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Record W2091995559 · doi:10.1139/s07-035

Management of waste from electrical and electronic equipment: The case of television sets and refrigerators

2008· article· en· W2091995559 on OpenAlexvenueno aff
K. Rousis, Κωνσταντίνος Μουστάκας, Marinos Stylianou, A. Papadopoulos, Maria Loizidou

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHazardous wasteElectronic equipmentElectronic wasteWaste managementEnvironmentally friendlyWaste streamWork (physics)Process (computing)Household hazardous wasteEnvironmental scienceWaste collectionMunicipal solid wasteEngineeringComputer scienceMobile incinerator

Abstract

fetched live from OpenAlex

Waste from electrical and electronic equipment (WEEE) constitutes one of the most complicated solid waste streams, in terms of its composition, and, as a result, it is difficult to be effectively managed. Waste from electrical and electronic equipment recycling is an important subject not only from the point of waste management, but also from the recovery of valuable materials. Characterization of this waste stream is of paramount importance for developing a cost-effective and environmentally friendly recycling system. Selective disassembly, targeting on singling out hazardous and (or) valuable components, is an indispensable process in the practice of WEEE recycling. It is very costly to perform manual dismantling of those products, due to the fact that brown goods contain very low-grade precious metals and copper. This work focuses on two major types of WEEE, television sets and refrigerators, giving analytical information on specific recovery and recycling procedures for their effective management.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.616
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.196
Teacher spread0.191 · 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 designBench or experimental
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

Citations9
Published2008
Admission routes1
Has abstractyes

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