{"id":"W1969258414","doi":"10.1364/ao.46.000076","title":"Fourier transform approach for thickness estimation of reflecting interference filters<br/> 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001105237,0.0001789346,0.0002632081,0.00008070398,0.0001435088,0.00007339411,0.0003702698,0.00008457155,0.00005266276],"category_scores_gemma":[0.0001270834,0.0001589512,0.00006001271,0.0001722635,0.0001613186,0.0002054446,0.00008057668,0.0001032374,0.000007702985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008375027,"about_ca_system_score_gemma":0.00007119749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001059847,"about_ca_topic_score_gemma":0.000004851877,"domain_scores_codex":[0.9982938,0.00004748696,0.0004480624,0.0003395653,0.0004219264,0.000449222],"domain_scores_gemma":[0.9990497,0.0002409293,0.0001744078,0.0003268531,0.0001445942,0.0000635406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000461475,0.0001065526,0.000001691584,0.0001929866,0.00001116377,4.091306e-7,0.0002775968,0.01623334,0.8410838,0.1368462,0.00008751415,0.004697251],"study_design_scores_gemma":[0.001324026,0.0001699294,0.00000669983,0.00003083635,0.00003402053,0.000003056301,0.0001806695,0.0775755,0.8355476,0.08465775,0.0002249438,0.0002450246],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.150566,0.00003068191,0.8317418,0.00002776541,0.00008168277,0.0008321272,0.00004237772,0.00005661507,0.01662095],"genre_scores_gemma":[0.6199238,0.000004345704,0.3793141,0.00002450334,0.00005316568,0.0002161036,0.00006006044,0.00002220805,0.0003817689],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4693578,"threshold_uncertainty_score":0.6481842,"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."}}