Infrasound detection of avalanches: operational experience from 26 systems in Canada, Switzerland and Norway - individual and multi-array based approaches
Bibliographic record
Abstract
During the winter seasons 2019-2020 and 2020-2021 as well as during the current season 26 avalanche infrasound detection systems (IDA®) were used by Parks Canada, the Norwegian Road Administration and Swiss cantons as support for operational decision making to minimize avalanche risk. In this period, more than 800 controlled avalanches and 2700 natural avalanches were detected and delivered in near-real-time to avalanche teams. Operational efficiency of the Trans-Canada Highway (BC) in the winter months is highly dependent on effective avalanche control. Here the goal of infrasound technology is to provide the Glacier National Park avalanche control team with information on avalanche activity in specific avalanche sectors and paths to reduce avalanche related road closure times. Infrasound technology has also been successfully applied in sparsely vegetated terrain at high altitudes and latitudes such as in Norway, where the Norwegian Public Roads Administration operates infrasound systems along remote road sections where visual observations are difficult to gather. Experiences from regional avalanche forecasting teams in Switzerland where multiple arrays are distributed over a larger area are also presented. In this work we want to give an overview of results obtained from the use of infrasound technology with particular focus on advantages and limitations encountered in various operational contexts. Recent results of ongoing developments such as multi array processing are also presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".