Cosmic Microwave Background B-Mode Polarization Experiment POLARBEAR-2
Bibliographic record
Abstract
Tomotake Matsumura1, Peter Ade2, Yoshiki Akiba3, Christopher Aleman4, Kam Arnold4, Matt Atlas4, Darcy Barron4, Julian Borrill6, Scott Chapman5, Yuji Chinone1, Ari Cukierman7, Matt Dobbs8, Tucker Elleflot4, Josquin Errard6, Giulio Fabbian9, Guangyuan Feng4, Adam Gilbert8, William Grainger10, Nils Halverson11, Masaya Hasegawa1, Kaori Hattori1, Masashi Hazumi1, William Holzapfel7, Yasuto Hori1, Yuki Inoue3, Greg Jaehnig11, Nobuhiko Katayama12, Brian Keating4, Zigmund Kermish12, Reijo Keskitalo6, Ted Kisner6, Adrian Lee7, Frederick Matsuda4, Hideki Morii1, Stephanie Moyerman4, Michael Myers7, Marty Navaroli4, Haruki Nishino12, Takahiro Okamura1, Christian Reichart7, Paul Richards7, Colin Ross5, Kaja Rotermund5, Michael Sholl7, Praween Siritanasak4, Graeme Smecher8, Nathan Stebor4, Radek Stompor9, Jun-ichi Suzuki1, Aritoki Suzuki7, Suguru Takada14, Satoru Takakura15, Takayuki Tomaru1, Brandon Wilson4, Hiroshi Yamaguchi1, Oliver Zahn7
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".