{"id":"W2618363144","doi":"10.15517/jte.v27i1.25429","title":"Usando la red de estaciones SIRGAS de Costa Rica para la cuantificación de las discrepancias respecto de un procesamiento PPP en línea","year":2017,"lang":"es","type":"article","venue":"Ingeniería","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002235067,0.0005932684,0.0003344625,0.002598779,0.001561778,0.002048903,0.0008869311,0.0003454209,0.004983814],"category_scores_gemma":[0.006858706,0.000287324,0.0004612597,0.007076753,0.0005127049,0.0007762656,0.001570744,0.0005514598,0.001180019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004407319,"about_ca_system_score_gemma":0.004928538,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5256438,"about_ca_topic_score_gemma":0.6391833,"domain_scores_codex":[0.9980952,0.0004158325,0.0001680359,0.0004215121,0.000714918,0.0001844568],"domain_scores_gemma":[0.9942961,0.0006002265,0.0005381909,0.0007437723,0.003681022,0.0001405896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003271944,0.00009441217,0.621995,0.001133297,0.0003330313,0.0007196688,0.0305432,0.01700836,0.007082297,0.006972952,0.03934401,0.2744466],"study_design_scores_gemma":[0.00001953699,0.00006656378,0.7680627,0.0004316557,0.0001598711,0.0002355246,0.02639709,0.01168762,0.005539306,0.001479354,0.1858309,0.00008980032],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8240464,0.002197599,0.02985753,0.001109321,0.0001765147,0.0006743264,0.04096476,0.001431802,0.09954163],"genre_scores_gemma":[0.9139092,0.001197418,0.03734858,0.0002729592,0.0000414551,0.0005336662,0.02289029,0.0003569469,0.02344947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5256438,"threshold_uncertainty_score":0.9542996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02983842170690823,"score_gpt":0.2769272961631349,"score_spread":0.2470888744562267,"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."}}