DEBRIS FLOW MODELS IN THE VALEMOUNT AREA , BRITISH COLUMBIA
Bibliographic record
Abstract
This report describes the data and methodological approaches used to assess the runout susceptibility to debris flow landslides along the Fraser River in east central British Columbia, Canada. Debris flow landslides are a relatively frequent phenomenon in this area and have a major impact mainly along roadways. The study area covers about 1200 sq. km. and has high and very high-resolution digital elevation models. In addition, a landslide inventory is available for this area in which past debris flows are delineated by including source areas and valley deposits. The inventory includes rapid slope and channelled flows, enabling the development of separate modelling for the two types of phenomena. More specifically, for hillslope debris flow, a supervised multivariate regression technique was used to identify the possible trigger areas for rapid flows. Then a conceptual model was trained and applied to simulate runout phenomena and classify areas according to runout susceptibility. Runout phenomena from hill-slopes can become sources of material for channelized ones. For this reason, the outputs of hillslope debris flow modelling became an input to characterize the portions of the channel network from which channelized flows are most likely to be triggered. Conceptual modelling was then applied to this second type of phenomena as well. The results of the two modelling were then appropriately combined in order to classify the area according to its predisposition to be involved in debris flow runout. Landslide datasets other than those used to train the models were used to optimize and validate the products.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".