Milk residues and performance of lactating dairy cows administered high doses of monensin.
Bibliographic record
Abstract
Milk residues and performance were evaluated in lactating cows that were fed up to 10 times the recommended dose of monensin. Following an acclimatization period of 14 d, during which cows were fed a standard lactating cow total mixed ration containing 24 ppm monensin, 18 lactating Holstein dairy cows were grouped according to the level of feed intake and then randomly assigned within each group to 1 of 3 challenge rations delivering 72, 144, and 240 ppm monensin. Outcome measurements included individual cow daily feed intakes, daily milk production, body weights, and monensin residues in composite milk samples from each cow. There were no detectable monensin residues (< 0.005 microg/mL) in any of the milk samples collected. Lactating cows receiving a dose of 72 ppm monensin exhibited up to a 20% reduction in dry matter intake, and a 5% to 15% drop in milk production from the pre-challenge period. Cows receiving doses of 144 and 240 ppm monensin exhibited rapid decreases in feed intake of up to 50% by the 2nd d and milk production losses of up to 20% and 30%, respectively, within 4 d. Lactating cows receiving up to 4865 mg monensin per day had no detectable monensin residues (< 0.005 microg/mL) in any of the milk samples collected. Results of this study confirm that food products derived from lactating dairy cattle receiving monensin at recommended levels are safe for human consumption.
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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".