From Fabric to Fallout: A Systematic Review of the Impact of Textile Parameters on Fibre Fragment Release
Bibliographic record
Abstract
With an expanding global clothing and textile industry that shows no signs of slowing, concerns over its environmental impacts follow. Fibre fragments (FFs)—short pieces of textiles that have separated from a textile construction—are a growing area of concern due to increasing evidence of their accumulation in the environment. Most of the existing research on this topic focuses on the role of consumer behaviour rather than the textiles themselves. A systematic literature review is used here to explore the key textile parameters that influence FF release. A search of articles published between 2011 and June 2024 was conducted following the PRISMA guidelines. Three databases (Scopus, Web of Science, and EBSCO) were used, and articles were screened to ensure that a minimum of one textile parameter was manipulated in the study. A total of 52 articles were selected and where appropriate, comparisons between samples used and key findings were made. The textile parameters that were found to reduce FF release include fibres of a longer length and higher tenacity, as well as filament yarns with low hairiness and higher twists. At the fabric level, tight fabric structures and high abrasion resistance show lower FF shedding. Mechanical finishes that reduce the number of protruding fibre ends or chemical finishes that increase abrasion resistance also prove to be beneficial. Lastly, sewing and cutting methods that enclose or seal the textile edge can reduce FF release. While optimal parameters have been identified, they are not applicable to all textile end-uses. Rather, these factors can serve as a guide during future production and be applied where possible to limit FF release.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".