Inter-annual and decadal variability on the sea level around the China seas
Bibliographic record
Abstract
Abstract All tide gauge stations around the China seas can be divided into two subregions according to their correlations before and after removing the seasonal cycle, within which the stations generally have very high correlations with each other. Region 1 is located in the Bohai Sea, the Yellow Sea, the East China Sea, and adjacent areas of China, which are in Korea and Japan; Region 2 is located in the South China Sea. EOF decomposition is performed on the tide gauge records within each subregion after removing the vertical land motion and the seasonal cycle, and then do the wavelet coherence analysis between the principal component (PC) time series of each subregion and the Southern Oscillation Index (SOI) as well as the Pacific Decadal Oscillation (PDO) time series. Results show that the first PC time series of Region 1 has no significant coherence with the SOI index on inter-annual timescales, but it proves strong coherence in the 8-to-16-year band; in contrast, the inter-annual variation of the sea level in the Region 2, which is mainly represented by the first mode, is consistent with the change of SOI revealing that the inter-annual variation of sea level in the South China Sea is closely related to ENSO. The wavelet coherence between the first PC time series of Region 1 and PDO index shows that they have strong coherence in the 8-to-16-year band. The wavelet coherence between the first PC time series of Region 2 and PDO index shows strong coherence in the 8-to-16-year band, and during 2000–2016, they also have strong coherence in the 3-to-7-year band.
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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 | 0.001 | 0.001 |
| 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".