Effect of antibiotic exposure on the characteristics of activated sludge in a landfill leachate biological treatment system
Bibliographic record
Abstract
Changes in the activated sludge performance in an anaerobic/aerobic biological treatment system for leachate was discussed under the condition of tetracycline (TC) exposure. The results show that a low concentration of TC did not have an obvious effect on the removal of chemical oxygen demand (COD) while a high concentration of TC had a certain promoting effect. Under the stimulation of TC, the particle size distribution of anaerobic/aerobic sludge tended to be more uniform, the particle size of anaerobic sludge decreased while the settleability increased; however, the particle size of aerobic sludge increased due to bulking. With the addition of TC, the concentration of most heavy metal ions in sludge samples increased.TC exposure results in the release of a large amount of extracellular polymeric substances (EPS), thus leading to a smoother surface of anaerobic sludge and a rougher surface of aerobic sludge. The high removal efficiency of COD under the high concentration of TC was also presumed to be due to EPS promoting the microbial absorption of anaerobic substances in the leachate. The results clearly showed that TC had a bacteriostatic effect. After antibiotic exposure, the abundance and diversity index of bacteria in each reactor decreased obviously, the microbial community evolved, and the dominant species at the genus and phylum levels of anaerobic/aerobic reactors changed. This study provides a better understanding the effect of TC on activated sludge and has reference value for the management of antibiotic exposure in leachate treatment facilities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".