{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00579977,0.0001267534,0.0002754473,0.0009688027,0.0001834243,0.0008339063,0.001242064,0.0001557571,0.0001909795],"category_scores_gemma":[0.0004304178,0.0001133329,0.0002063202,0.0006894324,0.0003014765,0.003129367,0.0001111499,0.0004805739,0.00003444219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003356648,"about_ca_system_score_gemma":0.0003278214,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01899003,"about_ca_topic_score_gemma":0.01026525,"domain_scores_codex":[0.9961556,0.0004273948,0.001567725,0.0001938011,0.001436234,0.000219295],"domain_scores_gemma":[0.9978329,0.0003763011,0.000382117,0.0003455772,0.0009067435,0.000156371],"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":[0.0001228443,0.0007475497,0.7958723,0.000475513,0.0007273981,0.00006511807,0.02039435,0.009459156,0.00003154512,0.1210786,0.002076962,0.04894871],"study_design_scores_gemma":[0.001835979,0.0002516343,0.2407133,0.002556002,0.0002013857,0.00007508275,0.08172485,0.1872568,0.00003137406,0.002950047,0.4814645,0.000939136],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8804221,0.0008502385,0.1096621,0.002571145,0.003600942,0.0004994142,0.0002703841,0.0001147266,0.00200903],"genre_scores_gemma":[0.9990779,0.00009288012,0.00002057266,0.00009281694,0.0005236334,0.000009886448,0.0001567127,0.000004974842,0.0000206028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.555159,"threshold_uncertainty_score":0.9875426,"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."}}