{"id":"W1969258414","doi":"10.1364/ao.46.000076","title":"Fourier transform approach for thickness estimation of reflecting interference filters&lt;br/&gt; 2 Generalized theory","year":2006,"lang":"en","type":"article","venue":"Applied Optics","topic":"Liquid Crystal Research Advancements","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Microstructural Sciences","funders":"","keywords":"Optics; Fourier transform; Bandwidth (computing); Interference (communication); Interference filter; Optical filter; Dielectric; Filter (signal processing); Fast Fourier transform; Discrete Fourier transform (general); Spectral density estimation; Spatial filter; Point (geometry); Computer science; Fractional Fourier transform; Algorithm; Materials science; Physics; Mathematics; Fourier analysis; Wavelength; Telecommunications; Mathematical analysis; Geometry; Optoelectronics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007077273,0.0006914957,0.000527249,0.001001721,0.0002091831,0.0006974622,0.0008624377,0.000827344,0.001849103],"category_scores_gemma":[0.001229242,0.0003512422,0.0007842859,0.0006092045,0.0005791076,0.001045104,0.0006132433,0.001032933,0.0006880685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006153838,"about_ca_system_score_gemma":0.000576345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001054033,"about_ca_topic_score_gemma":0.0006192733,"domain_scores_codex":[0.9996828,0.00009093016,0.00001358612,0.00004308024,0.0001445082,0.00002506326],"domain_scores_gemma":[0.9996626,0.0001418876,0.00002593029,0.00003996383,0.0001177467,0.00001182424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000125351,0.00005994404,0.0005573512,0.0004243071,0.0001043862,0.0003759223,0.0001689637,0.4014736,0.1142495,0.3226092,0.002907926,0.1569434],"study_design_scores_gemma":[0.000004611193,0.0000241538,0.0001620752,0.00001137248,0.00001108685,0.0001118898,0.00001075897,0.9745753,0.006580459,0.01673992,0.001753491,0.00001493724],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002272438,0.0001719612,0.9967685,0.00002519177,0.00001285382,0.000007007307,0.00001705253,0.0000501814,0.0006748615],"genre_scores_gemma":[0.1818474,0.001815123,0.8090272,0.0001287311,0.0001638127,0.0001606379,0.0002791608,0.0001760967,0.006401805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001849103,"threshold_uncertainty_score":0.006185889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03567840405586812,"score_gpt":0.3176256883556539,"score_spread":0.2819472842997858,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}