2023 AOS Peter R. Stettenheim Service Award to Scott Lanyon
Bibliographic record
Abstract
Journal Article 2023 AOS Peter R. Stettenheim Service Award to Scott Lanyon Get access Michael S Webster, Michael S Webster Cornell Lab of Ornithology, Cornell University, Ithaca, New York, USA Corresponding author: msw244@cornell.edu Search for other works by this author on: Oxford Academic Google Scholar Sara A Kaiser, Sara A Kaiser Cornell Lab of Ornithology, Cornell University, Ithaca, New York, USA https://orcid.org/0000-0002-6464-3238 Search for other works by this author on: Oxford Academic Google Scholar Erica Nol, Erica Nol Department of Biology, Trent University, Peterborough, Ontario, Canada https://orcid.org/0000-0001-8295-4550 Search for other works by this author on: Oxford Academic Google Scholar Sharon A Gill, Sharon A Gill Department of Biological Sciences, Western Michigan University, Kalamazoo, Michigan, USA https://orcid.org/0000-0002-4628-8922 Search for other works by this author on: Oxford Academic Google Scholar W Alice Boyle, W Alice Boyle Division of Biology, Kansas State University, Manhattan, Kansas, USA Search for other works by this author on: Oxford Academic Google Scholar Judith Scarl Judith Scarl Executive Director and CEO, American Ornithological Society, Chicago, Illinois, USA Search for other works by this author on: Oxford Academic Google Scholar Ornithological Applications, Volume 125, Issue 4, 6 November 2023, duad038, https://doi.org/10.1093/ornithapp/duad038 Published: 09 September 2023
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.133 |
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; both teacher heads agree on what is shown here.
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".