2024 AOS Peter R. Stettenheim Service Award to Jennifer Owen and a posthumous honorary award to Reed Bowman
Bibliographic record
Abstract
Jennifer Owen Reed Bowman The American Ornithological Society (AOS) offers the Peter R. Stettenheim Service Award to recognize AOS members who have provided continued, exceptional service to ornithology and society. This year, the AOS bestows the Peter R. Stettenheim Service Award to Jennifer Owen, and a posthumous honorary award to Reed Bowman for his long service to the society. Jennifer (Jen) Owen received her BS from the University of Montana’s School of Forestry in Wildlife Biology and her PhD from the University of Southern Mississippi, under the direction of Frank Moore, PhD Currently she is an associate professor and associate chair in the Department of Fisheries and Wildlife at Michigan State University, where she also serves as the director for the Corey Marsh Ecological Research Center and Michigan State Bird Observatory. Owen leads an interdisciplinary research program that addresses issues at the interface of health for wild birds, humans, and the environment. She investigates the role of migrating birds in the spread and maintenance of zoonotic pathogens. Currently, Owen and her students are studying how variation in habitat quality and access to adequate food affects a bird’s ability to maintain health during migration. Owen is a past AOS Student Research Grant winner and was elected an AOS Elective Member in 2008 and then a Fellow in 2014.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.574 | 0.304 |
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".