Presence of antibiotic-resistant commensal bacteria in samples from agricultural, city, and national park environments evaluated by standard culture and real-time PCR methods
Bibliographic record
Abstract
This study examined the presence of antibiotic-resistant commensal bacteria among cattle operations representing areas heavily affected by agriculture, city locations representing areas affected by urban activities and indirectly affected by agriculture, and a national park representing an area not affected by agriculture. A total of 288 soil, fecal floor, and water samples were collected from cattle operations, from the city of Fort Collins, and from Rocky Mountain National Park (RMNP) in Colorado. In addition, a total of 42 new and unused feed, unused bedding, compost, and manure samples were obtained from the cattle operations. Total, tetracycline-resistant, and ceftiofur-resistant bacterial populations were enumerated by both standard culture plating and real-time PCR methods. Only wastewater samples from the cattle operations demonstrated both higher tetracycline-resistant bacterial counts (enumerated by the culture plating method) and tetracycline resistance gene copies (quantified by real-time PCR) compared to water samples collected from non-farm environments. The ceftiofur resistance gene, blaCMY-2, was not detectable in any of the samples, while the tetracycline resistance genes examined in this study, tet(B), tet(C), tet(W), and tet(O), were detected in all types of tested samples, except soil samples from RMNP. Tetracycline resistance gene pools quantified from the tet(O) and tet(W) genes were bigger than those from the tet(B) and tet(C) genes in fecal and water samples. Although only limited resistance genes, instead of a full set, were selected for real-time PCR quantification in this study, our results point to the need for further studies to determine natural and urban impacts on antibiotic resistance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".