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Record W2262805455 · doi:10.4271/2004-01-0471

Separation and Liberation Factors in Designing for Automotive Materials Recovery

2004· article· en· W2262805455 on OpenAlexafffund
Edwin Tam, Lawrence J. Jekel

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutomotive industryLiberationComputer scienceEngineeringChemistry

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">One critical aspect of design-for-environment efforts is to increase the effectiveness of materials recovery from end-of-life vehicles. Recovery itself depends on both the amount of material recovered and the purity of the material stream. Shredding, and screening are often used to separate recyclable materials from wastes. However, with the increasing amount of composite components, particularly those made from plastics, separation processes may be inadequate. Instead, liberation processes, which reduce the physical joints between materials, are also important. In this research, samples of ABS and PVC plastics were assembled into various configurations, ground up, and then characterized by their size distributions and degrees of liberation. Two primary fastening methods - adhesive and riveting - were used to simulate how plastic components would be actually attached together. The results from this first phase of the research indicate that within the study parameters: 1) size distribution is largely independent of material type, material thickness, and fastening method; and 2) liberation varies with the type and degree of fastening.</div>

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 categoriesMeta-epidemiology (narrow)
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 score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.263
Teacher spread0.248 · 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.

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

Citations5
Published2004
Admission routes2
Has abstractyes

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