{"id":"W2953298990","doi":"10.48550/arxiv.1809.00957","title":"Road User Abnormal Trajectory Detection using a Deep Autoencoder","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Autoencoder; Focus (optics); Computer science; Artificial intelligence; Deep learning; Anomaly detection; Trajectory; Outlier; Computer vision; Pattern recognition (psychology); Machine learning","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"],"consensus_categories":[],"category_scores_codex":[0.0001832685,0.0002907928,0.0002343844,0.000328941,0.0003760028,0.0001415359,0.001244319,0.0003851874,0.00006474914],"category_scores_gemma":[0.000007474094,0.0003517779,0.0002371153,0.0005852039,0.0001304832,0.0004739234,0.001097856,0.0005046069,0.0001081937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003355746,"about_ca_system_score_gemma":0.0001498885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004098962,"about_ca_topic_score_gemma":0.00008188449,"domain_scores_codex":[0.9982306,0.00009388469,0.0002165637,0.001035305,0.00009285488,0.000330778],"domain_scores_gemma":[0.9981636,0.00002182123,0.00027465,0.001195226,0.0001999043,0.0001447475],"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":[0.0001200137,0.0006693709,0.003446372,0.0002537627,0.0004969854,0.000310631,0.001134735,0.8556618,0.003864304,0.09704892,0.000523121,0.03646997],"study_design_scores_gemma":[0.0001488177,0.00005616561,0.001503452,0.0000244147,0.00004955411,0.00001957706,0.00002112617,0.9851106,0.002333746,0.009002504,0.001313548,0.0004165168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2141962,0.00001855589,0.7835023,0.00001296074,0.0003001738,0.0002611577,0.000003866172,0.0006890706,0.001015693],"genre_scores_gemma":[0.9730043,0.00004071066,0.02602578,0.00005789761,0.0001661062,0.000004414373,0.00000313981,0.00002226975,0.0006754157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.758808,"threshold_uncertainty_score":0.9998934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05912866758396525,"score_gpt":0.2004498826492755,"score_spread":0.1413212150653103,"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."}}