Interactive effects of nanoplastics, multi-contaminants, and environmental conditions on prairie aquatic ecosystems: A factorial composite toxicity analysis within a Canadian context
Bibliographic record
Abstract
Limited data exist on the interactions between nanoplastics (NPs) and co-contaminants under diverse environmental conditions. Herein, a factorial composite toxicity analysis approach (FCTA) was developed to analyze the time-dependent composite effects of NPs (0 ∼ 60 mg/L), copper (Cu, 0.2 ∼ 6 mg/L) and phenanthrene (PHE, 0.001 ∼ 1 mg/L) on microalgae under diverse pH (6.7 ∼ 9.1), dissolved organic matter (DOM, 1.5 ∼ 25.1 mg/L), salinity (1 ∼ 417 mg/L) and temperature (23 ∼ 33 °C) within the Canadian prairie context. The toxic mechanism was revealed by multiple toxic endpoints. The combined toxicity of NPs, Cu and PHE within prairie aquatic ecosystems was assessed by the developed FCTA-multivariate regression model. Contrary to individual effects, NPs exhibited a promotional effect on microalgae growth under complex environmental conditions. Although Cu and PHE were more hazardous, NPs mitigated their single toxicity. Environmental conditions and exposure times significantly influenced the main effects and interactions of NPs, Cu and PHE. The synergistic effect of NPs*Cu and NPs*PHE on microalgae growth became antagonistic with increased pH or DOM. Microalgae in the Souris River, Saskatchewan, were projected to suffer the most toxic effects. Our findings have significant implications for the risk management of NPs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".