Long‐term modelling of landslides for different forest management practices
Bibliographic record
Abstract
Abstract Long‐term effects of different forest management practices on landslide initiation and volume were analysed using a physically based slope stability model. The watershed‐based model calculates the effects of multiple harvesting entries on slope stability by accounting for the cumulative impacts of a prior vegetation removal on a more recent removal related to vegetation root strength and tree surcharge. Four sequential clearcuts and partial cuts with variable rotation lengths were simulated with or without leave areas and with or without understorey vegetation in a subwatershed of Carnation Creek, Vancouver Island, British Columbia. The combined infinite slope and distributed hydrologic models used to calculate safety factor revealed that most of the simulated landslides were clustered within a 5 to 17 year period after initial harvesting in cases where sufficient time (c. 50 years) lapsed prior to the next harvesting cycle. Partial cutting produced fewer landslides and reduced landslide volume by 1·4‐ to 1·6‐fold compared to clearcutting. Approximately the same total landslide volume was produced when 100 per cent of the site was initially clearcut compared to harvesting 20 per cent of the area in successive 10 year intervals; a similar finding was obtained for partial cutting. Vegetation leave areas were effective in reducing landsliding by 2‐ to 3‐fold. Retaining vigorous understorey vegetation also reduced landslide volume by 3·8‐ to 4·8‐fold. The combined management strategies of partial cutting, increasing rotation length, provision of leave areas, and retention of viable understorey vegetation offer the best alternative for minimizing landslide occurrence in managed forests. Copyright © 2003 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".