{"id":"W2911439481","doi":"10.3390/ijerph16030488","title":"Mobilities of Older Chinese Rural-Urban Migrants: A Case Study in Beijing","year":2019,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Migration, Aging, and Tourism Studies","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China; La Trobe University","keywords":"Beijing; Mobilities; Environmental health; China; Geography; Gerontology; Economic geography; Socioeconomics; Demographic economics; Demography; Medicine; Sociology; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001115345,0.0007164778,0.0006734193,0.001117837,0.00704156,0.001115267,0.00115907,0.001414112,0.001734682],"category_scores_gemma":[0.00135764,0.0004128489,0.0004700419,0.001504578,0.001905294,0.001074861,0.002849664,0.0009130047,0.0002056979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003227656,"about_ca_system_score_gemma":0.001999611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03359694,"about_ca_topic_score_gemma":0.09865328,"domain_scores_codex":[0.9993978,0.0002271777,0.0000415549,0.00006140825,0.00006339062,0.0002086866],"domain_scores_gemma":[0.999472,0.00009947002,0.0001220309,0.00003510655,0.000045537,0.0002260078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001545536,0.0003830425,0.1650804,0.0003535005,0.00004231619,0.06031228,0.7529491,0.0001497132,0.001826735,0.0007628936,0.0009301638,0.01705536],"study_design_scores_gemma":[0.00001662596,0.0004594183,0.1297145,0.0001440587,0.00005276444,0.01000522,0.8539283,0.0002240342,0.0002753334,0.0002390378,0.004906663,0.00003414203],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9992433,0.0001575293,0.00003383664,0.0001612973,0.000003868807,0.00001953485,0.00001093112,0.000001172954,0.0003686815],"genre_scores_gemma":[0.9982501,0.0005038391,0.0001306954,0.0001339622,0.000008434786,0.00004136989,0.00002120922,0.000002773947,0.0009075323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03359694,"threshold_uncertainty_score":0.06680274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04100393581551124,"score_gpt":0.4056598831200305,"score_spread":0.3646559473045192,"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."}}