Database of Radiogenic Cancer in Experimental Animals Exposed to Low Doses of Ionizing Radiation
Bibliographic record
Abstract
For decades, there have been debates regarding the nature of the relationship between exposure to low doses of ionizing radiation and cancer risk. Under the linear no-threshold hypothesis, which serves as a theoretical basis for current radiation protection standards, the risk of cancer at low levels of exposure is presumed to be directly proportional to dose. Opponents of this hypothesis claim that there are threshold doses for radiation carcinogenesis, or even a reduction in cancer risk at low doses (a phenomenon referred to as "radiation hormesis"). Epidemiological, animal, molecular, and cellular studies were conducted to resolve this controversy, although each of these study types has its strengths and limitations. Although the results of animal experiments are not directly applicable to humans, data can substantially add to our knowledge on the form of relationship between radiation dose and cancer risk in a wide range of doses. Laboratory animals are a homogeneous population with little biological variability; animal experiments are conducted under controlled conditions with good estimates of radiation doses. In order to address the question of whether or not the dose-response curve for radiation carcinogens is linear at low doses, a comprehensive database of animal carcinogenesis experiments was assembled involving exposure to different types of ionizing gradation. The database includes virtually all publicly accessible data on the induction of radiogenic cancer in laboratory mammals. This review provides a descriptive overview of the experiments included in the database, along with a qualitative assessment of the shape of the dose-response relationship for radiation carcinogenesis at low doses in experimental animals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".