Day-to-day variation in trace and macro-mineral concentrations in corn and mixed grass–legume silages of Canadian commercial dairy herds
Bibliographic record
Abstract
The aims were to assess the day-to-day variation in trace minerals (TM), macro-minerals, dry matter, and physical effectiveness factor in grass–legume and corn silages and to evaluate the variance partition. Grass–legume and corn silage samples were collected in nine Canadian dairy herds during two episodes of five consecutive days at 4 weeks apart by the same individual. All variables were analyzed in duplicate. The proportion of variation due to the farm was more variable within TM than macro-minerals. Using TM software reference values of silages for formulating rations can lead to important errors. Except for physical effectiveness factor, the within-farm variations between sampling episodes were more marked for mixed grass–legume than corn silage. For most of the minerals and nutrients analyzed, the sampling + day-to-day variations were the main source of variability, accounting for over 50% of the within-farm variance for both silage types. The remaining within-farm variance was explained by subsampling and laboratory analyses. The high within-herd variation suggests that a silage sampling over more than 1 day can be useful to get a representative sample for TM analysis. Accurate nutrients and TM values when formulating cow diets is essential to cow health and productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".