Quantification of the relative efficiency of factory surveillance in the disclosure of tuberculosis lesions in attested Irish cattle
Bibliographic record
Abstract
In Ireland, factory surveillance of cattle for gross lesions is an important supplementary method for detecting herds infected with bovine tuberculosis (tb), and in recent years between 27 and 46 per cent of all new herd breakdowns in any year have been detected by this method. The aim of this study was to determine the relative efficiency of factories in detecting lesions among attested cattle slaughtered during 2003 and 2004. National databases were available on animal slaughter, programmes of tuberculin testing for bovine tb and laboratory confirmation of suspected lesions. Factories were ranked according to their submission risk (number of animals submitted with lesions/number of attested animals killed) and confirmation risk (number of animals with laboratory-confirmed lesions/number of animals submitted with lesions), adjusting for the risk profile of the animals slaughtered, including potential confounding factors such as their age and sex, whether they were purchased or homebred, the test history of their herd, the prevalence of bovine tb in the area and the season of slaughter. Approximately 3.7 million cattle were slaughtered in 42 Irish export-licensed factories during the two years. Complete data were available for 2,374,987 animals from 84,510 attested herds in 2845 District Electoral Divisions. Samples from 7398 animals with suspected tb lesions were submitted for laboratory examination; 4767 (64.4 per cent) were positive, 2011 were negative and 620 were inconclusive. The average unadjusted submission risk for all the factories was 22 per 10,000, ranging from 0 to 58 per 10,000. The unadjusted factory confirmation risk (excluding factories that had sent in fewer than 10 lesions) varied between 34.3 per cent and 86.3 per cent. The unadjusted and adjusted submission and confirmation risks were highly correlated, and animal-related factors (including their characteristics and origin) therefore did not contribute to the variations in factory-level submission and confirmation risks.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".