{"id":"W4401550853","doi":"10.1080/13658816.2024.2389410","title":"Quantifying local mobility patterns in urban human mobility data","year":2024,"lang":"en","type":"article","venue":"International Journal of Geographical Information Systems","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"European Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Destinations; Metric (unit); Measure (data warehouse); Mobile phone; Global Positioning System; Computer science; Mobility model; Geography; Scale (ratio); Individual mobility; Business; Data mining; Mathematics; Cartography; Statistics; Marketing; Tourism; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001274033,0.0003220193,0.0004935078,0.003312971,0.0003355727,0.0009751957,0.000539727,0.0004387539,0.001537631],"category_scores_gemma":[0.008386946,0.0001674061,0.0002879652,0.006228569,0.0003901616,0.00136926,0.001240408,0.0003797191,0.0006438528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006367705,"about_ca_system_score_gemma":0.0004266255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0168737,"about_ca_topic_score_gemma":0.03389304,"domain_scores_codex":[0.9988117,0.000492053,0.0001325643,0.0002358382,0.0002428039,0.00008505242],"domain_scores_gemma":[0.9963185,0.001657363,0.0007565749,0.000590978,0.000558837,0.0001178113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001565009,0.0001170503,0.8551272,0.0006108418,0.0002618501,0.0003065527,0.002144826,0.04070008,0.002567599,0.00720207,0.006501879,0.08430348],"study_design_scores_gemma":[0.00002115597,0.0002023581,0.8394816,0.0002121844,0.0001050645,0.0006293154,0.005977154,0.1200976,0.002079868,0.009404059,0.02169258,0.00009704075],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8966886,0.0006721515,0.05431348,0.0006219386,0.00004258175,0.0002156565,0.04051043,0.0004137598,0.00652132],"genre_scores_gemma":[0.9694736,0.0001786275,0.01555237,0.00003019521,0.0000175985,0.0001453567,0.01390468,0.00001798307,0.0006796581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0168737,"threshold_uncertainty_score":0.03355092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06256152996521608,"score_gpt":0.3786623658472948,"score_spread":0.3161008358820787,"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."}}