Remise en production des bétulaies jaunes résineuses dégradées : étude du succès d'installation de la régénération
Bibliographic record
Abstract
The abundance of poor quality stands in the North American hardwood and mixedwood forests poses important regeneration challenges. These stands have an open canopy with a well-developed shrub layer dominated by noncommercial species. The present study aims at testing the efficiency of a natural regeneration approach using a combination of brushing and spot scarification in the cleared strips. Four high-graded stands from the mixedwood zone in Quebec were selected and strips were cleared with a brush saw. Four microsite types created by the scarification were studied: 1-m and 2-m wide pockets, mounds and undisturbed forest floor. The amount of dispersed yellow birch seeds was adequate for two out of the three years of the study. Yellow birch establishment was in phase with seed years and was better on disturbed microsites. Best establishment was observed in seed spots and light conditions in these microsites after three years were better than on mounds or undisturbed ground. On the latter two, survival will likely be impaired by the poor light conditions. Seed spots remain receptive three years after scarification. Softwood regeneration was poor due to a lack of seed trees. The study has shown that seed tree abundance remains sufficient for natural regeneration even in these open stands. It also showed a very rapid regrowth of competing vegetation when root systems and seed banks were not removed by site preparation. Key words: yellow birch, Betula alleghaniensis, scarification, diameter-limit cutting, competition
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".