TRACK – A new method for the evaluation of low-level extinction coefficient in optical films
Bibliographic record
Abstract
We develop a rigorous methodology named TRACK based on the collection of multi-angle spectrophotometric transmission and reflection data in order to assess the extinction coefficient of quasi-transparent optical films. The accuracy of extinction coefficient values obtained by this method is not affected by sample non-idealities (thickness non-uniformity, refractive index inhomogeneities, anisotropy, interfaces, etc.) and therefore a simple two-layer (substrate/film) optical model can be used. The method requires the acquisition of transmission and reflection data at two angles of incidence: 10° and 65° in p polarization. Data acquired at 10° provide information about the film thickness and the refractive index, while data collected at 65° are used for absorption evaluation and extinction coefficient computation. We test this method on three types of samples: (i) a CR-39 plastic substrate coated with a thick protective coating; (ii) the same substrate coated with a thin TiO(2) film; (iii) and a thick Si(3)N(4) film deposited on Gorilla glass that presents thickness non-uniformity and refractive index gradient non-idealities. We also compare absorption and extinction coefficient values obtained at 410 and 550 nm by both TRACK and Laser Induced Deflection techniques in the case of a 1 micron thick TiO(2) coating. Both methods display consistent extinction coefficient values in the 10(-4) and 10(-5) ranges at 410 and 550 nm, respectively, which proves the validity of the methodology and provides an estimate of its accuracy limit.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".