{"id":"W3182718252","doi":"10.1177/14738716211021591","title":"PrAVA: Preprocessing profiling approach for visual analytics","year":2021,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Victoria","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Preprocessor; Visual analytics; Visualization; Profiling (computer programming); Data pre-processing; Data visualization; Analytics; Data mining; Data science; Process (computing); Scope (computer science); Interactive visual analysis; Data analysis; Artificial intelligence; Programming language","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":[0.0004560263,0.0001482397,0.0001658122,0.0002288979,0.0002651996,0.0009888798,0.0003090339,0.00009872529,0.00001033008],"category_scores_gemma":[0.0005443324,0.0001564853,0.00006713932,0.001232582,0.0000214488,0.004861175,0.0001371802,0.00005980635,0.00002322656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006112322,"about_ca_system_score_gemma":0.0002921072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001073822,"about_ca_topic_score_gemma":4.595018e-7,"domain_scores_codex":[0.9984525,0.0000537733,0.0006001086,0.0002577623,0.0004029918,0.0002328633],"domain_scores_gemma":[0.9980962,0.00004659963,0.0003348302,0.0003259226,0.001112291,0.00008418147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001224086,0.0002857363,0.001130938,0.0006011155,0.00006135647,0.000001050893,0.002237692,0.02469758,0.0006406814,0.941126,0.002802295,0.0264033],"study_design_scores_gemma":[0.0004313692,0.00002937096,0.00006885726,0.00002005709,0.00001687832,0.000009046774,0.000369097,0.9707237,0.01389351,0.0006218469,0.01361472,0.0002014782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000303672,0.00003110634,0.9956926,0.00008680502,0.0001644162,0.0002836344,0.00001650076,0.0002912625,0.003130046],"genre_scores_gemma":[0.3589953,0.00007602062,0.624274,0.004306623,0.000289537,0.0001401931,0.0110243,0.00004225562,0.0008518661],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9460262,"threshold_uncertainty_score":0.9535791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02990580318123262,"score_gpt":0.3319223175313368,"score_spread":0.3020165143501042,"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."}}