Sub-sampling strategies for analysis of small (<20 µm) microplastics in water
Bibliographic record
Abstract
Quantification of microplastics (MPs) in drinking water is typically achieved using spectroscopic techniques. However, due to the time-consuming nature of these analyses researchers typically apply sub-sampling strategies whereby particles in small areas of a filter are quantified and subsequently extrapolated to the entire area. This widely applied strategy has not been evaluated in terms of potential extrapolation error despite a wide range of sub-sampling methods having been reported. The current study examined the relationship between sub-sampling and extrapolation accuracy when considering 2-100 µm low-density polyethylene (LDPE), polypropylene (PP), and polystyrene (PS) fragments, with a specific focus on particles <20 µm in size as they are the most abundant and have the potential to exert adverse health impacts. A grid-based random sub-sampling method was developed to serve as a baseline such that extrapolation accuracy could be compared to several previously published methods. Results show that as sub-sampling area increases, error decreases following a power law trend. A minimum sub-sampling threshold was identified (approximately 6-8 % of total area) corresponding to an extrapolation error ranging from 8 to 17 %. Use of a log-normal model to describe particle size distributions was evaluated and found to be applicable to particles >2-5 µm. Findings arising from this study provide insight regarding optimal sub-sampling strategies for the analysis of MP in drinking water.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".