Playing It Safe: Selected Mountain Leadership Papers, Techniques and Reports of the Alpine Club of Canada
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.011 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".