2023 AOS Florence Merriam Bailey Award to Allison E. Huysman
Bibliographic record
Abstract
Allison E. Huysman The Florence Merriam Bailey Award—named for the first woman “associate” of the American Ornithologists’ Union (AOU) in 1885, who was also the first woman elected as a fellow of the AOU in 1929—recognizes an outstanding article published in Ornithology (odd-numbered years) or in Ornithological Applications (even-numbered years) by an early-career American Ornithological Society (AOS) member. The 2023 recipient of the AOS Florence Merriam Bailey Award is Allison E. Huysman for her paper “Strong migratory connectivity indicates Willets need subspecies-specific conservation strategies” (Huysman et al. 2023). Allison E. Huysman earned her B.S. in natural resources and animal science from Cornell University and her M.S. in natural resources: wildlife from Cal Poly Humboldt. Her M.S. research focused on the potential for Barn Owls to provide rodent pest control in vineyards. Since finishing her M.S., she has worked at the Smithsonian Migratory Bird Center, where she is contributing to research on migratory connectivity for North American birds. In this winning paper, “Strong migratory connectivity indicates Willets need subspecies-specific conservation strategies,” Huysman and her coauthors quantified migratory connectivity between breeding and nonbreeding areas in Willets at both the range-wide and subspecies levels. They consolidated tracking and banding data and characterized breeding and wintering areas for Western and Eastern Willets. The authors found strong migratory connectivity between breeding and nonbreeding areas, allowing them to identify threats such as impacts from climate change and anthropogenic development in both subspecies and an additional threat from harvest in the eastern subspecies. The authors recommend that conservation efforts for Willets entail managing by subspecies and protecting a variety of breeding and nonbreeding habitats. It is an honor to recognize Allison E. Huysman and her outstanding paper with the 2023 AOS Florence Merriam Bailey Award.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.715 | 0.645 |
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".