Rapid assessment of bovine colostrum quality: How reliable are transmission infrared spectroscopy and digital and optical refractometers?
Bibliographic record
Abstract
The objectives of this study were to evaluate the performance of the transmission infrared (IR) spectroscopic method and digital and optical Brix refractometers for measurement of colostral IgG concentration and assessment of colostrum quality of dairy cows. Colostrum samples (n = 258) were collected from Holstein cows on 30 commercial dairy farms in Nova Scotia and Newfoundland, Canada. Colostral IgG concentrations of 255 samples were measured by the reference radial immunodiffusion (RID) assay and IR spectroscopy. The Brix scores were determined on 240 of these samples using both the digital and optical Brix refractometers. Approximately half (48%) of the colostrum samples had RID IgG concentrations <50 g/L, which was the cut-point for poor quality. The correlation between RID and IR IgG concentrations was 0.88. The correlations between RID IgG concentration and Brix scores, as determined by the digital and optical refractometers, were 0.72 and 0.71, respectively. The optimal cutoff levels for distinguishing good- and poor-quality colostrum using IR spectroscopy, and digital and optical Brix refractometers were at 35 g/L and 23% Brix, respectively. The IR spectroscopy showed higher sensitivity (90%) and specificity (86%) than the digital (74 and 80%, respectively) and optical (73 and 80%, respectively) Brix refractometers for assessment of colostrum quality, as compared with RID. In conclusion, the transmission-IR spectroscopy is a rapid and accurate method for assessing colostrum quality, but is a laboratory-based method, whereas Brix refractometers were less accurate but could be used on-farm.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".