{"id":"W2796272306","doi":"10.1080/12265934.2018.1458639","title":"The strategies of advanced local spatial data infrastructure for Seoul Metropolitan Government","year":2018,"lang":"en","type":"article","venue":"International Journal of Urban Sciences","topic":"Energy and Environmental Systems","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Information and Communications Technology; Maturity (psychological); Sustainability; Government (linguistics); Local government; Business; Spatial data infrastructure; Regional science; Public administration; Geography; Political science; Spatial analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.00143128,0.00005635594,0.00009515911,0.00002772661,0.0003910905,0.0001270848,0.001700531,0.00003122024,0.00007658848],"category_scores_gemma":[0.0002517521,0.00003472551,0.00005022331,0.00008448707,0.001978535,0.0006158226,0.0001116917,0.00005689217,0.000001146608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002473521,"about_ca_system_score_gemma":0.0002280482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007223335,"about_ca_topic_score_gemma":0.002290284,"domain_scores_codex":[0.9980602,0.00007260679,0.0003156947,0.0001095383,0.001297387,0.0001446005],"domain_scores_gemma":[0.9990073,0.00024811,0.000428333,0.0001048973,0.0001590833,0.00005225421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005476387,0.0001220146,0.02518543,0.000009392872,0.0003811112,0.000007863627,0.01011211,0.002718747,0.003821819,0.7846845,0.02161292,0.1507964],"study_design_scores_gemma":[0.0008164459,0.001010952,0.01130402,0.0001002278,0.00003089541,0.00001975013,0.2422707,0.001504163,0.00416007,0.03161396,0.7069877,0.0001811101],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7636902,0.001853551,0.09053244,0.01025309,0.01694957,0.0004362621,0.0003000104,0.0000193284,0.1159655],"genre_scores_gemma":[0.9973528,0.00008851843,0.001077678,0.00003884402,0.001166528,9.710296e-7,0.000001056541,0.000002412878,0.0002712234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7530706,"threshold_uncertainty_score":0.7289995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02248206414560977,"score_gpt":0.3387962177669536,"score_spread":0.3163141536213438,"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."}}