Assessing the relationship between weather conditions and rockfall using terrestrial laser scanning to improve risk management
Bibliographic record
Abstract
Abstract. Since 1987, more than 13 200 rockfalls have been inventoried by the ministère des Transports du Québec (MTQ) as having impacted the national road Route 132 in northern Gaspésie. This natural hazard represents a nearly permanent danger for road users. Traditional mitigation measures can be ineffective on poorly consolidated, deformed and highly fractured rockwalls such as those found in northern Gaspésie. To address this issue, implementing preventive risk management based on the factors that trigger rock instabilities could be the most effective method. Earthquake, rainfall and freeze–thaw cycles are commonly considered to be the main rockfall-triggering factors. This study aims to better understand the climatic conditions conducive to rockfalls in northern Gaspésie in order to provide knowledge to implement an appropriate risk management strategy. Three rockwalls were scanned with terrestrial laser scanning (TLS) instruments during specific pre-targeted weather conditions. Over a period of 18 months, 17 surveys have allowed us to identify 1287 rockfalls with a magnitude above 0.005 m3 on a scanned surface of 12 056 m2. In addition, meteorological instruments and a 550 cm thermistor string have been installed directly on a vertical rockwall. It appears that some weather conditions influence the occurrence, frequency and magnitude of rockfalls. In winter, rockfall frequency is 12 times higher during a superficial thaw than during a cold period in which temperature remains below 0 ∘C. In summer, rockfall frequency is 22 times higher during a heavy rainfall event than during a mainly dry period. Superficial freeze–thaw cycles (< 50 cm) cause mostly a high frequency of small-magnitude events, while deeper spring thaw (> 100 cm) results in a high frequency of large-magnitude events. The influence of weather conditions on rockfall frequency and magnitude is crucial in order to improve risk management, since large-magnitude events represent higher potential hazards. This study provides a classification of weather conditions based on their ability to trigger rockfalls of different magnitudes. This knowledge could be used to implement a risk management strategy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".