A randomized herd-level field study of dietary interactions with monensin on milk fat percentage in dairy cows
Bibliographic record
Abstract
This field trial evaluated the effects of dietary supplementation with 16 mg/kg (based on total dry matter intake) of monensin sodium on bulk tank milk fat percentage (MFP) of commercial dairy herds. Interactions between monensin and nutritional factors on MFP were studied. The trial was conducted in 47 Holstein dairy herds in Québec, Canada, between November 2005 and May 2006. The herd was the unit of interest. Enrolled herds were followed for a 7-mo period. Monensin treatment was randomly allocated in a crossover design where monensin was supplemented to the lactating dairy cow diet for a consecutive 12-wk period. Twenty-four herds were allocated to monensin treatment for the first period of trial, and 23 herds were allocated for the second period. Diet composition and ration physically effective particle level were collected every 8 wk. Milk fat percentage data were retrieved from weekly bulk tank measures. Data were analyzed in linear mixed models using repeated measures within herd where MFP was considered the outcome variable. In addition to the main effect of monensin treatment, the following covariates were forced a priori into all statistical models: treatment period, weekly herd mean parity, and weekly herd mean days in milk. The majority of herds were fed a total mixed ration (n = 29) and were housed in tie-stalls (n = 42). Monensin significantly decreased bulk tank MFP by 0.12 percentage points. The reduction of MFP associated with monensin was larger for herds having a diet high (>39.7%) in nonfiber carbohydrates, having a low level of physically effective particles in ration (>45.0%; >or=8 mm), and not feeding dry hay as first meal in the morning. Significant interactions between monensin and nutritional factors on bulk tank MFP were related to nonfiber carbohydrate and fiber concentrations in the diet.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".