Abundance, Composition, and Factors Impacting the Formation of Microplastic-Associated Biofilm in Freshwaters
Bibliographic record
Abstract
Biofilm that colonizes on the surface of microplastics (MPs) may represent a potential health risk. The current study examined factors that influence MP-associated biofilm growth, including polymer type, degree of weathering, and source water quality. Weathered MPs were produced in-lab, and biofilm trials were conducted on-site at drinking water treatment facilities using a passive flow-through system. Biofilm abundance was quantified in terms of adenosine triphosphate (ATP); its composition was assessed via metagenomic sequencing. Biofilm growth was observed on MPs of all polymer types, and most prevalent on polyvinyl chloride (PVC) where ATP levels were 6 to 12 times higher when compared to other polymers. Pathogen-containing species including Salmonella enterica and Escherichia coli were observed on all polymers with relative abundance up to 13.7%; S. enterica was selectively enriched on weathered polymers in specific water matrices. These findings support the need to examine the impact of drinking water treatment to minimize potential health risks. As well they suggest the need for future studies to adopt the use of weathered polymers, as they were observed to increase biofilm growth.
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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 | 0.000 | 0.000 |
| 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".