Uncertainty budget for Sea-Bird Scientific radiometers following cross-site calibration
Bibliographic record
Abstract
In-situ and above water radiometers are a critical for validating Ocean Color Satellite measurements, used to monitor in-water constituents of the global ocean. The calibration process, instrument response characterization, and environmental measurement all contribute to the overall uncertainty budget of the radiometric measurement. An integral part of this uncertainty traceability chain is accurate laboratory calibration of radiometric sensors. Over its lifetime, the Sea-Bird Scientific Halifax site (formerly Satlantic, LP) participated in inter-laboratory comparisons to ensure the quality of its calibrations. These include: NASA’s Seventh SeaWIFS Intercalibration Round-Robin Experiment (SIRREX-7, Hooker et al. 2002), conducted in 1999, compared Halifax to the Center for Hydro-Optics and Remote Sensing (CHORS, San Diego State University, California, USA) and the Joint Research Centre (JRC, Ispra, Italy). More recently, Sea-Bird Scientific participated in the European Space Agency (ESA) sponsored Fiducial Reference Measurements for Satellite Ocean Colour (FRM4SOC) program. In 2017, Sea-Bird Scientific transitioned the manufacturing and calibration of radiometric products from the facility located in Halifax (HAL), Nova Scotia CA to the facility located in Philomath (PHI), Oregon USA (formerly WET Labs, Inc.). As part of this transition, the radiometer calibration facility was reproduced at the Philomath site and Sea-Bird Scientific conducted an extensive cross facility set of experiments to: 1. Quantify relative calibration uncertainties within and between Halifax and Philomath laboratories; 2. Quantify differences in repeatability relative to Halifax (established standard); 3. Compare relative laboratory calibration uncertainties to budget of estimated uncertainty sources; 4. Verify successful transfer of build and calibration processes at Philomath site.
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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 | 0.024 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".