MétaCan
Menu
Back to cohort
Record W7100606798

RECENT TRENDS AND PREVENTION STATEGIES FOR AVALANCHE AND SNOW IMMERSION RISK AT U.S. AND B.C. SKI AREAS

2016· article· en· W7100606798 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsnot available
Fundersnot available
KeywordsSnowPoison controlRisk assessmentInjury preventionOccupational safety and healthSuicide prevention
DOInot available

Abstract

fetched live from OpenAlex

Many ISSW participants are involved in snow safety operations at ski areas. Understanding recent changes in trends in avalanche and snow immersion risk at ski areas will help us better educate and protect ski area users. Analysis of the last 18 years found 6 ski area avalanche fatalities in bounds reflecting a trend in reduction. 60 fatalities (12 % of the total U.S. and 9 % of Canadian total) associated with leaving the ski area boundary indicate a flat trend in this risky in this behavior. Not well documented during the same 18 years, is an increasing trend in the risk of asphyxiation in deep snow in bounds at ski areas. 57 Non-Avalanche Related Snow Immersion Deaths (NARSID) occurred during the study period. NARSID currently accounts for 15 % of all ski area fatalities in the United States. In B.C., during the period from 1993 to 1998, it accounted for 25 % of all ski area in bounds fatalities. The NARSID risk in the U.S. is currently 15 times greater than the avalanche risk in bounds at a ski area. In 2005-06 alone, 40 % of the total snowboard fatalities at U.S. ski areas were NARSIDs. The greatest risk continues to be lack of awareness.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.300
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicLanguage and cultural evolutionFrench-language works237,207