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Record W4401877626 · doi:10.1021/cen-10226-scicon5

Slippery fabric finish cuts microplastic pollution

2024· article· en· W4401877626 on OpenAlexaboutno aff
Prachi Patel

Bibliographic record

VenueC&EN Global Enterprise · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsLaundryMicrofiberClothingWater repellentPolyesterTextilePollutionMaterials scienceWaste managementEngineeringForensic engineeringComposite materialPulp and paper industryLaw

Abstract

fetched live from OpenAlex

Nylon and polyester fabrics are a major cause of microplastic pollution. Around 500,000 metric tons of microfibers released from synthetic clothes during laundry ends up in the oceans every year. New fabric finishes that reduce friction could thwart the formation of these microfibers. The finishes can be water-wicking or water-repellent, depending on the properties needed for performance wear. The coated textiles remain as comfortable as traditional fabrics and can be laundered. When fabrics are worn or washed, individual fibers rub against each other and break, releasing pieces less than 500 µm in length. Some large synthetic apparel companies are trying to address this problem, mainly by focusing on consumer laundry habits or promoting washing machine filters that trap microfibers. “We want to stop creating microfibers in the first place,” said Kevin Golovin , a mechanical and industrial engineering professor at the University of Toronto. He and his colleagues are doing

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.006

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 head0.213
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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