Evaluation of a Brix refractometer to estimate serum immunoglobulin G concentration in neonatal dairy calves
Bibliographic record
Abstract
The objective of this study was to evaluate the utility of a digital Brix refractometer for the assessment of success of passive transfer of maternal immunoglobulin compared with the measurement of serum total protein (STP) by refractometry. Blood samples (n = 400) were collected from calves at 3 to 6d of age. Serum IgG concentration was determined by radial immunodiffusion (RID), and STP and percentage Brix (%Brix) were determined using a digital refractometer. The mean IgG concentration was 24.1g/L [standard deviation (SD) ± 10.0] with a range from 2.1 to 59.1g/L. The mean STP concentration was 6.0 g/dL (SD ± 0.8) with a range from 4.4 to 8.8 g/dL. The mean %Brix concentration was 9.2% (SD ± 0.9) with a range of 7.3 to 12.4%. Brix percentage was highly correlated with IgG (r = 0.93). Test characteristics were calculated to assess failure of passive transfer (FPT; serum IgG <10 g/L). The sensitivity and specificity of STP at 5.5 g/dL were 76.3 and 94.4%, respectively. A receiver operating characteristic curve was created to plot the true positive rate against the false positive rate for consecutive %Brix values. The optimal combination of sensitivity (88.9%) and specificity (88.9%) was at 8.4% Brix. Serum total protein was also positively correlated with %Brix (r = 1.00) and IgG (r = 0.93). Dairy producers can successfully monitor their colostrum management and the overall success of passive transfer using a digital Brix refractometer to estimate IgG concentration of colostrum and calf serum.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".