{"id":"W4288720071","doi":"10.3390/s22155659","title":"Plant Tissue Modelling Using Power-Law Filters","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Academy of Scientific Research and Technology","keywords":"Electrical impedance; Filter (signal processing); Power (physics); Dielectric spectroscopy; Equivalent circuit; Heuristic; Electronic engineering; Biological system; Computer science; Engineering; Electrical engineering; Voltage; Chemistry; Electrode; Electrochemistry; Physics; Artificial intelligence; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0000961805,0.00006769085,0.00006547934,0.00004265213,0.0005581151,0.00003588451,0.0003589884,0.00001367616,0.00004604085],"category_scores_gemma":[0.000001020608,0.00007457415,0.00002882003,0.0002280216,0.00002371017,0.0001051244,0.0002153599,0.0001124858,0.00002181035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005973276,"about_ca_system_score_gemma":0.00002619588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001688248,"about_ca_topic_score_gemma":0.000001278773,"domain_scores_codex":[0.9992428,0.00003532499,0.0001117223,0.0002342893,0.0002000178,0.0001758012],"domain_scores_gemma":[0.9995651,0.00002945369,0.00003996754,0.0002987355,0.00001645875,0.00005025473],"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":[8.262525e-7,0.00002550408,0.00000356799,9.774552e-7,0.000003883938,0.00001328882,0.0003542427,0.8397541,0.0006912141,0.1586046,0.0002431496,0.000304668],"study_design_scores_gemma":[0.00005746969,0.00002875617,0.000007966904,0.00000169485,0.000001841107,0.00007619669,0.00009608881,0.9557554,0.0004439024,0.003505865,0.0399231,0.0001017257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1999023,0.00009972121,0.7953451,0.000941606,0.0003479972,0.0001533939,0.00005346528,0.0001782212,0.00297818],"genre_scores_gemma":[0.8624903,0.000001594854,0.1368048,0.000292101,0.00003986458,0.0000132499,0.000009707781,0.00000798476,0.000340368],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.662588,"threshold_uncertainty_score":0.4292627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03047385689859506,"score_gpt":0.248767777815646,"score_spread":0.218293920917051,"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."}}