New developments in the determination of the complex refractive index of arbitrary absorptance thin films from envelope profiles of a single transmittance curve
Bibliographic record
Abstract
Recently, a simple and self-consistent formalism that accurately gives the complex refractive index η = n - iκ of arbitrary absorptance thin films from a single transmittance curve has been introduced. Without any approximation, this analysis method makes use of a “corrected transmittance curve” for which the transmittance maxima values reach 1. With actual values of n(λ) and κ(λ), this last condition must be fulfilled. When these dispersion curves are not known, the method remains valid, but one must rely on “initial approximate dispersion curves” obtained by any mean, including theoretical formulations. In addition, this method shows that when the envelope profiles of transmittance curves are known, the need to determine initial approximate dispersion curves is not required. The challenge lies in finding the actual envelope profiles. Here, we show new developments on procedures to extract the envelope profiles. In case of weak absorption, using cubic spline interpolations, this can be done with little to no error, except for experimental and computational ones. In case of strong absorption bands, the slope in the transmittance curve shifts the extrema, which no longer correspond to the tangent points with their respective envelopes. This is remedied by applying a “rectifying process” that gives a “partially corrected transmittance curve”, which then leads to a fully corrected curve. However, in case of strong and narrow absorption bands, the small number of transmittance fringes might reduce the accuracy. Then, the reflectance curve appears beneficial to circumvent this weakness.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| 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".