Shallow water depth (≤5 m) estimation based on single-beam echo sounder and optical satellites
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it