Nonlinear Canonical Correlation Analysis of the Tropical Pacific Climate Variability Using a Neural Network Approach
Bibliographic record
Abstract
Recent advances in neural network modeling have led to the nonlinear generalization of classical multivariate analysis techniques such as principal component analysis and canonical correlation analysis (CCA). The nonlinear canonical correlation analysis (NLCCA) method is used to study the relationship between the tropical Pacific sea level pressure (SLP) and sea surface temperature (SST) fields. The first mode extracted is a nonlinear El Nin o-Southern Oscillation (ENSO) mode, showing the asymmetry between the warm El Nin o states and the cool La Nin a states. The nonlinearity of the first NLCCA mode is found to increase gradually with time. During 1950-75, the SLP showed no nonlinearity, while the SST revealed weak nonlinearity. During 1976-99, the SLP displayed weak nonlinearity, while the weak nonlinearity in the SST was further enhanced. The second NLCCA mode displays longer timescale fluctuations, again with weak, but noticeable, nonlinearity in the SST but not in the SLP.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".