{"id":"W6979995919","doi":"","title":"Aproximación dinámica bayesiana para el consumo privado en el Perú","year":2018,"lang":"en","type":"dissertation","venue":"renati","topic":"Building materials and conservation","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Sequence (biology); Variable (mathematics); Inference; Bayesian inference; Process (computing); Linear model; Quarter (Canadian coin)","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":[],"category_scores_codex":[0.0002826268,0.0002910837,0.0003392451,0.0001244283,0.0002001017,0.000158043,0.0003572652,0.0003505517,0.003654864],"category_scores_gemma":[0.000106341,0.0002520966,0.00008804737,0.0001645555,0.00005287458,0.0001601288,0.000008153352,0.000186425,0.0006573761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009983848,"about_ca_system_score_gemma":0.0001623554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001818281,"about_ca_topic_score_gemma":0.002179685,"domain_scores_codex":[0.9984213,0.0001040602,0.0003694387,0.0004441802,0.0003425063,0.0003185383],"domain_scores_gemma":[0.9991243,0.0001002052,0.0002651038,0.0002951213,0.0001077198,0.0001074814],"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":[0.001574995,0.0002661294,0.3989253,0.003220074,0.0008363264,0.0001577393,0.007241409,0.0001178,0.01994184,0.008495755,0.05245619,0.5067664],"study_design_scores_gemma":[0.0005409151,0.0002241196,0.9283559,0.0004901054,0.0001701565,0.00002094652,0.0003510405,0.003677631,0.006690722,0.009465879,0.04895732,0.001055305],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9786924,0.0006607889,0.00004421856,0.0002150903,0.001674413,0.0003362959,0.000224115,0.0001092012,0.01804346],"genre_scores_gemma":[0.9875814,0.0002541776,0.001386992,0.0001874625,0.0006010017,0.000007533666,0.006358713,0.00001983637,0.003602875],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5294306,"threshold_uncertainty_score":0.9999931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01432082457576039,"score_gpt":0.2669447261625939,"score_spread":0.2526239015868335,"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."}}