{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001616517,0.001219998,0.001542478,0.001642816,0.0009590203,0.001190707,0.00252658,0.001727252,0.003080847],"category_scores_gemma":[0.005730849,0.0007309414,0.001701885,0.00296049,0.001203221,0.00278596,0.002829319,0.003516344,0.002483801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006958396,"about_ca_system_score_gemma":0.001581287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004337087,"about_ca_topic_score_gemma":0.004765207,"domain_scores_codex":[0.9986533,0.0002725656,0.0001202248,0.0004501375,0.0003949652,0.0001087132],"domain_scores_gemma":[0.9981122,0.000646258,0.0001511481,0.0005211645,0.0004901381,0.0000791319],"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":[0.000594116,0.0003472389,0.003368191,0.0002921839,0.0001535235,0.000268554,0.0003279353,0.2722513,0.02054483,0.01564888,0.0177944,0.6684088],"study_design_scores_gemma":[0.00002308682,0.00009291345,0.0003959209,0.00002433729,0.00001523684,0.0001210207,0.00005254078,0.9786767,0.005871536,0.007835696,0.006870153,0.00002095992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007383029,0.0002300019,0.9882079,0.0002367648,0.0001626486,0.0001232242,0.0005952711,0.002429756,0.0006314262],"genre_scores_gemma":[0.1173587,0.0003617622,0.8731009,0.0002793701,0.0002083811,0.000586287,0.005024571,0.0003322773,0.00274774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004337087,"threshold_uncertainty_score":0.01030648,"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."}}