Impacts of feeding monensin sodium on production and the efficiency of milk production in dairy cows fed total mixed rations: evaluation of a confounded literature
Bibliographic record
Abstract
The amount of milk Canadian dairy farmers produce is limited to a production quota expressed in milk fat. Because milk economic value is primarily based on fat and protein, it can be advantageous to decrease the milk fat to protein ratio. Monensin sodium has been suggested to reduce milk fat proportion and outputs, but not milk protein. Publications using lactating dairy cows were utilized to assess predictability of production responses to monensin feeding based upon their base production characteristics and diet composition. Predicted animal output changes due to monensin had poor fits with low r2 (0.31–0.44) and unevenly distributed residuals. Further assessment revealed that cow characteristics, and diets, were not independent of monensin feeding level. Thus, the 43 comparisons were clustered into levels of 10–12, 14–18, or 20–24 mg kg−1 of diet dry matter (DM). Milk fat yield reductions due to monensin differed (P < 0.05; 10–12 and 14–18 mg kg−1 DM), or tended to differ (P = 0.057; 20–24 mg kg−1 DM), from zero (i.e., no change). Monensin addition to total mixed rations of lactating dairy cows negatively impacted milk fat yield to a greater extent than milk protein.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".