Environmental Radiobiology
Bibliographic record
Abstract
Abstract This chapter aims to contribute to a comprehensive view of environmental radiobiology and discuss the effects of different kinds of ionizing radiation on ecosystems. The impact of ionizing radiation was considered on both organisms and the abiotic environment, assessing the fate of radionuclides in abiotic compartments (e.g., the movement through atmosphere, hydrosphere, and lithosphere) and in the trophic chains, with implications for human and non-human biota. The available methodologies for estimating radiation dose to biota were also addressed as well as the associated challenges. This chapter also focused on the impacts of ionizing radiation exposure on non-human biota from microorganisms to vertebrates, as well as on the basic concepts related to environmental radiobiology and the molecular effects associated with the exposure to different types of ionizing radiation. The particular context of Naturally Occurring Radioactive Material (NORM) contamination was also tackled, as well as its effects on non-human biota.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.120 | 0.057 |
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; both teacher heads agree on what is shown here.
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".