{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006840346,0.0006872487,0.0005790948,0.001152889,0.0006300209,0.002153292,0.0008948637,0.0009627559,0.007803932],"category_scores_gemma":[0.03252401,0.0005274887,0.001129126,0.001288326,0.0006666473,0.001624466,0.001520898,0.001938104,0.0005315717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002628237,"about_ca_system_score_gemma":0.002593594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1303523,"about_ca_topic_score_gemma":0.1035757,"domain_scores_codex":[0.9990391,0.0005561775,0.00003519024,0.0001477623,0.0001474725,0.0000744483],"domain_scores_gemma":[0.9928154,0.006031143,0.0003054378,0.0002196385,0.0005474896,0.00008084057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005877648,0.0002760451,0.06416382,0.0004547765,0.0006358001,0.0004364722,0.001900735,0.4785435,0.001559682,0.1393078,0.009096051,0.3030375],"study_design_scores_gemma":[0.00007928652,0.0001100092,0.01641657,0.0003119342,0.0002194886,0.0000692427,0.0006096682,0.8798846,0.0005691206,0.08918666,0.01248158,0.0000618342],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.485867,0.01042253,0.4504365,0.01995851,0.0002791184,0.0002208949,0.003221977,0.001140409,0.02845297],"genre_scores_gemma":[0.8835378,0.00842351,0.09015139,0.0003537492,0.0004638027,0.0001736496,0.001590359,0.0001840311,0.01512178],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1303523,"threshold_uncertainty_score":0.2591871,"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."}}