{"id":"W3047801338","doi":"10.1109/mdm48529.2020.00032","title":"Learning Semantic Relationships of Geographical Areas based on Trajectories","year":2020,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Leverage (statistics); Computer science; Trajectory; Space (punctuation); Focus (optics); Range (aeronautics); Scale (ratio); Latent semantic analysis; Data mining; Artificial intelligence; Data science; Geography; Cartography","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":[0.000580936,0.0008816565,0.0005648125,0.003121096,0.0004399939,0.001136931,0.001031441,0.0009224363,0.001086316],"category_scores_gemma":[0.004970316,0.0003280278,0.001078256,0.003298897,0.0006905744,0.002681253,0.001256884,0.001345595,0.0005801664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008971346,"about_ca_system_score_gemma":0.0008655487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01566334,"about_ca_topic_score_gemma":0.02340803,"domain_scores_codex":[0.9992806,0.0001554454,0.00005632538,0.0003346635,0.0001146008,0.00005833222],"domain_scores_gemma":[0.997964,0.001083377,0.0003436826,0.0002735539,0.000243506,0.00009190342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005378136,0.0005982361,0.1487482,0.0006385337,0.0004258052,0.0007721603,0.001801816,0.4500078,0.009528058,0.03167204,0.01117261,0.344097],"study_design_scores_gemma":[0.00001504361,0.00005970119,0.009876176,0.00005567869,0.00003551043,0.0001215514,0.0004635663,0.9645322,0.001270793,0.01953411,0.004012792,0.00002289689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3422816,0.001233539,0.6418943,0.001251834,0.0001032153,0.000251982,0.008502711,0.001694016,0.002786845],"genre_scores_gemma":[0.8456947,0.0008050518,0.139653,0.0001109928,0.00008314295,0.0001892851,0.01191389,0.000103223,0.001446601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01566334,"threshold_uncertainty_score":0.03114432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04067991884811699,"score_gpt":0.2817475723897181,"score_spread":0.2410676535416011,"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."}}