Snowpack tests for assessing snow-slope instability
Bibliographic record
Abstract
Abstract Information on snowpack instability is crucial for assessing avalanche risk in backcountry operations as well as for operational forecasting of the regional avalanche danger. Since slab avalanche release requires both fracture initiation and fracture propagation in a weak snowpack layer, field observations should ideally provide reliable information on the probability or propensity of both fracture processes. Even simple field observations that do not require digging a snow pit can provide useful information. Traditional snowpack tests include the shovel shear test, the shear frame test, the compression test (CT) and the rutschblock test (RB). Interpretation of the test results for the CTand RB has been improved by considering the appearance or type of the fracture in addition to the score. More recently, two tests have been developed that focus on fracture propagation rather than initiation: the extended column test (ECT) and the propagation saw test (PST). We compare the sensitivity, specificity and unweighted average accuracy of various stability tests. Comparative studies indicate that the RB, ECT and PST have comparable accuracy. For most test methods the unweighted average accuracy of a single test was 70–90% depending on the dataset. Test methods such as the RB, ECT and PST, which fracture an area large enough to include fracture propagation, are generally more accurate than test methods that fracture smaller areas (e.g. the CT). The threshold-sum method was also less accurate. Even with very experienced observers for the RB, ECT and PST an error rate of at least about 5–10% has to be expected. Performing a second, adjacent test on the same slope improves test reliability.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".