Molecular and microscopic assessment of the effects of caffeine, acetaminophen, diclofenac, and their mixtures on river biofilm communities
Bibliographic record
Abstract
The authors examined effects of three common contaminants, caffeine (CF), acetaminophen (AC), and diclofenac (DF), as well as their mixtures on the development, functioning, and biodiversity of river biofilm communities. Biofilms were cultivated in rotating annular reactors. Treatments included AC, CF, DF, AC + CF, AC + DF, CF + DF, AC + CF + DF at 5 µg/L, and their molar equivalent as carbon and nutrients. Incubations using ¹⁴C-labeled AC, DF, and CF indicated that 90% of the CF, 80% of the AC, and less than 2% of the DF were converted to CO₂. Digital imaging revealed a variety of effects on algal, cyanobacterial, and bacterial biomass. Algal biomass was unaffected by AC or CF in combination with DF but significantly reduced by all other treatments. Cyanobacterial biomass was influenced only by the AC + DF application. All treatments other than AC resulted in a significant decrease in bacterial biomass. Diclofenac or DF + CF and DF + AC resulted in increases in micrometazoan grazing. The denaturing gradient gel electrophoresis of Eubacterial community DNA, evaluated by principal component analysis and analysis of similarity, indicated that relative to the control, all treatments had effects on microbial community structure (r = 0.47, p < 0.001). However, the AC + CF + DF treatment was not significantly different from its molar equivalent carbon and nutrient additions. The Archaeal community differed significantly in its response to these exposures based on community analyses, confirming a need to integrate these organisms into ecotoxicological studies.
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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".