Bibliographic record
Abstract
Depuis 1996, la mise en œuvre de la directive IPPC (Integrated Pollution Prevention and Control) est au cœur des débats publics et politiques. Des non-conformités subsistent encore actuellement ; les points bloquants ne sont pas encore totalement levés que déjà une nouvelle directive sur les émissions industrielles se profile à l’horizon.Cette directive IPPC impose notamment aux industriels concernés par cette réglementation d’utiliser des « meilleures techniques disponibles » (MTD). Or, au niveau local, les exploitants n’ont pas les moyens suffisants de justifier qu’ils utilisent des MTD ou des techniques ayant des performances équivalentes. Cet article traite de l’évolution du cadre réglementaire lié à la mise en œuvre de la directive IPPC depuis 1996, en Europe et plus particulièrement en France. Il propose un bilan synthétique des principaux points bloquants à sa mise en œuvre. En conclusion, l’article montre, que grâce à l’analyse approfondie du contexte réglementaire d’application de l’IPPC en Europe, il est important de développer une méthodologie d’évaluation des performances environnementales au sens de l’IPPC.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| 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".