Bibliographic record
Abstract
2023 Cryogenic Engineering Conference Board of Directors Peter Bradley (President) National Institute of Standards and Technology Austin Capers (Exhibit/Sponsorship Chair) Scientific Instruments, Inc. Mark Derakshan (Exhibit/Sponsorship Vice Chair) Sumitomo (SHI) Cryogenics of America, Inc. Ram Dhuley (Program Vice Chair) Fermi National Accelerator Laboratory Michael DiPirro NASA/Goddard Space Flight Center Robert Duckworth (Scholarship Chair) Oak Ridge National Laboratory Benjamin Hansen Fermi National Accelerator Laboratory Wesley Johnson (Conference Chair) NASA Glenn Research Center Peter Kittel (Awards Chair) Consultant Jacob Leachman (Program Chair) Washington State University Robbi McDonald (Exhibit/Sponsorship Vice Chair) Westport Fuel Systems, Canada Franklin Miller (Awards Vice Chair) University of Wisconsin-Madison Holger Neumann (Awards Vice Chair) Karlsruhe Institute of Technology, Germany Sastry Pamidi (Scholarship Vice Chair) FAMU-FSU College of Engineering/CAPS John Pfotenhauer University of Wisconsin-Madison Wolfgang Stautner (Vice President) GE Research Michael Sumption The Ohio State University Srini Vanapalli (Scholarship Vice Chair) University of Twente, Netherlands John Weisend II (Chief Technical Editor) European Spallation Source, Sweden List of Awards Committee and Nominations Committee are available in this pdf.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.429 | 0.301 |
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".