Development of a GIS-based landslide management system for western Manitoba's highway network
Bibliographic record
Abstract
Slope failures are a significant issue in the sedimentary clay shales of western Manitoba. Fluvial induced erosion by under fit rivers of the glacial Lake Agassiz have carved into the Odanah and Millwood clay shales in many regions, exposing them along valley walls in many areas of Western Manitoba. Manitoba's highways and surrounding infrastructure are at risk in these areas, where significant damage and hazards to the public due to landslides are a large concern. In today's age of proactive risk assessment it has become necessary to develop a management approach to optimize allocation of resources to highway infrastructure maintenance and rehabilitation in these areas. Four previously investigated and documented failures are the focus for developing a predictive model of which environmental and geological attributes tend to lead to failure. In the fall of 2006, six sites were visited where the Saskatchewan risk assessment system (Kelly et al. 2004) was applied to establish probability, consequence and risk factors for each site. Results from the tour were used as the basis for developing a Geographic Information System (GIS) model to predict risk of failure. Development of the Manitoba Infrastructure and Transportation Risk Management System (MITRMS) includes mathematical processing of digital elevation models (DEMs), allocating buffer distances around water bodies and highway infrastructure, selction of a representative statistical distribution to describe the occurrence level of grade in spatially selected regions, and reviewing highway designation and their public dependency. By combining a GIS and manually observed site specific information, a priorized list of potentially hazardous landslide sites is created along western Manitoba's highway network.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".