Temporal Rockfall Forecasting Using Thermal Imaging and Meteorological Data
Bibliographic record
Abstract
ABSTRACT: Rockfall poses serious hazards to safety and infrastructure along both excavated and natural rock slopes. Significant progress has been made in forecasting time-to-failure for large-scale, progressive slope failures using full-spatial, real-time monitoring techniques. However, small-scale, brittle rockfall events typically occur with little to no detectable precursory movement, highlighting the value of identifying and characterizing the triggers for these events as a potential predictive approach. There is broad consensus within the geotechnical community that meteorological factors, such as heavy/cumulative rainfall and freeze/thaw, contribute to rockfall occurrence. However, quantitative documentation of these relationships has been limited by the lack of real-time rockfall monitoring. Quantifying the relationship between meteorological forces and rockfall events could be a critical first step towards higher confidence predictions of rockfall occurrence to support risk management. The University of Arizona's Geotechnical Center of Excellence has collected a unique dataset of observed rockfall events captured using thermal video from two mine sites in North America. These events were identified and evaluated alongside the concurrent meteorological data. Here, we present the results of preliminary predictive rockfall models developed for one of the two study sites with the goal of improving on-site safety and reducing economic losses from rockfall-related work interruptions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".