Differences in the occurrence of debris flows in tropical and temperate environments: field observations and geomorphologic characteristics in Serra do Mar (Brazil) and British Columbia (Canada)
Bibliographic record
Abstract
Debris flows are among the most destructive types of mass movements throughout the world and are not restricted to certain climate zones or geological environments. In opposite corners of the American continent, Canada and Brazil have experienced several historic debris flows. As part of a one-year international exchange program, the lead author compared morphological evidence related to debris flows and the morphometry of watersheds at sites in Canada and Brazil, with the ultimate goal of improving the understanding of debris flows in Brazil. Field surveys carried out in both areas in 2019 and 2020 permitted observation of the debris-flow signatures, as well as the physical aspects of the surrounding areas and morphometric mapping of watersheds. Both areas exhibit similar typical features of debris flows, and the morphometric results indicate differences that may influence the recurrence of events at the sites in Canada as compared to Brazil due to their higher values for the parameters area > 25° (A25), relief ratio (Rr), and Melton ratio (Mr) at the Canadian sites; however, this dataset is limited. Compared to results in the literature from around the world, values of morphometric parameters at Brazilian sites are within the ranges observed in other tropical climates.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".