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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".