SINGULAR VECTOR AND ENSO PREDICTABILITY IN A HYBRID COUPLED MODEL
Bibliographic record
Abstract
In this study, singular vector (SV) and retrospective ENSO (El Nino and Southern Oscillation) predictions were performed respectively for the period from 1876 to 2000 using a hybrid coupled model. Emphasis was placed on exploring the relationship between SV and ENSO predictability. It is found that a defined Nino3 index from the first singular vector of sea surface temperature anomaly (SSTA) is highly correlated with the predicted Nino3 SSTA index of 6-month leads and that the first singular value (FSV) is positively correlated with the predictive skill. These results and findings improve our knowledge and understanding to the relationship between SV and predictability. It was thought that the fastest growth rate of errors to be inversely related to the prediction skill. The reasons why there is such a relationship between SV and realistic predictability include: (1) the strong signals of ENSO variability that favour the growth of initial uncertainties also have significant contributions to the predictability; (2) the averaged climate state of the tropical Pacific Ocean simultaneously effects both SV and predictability.
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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 | 0.000 |
| 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".