Bibliographic record
Abstract
For their service as ad hoc editors of one or more manuscripts (for Ecology, Ecological Monographs, and Ecosphere during the past year (October 2014–September 2015) the Society is especially grateful to: Ali Arab Debra R. Ayres Jarrett J. Barber David Barnett Travis Brenden Burkhard Budel Peter Caley Stephanie M. Carlson Edward A. Codling Brian Cypher Darren Drewry Mark J. Ducey Evelyn E. Gaiser* Matthew J. Germino Kevin Gross Eric J. Gustafson Tim Haas Niall P. Hanan Ephraim M. Hanks* Florian Hartig Kathy A. Hibbard Scott Higgins Eve-Lyn S. Hinckley Gordon W. Holtgrieve David L. Hoover* Devin S Johnson Lucas N. Joppa Bonnie Keeler Devan A. McGranahan Peter McIntyre Joshua J. Millspaugh Seth Munson Beth A. Newingham Shuli Niu* Jonathan N. Pauli Jameal F. Samhouri Joel B. Sankey Erin Schliep Matthew Schofield Karen Seto Christopher T. Solomon R. Jan Stevenson Jeffrey Taylor* Wayne E. Thogmartin Kristin Vanderbilt Marcel G.A. van der Heijden M. Jake Vander Zanden Charles van Riper III Robert van Woesik Caryn C. Vaughn Jay M. Ver Hoef Matthew A. Whalen J. Wilson White Kerry D. Woods Elise F. Zipkin* Benjamin Zuckerberg
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 | 0.024 | 0.164 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.031 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.517 | 0.599 |
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".