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Record W4220823346 · doi:10.3390/textiles2010010

A Review on Textile Recycling Practices and Challenges

2022· review· en· W4220823346 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueTextiles · 2022
Typereview
Languageen
FieldArts and Humanities
TopicCrafts, Textile, and Design
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Manitoba
KeywordsReuseTextileWaste managementTextile industryMunicipal solid wasteBusinessEngineeringEnvironmental science

Abstract

fetched live from OpenAlex

The expansion of clothing and textile industry and the fast fashion trend among consumers have caused a rapid global increase in textile waste in the municipal solid waste (MSW) stream. Worldwide, 75% of textile waste is landfilled, while 25% is recycled or reused. Landfilling of textile waste is a prevalent option that is deemed unsustainable. Promoting an enhanced diversion of textile waste from landfills demands optimized reuse and recycling technologies. Reuse is the more preferred option compared with recycling. Various textile reuse and recycling technologies are available and progressively innovated to favor blended fabrics. This paper aims to establish reuse and recycling technologies (anaerobic digestion, fermentation, composting, fiber regeneration, and thermal recovery) to manage textile waste. Improved collection systems, automation of sorting, and discovering new technologies for textile recycling remains a challenge. Applying extended producer responsibility (EPR) policy and a circular economy system implies a holistic consensus among major stakeholders.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.496
GPT teacher head0.392
Teacher spread0.104 · 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