2024 AOS Elliott Coues Award to Rebecca T. Kimball
Bibliographic record
Abstract
The Elliott Coues Award recognizes outstanding and innovative contributions to ornithological research with no limitation to geographic area, subdiscipline(s) of ornithology, or the time course over which the work was done. The 2024 Elliott Coues Award is presented to Rebecca T. Kimball. Rebecca T. Kimball Rebecca T. Kimball received a PhD from the University of New Mexico, where her dissertation focused on sexual selection in House Sparrows. After completing postdoctoral work at both the University of New Mexico and The Ohio State University, she became a faculty member at the University of Florida in 2001, where she is now a professor in the Department of Biology. She has published more than 100 peer-reviewed papers in the areas of evolutionary biology and behavioral ecology. Within these broad areas, she utilizes modern molecular genomic techniques in combination with other types of data to address questions in phylogenetics, particularly to understand higher-level avian relationships; population and conservation genetics; the genetic and physiological mechanisms that underlie the evolutionary change in specific traits; the evolution of male secondary sexual traits and display behaviors; and the mating and social systems within and among species. Kimball is a past winner of an AOS-sponsored Student Membership and was elected as an Elective Member (2010) and then Fellow (2014) of the AOS, has chaired the American Ornithologists' Union (AOU) Student Travel and Presentation Awards Committee, has served as Treasurer of the AOU/AOS, has been a member of numerous AOS committees, and is currently an editor at the journal Ibis. The American Ornithological Society is honored to bestow the 2024 AOS Elliott Coues Award to Rebecca T. Kimball.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.510 | 0.316 |
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".