Reframing microplastics as a ligand for metals reveals that water quality characteristics govern the association of cadmium to polyethylene
Bibliographic record
Abstract
Environmental characteristics including water quality and sediment properties alter the hazard that metals pose to aquatic systems by governing the speciation and partitioning of metals between water, sediment, and biotic ligands; however, alternate ligands are being introduced into aquatic systems through anthropogenic activity. Microplastics are a ligand on which metals interact through adsorption to the plastic surface. It remains unknown what factors determine the amount of metal bound to microplastic. Using a combination of laboratory experiments and machine learning, we tested a suite of eighteen environmental parameters (inclusive of both water and sediment) to understand how they influence association of cadmium to a representative microplastic, polyethylene. From this, we developed and tested a predictive model that outlines the characteristics that favour the association of cadmium to microplastic. Alkalinity, humification index of dissolved organic matter, and pH (all of which are water quality characteristics) were the three factors determining the proportion of cadmium adsorbed to plastics. These results align with other predictive models, such as the Biotic Ligand Model in demonstrating the governance of metal behaviour by water quality characteristics. To assess the relationship of the amount of cadmium bound to microplastic and cadmium uptake, an exposure was completed in which fathead minnows (Pimephales promelas) were acclimated to environments representing each of the potential outcomes of the model. The uptake of cadmium was not significantly different between groups, indicating that the stress of alterations to water quality may be a confounding factor in determining the exposure risk of microplastics and cadmium.
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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 | 0.000 | 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".