Mapping of the North Sea turbid coastal waters using SeaWiFS data
Bibliographic record
Abstract
The spatial–temporal coverage provided by optical remote sensing can be effectively used to overcome some of the severe deficiencies in the current in situ monitoring programs for water-quality parameters in the coastal zone. We present the outcome of a project to map the concentrations fields of total suspended matter in the North Sea, based on data from the sea-viewing wide field-of-view sensor (SeaWiFS) instrument. Next to a good infrastructure and standard automatic processing, a reliable atmospheric correction proved to be essential. A single-band algorithm, based on a representative set of inherent optical properties, is presented. The satellite data and data from a standard in situ monitoring program near the Dutch coast are compared. For the year 2000, a total of 129 images could be used to present actual information and to derive monthly mean patterns and trends in the dynamic North Sea system. The results show the capacity of satellite data to provide excellent temporal coverage. Spatial variations of suspended sediment, often caused by input from rivers plus wind- and wave-induced resuspension over shallow areas, are covered.
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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 | 0.002 | 0.001 |
| 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) | 0.001 | 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".