Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks
Bibliographic record
Abstract
A data set comprising milk-recording and conformation data was used to investigate the usefulness of artificial neural networks in detecting influential variables in the prediction of incidences of clinical mastitis. Specifically, these data contained test-day records from dairy herd analysis, phenotypic cow scores for conformation and genetic conformation proofs for cows and their sires. The data were analysed using the milk-recording data only, the conformation data only, and a combination of the two. Results from sensitivity analyses, performed with trained neural nets, indicated that stage of lactation, milk yield on test day, cumulative milk yield and somatic cell count were the major production factors influencing the ability to detect the occurrence of clinical mastitis. Among the conformation traits, such variables as phenotypic scores for rear-teat placement, dairy character and size, cow proof for dairy character, sire reliability for final score and sire proofs for pin-setting (desirability) and loin strength were found to have some influence on the network's predictive ability, although they were all very minor in relation to the production variables mentioned. As a group, cow genetic proofs seemed more important than either sire genetic proofs or cow phenotypic scores. Given the neural network's general abilities to determine the major factors related to the presence or absence of mastitis on a given test day, it may be appropriate to investigate the possibility of using this technology for actual prediction purposes. Key words: Artificial neural networks, clinical mastitis, milk-recording data, conformation traits, sensitivity analysis, milk production
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".