Bibliographic record
Abstract
Snow avalanches occur in snow covered mountain regions throughout the world and have caused natural disasters as long as mountainous areas have been inhabited. Their occurrences affect ski resorts, roads, railways, power lines, communication lines, forests, backcountry recreationists, residential areas, and industrial facilities (e.g., mining) (Table 1). The number of fatalities per year due to snow avalanches is estimated to be about 250 worldwide. Within the last ten years (1993-1994 to 2002-2003) 419 people were killed in North America (U.S. and Canada) (Figure 1). In Canada, for example, the direct and indirect costs amount to over CAD$5 million per year. Most of the fatalities involve personal recreation on public land (Jamieson et al., 2002). Avalanche mitigation includes temporary measures (forecasting and road closure) and permanent measures (landuse planning, protective means such as snow sheds or tunnels, and reforestation). By combining temporary and permanent measures in a cost efficient way, also called integral risk management, the avalanche risk can be reduced to an acceptable level. Since snow avalanches are still relatively rare events, personal experience is limited and expertise is usually not readily available. Therefore, it is essential for hazard mitigation to increase the awareness of land managers, consultants, govern- mental agencies and individual recreationists about snow avalanches.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".