Reflectance properties of grey-scale Spectralon® as a function of viewing angle, wavelength, and polarization
Bibliographic record
Abstract
The remote-sensing community uses grey-scale Spectralon to evaluate the performance of instruments designed to observe geologic surfaces with a range of reflectance values. This article presents the normalized biconical reflectance factor of a series of grey-scale Spectralon targets taken at viewing angles ranging from 10° to 80°. The darkest Spectralon standard, when illuminated at nadir incidence with s-polarized 1064 nm light, first undergoes a decrease in reflectance factor with increasing viewing angle, then encounters a minimum, after which the reflectance factor increases with increasing viewing angle. When progressively brighter Spectralon samples are measured, the reflectance factor dip at lower emergence angles becomes less pronounced and the slope at higher emergence angles is more gradual until, for the 20% Spectralon sample, the curve dips downward at the highest emergence angles. Measuring the grey-scale Spectralon set of targets using p-polarized incident light, the same trends described above are seen, except for the darkest Spectralon target, which monotonically decreases in reflectance factor. The data show a total decrease of 44%. The reflectance factor curves observed at 852 nm are similar to those seen at 1064 nm in that the same number of inflection points and the same sign of slope is seen at both wavelengths. However, comparing two different incidence angles using 1064 nm s polarized incident light, the slope of the reflectance factor curve does change significantly. For the brightest Spectralon target, the slope is positive for = 60° and negative for = 0° at all viewing angles measured. For the darkest Spectralon target, the slope is larger for = 60° than for = 0° at all viewing angles measured. At = 60°, the data show a total increase of 1360% in reflectance factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".