{"id":"W4281686790","doi":"10.5194/isprs-archives-xliii-b4-2022-247-2022","title":"A REVIEW OF URBAN HUMAN MOBILITY RESEARCH BASED ON CROWD-SOURCED DATA AND SPACE-TIME AND SEMANTIC ANALYSIS","year":2022,"lang":"en","type":"review","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Metadata; Computer science; Data science; Urbanization; Urban planning; World Wide Web; Engineering","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":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.008634853,0.0005050223,0.001203506,0.002252265,0.003144818,0.0007828095,0.002863384,0.0001393432,0.00005356148],"category_scores_gemma":[0.002992979,0.0003330183,0.0006778094,0.003037253,0.008200706,0.0003799344,0.001792228,0.0008211003,0.000001823174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020591,"about_ca_system_score_gemma":0.0007688139,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8740059,"about_ca_topic_score_gemma":0.3496111,"domain_scores_codex":[0.9913781,0.00206164,0.002123477,0.0007335402,0.003140619,0.0005626697],"domain_scores_gemma":[0.9914026,0.004197531,0.002639917,0.001107895,0.0004310246,0.0002210442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004613328,0.000039184,0.00006112552,0.001234444,0.0002917238,2.185029e-7,0.003062996,0.0005123933,0.00001993863,0.0000108189,0.00006209192,0.9946589],"study_design_scores_gemma":[0.0002928828,0.0001290289,0.0002468029,0.004243487,0.0005075876,0.00001445363,0.001776408,0.9081116,0.00003187988,0.0009684653,0.08337121,0.000306141],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008898046,0.0323479,0.9388622,0.006787805,0.0010652,0.003629931,0.001121034,0.00008531187,0.01521079],"genre_scores_gemma":[0.7996898,0.1979654,0.000726171,0.0008707172,0.0001649471,0.000001872532,0.0004002162,0.00001975808,0.0001611277],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9943528,"threshold_uncertainty_score":0.9999122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07055104192569583,"score_gpt":0.3679538956010373,"score_spread":0.2974028536753415,"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."}}