2022 Brina C. Kessel Award to Benjamin M. Winger and Teresa M. Pegan
Bibliographic record
Abstract
(left) Teresa Pegan and (right) Benjamin Winger Every two years, the American Ornithological Society bestows the Brina C. Kessel Award for a paper published during the preceding two years in Ornithology (formerly The Auk: Ornithological Advances) that has made an exceptional contribution to ornithology. Given in even-numbered years and consisting of a cash prize of $1,000, the Kessel Award is given in honor of Brina Kessel, former President of the AOU (1992–1994) and beloved leader and mentor in ornithology. The Kessel Award for 2022 is presented to Benjamin M. Winger and Teresa M. Pegan for their paper “Migration distance is a fundamental axis of the slow-fast continuum of life history in boreal birds,” published in 2021. Winger, B. M., and T. M. Pegan (2021). Migration distance is a fundamental axis of the slow-fast continuum of life history in boreal birds. Ornithology 138:ukab043. https://doi.org/10.1093/ornithology/ukab043 In their paper, Winger and Pegan show connections between migration distance and key life-history traits. They show that, counterintuitively, birds that breed in boreal North America and migrate long distances for the winter than those that migrate shorter distances and that long-distance migrants also lay fewer eggs each year than do short-distance migrants. This study reveals how the evolution of a remarkable behavioral adaptation—long-distance migration— simultaneously shapes and is shaped by the fundamental balance of reproduction and survival. It is an honor to recognize Benjamin M. Winger and Teresa M. Pegan for their paper with the Brina C. Kessel Award. Please check Wing Beat (https://americanornithology.org/blog/) for more detailed profiles of the award winners.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.551 | 0.496 |
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".