A fit-for-purpose categorization scheme for microplastic morphologies
Bibliographic record
Abstract
Microplastic categorization schemes are diverse, thereby posing challenges for cross-study comparisons. Further, categorization schemes are not necessarily aligned with and, thus, useful for applications such as source reduction initiatives. To address these challenges, we propose a hierarchical categorization approach that is "fit for purpose" to enable the use of a scheme that is tailored to the study's purpose and contains categories, which, if adopted, would facilitate interstudy comparison. The hierarchical categorization scheme is flexible to support various study purposes (e.g., to support regulation and toxicity assessment) and it aims to improve the consistency and comparability of microplastics categorization. Categorization is primarily based on morphology, supplemented by other identification methods as needed (e.g., spectroscopy). The use of the scheme was illustrated through a literature review aimed at critically evaluating the categories used for reporting microplastic morphologies in North American freshwater environments. Categorization and grouping schemes for microplastic particles were highly variable, with up to 19 different categories used across 68 studies, and nomenclature was inconsistent across particle morphologies. Our review demonstrates the necessity for a "fit for purpose" categorization scheme to guide the information needs of scientists and decision-makers for various research and regulatory objectives across global, regional, and local scales. Integr Environ Assess Manag 2023;19:422-435. © 2022 SETAC.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".