Bibliographic record
Abstract
Beginning with this issue, the journal Biospectroscopy will be published as Biopolymers: Biospectroscopy. I would like to take this opportunity to thank all those who served on the Editorial Advisory Board of Biospectroscopy in any capacity over its first 5 years of publication. The Board members who have served with Biospectroscopy are Hassan Y. Aboul-Enein, King Faisal Specialist Hospital & Research Centre, SAUDIA ARABIA Sanford A. Asher, University of Pittsburgh, USA Gerald T. Babcock, Michigan State University, USA Alessandro Bertoluzza, Università di Bologna, ITALY Steven G. Boxer, Stanford University, USA Gary W. Brudvig, Yale University, USA Robert Callender, City College of New York (CUNY), USA Paul R. Carey, Case Western Reserve University, USA Pedro Carmona, Instituto de Estructura de la Materia (CSIC), SPAIN Paul M. Champion, Northeastern University, USA Therese M. Cotton, Iowa State University, USA Robin L. Garrell, University of California, Los Angeles, USA Jan Greve, University of Twente, The NETHERLANDS Robin M. Hochstrasser, University of Pennsylvania, USA Dewey Holten, Washington University, USA Bruce S. Hudson, Syracuse University, USA W. Curtis Johnson, Oregon State University, USA Kathryn S. Kalasinsky, Armed Forces Institute of Pathology, USA Timothy A. Keiderling, University of Illinois at Chicago, USA Teizo Kitagawa, Institute for Molecular Science, JAPAN Nikolai I. Koroteev, Moscow State University, RUSSIA Yasushi Koyama, Kwansei Gakuin University, JAPAN Joseph R. Lakowicz, University of Maryland, School of Medicine, USA Ira W. Levin, National Institutes of Health, USA Wolfgang W. B. Lubitz, Technische Universität Berlin, GERMANY Marc Lutz, CEA, Centre d'Etudes de Saclay, FRANCE Akio Maeda, Kyoto University, JAPAN Michel Manfait, Université de Reims, FRANCE Richard A. Mathies, University of California, Berkeley, USA Linda B. McGown, Duke University, USA Donald McNaughton, Monash University, AUSTRALIA Richard Mendelsohn, Rutgers University, USA Dieter Naumann, Robert-Koch-Institut, GERMANY Warner L. Peticolas, University of Oregon, USA Michel Pezolet, Laval University, CANADA Fred W. Schneider, Universität Würzburg, GERMANY Friedrich Siebert, Albert-Ludwigs-Universität, GERMANY Giulietta Smulevich, University of Florence, ITALY Thomas G. Spiro, Princeton University, USA Hideo Takeuchi, Tohoku University, JAPAN Lansing Taylor, Carnegie–Mellon University, USA Theo Theophanides, National Technical University of Athens, GREECE George J. Thomas, University of Missouri–Kansas City, USA Jane Vanderkooi, University of Pennsylvania, USA Rienk van Grondelle, Vrije Universiteit, The NETHERLANDS William H. Woodruff, Los Alamos National Laboratory, USA Robert W. Woody, Colorado State University, USA Jinguang Wu, Peking University, CHINA Nai-Teng Yu, Hong Kong University of Science & Technology, HONG KONG
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 | 0.018 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.078 | 0.062 |
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".