Environmental Growth of Enterococci and <i>Escherichia coli</i> in Feedlot Catch Basins and a Constructed Wetland in the Absence of Fecal Input
Bibliographic record
Abstract
Population structures of fecal indicator bacteria (FIB) isolated from catch basins, a constructed wetland, and feces from a beef cattle feedlot were compared over a two-year period. Enterococcus hirae accounted for 92% of the fecal isolates, whereas secondary environments were characterized by greater relative abundance of environmentally adapted species including Enterococcus casseliflavus. While enterococci densities in the catch basins and wetland were similar under wet and drought conditions, E. hirae predominated during rainy periods, while E. casseliflavus predominated during drought conditions. Environmentally adapted species accounted for almost half of the erythromycin resistant enterococci isolated from the wetland. Densities of Escherichia coli were also comparable during wet versus drought conditions, and the relative abundance of strains from environmentally adapted clades was greater in secondary environments compared to feces. Unlike enterococci, fewer environmentally adapted E. coli strains were isolated on selective media containing ceftriaxone from the wetland compared to feces, suggesting resistance to this antibiotic may not be well maintained in the absence of selective pressure. Overall, these findings suggest that secondary environments select for environmentally adapted FIB. While these species and clades tend to be of limited clinical relevance, they could potentially serve as reservoirs of antimicrobial 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 | 0.000 | 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 | 0.001 | 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".