A dynamic sediment model based on satellite‐measured concentration of the surface suspended matter in the East China Sea
Bibliographic record
Abstract
Abstract The concentration of total suspended matter (TSM) at the sea surface is derived from satellite data using a complex proxy TSM model in East China Sea from 1997 to 2008. The structure of the mean TSM image is similar to that of the topography, indicating that the distribution of the surface concentration is strongly related to the water depth. A dynamic sediment model (DSM) is constructed to relate the TSM concentration at the sea surface with suspended sediment at the benthic boundary layer, the Rouse number, and the water depth. The DSM model is improved through iteration with a convergence identified by the mean relative difference between two adjacent bottom TSM images which becomes smaller with the more iterations and the value is less than 1% after 50 iterations. The performance of the DSM model is validated by satellite‐measured concentration with a mean relative error of 5.2% for the monthly mean images. The DSM model is used to deduce the bottom TSM concentration at the benthic boundary layer and the distribution of the Rouse number. The spatial distribution of the sea surface TSM concentration is determined predominately by both the bottom suspended sediment concentration and water depth. The temporal variation of the sea surface concentration mainly depends upon the Rouse number in the water column. Our result shows that the discharge of the Changjiang River can change the distribution of the Rouse number to form a band‐shaped region in the Changjiang Estuary. The DSM model provides a framework for understanding some of the mechanisms of the formation and variation of the primary TSM plume and the secondary plume in the ECS. The primary TSM plume corresponds approximately to the region with depth shallower than 20 m and the secondary plume corresponds to the region with depths between 20 and 50 m.
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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 | 0.000 |
| 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 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".