{"id":"W3087959594","doi":"10.1109/compsac48688.2020.00-77","title":"Data-Driven Adaptive Regularized Risk Forecasting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Risk analysis (engineering); Business","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":[0.001299132,0.0005406327,0.0008179402,0.0004210421,0.0001834479,0.0008403839,0.0008374625,0.0008016022,0.0009279798],"category_scores_gemma":[0.003737587,0.0003276346,0.0006769574,0.0005151856,0.0003850211,0.0008078981,0.0006127768,0.00116714,0.0002292434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004289659,"about_ca_system_score_gemma":0.0008607067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003122275,"about_ca_topic_score_gemma":0.001954285,"domain_scores_codex":[0.9994988,0.0001690157,0.00003450981,0.0001089053,0.0001446232,0.00004397712],"domain_scores_gemma":[0.9987746,0.0005743237,0.0001494426,0.0001555655,0.0003151275,0.00003092778],"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":[0.00003837401,0.00001700368,0.000454086,0.00002288731,0.00002809168,0.00003150091,0.00002028658,0.9700792,0.001347812,0.006991857,0.0009234956,0.02004539],"study_design_scores_gemma":[0.000001262989,0.000002410872,0.00003527427,7.324406e-7,9.533531e-7,0.000002207958,7.510631e-7,0.9985048,0.0001235122,0.001233741,0.00009271314,0.000001689652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01841853,0.0001285051,0.9800611,0.000169217,0.00003518948,0.00002156522,0.0001003388,0.000295446,0.0007701034],"genre_scores_gemma":[0.7486831,0.0003723849,0.2472217,0.0001295374,0.0001168996,0.0001428319,0.0006864332,0.0001345396,0.002512454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003122275,"threshold_uncertainty_score":0.006870508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07035496042861558,"score_gpt":0.223653774666605,"score_spread":0.1532988142379894,"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."}}