{"id":"W2996118209","doi":"10.1109/visual.2019.8933542","title":"Uncovering Data Landscapes through Data Reconnaissance and Task Wrangling","year":2019,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Data science; Computer science; Task (project management); Domain (mathematical analysis); Visualization; Set (abstract data type); Data visualization; Data mining; Systems engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04477216,0.001758788,0.001075151,0.006441453,0.003744355,0.009685013,0.003996475,0.002427244,0.002242124],"category_scores_gemma":[0.08559512,0.001644156,0.001499434,0.002836885,0.008476706,0.01496233,0.01017747,0.004789452,0.0007741053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002954905,"about_ca_system_score_gemma":0.004072247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002691399,"about_ca_topic_score_gemma":0.005471342,"domain_scores_codex":[0.971217,0.01898886,0.00123438,0.004104343,0.003255863,0.00119949],"domain_scores_gemma":[0.8847605,0.07810877,0.007674443,0.02012047,0.007083938,0.00225191],"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":[0.0006195332,0.0008126427,0.03010572,0.003035493,0.0001640978,0.001162556,0.2541367,0.01678525,0.03652327,0.08579069,0.005798675,0.5650653],"study_design_scores_gemma":[0.0002601738,0.001970291,0.04187455,0.003005916,0.0002570779,0.003345009,0.2268763,0.1232664,0.04977166,0.3676922,0.1808833,0.0007971543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.18523,0.001079274,0.7946513,0.004386391,0.00006608183,0.001419454,0.0002592494,0.001542412,0.01136573],"genre_scores_gemma":[0.3408927,0.0004439557,0.6550007,0.000466545,0.00002519866,0.0009002506,0.0003195835,0.0003257609,0.001625401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04477216,"threshold_uncertainty_score":0.2367806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08160704378035767,"score_gpt":0.330723064920984,"score_spread":0.2491160211406264,"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."}}