Influence of Potential Future Sea-Level Rise on Tides in the China Sea
Bibliographic record
Abstract
Kuang, C.; Liang, H.; Mao, X.; Karney, B.; Gu, J.; Huang, H.; Chen, W., and Song, H., 2017. Influence of potential future sea-level rise on tides in the China Sea.This study investigates the diurnal and semidiurnal tidal responses of the entire China Sea to a potential rise in sea level of 0.5–2 m. A modified two-dimensional tidal model based on MIKE21 is primarily configured and validated for the present situation; then, three (0.5, 1, 2 m) sea-level rise (SLR) scenarios are simulated with this model. The predicted results show that the principal lunar semidiurnal (M2) and diurnal (K1) tidal constituents respond to SLR in a spatially nonuniform manner. Generally, changes of M2 and K1 amplitudes in shallow waters are larger than those in the deep sea, and significant tidal alterations mainly occur in the Bohai and Yellow seas, Jianghua Bay, Hangzhou Bay, Taiwan Strait, Yangtze River estuary, Pearl River estuary, and Beibu Bay. Possible mechanisms further discussed for these changes mainly relate to bottom friction decreasing, amphidromic point migration, and resonant effect change. Additionally, simulated changes in M2 and K1 amplitudes in response to three SLR scenarios imply that M2 amplitude changes are proportional to the magnitude of SLR, whereas this proportionality does not hold for K1 amplitudes. Identifying the response of tides in the China Sea to SLR not only increases our knowledge of tidal systems, but also assists in setting conservation requirements and management plans in coastal areas.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".