SPECTRUM OF THE LAKES: USING SATELLITE REMOTE SENSING TO UNVEIL WATER COLOR IN MINNESOTA'S SENTINEL LAKES FOR WATER QUALITY MONITORING
Bibliographic record
Abstract
Traditional water quality monitoring methods often face limitations due to equipment costs and labor demands. To effectively assess the ecological health of inland lakes across vast areas, innovative approaches are needed. This research explores the application of free, publicly available water color chromaticity analysis for midcontinent lakes in Minnesota, USA. We analyzed water color variations in Minnesota's Sentinel Lakes. Using Landsat 8 OLI data, we analyzed surface reflectance samples collected from the deepest area within each lake during the late summer, corresponding to peak annual insolation and trophic activity. The median dominant visible wavelength was used to characterize water color. Results indicate a prevalence of green-yellow hues (∼575 nm), indicating the presence of photosynthetic activity and suspended solids. Regional variations were also observed across Minnesota. Red colors were common in the northeast and south, while blue colors were scarce. Statistical analysis revealed color was not unique to any ecoregions however, sentinel lakes within an ecoregion were proven to have the same color. In the Canadian Shield ecoregion, annual water color variations were attributed to forested catchments and undisturbed hydrology. Using mDVW (median dominant visible wavelength) to observe decadal patterns of water color can serve as a baseline for identifying anomalies and guide resourceful investigations.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".