{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000650908,0.0006999202,0.0008236152,0.0009085226,0.0002795143,0.0006806231,0.001113504,0.0008686789,0.000808201],"category_scores_gemma":[0.001203361,0.000386607,0.0004966266,0.0003982793,0.0007054308,0.001624292,0.0009174796,0.0007532201,0.0003799014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000368135,"about_ca_system_score_gemma":0.0001697518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001778128,"about_ca_topic_score_gemma":0.0003576142,"domain_scores_codex":[0.9988726,0.000152223,0.00005444605,0.000346837,0.0005288123,0.00004510569],"domain_scores_gemma":[0.9993414,0.0002430138,0.0001058533,0.0001518667,0.0001322983,0.00002540168],"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":[0.000299089,0.0001304409,0.002017861,0.0002599719,0.00009640458,0.0001669201,0.0001476556,0.01688181,0.7443094,0.009858582,0.0007478319,0.225084],"study_design_scores_gemma":[0.00003824654,0.0006182223,0.002701224,0.00004826192,0.0001165026,0.0008145621,0.00007709035,0.4486794,0.5250242,0.007959445,0.0138015,0.0001214431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05509235,0.001168867,0.9395186,0.0002099067,0.0001455596,0.00007512255,0.000046744,0.0008240781,0.002918818],"genre_scores_gemma":[0.5367678,0.0005423851,0.4598332,0.0001889361,0.0001261766,0.0001267785,0.00008291593,0.00006717273,0.002264576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001113504,"threshold_uncertainty_score":0.003442407,"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."}}