Quantitative prediction model for landslide hazardmapping: Tsitika and Schmidt Creek Watersheds, Northern Vancouver Island, British Columbia, Canada
Bibliographic record
Abstract
The management of unstable terrain has long posed a problem to geological engineers, foresters, construction companies, urban planners and others who have expanded their focus of attention toward steeper and more hazardous terrain. With this increased activity on steeper slopes comes an increasing frequency in slope failure problems including rising economic costs and the potential for loss of life ( Schuster 1996 ). One of the best methods of mitigating landslide hazard issues is to properly identify those areas most likely to undergo slope failure. Proper mapping for landslide hazards has long been recognized as one method for identifying and managing regions prone to mass movement and a variety of methods have been proposed (cf. Carrara 1983 ; Carrara et al. 1992 ; Chatwin et al. 1994 ; Wang & Unwin 1992 ). More recently, mathematical analyses and models have been put forward for landslide hazard mapping (cf. Jibson et al. 1998 ). Soeters & van Westen (1996 ) provide a good review of landslide hazard zonation mapping including inventory, heuristic, statistical and deterministic analyses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".