Transport of microplastic-antibiotic co-contaminants in tidal zones
Bibliographic record
Abstract
Microplastics (MPs) and antibiotics (ATs) are emerging contaminants with recognized negative effects on marine ecosystems. MPs can adsorb and transport ATs, posing combined toxic effects to marine organisms. Despite growing concerns, research remains limited on the MP-AT co-contaminants in tidal zones, which are home to numerous aquatic species and represent a particularly susceptible ecosystem. This study used polyethylene (PE) MPs and tetracycline (TC) to investigate the influence under various conditions, including sediment sizes, tidal cycles, and MP sizes, on the transport of MP-AT co-contaminants in tidal zones using a tidal cycle simulation system, which was designed to replicate the tidal dynamics and provide insights into the movement and behavior of contaminants. It was observed that MP-AT co-contaminants in tidal sediments exist in three distinct transport states. Smaller MP-AT co-contaminants (State 1) pass through sand pores and are widely distributed in the upper sediment layers, whereas larger MP-AT co-contaminants (State 2) concentrate in layers 1–5 due to size limitations. Agglomerated MP-AT co-contaminants (State 3), unable to pass through sand pores, accumulate at the bottom. Tidal cycles enhance MP-AT co-contaminant retention, while sand size (125–212 μm) limitedly affects their distribution. MP size played a crucial role, with larger MPs settling in layers 1–5 and smaller MPs remaining more dispersed. These findings emphasize the importance of MP size in affecting contaminant transport in tidal environments. Results from this research will contribute to the development of transport models and help predict the long-term environmental impact of MP-AT co-contaminants. • First report on PE microplastic (MP)-antibiotic (AT) transport in tidal zones. • MP size governs the vertical distribution of MP-AT co-contaminants. • Tidal cycles markedly enhance MP-AT co-contaminant retention in tidal sediments. • Smaller MPs disperse widely, but larger ones settle in lower tidal sediment layers. • The sand grain size has little effect on MP-AT co-contaminant retention.
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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) | 0.000 | 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".