Ensemble learning for heterogeneous biomarker discovery in precision dairy farming
Bibliographic record
Abstract
Metabolic biomarkers can act as powerful indicators of dairy cow welfare. Machine learning methods have been applied to discover discriminative biomarkers that can help predict potential risks to cow health, and consequently support early interventions to avoid decline in animal welfare and production loss. Previous studies on predictive models based on metabolic profiling have considered a limited scope of biomarkers, and have mostly been restricted to prediction of disease occurrence. This work proposes an ensemble supervised learning approach based on heterogeneous biomarker attributes obtained from dairy cow profile and history, and two metabolic profiling methods, to predict potential risks for dairy cow health, reproduction performance, and milk production loss. Best performing models are composed of either Random Forest, Multilayer Perceptropn and Extra Trees classifiers, and achieved F1 scores of 0.81, 0.86, 0.98 and 0.75 when predicting ‘Non-diseased’, ‘Bad’ or ‘Good’ reproduction performance, and ‘0’ production loss for an upcoming lactation. The source code and sample datasets for this work are made publicly available at https://github.com/bioinfoUQAM/dairy_biomarkers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".