An Example Crossover Experiment for Testing New Vicarious Calibration Techniques for Satellite Ocean Color Radiometry
Bibliographic record
Abstract
Abstract Vicarious calibration of ocean color satellites involves the use of accurate surface measurements of water-leaving radiance to update and improve the system calibration of ocean color satellite sensors. An experiment was performed to compare a free-fall technique with the established Marine Optical Buoy (MOBY) measurement. It was found in the laboratory that the radiance and irradiance instruments compared well within their estimated uncertainties for various spectral sources. The spectrally averaged differences between the National Institute of Standards and Technology (NIST) values for the sources and the instruments were <2.5% for the radiance sensors and <1.5% for the irradiance sensors. In the field, the sensors measuring the above-surface downwelling irradiance performed nearly as well as they had in the laboratory, with an average difference of <2%. While the water-leaving radiance Lw calculated from each instrument agreed in almost all cases within the combined instrument uncertainties (approximately 7%), there was a relative bias between the two instrument classes/techniques that varied spectrally. The spectrally averaged (400–600 nm) difference between the two instrument classes/techniques was 3.1%. However, the spectral variation resulted in the free-fall instruments being 0.2% lower at 450 nm and 5.9% higher at 550 nm. Based on the analysis of one matchup, the bias in Lw was similar to that observed for Lu(1 m) with both systems, indicating the difference did not come from propagating Lu(1 m) to Lw.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".