Evaluation of an alternative method of herd classification for infection with paratuberculosis in cattle herds in the United States
Bibliographic record
Abstract
OBJECTIVE: To develop a better system for classification of herd infection status for paratuberculosis (Johne's disease [JD]) in US cattle herds on the basis of the risk of potential transmission of Mycobacterium avium subsp paratubeculosis. SAMPLE: Simulated data for herd size and within-herd prevalence; sensitivity and specificity for test methods obtained from consensus-based estimates. PROCEDURES: Interrelationships among variables influencing interpretation and classification of herd infection status for JD were evaluated by use of simulated data for various herd sizes, true within-herd prevalences, and sampling and testing methods. The probability of finding ≥ 1 infected animal in herds was estimated for various testing methods and sample sizes by use of hypergeometric random sampling. RESULTS: 2 main components were required for the new herd JD classification system: the probability of detection of infection determined on the basis of test results from a sample of animals and the maximum detected number of animals with positive test results. Tables were constructed of the estimated probability of detection of infection, and the maximum number of cattle with positive test results or fecal pools with positive culture results with 95% confidence for classification of herd JD infection status were plotted. Herd risk for JD was categorized on the basis of 95% confidence that the true within-herd prevalence was ≤ 15%, ≤ 10%, ≤ 5%, or ≤ 2%. CONCLUSIONS AND CLINICAL RELEVANCE: Analysis of the findings indicated that a scientifically rigorous and transparent herd classification system for JD in cattle is feasible.
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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 | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".