Deep learning approaches to predict sea surface height above geoid in Pekalongan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rising sea surface height is one of the world's vital issues in marine ecosystems because it greatly affects the ecosystems as well as the socio-economic life of the surrounding environment. Pekalongan is one area in Indonesia facing the effects of this phenomenon. This problem deserves to be explored further with complex approaches. One of them is a neural network to perform forecasting more accurately. In neural networks, the time series approach can be used with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). By adding the bidirectional method to each of these two approaches, we will find the best method to use to perform the analysis. The best results were obtained by forecasting for 960 days using Vanilla BiGRU. The results can be interpreted from multiple perspectives. The forecasting results showed a fluctuating pattern as in previous periods, so it can be said that the pattern is still quite normal, which indicates that the terminal can continue to operate normally. However, the forecasting results from this study are expected to be a reference for information for the government to prevent future dangers.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.001 |
| 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 it