Presentation and comments on EU legislation related to food industries – environment interactions and waste management
Bibliographic record
Abstract
Summary Although environment remained for a long time at the very top of most advanced countries governments’ agenda priorities, and a series of protocols like Montreal (1987), Kyoto (2002), Conference in Rio de Janeiro (1992) and legislations (White and Green Paper, 2001 and 1996, respectively) were put forward among others, the global awareness towards the environment continues to be at a very low level. The main problem towards enforcing legislation is the high cost invoked by most industries and municipalities. However, recent advances in remediation, composting, recycling technology have shown that waste treatment can result in high added value products (i.e. biodiesel, fertiliser) and advantageous to the environment as well. European Union (EU) legislation is currently considered one of the well‐compiled and strict legislations compared with other advanced countries (USA, Canada and Japan). Although food industries are not included in the highly polluting ones, their great volume of waste materials generated, makes imperative their undertaking actions in this direction. This review aims at presenting all the EU laws (from the waste management perspective) in connection with the food industries and their interactions with the environment and vice versa.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.018 |
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".