{"id":"W3006195787","doi":"10.1109/siitme47687.2019.8990804","title":"Analysis of activity detection data pre-processing","year":2019,"lang":"en","type":"article","venue":"","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Computer science; Raw data; Process (computing); Data processing; MATLAB; Activity recognition; Data mining; Real-time computing; Database; Artificial intelligence; Operating system","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005894781,0.001035983,0.0007442878,0.002122225,0.0003401422,0.001059024,0.00074948,0.0004525455,0.004670211],"category_scores_gemma":[0.003412453,0.000181355,0.0007371145,0.001331193,0.0002573691,0.0006522354,0.0005376373,0.000725772,0.003364138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003817622,"about_ca_system_score_gemma":0.0008957022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002813781,"about_ca_topic_score_gemma":0.002151451,"domain_scores_codex":[0.9989911,0.00009564674,0.00008810264,0.0002151992,0.000502118,0.0001078785],"domain_scores_gemma":[0.9977291,0.0005761565,0.0001204701,0.0002402366,0.001242182,0.00009185533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006139596,0.0003881582,0.02025327,0.0008051505,0.0001116458,0.0006780712,0.0003457748,0.03332455,0.09570339,0.002926742,0.01045118,0.8343981],"study_design_scores_gemma":[0.00004300244,0.0007484165,0.09467392,0.0001872628,0.0001454439,0.00115957,0.0006873406,0.6568519,0.1894748,0.005534368,0.05034817,0.0001458217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1384553,0.001015057,0.8406892,0.0005246648,0.0004673534,0.0006143763,0.004763571,0.006163651,0.007306782],"genre_scores_gemma":[0.5873302,0.001030378,0.3880776,0.0001950616,0.000186544,0.0007860286,0.01129816,0.0006320231,0.01046399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004670211,"threshold_uncertainty_score":0.01562339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05009296384675027,"score_gpt":0.3013760072303821,"score_spread":0.2512830433836318,"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."}}