Répercussions météorologiques découlant de modifications naturelles ou délibérées de la surface. Principes généraux et prospectives, région de la Baie James
Bibliographic record
Abstract
Les régions arctique et subarctique se prêtent merveilleusement à l'expérimentation du fait de la grande variabilité saisonnière de l'état de leurs surfaces glacées et neigeuses. Ces régions se prêtent bien aux études d'évolution climatique car en changeant les paramètres de surface on arrive à modifier significativement les processus d'échange par chaleur sensible et chaleur latente. À l'Université McGill on a élaboré un modèle numérique, utilisant aussi bien les données synoptiques des stations météorologiques, les données de point de grille que les données climatiques moyennes afin de calculer les différents termes du bilan énergétique. La région de la Baie de James est intéressante à étudier dans cette optique. L'article décrit un traitement numérique expérimental de la région.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".