A qualitative study to identify potential biosecurity risks associated with feed delivery
Bibliographic record
Abstract
Objectives: To identify management and operational functions, recommended by feed-company personnel and swine producers, that have the potential to decrease the risk of pathogens being transmitted among swine farms through movement of feed trucks. Materials and methods: Focus groups and key-informant interviews were conducted with feed company representatives (21), including managers, dispatchers, and truck drivers, and also with swine producers (15). Questions explored biosecurity measures that would reduce risk of pathogen transmission at the farm, feed-company, and feed-truck levels. Participants were asked to rate these biosecurity management changes by economic and logistic feasibility and likelihood of reducing pathogen transmission. Results: The results provide an understanding of the roles of the farm, feed truck, and feed company in biosecurity management surrounding delivery of feed to swine farms and the need for education about how pathogens move among farms. Examples include pest control and truck washing, dispatching trucks according to farm disease status, drivers not entering the barn, reducing exposure of trucks to deadstock and manure, and educating all industry personnel. Implications: All swine industry personnel must think about their roles in pathogen transmission associated with feed delivery and consider implementing changes and developing an industry standard that could reduce this risk. Veterinarians may take the responsibility of educating others in the industry about risks identified in the scientific literature that are associated with pathogen transmission. Biosecurity is everyone’s concern: everyone has a role to play in reducing the potential risk.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".