{"id":"W2823573924","doi":"10.1109/mdm.2018.00024","title":"Tensor Methods for Group Pattern Discovery of Pedestrian Trajectories","year":2018,"lang":"en","type":"article","venue":"","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cluster analysis; Trajectory; Group (periodic table); Data mining; Tensor (intrinsic definition); Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002024518,0.00005991578,0.00009509937,0.00004215743,0.00008311604,0.00005325469,0.0003324696,0.00003323332,0.00001138879],"category_scores_gemma":[0.00001658778,0.00004720218,0.00006675994,0.000180723,0.00006298724,0.0002585367,0.00006229195,0.00002489341,0.000002875333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009205152,"about_ca_system_score_gemma":0.00001616105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000105741,"about_ca_topic_score_gemma":0.00002898874,"domain_scores_codex":[0.999478,0.00002222805,0.0001529635,0.000184463,0.00004725633,0.0001150937],"domain_scores_gemma":[0.9994081,0.00009813354,0.00006485933,0.0003262143,0.00007661351,0.00002613274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006081189,0.00007396402,0.000782176,0.0000211631,0.00001484793,7.180734e-8,0.0001930316,1.428679e-7,0.03065625,0.1785852,0.001135063,0.7885321],"study_design_scores_gemma":[0.0003258543,0.0009974701,0.007847327,0.00001162827,0.0000150033,0.0000071597,0.00009295901,0.01780637,0.8488881,0.05872182,0.06499592,0.000290399],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004018656,0.000009973292,0.9940903,0.0002858843,0.00009635086,0.0002077462,0.000004101635,0.0001606971,0.001126254],"genre_scores_gemma":[0.4626335,0.000002796557,0.5366917,0.00008578477,0.00006887176,0.00006647791,6.907682e-7,0.000003469516,0.0004467489],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8182318,"threshold_uncertainty_score":0.1924849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050881979361468,"score_gpt":0.3519156673488131,"score_spread":0.3214068475551984,"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."}}