{"id":"W2965290353","doi":"10.2139/ssrn.3411653","title":"Data Aggregation and Demand Prediction","year":2019,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Data science; Data mining","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":[0.002211744,0.0007359379,0.0009204877,0.002785271,0.0006042796,0.002289231,0.0006282928,0.0006348266,0.00400555],"category_scores_gemma":[0.00894932,0.0005167178,0.0006373592,0.00462446,0.0002551565,0.001929442,0.0008649811,0.0008997606,0.002586172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007011159,"about_ca_system_score_gemma":0.0008248047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006974173,"about_ca_topic_score_gemma":0.005354436,"domain_scores_codex":[0.9984419,0.00048514,0.0001681285,0.0003383317,0.0004440623,0.0001224533],"domain_scores_gemma":[0.9955018,0.002039177,0.0002745587,0.001300599,0.0007618787,0.0001218618],"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":[0.0009458297,0.000540791,0.05376196,0.0002153051,0.0002022998,0.0003086468,0.0002028264,0.1136,0.00860739,0.0141798,0.03574623,0.7716889],"study_design_scores_gemma":[0.00002171434,0.00006650621,0.008574887,0.00002388103,0.00003775724,0.0000889249,0.00008377455,0.9602513,0.005135763,0.0167634,0.008927363,0.00002471466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.196329,0.002363693,0.7555199,0.002812615,0.0009447953,0.0004019707,0.01294526,0.01505789,0.0136249],"genre_scores_gemma":[0.8439001,0.0006857986,0.139459,0.0002492463,0.0004613306,0.0001974491,0.01028799,0.0002255927,0.004533519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006974173,"threshold_uncertainty_score":0.01386714,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01476346591750275,"score_gpt":0.2302477473568043,"score_spread":0.2154842814393016,"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."}}