Identification of natural and logging-related landslide in the Capilano River basin (coastal British Columbia) : a comparison between remotely sensed survey and field survey
Bibliographic record
Abstract
In the Pacific Northwest landslide inventories are routinely compiled by means of aerial photo interpretation. When examining photo pairs the forest canopy, notably in old-growth forest, hides a population of "not visible" landslides. The present study attempts to estimate how important is the contribution of landslides not detectable from aerial photographs, to the global mass of sediment production from mass failures on forested terrain of the Capilano basin. To achieve this, aerial photo interpretation has been coupled with intensive fieldwork for identification and measurement of all landslides. In order to minimise bias in the comparison and integration of field-collected and air photo-collected data it was decided to define a 30-year time window. Incidentally, it has been possible to prove how landslide scars that appear on a single photo set would date further back than 30 years. Results show that "not visible" landslides can represent up to 85 percent of the total number of failures and can account for up to 30 percent the total volume of debris mobilised. Rates of sediment production differ greatly (one order of magnitude) between two sub-basins of the study area, suggesting that such figures should be generalised with care within a physiographic region. The difference in denudation rate is explained qualitatively by GISbased analysis of slope frequency distributions, drainage density and spatial distribution of surficial materials. Fieldwork has demonstrated that gully-related failures have a greater importance than one could expect from air photo interpretation. ANOVA and nonparametric tests indicate that careful logging in East Cap Creek has produced no detectable effects on mass wasting. Similarly, Sisters Creek, where timber harvesting stopped about 20 years before the start of our 30-year time window, has apparently recovered from the signs of past extensive logging. The existence of "not visible" events affected in a minor way conclusions about the impact of logging on slope stability in terms of land use (management) effects. It had a major impact on the nature of landslide magnitude-frequency relations and, finally is demonstrated to have implications for British Columbia Terrain Stability Classification from the terrain sensitivity point of view.
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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) | 0.001 | 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".