The effects of UV-B radiation on crop yields across 4 countries from 1966-2021
Bibliographic record
Abstract
In the late 1970s, one of the most serious environmental issues was the hole in the ozone. Scientists proved that ozone depletion was caused by the chlorofluorocarbons (CFCs) produced by factories and that it was causing an increase in UV-B radiation, a serious cause of skin cancer. The Montreal Protocol (1987) was a groundbreaking policy that called for an immediate halt in CFC production. Although the ozone hole has begun shrinking, its interactions with global climate change are much less understood and can have major influences on human health. One of these factors is accessibility to food. The effects of UV-B radiation on crop yields are poorly researched and understood. Some lab experiments have been criticized for having defective methods creating biased results. Additionally, the results produced cannot be translated to other plants as UV-B radiation effects are species dependent. This new review and analysis study aims to discover the effects of UV-B radiation on crop yields using crop yield data. This study concludes that there is no statistically significant impact of ozone concentration on crop yields. Despite that this study is based on the best available data on ozone concentration, it must be recognized that it is limited as only 8 ground-based monitoring stations exist around the world. The results stress the importance of continued stratospheric ozone monitoring as climate change worsens. This study is a call to action for government agencies to invest more resources in this endeavor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".