How hard do we tap during snow stability tests?
Bibliographic record
Abstract
Abstract. This study examines the impact force applied from hand taps during Extended Column Tests (ECT), a common method of assessing snow stability. The hand-tap loading method has inconsistencies across the United States, Canadian, and Norwegian written standards, as well as inherent subjectivity. We developed a device, the “tap-o-meter”, to measure the force-time curves during these taps and collected data from 286 practitioners, including avalanche forecasters and mountain guides in Scandinavia, Central Europe, and North America. Peak forces and loading rates are the metrics chosen to quantitatively compare the data. The mean, median, and inner quartile peak forces are distinctly different for each loading step (wrist, elbow and shoulder), as are the loading rates. However, there is significant overlap across the range of measurements and examples of participants with higher force wrist taps than other participants' shoulder taps. This overlap challenges the reliability and reproducibility of ECT results, potentially leading to dangerous interpretations in avalanche decision-making, forecasting and risk assessments. Therefore, we recommend updating the standards for the ECT. We propose two viable paths for future action: (1) define a target impact force-time curve for each tap level and develop tools and training to minimize variability in tapping force (2) assess the significance of the information derived from the number of taps. If deemed not highly valuable, we should consider reverting to a simpler binary interpretation that focuses exclusively on crack propagation.
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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 | 0.009 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".