{"id":"W3135754616","doi":"10.1371/journal.pone.0247996","title":"Analysis of mobility homophily in Stockholm based on social network data","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Teck Resources; Horizon 2020; Horizon 2020 Framework Programme; Kungliga Tekniska Högskolan; Fondazione Centro Studi Enel; Governo Brasil; Ford Foundation","keywords":"Homophily; Socioeconomic status; Proxy (statistics); Similarity (geometry); Metric (unit); Geographical distance; Geography; Social network (sociolinguistics); Demography; Social media; Computer science; Sociology; Statistics; World Wide Web; Mathematics; Economics; Population; Social science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009531053,0.0002913415,0.0003439945,0.004891957,0.0004776687,0.001068169,0.000367034,0.0003155672,0.00131293],"category_scores_gemma":[0.005648467,0.0001451975,0.0004323068,0.004096611,0.0003880533,0.001084781,0.001468348,0.0003250077,0.0003649223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003573929,"about_ca_system_score_gemma":0.0002859181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003930952,"about_ca_topic_score_gemma":0.005721812,"domain_scores_codex":[0.9990124,0.0004339738,0.000101138,0.0001693488,0.000187672,0.00009555869],"domain_scores_gemma":[0.9964161,0.002059441,0.0008101867,0.0003361137,0.0002111051,0.0001669905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001944321,0.0001073133,0.9414074,0.0001717316,0.0006769147,0.0005292704,0.002257119,0.01972993,0.002319706,0.004783943,0.00139816,0.02642381],"study_design_scores_gemma":[0.00002792874,0.0001857072,0.8839541,0.0001111763,0.0002427749,0.0009496931,0.005708316,0.09281217,0.001717722,0.007788843,0.0064404,0.0000610857],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928464,0.0001471362,0.003827546,0.00008711455,0.00001012648,0.00001456331,0.001605678,0.00004761939,0.001413661],"genre_scores_gemma":[0.9967648,0.00006918544,0.001491164,0.000005260087,0.000007636553,0.00002050668,0.001450529,0.000005306415,0.0001856182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004891957,"threshold_uncertainty_score":0.007816136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1369669691760793,"score_gpt":0.3313753418243833,"score_spread":0.194408372648304,"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."}}