{"id":"W4391048039","doi":"10.2139/ssrn.4701536","title":"A Geographic-Semantic Context-Aware Urban Commuting Flow Prediction Model Using Graph Neural Network","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Leverage (statistics); Computer science; Artificial intelligence; Adjacency list; Dependency (UML); Graph; Machine learning; Spatial network; Artificial neural network; Context (archaeology); Gravity model of trade; Data mining; Data science; Geography; Theoretical computer science; Mathematics","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":[0.0003152105,0.0006729939,0.0008848247,0.00106009,0.0003371133,0.000570578,0.001326433,0.0008559712,0.001520632],"category_scores_gemma":[0.0007079931,0.0003773326,0.0006953302,0.001307845,0.0002375182,0.0009148354,0.0005204696,0.0007693945,0.0003545627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008209954,"about_ca_system_score_gemma":0.000779752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0441935,"about_ca_topic_score_gemma":0.04397574,"domain_scores_codex":[0.9998566,0.00001780978,0.000008045798,0.0000780093,0.00001723834,0.0000222112],"domain_scores_gemma":[0.9998259,0.00008035879,0.00001732331,0.00001255352,0.00004996162,0.00001397266],"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":[0.0001426111,0.0002300939,0.004091018,0.00004852039,0.0001010121,0.00006471708,0.00002751525,0.9129768,0.0009119224,0.001326927,0.001910533,0.07816827],"study_design_scores_gemma":[0.000001980678,0.000004941634,0.0002244841,0.000001455636,0.000006506568,0.000002021017,0.000001930834,0.9993966,0.00004442592,0.0002763227,0.00003795112,0.000001400676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3152315,0.001620043,0.6724049,0.001038567,0.0003237682,0.0001447474,0.002505243,0.002272106,0.004459175],"genre_scores_gemma":[0.9620044,0.0002985044,0.03388619,0.0001060318,0.00006473958,0.00009232712,0.0012033,0.00003606071,0.002308362],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0441935,"threshold_uncertainty_score":0.08787256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101638659110272,"score_gpt":0.285528178253029,"score_spread":0.2645117916619263,"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."}}