{"id":"W2609459841","doi":"10.2495/sdp-v12-n7-1132-1141","title":"Japanese metropolitan structure defined through correlated demographics and local service sector employment provision","year":2017,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Urbanization and City Planning","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Demographics; Business; Service (business); Tertiary sector of the economy; Regional science; Economic growth; Demographic economics; Economic geography; Geography; Marketing; Economics; Demography; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004603535,0.0001106301,0.0001489497,0.0001834124,0.001053044,0.0005104702,0.0003188672,0.00009872305,0.00004107555],"category_scores_gemma":[0.0003358965,0.000098218,0.0000212506,0.0001088009,0.0001180604,0.000785342,0.0001140574,0.0001982266,5.763225e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002314426,"about_ca_system_score_gemma":0.0002884017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008669032,"about_ca_topic_score_gemma":0.0001953445,"domain_scores_codex":[0.9987822,0.00005001103,0.0002928675,0.0001225796,0.0005283125,0.0002239826],"domain_scores_gemma":[0.9983388,0.00008141459,0.0004078807,0.0000559639,0.000999174,0.0001168067],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005502694,0.00009511863,0.5482615,0.0001029512,0.0006987302,0.002061729,0.3832974,0.0003397164,0.0001228698,0.05585816,0.002225074,0.006386591],"study_design_scores_gemma":[0.002198474,0.000102617,0.1926903,0.0004868799,0.00006663132,0.0001587574,0.6792424,0.0004691193,0.0002599682,0.00343888,0.1204027,0.0004832613],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942379,0.0004373517,0.001056185,0.001583064,0.0004250126,0.00009674703,0.000002528367,0.00001437036,0.002146855],"genre_scores_gemma":[0.9970108,0.00005839279,0.001664074,0.0002975942,0.0001612427,8.475533e-7,0.0000111435,0.000008070765,0.0007878295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3555712,"threshold_uncertainty_score":0.8099272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02402069281517411,"score_gpt":0.2996044021834662,"score_spread":0.2755837093682921,"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."}}