Results of the Second SeaWiFS Data Analysis Round Robin, March 2000 (DARR-00)
Bibliographic record
Abstract
The accurate determination of upper ocean apparent optical properties (AOPs) is essential for the vicarious calibration of the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument and the validation of the derived data products. To evaluate the importance of data analysis methods upon derived AOP values, the Second Data Analysis Round Robin (DARR-00) activity was planned during the latter half of 1999 and executed during March 2000. The focus of the study was the intercomparison of several standard AOP parameters: (1) the upwelled radiance immediately below the sea surface, L(sub u)(0(-),lambda); (2) the downward irradiance immediately below the sea surface, E(sub d)(0(-),lambda); (3) the diffuse attenuation coefficients from the upwelling radiance and the downward irradiance profiles, L(sub L)(lambda) and K(sub d)(lambda), respectively; (4) the incident solar irradiance immediately above the sea surface, E(sub d)(0(+),lambda); (5) the remote sensing reflectance, R(sub rs)(lambda); (6) the normalized water-leaving radiance, [L(sub W)(lambda)](sub N); (7) the upward irradiance immediately below the sea surface, E(sub u)(0(-)), which is used with the upwelled radiance to derive the nadir Q-factor immediately below the sea surface, Q(sub n)(0(-),lambda); and (8) ancillary parameters like the solar zenith angle, theta, and the total chlorophyll concentration, C(sub Ta), derived from the optical data through statistical algorithms. In the results reported here, different methodologies from three research groups were applied to an identical set of 40 multispectral casts in order to evaluate the degree to which differences in data analysis methods influence AOP estimation, and whether any general improvements can be made. The overall results of DARR-00 are presented in Chapter 1 and the individual methods used by the three groups and their data processors are presented in Chapters 2-4.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".