Characterization of Alberta Backyard Poultry Flocks and the Submission Level Prevalence of Infectious Laryngotracheitis Within This Sector
Bibliographic record
Abstract
The endemic nature of the provincially reportable disease, Infectious Laryngotracheitis (ILT), within backyard flocks in the western Canadian province of Alberta (AB) is of interest due to its clinical similarities to the federally reportable and potentially zoonotic respiratory diseases, Avian Influenza and Virulent Newcastle Disease, and the risk ILT-infected flocks may pose to Alberta’s commercial poultry population. This study utilized a voluntary online survey, and Government of Alberta surveillance and traceability program data to gain insight into the size, characteristics of, and submission level prevalence of ILT within Alberta’s backyard poultry population. Survey respondents were primarily middle-aged, well-educated females from acreage locations, largely new to keeping poultry, and kept fewer than 50 birds. Thirty one percent had noted respiratory disease signs in the preceding year, 3% had had a case of ILT within the preceding five years, and 13% regularly vaccinated for ILT. Twenty seven percent did not participate in the Premises Identification (PID) Program. The addition of new birds to an existing flock was found to be associated with having a history of respiratory disease signs. Predation losses and non-participation in the PID program were more common for those in acreage locations than urban or farm locations. There were estimated to be over 10,300 backyard flocks within AB in 2020, representing 128,350 - 409,000 laying birds. Nearly 35% of Ministry of Agriculture and Forestry “Non-Quota, Non-Commercial Disease Investigation Poultry Program” submissions had a history of respiratory signs, and the submission level prevalence of ILT from 2015 thru 2019 was 11.1%. Flock size, bird age, year of submission and season were not found to be associated with flocks that had been diagnosed with ILT. Other infectious diseases that can have asymptomatic carriers that were found with regularity within surveillance program submissions during this time-period were Marek’s Disease (18.6%) and Mycoplasmosis (11.7%). Educational materials and disease mitigation strategies can best be disseminated to backyard flock keepers by way of farm/feed/supply stores and social media sites, and should be aimed at acreage owners, those expanding their flocks, and those new to poultry keeping.
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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 | 0.000 |
| Research integrity | 0.000 | 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".