Elliott Coues Award 2017, to Kevin J. McGraw
Bibliographic record
Abstract
The American Ornithological Society (AOS) is pleased to give the 2017 Elliott Coues Award, in recognition of outstanding and innovative contributions to ornithological research, to Dr. Kevin J. McGraw. Kevin is a professor of evolutionary and systems biology at Arizona State University. His research has focused on identifying the chemical nature, distribution, and functions of natural carotenoid pigments. This innovative work on animal coloration constitutes an outstanding example of integrating multiple levels of biological organization; it relates animal coloration to immune activity and infections, sensory perception, social behavior, nutrition, endocrine physiology, and effects of urbanization. Kevin's work has focused broadly on birds of the world, from passerines to hummingbirds to parrots to penguins. His research has wide-ranging implications for understanding organismal adaptations to environmental constraints. He has authored well over 150 scientific articles, in addition to coediting a well-received two-volume book, Bird Coloration (2006). Moreover, Kevin has mentored an impressive number of Ph.D. and undergraduate students. The Elliott Coues Award recognizes extraordinary contributions to ornithological research. The award is named in honor of Elliott Coues, a pioneering ornithologist of the western United States and a founding member of the American Ornithologists' Union. There is no limitation with respect to geographic area, subdiscipline of ornithology, or time course over which the work was done. The award consists of a medal and an honorarium provided through the endowed Elliott Coues Achievement Award Fund. To read more about the award, go to http://www.americanornithology.org/content/aos-coues-award. To see a list of previous recipients, go to http://www.americanornithology.org/content/aos-coues-award-recipients. Kevin J. McGraw
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.297 | 0.158 |
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".