, etc., along with the fire characteristics in those areas, were compiled from existing satellite-derived products. These variables are being modelled to analyse the factors that determine and explain fire occurrence.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".