The Propagation Saw Test (PST): A Review of its Development, Applications, and Recent Research
Bibliographic record
Abstract
ABSTRACT: The Propagation Saw Test (PST) method was developed independently in Switzerland and in Canada in 2005 and 2006. It was inspired partly by observations of fracture propagation in earlier cantilever beam tests, and was partly based on notched-specimen tests that are common in fracture mechanics research. The PST has been used- in various modified forms- for investigation and verification of both shear-fracture and weak layer collapse failure models, for spatial variability studies, and for real-world avalanche forecasting and instability assessment. Many PST research and development studies have been conducted over the last several years, and recently there has been some debate regarding the best methodology and interpretation or application of test results. For these reasons, and given the recent or forthcoming adoption and standardization of the PST method by a number of international avalanche associations, we feel that researchers and practitioners could benefit from a comprehensive review of the development of the PST, its applications, and recent research which makes use of it. In this paper, we briefly review peer-reviewed articles, conference papers and presentations, and academic theses related to: the general test configuration and its relationship to fracture mechanics; the results of field experiments designed to test the influence of column size, cut direction, and slope angle on the test method; observations of ‘critical ’ cut lengths, independent of column size in standard and non-standard test geometries; the empirical relationship between snowpack parameters and PST results; the basis for the original and modified test dimensions and methods; the verification of the method and validation of its application to the initiation and propagation of fracture; its applications to other research problems, such as fracture mechanics and spatial variability.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".