Microplastics and their potential effects on the aquaculture systems: a critical review
Bibliographic record
Abstract
Abstract According to the statistics, 8.3 billion metric tonnes of plastics have been produced since 1950s, which is far more than other synthetic materials and the annual production which are about 500 million tonnes per year at present. The production of plastics makes microplastics pollution extremely widespread distribution, which will have a lasting impact on the global environment, especially on the aquaculture systems. And the distribution of the microplastics is extremely imbalanced around the global waters. In the present review, we have summarized the development of aquaculture in the World and China based on the existing data sources. And the total aquaculture production of the World will over 90 million tonnes, which will exceed the capture production in 2020. Aquaculture products will become one of the most important sources of high‐quality protein. However, we found that many kinds of microplastics are detected and enriched in both farmed and captured species. Both endogenous and exogenous factors like the use of fishing plastic products, factory farming facility and equipment, natural and synthetic feed, animal health products, aquaculture fortifier and aquatic food additives make accumulation of microplastics easier. In addition, the safety of aquaculture products is closely related to human health because the residues of microplastics in fish leading to various potential hazards. In summary, this paper reviewed the relationship between microplastics and aquaculture, aimed at calling for the rational and restricted use of plastic products in the aquaculture ecosystems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".