Regional patterns of postfire canopy recovery in the northern boreal forest of Quebec: interactions between surficial deposit, climate, and fire cycle<sup>1</sup>This article is one of a selection of papers from the 7th International Conference on Disturbance Dynamics in Boreal Forests.
Bibliographic record
Abstract
In many northern forest ecosystems, the postfire transition from a closed-crown forest to open woodland is often observed but poorly understood. This paper looks at the effect of interactions between surficial deposit, climate, and fire cycle on postfire forest recovery within a large territory (190 000 km2) of the boreal forest of eastern Canada. Postfire recovery was estimated using the time elapsed to move from the burnt stage to the regenerated stage and the young forest stage. The main objective was to determine if forests situated in dry regions (characterized by a high proportion of dry coarse surficial deposits, low precipitation, and short fire cycle) tend to reestablish more slowly after fire, obtaining a more open stand compared with wetter regions characterized by a longer fire cycle. To identify the best explanatory model for postfire recovery, multinomial logistic regressions with the Akaike information criterion were conducted using a combination of physicoclimatic factors. Our best model suggests that the most significant predictors of postfire recovery are time since fire (χ2 = 1370.06), surficial deposit type (χ2 = 651.95), the Canadian Drought Code (χ2 = 247.75), and the growing season precipitation (χ2 = 102.80). Fast recovery and dense forest regeneration are associated with subhydric till deposits only in the regions characterized by a long fire cycle (>500 years). Conversely, slow regeneration conducive to a sparse young forest was usually associated with regions characterized by a short fire cycle (<200 years) underlain by dry coarse deposits such as juxtaglacial but also mesic deposits in some cases. Our results also show that slow recovery and reduced forest regeneration are most likely to occur following fires that occurred in dry years, regardless of the deposit type and 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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".