Education and Training for Radiation Scientists: Radiation Research Program and American Society of Therapeutic Radiology and Oncology Workshop, Bethesda, Maryland, May 12–14, 2003
Bibliographic record
Abstract
Coleman, C. N., Stone, H. B., Alexander, G. A., Barcellos-Hoff, M. H., Bedford, J. S., Bristow, R. G., Dynlacht, J. R., Fuks, Z., Gorelic, L. S., Hill, R. P., Joiner, M. C., Liu, F-F., McBride, W. H., McKenna, W. G., Powell, S. N., Robbins, M. E. C., Rockwell, S., Schiff, P. B., Shaw, E. G., Siemann, D. W., Travis, E. L., Wallner, P. E., Wong, R. S. L. and Zeman, E. M. Education and Training for Radiation Scientists: Radiation Research Program and American Society of Therapeutic Radiology and Oncology Workshop, Bethesda, Maryland, May 12–14, 2003. Radiat. Res. 160, 729–737 (2003).Current and potential shortfalls in the number of radiation scientists stand in sharp contrast to the emerging scientific opportunities and the need for new knowledge to address issues of cancer survivorship and radiological and nuclear terrorism. In response to these challenges, workshops organized by the Radiation Research Program (RRP), National Cancer Institute (NCI) (Radiat. Res. 157, 204–223, 2002; Radiat. Res. 159, 812–834, 2003), and National Institute of Allergy and Infectious Diseases (NIAID) (Nature, 421, 787, 2003) have engaged experts from a range of federal agencies, academia and industry. This workshop, Education and Training for Radiation Scientists, addressed the need to establish a sustainable pool of expertise and talent for a wide range of activities and careers related to radiation biology, oncology and epidemiology. Although fundamental radiation chemistry and physics are also critical to radiation sciences, this workshop did not address workforce needs in these areas. The recommendations include: (1) Establish a National Council of Radiation Sciences to develop a strategy for increasing the number of radiation scientists. The strategy includes NIH training grants, interagency cooperation, interinstitutional collaboration among universities, and active involvement of all stakeholders. (2) Create new and expanded training programs with sustained funding. These may take the form of regional Centers of Excellence for Radiation Sciences. (3) Continue and broaden educational efforts of the American Society for Therapeutic Radiology and Oncology (ASTRO), the American Association for Cancer Research (AACR), the Radiological Society of North America (RSNA), and the Radiation Research Society (RRS). (4) Foster education and training in the radiation sciences for the range of career opportunities including radiation oncology, radiation biology, radiation epidemiology, radiation safety, health/government policy, and industrial research. (5) Educate other scientists and the general public on the quantitative, basic, molecular, translational and applied aspects of radiation sciences.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.146 | 0.096 |
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".