{"id":"W3128840626","doi":"10.1016/j.eneco.2021.105711","title":"Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels","year":2021,"lang":"en","type":"article","venue":"Energy Economics","topic":"Merger and Competition Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; University of Alberta","keywords":"Kurtosis; Skewness; Complement (music); Confusion; Confusion matrix; Gasoline; Econometrics; Variance (accounting); Economics; Computer science; Artificial intelligence; Statistics; Engineering; Psychology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001380357,0.0005433754,0.0008123752,0.001093673,0.0006678737,0.0009009367,0.001548704,0.001756974,0.001869313],"category_scores_gemma":[0.004130315,0.0003424459,0.0004661519,0.000578353,0.0005615946,0.001174485,0.0008182162,0.001323358,0.0005614108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000684563,"about_ca_system_score_gemma":0.001347169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01194376,"about_ca_topic_score_gemma":0.02221695,"domain_scores_codex":[0.9994362,0.0002370458,0.00002844017,0.0001381425,0.00008824804,0.00007187458],"domain_scores_gemma":[0.997219,0.001732099,0.0002892309,0.0001892595,0.0004132391,0.0001572641],"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":[0.001079412,0.001831042,0.04603633,0.0001677851,0.0002571696,0.0003939589,0.0002040945,0.4002218,0.003942579,0.009098331,0.02836131,0.5084062],"study_design_scores_gemma":[0.00001431371,0.00002398147,0.0009758722,0.000004470031,0.00000601777,0.00001278731,0.00001888408,0.996208,0.0004045923,0.002060828,0.0002653323,0.000004942809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5387461,0.0007818314,0.4443046,0.002862557,0.0001541257,0.0002909547,0.001453595,0.003448796,0.007957499],"genre_scores_gemma":[0.9208112,0.00007856489,0.0730819,0.0002618563,0.0001015808,0.00007047947,0.001138396,0.00006284413,0.004393198],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01194376,"threshold_uncertainty_score":0.02374846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03101210708372109,"score_gpt":0.2022139007775275,"score_spread":0.1712017936938064,"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."}}