Risk characterization of microplastics in San Francisco Bay, California
Bibliographic record
Abstract
Abstract Assessing microplastics risk to aquatic ecosystems has been limited by lack of holistic exposure data and poor understanding of biological response thresholds. Here we take advantage of two recent advances, a toxicological meta-analysis that produced biotic response thresholds and a method to quantitatively correct exposure data for sampling methodology biases, to assess microplastic exposure risk in San Francisco Bay, California, USA. Using compartment-specific particle size abundance data, we rescaled empirical surface water monitoring data obtained from manta trawls (> 333 μm) to a broader size (1 to 5000 μm) range, corrected for biases in fiber undercounting and spectroscopic subsampling, and assessed the introduced uncertainty using probabilistic methods. We then compared these rescaled concentrations to four risk thresholds developed to inform risk management for California for each of two effect categories/mechanisms - tissue translocation-mediated effects and food dilution - each aligned to ecologically relevant dose metrics of surface area and volume, respectively. More than three-quarters of samples exceeded the most conservative food dilution threshold, which rose to 85% when considering just the Central Bay. Within the Central Bay, 38% of the samples exceeded a higher threshold associated with management planning, which was statistically significant at the 95% confidence interval. For tissue translocation-mediated effects, no samples exceeded any threshold with statistical significance. The risk associated with food dilution is higher than that found in other systems, which likely reflects this study having been conducted for an enclosed water body. A sensitivity analysis indicated that the largest contributor to assessment variability was associated with estimation of ambient concentration exposure due to correcting for fiber undercounting. Even after compensating for biases associated with fibers and other small particles, concentrations from the trawl samples were still significantly lower than the 1-L grab samples taken at the same time, suggesting our SFB risk estimates are an underestimate. We chose to rely on the trawl data because the 1-L grab sample volume was too small to provide accurate spatial representation, but future risk characterization studies would be improved by using in-line filtration pumps that sample larger volumes while capturing a fuller range of particle size than a towed net.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".