DeepBrucel: A Deep Learning Approach for Automated Risk Detection of Brucellosis in Cattle Farms in Ecuador
Bibliographic record
Abstract
An automated risk model for Brucellosis detection in cattle farms, termed DeepBrucel, was developed and validated. A comprehensive survey encompassing 51 variables related to farm characteristics, management practices, and reproductive pathologies was administered across 632 cattle farms in Ecuador. The extensive dataset thus obtained was utilized to implement and compare classifiers based on regression, neural networks, and deep learning methodologies. A wide-ranging primary experimentation protocol enabled the identification of critical variables and the optimal topology for the neural networks. Superior performance was exhibited by a deep neural network model with three hidden layers, which achieved an impressive accuracy of 98.4% in predicting Brucellosis risk. DeepBrucel, now publicly available, provides a highly accessible and robust tool for the diagnosis and control of Brucellosis in cattle farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".