Bibliographic record
Abstract
The need for this book arose from my teaching, engineering, and - search experience in the non-power aspects of nuclear technology. The lack of a comprehensive textbook in industrial applications of radiation frustrated my students, who had to resort to a multitude of textbooks and research publications to familiarize themselves with the fundam- tal and practical aspects of radiation technology. As an engineer, I had to acquire the design aspects of radiation devices by trial-and-error, and often by accidental reading of a precious publication. As a researcher and a supervisor of graduate students, I found that the needed literature was either hard to find, or too scattered and diverse. More than once, I discovered that what appeared to be an exciting new idea was an old concept that was tried a few decades earlier during the golden era of "Atom for Peace". I am hoping, therefore, that this book will serve as a single comprehensive reference source in a growing field that I expect will continue to expand. This book is directed to both neophytes and experts, and is written to combine the old and the new, the basic and the advanced, the simple and the complex. It is anticipated that this book will be of help in - viving older concepts, improving and expanding existing techniques and promoting the development of new ones
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.029 |
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".