Microplastics and their potential effects on the aquaculture systems: a critical review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it