Combination of statistical and conceptual approaches for debris-flow susceptibility modelling at a regional scale, British Columbia, Canada
Bibliographic record
Abstract
This paper describes the data and methodological approaches used to assess the initiation and runout susceptibility of debris-flows in the Valemount area, east-central British Columbia, Canada. Debris-flows are frequent in this area and have impacted the roads and dwellings. The study area covers about 1200 km 2 . A landslide inventory for this area delineates past debris-flows, including their source areas and deposits. This inventory includes hillslope and channelized debris-flows, enabling the development of separate models for each type of event. For hillslope debris-flows, a supervised multivariate regression technique was used to identify possible initiation zones. Subsequently, a conceptual model was trained and applied to simulate runout and classify areas according to runout susceptibility. Modeled hillslope debris-flow deposits reaching the main valley channels were considered as a proxy for potential source areas for channelized debris-flows, even though source sediments may also result from other processes, including gradual erosion or mass movements from adjacent slopes. Conceptual modelling was then applied to this second type as well. The results of the two models were combined to classify the area according to its predisposition to debris-flow runout. Debris-flow datasets other than those used to train the models, were used to optimize and validate the models. Results indicate that, considering both debris-flow types, there is a 75 % of agreement between the modeled susceptible areas and the validation debris-flow fans. This suggests that the models can effectively distinguish between potential debris-flow fan areas and non-debris-flow areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".