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Record W2977953709

Development of Avalanche Safety and Control Programs in the Canadian Rocky Mountain National Parks - A Historical Perspective

2002· article· en· W2977953709 on OpenAlexaboutno aff
Brad J. White

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)GeographyAeronauticsEngineeringComputer science
DOInot available

Abstract

fetched live from OpenAlex

The Parks Canada Warden Service has the responsibility for avalanche safety and control programs within the Canadian Rocky Mountains National Parks. The avalanche program has developed over the years in response to avalanche accidents, changing visitor use patterns and technological advances. Ski touring as a recreation began in the parks in the late 1920's and several avalanche accidents occurred in the early 1930's that the wardens responded to as rescuers. With the advent oflift serviced skiing after the second world war, it was recognized that the Wardens needed training in both skiing skills and avalanche snowcraft in order to adequately protect the public and respond to accidents. Noel Gardner instituted the first skiing and avalanche awareness schools for Wardens in the early 1950's. In 1955 a group of three select wardens traveled to Alta, Utah to study avalanche practices under Monty Atwater. Also in 1955 the Parks hired a Swiss mountain guide at the chief park warden level to administer the mountain rescue and travel training in the National Parks. During the 1960's considerable growth occurred in the ski areas in the parks, and the Wardens performed ski patrol and avalanche duties at the ski hills and began to experiment with explosive control methods to protect ski terrain and Park's roadways. The first avalanche danger bulletins and public information dissemination began. Record snowfalls and avalanches that created

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.882

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)0.0010.000

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 head0.018
GPT teacher head0.202
Teacher spread0.185 · 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 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

Citations0
Published2002
Admission routes1
Has abstractyes

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