Bibliographic record
Abstract
The following physicians provided critical peer review for manuscripts submitted to the Journal of Bronchology during the past year. On behalf of the Editorial Board of the Journal, I gratefully acknowledge and thank these reviewers for giving their valuable time to provide constructive critiques and suggestions. Without their help, it would not be possible to publish the high-quality articles that appear in the Journal of Bronchology. Robert P. Baughman, MD Cincinnati, Ohio John F. Beamis, Jr., MD Burlington, Massachusetts Stephen D. Cassivi, MD Rochester, Minnesota Henri G. Colt, MD Irvine, California Eric S. Edell, MD Rochester, Minnesota Armin Ernst, MD Boston, Massachusetts David Feller-Kopman, MD Boston, Massachusetts Willane S. Krell, MD Detroit, Michigan Paul A. Kvale, MD Detroit, Michigan Stephen Lam, MD Vancouver, British Columbia, Canada Kaiser Lim, MD Rochester, Minnesota Robert Loddenkemper, MD Berlin, Germany Praveen N. Mathur, MD Indianapolis, Indiana Atul C. Mehta, MD Cleveland, Ohio Mark L. Metersky, MD Farmington, Connecticut David E. Midthun, MD Rochester, Minnesota Marc Noppen, MD, PhD Brussels, Belgium James M. Parish, MD Scottsdale, Arizona Charles A. Read, MD Washington, DC Mark J. Rosen, MD New York, New York Edward C. Rosenow III, MD Rochester, Minnesota Gerard A. Silvestri, MD Charleston, South Carolina Michael J. Simoff, MD Detroit, Michigan Daniel H. Sterman, MD Philadelphia, Pennsylvania Karen L. Swanson, DO Rochester, Minnesota Zelalem Temesgen, MD Rochester, Minnesota J. Francis Turner, Jr., MD Las Vegas, Nevada Michael Unger, MD Philadelphia, Pennsylvania James P. Utz, MD Rochester, Minnesota Jeanne M. Wallace, MD Sylmar, California
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 | 0.019 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.586 | 0.494 |
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".