Bibliographic record
Abstract
Priyanga Amarasekare's pathbreaking research career as an ecologist was eloquently outlined in her outstanding MacArthur lecture, "Non-linearity, variability and emergent patterns: an integrative Perspective" presented at the 2023 Ecological Society Meeting in Portland, Oregon.Her initial work on coexistence of competitors in metacommunities showed how competition-dispersal trade-offs generalize beyond plants and was used to provide vital insights into biological control of insect pests in Southern California.She has since extended the classical framework of species interactions to highlight multiple conflicting eco-evo drivers of coexistence and exclusion.In her MacArthur lecture, she outlined a new synthetic theory of how rate-controlled traits and thermal reaction norms are likely to determine persistence, coexistence, and range expansion/contraction under ongoing climate change (available from ESA at https:// www. youtu be. com/ watch? v= lOO-vzAZHsk).Priyanga began her dual research programs on theory and experiments as a Ph.D. student with Dr. Steve Frank at UC Irvine after a Master's at the University of Hawaii.She developed a new spatial theory for species interactions as a member of the first outstanding crop of NCEAS postdocs, a group that produced many of the leading ecologists of her generation.Her 2-year postdoc at NCEAS was highly productive, but all too brief, as word got out and soon Yale, the University of Washington, and the University of Chicago were competing for her.She quickly found herself as an Assistant Professor in UChicago's prestigious Department of Ecology and Evolution.In 2001, as a freshly minted assistant professor at UChicago, Dr. Amarasekare won the Young Investigator Prize from the American Society of Naturalists.She continued to work on the mathematics of dispersal and coexistence in metacommunities, and the stimulating intellectual environment at UChicago encouraged her to think about the evolutionary underpinnings of population persistence COMMENTARIES
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.018 |
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".