{"id":"W2032892927","doi":"10.1057/palgrave.ivs.9500015","title":"Representing High-Dimensional Data Sets as Closed Surfaces","year":2002,"lang":"en","type":"article","venue":"Information Visualization","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Visualization; Information visualization; Set (abstract data type); ENCODE; Data visualization; Similarity (geometry); Scientific visualization; Surface (topology); Measure (data warehouse); Data set; Data mining; Interactive visualization; Theoretical computer science; Artificial intelligence; Image (mathematics); Geometry","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":[0.001221214,0.0008168478,0.000707983,0.00248127,0.0007365847,0.005384685,0.001355703,0.00137036,0.003714622],"category_scores_gemma":[0.006684374,0.0004542003,0.0009583694,0.002854058,0.001758264,0.003383061,0.002808145,0.001359976,0.001014382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004377859,"about_ca_system_score_gemma":0.000696225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001150744,"about_ca_topic_score_gemma":0.001143273,"domain_scores_codex":[0.9988436,0.0004245195,0.00009115368,0.0001539254,0.0004187874,0.00006796025],"domain_scores_gemma":[0.9974195,0.001528608,0.0001459758,0.0005012377,0.0003144566,0.0000901492],"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":[0.0002817276,0.0001808421,0.004236713,0.0008625558,0.0001203527,0.001002556,0.006611538,0.2826726,0.04292639,0.3125372,0.01097655,0.3375909],"study_design_scores_gemma":[0.00003786781,0.0001097864,0.001503568,0.00008786512,0.00002726577,0.0006166471,0.001558704,0.7584547,0.01112407,0.190118,0.03627133,0.00009021794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02027095,0.0001206387,0.9750224,0.0003727104,0.00004817871,0.00007938948,0.0003673258,0.001490529,0.00222793],"genre_scores_gemma":[0.2525851,0.0004578968,0.7432681,0.0001299793,0.00004127585,0.0002986373,0.0009899603,0.0003722153,0.001856862],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005384685,"threshold_uncertainty_score":0.01242667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0523778027268075,"score_gpt":0.3275444828299672,"score_spread":0.2751666801031597,"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."}}