{"id":"W2090112467","doi":"10.1016/j.epsr.2013.10.005","title":"Improvement of vector fitting by using a new method for selection of starting poles","year":2013,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Selection (genetic algorithm); Vector (molecular biology); Support vector machine; Computer science; Mathematics; Artificial intelligence; Pattern recognition (psychology); Algorithm; Control theory (sociology); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001387118,0.001993438,0.001179207,0.001922335,0.0006044994,0.001105056,0.001370656,0.001688791,0.00974812],"category_scores_gemma":[0.005294336,0.0006332317,0.0008411558,0.001530884,0.0002680513,0.001807685,0.00083248,0.001495494,0.004723887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002537961,"about_ca_system_score_gemma":0.0007237645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001121388,"about_ca_topic_score_gemma":0.001646243,"domain_scores_codex":[0.9990817,0.000306763,0.00007239383,0.0001816357,0.0003122024,0.00004529374],"domain_scores_gemma":[0.9976896,0.0008580774,0.0001233901,0.0004342254,0.0008155062,0.00007925514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000399319,0.0003241164,0.001213091,0.0004712306,0.0001201149,0.0002219383,0.0002776114,0.04713978,0.1191902,0.009539634,0.005280082,0.8158228],"study_design_scores_gemma":[0.0001119345,0.0002252408,0.002120629,0.00006380206,0.0001240176,0.0005916342,0.00007991725,0.9028512,0.06520895,0.005598412,0.02291047,0.0001137734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002076881,0.00005599096,0.9963486,0.00001976989,0.00004738663,0.00001952024,0.0000199534,0.0009071418,0.0005048008],"genre_scores_gemma":[0.03302502,0.00009957055,0.9646846,0.00003459899,0.00003709632,0.00005384198,0.000175878,0.0004964004,0.001393043],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00974812,"threshold_uncertainty_score":0.03261077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03109108496042854,"score_gpt":0.3405786872435825,"score_spread":0.3094876022831539,"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."}}