{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002483492,0.0004602068,0.0009685198,0.0003772448,0.0009404779,0.001178389,0.001196693,0.0005238804,0.0002676769],"category_scores_gemma":[0.001681753,0.0005310383,0.0005338342,0.0002272836,0.0004112907,0.0004782732,0.000282438,0.0005016314,0.0003115753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007979884,"about_ca_system_score_gemma":0.0004434097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003334941,"about_ca_topic_score_gemma":0.0005639365,"domain_scores_codex":[0.9966577,0.0002407138,0.001007216,0.0009529438,0.0001023076,0.001039095],"domain_scores_gemma":[0.9966298,0.0005643776,0.001156737,0.001113933,0.00007925311,0.0004558651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003610007,0.0003896188,0.9093098,0.0001457994,0.0007491888,0.0002161978,0.003440941,0.0008207837,0.0003836656,0.07684105,0.00188189,0.005460053],"study_design_scores_gemma":[0.001401401,0.0001116537,0.6649872,0.0001493976,0.0002401981,0.00009642462,0.0003091984,0.02075205,0.001393533,0.03853717,0.2710785,0.0009432723],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965754,0.003103142,0.005715556,0.005470302,0.0001906752,0.0002863867,0.000298263,0.00006642689,0.01911519],"genre_scores_gemma":[0.9881965,0.005253695,0.00263839,0.0003238116,0.0005086226,0.00007904349,0.00003252192,0.00008472379,0.002882671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2691966,"threshold_uncertainty_score":0.9998585,"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."}}