Towards a portrait of avalanche hazard in Nunavik
Bibliographic record
Abstract
In Nunavik, the hilly territory of northern Quebec north of the 55th parallel, snow avalanches have only recently attracted scientific attention. The first reports followed the January 1, 1999 avalanche that killed nine people and injured 25 others in Kangiqsualujjuaq (northeast Nunavik). Since then, only a few studies have been carried out on snow and weather data related to this event. New data productions started in 2015-2016, when a team from OHMi Nunavik, interested in studying slope dynamics at different sites, recognized the geomorphological evidence of avalanches.To identify active avalanche paths, automatic time-lapse cameras (type ReconyxTM Hyper Fire2) were installed at the foot of several slopes. Operating year-round, and recording an hourly image of the slope during the day (between 9:00 am and 4:00 pm in most cases), the collection of images from these cameras led to the development of a first avalanche portrait at the scale of three Nunavik sites: Umiujaq, Wiyâshâkimî meteorite crater lake, and Kangiqsualujjuaq. Set up at different dates, the devices collected more than 40,000 images between 2017 and 2022. The avalanche deposits visible on these images enables to establish a winter and spring calendar of occurrence, to delimit their contours, to estimate their runout distances, and the weather conditions during the triggering phase. The Umiujaq site, which had not been identified as a potential avalanche site following the 1999 disaster in Kangiqsualujjuaq during a rapid study of villages in a likely vulnerable situation, is the one of the three study sites that has the most avalanches captured by our device.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".