On the need to avoid apple-to-orange comparisons in microplastic research
Bibliographic record
Abstract
Here, we discuss several key issues in the recent microplastic risk assessment conducted by Hataley et al. (2023. Can. J. Fish. Aquat. Scie 80 (10): 1669-1678) for the Great Lakes ecosystem. First, we note that the risk characterizations are incorrect due to errors in parameterizations of the calculations necessary to align exposure and effects data, as well as the corrections for bioaccessibility cutoffs. Second, the lack of quantification of uncertainty in the risk assessment raises concerns. Previous assessments that employed similar methods addressed uncertainties arising from the calculations, revealing that the probabilistic uncertainty inherent in risk characterization can span significant magnitudes. Third, we highlight the problematic use of species sensitivity distributions (SSDs) designed for marine systems in a freshwater context. We emphasize the importance of utilizing SSDs that incorporate relevant freshwater species data, and identify recent studies that provide such data for use in future risk assessments. Based on a previously published assessment, we suggest initiating measures to reduce the release of plastic debris into the watershed and advise research, monitoring, and mitigative strategies to address potential threats to water quality.
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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 | 0.155 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".