Black spruce and jack pine dynamics simulated under varying fire cycles in the northern boreal forest of Quebec, Canada
Bibliographic record
Abstract
The postfire regeneration dynamics of black spruce and jack pine were documented by a study of three successive cohorts (woody debris, snags, seedlings) within a large area burnt in 1989. The objectives of this study were (i) to describe how fire interval can influence the abundance of regenerating black spruce and jack pine and (ii) to model the future abundance trends of these two species for fire cycles of different lengths. The transition probabilities after fire were calculated for mixed stands of black spruce and jack pine for fire intervals of 47 and 67 years in well-drained sites and for fire intervals varying between 92 and 270 years in poorly drained sites. These probabilities were incorporated into a model of regeneration dynamics that took into account the drainage type, the regeneration potential, and the natural mortality rate of both species. After the 1989 fire, jack pine seedlings made up 55%82% of the regeneration in well-drained sites and 11%40% in poorly drained sites. Model simulations show that fire intervals <60 years lead to the local extinction of black spruce, and those >220 years lead to that of jack pine. The simulation results also suggest that jack pine could expand its populations under a fire cycle of 50 years or after short fire intervals during longer fire cycles. Thus, in the short term and medium term (i.e., ca. <100 years), the length of the interval between two consecutive fires is a better predictor of the abundance, extinction, or local expansion of black spruce and jack pine than the fire cycle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".