Sea surface multispectral index model for estimating chlorophyll<i>a</i>concentration of productive coastal waters in Thailand
Bibliographic record
Abstract
Chlorophyll a (Chl a) concentration in water can be estimated using remote sensing methodology. This study uses Chl a high absorption and high reflectance wavelengths to produce indices stable for Chl a monitoring. The relationships between the indices and Chl a are determined and then applied to satellite bands for model development. The sea surface spectrum was measured in situ using a portable spectrometer, and Chl a concentration was analyzed in the laboratory. Satellite images were obtained from Landsat-5 for the day of sampling. The results of a three-band index from the spectrometer composed of wavelengths 435, 488, and 692 nm indicate the most significant correlation with Chl a concentration. The exponential relation was observed with the highest correlation coefficient of r2 = 0.83. The index model was applied to Landsat-5 data, and the observed Chl a dataset was compared with that estimated from Landsat-5. This analysis gave a high correlation of r2 = 0.73.
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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) | 0.000 | 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".