{"id":"W2897238282","doi":"10.1007/s11205-018-2015-z","title":"An Analysis of Well-Being Determinants at the City Level in China Using Big Data","year":2018,"lang":"en","type":"article","venue":"Social Indicators Research","topic":"Urban Green Space and Health","field":"Environmental Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Per capita; Population; China; Geography; Quality of Life Research; Quarter (Canadian coin); Population size; Demographic economics; Human geography; Well-being; Economics; Demography; Socioeconomics; Agricultural economics; Economic geography; Public health; Psychology; Sociology; Medicine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003685461,0.00008231054,0.0002112446,0.0004081285,0.0009932434,0.0000292718,0.001128129,0.0001074602,0.001189686],"category_scores_gemma":[0.00007170546,0.00006337791,0.00004460871,0.003558939,0.001547798,0.0001763276,0.001239144,0.0003236775,0.00008364864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004591692,"about_ca_system_score_gemma":0.0001137348,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06122181,"about_ca_topic_score_gemma":0.2033057,"domain_scores_codex":[0.9974362,0.0004902289,0.0002353535,0.0004054456,0.0008888938,0.000543864],"domain_scores_gemma":[0.9989855,0.00008270425,0.0001039472,0.0006985093,0.00001320573,0.0001161415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001939572,0.00006416325,0.984762,0.000003530752,0.00002360159,0.000002702286,0.005288546,0.00000290497,0.0008726118,0.00001052994,0.000450679,0.008499308],"study_design_scores_gemma":[0.00009025575,0.00004801521,0.9942549,0.000004490502,0.00003173118,2.431302e-7,0.0006146107,0.003766975,0.0005244169,0.000111189,0.0004828572,0.00007033323],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975371,0.00001089952,0.0000368796,0.0002287827,0.0000368348,0.0001663574,0.0000433416,0.000005191238,0.001934553],"genre_scores_gemma":[0.9994047,0.00001444868,0.00005940195,0.00004049072,0.000171797,0.000003070352,0.0000165122,0.00001010866,0.000279527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1420839,"threshold_uncertainty_score":0.9997234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2574381254914193,"score_gpt":0.4611157440473351,"score_spread":0.2036776185559159,"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."}}