Assessment of Brix refractometry to estimate immunoglobulin G concentration in beef cow colostrum
Bibliographic record
Abstract
BACKGROUND: Brix refractometry can be used to assess colostral immunoglobulin G (IgG) concentration, but studies identifying Brix percentages to detect high- and low-IgG colostrum are lacking for beef cows and interlaboratory agreement is unknown. OBJECTIVES: Evaluate Brix refractometer performance and interlaboratory agreement for assessing beef cow colostrum IgG concentration, including determination of thresholds to identify colostrum containing IgG concentrations <100 g/L and ≥150 g/L. ANIMALS: Beef cows (n = 416) from 11 cow-calf operations in Alberta, Canada. METHODS: Colostral IgG concentrations were measured using radial immunodiffusion (RID) and estimated by Brix refractometry for this retrospective study. Spearman correlation coefficients were assessed between RID and Brix refractometry. Likelihood ratios and misclassification cost-term analysis were used to determine optimal Brix percentages for detecting colostrum containing IgG concentrations <100 g/L and ≥150 g/L. Concordance correlation coefficient (CCC) and Bland-Altman analyses were performed for Brix percentages obtained at 3 different laboratories. RESULTS: Brix percentages obtained at 3 laboratories were positively correlated with IgG results (r = 0.72, 0.68, and 0.76, respectively). Colostrum Brix percentages of <24% and ≥30% were optimal for indicating IgG concentrations of <100 g/L and ≥150 g/L, respectively. Interlaboratory agreement was substantial, with CCC ranging from 0.89 to 0.96 and Bland-Altman analysis showing small mean differences (-1.2% to 0.09% Brix) and narrow limits of agreements (-4.8% to 2.4% Brix) among laboratories. CONCLUSIONS AND CLINICAL IMPORTANCE: Brix refractometry shows good potential for reliably estimating IgG concentrations in beef cow colostrum across multiple laboratories and can be recommended to aid colostrum management decisions on farms.
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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 | 0.007 | 0.010 |
| 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 | 0.001 | 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".