Avalanche terrain analysis in Nunavik, Québec: a multi-criteria approach combining topographic and meteorological data
Bibliographic record
Abstract
• Mapping avalanche-prone areas near Inuit villages in Nunavik, Quebec. • Use of a multi-criteria analysis method to overcome the lack of historical data. • Key roads and residential areas overlap with identified avalanche runout zones. • Windblown snow drives avalanche triggers in treeless northern landscapes. To address the knowledge gaps regarding snow avalanches in Nunavik, whose frequency and intensity may increase due to current climate change, this research aims to characterize avalanche potential areas located in or near local villages. To achieve this goal, a method inspired by various existing approaches, such as a multi-criteria analysis and the application of an empirical model to estimate maximum runout distances, was developed to better address the specific conditions of the study regions and the available data. Thus, various topographic and meteorological analyses were carried out near or in the villages of Umiujaq and Kangiqsualujjuaq (Nunavik, Québec). The following variables, ranked by their weighted importance, were used: slope steepness, the presence of leeward topographic depressions that promote snow accumulation, and the elevation of the slope. The final maps indicate that there are some issues for both villages, as the avalanche runout overlaps with roads and areas used for various activities. In Kangiqsualujjuaq, a potentially avalanche-prone area reaches the inhabited area. Overall, a total of 4.72 km 2 of avalanche-prone terrains was identified in Umiujaq and 1.47 km 2 in Kangiqsualujjuaq. The study also highlights the significant role of windblown snow transport in potential snow avalanche release areas and evaluates the most favourable wind directions for each village.
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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".