Relation between inherent optical properties and land use and land cover across <scp>G</scp>ulf <scp>C</scp>oast estuaries
Bibliographic record
Abstract
Abstract Land use and land cover (LULC) can affect the watershed exports of optically active constituents such as suspended particulate matter and colored dissolved organic matter, and in turn affect estuarine optical properties. We collected optical data from six estuaries in the northeastern Gulf of Mexico with different watershed LULC characteristics and investigated how estuarine optical properties varied across these systems. Differences in LULC corresponded with significant differences in the estuarine inherent optical properties and specific inherent optical properties (SIOPs), which are known surrogates for phytoplankton cell size, organic particle concentration, and the amount of terrigenous dissolved organic carbon. The results indicated that increasing proportions of developed land use (urban + agriculture) in the watersheds resulted in a linear increase in light attenuation in the estuaries primarily through increased absorption by phytoplankton. Estuarine SIOPs were also linearly related to the proportion of developed land. These findings were used to demonstrate how improved knowledge of the factors regulating estuarine SIOPs may be used to increase the accuracy of semianalytical ocean color remote sensing algorithms in optically complex estuaries.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".