The identification of debris torrent basins using morphometric measures derived within a gis
Bibliographic record
Abstract
The identification of drainage basins susceptible to debris torrents has advanced in a somewhat ad hoc fashion with a variety of morphometric measures being employed. This study reports on a systematic and automated approach using morphometric measures derived from a digital elevation model (DEM), namely the first and second deravitives of elevation, slope gradient, slope aspect, profile curvature, plan curvature and mean curvature. Descriptive statistics such as the mean, standard deviation, skewness and kurtosis are calculated within a geographic information system (GIS) and used to describe the frequency distribution of the morphometric measures within each basin. For comparison purposes, morphometric measures previously employed to identify debris torrent basins, such as Melton's basin ruggedness R, basin area, and an elevation‐relief ratio, are also included in the GIS database. Logistic regression and discriminant analyses indicate that the standard deviations of slope gradient and slope aspect are the strongest predictors of the variables tested. In comparison, Melton's R and basin area, although proving to be significant predictors and thereby supporting previous studies, are shown to be weaker than the two strongest DEM‐derived variables. The results suggest that important morphometric indicators of debris torrent activity can be derived from DEMs, thus providing a systematic basis for future research into the identification of debris torrent basins.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".