The origin of microplastics of offshore discharge: A review in assessing the relationship between microplastics content and other contaminants
Bibliographic record
Abstract
The article reviewed migration, degradation, toxicity, and distribution of microplastics, which was focused on data enumeration of emission samples from countries around the North Pacific Ocean, South Atlantic Ocean, and Circumpolar oceans. Microplastic particles are easily absorbed by animals and spread to the whole food chain, and they have been confirmed to exist in the human body. It was well established that high abundance microplastics were trapped by ocean currents and accumulated in surface and sediment in convergence zones of the five subtropical gyres. While microplastic itself leaches out the toxin in the seawater, synergistic effects between microplastic and other pollutants increase microplastic toxicity for organisms. The monomers of 16 out of 55 plastic polymers were carcinogenic and mutagenic or toxic for reproduction. Additives used in the process are also dangerous polypropylene (PP), and polyethylene (PE) prefer to sorb persistent organic pollutants (POPs) and have an extremely slow rate of desorption, which form synergic effects and increase the toxicity of microplastics (MPs). For other plastic polymers, the sorption and desorption of pollutants by MPs depends on the concentration of POPs, so the toxicity of MPs varies with the content of pollutants. But for some types of MPs and POPs, the concentration of POPs controlled by microplastics also can decrease the lethal toxicity of POPs. Higher concentrations of MPs in the seawater cause larger MPs consumptions of marine organisms, especially in polar regains that have the highest MPs concentrations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".