Un simulateur spatial pour mieux comprendre les effets des traitements sylvicoles
Bibliographic record
Abstract
Les modèles de croissance forestière prévoient les effets à long terme des traitements sylvicoles, sans toutefois tenir compte de la structure spatiale des peuplements. Or, la manière dont les arbres sont répartis dans l’espace influence le degré de compétition entre eux, la dynamique de la régénération et, par conséquent, l’évolution de la structure du peuplement. Cette répartition est souvent modifiée par les interventions sylvicoles, en particulier si elles impliquent une sélection ciblée des arbres à récolter. Un nouveau simulateur spatial a été conçu pour intégrer cette dimension. À partir d’un simple inventaire, il permet de générer des peuplements avec une structure spatiale réaliste et d’y appliquer virtuellement différents traitements sylvicoles ayant pour effet de modifier la structure spatiale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".