Bibliographic record
Abstract
The editors and Karger Publishers would like to thank the following reviewers for the ongoing support in reviewing manuscripts for Journal of Vascular Research:Christian Aalkjær, Aarhus, DenmarkChris Barrett, Omaha, NE, USAKayla Bayless, Bryan, TX, USAErik Behringer, Loma Linda, CA, USAShawn Bender, Columbia, MO, USAMarie Billaud, Boston, MA, USAErika Boerman, Columbia, MO, USAGeorg Breier, Dresden, GermanyMatt Brothers, Arlington, TX, USAAlbert Busch, Dresden, GermanyJoshua T. Butcher, Stillwater, OK, USAYujun Cai, New Haven, CT, USAJorge Augusto Castorena-Gonzalez, New Orleans, LA, USASanjukta Chakraborty, Bryan, TX, USAJohn C. Chappell, Roanoke, VA, USAStephen Cheng, Hong Kong, Hong Kong SARGeraldine Clough, Southampton, UKAntonio Colantuoni, Naples, ItalyCor de Wit, Luebeck, GermanyLeon DeLalio, Pittsburgh, PA, USAJudy Muller Delp, Tallahassee, FL, USAEvan DeVallance, Morgantown, WV, USARaul Devia Rodriguez, Groningen, The NetherlandsStephanie J. Frisbee, London, ON, CanadaAgne Frismantiene, Bern, SwitzerlandVijay Ganta, Augusta, GA, USABradley Gelfand, Charlottesville, VA, USADaniel Goldman, London, ON, CanadaMiranda Elizabeth Good, Boston, MA, USAClare Louise Hawkins, Copenhagen, DenmarkTravis Hein, Bryan, TX, USADaniel Henrion, Angers, FranceYusuke Higashi, New Orleans, LA, USAMichael Hill, Columbia, MO, USAWilliam F. Jackson, East Lansing, MI, USAAnne Joutel, Paris, FranceElena Kashuba, Stockholm, SwedenPanagiotis Koutakis, Waco, TX, USAFong Wilson Lam, Houston, TX, USAPaula Lamagna, Boston, MA, USAVictor Lamin, Iowa City, IA, USATing-Yim Lee, London, ON, CanadaLiwu Li, Blacksburg, VA, USAVolkhard Lindner, Scarborough, ME, USAAlexander W Lohman, Calgary, AB, CanadaDuosheng Luo, Guangzhou, ChinaMelissa Luse, Charlottesville, VA, USAJulia J. Mack, Los Angeles, CA, USATakayuki Matsumoto, Tokyo, JapanCameron G. McCarthy, Columbia, SC, USAJoseph McClung, Greenville, SC, USAGordon Mclennan, Aurora, CO, USACoral Murant, Guelph, ON, CanadaShayn Pierce-Cottler, Charlottesville, VA, USAGirija Regmi, Oklahoma City, OK, USAUlka Sachdev, Pittsburgh, PA, USAOtto A. Sanchez, Minneapolis, MN, USATakeshi Sasaki, Hamamatsu, JapanMicah B. Schott, Omaha, NE, USATimothy Secomb, Tucson, AZ, USAUtpal Sen, Louisville, KY, USAUlf Simonsen, Aarhus, DenmarkPerumana R. Sudhakaran, Thiruvananthapuram, IndiaAlain Tedgui, Paris, FranceYuqian Tian, Omaha, NE, USAQiwei Wang, Boston, MA, USACamilla Ferreira Wenceslau, Columbia, SC, USAShawn Whitehead, London, ON, CanadaHao Yin, London, ON, CanadaSilvio Zaina, León, MexicoScott Zawieja, Columbia, MO, USAHanming Zhang, New Haven, CT, USAZhen Zhou, Houston, TX, USA
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 | 0.014 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.311 | 0.225 |
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".