Structural and functional responses of river biofilm communities to the nonsteroidal anti-inflammatory diclofenac
Bibliographic record
Abstract
Diclofenac is a nonsteroidal anti-inflammatory drug (NSAID) that has been detected widely in surface waters in North America and Europe. The impact of diclofenac on river biofilm communities was investigated at exposures of 10 and 100 microg L(-1) of diclofenac or its molar equivalent in carbon and nutrients. Experiments were carried out with river water during spring and summer using rotating annular reactors as model systems. Diclofenac or nutrients at 10 microg L(-1) were observed to have no significant effect on algal, bacterial, and cyanobacterial biomass in spring, whereas in the summer the nutrient equivalent reduced algal biomass and diclofenac reduced cyanobacterial biomass relative to control biofilms (p < 0.05). In contrast, at 100 microg L(-1) diclofenac or nutrients, the result was increased cyanobacterial and bacterial biomass, respectively, relative to control biofilms in spring. In summer, 100 microg L(-1) diclofenac significantly increased bacterial biomass and the nutrient treatment had no significant effect (p < 0.05); both treatments resulted in increased biofilm thickness. The glycoconjugate composition of the exopolysaccharide matrix was influenced differentially by the treatments in both seasons. Biolog assessments of carbon use indicated that 100 microg L(-1) diclofenac or nutrients resulted in significant depressions in the use of carbon sources in summer and significant increases in spring. Impacts on protozoan and micrometazoan populations also were assessed. Denaturing gradient gel electrophoresis analyses of community DNA and fluorescent in situ hybridization studies indicated that diclofenac had significant effects on the nature of the bacterial community in comparison with control and nutrient-treated river biofilm communities.
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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".