Enhancing Few-Shot Prediction of Ocean Sound Speed Profiles through Hierarchical Long Short-Term Memory Transfer Learning
Bibliographic record
Abstract
The distribution of ocean sound speed profiles (SSPs) profoundly influences the design of underwater acoustic communication and positioning systems. Conventional methods for measuring sound speed by instruments entail high time costs, while sound speed inversion methods offer rapid estimation of SSPs. However, these methods heavily rely on sonar observational data and lack the capacity to swiftly estimate SSPs in arbitrary oceanic regions, particularly in scenarios with few-shot data. Precisely estimating non-cooperative maritime SSPs under such conditions poses a significant challenge. To explore temporal distribution patterns of sound speed and achieve precise SSP predictions with limited data, we propose a hierarchical long short-term memory transfer learning (H-LSTM-TL) framework. The core idea involves pre-training the base model on extensive public datasets, transferring the acquired knowledge to task models, and fine-tuning the task model on few-shot data to predict future SSPs. Through H-LSTM-TL, it accelerates model convergence, enhances sensitivity to few-shot input data, alleviates overfitting issues, and notably improves the accuracy of SSP predictions. Experimental results demonstrate that the H-LSTM-TL model exhibits strong generalization capabilities in few-shot data scenarios, effectively reducing overfitting problems and proving its applicability for rapid prediction of SSPs.
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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 | 0.000 |
| 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) | 0.001 | 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".