Evaluation of on-farm tools for colostrum quality measurement
Bibliographic record
Abstract
The objectives of this study were to determine the immunoglobulin G (IgG) content of colostrum on Alberta dairy farms and to determine which on-farm tool, the colostrometer or the Brix refractometer, was more highly correlated with IgG content as determined by radial immunodiffusion (RID). Colostrum samples (n=569) were collected between February and July 2012 from 13 commercial dairy farms in central Alberta, with herds ranging in size from 60 to 300 lactating cows. Immunoglobulin G content was determined directly by RID and indirectly by a colostrometer (specific gravity) and Brix refractometer (total solids). The Spearman correlation was used for the colostrometer and Brix refractometer data. According to RID analysis, 29.1% of the colostrum samples contained <50 mg/mL IgG. Concentrations ranged from 8.3 to 128.6 mg/mL IgG, with a median of 65.1 mg/mL. Third or greater parity cows had higher colostral IgG content (69.5±1.98 mg/mL) than second parity (59.80±2.06 mg/mL) or first parity (62.2±1.73 mg/mL) cows. The colostrometer data were more highly correlated with RID results (r=0.77) than were the Brix refractometer data (r=0.64). Specificity and sensitivity were determined for the colostrometer and Brix refractometer compared with a cut-point of 50 mg/mL IgG as determined by RID. The highest combined value for sensitivity and specificity occurred at 80 mg/mL for the colostrometer (84.1 and 77.0%, respectively) and 23% Brix (65.7 and 82.8%, respectively). This study indicates that although the colostrometer data are better correlated with true IgG values, the user-friendly Brix refractometer is a more specific tool to detect colostrum of adequate quality.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".