Is It or Isn't It: The Importance of Visual Classification in Microplastic Characterization
Bibliographic record
Abstract
Microplastics are a diverse category of pollutants, comprising a range of constituent polymers modified by varying quantities of additives and sorbed pollutants, and exhibiting a range of morphologies, sizes, and visual properties. This diversity, as well as their microscopic size range, presents numerous barriers to identification and enumeration. These issues are addressed with the application of physical and chemical analytical procedures; however, these present new problems associated with researcher training, facility availability and cost, especially for large-scale monitoring programs. Perhaps more importantly, the classifications and nomenclature used by individual researchers to describe microplastics remains inconsistent. In addition to reducing comparability between studies, this limits the conclusions that may be drawn regarding plastic sources and potential environmental impacts. Additionally, where particle morphology data is presented, it is often separate from information on polymer distribution. In establishing a more rigorous and standardized visual identification procedure, it is possible to improve the targeting of complex analytical techniques and improve the standards by which we monitor and record microplastic contamination. Here we present a simple and effective protocol to enable consistent visual processing of samples with an aim to contribute to a higher degree of standardization within the microplastic scientific community. This protocol will not eliminate the need for non-subjective methods to verify plastic objects, but it will standardize the criteria by which suspected plastic items are identified and reduce the costs associated with further analysis.
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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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".