Influence of sewage and pharmaceuticals on soil microbial function
Bibliographic record
Abstract
Although sewage effluent application to land is a common approach to recycle water and provide nutrients to plants, bioactive pharmaceuticals contained in sewage may change soil quality by affecting soil microbial communities. Establishing causal effects, however, is difficult, because trace levels of pharmaceuticals are confounded with other effluent constituents. Therefore, two originally similar soil microbial communities, one irrigated in situ with sewage effluent for 12 years and another nonirrigated, were exposed to high levels of acetaminophen, aspirin, carbamazepine, chlorpromazine, and tetracycline. The objectives of the current study were to determine the influence of high levels of pharmaceuticals on several soil microbial properties, the effect that prolonged effluent irrigation with ambient levels of pharmaceuticals had on soil microbial function, and how this effect would change in response to pharmaceutical exposure. Several pharmaceuticals, at high exposure levels, imposed stress on the soil microbial community as judged by increased CO(2) respiration, decreased biomass carbon, and altered substrate utilization affinities. Prolonged effluent irrigation, which altered the genetic fingerprint of the microbial community, also mitigated the response that exposure to pharmaceuticals had on the microbial community and enabled degradation of the antimicrobial salicylic acid after aspirin exposure. In conclusion, prolonged irrigation with sewage effluent containing pharmaceuticals at ambient levels influenced the microbial community so that they were able to better cope with sudden exposure to high levels of pharmaceuticals.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".