Bibliographic record
Abstract
Many anthropogenic compounds, such as antibiotics, are found at trace levels (-1) in aquatic and terrestrial systems. The effect of these compounds on the metabolism and function of microbes are difficult to assess because the assays used, such as the minimum inhibitory concentration (MIC) and the disk diffusion methods, lack the sensitivities to measure bacterial response to these very low levels of antibiotics on bacterial populations. Therefore, we theorized that the [3H] thymidine incorporation into DNA method might be sensitive in determining the effect of DNA inhibiting antibiotics on DNA production in planktonic bacteria in aquatic systems. Utilizing the 3H thymidine method, we measured the effects of ciprofloxacin on DNA production on planktonic bacteria in river and pond waters. Ciprofloxacin significantly (P -1 but significant inhibition (P -1in pond water. The very low concentration required to inhibit DNA production in river water bacteria indicates that bacteria are extremely sensitive to antibiotics at very low concentrations. A likely reason for the differences in inhibition between the two waters is due to ciprofloxacin becoming bound, and possibly becoming biologically inactive, in the pond water due to higher dissolved organic carbon content. This work demonstrates that bacteria in some aquatic systems can be significantly impacted by low concentrations of anthropogenic antibiotics finding their way into these systems and that our assumptions as to the concentrations at which antibiotics affect microbes are highly underestimated.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".