Landslide Sediment Flux and Forest Management in Northeastern Vancouver Island, Coastal British Columbia
Bibliographic record
Abstract
To evaluate landslide response of mountain drainage basins to forest management we examine a historical \ninventory in northeastern Vancouver Island. The study area is underlain by extrusive (upper Triassic Karmutsen \nFormation) and intrusive rocks (Jurassic Island Intrusions). The dataset, compiled via interpretation of sequential aerial photosets, helicopter traverses, and extensive fieldwork, comprises a total of 1961 sediment sources, including 798 field-measured events. Field- and photo-based data cover a time window of approximately 70 \nyears. The effects of forest management on landslide activity are assessed in terms of landslide density, sediment \nproduction, landslide geometry, landslide magnitude-frequency relations, topographic conditions of landslide initiation and deposition, and sediment redistribution across landscape components. Results indicate that forest management alters natural landslide dynamics in many respects. Logging-related debris avalanches are typically smaller. Consequently, the magnitude-frequency relation in logged terrain occupies the small-medium magnitude spectrum (< 6000 m3), with frequencies increasing by over an order of magnitude. Lithologic effects on sediment production appear amplified, in that terrain underlain by extrusive rocks become increasingly more unstable than intrusive ones. Analysis of landslide initiation and deposition zones reveals that forest management promotes colluvial aggradation on mid and lower hillslopes and in gullies. This pattern, which accelerates sediment recharge of gully systems, has the potential of increasing the frequency of channelized debris-flows, hence cause an extended period of disturbance, before sediment dynamics recover to pre-logging conditions. The effects of forest clearing on hillslope-channel coupling are composite: in cutblocks the percentage of sediment delivered to streams is reduced by 20-60% whereas road-related landslides are associated with highestconnectivity to streams.
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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 | 0.002 | 0.003 |
| 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".