Pandemic Legacy: Medical Facemasks as a Potential Source of Marine Microplastic?
Bibliographic record
Abstract
Abstract Understanding the main sources of microplastic pollution is key towards developing efficient measures to reduce microplastic loadings to marine waters. Yet identifying the main sources of marine microplastic is challenging. Source tracking should be easier in marine bays where inputs are limited. In 2021 we determined the concentrations and characteristics of microplastics > 300 μm in surface waters of Placentia Bay, Newfoundland; a bay with negligible river input in an area of low population density, and no plastic processing plants in the vicinity. Microplastics contributed 2-14% to particulate organic carbon (> 300 μm), and concentrations ranged from 0.11 to 0.67 particles m -3 , a relatively high level given the region’s low population density. Microplastic diversity was low; fiber and fragment concentrations dwarfed those of other shapes, and polypropylene (PP) dominated, with transparent PP fibers specifically contributing near 50% to the total microplastic inventory. The overwhelming dominance of transparent PP fibers, as well as the exceptionally high proportion of long fibers, suggest that a distinctive input of large, transparent PP fibers overlaid “background” inputs from other sources. A ballpark estimate indicates that weathering of medical facemasks used during the COVID-19 pandemic are a likely explanation for the dominance of transparent PP fibers in Placentia Bay in 2021. Similar inputs may have affected many other aquatic environments globally, but might not have been observable in systems where other continuous input pathways are high.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".