{"id":"W4284887612","doi":"10.1287/opre.2022.2301","title":"Data Aggregation and Demand Prediction","year":2022,"lang":"en","type":"article","venue":"Operations Research","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Aggregate (composite); Cluster analysis; Data aggregator; Benchmark (surveying); Data set; Data mining; Stock (firearms); Flexibility (engineering); Econometrics; Artificial intelligence; Statistics; Economics; Mathematics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.008437305,0.00003990652,0.00005968335,0.00029704,0.002347634,0.0004337765,0.0008708832,0.00001997646,0.0008192401],"category_scores_gemma":[0.001801788,0.00003382787,0.000009864391,0.001244327,0.0001193045,0.0003648139,0.001437726,0.0002844802,0.00005678933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004292788,"about_ca_system_score_gemma":0.0001091423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001341149,"about_ca_topic_score_gemma":0.0001922443,"domain_scores_codex":[0.9974583,0.0004299573,0.0002465932,0.0004233352,0.001296422,0.0001454078],"domain_scores_gemma":[0.9981375,0.0004188559,0.00001694124,0.001035757,0.0003316412,0.00005930533],"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":[0.00002602933,0.0002589003,0.007200498,0.000004195137,0.00001326171,0.000004428327,0.00103279,0.01634517,0.003789202,0.1245365,0.5838788,0.2629102],"study_design_scores_gemma":[0.00009344039,0.00009199684,0.002397426,0.000002341409,0.000001914011,0.00002337204,0.0006369504,0.6326427,0.0001883115,0.01470948,0.3491615,0.00005056908],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8572546,0.0005204451,0.09500895,0.02142024,0.0002296288,0.001846791,0.002586439,0.0002336149,0.02089931],"genre_scores_gemma":[0.9863148,0.0000340101,0.00733823,0.00004100765,0.00006435577,0.0002889647,0.0002413088,0.00000628917,0.00567106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6162975,"threshold_uncertainty_score":0.9989512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6061308537931067,"score_gpt":0.5596009659608996,"score_spread":0.04652988783220713,"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."}}