Mapping and Classification of Potential Avalanche Sites in the Chic-Chocs Mountains, Quebec, Canada, Using Geographic Information Systems
Bibliographic record
Abstract
Avalanche sites mapping and classification are tools that have been frequently used for managing avalanche risks. The use of geographic information systems (GIS) for such applications has great potential although it is still in development. The potential avalanche sites of the Chic-Chocs Mountains, Quebec, Canada, was mapped with GIS technology, satellite images, aerial photos and 1:20 000 topographic maps. A forest map, including three different levels of forest density, was generated from the satellite image. A total of 59 potential avalanche zones were characterized in this area, including 249 avalanches paths, Moreover, in order to build an institutional memory bank of one of the most frequented area by winter sports adepts in Quebec, a system was created to allow future cataloguing of avalanche occurrences inside the potential avalanche location map. Another terrain analysis was also performed to address the challenge of the access restrictions of Mount-Albert in Gaspesie National Park. A terrain classification by exposure to avalanches based on Parks Canada’s technical model was performed in order to help safer management of the park’s winter activities. The database linked to a GIS is the basis for the study of potential correlation between topographic parameters and weather patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".