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Record W1990735894 · doi:10.1080/00207230600802098

Automotive coatings with improved environmental performance

2006· article· en· W1990735894 on OpenAlex
Lindita Prendi, Paul Henshaw, Edwin Tam

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Environmental Studies · 2006
Typearticle
Languageen
FieldMaterials Science
TopicPolymer crystallization and properties
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAutomotive industryCoatingLegislationLife-cycle assessmentEnvironmental impact assessmentProcess engineeringMaterials scienceManufacturing engineeringEnvironmental scienceEngineeringNanotechnologyProduction (economics)

Abstract

fetched live from OpenAlex

The automotive coating processes contribute significantly to the environmental burden compared to other stages of vehicle manufacturing. Efforts are being made to reduce this impact through legislation, resulting in the introduction of new coating formulations and application technologies. Water‐borne, powder and UV‐cured coatings are seen as alternatives to solvent‐borne coatings. It is not clear which type of coating is superior in terms of impacts on the environment. This paper discusses the stages involved in the automotive paint application phase, together with materials involved. Next, the composition and properties of water‐borne coatings are discussed briefly. A summary of developments in powder coatings and research related to their properties and application follows. Finally, emphasis is placed on life cycle assessment (LCA) studies conducted to identify and quantify the environmental impacts and trade‐offs in the use of alternative coatings.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.522

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 head0.008
GPT teacher head0.213
Teacher spread0.205 · 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