Rockfall forecasting models along the roads of northern Gaspésie (Eastern Canada)
Bibliographic record
Abstract
Rockfalls are major natural hazard for road users and infrastructures in northern Gaspésie (Eastern Canada) where nearly 25 kilometers of road runs along 10 to 100 m high flysch rockwall. The Ministère des Transports du Québec (MTQ) has recorded more than 17 500 rockfalls that have reached the roadway since 1987, which represents a nearly permanent danger for users. In the late 90s, protective berms were erected to reduce the number of rocks reaching the roadway. Despite the efficiency of these infrastructures, more than a hundred events are still recorded each year. Based on previous studies showing that rock instabilities in this type of geology is strongly correlated with meteorological events, we used different machine learning methods (logistic regression, classification tree, random forest, neural network) to design the best operational rockfall prediction model. Three event variables based on different rockfall frequency and magnitude thresholds were created. 94 weather variables were used to explain and predict events. Results show that 24h to 120h mean daily temperature above 0oC and thawing degree-days are the most effective variables explaining the occurrence of winter and spring rockfall events. In summer, rainfall intensity is the most effective explanatory variable. The performance of the models has been optimized using a testing data set and then tested in an operational context using Environment Canada 24h and 48h HRDPS (high resolution deterministic prediction system) weather forecast model. A rockfall danger scale based on the probability of occurrence of medium and large magnitude rockfalls is proposed. These models can be used operationally as decision support tools to predict days with high event probability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".