A Machine Learning-Based Regional Hybrid Model for Remote Retrieving Turbidity From Landsat Imagery
Bibliographic record
Abstract
Turbidity [nephelometric turbidity unit (NTU)] monitoring is of great interest to water quality stakeholders. Traditional monitoring programs are limited in time and space, are expensive, and do not reflect the true extent of NTU. In contrast, remote sensing data are able to model the NTU, to monitor its spatial expansion, and are cost-effective. Models developed are usually a single-based function. This study presents a simple machine learning-based Regional hybrid model (R-HM) for NTU retrieval. The R-HM allows prior recognition of the NTU level concentration (high or low) before estimation. The calibration step highlighted that low and high NTUs are sensitive to different spectral regions, but mainly controlled by the red part. Validation was satisfactory with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2} = 0.99$ </tex-math></inline-formula> , although high NTUs tend to be underestimated (BIAS = −14%). Landsat (LS) NTU products derived from R-HM were found to be only sensitive to turbidity, even under conditions of high algal blooms.
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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 | 0.001 |
| 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".