Bibliographic record
Abstract
Description This one-of-a-kind publication provides 30 peer-reviewed, award-winning papers from recipients of the William J. Kroll Zirconium Medal, named in honor of Dr. William J. Kroll, one of the foremost metallurgists of the 20th Century. The Kroll Medal was established to recognize outstanding achievement in the scientific, technological, or commercial aspects of zirconium production and utilization, and to encourage future efforts, studies, and research. These peer-reviewed papers cover all aspects of zirconium technology, including history, ore refinement, production, fabrication, mechanical properties, and physical properties. Many of the early papers deal with state-of-the-art ore refinement and Zr production processes, and the later papers with highly sophisticated metallurgical and scientific technologies. Of the 33 medals awarded, 31 have been for work in the commercial nuclear power industry. This retrospective covers work conducted in the United States, Canada, Japan, Russia, and Western Europe, including, France, Germany, Sweden, and United Kingdom. The papers include a whole range of zirconium alloy technical topics, including: Taken overall, the papers provide a comprehensive technical history of zirconium technology and will be of great interest to both young and experienced workers in the field.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.053 | 0.049 |
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".