{"id":"W2766489335","doi":"","title":"Madrid como ciudad amigable con las personas mayores. Escenarios de diagnóstico para políticas públicas","year":2017,"lang":"es","type":"article","venue":"","topic":"Aging, Health, and Disability","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Persona; Humanities; Political science; Sociology; Art; Demography","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00133164,0.0006877091,0.001246543,0.0001334506,0.001523169,0.0005623684,0.0008135823,0.0005927528,0.00519817],"category_scores_gemma":[0.002944552,0.0005953306,0.0003706597,0.0001308619,0.001573167,0.0003560996,0.000372303,0.001014544,0.002253275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006701638,"about_ca_system_score_gemma":0.001252116,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0158431,"about_ca_topic_score_gemma":0.00117429,"domain_scores_codex":[0.9950973,0.0002735292,0.0008760691,0.001129807,0.0007708555,0.001852443],"domain_scores_gemma":[0.9940051,0.0007673363,0.0004772752,0.002846468,0.0002575721,0.001646262],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005494046,0.001485592,0.922918,0.001950689,0.000181275,0.0003714772,0.00286603,0.000002396921,0.0003548378,0.03657496,0.02841424,0.004331085],"study_design_scores_gemma":[0.006385006,0.0008075175,0.8918456,0.0009097162,0.0007847448,0.0002129043,0.004832122,0.005491372,0.001574251,0.001676392,0.08442833,0.001052079],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8118907,0.001755505,0.0006819459,0.03288161,0.000935548,0.001561411,0.000125409,0.0003273359,0.1498405],"genre_scores_gemma":[0.9762433,0.001351627,0.000698656,0.005836753,0.001320437,0.00006153466,0.00005044525,0.0001028077,0.01433443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1643526,"threshold_uncertainty_score":0.9997767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0447957280508542,"score_gpt":0.3636333183871994,"score_spread":0.3188375903363452,"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."}}