Optical oceanography: Recent advances and future directions using global remote sensing and in situ observations
Bibliographic record
Abstract
The present review describes progress in addressing and solving several fundamental and applied problems involving optical oceanography. These problems include: primary productivity, ecosystem dynamics, biogeochemical cycling, upper ocean heating, and the impacts of anthropogenic disturbances on ocean dynamics. Technological advances in optical sensors and ocean observing platforms are being used to increase the variety and quantity of optical observations and to greatly expand their sampling capabilities in time and space. Remote sensing of ocean color from aircraft‐ and satellite‐borne instruments is vital to obtain regional‐ and global‐scale optical data synoptically. In situ observations provide complementary subsurface data sets with high temporal and spatial resolution. In situ observations are also essential for calibration and validation of remotely sensed data as well as for algorithm development and data assimilation models. Important challenges remain to synthesize regional and global optical data sets obtained from optical sensors and oceanographic platforms and to utilize these data sets in predictive models of oceanic optical, physical, and biogeochemical dynamics.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".