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Record W2907868777 · doi:10.1016/j.wem.2018.10.005

Rock Climber Self-Rescue Skills

2019· article· en· W2907868777 on OpenAlexaff
Alana Hawley, Andrew O’Farrell, Mathew Mercuri, Scott McIntosh

Bibliographic record

VenueWilderness and Environmental Medicine · 2019
Typearticle
Languageen
FieldMedicine
TopicOrthopedic Surgery and Rehabilitation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsClimbingRescue robotPopulationPsychologySearch and rescueConfidence intervalApplied psychologyMedicineEngineeringComputer scienceEnvironmental healthArtificial intelligence

Abstract

fetched live from OpenAlex

INTRODUCTION: Rock climbing involves some inherent danger, and rock climbers should be able to carry out basic rescue techniques for their own safety. This study seeks to assess such abilities by examining self-rescue skills in a cohort of rock climbers. METHODS: Climbers who participate in multipitch sport or traditional climbing styles were recruited via posters at a local climbing gym and on social media. Participants completed a survey assessing climbing history and confidence in their rescue skills and then were evaluated on 3 rescue scenarios in an indoor, standardized setting. Scenario pass rates were calculated and compared with rescue skill confidence on the survey. RESULTS: Twenty-five climbers participated in the study. Mean confidence in rescue skills varied from 4 to 4.5 (on a 7-point scale). The pass rates for the 3 scenarios were 28%, 68%, and 52%. Only 24% of climbers passed all 3 scenarios. Surveyed confidence in rescue skills and pass rate statistically correlated in only 1 scenario. CONCLUSIONS: Self-rescue skills were generally lacking in our study population. Climber confidence, experience, training, and climbing frequency did not appear to be associated with a higher level of rescue skills. Self-rescue skills should be emphasized in climbing instruction and courses to increase overall safety.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.999

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)0.0020.000

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 head0.216
Teacher spread0.212 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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