Dissipation of Antimicrobials in Feedlot Manure Compost after Oral Administration versus Fortification after Excretion
Bibliographic record
Abstract
Fortification of manure with antimicrobials is one approach to studying their dissipation. However, fortified antimicrobials may not accurately model dissipation that occurs after antimicrobials have been administered to livestock in feed and excreted in manure. This study examined the dissipation of antimicrobials excreted in manure versus those added directly to manure (fortified). Steers were fed a diet containing (kg feed) (i) 44 mg chlortetracycline, (ii) 44 mg each of chlortetracycline and sulfamethazine, (iii) 11 mg tylosin, and (iv) no antimicrobials (control). Fortified antimicrobial treatments were prepared by adding antimicrobials to control manure. Manure was composted for 30 d, sampled every 2 to 3 d, and analyzed for antimicrobials and compost properties. Antimicrobial dissipation followed first-order kinetics. The dissipation rate constant was significantly greater (based on 95% confidence limit) for excreted (0.29-0.54 d) than for fortified chlortetracycline (0.11-0.13 d). In contrast, dissipation rate constants were significantly greater for fortified sulfamethazine (0.47 d) and tylosin (0.31 d) than when the same antimicrobials were excreted (0.08 and 0.07 d, respectively). On average, 85 to 99% of the initial antimicrobial concentrations in manure were dissipated after 30 d of composting. The degree of dissipation was greater ( < 0.0001) for fortified (99%) than for excreted tylosin (85%). Composting can be used to reduce environmental loading of antimicrobials before field application of beef cattle manure. Dissipation rates of fortified antimicrobials during manure composting may not accurately reflect those of antimicrobials that are consumed and excreted by cattle.
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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) | 0.000 | 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".