{"id":"W2089192721","doi":"10.1364/ao.45.005636","title":"Fourier transform approach for thickness estimation of reflecting interference filters","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; National Research Council Canada","funders":"","keywords":"Optics; Fourier transform; Interference (communication); Fractional Fourier transform; Dielectric; Bandwidth (computing); Spatial filter; Spatial frequency; Fast Fourier transform; Materials science; Optical filter; Discrete Fourier transform (general); Computer science; Fourier analysis; Algorithm; Telecommunications; Physics; 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":[0.0004690676,0.0001123563,0.0001642732,0.00005424395,0.00009147915,0.00004723251,0.0002472661,0.00005249885,0.00002185743],"category_scores_gemma":[0.00007142262,0.0001026835,0.00003413535,0.000132474,0.0001009196,0.0001619338,0.00004834769,0.00007662325,0.000005982788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006130393,"about_ca_system_score_gemma":0.00004808466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001677006,"about_ca_topic_score_gemma":0.000004051628,"domain_scores_codex":[0.9988192,0.0000119454,0.0003085279,0.000232875,0.0003084183,0.0003190176],"domain_scores_gemma":[0.9994107,0.0001276376,0.0001115398,0.0002106281,0.00009693805,0.00004249237],"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":[0.000212139,0.0001079547,0.00000515268,0.0002710905,0.000006472629,3.141259e-7,0.0002435577,0.02818153,0.9335486,0.02906848,0.00007674031,0.008277949],"study_design_scores_gemma":[0.0006797395,0.0001466531,0.00001200182,0.00002494397,0.00001600823,0.000001840534,0.000229289,0.09629355,0.8840573,0.01826952,0.0001042876,0.0001648884],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1372085,0.000009024114,0.8461993,0.00003073995,0.00005639822,0.0006038085,0.00002507013,0.00003837667,0.01582884],"genre_scores_gemma":[0.641797,0.00000137915,0.3578749,0.00001257075,0.00002820751,0.0001216454,0.00003029521,0.00001226019,0.0001217283],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5045885,"threshold_uncertainty_score":0.4187312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04005787468468758,"score_gpt":0.3254602672592316,"score_spread":0.285402392574544,"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."}}