Le plan intercommunal de sauvegarde : « de la conception à la mise en œuvre »
Bibliographic record
Abstract
The world is facing an increase in natural disasters, such as heatwaves, wildfires, floods, storms, earthquakes, and volcanic eruptions. These events, which are becoming more frequent and intense, are causing significant damage and loss of life. In France, natural risks are particularly high, as evidenced by the 2003 heatwave, the 2010 Xynthia storm, the 2017 Hurricane Irma, and the 2022 wildfires.In the face of these growing threats, it is crucial to implement risk prevention and management measures. Communal Safeguard Plans (PCS) and Intercommunal Safeguard Plans (PICS) play an essential role in preparing municipalities and inter-municipalities to deal with natural disasters.PICS, in particular, allow the member municipalities of an inter-municipality to pool their resources and coordinate their actions in the event of a crisis. They must be adapted to the specificities of each territory and take into account the diversity of risks to which populations are exposed. The implementation of these plans requires close collaboration between municipalities, government departments, and other local actors.Preparedness and risk management are essential to protect populations and limit the damage caused by these events. PICS, if well-designed and implemented, can be valuable tools for strengthening the resilience of territories.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".