Assessing the utility of leukocyte differential cell counts for predicting mortality risk in neonatal Holstein calves upon arrival and 72 hours postarrival at calf rearing facilities
Bibliographic record
Abstract
There is growing concern about the level of antimicrobial use and antimicrobial resistance in food producing animals. An area of opportunity to reduce antimicrobial use could be in the treatment of young calves during the first week following arrival at calf rearing facilities. Group metaphylaxis is common due to the unknown age and history of calves that undergo several stressful events prior to arrival, such as transportation, co-mingling and variable periods of fasting. It may be possible to reduce antimicrobial use at this stage in the production cycle without sacrificing animal health and welfare if the calves at highest risk of morbidity and mortality could be identified and treated in a highly selective manner. Recent studies have identified indicators of future risk for morbidity and mortality that can be measured at arrival such as biomarkers and physical exam factors. Bovine haematology, when used in conjunction with clinical examination findings, could be used to improve disease diagnosis. The objective of this study was to assess the utility of leukocyte differential cell counts taken at the time of arrival at a calf rearing facility and 72 hours post arrival for determining mortality risk during the production cycle.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".