Bibliographic record
Abstract
The focus of the September-October issue is on plant maintenance and advanced reactors. Major articles/reports in this issue include: Technologies of national importance, by Tsutomu Ohkubo, Japan Atomic Energy Agency, Japan; Modeling and simulation advances brighten future nuclear power, by Hussein Khalil, Argonne National Laboratory, Energy and desalination projects, by Ratan Kumar Sinha, Bhabha Atomic Research Centre, India; A plant with simplified design, by John Higgins, GE Hitachi Nuclear Energy; A forward thinking design, by Ray Ganthner, AREVA; A passively safe design, by Ed Cummins, Westinghouse Electric Company; A market-ready design, by Ken Petrunik, Atomic Energy of Canada Limited, Canada; Generation IV Advanced Nuclear Energy Systems, by Jacques Bouchard, French Commissariat a l'Energie Atomique, France, and Ralph Bennett, Idaho National Laboratory; Innovative reactor designs, a report by IAEA, Vienna, Austria; Guidance for new vendors, by John Nakoski, U.S. Nuclear Regulatory Commission; Road map for future energy, by John Cleveland, International Atomic Energy Agency, Vienna, Austria; and, Vermont's largest source of electricity, by Tyler Lamberts, Entergy Nuclear Operations, Inc. The Industry Innovation article is titled Intelligent monitoring technology, by Chris Demars, Exelon Nuclear.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".