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Record W196758170

La ‘mort blanche’ au Québec (Canada) depuis 1825 : de la prise de conscience du pr oblème à la gestion du risque

2009· article· fr· W196758170 on OpenAlexaboutno aff
Dominic Boucher, Bernard Hétu

Bibliographic record

VenueInternational Snow Science Workshop, Davos 2009, Proceedings · 2009
Typearticle
Languagefr
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.004
Scholarly communication0.0010.003
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.238
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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