{"id":"W4405351294","doi":"10.2139/ssrn.5040589","title":"Product Complexity, Investor Experience, and Returns","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Business; Product (mathematics); Computer science; Industrial organization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001188186,0.0001675733,0.0002914176,0.0007210218,0.0001558646,0.001937355,0.0001428246,0.0005203421,0.009079514],"category_scores_gemma":[0.01416871,0.0001259632,0.0001818906,0.000679397,0.0005622974,0.001717797,0.0006999763,0.0006266608,0.0003930201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002745355,"about_ca_system_score_gemma":0.0001428377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005888144,"about_ca_topic_score_gemma":0.0007068494,"domain_scores_codex":[0.9996477,0.0001146554,0.00002901955,0.00005809135,0.00009526048,0.00005524149],"domain_scores_gemma":[0.971212,0.01980304,0.005705285,0.000986028,0.0004480761,0.00184555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000702484,0.0004514023,0.9581681,0.00004269768,0.0001793475,0.0003727486,0.0005657846,0.004120259,0.0008666556,0.008545353,0.0006360526,0.02534895],"study_design_scores_gemma":[0.00004318457,0.0003309524,0.9659193,0.00001897203,0.0000804779,0.0004076351,0.0005515203,0.00565087,0.0003101601,0.0255195,0.001140734,0.00002662575],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936878,0.0003942047,0.0007152715,0.0001623764,0.000004389245,0.000005262772,0.0000780866,0.000004504153,0.004948155],"genre_scores_gemma":[0.9986708,0.0001473067,0.0001197447,0.00001345082,0.00002018899,0.000001876893,0.00006544484,0.000002656962,0.0009584957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009079514,"threshold_uncertainty_score":0.03037399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07381452281432536,"score_gpt":0.2587502965472366,"score_spread":0.1849357737329113,"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."}}