{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001431498,0.0002859773,0.000412341,0.0002369252,0.0008252611,0.0002224574,0.0007327637,0.0004845487,0.001289774],"category_scores_gemma":[0.0004670304,0.0003544985,0.000336602,0.001188592,0.0006786186,0.000446473,0.0004811715,0.0006371266,0.00003839223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006089669,"about_ca_system_score_gemma":0.001790041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01606279,"about_ca_topic_score_gemma":0.04416922,"domain_scores_codex":[0.996893,0.0008882122,0.0003312018,0.001203716,0.0002975771,0.0003862585],"domain_scores_gemma":[0.9976385,0.0002365817,0.0003428851,0.0006668555,0.000874536,0.000240613],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003267154,0.003408844,0.8136546,0.00187414,0.002427879,0.0003590645,0.05837886,0.1078606,0.00003507782,0.006045339,0.001143154,0.004485726],"study_design_scores_gemma":[0.004238321,0.0004807174,0.4921788,0.001739798,0.007464815,0.000004043008,0.2370596,0.1309778,0.0005022197,0.1102733,0.009525748,0.005554806],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9503143,0.0001050092,0.04422178,0.0002360914,0.0003529802,0.0006341835,0.000150145,0.0002424972,0.003742939],"genre_scores_gemma":[0.9968631,0.0001744982,0.00005341066,0.00006625003,0.0002131854,0.000007034588,0.0005628556,0.00001514614,0.002044495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3214758,"threshold_uncertainty_score":0.9998907,"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."}}