2023 AOS Marion Jenkinson service award to James Rivers
Bibliographic record
Abstract
James River The American Ornithological Society (AOS) offers two service awards, the Peter R. Stettenheim Service Award and the Marion Jenkinson Service Award, to recognize AOS members who have provided continued, exceptional service to ornithology and to the Society. This year, James Rivers is awarded the Marion Jenkinson Service Award. James Rivers is an Assistant Professor of Wildlife Ecology, Forest Engineering, Resources & Management, at Oregon State University, Portland. Dr. Rivers has served on numerous AOS committees, starting when he was a graduate student. Most notable is Dr. Rivers’ leadership in student affairs, including serving as chair of the Student Affairs Committee shortly after its establishment. His leadership of this committee led to improved services for students and early professionals, ensuring more impactful meetings (including initiating the very popular Quiz Bowl at the annual meetings), and expanding professional development. Dr. Rivers continued these efforts for several years as an active member of the Early Professionals Committee as well as while serving as an AOS Elective Councilor. In addition to being an enduring advocate for students and early professionals in the society, Dr. Rivers has contributed directly to several AOS conferences, has helped develop new awards to recognize significant contributions, and continues to serve as an associate editor for Ornithology. Dr. Rivers also received the 2012 Ned K. Johnson Early Investigator Award and was elected as an AOS Elective Member in 2007 and an AOS Fellow in 2015. In recognition of his outstanding and diverse service to AOS, the society is proud to name James Rivers as the recipient of the 2023 AOS Marion Jenkinson Service Award.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.780 | 0.684 |
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".