A Time Series Prediction Method for the Subsurface Thermal Structure of the South Yellow Sea Cold Water Mass
Bibliographic record
Abstract
Ocean subsurface thermal structure prediction is an area of active research field because of its scientific importance attach to ocean dynamic, air-sea interaction, and climate change, but currently, most of the ocean temperature predictions are oriented to the sea surface temperature (SST) due to the lack of observed profile data inside the ocean, especially in some marginal sea areas, such as the South Yellow Sea Cold Water Mass (SYSCWM). In fact, the prediction for ocean subsurface thermal structure is more important than SST in some ocean fields. In this letter, a dynamic coupling vertical multifeature difference time series prediction model based on bi-long short-term memory (DVMFD-Bi-LSTM) is proposed for the subsurface thermal structure prediction in the SYSCWM. Bi-LSTM with the “bi-directional” structure enables information association in temporal dimension. The dynamic coupling vertical mechanism is used to realize the spatial correlation between two adjacent layers of the subsurface ocean, and the difference algorithm is introduced to ensure the accuracy and robustness of the new method. Besides, we construct multifeature datasets to improve data scale and quality and rely on a multistep prediction strategy for multiday prediction. For a more comprehensive evaluation, multiple groups of experiments are set up for comparison, and the RMSE of the new model is reduced to 0.517, and R2 is increased to 0.937, which verifies the good performance of it in both temporal and spatial dimensions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".