Validation of an automated cell counter to determine leukocyte differential counts in neonatal Holstein calves
Bibliographic record
Abstract
Morbidity and mortality in calf rearing facilities is known to be high early in the production cycle causing large amounts of antimicrobials to be used for group metaphylaxis after arrival. With recent work focusing on identifying risk factors and biomarkers associated with mortality risk in calves arriving at veal facilities, it may be possible to reduce antimicrobial use after arrival by selectively treating only high risk calves. For a selective antimicrobial therapy protocol to be implemented without a decline in animal health and welfare, further refinement of calf risk assessment is required to increase the sensitivity and specificity of risk status determination. Rapid, on-farm white blood cell differential counts presents an opportunity to improve disease risk assessment in young calves when interpreted with clinical exam findings. The objective of this prospective cross-sectional diagnostic accuracy study was to validate an automated leukocyte cell counter, the QScout BLD test (Advanced Animal Diagnostic, Morrisville, NC), in its ability to determine leukocyte differential cell counts in neonatal Holstein calves.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".