{"id":"W2094840417","doi":"10.1109/tvcg.2013.61","title":"ParaGlide: Interactive Parameter Space Partitioning for Computer Simulations","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Parameter space; Workload; Visualization; Process (computing); Domain (mathematical analysis); Set (abstract data type); Task (project management); Data mining; Human–computer interaction; User interface; Interactive visual analysis; Space (punctuation); Interface (matter); Graphical user interface; Data visualization; Systems engineering; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001258405,0.0002643861,0.0002437103,0.0004278298,0.0004993369,0.0008022376,0.000299401,0.0001165832,0.00005996304],"category_scores_gemma":[0.000008616702,0.0002650785,0.0001301965,0.0007378593,0.00008467738,0.001240237,0.00001109293,0.0001555677,0.00003625369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002886622,"about_ca_system_score_gemma":0.00003559862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001717671,"about_ca_topic_score_gemma":0.00001339358,"domain_scores_codex":[0.998347,0.000120295,0.0004258707,0.0005522409,0.0002582188,0.0002963978],"domain_scores_gemma":[0.9983684,0.0004983894,0.0001578948,0.0003897636,0.0004028364,0.0001827032],"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":[0.00001849843,0.0006684865,0.0002083759,0.00007251391,0.000197225,0.000001970034,0.001684202,0.03261175,0.00003632966,0.9368012,0.00456841,0.02313101],"study_design_scores_gemma":[0.0006214119,0.0002529145,0.0002941348,0.0000516764,0.00002840231,0.000007752373,0.00002363582,0.9903589,0.0006898275,0.003473973,0.003881262,0.0003160778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00356736,0.00001060253,0.9944991,0.0002671582,0.0007280515,0.0005633443,0.00003967007,0.0003076827,0.00001698391],"genre_scores_gemma":[0.9566309,0.00006049813,0.03883852,0.003955489,0.0001333366,0.0001146126,0.00008116711,0.00003447104,0.0001509878],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9577472,"threshold_uncertainty_score":0.9999802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02655409984300039,"score_gpt":0.3052135667651903,"score_spread":0.2786594669221899,"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."}}