{"id":"W4226246217","doi":"10.48550/arxiv.2201.05709","title":"How easy is it for investment managers to deploy their talent in green and brown stocks?","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Business; Investment (military); Finance; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001679765,0.0001892428,0.0002967142,0.0009338177,0.0005708601,0.002272172,0.0003226219,0.0006035388,0.002789258],"category_scores_gemma":[0.009627271,0.0001173508,0.00011922,0.0006201238,0.0008175483,0.00241772,0.001045351,0.0006237517,0.000650463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006258997,"about_ca_system_score_gemma":0.0004231475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00390114,"about_ca_topic_score_gemma":0.00452447,"domain_scores_codex":[0.9994585,0.00009086209,0.0000257908,0.00009507819,0.0001181968,0.0002116301],"domain_scores_gemma":[0.994426,0.001446461,0.002175592,0.0002228374,0.0004507535,0.00127842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002915231,0.000198287,0.8789752,0.00007617726,0.0001178131,0.0002802901,0.002623877,0.002486191,0.002591457,0.00773133,0.003033986,0.1015939],"study_design_scores_gemma":[0.00002498125,0.0002162127,0.9740669,0.00004342015,0.00003053105,0.0001017157,0.006503887,0.00304988,0.0009086623,0.01183251,0.003185701,0.00003572997],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940006,0.0002740346,0.0005248117,0.001021299,0.00001519856,0.000005138414,0.0000737306,0.00000948342,0.004075743],"genre_scores_gemma":[0.9992723,0.00007837298,0.000131499,0.00007019796,0.0000210115,0.000003002601,0.00003414454,0.000002130482,0.0003873899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00390114,"threshold_uncertainty_score":0.009330988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09636330387271932,"score_gpt":0.1868496325026382,"score_spread":0.09048632862991887,"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."}}