Determination of the Reliability and Accuracy of the Brix Refractometer for the Detection of Colostrum Quality in Sheep
Bibliographic record
Abstract
This study was aimed at the assessment of the reliability and performance of the Brix refractometer for the determination of colostral IgG concentrations in sheep. A total of 190 colostrum samples were collected from 150 ewes of various breeds. Out of the colostrum samples, 101 were taken at parturition, 10 at 6 h postpartum, 6 at 8 h postpartum, 2 at 10 h postpartum, 65 at 12 h postpartum and 6 at 24 h postpartum. The Brix percentages of the colostrum samples were determined using a Brix refractometer, and the colostral IgG concentrations were measured with the radial immunodiffusion (RID) technique. Correlation coefficients were calculated between the Brix percentages and RID-IgG values. Receiver operating characteristics (ROC) analyses were performed to determine the optimal Brix thresholds for the prediction of colostral IgG concentrations using <20, <50, <75 and <100 g/L thresholds. The misclassification cost term (MCT) analyses were performed to test the robustness of the Brix thresholds based on the relative costs of misclassifications. The mean IgG concentration and Brix percentage of the ewe colostrum samples were determined as 62.7 g/L and 27.2%, respectively. A high correlation was detected between the colostral RID-IgG concentrations and Brix percentages (r = 0.83). Brix percentages of ≤25.0%, ≤25.9%, ≤27.5% and ≤31.9% indicated IgG concentrations of <20, <50, <75 and <100 g/L, respectively. The Brix refractometer was demonstrated to be a low-cost, practical and reliable tool for the determination of colostrum quality in sheep. It is recommended that the four Brix thresholds determined in this study be used strategically in feeding lambs in need of colostral supplementation due to inadequate or poor-quality colostrum production by their dams.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".