Researchers at Natural Resources Canada's Canadian Forest Service have recently used a new model for estimating the risk of large forest fires (over 200 hectares) in various regions across Canada.
Bibliographic record
Abstract
The risk is mainly related to the increase in summer temperatures and droughts 1 . However, close observation of changes in precipitation and evapotranspiration reveals unexpected effects on drying rates in the deep layers of the forest floor. This phenomenon is not uniform across the different regions studied. This new information indicates that the risk of large fires is declining south of Hudson Bay, in western Canada and in the eastern Maritimes. These regions were considered to be at high risk in the early 20th century, with one year of large fires every five years, but now the risk is low to moderate, with one year of large fires every seven years. In the forests of Central Canada and those north of the Great Lakes, the risk of large fires has changed from extreme (one in five years) to one in fourteen years. On the other hand, in the taiga regions the risk has risen from one in seven years to one in five.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".