Morphometric controls and basin response in the cascade mountains
Bibliographic record
Abstract
Morphometric variables associated with 36 debris torrent, 78 snow avalanche, 45 composite debris torrent and snow avalanche and 14 streamflow basins in the Cascade Mountains of southwestern British Columbia, Canada are examined. The results show significant statistical differences in top and bottom elevations, relief, channel length and gradient, basin area, fan gradient and area, and basin ruggedness between snow avalanche basins and the two basin types affected by debris torrents, reflecting the very different nature of these processes. Only top and bottom elevations and fan area differ significantly between debris torrent and debris torrent‐snow avalanche basins, implying that the latter are probably debris torrent basins in origin. As many as six morphometric variables are significantly different between streamflow basins and the other basin types, allowing the former to be differentiated despite their small, steep character. Discriminant analysis indicates that bottom elevation and channel or path gradient are the best variables for classifying the four basin types by process. Generally strong correlations exist between basin area on the one hand and relief, channel length and channel gradient on the other in debris torrent, debris torrent‐snow avalanche, and streamflow basins. Fan gradient and area are, however, weakly or modestly correlated with basin area or ruggedness. No such morphometric relations are present in snow avalanche basins. The results of this study also indicate that in debris torrent‐prone basins the fan gradient and Melton's R have identifiable lower thresholds while basin area has an upper threshold, but use of these thresholds for identification of debris torrent hazard is complicated by overlapping thresholds for streamflow basins.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".