{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000755789,0.0001328216,0.0001863057,0.0001349575,0.0009389145,0.000177043,0.0005797626,0.0001153629,0.006482612],"category_scores_gemma":[0.0003641611,0.0001370425,0.0001490339,0.0005422241,0.0002499291,0.0006297766,0.00006828739,0.0001923431,0.003427998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000147623,"about_ca_system_score_gemma":0.0001389289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003406378,"about_ca_topic_score_gemma":0.008887506,"domain_scores_codex":[0.9982098,0.0003483635,0.000322301,0.0003983428,0.000399513,0.0003216358],"domain_scores_gemma":[0.9982471,0.0002633596,0.00008716308,0.001108595,0.0001708903,0.0001228999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002379759,0.001114691,0.2847053,0.00006205781,0.0002091369,0.000006407106,0.0949783,0.0008138325,0.01459353,0.1441785,0.4529069,0.006407578],"study_design_scores_gemma":[0.0005513287,0.00008723201,0.0157579,0.00002321368,0.00008721892,2.091609e-7,0.01112741,0.0001943996,0.0008783243,0.01440172,0.9562712,0.0006197932],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9322181,0.00003819474,0.001855172,0.03522222,0.0002957495,0.0007142736,0.000009557358,0.0002681871,0.02937852],"genre_scores_gemma":[0.9841303,0.000004961251,0.0002128028,0.003686559,0.0005213185,0.0001134349,0.00008086752,0.00001352906,0.01123623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5033644,"threshold_uncertainty_score":0.997348,"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."}}