Cartographie et classification du terrain à potentiel avalancheux des Chics-Chocs, Québec, Canada, à l'aide d'un système d'information géographique
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, Québec, 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 zones composed of 249 sub-zones and paths, were localized and characterised by 16 attributes. Moreover, in order to build the institutional memory bank of one of the most frequented area by winter sports adepts in Québec, a system was created to allow future cataloguing of avalanche occurrences inside the potential avalanche location map. The database, currently having 48 events dated between 1987 and 2006, linked to a GIS allows the visualisation of the spatial distribution of avalanche occurrences and is the basis for the study of potential correlation topographic parameters and weather patterns. Another terrain analysis was also performed to address the challenge of the access restrictions of Mount-Albert in Gaspésie 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 results show that in surface, 41% of the terrain analysed has a high degree of exposition to avalanches. An English version of this thesis is summarized in annex 7.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".