Bibliographic record
Abstract
The winter of 2008 produced a series of persistent weak layers that plagued backcountry skiers from the very beginning of the season. These layers formed a snowpack, which consistently delivered a large number of anomalous avalanches throughout the Columbia and Rocky Mountains. Some of the greatest learning tools available to avalanche professionals are the stories and pictures passed on by people who have lived through years and conditions similar to those of 2008 in western Canada. This presentation will be a series of short case studies of significant avalanches that occurred in guiding operations focusing on the February 26 th surface hoar layer. This persistent weak layer created an unusual set of problems, which broke the rules of terrain and snowpack assessment. In the guiding world, where “the show must go on”, these conditions had to be managed in very unique and creative ways and forced operations to think outside of the box.
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 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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.043 | 0.016 |
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".