Statistical Analysis of Stratospheric Ozone Trends over the San Francisco Bay Area
Bibliographic record
Abstract
Stratospheric ozone plays a critical role in shielding life on Earth from harmful ultraviolet radiation.This study investigates ozone variability over the San Francisco Bay Area by applying a variety of statistical methods on NASA's OMI OMTO3d (2004-2025) and OMPS NMTO3-L3_DAILY (2012-2025) datasets.Results revealed strong seasonal variation, with ozone levels peaking in April-May and dipping in October-November, consistent with Brewer-Dobson circulation and sunlight cycles.A gradual upward trend suggests gradual ozone recovery following the Montreal Protocol.Spectral analysis confirmed a dominant annual cycle.Solar influence was investigated using sunspot data from WDC-SILSO's (World Data Center for Sunspot Index and Long-term Solar Observations) Northern Hemisphere records; however, Granger causality testing indicated a weak predictive relationship at short lags (p<0.07),but not significant under standard criteria (p<0.05) between sunspots and ozone levels.Cross-correlation analysis revealed a moderate association.This research provides new insights into regional ozone behavior and highlights the importance of continued localized monitoring.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".