Holistic Assessment of Microplastics and Other Anthropogenic Microdebris in an Urban Bay Sheds Light on Their Sources and Fate
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The physical and chemical properties of microplastics and their environmental distributions may provide clues about their sources and inform their fate. We demonstrate the value of extensive monitoring of microplastics in an urban bay, San Francisco Bay. Surface water, fish, sediment, stormwater runoff, and treated wastewater were sampled across the bay and adjacent national marine sanctuaries (NMS). We found microplastics and other anthropogenic microdebris (“microdebris”) in all sample types. Concentrations were higher in the bay than in the NMS, and within the bay, concentrations were higher during the wet season than during the dry season. The fate of microdebris varied depending on their morphologies and densities: fibers were dominant in fish, black rubbery fragments were common in sediment, as were fibers, while buoyant fragments and fibers were widely observed in surface waters. Notably, we found large amounts of black rubbery fragments, an emerging contaminant, in stormwater. Moreover, stormwater was a significant pathway of microdebris, with concentrations roughly 140 times greater than those found in wastewater, which was dominated by fibers. Overall, we demonstrate the value of multimatrix regional monitoring to evaluate the sources and fate of microplastics, which can inform effective mitigation for other urban bays around the world.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".