{"id":"W2976918864","doi":"10.22215/etd/2018-12627","title":"Synthetic Data Generator for User Behavioral Analytics Systems","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Generator (circuit theory); Synthetic data; Analytics; Data analysis; Data mining; Process (computing); Behavioral pattern; Data science; Artificial intelligence; Power (physics); Software engineering","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":[0.000374128,0.0002704393,0.0004327845,0.0001528861,0.0002143955,0.000853347,0.002415058,0.0002107704,0.00008934543],"category_scores_gemma":[0.00004265229,0.0002250866,0.0001460137,0.0003003215,0.00001948267,0.0005199796,0.000255919,0.0001017673,0.00005513041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003489077,"about_ca_system_score_gemma":0.0001394204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002086988,"about_ca_topic_score_gemma":0.0006347369,"domain_scores_codex":[0.9979405,0.00002434392,0.00048361,0.0009034623,0.0003201676,0.0003278503],"domain_scores_gemma":[0.9969077,0.00003838725,0.0003249171,0.002217314,0.0004113324,0.0001003848],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001196834,0.0009128927,0.0003668072,0.001791711,0.002086908,0.00004894104,0.003213201,0.0007444756,0.002361955,0.171986,0.6275295,0.1888379],"study_design_scores_gemma":[0.0001184665,0.0001266005,0.00004235846,0.00009118143,0.0003701072,0.00000500891,0.0003913653,0.8945914,0.0003861245,0.0000540642,0.1033518,0.0004714935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01746139,0.001242815,0.9624902,0.00007352541,0.006998485,0.001163731,0.0005217626,0.0004306451,0.009617473],"genre_scores_gemma":[0.2058586,0.00009993964,0.4260347,0.000146267,0.003714923,0.0002334576,0.01909145,0.000260189,0.3445605],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8938469,"threshold_uncertainty_score":0.9178764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08022493173138308,"score_gpt":0.3224869765057953,"score_spread":0.2422620447744123,"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."}}