{"id":"W2557755265","doi":"10.1002/fut.21958","title":"Multivariate constrained robust M‐regression for shaping forward curves in electricity markets","year":2018,"lang":"en","type":"preprint","venue":"Journal of Futures Markets","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"KU Leuven; Fonds Wetenschappelijk Onderzoek","keywords":"Outlier; Multivariate statistics; Electricity; Arbitrage; Econometrics; Electricity market; Robust regression; Regression; Economics; Computer science; Mathematical optimization; Mathematics; Statistics; Financial economics; Engineering","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01631893,0.0005803714,0.001563985,0.001212495,0.000179838,0.0002991255,0.002028748,0.0006577419,0.000292632],"category_scores_gemma":[0.02515301,0.0003799183,0.0007809894,0.0005855937,0.0001727433,0.0003051627,0.0005139786,0.001425485,0.000005347075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003125926,"about_ca_system_score_gemma":0.0008337584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009836608,"about_ca_topic_score_gemma":0.00001287582,"domain_scores_codex":[0.9933008,0.0008409168,0.002587433,0.0007560784,0.001858639,0.0006561137],"domain_scores_gemma":[0.9894625,0.00504445,0.002704284,0.0007336685,0.001742907,0.0003121883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.009635152,0.000926492,0.003915573,0.002876124,0.0009029298,0.0004957326,0.001231201,0.04534281,0.001506703,0.0004598393,0.8212376,0.1114699],"study_design_scores_gemma":[0.01233225,0.00145522,0.2754962,0.03797232,0.0008497638,0.0009458431,0.0006355927,0.4689707,0.001244422,0.151128,0.04517261,0.003797057],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09150616,0.01961598,0.863129,0.004036,0.01516007,0.002938899,0.0002430189,0.0001071899,0.003263664],"genre_scores_gemma":[0.8654565,0.002420242,0.1260647,0.0006308099,0.003993138,0.00007403798,0.00002496614,0.0001258662,0.00120971],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7760649,"threshold_uncertainty_score":0.9998653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1093380463171657,"score_gpt":0.3644222952349074,"score_spread":0.2550842489177416,"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."}}