Effects of Singular-Vector-Type Initial Errors on the Short-Range Prediction of Kuroshio Extension Transition Processes
Bibliographic record
Abstract
Abstract The effects of optimal initial error on the short-range prediction of transition processes between the Kuroshio Extension (KE) bimodalities are analyzed using a reduced-gravity shallow-water model and the singular vector (SV) approach. Emphasis is placed on the spatial structures, growing processes, and effects of the SVs. The results show that the large values of the SVs are mainly located in the first crest region of the KE (around 35°N, 144°E) and in the Kuroshio large meander (KLM) region south of Japan (around 32°N, 139.5°E). The fast growths of the SVs have important impacts on the prediction of transition of the KE bimodality. The initial error with +SV pattern (with positive anomalies in the first crest region of the KE and negative anomalies in the KLM region) tends to strengthen the KE and shift it toward the high-energy state, while the error with −SV pattern is prone to weaken the KE and shift it toward the low-energy state. In addition, the SV-type initial errors grow more quickly in the transition phase of the KE from the high-energy to the low-energy state than in the opposite transition phase. A perturbation energy analysis illustrates that different physical processes are responsible for the error growth in the KE region for different transition phases of the KE; barotropic instability plays a dominant role in the error growth in the low-to-high (LH) energy phase, while the error evolution in the high-to-low (HL) energy phase is mainly caused by advection processes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".