Biological Effects of Ionizing and Non-Ionizing Radiation: A Comprehensive Review of Health and Environmental Impacts
Bibliographic record
Abstract
Radiation impacts living organisms in several ways, and its biological effects vary depending on the type and dose of exposure.It can damage DNA, potentially causing mutations and increasing the risk of cancer.Ionizing radiation can harm the human body through two primary mechanisms.Direct damage occurs when radiation strikes essential biological molecules, such as DNA, breaking or altering their structure.Indirect damage arises when radiation interacts with water molecules-abundant in cells-generating free radicals, which are highly reactive and can harm cellular components.These free radicals can react with DNA and other cellular components, leading to molecular damage.Depending on the radiation dose and exposure rate, the biological effects can range from minor, repairable molecular damage to cell and tissue death.These outcomes may result in acute or chronic health conditions, such as Acute Radiation Syndrome (ARS) and an increased risk of cancer.Additional consequences may include cardiovascular diseases and thermal effects, which can cause localized tissue overheating and damage.Radiation exposure can also suppress the immune system, thereby increasing susceptibility to infections.Negative effects on mental and physical development or birth defects may result from exposure during developmental stages, such as pregnancy.Exposure to radiation in specific situations, like nuclear accidents, can result in mental health issues like depression and anxiety.Proper precautions must be taken when handling radiation sources, as health risks generally increase with higher exposure doses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".