{"id":"W3003718360","doi":"10.1177/1473871619896101","title":"Visualization in the preprocessing phase: Getting insights from enterprise professionals","year":2020,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canadian Bureau for International Education","keywords":"Computer science; Visualization; Data science; Workflow; Data visualization; Process (computing); Raw data; Scope (computer science); Set (abstract data type); Data pre-processing; Preprocessor; Data mining; Knowledge management; Database; Artificial intelligence","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":[0.02283765,0.0008656688,0.0007132031,0.003638373,0.004090487,0.00726665,0.001053297,0.002242692,0.001772473],"category_scores_gemma":[0.05939208,0.0007222597,0.0005638596,0.002425238,0.00311513,0.008395302,0.005854632,0.002762644,0.0006974646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002543142,"about_ca_system_score_gemma":0.004036296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002663202,"about_ca_topic_score_gemma":0.004860415,"domain_scores_codex":[0.9865137,0.008310375,0.0006076276,0.0009762624,0.001827984,0.00176401],"domain_scores_gemma":[0.9371006,0.04967363,0.001885627,0.001703986,0.0072841,0.002352055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001127859,0.0001694296,0.01624499,0.0005345754,0.0000128609,0.001224858,0.919671,0.0002179575,0.003620463,0.003118482,0.005090749,0.04998185],"study_design_scores_gemma":[0.00002302743,0.0002295988,0.009911085,0.0009734262,0.00002807158,0.001033492,0.9319177,0.001892415,0.002976644,0.006384412,0.04455151,0.00007861944],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9265094,0.001655311,0.0488999,0.01140984,0.0001771521,0.0003619817,0.0003583406,0.0002525919,0.01037544],"genre_scores_gemma":[0.9595829,0.001778715,0.03334855,0.001893884,0.00008735833,0.0003301195,0.0003756094,0.000198167,0.002404708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02283765,"threshold_uncertainty_score":0.1207785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754475676661746,"score_gpt":0.3509752933536995,"score_spread":0.323430536587082,"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."}}