The effects of surficial deposit–drainage combinations on spatial variations of fire cycles in the boreal forest of eastern Canada
Bibliographic record
Abstract
Spatial variations in the fire cycle of a large territory (190 000 km2) located in the boreal forest of eastern Canada were assessed using random sampling points. Our main objective was to determine if regions characterised by a large proportion of dry surficial deposit–drainage (SDD) burn more frequently than regions with a smaller proportion. Through a regionalisation of the landscape units, we analysed the effects of SDD on spatial variations of the fire cycle. A discriminant analysis involving the SDD and other physical variables (precipitation, temperature, aridity index, water bodies, elevation and slope) made it possible to identify a combination of variables characterising each region. A considerable variation in fire cycle was observed among the different SDD types (from 144 to 425 years) and between regions (from 90 to 715 years). Through the discriminant analysis, this study suggests that a combination of possible climatic top-down (precipitation R2 = 0.727, aridity index R2 = 0.663 and temperature R2 = 0.574) and bottom-up factors (xeric undifferentiated till R2 = 0.819 and humid undifferentiated till R2 = 0.691) could explain this variation at the regional scale. Implications of those results for forest protection against fire and regional development are briefly discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".