Clarifying the absence of evidence regarding human health risks to microplastic particles in drinking-water: High quality robust data wanted
Bibliographic record
Abstract
In a recently published article, Leslie and Depledge (2020) raise concerns regarding statements on the risk that microplastic particles represent to human health and which have been attributed to reports published by both the Science Academies’ Group, Science Advice for Policy (SAPEA) (part of the European Commission’s Science Advice Mechanism) and the World Health Organization (WHO) (SAPEA. Science Advice for Policy by European Academies, 2019, WHO, 2019). Leslie and Depledge (2020), for instance, suggest that WHO (2019) conclude that there is ‘no evidence to indicate a human health concern.’ This statement, taken out of context from the WHO report (WHO, 2019), is then used to imply that the WHO conclude there is ‘no risk’ related to the exposure of microplastic particles (Leslie and Depledge, 2020). While, Leslie and Depledge (2020) highlight the importance of debate and systematic assessment of claims related to the assessment of risk, observations that we agree are important to highlight, there are a number of points raised in the article that require clarification.
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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 | 0.072 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.069 | 0.042 |
| Insufficient payload (model declined to judge) | 0.003 | 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".