Bibliographic record
Abstract
High praise continues to come in for the 4th volume of Jake Van Dyk's The Modern Technology of Radiation Oncology. From Peter Metcalfe in Physical and Engineering Sciences in Medicine… "Thank goodness medical physics has Jacob Van Dyk. Like Tiger Woods and Phil Mickelson in golf, his textbooks continue to make major comebacks. He has managed to assemble the most talented among us to sustain the up-to-date knowledge that is essential to our profession. Reference knowledge from this textbook will help ensure the medical physics profession is at the cutting edge of cancer research and clinical treatment. This textbook has taken pride of place on my bookshelf, right next to my most treasured Porsche magazines. I could not give it a higher accolade than that." From Rajesh A. Kinhikar in Journal of Medical Physics…"This resourceful book has aimed to serve as a comprehensive textbook for the practicing radiotherapy professionals. I would like to congratulate the authors and the Editor for such a high?quality scientific feast and strongly recommend the fourth volume of The Modern Technology of Radiation Oncologyto the clinical medical physicists and radiation oncology professionals involved with the rapidly evolving radiotherapy." New topics addressed in volume 4 include surface-guided radiation therapy (RT), PET/MRI, real-time MRI guidance, robust optimization, automated treatment planning, artificial intelligence, adaptive RT, machine learning, big data, radiomics, particle therapy RBE, nanoparticle applications, economic considerations, global medical physics activities, global access to RT, and FLASH RT. The volumes in this series have not only been valued by medical physicists and radiation oncologists in clinical practice around the world, but have also provided an important learning resource for residency programs, radiation technologists, dosimetrists, research students, biomedical engineers, and ancillary professionals related with radiotherapy. Administrators and scientists affiliated with the practice of radiation therapy will also find this book a useful resource.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.066 | 0.053 |
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".