THE BIOLOGICAL EFFECTS OF IONIZING RADIATION: EPIDEMIOLOGICAL SURVEYS AND LABORATORY ANIMAL EXPERIMENTS. IMPLICATIONS FOR RISK EVALUATION AND DECISION PROCESSES
Bibliographic record
Abstract
served faithfully and effectively to discuss, to review, to evaluate and to report on three important matters of societal concern: (1)) to place into perspective the actual and potential harm to the health of man and his decendants to be expected in the present and in the future from those societal activities involving the use of ionizing radia tions; (2) to develop quantitative indices of harm based on doseresponse relationships to provide a scientific basis for the evaluation of somatic and genetic risk and protection of human populations exposed to low-level radiation; and (3) to identify the sources and levels of radiation which could cause harm, to assess their relative importance, and to provide a framework on how to reduce unnecessary radiation exposure to human populations.To a greater or lesser extent, each advisory committee on radiation-such as the UNSCEAR.2 the ICRP.3 the NCRP.4 the NRPB,5 and others in France, Canada, and elsewhere in Europe and Japan, and the BEIR Committee-have dealt with these matters.But significant differ ences occur in the scientific reports of these various bodies, and we should expect differences to occur, because of the charge, the scope, and the composition of each committee, and probably most important, because of public attitudes existing at the 2
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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 | 0.210 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.015 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".