{"id":"W3198292399","doi":"10.48550/arxiv.2109.02715","title":"Individual Mobility Prediction via Attentive Marked Temporal Point Processes","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Point (geometry); Psychology; Cognitive psychology; Computer science; Artificial intelligence; History; 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":[0.0007265765,0.0006889419,0.0006842552,0.0007478344,0.0002916519,0.0006104719,0.001588897,0.0008860252,0.001045825],"category_scores_gemma":[0.002384703,0.0004646329,0.0008306424,0.0007306969,0.0006086952,0.001383442,0.001062875,0.001487068,0.0003613514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006038221,"about_ca_system_score_gemma":0.0004832526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01044231,"about_ca_topic_score_gemma":0.009797333,"domain_scores_codex":[0.9997568,0.00005502005,0.00001011894,0.0001034322,0.00003862773,0.00003601681],"domain_scores_gemma":[0.9990521,0.0005656998,0.0001315563,0.00006798436,0.0001130398,0.00006953016],"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":[0.0001439387,0.00008912009,0.008059561,0.00003886383,0.00007056141,0.0001251155,0.0001196482,0.94216,0.0013779,0.008596689,0.001153969,0.03806463],"study_design_scores_gemma":[0.000002171164,0.000008921142,0.0002995444,0.000001763865,0.000004406025,0.000006064909,0.00000395803,0.9972003,0.0001032981,0.002289281,0.0000779106,0.000002351355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2292552,0.0005210096,0.7667397,0.0006449895,0.00009395168,0.00004522956,0.0005436212,0.0006210948,0.001535214],"genre_scores_gemma":[0.9761378,0.0002686398,0.02128682,0.00007881277,0.00007583376,0.00004464977,0.000570234,0.00002257156,0.001514642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01044231,"threshold_uncertainty_score":0.0207631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06713472222039917,"score_gpt":0.2176150845168542,"score_spread":0.1504803622964551,"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."}}