First approach for on-ground radiometric characterization of the new infrared sensor technology camera
Bibliographic record
Abstract
The aim is to present a first approach for the on-ground radiometric characterization of the new infrared sensor technology (NIRST) instrument. NIRST is an infrared radiometer on board the SAC-D/Aquarius mission, launched on June 10, 2011. It is composed of a middle-wave infrared and a long-wave infrared camera, with three arrays of 512 microbolometers each, and has also a pointing Be mirror. In order to perform the on-ground radiometric characterization, several measurements are taken using blackbody sources. Aiming to obtain a set of absolute radiometric coefficients for each pixel of each microbolometer array, relating digital numbers and brightness temperature or its equivalent in radiated power, polynomial fits are performed. Interpixel characterization to obtain relative calibration coefficients is also performed, relating the counts of an arbitrary pixel to those of a reference pixel. The choice of polynomial order for both absolute and relative calibration functions, as well as the election of reference pixels, are analyzed. Finally, a pointing angle characterization is performed. This approach leads to high polynomial orders for both absolute and relative calibrations, indicating that a new approach for NIRST radiometric characterization is required to catch-up the nonlinearity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".