Global vs Local Random Forest Model for Water Quality Monitoring: Assessment in Finger Lakes Using Sentinel-2 Imagery and Gloria Dataset
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Machine learning (ML) methods such as Random Forest (RF) have shown promises to estimate Secchi Disk Depth (Zsd). However, lack of a comprehensive dataset has been a long-lasting issue for training ML models in remote sensing of water quality. To aid the training process, the GLORIA dataset has recently provided access to hyperspectral in-situ measurements of remote sensing reflectance (Rrs) along with associated water quality parameters for globally representative inland and coastal waters. We use simulated Sentinel-2 Rrs to train a global model using GLORIA and then validate it on independent data from Finger Lakes, USA. When compared to RF model trained on Finger Lakes data, the validation results indicate better performance (Mean Absolute Error (MAE) 37%) as compared to the global model trained on GLORIA (MAE 94%). However, when the global model was validated on independent dataset from GLORIA (i.e. Lake Erie), the results were promising (MAE 34%). Therefore, the models can be used to estimate Zsd globally, provided the uncertainties in deriving satellite based Rrs are accounted for.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 it