{"id":"W2057651209","doi":"10.1109/isplc.2012.6201319","title":"Parametric and nonparametric methods for power line network topology inference","year":2012,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Nonparametric statistics; Topology (electrical circuits); Computer science; Parametric statistics; Inference; Network topology; Algorithm; Frequency domain; Line (geometry); Reflectometry; Power (physics); Process (computing); Time domain; Mathematics; Artificial intelligence; Statistics","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":[0.006219317,0.001070681,0.001050631,0.002038822,0.000495849,0.001262247,0.001807943,0.001475441,0.001323895],"category_scores_gemma":[0.03778127,0.000598543,0.0009270399,0.001686283,0.001700108,0.002455494,0.001739803,0.002102462,0.0004055492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006367693,"about_ca_system_score_gemma":0.0009773136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002017222,"about_ca_topic_score_gemma":0.00162579,"domain_scores_codex":[0.9949622,0.00286164,0.0001612736,0.0006867412,0.001195263,0.0001328731],"domain_scores_gemma":[0.97954,0.01552728,0.001611674,0.002134113,0.001056898,0.000130118],"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":[0.0001385498,0.00008978519,0.002597172,0.000195139,0.0001685712,0.000128705,0.0001324013,0.6434817,0.003413647,0.08705784,0.001635132,0.2609614],"study_design_scores_gemma":[0.00001182233,0.0000355286,0.0006372594,0.00001450075,0.00001382379,0.0001045475,0.00001933003,0.9604385,0.001051521,0.03635824,0.001282868,0.0000320076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001257723,0.0001424405,0.9981872,0.00005207905,0.000008958171,0.0000100852,0.00002939165,0.00008736601,0.0002246212],"genre_scores_gemma":[0.2592251,0.0008316431,0.7366704,0.0001423334,0.0002449965,0.0003712743,0.0003371733,0.0001281895,0.002048909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006219317,"threshold_uncertainty_score":0.03289127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03918122855662481,"score_gpt":0.3704161790245766,"score_spread":0.3312349504679518,"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."}}