Zoonotic Diseases: Health Aspects of Canadian Geese
Bibliographic record
Abstract
OBJECTIVE: Review zoonotic diseases associated with Canadian geese. STUDY DESIGN: Review article: A review of the multiple physical, microbiologic and safety concerns, and methods used in controlling this potential problem. RESULTS: Over the last decade the Canadian goose population (protected by international treaties and protection acts) has increased rapidly such that in many cities they have become a pest rather than an admired wild bird. Their increasing numbers have caused a number of potential healthcare concerns including: physical, bacterial, parasitic, allergic and viral potential problems. The Canadian goose fecal droppings of one per minute have caused falls and the flying geese have caused air traffic accidents. Bacterial concerns, including botulism, salmonella and E. coli have all been reviewed and presented concerns. The viral Newcastle disease may be detected with hemagglutination studies and the Giardia psittaci parasites have been repeatedly found in their droppings. The Cryptosporidium parvum oocytes have been present on stool study. CONCLUSIONS: Definite links to human infectious diseases have been difficult to prove. Revision of the current laws and new control programs must be developed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".