Maximum UV Index Records (2010–2014) in Quito (Ecuador) and Its Trend Inferred from Remote Sensing Data (1979–2018)
Bibliographic record
Abstract
To prevent adverse health effects, the World Health Organization promotes the diffusion of the ultraviolet radiation index (UVI), with messages promoting precautionary behaviors, through a scale that considers extreme UVI values to be larger than 11.0. This scale came from a proposal from Canada, a country with a mostly light-skinned population, which experiences maximum UVI values up to 10.0. A modified scale was proposed, adapted to the skin types and the UVI levels in South America, which considers extreme values larger than 16.0. The records from 2010 to 2014 indicated that UVI is frequently larger than 11.0 (40.0–76.1% of the days per month) in Quito (Ecuador). The number of days per month with levels larger than 16.0 varied between 0.7% and 32.0%. We found that the maximum UV index levels do not occur necessarily around the local solar noontime. As the basis for a self-warning system in Quito and based on their skin type and UVI levels, people should know the exposure time before damage can take place. The Tropospheric Emission Monitoring Internet Service (TEMIS) computed the UVI at local solar noontime and under clear-sky conditions. The records from 2010 to 2014 were congruent with the corresponding TEMIS values. We did not identify any trend of the daily TEMIS UVI values during 1979 to 2018, which, used as a proxy, suggested the real UVI levels in Quito during 2010 to 2018 varied in a range similar to 1979–2009.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".