Effets d’héritage de traitements sylvicoles sur la régénération naturelle postincendie de Picea mariana et Pinus banksiana dans la pessière à mousse de la forêt boréale québécoise
Bibliographic record
Abstract
In Québec, the large wildfires of 2023 were particularly devastating in managed boreal forests, especially in stands originating from clearcuts. These mostly just-mature stands faced high risks of postfire regeneration failure. Many of them had nonetheless undergone silvicultural treatments, such as precommercial thinning and planting, to enhance yield. This study evaluates the postfire legacy effects of these treatments on Picea mariana (black spruce) and Pinus banksiana (jack pine) in the boreal forest. Natural regeneration was assessed across five silvicultural scenarios in 89 stands within the Lebel-sur-Quévillon fires, two years after the events. We found that 30–50-year-old forests showed high regeneration failures due to insufficient stand maturity at the time of the fires. Overall, jack pine regeneration exceeded that of black spruce, and silvicultural scenario effects differed by species. Jack pine plantings appeared particularly effective in accelerating stand maturity, whereas only mature forests reached sufficient regeneration thresholds for black spruce. The forestry sector will need to adapt toward more sustainable practices compatible with the emerging climate regime to cope with increasingly frequent fires.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".