{"id":"W2761190345","doi":"10.1109/iceaa.2017.8065601","title":"Power-flow considerations for optimal wireless power transfer networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Wireless Power Transfer Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Wireless power transfer; Realizability; Computer science; Transmitter; Wireless; Power (physics); Power flow; Maximum power transfer theorem; Field (mathematics); Electrical engineering; Electric power system; Electronic engineering; Telecommunications; Engineering; Mathematics; Channel (broadcasting); Physics; Algorithm","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001697598,0.0002805787,0.0003444001,0.00005759232,0.0004473561,0.0003823331,0.0003007992,0.0002080136,0.0005120508],"category_scores_gemma":[0.00001878833,0.000276786,0.0001795362,0.00003074392,0.000086627,0.0003833623,0.00001373784,0.000192227,0.00004172183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003868607,"about_ca_system_score_gemma":0.00002881934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001846699,"about_ca_topic_score_gemma":0.0001284165,"domain_scores_codex":[0.9986952,0.00001599326,0.0003610144,0.0002813386,0.0001547346,0.0004917221],"domain_scores_gemma":[0.9988378,0.0001499165,0.00001387036,0.0007516784,0.00009210599,0.0001546163],"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":[0.0001213317,0.0002225807,0.001412424,0.0003530137,0.001153013,0.00007215625,0.004430789,0.7729321,0.01053696,0.1183562,0.08694258,0.003466839],"study_design_scores_gemma":[0.002989225,0.0001763674,0.002302055,0.0001193141,0.00007124121,0.00004402368,0.0002312635,0.9660764,0.01067777,0.0002338607,0.01586824,0.001210271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07713291,0.0001133412,0.8932711,0.0003802691,0.00225841,0.0006844193,0.00006990605,0.0005863075,0.02550332],"genre_scores_gemma":[0.9958017,0.00001553949,0.00326515,0.00007981608,0.0001559192,0.0001619376,0.000009918297,0.0001008553,0.0004091043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9186689,"threshold_uncertainty_score":0.9999684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608874058366019,"score_gpt":0.2289226412616848,"score_spread":0.2128339006780246,"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."}}