{"id":"W2037520224","doi":"10.1109/smartgridcomm.2013.6687980","title":"Power grid topology inference using power line communications","year":2013,"lang":"en","type":"article","venue":"","topic":"Power Line Communications and Noise","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Topology (electrical circuits); Power (physics); Line (geometry); Power grid; Inference; Grid; Power-line communication; Network topology; Distributed computing; Electrical engineering; Computer network; Engineering; Artificial intelligence; Mathematics; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00006964688,0.000113887,0.0001259828,0.00008463538,0.0001034768,0.00003867435,0.0006736181,0.00007267497,0.002957444],"category_scores_gemma":[0.00004066531,0.0001062418,0.00003959571,0.0001746433,0.00009284745,0.0002141673,0.0002760452,0.0002118195,0.0004949161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003500718,"about_ca_system_score_gemma":0.00001842175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001739224,"about_ca_topic_score_gemma":0.00004807284,"domain_scores_codex":[0.9993973,0.00003336474,0.0002304294,0.00008583497,0.00005369648,0.0001993222],"domain_scores_gemma":[0.9981292,0.0001240624,0.00002204226,0.001551592,0.00009813204,0.00007494634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002038225,0.002057477,0.02763616,0.0001338433,0.0009244132,0.000009347154,0.006448919,0.0374608,0.2720754,0.4282776,0.2016909,0.02326477],"study_design_scores_gemma":[0.0008730183,0.0001706878,0.01886284,0.00007378005,0.0000412701,0.00004079923,0.0008473718,0.5577128,0.004490498,0.00546603,0.4101322,0.00128874],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.571074,0.003090104,0.04324174,0.003610411,0.0009475055,0.0005184063,0.00002379836,0.001174265,0.3763197],"genre_scores_gemma":[0.9754577,0.0002977956,0.02361576,0.0002115971,0.00001888605,0.00002688835,0.00001545471,0.00002052128,0.0003354479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.520252,"threshold_uncertainty_score":0.997954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03623877953451769,"score_gpt":0.2973593466477615,"score_spread":0.2611205671132438,"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."}}