{"id":"W6925191901","doi":"10.17632/92g8n7pjp2","title":"Electricity Energy Consumption in the Gran Buenos Aires (metropolitan area) from 2012 to 2018 --- CAMMESA data","year":2023,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"Economic Zones and Regional Development","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Metropolitan area; Electricity; Consumption (sociology); Population; Energy consumption","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002548615,0.0007145522,0.0005827514,0.001676123,0.0003200227,0.0008362966,0.0008876231,0.0004659129,0.01432005],"category_scores_gemma":[0.001515433,0.0002036303,0.0004451762,0.003055132,0.0001219809,0.0004370048,0.0004718038,0.0007974113,0.008844479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009143414,"about_ca_system_score_gemma":0.0006998682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1165999,"about_ca_topic_score_gemma":0.145036,"domain_scores_codex":[0.9996516,0.00004167341,0.00002663291,0.0001186521,0.0001108742,0.00005073674],"domain_scores_gemma":[0.9992866,0.00007513014,0.00009449584,0.00007477638,0.0004022867,0.00006681665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001865435,0.0001092009,0.03838081,0.000508843,0.00007820771,0.0001008167,0.00007005635,0.001426351,0.0003005248,0.0006368082,0.9474523,0.01074956],"study_design_scores_gemma":[0.0001198985,0.00005177797,0.321834,0.0004055442,0.00005440062,0.0001107077,0.0006524437,0.002942329,0.0005755643,0.0004004756,0.672798,0.0000548774],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01387102,0.0002594611,0.0000922587,0.0002049824,0.00006180798,0.00002787015,0.9818362,0.0002050672,0.003441356],"genre_scores_gemma":[0.01448249,0.000238344,0.0003042448,0.00005826517,0.00003419837,0.0001158116,0.9823829,0.00003348256,0.002350152],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1165999,"threshold_uncertainty_score":0.2318425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1881675676103195,"score_gpt":0.287226990091528,"score_spread":0.0990594224812085,"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."}}