Holocene landslides in the Fraser Canyon Corridor and implications for ‘ecohazard’ assessment
Bibliographic record
Abstract
Abstract Landslides are important natural hazards to infrastructure and humans, but they also pose a hazard to the biosphere and constitute an ‘ecohazard’. Recent and prehistoric landslides in southwest British Columbia have significantly impacted local fluvial processes and nearby ecosystems. For example, the 2018 Big Bar landslide caused a major hydraulic barrier in the Fraser River, which impeded upstream salmon migration for 2 years. To better understand the impact of landslides on the biosphere, defined here as ’ecohazard’ impacts, we assessed the distribution, size and character of landslides near the Fraser River over the past 12 to 15 ka. We created a landslide inventory using 2560 km2 of airborne lidar along the Fraser Canyon Corridor, a 375‐km stretch of the Fraser River, which flows through varied topography and bedrock. We mapped 274 landslides with planform areas between 2 × 103 and 2 × 106 m2 and estimated volumes that range from 600 to 1.05 × 108 m3. While the landslides with the largest area were flows/spreads in volcanic rocks, landslides in intrusive and metamorphic rocks had the highest median area. Area–volume relations were consistent across lithology and failure styles. Area–frequency and volume–frequency distributions have a positive skew, in agreement with other landslide inventories. Area–runout relations were also consistent across classifications, meaning the likelihood of a given landslide event reaching the Fraser River is primarily controlled by valley morphology rather than lithology or specific failure mechanics. We propose an ecohazard impact framework to classify the risk posed to salmon migration for the different classes of landslides documented in our inventory. We found that landslides of similar size and character to the Big Bar landslide are relatively common within the Fraser River Corridor; however, their location within the valley will determine how likely they are to significantly impact the ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".