{"id":"W4285013258","doi":"10.3390/jrfm15070302","title":"Artificial Intelligence and Firm Performance: Does Machine Intelligence Shield Firms from Risks?","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Stock market; Portfolio; Pandemic; Business; Coronavirus disease 2019 (COVID-19); Stock (firearms); Financial economics; Economics; Finance; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002551665,0.0003432363,0.0004172453,0.001294699,0.000389374,0.00282574,0.0004201136,0.001173761,0.001741142],"category_scores_gemma":[0.01582042,0.0001274654,0.0003169505,0.001633804,0.001019719,0.002538096,0.0008031062,0.001007229,0.0003586829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001099896,"about_ca_system_score_gemma":0.0007009599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0045867,"about_ca_topic_score_gemma":0.003750392,"domain_scores_codex":[0.9989746,0.000328989,0.00005534102,0.0001243675,0.0002371029,0.0002796485],"domain_scores_gemma":[0.982144,0.008621179,0.00625306,0.0007712336,0.001034359,0.001176109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003353912,0.0003875553,0.8684934,0.0001209262,0.0005086002,0.0004007184,0.000384837,0.03095681,0.0006092078,0.01592353,0.002830816,0.07904827],"study_design_scores_gemma":[0.00005301389,0.0007268273,0.8669427,0.00016422,0.0001994328,0.000151354,0.001470499,0.07543398,0.001037219,0.04804684,0.005702923,0.00007093656],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9716038,0.002834431,0.002744723,0.008227099,0.00007236344,0.00002299163,0.0001895648,0.00003085687,0.0142742],"genre_scores_gemma":[0.9990693,0.0002433218,0.0001689423,0.0001575648,0.00005596931,0.000002706836,0.00004594812,0.000001495996,0.0002548435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0045867,"threshold_uncertainty_score":0.01349461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04023567316832886,"score_gpt":0.2549163315522594,"score_spread":0.2146806583839306,"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."}}