{"id":"W2246771206","doi":"10.1364/bgpp.2007.jwa31","title":"Numerical optimization of passband fiber Bragg gratings","year":2007,"lang":"en","type":"article","venue":"Bragg Gratings, Photosensitivity, and Poling in Glass Waveguides","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Fiber Bragg grating; Passband; Materials science; Optics; PHOSFOS; Dispersion (optics); Grating; Encoding (memory); Fabrication; Computer science; Graded-index fiber; Optical fiber; Optoelectronics; Physics; Fiber optic sensor; Band-pass filter","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007674035,0.000389708,0.0005552149,0.00033128,0.0001125575,0.00005701957,0.0001009503,0.0002310496,0.0000188201],"category_scores_gemma":[0.000536884,0.0004134092,0.00009134882,0.0005420865,0.0001725238,0.0002774062,0.00005309037,0.0003589806,0.000003598536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097536,"about_ca_system_score_gemma":0.00003158454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004718387,"about_ca_topic_score_gemma":0.0002355656,"domain_scores_codex":[0.997735,0.00006302424,0.0008111682,0.0003967115,0.0003703605,0.0006237437],"domain_scores_gemma":[0.9984543,0.0007615593,0.0001870275,0.0002638282,0.0001667148,0.0001665337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000107452,0.0001689046,0.0102807,0.0005195178,0.00009869334,0.0001676697,0.004239812,0.8945543,0.07281519,0.0008715159,0.0005372668,0.015639],"study_design_scores_gemma":[0.001424416,0.0001472152,0.01010927,0.0005842892,0.00004714014,0.0002651931,0.0006362693,0.7631589,0.2212719,0.0006063103,0.0007293163,0.001019723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9522248,0.0002621431,0.0348242,0.0000559704,0.0002995976,0.0003484589,0.00002998106,0.0002799688,0.01167488],"genre_scores_gemma":[0.9647093,0.0001500853,0.03461915,0.0001066385,0.0001828295,0.000005941354,0.00002168535,0.00008847559,0.0001159206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1484567,"threshold_uncertainty_score":0.9998318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008622334356289037,"score_gpt":0.2307010060567248,"score_spread":0.2220786717004358,"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."}}