SINGULAR VECTOR AND ENSO PREDICTABILITY IN A HYBRID COUPLED MODEL
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it