Machine-learning assisted development of a knowledge-based system in dairy farming
Bibliographic record
Abstract
The goal of this research was to explore the use of machine learning to assist in the development of knowledge-based systems (KBS) in dairy farming. A framework was first developed which described the various types of management and control activities in dairy farming and the types of information flows among these activities. This framework provided a basis for the creation of computerized information systems and helped to identify the analysis of group-average lactation curves as a promising area of application. A case-acquisition and decision-support system was developed to assist a domain specialist in generating example cases for machine learning. The specialist classified data from 33 herds enrolled with the Quebec dairy herd analysis service, resulting in 1428 lactations and 7684 tests of individual cows, classified as outlier or non-outlier, and 99 interpretations of group-average lactation curves. To enable the performance analysis of classifiers, generated with machine learning from these small data sets, a method was established involving cross-validation runs, relative operating characteristic curves, and analysis of variance. In experiments to filter lactations and tests, classification performance was significantly affected by preprocessing of examples, creation of additional attributes, choice of machine-learning algorithm, and algorithm configuration. For the filtering of individual tests, naive-Bayes classification showed significantly better performance than decision-tree induction. However, the specialist considered the decision trees as more transparent than the knowledge generated with naive Bayes. The creation of a series of three classifiers with increased sensitivity at the expense of reduced specificity per classification task, allows users of a final KBS to choose the desired tendency of classifying new cases as abnormal. For the main interpretation tasks, satisfactory performance was achieved. For the filtering tasks, performance was fai
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".