Cooperative Work is Needed Between Textile Scientists and Environmental Scientists to Tackle the Problems of Pollution by Microfibers
Bibliographic record
Abstract
It is clear that plastic pollution is a severe problem in the ocean. Photographs document beaches all around the world covered with plastic bottles, bags, straws, etc. (e.g. Gregory 2009, figs 1-3). Billions of pieces of plastic are floating in the oceans. Their effects are also sufficiently well-known: marine animals swallow them or get tangled up in them, which causes many of them to die. Hundreds of scientific reports (Gall and Thompson, 2015; Rochman et al., 2016) demonstrate the many ways in which plastic is maiming and killing marine animals. One particularly insidious form of plastic pollution that does not appear in the pictures is microplastics, which are tiny pieces ranging from a few millimeters in size down to microscopic. Microplastics come from various sources including the breaking-up of larger plastic pieces, pre-production pellets, and microbeads that are added to personal care products for their abrasive qualities. Microbeads have been banned in personal care products in some countries, including the US, Holland and Canada (Indy100.com)
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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 | 0.040 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.054 | 0.022 |
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".