Ozone Layer Depletion and Its Impacts on the Environment and Public Health: A Review
Bibliographic record
Abstract
The level of solar UV radiation is increasing worldwide due to the depletion of the stratospheric ozone layer. The ozone layer is considered a protective shield from harmful ultraviolet radiation emitted from the sun to humans and the environment. In this narrative review, the paper investigates the cause of Ozone depletion, its impacts on the environment and human health, and initiatives to protect the ozone layer. It gives an overview of the depletion of the ozone layer from natural and anthropogenic processes, which has led to numerous harmful effects on human health and the environment. The exposure to harmful UV radiation, which has led to increased long-term effects including the rise of melanoma, is estimated to rise to 510,000 new cases and 96,000 deaths by 2040, with a decline in crop productivity by about 3% for every 10% increase in UVB radiation. However, the ratification of the Montreal Protocol globally has led to combined efforts to recover the stratospheric ozone layer. The challenges of limited funding to implement activities tailored towards abating ozone layer depletion, and porous borders in many countries still hinder the initiatives to save the ozone layer.
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 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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".