LINKING LANDSLIDE VELOCITY CHANGES AND CLIMATE IN THE WESTERN CANADIAN SEDIMENTARY BASIN
Bibliographic record
Abstract
Slowly moving landslides in the weak glacial sediments and shale bedrock of the Western Canada Sedimentary Basin (WCSB) are intersected by both linear infrastructure and settlements and have been documented to cost infrastructure owners over CDN $ 400 Million annually (Porter et al, 2019) in relation to maintenance and repairs. Although there have been numerous high activity years documented in the past decades, the relation between hydroclimatic conditions and landslide velocity change has not been well quantified. In order to develop a quantitative regional prediction model that correlates landslide velocity and hydroclimatic factors such as, precipitation, snow melt and soil moisture, a series of private companies and government agencies have contributed to the assembly of a regional data set of landslide velocity data. This initial phase of this week has involved the discovery and compilation of both continuous and discontinuous geotechnical monitoring data, supplemented with space-based InSAR data. This displacement data has been linked hydroclimatic data derived from both sensor and satellite data to develop preliminary regional thresholds. The next phases will integrate the various data sets into machine learning models to support the development of regional predictive models to support operational response.
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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 | 0.002 |
| 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 | 0.000 |
| 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".