Bibliographic record
Abstract
Experience from avalanche rescues and rescue drills reveals that often the buried victim is not found on the first or even second pass of an organized probe line. Traditional probe spacing used in North America is based on assumptions and practice done nearly 40 years ago (Schild, 1963 and 1973). More recent work by Jamieson and Auger (1997) challenged those assumptions and presented evidence that the original probabilities of detection (POD) were high, but still their probe targets were not human shaped, and thus did not offer a realistic search target. We developed a computer program PROBE that simulates a fully articulated human body, “buries” the body, and then implements the probing technique specified by various command line options. The derived “bodies” offer realistic targets, and the program can compare the PODs for different probe-pole grid patterns. 10,000 trials were run for a variety of probegrid spacings, including the standard coarse probe, Canadian 3-holes-per-step, and European methods. Results suggest significantly lower PODs for the commonly used probe-grid patterns. Search and rescue leaders should reconsider their use of the traditional techniques. Some options are offered that may make searching more efficient, for the sake of the searchers and of the buried victim.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".