I: Interannual variability of stratospheric ozone and temperature. II: Seasonal cycle of N2O
Bibliographic record
Abstract
I would like to thank the many people without whom this thesis would not have been possible. Foremost is my advisor, Yuk L. Yung, whom I thank for his insightful guidance and generous support. I also want to thank Dr. Runlie Shia, who helped my understanding of the chemistry and transport model, in addition Andrew Ingersoll, Paul Wennberg, and Tapio Schneider for their advice in the thesis research. I would like to thank Alexander Ruzmaikin, Joan Feynman, and Duane Waliser at JPL. I also want to thank Dylan Jones at the University of Toronto, Var Limpasuvan at Coastal Carolina University, and Steven Pawson and Eric Nielsen in the Global Modeling and Assimilation Office, for their advice and assistance in the research. I would like to thank the following graduate students and postdocs for their help and discussions. David Camp, Dave Noone, Chris Walker, Dan Feldman, Maochang Liang,
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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; 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".