{"id":"W4388860803","doi":"10.1145/3615885.3628008","title":"A Data Augmentation Algorithm for Trajectory Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Horizon 2020 Framework Programme; Canada First Research Excellence Fund; Ocean Frontier Institute; Dalhousie University","keywords":"Trajectory; Computer science; Data mining; Perturbation (astronomy); Raw data; Machine learning; Field (mathematics); Data point; 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":[0.0006739915,0.00007608446,0.00007227101,0.0001055901,0.00009070934,0.0002829001,0.005064747,0.00001649402,0.00002397245],"category_scores_gemma":[0.00002597464,0.00006852287,0.00001243174,0.0004616303,0.00001298674,0.003154471,0.00369613,0.00002980298,0.0002753649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007915048,"about_ca_system_score_gemma":0.00002596626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002247867,"about_ca_topic_score_gemma":0.00002157294,"domain_scores_codex":[0.9988139,0.00001525015,0.0001335673,0.0006272632,0.0002001576,0.0002098704],"domain_scores_gemma":[0.9969609,0.00007808544,0.00003403673,0.002869387,0.00001755464,0.00003998944],"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":[3.117592e-7,0.00001191234,0.000003134984,0.000005294243,0.00001207791,0.000002710232,0.00001523019,0.000001811985,0.00001062546,0.002509163,0.3404072,0.6570206],"study_design_scores_gemma":[0.0002496231,0.00001539352,0.0001888776,0.000002379509,0.000006552394,4.903304e-7,0.00004168267,0.7872596,0.00002748892,0.00064202,0.2114822,0.00008372679],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001518006,0.0000157831,0.9953166,0.0010253,0.0005890381,0.0002650087,0.001153549,0.0005180705,0.001101532],"genre_scores_gemma":[0.0001359787,0.00004686484,0.972515,0.0004823315,0.0002366544,0.00002866141,0.01888878,0.0000111641,0.007654577],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7872577,"threshold_uncertainty_score":0.9411644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1781519273023425,"score_gpt":0.3579250594167012,"score_spread":0.1797731321143587,"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."}}