{"id":"W3184219015","doi":"10.1016/j.compenvurbsys.2021.101684","title":"Measuring urban regional similarity through mobility signatures","year":2021,"lang":"en","type":"article","venue":"Computers Environment and Urban Systems","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Similarity (geometry); TRIPS architecture; Geography; Population; Economic geography; Computer science; Real estate; Regional science; Business; Demography; Artificial intelligence; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006948963,0.0001465417,0.0002538591,0.00003070218,0.0007172225,0.0001644959,0.0001767379,0.0001318968,0.0001140115],"category_scores_gemma":[0.00003295753,0.0001511025,0.0001171174,0.0001249223,0.0003717582,0.0001663999,0.00007253545,0.0001638518,0.00001701031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001964194,"about_ca_system_score_gemma":0.00008034275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001292591,"about_ca_topic_score_gemma":0.0002427689,"domain_scores_codex":[0.9979715,0.0005265885,0.0002718831,0.0004791435,0.0004886867,0.0002622401],"domain_scores_gemma":[0.9991941,0.000209219,0.00009127206,0.00033011,0.00003558828,0.0001396363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007877412,0.002055106,0.6315413,0.0007406332,0.001030325,0.0001294704,0.1218604,0.03242627,0.001274504,0.08772277,0.1116706,0.00946983],"study_design_scores_gemma":[0.0007959248,0.00006883102,0.06510884,0.0001835341,0.0001699537,0.000003822706,0.01068749,0.01302545,0.0001523943,0.001374188,0.9075899,0.000839673],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8588457,0.03564385,0.08561373,0.004625339,0.001696471,0.001350668,0.00003771621,0.0003460789,0.01184041],"genre_scores_gemma":[0.9974685,0.0002477841,0.0002108377,0.0002285942,0.0005157012,0.00001955543,0.0000278686,0.000008118115,0.001273084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7959193,"threshold_uncertainty_score":0.6161783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04673851890202465,"score_gpt":0.2421972248214527,"score_spread":0.195458705919428,"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."}}