Norfloxacin affects inorganic nitrogen compound transformation in tailwater containing Corbicula fluminea
Bibliographic record
Abstract
The Asian clam, Corbicula fluminea , commonly used in engineered wetlands receiving tailwater, affects nitrogen compound transformation in water. This study investigates how a commonly observed antibiotic in tailwater, norfloxacin , impact nitrogen compound transformation in tailwater containing C. fluminea . The clam was exposed to artificial tailwater with norfloxacin (0, 0.2, 20, and 2000 μg/L) for 15 days. Water properties, C. fluminea ecotoxicity responses, microorganism composition and nitrification- or denitrification-related enzyme activities were measured. Results revealed norfloxacin-induced increases and reductions in tailwater NH 4 + and NO 2 − concentrations, respectively, along with antioxidant system inhibition, organ histopathological damage and disruption of water filtering and digestion system. Microorganism composition, especially biodiversity indices, varied with medium (clam organs and exposure water) and norfloxacin concentrations. Norfloxacin reduced NO 2 − content by lowering the ratio between microbial nitrifying enzyme (decreased hydroxylamine oxidoreductase and nitrite oxidoreductase activity) and denitrifying enzyme (increased nitrate reductase and nitrite reductase activity) in tailwater. Elevated NH 4 + content resulted from upregulated ammonification and inhibited nitrification of microorganisms in tailwater, as well as increased ammonia emission from C. fluminea due to organ damage and metabolic disruption of the digestion system. Overall, this study offers insights into using benthic organisms to treat tailwater with antibiotic residues, especially regarding nitrogen treatment.
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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".