Migratory connectivity of Blue-winged Teal: risk implications for avian influenza virus introduction to Cuba
Bibliographic record
Abstract
In recent years, the complex evolution of the highly pathogenic avian influenza (HPAI) situation reflects a change in the eco-epidemiology of the causative agent which, among other demands, renews the need for better understanding of the connectivity between countries through the main virus reservoirs to improve prevention, early warning and mitigate the associated risks. Our objective was to determine migratory connectivity of Blue-winged Teal and evaluate the risk of AIV introduction to Cuba by this species. The stable hydrogen isotope (δ2H) value in flight feathers was analyzed. Individuals were sampled during the migratory season of 2021 (N=126) and winter residence of 2020 (N=152), in western and central Cuba, respectively. Based on banding records from 1955 to 2018, the transition probabilities from 3 breeding areas in North America to 9 wintering areas was estimated with a Burnham’s live-recapture dead-recovery modeling framework. A map of likely origin in North America of the individuals sampled for each season was generated, combining the isotopic information and transition probabilities. Evidence of an age-related and phenological pattern in migratory origins was identified. Individuals harvested in the migratory season in western Cuba were most likely from molt or natal areas in the prairies and forest regions from United States and southern Canada. Alternatively, individuals harvested in winter in the center of Cuba had the most likely origin in the U.S. prairie region. The spatial-temporal pattern of AIV prevalence in the estimated region was analyzed and suggestions are made to assist the Cuban system of active AIV surveillance of wild birds.
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) | 0.000 | 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) | 0.001 | 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".