{"id":"W2993447191","doi":"10.1109/access.2019.2956568","title":"A Self-Adaptive and Wide-Range Conductivity Measurement Method Based on Planar Interdigital Electrode Array","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"South-Central University for Nationalities; Natural Science Foundation of Hubei Province; Central South University; National Natural Science Foundation of China","keywords":"Planar; Conductivity; Electrode; Materials science; Fabrication; Range (aeronautics); Computer science; Sensitivity (control systems); Optoelectronics; Planar array; Electronic engineering; Biological system; Acoustics; Composite material; Telecommunications; Chemistry; Physics; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002601155,0.0003376537,0.0004227911,0.0004713361,0.0001872487,0.0004019126,0.001022019,0.0007116002,0.0006680707],"category_scores_gemma":[0.0006878112,0.0002861556,0.0003058396,0.0005091291,0.0002399336,0.0009859519,0.0004825157,0.0005307589,0.0004354481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002672368,"about_ca_system_score_gemma":0.0002156965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002411263,"about_ca_topic_score_gemma":0.0003118936,"domain_scores_codex":[0.9993205,0.00007902169,0.00003122932,0.0002069767,0.0003329137,0.00002937837],"domain_scores_gemma":[0.9995885,0.00008620154,0.00006449188,0.00006877707,0.0001720084,0.00001994617],"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":[0.00006898974,0.00005529067,0.0006697005,0.0001474303,0.00003197288,0.0001002694,0.00007210988,0.005074181,0.8393406,0.001587815,0.0006570073,0.1521946],"study_design_scores_gemma":[0.00002565046,0.0004372044,0.001856862,0.00001153909,0.00004376322,0.0009656663,0.00005472526,0.1593776,0.8266841,0.0007181337,0.009720816,0.0001040355],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05188169,0.0009111749,0.9422742,0.0002479632,0.0001812391,0.00006982633,0.0000703122,0.001578346,0.00278524],"genre_scores_gemma":[0.4385344,0.0005982979,0.5569963,0.0002273024,0.00007057058,0.00008439066,0.000106643,0.00005352954,0.003328532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001022019,"threshold_uncertainty_score":0.002234936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03413976493573297,"score_gpt":0.2776910694296367,"score_spread":0.2435513044939037,"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."}}