Correction of the calibration measurement by taking into account the Spectralon spectro-polarimetric BRDF model
Bibliographic record
Abstract
The Spectralon is one of the best materials for the calibration of spectral measurements. Normally, the Spectralon must be illuminated and measured at right angle, but, this is not always possible, particularly for outdoor measurements where we cannot control the Sun position. A Spectralon plate has a BRDF (Bidirectional Reflectance Distribution Function), which is not completely flat as a perfect Lambertian surface and this affects the calibration. Furthermore, when illuminated or observed at angles, the Spectralon polarizes the light and it can also create reddening effects in some conditions. Moreover, several sensors (most of the spectrometers) are sensitive to the polarization and are prone to create reading artefacts. First, this paper presents a calibration procedure adapted to these situations; it takes into account the polarization and the Spectralon BRDF. Second, this paper presents also a Spectralon BRDF model that is required to calculate its reflectance at the encountered angular conditions. This BRDF model is an algorithm framework (based on curve fitting methods) that was deduced from the analysis of thousands Spectralon measurements done with various illumination, viewing and polarization angles. The model is decomposed into several sub-models for the various encountered scattering types (backscattering and deep, forward and sub-surface scatterings), plus models for the polarization contrast and reddening.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".