Cost-aware Inference of Bovine Respiratory Disease in Calves using Precision Livestock Technology
Bibliographic record
Abstract
Bovine Respiratory Disease (BRD) is the second leading cause of death in young dairy calves, and is associated with less growth, and reduced long-term performance such as less milk production, which makes BRD a financial burden on a farm’s economy. Precision technologies, such as accelerometers, automatic feeders, and cameras have been extensively used to collect, summarize, and interpret changes in baseline dairy cattle behavior. While some efforts to evaluate the presence of statistical relationships between calves’ behavior and BRD status have been made, there is very little research in pairing such technologies with manual examinations to improve the accuracy and cost of BRD monitoring. In this paper, we propose a framework for diagnosis and early prediction of BRD in calves. This framework is composed by a machine learning model as well as by a cost-sensitive feature selection problem called Cost Optimization Worth (COW). COW maximizes prediction accuracy given a budget constraint. We show that COW is NP-Hard and propose an efficient heuristic with polynomial complexity. We validate our methodology on a real dataset of 46 automatic and manually collected features, representing 106 calves observed during the preweaning period of 50 days. Our results show that our machine learning model can correctly classify a sick cow with a 97% accuracy and up to 5 days prior to BRD diagnosis, outperforming a recent state-of-the-art approach. Furthermore, our feature selection results show that in a low-budget scenario, manually collected features are more valuable than automated features in detecting sick cows. Conversely, in a high-budget scenario, automated features report higher accuracy for the early prediction of BRD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".