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Playing It Safe: Selected Mountain Leadership Papers, Techniques and Reports of the Alpine Club of Canada

2001· article· en· W2141019276 on OpenAlexaboutno aff
Luanne Freer

Bibliographic record

VenueWilderness and Environmental Medicine · 2001
Typearticle
Languageen
FieldMedicine
TopicWinter Sports Injuries and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsMountaineeringWildernessAdventureClimbingRecreationPsychologyOperations researchHistoryEngineeringComputer sciencePolitical scienceLawArchaeologyArtificial intelligenceEcology

Abstract

fetched live from OpenAlex

The information in Playing It Safe covers many aspects of the climbing game. From sport climbing to ice climbing, ski tours to expeditions, there is plenty of sage advice gained from trial and error. Equipment, techniques, training, logistics, risk analysis, rescue protocol, and the mental and physical aspects are covered with a focus on reducing the amount of danger to which we, as climbers, are exposed.From the “Introduction” by Conrad Anker Not being a terribly experienced mountaineer myself, I wasn’t sure what sort of meaningful review I could give this book. What I was pleased to discover was that most of the content is useful and interesting not only to seasoned expedition planners, but to clients of the smallest wilderness outing as well. Toft edits well-written chapters from various well-known and respected wilderness adventurers on a variety of topics. From group dynamics to risk management, helicopter safety, and emergency situation management, many readers would find most chapters a good review before embarking on any expedition—mountaineering or otherwise. Several more technically advanced chapters focus on topics such as snow evaluation, sling anchors, and knot strength, topics likely to be interesting only to the experienced mountaineer. Playing It Safe is an inexpensive, easily packed, and useful addition to any adventurer's library. It should be on the must-read list for expedition leaders.

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 score0.029
Threshold uncertainty score0.986

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 head0.011
GPT teacher head0.206
Teacher spread0.196 · 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
Published2001
Admission routes1
Has abstractyes

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