Validation of serum gamma-glutamyl transferase activity and body weight information for identifying dairy calves that are too young to be transported to auction markets in Canada
Bibliographic record
Abstract
Dairy calves are at risk of being stressed when transported during the first week of life. A new Canadian federal rule will forbid transportation of calves younger than 9 d old to auction market. However, in the absence of reliable information to determine birth date, other indirect methods would be of interest. This study aimed to determine the prediction accuracy of body weight, Brix refractometry, and serum gamma-glutamyl transferase (GGT) activity for determining if a calf was not fit to be transported (i.e., <9 d old). For this purpose, we used 284 calves with a known birth date from a cross-sectional and a prospective cohort study. A logistic regression model was built based on multivariable analysis as well as a misclassification cost term analysis. Because of the collinearity between GGT activity and Brix value and lower discrimination of Brix value, the GGT activity was retained for the main model. The final logistic regression model contained body weight and log-transformed GGT activity value. The misclassifications of the logistic model was minimized using a model probability threshold ≥0.55 with a sensitivity of 70.4% and a specificity of 77.3%. This probability threshold was relatively robust for various prevalence and false negative to false positive cost ratios. The prediction accuracy of this model was moderate at the individual level, but is helpful in calves with a reasonable suspicion of being less than 9 d old.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".