MétaCan
Menu
Back to cohort

Presentation and comments on EU legislation related to food industries – environment interactions and waste management

2006· article· en· W1967146527 on OpenAlexaboutno aff
Ioannis S. Arvanitoyannis, Persefoni Tserkezou, Stefania Choreftaki

Bibliographic record

VenueInternational Journal of Food Science & Technology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationEuropean unionBusinessPresentation (obstetrics)White paperInternational tradeGlobal environmental analysisEnvironmental planningPolitical scienceMarketingLawEnvironmental science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.255
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Food Science & TechnologySame topicFood Waste Reduction and SustainabilityFrench-language works237,207