{"id":"W2031464118","doi":"10.1109/mei.2009.4802596","title":"EIC volunteer profile","year":2009,"lang":"en","type":"article","venue":"IEEE Electrical Insulation Magazine","topic":"Power Systems and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Desk; Variety (cybernetics); Set (abstract data type); Engineering; Management; Library science; Computer science; Artificial intelligence; Mechanical engineering; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000575053,0.0001231792,0.0001452719,0.0001508694,0.00003346412,0.00002755121,0.0001076933,0.0001250214,0.0000535678],"category_scores_gemma":[0.0000344824,0.0001129653,0.00004019394,0.0005643276,0.00001093066,0.0001248476,0.000004052174,0.0001782869,0.0005261597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007138963,"about_ca_system_score_gemma":0.000007505776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001430283,"about_ca_topic_score_gemma":8.766689e-7,"domain_scores_codex":[0.9992505,0.000008978337,0.000212346,0.0001344474,0.0001433571,0.0002503915],"domain_scores_gemma":[0.9996912,0.00001868408,0.00002335981,0.0001821864,0.00004120342,0.00004336394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002782675,0.0001650115,0.001802702,0.00004083263,0.00006120834,0.00003593443,0.00009918385,0.007413398,0.5500764,0.01357428,0.1407733,0.28593],"study_design_scores_gemma":[0.0009184537,0.0004246422,0.2342793,0.00003308973,0.00002330535,0.00004318016,0.000002941183,0.5548636,0.04937777,0.006854564,0.1524455,0.000733703],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8169547,0.002241061,0.09800002,0.0006607445,0.001307566,0.0006600073,0.000011374,0.006062105,0.07410242],"genre_scores_gemma":[0.9977984,0.00002444061,0.0007363297,0.00005526706,0.0001352429,0.00001124499,0.000007107973,0.00001462483,0.001217317],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5474502,"threshold_uncertainty_score":0.6762893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009402606655759179,"score_gpt":0.218764969148556,"score_spread":0.2093623624927969,"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."}}