Méthodologie d'évaluation des performances environnementales de techniques en vue de les comparer puis de les valider "meilleures techniques disponibles
Bibliographic record
Abstract
The European Directive, known as IPPC (Integrated Pollution Prevention and Control) imposes to the more pollutant industries to apply best available techniques (BAT) in order to reach an adequate level of the protection of the environment in the European Union. The French transposition of the European directive‟s obligations enforces notably industries to carry out a comparative study of their processes of production to techniques standing for BAT through a "decennial working report". Standard best available techniques are gathered by branch of activity in technical guides that are developed in the European level (BREF). The analysis of the European and French context showed that the main barriers are based on the comprehension of the BAT concept and the technical guides (BREFs), on the little synergy between the stakeholders but also on the lack of accepted assessment methodologies from the IPPC directive point of view, despite the twelfth considerations listed in this directive. The assessment and the application are even less undemanding than a technique could perform a BAT performance according to the local context. Hence, the culture of the sensitivity of the natural and human environment is essential. This thesis proposes an environmental performance assessment methodology of existing techniques in order to compare and to validate them as best available techniques, underlining its assets and limits. As far as the methodology is concerned, we decided to strengthen the legitimacy of our method by creating a voluntary technical working group which took part in the construction of our methodology. An approach in four steps ((1) analysis of the level of performance of existing technologies, step by step (2) analysis of the level of performance of the environmental and risk management system (3) analysis of the level of global performance of an installation (4) analysis of the level of performance of an installation with taking into account the sensitivity of the media) and three tools ((1) a simplified BREF reading grid (2) a thematic assessment form (3) an assessment grid of the sensitivity of the environmental area) have been defined. Besides, our methodology proposes an overall "multiple-media" assessment grid of the sensitivity of the local context. On the top of that, a theoretical reflection led to the fact that our methodology can be compared to existing assessment methodologies such as the life cycle analysis (LCA) or the environmental performance assessment (EPA).
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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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".