Detecting Local Drivers of Fire Cycle Heterogeneity in Boreal Forests: A Scale Issue
Bibliographic record
Abstract
Severe crown fires are determining disturbances for the composition and structure of boreal forests in North America. Fire cycle (FC) associations with continental climate gradients are well known, but smaller scale controls remain poorly documented. Using a time since fire map (time scale of 300 years), the study aims to assess the relative contributions of local and regional controls on FC and to describe the relationship between FC heterogeneity and vegetation patterns. The study area, located in boreal eastern North America, was partitioned into watersheds according to five scales going from local (3 km2) to landscape (2800 km2) scales. Using survival analysis, we observed that dry surficial deposits and hydrography density better predict FC when measured at the local scale, while terrain complexity and slope position perform better when measured at the middle and landscape scales. The most parsimonious model was selected according to the Akaike information criterion to predict FC throughout the study area. We detected two FC zones, one short (159 years) and one long (303 years), with specific age structures and tree compositions. We argue that the local heterogeneity of the fire regime contributes to ecosystem diversity and must be considered in ecosystem management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".