{"id":"W3105110352","doi":"10.1109/wimob50308.2020.9253403","title":"RNN-Based User Trajectory Prediction Using a Preprocessed Dataset","year":2020,"lang":"en","type":"article","venue":"","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Trajectory; Mobility model; Focus (optics); Recurrent neural network; Artificial intelligence; Mobile device; Mobile computing; Machine learning; Cellular network; Deep learning; Quality of service; Face (sociological concept); Mobile telephony; Mobile service; Artificial neural network; Service (business); Distributed computing; Computer network; Mobile radio; World Wide Web","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":[0.0003242033,0.0008047731,0.0003863977,0.0006986095,0.0002524795,0.0002344662,0.0006600709,0.000501331,0.001018778],"category_scores_gemma":[0.001594103,0.0002097311,0.0003973494,0.0007544833,0.0001387455,0.0005712479,0.0002996935,0.0006926279,0.0005896613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006471777,"about_ca_system_score_gemma":0.0006285423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03483783,"about_ca_topic_score_gemma":0.03843666,"domain_scores_codex":[0.9998617,0.00002293866,0.0000119485,0.00005534222,0.00002242295,0.00002571366],"domain_scores_gemma":[0.9995808,0.000125624,0.00004459492,0.00008200071,0.0001440177,0.00002293254],"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":[0.0004849949,0.0003317028,0.02239797,0.0001526669,0.0001443953,0.0004329916,0.00009914274,0.7268937,0.009399493,0.0007186902,0.00597054,0.2329737],"study_design_scores_gemma":[0.000003171088,0.00002952566,0.002407196,0.000004274183,0.000007998088,0.00002791629,0.00001149722,0.9954483,0.001634025,0.0001843461,0.0002373885,0.000004299017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7497055,0.0005822144,0.2363649,0.0003766965,0.000209533,0.000138641,0.006722296,0.0040061,0.00189398],"genre_scores_gemma":[0.9378374,0.0002015896,0.05064685,0.00003456872,0.00002802658,0.0000880615,0.009847951,0.00003456348,0.001280903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03483783,"threshold_uncertainty_score":0.06927013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07713782862653798,"score_gpt":0.3313304421059023,"score_spread":0.2541926134793644,"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."}}