10.1016/s0967-0653(97)85475-0
Bibliographic record
Abstract
Analysis of digital imagery from the 100 m resolution airborne Multispectral Atmospheric Mapping Sensor (MAMS) indicates that scatter plots of remotely sensed sea surface temperature versus visible and near infrared subsurface reflectance can be used to quantitatively distinguish coastal water types. The Louisiana Gulf coast, a complex region of deltas, estuaries, and marshy wetlands, is the setting for this work. Gulf inner shelf and Mississippi River waters are the primary source water types, but four additional water types are formed locally and are readily detectable with MAMS including: shallow ambient bay water, fresh marsh water drainage, salt marsh drainage, and soil water drainage. In situ measurements of suspended sediment concentration and sea surface temperature in the Atchafalaya and adjacent bays have verified the characteristics of these water types. It is found that under the forcing of atmospheric cold front passages, water type differentiation is enhanced, creating a mosaic of water types in these coastal waters. Multispectral remote sensing of these complex, variably turbid coastal waters, provides data useful for studies of water type formation and coastal circulation processes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.992 | 0.993 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".