Modeling the travel distances of debris flows and debris slides: quantifying hillside morphology
Bibliographic record
Abstract
A travel distance model for debris flows and slides is presented based on information collected in southeast British Columbia, Canada. The model incorporates a variable that represents terrain morphology by a single number, quantification made using a one-to-one correspondence between the binary and decimal numeration systems. The terrain morphology coding has a site-specific character, providing a process-based representation of local conditions. Multiple regression analysis was used to assess the dependence of event travel distance on terrain morphology, slope, stand height, terrain curvature and canopy closure (R2 = 0.975, p < 0.001). The model fulfills all the assumptions and requirements of regression analysis (i.e. normality, homoscedasticity, non – correlated errors, lack of colinearity or outliers). An independent data set was used to test the model. The model successfully predicted all but one of the test dataset events, and one of four outliers. The model consists of an equation that can be used in mass movement risk assessment associated, with different forest activities (e.g. harvesting, road building).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".