PREDICTIONS OF THE PROPAGATION SAW TEST: COMPARISONS WITH OTHER INSTABILITY TESTS AT SKIER TESTED SLOPES
Bibliographic record
Abstract
ABSTRACT: Several new fracture propagation field tests have been presented in recent years. These are designed to provide specific information about propagation propensity; however, each of the more common test methods are thought to be demonstrating at least part of the propagation process, and recent research has shown that the new propagation tests perform well at predicting skier-triggered avalanches. But which test performs best under which conditions? To address this question we compared the predictive success of the new Propagation Saw Test (PST) with that of the Compression test (CT), the Rutschblock test (RB), and the Yellow Flags structural instability index (YF) on skier-tested slopes that did and did not release avalanches in the Columbia Mountains of British Columbia, Canada. The results show that, for our dataset, the combined success rate of the PST in predicting stable and unstable conditions was the highest of the group, although it also had a much larger proportion of potentially dangerous ‘false stable ’ results than the other tests. The CT, RB, and YF methods tended to overestimate instability, but often made correct predictions where the PST was incorrect. Overall, the tests usually performed better in combination than on their own, as each provided slightly different instability information.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".