2024 AOS Brina C. Kessel Award to Andrew J. Laughlin
Bibliographic record
Abstract
Lars Pomara (left) and Andrew J. Laughlin (right). The American Ornithological Society (AOS) Brina C. Kessel Award recognizes the most outstanding article by a single or multiple authors published in Ornithology over the preceding two-year period. The award is made in even-numbered years to complement the Painton Award, which is given in odd-numbered years for the best paper published in Ornithological Applications during the preceding two-year period. The 2024 recipient of the Brina C. Kessel Award is Andrew J. Laughlin for his paper, “Winter range shifts and their associations with species traits are heterogeneous in eastern North American birds” (Laughlin and Pomara 2023). Andrew J. Laughlin is an associate professor of environmental science at the University of North Carolina Asheville, where he has taught since 2015. He teaches classes in ecology, ornithology, wildlife management, urban ecology, and global change. His research interests focus on animal responses to environmental change, with a focus on birds. His avian research takes place in a variety of settings, from urban centers to relict southern Appalachian spruce-fir forests. Currently, he and his undergraduate research students are resurveying bird communities in portions of the Great Smoky Mountains National Park to understand how bird communities are responding to climate change and the recent Eastern hemlock die-off. He received his MS in Biological Sciences from East Tennessee State University in 2010, under Fred Alsop, PhD; and his PhD in Ecology and Evolutionary Biology from Tulane University in 2015, under Caz Taylor, PhD Lars Pomara, the paper’s coauthor, is a research ecologist at the USDA Forest Service’s Southern Research Station, with interests in landscape ecology, ornithology, and conservation. He contributed expertise in ecological modeling and climate change ecology to the analysis and writing of the paper.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.617 | 0.351 |
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".