Bibliographic record
Abstract
This review provides an account of fish species contaminated with microplastics (MPs) across the globe (seven continents). A total of 887 fish species were found contaminated with MPs based on MPs in the gastrointestinal tract/GI. The most MPs contaminated-fish species found were marine and demersal species. Globally 45 % of fish ingested MPs with an average concentration of 5.93 MPs particles per fish species. Among all the countries, China had the highest number of fish species contaminated with MPs in the followingorders: China (176 species), Brazil (84), the USA (48), India (35), the Atlantic Ocean (31), Iran (30), Bangladesh (28), Turkey (26), Indonesia (25), the UK (23), Saudi Arabia (23), Thailand (21), Portugal (20), Australia (20), Italy (18), South Africa (18), Argentina (15), Chile (14), Galapagos Islands (Ecuador) (14), the North Pacific Gyre (14), Samoa (13), Malaysia (12), Colombia (11), New Zealand (11), Fiji (10), Spain (10), the North Sea (09), South Korea (09), Tahiti (09), Vanuatu (09), Ghana (08), Canada (07), Japan (07) and Nigeria (07) and others. MPs ingestion in fishes varied (high, medium, and low) among the locations/countries. In several locations/countries, MPs ingestion/contamination occurred in up to 100 % of fish samples. Because of MPs contamination, seafood fisheries, and the livelihoods of people associated with fishing, aquaculture, and seafood business, can be threatened. It may also increase health risks to seafood fish consumers since there is a probability that high risks pollutants adsorbed in MPs can be transferred to humans via the food chain.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".