Spatial variability in biomass burning in the northern extratropics since the Last Glacial Maximum
Bibliographic record
Abstract
Fire is an important environmental and ecological process in northern high latitude environments. It is currently unclear how fire regimes will change in response to current environmental change in this region and the implications this may have for ecosystem processes and human societies. We reconstruct changes in biomass burning since the Last Glacial Maximum in the northern extratropics (>45°N), using data from the Global Charcoal Database complemented by new records from Canada, Beringia and Russia. A clustering machine-learning algorithm (K-means) is used to delimit regions that show similar burning histories. Comparison of the regional trajectories of change in biomass burning provides insights into the environmental drivers of fire. Generalised linear modelling is then used to explore the independent roles of climate, vegetation changes and human activities on changes in fire regimes for each region and for the northern extratropics as a whole. This study provides quantitive information about the differential importance of the drivers of changes in fire regimes in different regions and at different timescales since the Last Glacial Maximum, and provides insights about how these may influence future fire regimes across this region.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".