Historical wave height trends in the South and East China Seas, 1911–2010
Bibliographic record
Abstract
Abstract This study reconstructs 6 hourly significant wave heights (Hs) in the South and East China Seas for the period 1871–2010, using the Twentieth Century Reanalysis ensemble of mean sea level pressure (SLP) fields and a multivariate regression model to represent the Hs‐SLP relationship in each study area. The regression model was calibrated and validated using the ERA‐Interim reanalysis of Hs and SLP for the period 1981–2010. These reconstructions were found to reproduce reasonably well the seasonal mean and maximum Hs climates as represented by the ERA40 and ERA‐Interim wave reanalyses. For each study area, an ensemble of 56 members of 6 hourly Hs was reconstructed for each grid point. The regional mean series of the ensemble mean of the reconstructed consecutive monthly mean Hs was tested for temporal homogeneity, which identified a few discontinuities in the pre‐1946 period and led to the exclusion of the reconstructed Hs for 1871–1910 from trend analysis (due to data uncertainty and inhomogeneity). Each 6 hourly Hs time series for the period 1911–2010 was homogenized for the identified discontinuities, before being used to derive annual and seasonal mean and maximum Hs for trend analysis. The trend analysis results show that, in both study areas, the 1911–2010 wave height trends are dominantly negative, with the exception that the seasonal maximum significant wave heights seem to have increased in summer and spring in the central South China Sea and in summer in the East China Sea.
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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 | 0.001 | 0.002 |
| 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".