Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada
Bibliographic record
Abstract
This research activity aimed at reducing risk to infrastructure, such as a proposed pipeline route roughly parallel to the Yukon Alaska Highway Corridor (YAHC), by filling geoscience knowledge gaps in geohazards. Hence, the Geological Survey of Canada compiled an inventory of landslides including debris flow deposits, which were subsequently used to validate two different debris flow susceptibility models. A qualitative heuristic debris flow susceptibility model was produced for the northern region of the YAHC, from Kluane Lake to the Alaska border, by integrating data layers with assigned weights and class ratings. These were slope angle, slope aspect, surficial geology, plan curvature, and proximity to drainage system. Validation of the model was carried out by calculating a success rate curve which revealed a good correlation with the susceptibility model and the debris flow deposit inventory compiled from air photos, high-resolution satellite imagery, and field verification. In addition, the quantitative Flow-R method was tested in order to define the potential source and debris flow susceptibility for the southern region of Kluane Lake, an area where documented debris flow events have blocked the highway in the past (e.g. 1988). Trial and error calculations were required for this method because there was not detailed information on the debris flows for the YAHC to allow us to define threshold values for some parameters when calculating source areas, spreading, and runout distance. Nevertheless, correlation with known documented events helped define these parameters and produce a map that captures most of the known events and displays debris flow susceptibility in other, usually smaller, steep channels that had not been previously documented.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".