Direct and indirect contact rates among livestock operations in Colorado and Kansas
Bibliographic record
Abstract
OBJECTIVE: To characterize direct and indirect contacts among livestock operations in Colorado and Kansas. DESIGN: Cross-sectional quarterly survey. SAMPLE: 532 livestock producers. PROCEDURES: Livestock producers in Colorado and Kansas were recruited by various means to participate in the survey, which was sent out via email or postal mail once quarterly (in March, June, September, and December) throughout 2011. Data were entered into an electronic record, and descriptive statistics were summarized. RESULTS: Large swine operations moving animals to other large swine operations had the highest outgoing direct contact rates (range, 5.9 to 24.53/quarter), followed by dairy operations moving cattle to auction or other dairy operations (range, 2.6 to 10.34/quarter). Incoming direct contact rates for most quarters were highest for large feedlots (range, 0 to 11.56/quarter) and dairies (range, 3.90 to 5.78/quarter). For large feedlots, mean total indirect contacts through feed trucks, livestock haulers, and manure haulers each exceeded 725 for the year. Dairy operations had a mean of 434.25 indirect contacts from milk trucks and 282.25 from manure haulers for the year. CONCLUSIONS AND CLINICAL RELEVANCE: High direct contact rates detected among large swine operations may suggest a risk for direct disease transmission within the integrated swine system. Indirect contacts as well as incoming direct contacts may put large feedlots at substantial risk for disease introduction. These data can be useful for establishing and evaluating policy and biosecurity guidelines for livestock producers in the central United States. The results may be used to inform efforts to model transmission and control of infectious diseases such as foot-and-mouth disease in this region.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".