A Video Dataset for Nearshore Wave Breaking Type Classification
Bibliographic record
Abstract
Wave breaking type is a fundamental indicator of nearshore hydrodynamic processes, directly reflecting wave energy dissipation mechanisms. With the advancement of shore-based video monitoring, remote sensing has emerged as an efficient tool for identifying wave breaking types. However, existing studies predominantly rely on static single-frame imagery, limiting the ability to capture the dynamic evolution of breaking events. In this work, we present the first publicly available video dataset dedicated to wave breaking type classification. The dataset comprises 9,000 labeled wave breaking clips collected from 15 cameras across six morphologically diverse coastal sites, encompassing three primary breaking types: Spilling, Plunging, and Surging. To enhance the dataset's quality and consistency, a rigorous data curation workflow was implemented, including video segmentation, cropping, labeling, and frame extraction. Classification experiments using a well-established deep learning architecture combing CNN and RNN achieved state-of-the-art performance.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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".