{"id":"W4408061162","doi":"10.14778/3705829.3705830","title":"RED: Effective Trajectory Representation Learning with Comprehensive Information","year":2024,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Trajectory; Representation (politics); Computer science; Artificial intelligence; Human–computer interaction; Political science; Physics; Politics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001587761,0.00009620791,0.00009326392,0.0001207553,0.00008824639,0.0003065516,0.0004567698,0.00001712298,0.000003313461],"category_scores_gemma":[0.00002344561,0.00006040655,0.00004611606,0.0005008037,0.00003442031,0.002183919,0.0002936185,0.0001305749,0.00001405101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005159832,"about_ca_system_score_gemma":0.00001156407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002086561,"about_ca_topic_score_gemma":3.379166e-7,"domain_scores_codex":[0.9991893,0.000008761836,0.0001524246,0.0001822583,0.0003394901,0.0001278246],"domain_scores_gemma":[0.9996009,0.00004303307,0.0001115183,0.0001022394,0.0001197853,0.0000224882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001113949,0.0001304038,0.004714446,0.001595738,0.000576887,0.000006207413,0.01658205,0.002934333,0.02219659,0.2243007,0.02151724,0.7053341],"study_design_scores_gemma":[0.003112573,0.001867511,0.06459003,0.002082548,0.0002953078,0.00008982325,0.006575787,0.3643622,0.3311724,0.0129979,0.2116327,0.001221195],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.504823,0.001048311,0.3060966,0.01228929,0.003999325,0.008744637,0.00002257394,0.002765713,0.1602106],"genre_scores_gemma":[0.991616,0.00003139092,0.00779383,0.00008765902,0.00004694363,0.00009178045,0.000004958463,0.000006500513,0.0003209798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7041128,"threshold_uncertainty_score":0.2956085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00837476942388125,"score_gpt":0.2239230127017759,"score_spread":0.2155482432778947,"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."}}