Landslide Frequencies and Logging on Vancouver Island: An Analog Showing Varied yet Significant Changes
Bibliographic record
Abstract
Despite a long history of landslide research in British Columbia, there remain few data on the actual change in rates of landslides following harvesting activities other than from the Queen Charlotte Islands and the Clayoquot Sound region of Vancouver Island. The application of these data to other areas in British Columbia is problematic. This paper discusses implications of the results of a study of three watersheds on Vancouver Island: Macktush Creek, Artlish River, and Nahwitti River watersheds. Some 363 landslides, from 0.02 to> 1 ha, were identified in three watersheds from air photographs, beginning at a date that essentially preceded logging up to the present. Landslide frequencies increased in Macktush Creek, Artlish River, and Nahwitti River by approximately 11, 3, and 16 times, respectively. Two to 13 times more landslides reached streams following logging; most of these were between 0.2 and 1 ha. Landslide density analyses produced variable results, ranging from 2.4 to 24 times increases in number of landslides. Road landslide frequencies increased by 27, 12, and 94 times for Macktush, Artlish, and Nahwitti, respectively. Landslide frequencies need to be determined for many more watersheds to provide better information on the effects of logging activities on both Vancouver Island and the Interior of British Columbia. It is against this baseline that geoscientists and geotechnical engineers practising in the forest sector can measure their successes at reducing the impact of landslides in British Columbia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".