{"id":"W2945319864","doi":"10.1145/3331651.3331653","title":"Urban Human Mobility","year":2019,"lang":"en","type":"article","venue":"ACM SIGKDD Explorations Newsletter","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":107,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Multidisciplinary approach; Data science; Context (archaeology); Mobility model; Perspective (graphical); Field (mathematics); Global Positioning System; Urban computing; Human dynamics; Human–computer interaction; Artificial intelligence; Telecommunications; Sociology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002519001,0.0003688185,0.0002175946,0.001098971,0.0006082617,0.001404399,0.0005197416,0.0005150872,0.009492499],"category_scores_gemma":[0.00133685,0.0001695871,0.0003505315,0.002411705,0.0005238373,0.002050304,0.001202847,0.0004736707,0.001712601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204019,"about_ca_system_score_gemma":0.0008329767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00910354,"about_ca_topic_score_gemma":0.01072676,"domain_scores_codex":[0.9997316,0.00007308069,0.00001216038,0.00006772072,0.00006797424,0.0000474917],"domain_scores_gemma":[0.9997645,0.00006662401,0.00003391505,0.00003757349,0.00007662112,0.00002068],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00004255321,0.00003081391,0.01885827,0.0006605436,0.0000761215,0.0003142694,0.001400452,0.03941762,0.001048311,0.6163098,0.07855998,0.2432813],"study_design_scores_gemma":[0.00001097019,0.00005605209,0.02008269,0.0005067254,0.00006362519,0.0007751039,0.003324388,0.0594993,0.0009869995,0.1968902,0.7177368,0.00006724621],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1099405,0.03508145,0.2989787,0.01839554,0.001543793,0.0003538232,0.01508355,0.001487348,0.5191354],"genre_scores_gemma":[0.8824435,0.03045267,0.0278974,0.001085739,0.0004781792,0.0002627802,0.00694996,0.0001306654,0.05029897],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009492499,"threshold_uncertainty_score":0.03175551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0367557887767918,"score_gpt":0.3235828484170946,"score_spread":0.2868270596403028,"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."}}