Bibliographic record
Abstract
Abstract This article reviews the performance of optical filters for the 0.005 to 1000 µm spectral region. The filters described operate in transmitted or reflected light and are intended for use in free‐space optics. The theory of two or more filters placed in series or in parallel is presented. The effect of surface reflections on the performance of the filters is discussed. Optical filters can be based on many different physical principles. Brief explanations are given of the modes of operation of filters based on absorption, reflection, interference in thin films, holography, scattering, diffraction, and interference of polarized light. The principal advantages and disadvantages of filters based on these phenomena are discussed. Filters can also be classified according to the functions that they are intended to perform. In this article the following generic filter types are considered: antireflection coatings, neutral attenuators, narrow band, medium‐band and wideband reflectors, short‐ and long‐wavelength cutoff filters, narrowband transmission filters, rejection filters, neutral‐ and color‐selective beam splitters and correction or gain flattening filters. For each of the above filter types, spectral transmittance or reflectance curves are presented that correspond to representative filters based on a number of the above‐mentioned physical principles. Information is provided on how to specify the performance of the various filter types.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.033 |
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".