{"id":"W4307551435","doi":"10.3390/s22218203","title":"Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Advanced Fiber Optic Sensors","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Grating; Fiber Bragg grating; Bandwidth (computing); Computer science; Sorting; Electronic engineering; Genetic algorithm; MATLAB; Materials science; Optical fiber; Engineering; Optoelectronics; Algorithm; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007288035,0.0002086829,0.0001695892,0.0001059776,0.0005367546,0.00004365822,0.0002475182,0.00006685057,0.000009837639],"category_scores_gemma":[0.0001999609,0.0001619498,0.00006480569,0.0005987428,0.00004255525,0.0000505461,0.00004642542,0.0008919445,0.000004656164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002404003,"about_ca_system_score_gemma":0.00001782875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007101111,"about_ca_topic_score_gemma":0.0000249141,"domain_scores_codex":[0.9983747,0.0002534999,0.0003274464,0.000271028,0.0003920973,0.000381215],"domain_scores_gemma":[0.9985106,0.0009012914,0.00008268558,0.0004512584,0.00001310244,0.00004104933],"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":[0.00001488694,0.00001146072,0.00001585577,0.000003693103,0.000007689597,0.000006228539,0.00199478,0.9852532,0.007788833,0.00002473637,0.0002569079,0.004621725],"study_design_scores_gemma":[0.0004798119,0.00005952325,0.00001853602,0.00002262078,0.00001517411,0.00001458166,0.005976787,0.9722174,0.01727377,0.00004925508,0.003625584,0.0002469339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9896181,0.00003131788,0.007446259,0.0008507044,0.0003396716,0.0007677281,0.000016194,0.0002034007,0.0007266176],"genre_scores_gemma":[0.9946409,0.000002368242,0.004536708,0.0005471312,0.0000914005,0.00007202511,0.00002321644,0.00005766878,0.00002856329],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01303578,"threshold_uncertainty_score":0.6604123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01543708693283775,"score_gpt":0.2182210605192628,"score_spread":0.2027839735864251,"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."}}