Bibliographic record
Abstract
Several AGU members are among scientists recently honored by the Royal Society of Canada (RSC). Keith Hipel, with the Department of Systems Design Engineering at the University of Waterloo, Waterloo, Ontario, Canada, received the Sir John William Dawson Medal in recognition of “important and sustained contributions in two domains of interest to RSC or in interdisciplinary research.” Andrew Weaver, with the School of Earth and Ocean Sciences at the University of Victoria, Victoria, British Columbia, Canada, received the Miroslaw Romanowski Medal “for significant contributions to the resolution of scientific aspects of environmental problems or for important improvements to the quality of an ecosystem in all aspects—terrestrial, atmospheric and aqueous—brought about by scientific means.” In addition, the following AGU members were honored as new RSC fellows: Bernard Paul Boudreau, Department of Oceanography, Dalhousie University, Halifax, Nova Scotia, Canada; Dante Canil, School of Earth and Ocean Sciences, University of Victoria; Raymond Desjardins, Research Branch, Agriculture and Agri‐Food Canada, Ottawa, Ontario, Canada; Keiko Hattori, Department of Earth Sciences, University of Ottawa, Ottawa, Ontario, Canada; andDanny Summers, Department of Mathematics and Statistics, Memorial University of Newfoundland, St. John's, Newfoundland and Labrador, Canada.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.330 | 0.197 |
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".