Summertime total ozone variations over middle and polar latitudes
Bibliographic record
Abstract
The statistical relationship between springtime and summertime ozone over middle and polar latitudes is analyzed using zonally averaged total ozone data. Short‐term variations in springtime midlatitude ozone demonstrate only a modest correlation with springtime polar ozone variations. However by early summer, ozone variations throughout the extratropics are highly correlated. Analysis of correlation functions indicates that springtime midlatitude ozone, not polar ozone, is the best predictor for summertime polar ozone. Long‐term total ozone trends at middle and high latitudes are also different for spring and nearly identical for summer. About 39% of the observed southern midlatitude ozone decline in December can be attributed to the polar ozone depletion up to November. In the Northern Hemisphere, the corresponding contribution is about 15%, but the error bars are too large to make an accurate estimate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".