WEST NILE VIRUS INFECTION IN RUFFED GROUSE (BONASA UMBELLUS) IN PENNSYLVANIA, USA: A MULTI-YEAR COMPARISON OF STATEWIDE SEROSURVEYS AND VECTOR INDICES
Bibliographic record
Abstract
Eastern populations of Ruffed Grouse (Bonasa umbellus) have been in a decades-long decline across the mid-Atlantic and southern Appalachian Mountains of the US. West Nile virus (WNV), which first arrived in the US in 1999, is suspected to have contributed to these declines based on decreased population indices since the arrival of WNV in Pennsylvania as well as on high, experimentally induced WNV-associated morbidity rates. A 3-yr statewide survey was conducted across Pennsylvania to measure flavivirus (i.e., WNV) seroprevalence among hunter-harvested grouse. The overall seroprevalence from 2015-17 was 14.4% (81/563); annual seroprevalence ranged from 2.8% (4/145) in the 2017 hunt year to 22.6% (52/230) in 2016-17. We analyzed the effects of numerous variables (i.e., Ruffed Grouse age and sex, hunt year, WNV vector index [VI], and region of Pennsylvania) on WNV serostatus by logistic regression. While there was no significant difference in WNV seroprevalence between sex and age group, there was significant variation in seroprevalence between geographic regions of Pennsylvania and across hunt years. Additionally, there was a negative correlation between WNV seroprevalence and VI. Low seroprevalence rates among Ruffed Grouse corresponded to years with a high VI, supporting experimental findings that Ruffed Grouse may be highly susceptible to WNV-associated disease. Additional strategic research efforts are essential to more effectively measure the effects of WNV on Ruffed Grouse and other vulnerable avian species.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".