Using <scp>L</scp>andsat 8 data to estimate suspended particulate matter in the <scp>Y</scp>ellow <scp>R</scp>iver estuary
Bibliographic record
Abstract
Abstract The distribution of suspended particulate matter (SPM) and its variations in estuary regions are key to promoting carbon, oxygen, and nutrient cycling in coastal regions and nearby seas. This study presents SPM estimations for the Yellow River estuary from Landsat 8 Operational Land Imager (L8/OLI) data from 2013 to 2016. L8/OLI‐measured remote sensing reflectance (Rrs) was cross‐validated with Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, and SPM concentrations calculated from the tuned retrieval model were validated with in situ observations. The validation shows that L8/OLI can provide reasonably Rrs, which can be used to quantify SPM distributions and variations in the Yellow River estuary. Three year averaged SPM maps show that highly turbid waters are mostly found in an ovate area surrounding the mouth of the Yellow River. The corresponding area proportion is less than 30%, with SPM concentrations greater than 100 g m−3. High variations of SPM distributions are consistent with high SPM concentrations, and vice versa. Significant difference is observed between dry and wet seasons. Higher SPM in the dry season is observed both in range and intensity compared to those of the wet season. Furthermore, multiyear averaged SPM distributions with high concentrations are mainly attributable to currents. Significant seasonal variations are mainly controlled by sediment resuspension processes driven by wind‐wave forces. Due to human interventions, seasonal variability in river runoff and sediment discharge from the Yellow River has decreased in recent years. Accordingly, seasonal variability in SPM distributions in the Yellow River estuary due to sediment discharge has decreased.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".