Preliminary Canadian Landslide Database
Bibliographic record
Abstract
This preliminary Canadian landslide database is a publicly available compilation of existing landslide inventories and original mapping. Version 12.0 of the database contains 25,500 entries of both landslide events (discrete recorded period of movement) and landslide features (slope with morphology consistent with past or ongoing movement). Landslide locations are provided as point features and include attributes for landslide type, material type (surficial, rock, ice, anthropogenic), point location type (headscarp, source, transport, deposit), qualitative location confidence (low, moderate, high), and a field for tracking updates to an entry. Where available additional attributes such as volume estimate, date of occurrence, trigger, contributing factor, and reference to previous work are provided. Most landslides in the database have been identified using Google Earth and publicly available lidar. Online mapping applications such as HazMapper by Scheip and Wegman (2021) and Arctic Landscape EXplorer (ALEX) by Lübker et al. (2024) have also been used to identify landslides based on the changes in multi-spectral indices derived from satellite acquired datasets. As most of the landslides have been identified using remote sensing techniques (optical, multi-spectral, lidar, InSAR), landslide type attribution should be considered preliminary, and no characterization of the current level of landslide activity or hazard are provided. The database spatial sampling biases includes detailed representation of areas with existing inventory and where lidar is available which allows for the identification of landslide features in forested terrain. Based on these limitations, the preliminary Canadian landslide database is appropriate for research projects and for use as part of the initial desktop review but should not solely relied on for formal landslide hazard assessments. Version 12.0 includes the addition of 12,740 landslide features over version 11.0. Highlights of this version include the addition of Antoni Lewkowicz, (retrogressive thaw slumps from Canadian Arctic), Cory McGregor (landslides from Haida Gwaii and Akie River Valley, British Columbia), Jennifer Clarke (landslides from Okanagan and Shuswap, British Columbia), Aaron Steelquist (landslides from Fraser Canyon, British Columbia), and Caleb Ring (landslides from Fraser Canyon, British Columbia) as co-authors. This version also includes the addition of 694 new post-wildfire landslides by Carie-Ann Hancock along with updates (e.g. better constrained initiation date, location, landslide type) to 508 existing entries. Version 12.0 standardizes the date format to YYYYMMDD. Effort was started and is still underway to standardize volume class categories (giant, large, medium, small) according to the classification proposed by McColl and Cook (2024). Point location and attribute data are provided as .csv file which can be imported in GIS software and as .kmz file for visualization using Google Earth. Summary statistics are provided in a separate spreadsheet. Summary statistics from previous versions are now provided in the different spreadsheet tabs. Release notes from this and previous versions are compiled in an accompanying pdf document.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.078 | 0.042 |
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".