Impact of Global Warming on Cancer Development: A Review of Environmental Carcinogens and Human Immunogenetics
Bibliographic record
Abstract
This paper examines the impact of global warming on cancer development, specifically focusing on the intensified effects of environmental carcinogens such as ultraviolet (UV) radiation and air pollutants. Our review elucidates the intricate interplay between global warming, ecological carcinogens, human immunogenetics, and cancer susceptibility. The analysis highlights the exacerbating effects of rising temperatures and changes in atmospheric conditions on exposure to UV radiation and air pollutants, including particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), nitrogen dioxide (NO2), nitrogen oxides (NOx), and ground-level ozone (O3). Furthermore, the study explores the pivotal role of human immunogenetics in modulating individual responses to carcinogen exposure and shaping cancer susceptibility and progression. Genetic variations in key immune-related genes and their influence on the interplay between environmental carcinogens and cancer development are discussed. The paper underscores the importance of longitudinal cohort studies, integrative approaches, and interdisciplinary collaborations to advance our understanding of the complex interactions between global warming, environmental carcinogens, human immunogenetics, and cancer biology. Additionally, evidence-based public health interventions targeting environmental carcinogens and personalized prevention strategies based on genetic susceptibility profiles and environmental exposure assessments are proposed to address the growing challenges of environmentally induced cancers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".