Microplastics in bivalves and their habitat in relation to shellfish aquaculture proximity in coastal British Columbia, Canada
Bibliographic record
Abstract
Shellfish aquaculture often uses large amounts of plastic equipment and has been suggested as a potential source of microplastic contamination in the marine environment.To determine the influence of shellfish aquaculture on microplastic concentrations in bivalves and their environment, we compared microplastic particle (MP) concentrations in Manila clams Venerupis philippinarum and Pacific oysters Crassostrea gigas grown on commercial shellfish beaches with those in individuals of the same species grown on nearby non-aquaculture beaches in 6 regions of coastal British Columbia, Canada.MP concentrations did not differ between shellfish aquaculture and non-aquaculture sites for either bivalve species, sediment, or water samples.Plastic presence differed by site and oysters on sites with many synthetic anti-predator nets contained significantly, yet marginally, more MPs than those on sites without (0.05 vs. 0.03 g -1 dry-tissue weight on average).However, analysis of suspected MPs using Fourier-transform infrared spectroscopy in dicated a predominance of fibres from textiles (including nylon and polyester), which are not typically used in shellfish aquaculture, suggesting that this may be caused by the larger average body weight of oysters grown at non-aquaculture sites rather than by the degradation of aquaculture infrastructure.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".