Does blending canola meal with other protein sources improve production responses in lactating dairy cows? A multilevel mixed-effects meta-analysis
Bibliographic record
Abstract
The objective of this meta-analysis was to evaluate the effect of blending canola meal (CM) with other protein sources on production responses in lactating dairy cows. To evaluate this effect, a data set was assembled containing 22 studies reporting at least 3 isonitrogenous dietary treatments (total of 74 treatment means). Each study needed to report 1 diet with CM <0.3 kg/d, 1 or more diets consisting of CM blended with another protein source, and 1 diet with CM as the main protein source in the protein supplement (>85%). The crude protein (CP) concentration of CM averaged 37.4 ± 3.09% (dry matter basis), and the predictor of interest was the intake of CP from CM, which averaged 0.46 ± 0.413 kg/d among studies. The maximal CP from CM ranged from 0.47 to 1.55 kg/d among studies. The quadratic relationship between CP from CM and responses in milk true protein concentration was significant, the maximum response (3.19%) being reached at 0.79 kg of CP from CM; the quadratic relationships were not significant for the other dependent variables. Responses in dry matter intake; yields of milk, energy-corrected milk, and milk true protein; and apparent N efficiency were related positively to CP from CM and negatively for responses in milk fat and milk urea N concentrations. Remembering that diets were isonitrogenous within studies, this indicates no nutritional benefit of blending CM with another protein source. Taken together, the results indicate that the whole-body N utilization efficiency by the dairy cow improved and that more dietary protein was used to synthesize milk protein when CM was used as the sole protein source in the protein supplement up to 1.55 kg/d.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".