Phenotypic investigation of fine milk components in bovine milk and their prediction using mid-infrared spectroscopy
Bibliographic record
Abstract
This study aimed to examine the phenotypic variation observed in fine milk components, the use of mid-infrared (MIR) spectroscopy to predict these components, and the correlations with other milk production traits. A total of 4116 milk samples were collected from 414 dairy cows from 44 Canadian herds. Overall 3117 samples were analyzed for casein micelle size, 986 and 937 for total and soluble calcium, respectively, and 2054 for lactoferrin. Individual milk MIR spectra were obtained. Linear mixed models with repeated records were used to test the effects of herd nested within breed, breed, days in milk class, parity, and season on milk components. Lactoferrin content in milk increased with stage of lactation and parity number. Prediction equations were developed by partial least squares regression to predict each milk component using MIR spectroscopy, with the greatest coefficient of determination achieved for lactoferrin (0.55). Pearson correlation coefficients for lactoferrin with somatic cell score and protein percentage were 0.49% and 0.40%, respectively. The estimated correlation increased to 0.53% for predicted lactoferrin and protein percentage. The examined fine milk components exhibited variation, but low accuracies for their MIR prediction may limit the utility of this technology for their phenotyping.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".