Évaluation de modèles servant à prédire les assemblages aviaires dans l'est du Canada
Bibliographic record
Abstract
Au cours des quinze dernières années, de nombreuses initiatives canadiennes ont contribué au développement de modèles de prédiction des oiseaux nicheurs afin de répondre aux besoins urgents de conservation de l’avifaune forestière du Canada. Le projet IRMA (Inférences Régionales par Modélisation Aviaire ), soutenu par Environnement Canada, ainsi que le projet BAM (Boreal Avian Modelling), soutenu également en bonne partie par Environnement Canada, sont deux initiatives indépendantes qui visent à décrire la répartition géographique et l’abondance des oiseaux nicheurs en forêt boréale. Le présent travail vise à valider les modèles utilisés par ces deux projets en plus d’évaluer leurs forces et leurs faiblesses respectives au moyen de divers sous-ensembles de données recueillies dans la forêt boréale ontarienne et québécoise. Over the last 15 years, there have many initiatives to develop predictive breeding bird habitat models to meet the urgent needs for the conservation of Canada’s forest birds. The IRMA project (Regional Inferences by Avian Modelling), supported by Environment Canada, and the Boreal Avian Modelling Project (BAM, also largely supported by Environment Canada), were two independent initiatives seeking to describe the distribution and abundance of breeding birds in boreal forests. This study aimed to validate the predictive distribution and abundance models developed by these two projects and to evaluate their respective strengths and weaknesses by using various avian datasets collected from boreal regions of Ontario and Québec.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".