{"id":"W2953769670","doi":"","title":"Estrategias para la inversión extranjera en el Derecho Tecnológico Global: Reporte Fintech 2018: Cómo aprovechar las oportunidades del Brexit y del USMCA","year":2019,"lang":"es","type":"article","venue":"Unión Europea Aranzadi","topic":"Business, Innovation, and Economy","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; Political science; Geography; Art","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001674817,0.001029584,0.001427591,0.0006971231,0.0003515062,0.0007950612,0.001308539,0.0007618885,0.001401534],"category_scores_gemma":[0.0001838256,0.001249382,0.0005567061,0.001508426,0.0003683617,0.001577085,0.0003186885,0.0008960413,0.007100516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005135385,"about_ca_system_score_gemma":0.0004832357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001704242,"about_ca_topic_score_gemma":0.000050772,"domain_scores_codex":[0.9939998,0.0001323664,0.002572252,0.00189404,0.0001902975,0.001211259],"domain_scores_gemma":[0.9955193,0.0001416988,0.001834623,0.001834986,0.0003311546,0.0003382008],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003223014,0.0008122162,0.583412,0.0008373839,0.0008700064,0.0004670016,0.000577465,0.001262268,0.0002949093,0.4002325,0.006425345,0.004486649],"study_design_scores_gemma":[0.004809624,0.0004852651,0.4078196,0.0006203348,0.0001795432,0.0006066654,0.0004256888,0.003968946,0.0002135756,0.02779112,0.5501868,0.002892867],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8090475,0.003707407,0.004334107,0.001030574,0.002012108,0.001085208,0.0006529213,0.0003046968,0.1778255],"genre_scores_gemma":[0.9858369,0.002984728,0.001263019,0.001638789,0.0005254151,0.00002713915,0.0003061782,0.0002213356,0.007196496],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5437614,"threshold_uncertainty_score":0.9995113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03429955234309555,"score_gpt":0.2433899104944159,"score_spread":0.2090903581513204,"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."}}