{"id":"W2996501032","doi":"10.3390/jrfm12040189","title":"Risk Capital and Emerging Technologies: Innovation and Investment Patterns Based on Artificial Intelligence Patent Data Analysis","year":2019,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Private Equity and Venture Capital","field":"Business, Management and Accounting","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Venture capital; Investment (military); Emerging technologies; Business; Emerging markets; Capital (architecture); Quality (philosophy); Industrial organization; Finance; Knowledge management; Artificial intelligence; Computer science; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009686677,0.0001798823,0.0002985978,0.00111057,0.0001635323,0.0002400286,0.0002703873,0.00007142694,0.00001769136],"category_scores_gemma":[0.0001988518,0.0001484552,0.0000512941,0.0008170822,0.00005879373,0.0006547669,0.0005593044,0.0002745327,0.000006383126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002465869,"about_ca_system_score_gemma":0.000008753562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008845703,"about_ca_topic_score_gemma":0.000054833,"domain_scores_codex":[0.9986652,0.0000171807,0.0005047745,0.0003254526,0.0002943002,0.0001931179],"domain_scores_gemma":[0.9988667,0.00004197255,0.0006688404,0.0003143433,0.0000934508,0.00001471085],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002229177,0.000209547,0.3214593,0.0002356264,0.0002080865,0.0000540641,0.0001856184,0.0008448653,0.00001740806,0.09999467,0.0001687803,0.5763991],"study_design_scores_gemma":[0.001394981,0.0004268837,0.7674865,0.0003869314,0.002726202,0.000005190915,0.006076616,0.07996429,0.00007498852,0.1197763,0.02084738,0.0008337552],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.942847,0.0002972932,0.05577234,0.0003899494,0.0003013037,0.0002170693,0.00002616863,0.00002467207,0.0001242619],"genre_scores_gemma":[0.9976015,0.001121549,0.0006359126,0.0003996177,0.0001952238,0.000002740196,0.00002920041,0.00000886924,0.00000536239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5755653,"threshold_uncertainty_score":0.6053827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02929529452085738,"score_gpt":0.2356577443947123,"score_spread":0.2063624498738549,"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."}}