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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.096 | 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 teacher head, 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".