Accurate computation of the Briot–Sellmeier and Briot–Cauchy chromatic dispersion coefficients from the transmittance spectrum of thin films of arbitrary absorptance
Bibliographic record
Abstract
An exact formalism devoted to the determination of dispersion coefficients is described. The method takes into account two frequent experimental configurations: a solid thin layer on a substrate and a fluid, or solid, layer between a substrate and a superstrate. Introducing the concepts of reduction and reduced finesse, this method is based entirely on the fringes' spectral position of the maxima in the transmittance spectrum. It is found that the chromatic dispersion does not affect the spectral position of the minima in the same way as it does for the maxima. There is no need to get the refractive-index curve, n(lambda), to determine the dispersion coefficients nor to work at multiple incidence angles. Bringing together the possible nonrestrictive approximations, the method becomes easy and simple to implement from a spectrophotometer in tandem with a computer. In addition, the spectrometer does not require ordinate-axis calibration, and knowledge of the substrate's and superstrate's refractive index is not required. Alternatively, the method can be easily used to accurately determine the thickness of thin layers. A numerical example using a thin layer of 2-methyl-4-nitroaniline (MNA) is given.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".