Description et guide d'utilisation du programme HSG de modelisation de la reserve de bois
Bibliographic record
Abstract
Le mandat de l'Institut forestier national de Petawawa, comme celui des autres etablissements de Forets Canada, est de promouvoir une meilleure gestion et une utilisation plus rationnelle des ressources forestieres du Canada, pour Ie bien economique et social de tous les Canadiens. Les objectifs des activites des programmes menes a l'Institut appuient ce mandat a travers la decouverte, Ie developpement, la demonstration, l'application et Ie transfert des innovations. En tant qu'institut national, il doit s'attacher a des problemes qui debordent Ie cadre regional ou qui necessitent des competences particulieres de.mcme qu'un equipement non disponible aux installations regionales de ForNs Canada. La plupart du temps, les recherches sont effectuees en etroite collaboration avec Ie personnel des centres regionaux, des services forestiers des provinces et de l'industrie forestiere. Les travaux de recherche et les services techniques de l'Institut sont regroupes autour de cinq principales activites: GENETIQUE FORESTIERE ET BIOTECHNOLOGIE- Ce programme encadre des etudes sur la genetique forestiere, la microbiologie, la micropropagation, la genetique moleculaire et la recherche sur les semences. II comprend egalement les services a la clientele et la banque de semences du Centre national de semences forestieres. Lie a plusieurs organismes internationaux, ce centre existe depuis longtemps.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".