Correlation of Remotely Sensed Surface Reflectance With Forcing Variables in Six Different Estuaries
Bibliographic record
Abstract
Abstract This study examines the links between estuarine dynamics and longitudinal distributions of remotely sensed reflectance in estuaries. Reflectance at 655 nm from Landsat‐8 correlates with in situ measurements of surface turbidity. Images collected from 2013 to 2018 are used to investigate the spatial and temporal characteristics of reflectance distribution in six selected estuaries with different dynamics. The results show that the maximum magnitude (C ) and location (X ) of the reflectance are functions of two major forcing variables, the freshwater discharge and tidal stage. Salt wedge estuaries (i.e., the Fraser River, Connecticut River, and Columbia River estuary) are affected strongly by river discharge and are relatively less affected by tidal forcing. In salt wedge estuaries, the C values along the river channel are correlated with river discharge, while the X values generally are not. In partially mixed to strain‐induced periodic stratification (SIPS) estuaries (i.e., Delaware River) and SIPS‐to‐well‐mixed estuaries (i.e., Gironde estuary), the X values are correlated with river discharge, but the C values are not. SIPS‐to‐well‐mixed estuaries (i.e., Gironde estuary) and highly time‐dependent salt wedge to well‐mixed estuaries (i.e., Merrimack River estuary) are affected by tidal forcing. In these estuaries, the C values are correlated with tidal stage. The C values tend to be lower around high tide than low tide. Overall, the results demonstrate that remote sensing observations of ocean color can be utilized to infer subsurface estuarine processes. Satellite ocean color is a potential tool to monitor river discharge and to classify estuaries.
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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 | 0.001 |
| 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 | 0.000 |
| 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".