Shallow water depth (≤5 m) estimation based on single-beam echo sounder and optical satellites
Bibliographic record
Abstract
This study combined single-beam echo sounder data with PlanetScope multispectral data to invert shallow water depth (≤5 m) around Weizhou Island, analyzing how each band's reflectance varies with depth by establishing their quantitative relationship and building nine statistical regression and machine learning models. In the inversion of water depths less than 5 m, the correlation R2 between the blue and green bands and water depth was less than 0.1, while the R2 between the red-edge band and water depth was 0.73. In addition, after classification of the sediment type, water depth inversion improved the correlation between the water depth and reflectance. The random forest (RF) and support vector regression (SVR) models demonstrated the highest accuracy in terms of water depth inversion, with R2 = 0.87, RMSE = 0.28 m and MAE = 0.18 m.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".