2023 AOS William Brewster Memorial Award to Cristina Yumi Miyaki and Maren Vitousek
Bibliographic record
Abstract
Journal Article 2023 AOS William Brewster Memorial Award to Cristina Yumi Miyaki and Maren Vitousek Get access Marty Leonard, Marty Leonard Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada Corresponding author: mleonard@dal.ca Search for other works by this author on: Oxford Academic Google Scholar Mark Hauber, Mark Hauber Department of Animal Biology, University of Illinois, Urbana, Illinois, USA https://orcid.org/0000-0003-2014-4928 Search for other works by this author on: Oxford Academic Google Scholar Helen F James, Helen F James Division of Birds, National Museum of Natural History, Smithsonian Institution, Washington, District of Columbia, USA https://orcid.org/0000-0002-2495-6133 Search for other works by this author on: Oxford Academic Google Scholar Tony D Williams, Tony D Williams Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada Search for other works by this author on: Oxford Academic Google Scholar Karen Wiebe Karen Wiebe Department of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada https://orcid.org/0000-0002-3148-8278 Search for other works by this author on: Oxford Academic Google Scholar Ornithology, Volume 140, Issue 4, 5 October 2023, ukad039, https://doi.org/10.1093/ornithology/ukad039 Published: 29 August 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 | 0.000 |
| 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) | 0.002 | 0.012 |
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".