Development and Validation of an Efficient Method for Processing Microplastics in Biota Samples
Bibliographic record
Abstract
= 4:1 by volume) as digestion reagents, which completely digested biota samples <5 g weight within 30 min at 50 °C. A density separation step was subsequently used to remove organic residues as necessary. The efficiency and suitability of this method were tested by spiking microplastics of 7 different types and of various sizes (1000, 900, 675, 300, 250, and 150 μm) into mussel and gastrointestinal tracts of fish. The recoveries of microplastics ranged from 90 to 100%. No significant changes in weight, surface area, and particle size (t test, p > 0.05) were observed for all tested polymers. Fourier transform infrared spectral analyses demonstrated that the method did not degrade any of the polymers except for polyethylene terephthalate. The method was demonstrated with mussel and fish samples collected from the Pearl River delta, south China, and was able to recover microplastics effectively. Overall, the present method is time-saving and easy to operate, with low procedural cross-contamination. The properties of microplastics recovered by the present method remained largely intact, greatly benefiting subsequent qualitative and quantitative analyses. Environ Toxicol Chem 2019;38:1400-1408. © 2019 SETAC.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".