Nonlinear modes of decadal and interannual variability of the subsurface thermal structure in the Pacific Ocean
Bibliographic record
Abstract
The nonlinear principal component analysis, a neural network technique, is applied to the observed upper ocean heat content anomalies (HCA) in the Pacific basin from 1961 to 2000. By applying the analysis to high‐passed and low‐passed data, nonlinear interannual and decadal modes are extracted separately. The first nonlinear interannual mode is mainly characterized by the El Niño‐Southern Oscillation (ENSO) structure in the tropical Pacific, with considerable asymmetry between warm El Niño and cool La Niña episodes; for example, during strong El Niño, the negative HCA in the western tropical Pacific is much stronger than the corresponding positive HCA during strong La Niña. The first nonlinear decadal mode goes through several notable phases. Two of the phases are related to decadal changes in the La Niña and El Niño characteristics, revealing that the decadal changes for La Niña episodes are much weaker than the changes for El Niño episodes. Other phases of the decadal mode show a possible anomaly link from the middle latitudes to the western tropical Pacific via the subtropical gyre. The decadal changes in the HCA around 1980 and around 1990 were compared and contrasted.
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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) | 0.000 | 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 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".