Steps Scientists Can Take to Inform Aquatic Microplastics Management: A Perspective Informed by the California Experience
Bibliographic record
Abstract
Recent evidence suggests that microplastic particles are pervasive and potentially of great risk to both animal and human health. The California legislature has responded to this information by enacting two new bills that require quantification of microplastics in various media and development of new management strategies to address microplastic pollution. However, there are several scientific gaps that impede the development and implementation of necessary management strategies to address microplastic pollution. In this paper, we use the California experience as a case study to provide perspective on those science gaps, the current barriers to science affecting management, and the actions scientists can take to best ensure their efforts are of greatest value to policymakers and the management community.
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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 | 0.079 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.028 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.025 | 0.041 |
| 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 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".