Bibliographic record
Abstract
Since 1971, we use CLPA maps (past and geomorphic map of avalanche), as the main registration maps concerning the major threatened areas, especially the villages. Few years later (1984), in order to perform the land planning process in which we have to assess what the avalanche hazard level might be, appeared the first legal avalanche risk zoning maps (called PPR - Risk Prevention Plan). \nThese two supports are not easily connected but significant progress in the methods appeared after 1999. This poster details the most recent evolutions of avalanche mapping methods:\nIn the first part, the main evolutions of CLPA production process are described with a special focus put on the better coordination between forest rangers (in charge of a permanent avalanche survey) and avalanche mapping specialists. To improve this preventive action, an annual update distribution of digital files and via internet is set up now.\nThe second part details the main expert assessments to be used in the PPR mapping process: from the determination of potential hazard (considering the return period and the power of avalanche) to the representation by zones of different land-use prescription. Corresponding to each zone, specific rules on housing and land planning process are applied.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".