{"id":"W7108249342","doi":"","title":"Metis y ciudades latinoamericanas: Talca y sus muchos pequeños estacionamientos","year":2025,"lang":"es","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Latin American Urban Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metis; Urban planning; Theme (computing); Metropolitan area; Context (archaeology)","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","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.002705754,0.0009337312,0.002392097,0.001882773,0.001999105,0.004232749,0.006607794,0.0003025224,0.01573],"category_scores_gemma":[0.002952493,0.0008936204,0.0007095788,0.007051555,0.00294556,0.003193273,0.00346772,0.0009883333,0.0001003433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008033431,"about_ca_system_score_gemma":0.001318152,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03086052,"about_ca_topic_score_gemma":0.0007573701,"domain_scores_codex":[0.9910559,0.00178711,0.002090997,0.001297989,0.002357264,0.001410714],"domain_scores_gemma":[0.9919136,0.002432957,0.002786345,0.001082107,0.001168088,0.0006169587],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002137667,0.0006515973,0.7637692,0.0002601902,0.001693267,0.00007624779,0.00451613,0.00007423601,0.006304984,0.005030425,0.133955,0.08345502],"study_design_scores_gemma":[0.00103851,0.0000461048,0.7510497,0.001721361,0.0007305192,0.000004571551,0.01000698,0.00007739341,0.002814305,0.007465254,0.2237163,0.001328952],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5912499,0.07497707,0.0005009325,0.005566632,0.003135283,0.002789666,0.0004035134,0.0002520433,0.3211249],"genre_scores_gemma":[0.9328593,0.05716246,0.0005492309,0.00109811,0.0004544516,0.0001606415,0.00001320263,0.00009503623,0.007607565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3416094,"threshold_uncertainty_score":0.9997678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1945453122479855,"score_gpt":0.5895684682577776,"score_spread":0.3950231560097921,"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."}}