Forest harvesting is associated with increased landslide activity during an extreme rainstorm on Vancouver Island, Canada
Bibliographic record
Abstract
Abstract. Safe operations of forest practices in mountainous regions require effective development planning to mitigate hazards posed by landslides. British Columbia, Canada, has for the past 2 decades implemented landslide risk management policies aimed at reducing the impacts of the forestry industry on landslides. Consequently, it is required that timber harvesting sites be evaluated for their potential or existing impacts on terrain stability. Statistical landslide susceptibility modelling can enhance this evaluation by geographically highlighting potential hazardous areas. In addition, these statistical models can also improve our understanding of regional landslide controlling factors. The purpose of this research was to explore the regional effects of forest harvesting activities, topography, precipitation and geology on landslides initiated during an extreme rainfall event in November 2006 on Vancouver Island, British Columbia. These effects were analyzed with a nonparametric statistical method, the generalized additive model (GAM). Although topography was the strongest predictor of landslide initiation, low density forest interpreted as regrowth areas and proximity to forest service roads were jointly associated with a 6- to 9-fold increase in the odds of landslide initiation, while accounting for other environmental confounders. This result highlights the importance of continuing proper landslide risk management to control the effects of forest practices on landslide initiation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".