{"id":"W4399279370","doi":"10.3386/w32515","title":"Understanding Expert Choices Using Decision Time","year":2024,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Computer science; Data science; Management science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01449089,0.0006476617,0.0008173512,0.001722676,0.0005365957,0.003354054,0.0007570876,0.00152514,0.01051375],"category_scores_gemma":[0.1291153,0.0004771044,0.0008419079,0.001432377,0.001515443,0.005265476,0.001299948,0.001941103,0.0007233029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001418862,"about_ca_system_score_gemma":0.0008260274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0022364,"about_ca_topic_score_gemma":0.001586487,"domain_scores_codex":[0.9935476,0.003720119,0.0003482304,0.00125343,0.000803474,0.0003271708],"domain_scores_gemma":[0.781258,0.1842919,0.02334859,0.006564255,0.002541596,0.001995694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004079882,0.002270385,0.3734732,0.0007449102,0.001250323,0.0006546396,0.006554901,0.1688911,0.01194371,0.2001609,0.005757771,0.2242183],"study_design_scores_gemma":[0.0004919922,0.001353607,0.1723747,0.0001220031,0.0002511701,0.0004288731,0.001627306,0.3530887,0.003469483,0.4609805,0.005565059,0.0002465847],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8613042,0.0006947703,0.1119383,0.001564667,0.00006488567,0.0001710023,0.0004896476,0.0001239284,0.02364864],"genre_scores_gemma":[0.9881334,0.0002122957,0.01001367,0.0001888964,0.00003502366,0.00009775566,0.0001781714,0.00002180826,0.00111905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01449089,"threshold_uncertainty_score":0.07663608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7772247054156955,"score_gpt":0.5597487248326142,"score_spread":0.2174759805830814,"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."}}