Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Snow avalanches are a serious threat to traffic in the northern Gaspésie region. In this study, we look at the development of different forecasting models using machine learning (ML), based on snow avalanche events recorded by Quebec's Ministry of Transportation (MTMQ), meteorological data from the Cap-Madeleine station and Environment Canada weather forecast data. The models were trained and tested on <em>Train</em> and <em>Test</em> datasets with meteorological and weather forecasts recorded at the Meteorological Station. Unsupervised learning models were compared to expert models where only 4 variables were selected with avalanche expertise in mind, yielding similar results in prediction. The ML models were then tested in a realistic forecasting context over the year 2019 with weather data from a forecasting station (Hindcast) and with forecast data over 24 h and 48 h (GEMLAM 24 h). The LR and RF models show that model performance can match or exceed that of current forecasting tools, enhancing hazard anticipation while maintaining a user-friendly framework suitable for real-time application. In conclusion, recommendations on forecast-based operational procedures are proposed.
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.
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 | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it