Representation of the quasi‐biennial oscillation in the tropical stratospheric wind by nonlinear principal component analysis
Bibliographic record
Abstract
The zonal winds at several levels between 70 and 10 hPa (roughly 20–30 km) measured at near‐equatorial stations during 1956–2000 were analyzed to produce a one‐dimensional approximation. The neural network‐based technique applied was the circular nonlinear principal component analysis (NLPCA.cir) designed to characterize quasiperiodic phenomena. The reconstructed height‐time series of wind based on the one‐dimensional NLPCA.cir captures many of the characteristic features of the observed quasi‐biennial oscillation (QBO). The nonlinear results were evaluated relative to comparable linear principal component analysis and found to produce a superior one‐dimensional representation of the data. The NLPCA.cir analysis produces a single time series of QBO phase based on data at all levels. This phase was then applied to demonstrate a strong correlation of the state of the tropical QBO and boreal winter high‐latitude stratospheric temperatures.
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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 | 0.000 | 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) | 0.001 | 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".