Nonlinear multichannel singular spectrum analysis of the tropical Pacific climate variability using a neural network approach
Bibliographic record
Abstract
Singular spectrum analysis (SSA), a linear (univariate and multivariate) time series technique, performs principal component analysis (PCA) on an augmented data set containing the original data and time‐lagged copies of the data. Neural network theory has meanwhile allowed PCA to be generalized to nonlinear PCA (NLPCA). In this paper, NLPCA is further extended to perform nonlinear SSA (NLSSA): First, SSA is applied to reduce the dimension of the data set; the leading principal components (PCs) of the SSA then become inputs to an NLPCA network (with a circular node at the bottleneck). This network performs the NLSSA by nonlinearly combining all the input SSA PCs. The NLSSA is applied to the tropical Pacific sea surface temperature anomaly (SSTA) field and to the sea level pressure anomaly (SLPA) field for the 1950–2000 period. Unlike SSA modes, which display warm and cool periods of similar duration and intensity, NLSSA mode 1 shows the warm periods to be shorter and more intense than the cool periods, as observed for the El Niño‐Southern Oscillation. Also, in SSTA NLSSA mode 1 the peak warm event is centered in the eastern equatorial Pacific, while the peak cool event is located around the central equatorial Pacific, an asymmetry not found in the individual SSA modes. A quasi‐triennial wave of about a 39 month period is found in NLSSA mode 2 of the SSTA and of the SLPA.
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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".