Review and latest insights into rock fall temporal variability associated with weather
Bibliographic record
Abstract
This paper presents a review of historical and latest insights into rock fall hazard temporal variability associated with weather. Reviewed research expands several locations around the world; however, focus has been on the recent advances in western Canada and from the author's experience. The recent research reviewed has provided new insights into the relationships between weather and rock fall occurrences and the recent focus on probabilistic approaches appears to be the way forward for rock fall hazard management. This paper also references some statistical tools for quantification of weather–rock fall relationships that allows better understanding of the stochastic nature of the phenomena. A decisive strength of how these methodologies are developing lies in the fact that adoption of probabilistic tools allows direct translation into rock fall hazard quantification that reflects its temporal variability. When coupled with weather forecasting, these tools can provide real-time forecasting of rock fall hazard. Moreover, some of these tools provide a way forward for forecasting the effects of climate change on rock fall hazards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".