Bibliographic record
Abstract
Accurate tide prediction is required for safe marine navigation in shallow waters as well as for other marine operations. Traditionally, tide prediction was carried out using the harmonic method, which is based on the identification of the harmonic tidal constituents existing in the tidal record. Unfortunately, however, unless long tidal records are available at the tide gauges, some important tidal constituents may not be identified. This, in turn, deteriorates the accuracy of the tidal prediction. More recently, a sequential least-squares prediction method capable of using relatively short tidal records was developed. This method allows for the modifications and corrections of the original solution of the tidal constituents when new observations and parameters are included. Although it reduces the computation time significantly compared to the batch harmonic method, this method exhibits large residuals particularly when very short tidal records are used. To overcome the limitations of the sequential least-squares method, a neural network-based model is developed for sequentially predicting the tidal heights using tide data series collected at various tide gauges. A modular, three-layer feedforward neural network trained using the back-propagation algorithm is used for this purpose. Tide data from three tide gauges are used to validate the model. A comparison is made between the developed neural network model and the sequential least squares method for tidal prediction. It is shown that the accuracy level of the tidal prediction has improved by a factor of 5 when using the neural network model.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".