Preliminary Investigation of Bird and Human Movements and Disease-Management Practices in Noncommercial Poultry Flocks in Southwestern British Columbia
Bibliographic record
Abstract
Understanding normal movement patterns and husbandry practices of poultry production systems is important for understanding the dynamics of disease spread, and for controlling outbreaks of highly infectious diseases, such as highly pathogenic avian influenza. To learn about these patterns in the noncommercial or "backyard" poultry-keeping sector, an open-ended questionnaire was administered to 18 backyard-flock owners in British Columbia, Canada, and responses were analyzed descriptively. Six participants reported that they visited premises that were part of the commercial poultry system in the last year; however, bird movements between commercial and noncommercial farms were always unidirectional, from commercial to backyard. Bird movements into and out of participants' flocks occurred multiple times per month (two flocks), three times per year (five flocks), once or twice a year (nine flocks) and every 3-5 yr (two flocks). Visitors had direct contact with three participants' flocks multiple times per week; for other flocks, visitors had direct contact three times or less per year. Fourteen participants rarely had direct contact with other backyard flocks, three had contact more than once per week, and one had contact every 3 mo. Participants stated that the health of their birds was excellent (7), very good (3), good (6), O.K. (1), and all right (1), and used a median of two biosecurity practices to maintain health in their flock. Our findings suggest that bird movements are not likely to transmit disease from backyard to commercial flocks; however, human movements between backyard and commercial premises could transmit diseases. Within the backyard-flock sector, the majority of small flocks appear to pose little risk of disease transmission because they are maintained in semi-isolation from other flocks; however, a minority of flocks has high contact levels with other flocks and could be important in disease spread.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".