Examining ingested microplastics in fish: Considerations on filter pore size, analysis time, and material costs to design cost-effective projects
Bibliographic record
Abstract
In recent years the microplastics research community has called for methods harmonization and standardized metrics of reporting microplastic attributes. While alignment of research practices is essential in obtaining robust microplastic data, resource managers need to balance how the cost and effort of methodologies compare to data output. The intention of this study is to compare two recommended methods for isolating anthropogenic microparticles in fish gastrointestinal tracts. Using Icelandic capelin ( Mallotus villosus ) as a study species, with potassium hydroxide (KOH) digestion, we compared a 1.2 μm filtration and 45 μm sieving protocols for isolating ingested anthropogenic microparticles. We compared methods based on the amount of time they took to conduct, the cost of the materials and equipment required, levels of procedural contamination, and data output. We found no significant differences in the materials costs or procedural contamination between the two methods. However, the two protocols resulted in anthropogenic microparticles with significantly different characteristics (i.e. colour, length, morphology), and the 45 μm sieving protocol took longer to conduct per sample. Our results contribute towards a more holistic understanding of microplastic research methods, their relative costs, and how they contribute to data outputs and development of large-scale monitoring programs. • Methods applied in the laboratory influence the results in studies of microplastics. • We tested and compared two common microplastic laboratory methods. • Sieving protocols took more time to conduct than the filtration protocols. • Particle characteristics were significantly different between sieved and filtered samples. • No difference in the quantity or character of procedural contamination between methods. • Material costs were not different between methods for filtered and sieved samples
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".