Landslide response to the 27 October 2012 earthquake (MW 7.8), southern Haida Gwaii, British Columbia, Canada
Bibliographic record
Abstract
Abstract In this paper, we examine the influence of the 27 October 2012, Mw7.8 earthquake on landslide occurrence in the southern half of Haida Gwaii (formerly Queen Charlotte Islands), British Columbia, Canada. Our 1350 km2study area is undisturbed, primarily forested terrain that has not experienced road building or timber harvesting. Our inventory of landslide polygons is based on optical airborne and spaceborne images acquired between 2007 and 2018, from which we extracted and mapped 446 individual landslides (an average of 33 landslides per 100 km2). The landslide rate in years without major earthquakes averages 19.4 per year, or 1.4/100 km2/year, and the annual average area covered by non-seismically triggered landslides is 35 ha/year. The number of landslides identified in imagery closely following the 2012 earthquake, and probably triggered by it, is 244 or an average of about 18 landslides per 100 km2. These landslides cover a total area of 461 ha. In the following years—2013–2016 and 2016–2018—the number of landslides fell, respectively, to 26 and 13.5 landslides per year. In non-earthquake years, most landslides happen on south-facing slopes, facing the prevailing winds. In contrast, during or immediately after the earthquake, up to 32% of the landslides occurred on north and northwest-facing slopes. Although we could not find imagery from the day after the earthquake, overview reconnaissance flights 10 and 16 days later showed that most of the landslides were recent, suggesting they were co-seismic.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".