Un système d'alerte pour les drogues illégales: développement de scénarios.
Bibliographic record
Abstract
[Table des matières] Résumé. Zusammenfassung. 1 Introduction. 2 Mandat et méthode. 3 Problématique : 3.1 Qu'est-ce qu'un système d'alerte ? 3.2 Objectif opérationnel d'un système d'alerte. 3.3 Fonctionnement d'un système d'alerte. 3.4 Synthèse. 4 Les systèmes d'alerte au niveau international : 4.1 Méthode. 4.2 Canadian Community Epidemiology Network on Drug Use. 4.3 Drug abuse warning network (DAWN). 4.4 Ohio substance abuse monitoring network (OSAM) 4.5 The Maryland drug early warning system (DEWS). 4.6 The Australian illicit drug reporting system (IDRS). 4.7 Tendances récentes et nouvelles drogues (TRENDS). 4.8 European joint action on new synthetic drugs. 4.9 European emerging trends projekt. 4.10 Synthèse. 5 La situation en Suisse : 5.1 Développements méthodologiques. 5.2 Développements organisationnels. 6 Besoins et ressources au niveau fédéral. 7 Besoins et ressources dans les cantons. 8 Eléments pour l'élaboration de scénarios pour un système d'alerte fédéral et recommandations. 8.1 Objectifs d'un système d'alerte ? 8.2 Les systèmes existants. 8.3 La situation en Suisse. 8.4 Les besoins au niveau fédéral. 8.5 La situation dans les cantons. 9 Scénarios : 9.1 Changement minimum. 9.2 Un système d'alerte inspiré de l'OSAM. 9.3 Un système de surveillance et de contrôle des substances. Bibliographie. Annexes.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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 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".