What does landslide triggering rainfall mean?
Bibliographic record
Abstract
Landslide-triggering rainfall thresholds are often subject to both false negatives (landslides where none are expected) and false positives (no landslides despite thresholds being exceeded). Debris flows and shallow landslides impact communities and infrastructures worldwide. Refinement of the relation between rainfall intensity and landslide occurrence would help remove the imprecise nature of this tool moving forward. Continuous 6-hour gridded precipitation data from over a five-year interval 900 km2, combined with a complete, time-constrained, landslide data base over the same period, are used to derive relations for the probability of shallow landslides with rainfall intensity measured over 6-hour, 12-hour, or 24-hour durations. Previously published and widely used thresholds are quantified in terms of landslide probability per unit area and demonstrate, for different sized study areas, the likelihood that at least one landslide will be initiated at different intensities and durations. Probabilistic distribution of landslides for a given study area and rainfall intensity can be easily derived using the binomial method from these relations.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".