La ‘mort blanche’ au Québec (Canada) depuis 1825 : de la prise de conscience du pr oblème à la gestion du risque
Bibliographic record
Abstract
Au Quebec, les avalanches de neige affectent les activites recreatives, les routes et les habitations dans presque toutes les regions de la province. Depuis 1825, le bilan provisoire s’eleve a plus de 70 morts dont pres d’une trentaine au cours des 40 dernieres annees. Ces chiffres placent les avalanches de neige au second rang des risques naturels les plus meurtriers derriere les eboulements et les glissements de terrain. L’idee d’un centre d’expertise sur les avalanches de neige au Quebec s’est imposee apres les nombreux incidents qui se sont produits dans les monts Chic-Chocs au cours des annees 1980 et 1990 et, surtout, apres les accidents de Blanc-Sablon en 1995 (2 morts) et celui de Kangiqsualujjuaq en 1999 (9 morts, 25 blesses) qui ont agi comme element declencheur aupres des decideurs. L’objectif du Centre d’avalanche de la Haute-Gaspesie vise a ameliorer la securite en avalanche au Quebec. Ses activites et services incluent la sensibilisation du public, la formation professionnelle, la prevision dans les monts Chic-Chocs et le support au developpement et a la recherche dans le domaine. Apres avoir expose les resultats d’une enquete historique (73 deces depuis 1825) qui permet de mieux comprendre la problematique des avalanches de neige au Quebec, nous presenterons les activites et les services du Centre d’avalanche de la HauteGaspesie qui constituent une solution adequate et efficace pour reduire les risques et ainsi sauver des vies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".