{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005567277,0.0003210614,0.0004893925,0.005857619,0.0003901498,0.00140462,0.000554611,0.0005735333,0.001459958],"category_scores_gemma":[0.004416069,0.0001716494,0.0004109988,0.004394255,0.0003889943,0.001692371,0.001290133,0.0002890966,0.0006909878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004642131,"about_ca_system_score_gemma":0.0004841206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003306837,"about_ca_topic_score_gemma":0.00395627,"domain_scores_codex":[0.9991338,0.0002316865,0.00005880385,0.000192785,0.0002793713,0.0001035129],"domain_scores_gemma":[0.9979621,0.000640256,0.0004999359,0.0002623776,0.0005103416,0.0001249645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001007576,0.0004387639,0.5357028,0.0002670489,0.0005258203,0.0004081613,0.000928092,0.07136964,0.03067637,0.01311202,0.002884323,0.3426793],"study_design_scores_gemma":[0.00003389945,0.0003827106,0.3013328,0.00004530125,0.0002711809,0.001225034,0.002553051,0.6594351,0.01760911,0.01250343,0.004525152,0.00008322789],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8721907,0.0001877297,0.1207644,0.00008423036,0.00003027881,0.00007953633,0.001018282,0.0005041513,0.005140608],"genre_scores_gemma":[0.9890007,0.00004923345,0.01017171,0.000006808079,0.00001361919,0.00001733717,0.000370583,0.00001752361,0.0003524186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005857619,"threshold_uncertainty_score":0.006575167,"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."}}