The relation between spectral reflectance and dissolved organic carbon in lake water: Kejimkujik National Park, Nova Scotia, Canada
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Bibliographic record
Abstract
The ability to predict dissolved organic carbon (DOC) concentrations based on spectral reflectance of lake water was examined in Kejimkujik National Park. Spectral reflectance from both ground and satellite remote sensing platforms were used to create regression models for the prediction of DOC with r 2 values of 0.94 and 0.72 respectively. The location of the peak wavelength of the ground spectral measurements and a cluster analysis of the satellite measurements both separated the lakes into two distinct groups with different DOC concentrations. An analysis of the potential sources of DOC identified three variables important for the prediction of DOC concentrations within the lake, flushing rate and the area of both deciduous forest and open area within the watershed ( r 2 = 0.41). As DOC concentrations are related to mercury concentrations ( r 2 = 0.86) these models could be used to assist in the identification of lakes that are sensitive to mercury pollution.
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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 | 0.000 |
| 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 it