{"id":"W2983609705","doi":"10.3390/s19214766","title":"Intelligent Sensing Using Multiple Sensors for Material Characterization","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Higher Education and Scientific Research; King Saud University; CMC Microsystems","keywords":"Microwave; Microstrip; Resonator; Computer science; Wideband; Planar; Artificial neural network; Electronic engineering; Modulation (music); Acoustics; Field (mathematics); Materials science; Engineering; Optoelectronics; Artificial intelligence; Physics; Telecommunications; Mathematics","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":[0.000131303,0.0001554434,0.0001747387,0.0001078146,0.00004052985,0.00004188225,0.00004974062,0.00009161316,0.00005601232],"category_scores_gemma":[0.0000260503,0.0001628394,0.00006266787,0.0001013835,0.000007968752,0.00006483767,0.00001039765,0.00006087515,0.00003852639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008317812,"about_ca_system_score_gemma":0.000008658875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000143585,"about_ca_topic_score_gemma":0.000002486229,"domain_scores_codex":[0.9992098,0.00001996877,0.0002246413,0.0001692791,0.0001062814,0.0002700821],"domain_scores_gemma":[0.9996611,0.00002951728,0.00003990624,0.0001673052,0.00006013784,0.00004202928],"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":[0.0000228176,0.000005419388,0.0001598976,0.0000815783,0.00002045612,0.000001139838,0.0001178913,0.003269224,0.9922606,0.0000202153,0.0001001496,0.003940665],"study_design_scores_gemma":[0.0001204104,0.00001969741,0.00005386543,0.00002861253,0.00001093531,0.000005963255,0.00002276262,0.3258046,0.6686105,0.00001891854,0.005148746,0.0001550479],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9544258,0.00001116567,0.04349567,0.000009290607,0.0006797306,0.0005613954,0.00002471592,0.000436501,0.0003557054],"genre_scores_gemma":[0.9940922,0.0000232432,0.005383865,0.00003666671,0.0001826935,0.000002727513,0.00008167091,0.00005448769,0.0001424082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3236501,"threshold_uncertainty_score":0.6640396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02544864031921401,"score_gpt":0.2281048795655103,"score_spread":0.2026562392462962,"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."}}