{"id":"W3200387212","doi":"","title":"Carta de China: Cuestión de tamaño","year":2021,"lang":"es","type":"article","venue":"Política exterior","topic":"Economic Zones and Regional Development","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Humanities; China; Political science; Population; Philosophy; Demography; Sociology; Law","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":[0.000479133,0.0003283493,0.000685783,0.0001520124,0.0002142825,0.0003265982,0.0003320095,0.0002675584,0.001990622],"category_scores_gemma":[0.0001771664,0.0004129129,0.0002848487,0.0002231383,0.0001262545,0.0001525704,0.0002411343,0.0002743842,0.001888553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005356825,"about_ca_system_score_gemma":0.0005306865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003314849,"about_ca_topic_score_gemma":0.00003011362,"domain_scores_codex":[0.9974883,0.0000467771,0.0008309645,0.0007137583,0.00004574335,0.0008745022],"domain_scores_gemma":[0.9986624,0.00007761761,0.0002920057,0.0005497258,0.00004101403,0.0003772511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00005403874,0.0006243561,0.2911434,0.000502212,0.0004529094,0.0005366526,0.002237541,0.00008907782,0.0009760046,0.68501,0.006470818,0.01190307],"study_design_scores_gemma":[0.0007478217,0.0000738061,0.5792956,0.0002003275,0.00003981396,0.000273682,0.0001231559,0.001905475,0.0006662338,0.02705613,0.3889149,0.0007030948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9312179,0.01608503,0.001378117,0.02050563,0.001911633,0.0002032183,0.0002762142,0.00005628592,0.02836597],"genre_scores_gemma":[0.9702151,0.008792422,0.007081177,0.001534527,0.000652601,0.00003914391,0.00003227957,0.00007480384,0.01157796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6579538,"threshold_uncertainty_score":0.9998323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01592938235551456,"score_gpt":0.2361400562548437,"score_spread":0.2202106738993292,"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."}}