Antimicrobial Effects of Sulfadimethoxine on Salmonella, Escherichia coli and Aerobic plate count (APC) in Small-Scale Broiler Operations
Bibliographic record
Abstract
Poultry is a source of Salmonella and Escherichia coli. Antibiotics can be used to reduce the enumeration and prevalence of these bacteria. The objective of this study was to determine the effects of sulfadimethoxine antibiotic on the enumeration and prevalence of Salmonella, Escherichia coli and aerobic plate count in broilers. Broilers (n = 600) were allotted to two treatments, each with twelve replications. The treatments were control (drinking water without antibiotic) and with antibiotic at 0.05% (wt/vol) sulfadimethoxine. After a six-week period, the enumeration of Salmonella in the experimental treatment was detected at 2.55 log CFU/g. This value is not different (P>0.05) than that detected in the control at 2.81 log CFU/g. With respect to prevalence, there was a difference (P<0.05) between the experimental treatment at 90.0% and the control at 100%. The enumeration of E. coli in the experimental treatment was detected at 3.97 log CFU/g. This value is lower (P<0.05) than that detected in the control treatment at 4.37 log CFU/g. With respect to prevalence, there was no difference (P>0.05) between the experimental treatment at 100% and the control at 100%. The enumeration of aerobic plate count in the antibiotic treatment were detected at 6.62 log CFU/g. This value is lower (P<0.05) than that detected in the control at 7.50 log CFU/g. With respect to prevalence, there was no difference (P>0.05) between treatments. Our overall findings suggest that the use of the antibiotic sulfadimethoxine can reduce the number of E. coli, Salmonella and aerobic plate count in the small-scale broiler operations.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".