A Case-Acquisition and Decision-Support System for the Analysis of Group-Average Lactation Curves
Bibliographic record
Abstract
A case-acquisition and decision-support system was developed to support the analysis of group-average lactation curves and to acquire example cases from domain specialists. This software was developed through several iterations of a three-step approach involving 1) problem analysis and formulation in consultation with two dairy nutrition specialists; 2) development of a case-acquisition and decision-support prototype by the system developer; and 3) use of the prototype by the domain specialists to analyze and classify milk-recording data from example herds. The overall problem was decomposed into three subproblems: removal of outlier tests and lactation curves of individual cows; interpretation of group-average lactation curves; and diagnosis of detected abnormalities at the herd level through the identification of potential management deficiencies. For each subproblem, a software module was developed allowing the user to analyze both graphical and numerical performance representations and classify these representations using predefined linguistic descriptors. The example-based method for the development of the program proved to be very useful, facilitating the communication between system developer and domain specialists, and allowing the specialists to explore the appropriateness of the various prototypes developed. The resulting software represents a formalization of the approach to group-average lactation curve analysis, elicited from the two domain specialists. In future research, the case-acquisition and decision-support system will be complemented with knowledge to automate identified classification tasks, which will be captured through the application of machine-learning techniques to example cases, acquired from domain specialists using the software.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".