Waterbird disease in the United States Laurentian Great Lakes under climate change
Bibliographic record
Abstract
Since the turn of the 21st century, anecdotal evidence suggests that incidences of avian disease in natural populations in the Laurentian Great Lakes are increasing, with recent high-profile outbreaks including avian botulism in 2020 and avian influenza in 2022. To understand avian diseases, we must understand environmental associations and their relationships with disease outbreaks. Here, we conducted a scoping review on avian disease in the Laurentian Great Lakes using concept pools and key words, with specific attention to Green Bay, Lake Michigan and the endangered piping plover ( Charadrius melodus ). Green Bay represents a mesocosm of environmental stressors that continue to disrupt similar Great Lakes ecosystems, has a rich assemblage of waterbirds, including species of concern, and has intensive conservation investment and management. We sought to 1) synthesize the general knowledge of avian disease in the watershed, 2) understand how species’ biology may impact transmission, and 3) identify potential drivers (i.e. water quality, climate) that may influence avian disease patterns. We identified and provided descriptions and histories of three viruses (avian influenza, duck plague, and Newcastle disease), two bacteria (avian cholera and Salmonella ), and two toxins (botulism types C and E). Overall, density dependent effects including carcass abundance, waterbird community, and population structure, as well as environmental conditions such as temperature, Cladophora presence, and water pH need to be considered for mitigating disease outbreaks. Future waterbird management will require rapid responses to contend with increasing disease outbreaks ostensibly linked to climate change, and requires incorporating climate change into disease modeling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".