{"id":"W2220644283","doi":"10.1299/jsmecmd.2012.25._f-4_","title":"F102 Implementation of Large Scale Visualization in AVS/Express","year":2012,"lang":"en","type":"article","venue":"Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cybernet Systems Corporation (Canada)","funders":"","keywords":"Visualization; Computer science; Scale (ratio); Computer graphics (images); Human–computer interaction; Data mining; Cartography; Geography","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":[0.001661078,0.001192006,0.0006084979,0.0007934074,0.0004998463,0.00245335,0.002304358,0.0008262474,0.03013056],"category_scores_gemma":[0.003623999,0.0008051064,0.001036919,0.0005605101,0.0005964306,0.002407685,0.001608957,0.001577294,0.01038127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006639292,"about_ca_system_score_gemma":0.0009942173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004156117,"about_ca_topic_score_gemma":0.002137075,"domain_scores_codex":[0.998839,0.0001470371,0.0001047274,0.0001795388,0.0005513574,0.0001783684],"domain_scores_gemma":[0.9985978,0.00040165,0.00006573203,0.0004127581,0.0004151499,0.0001070091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004484262,0.0005804054,0.008155323,0.0008195784,0.0002755197,0.001958067,0.002780773,0.01690865,0.1763819,0.04966785,0.2004998,0.5374879],"study_design_scores_gemma":[0.0006042715,0.0003800364,0.003414357,0.0001721724,0.000105998,0.00140306,0.0002429585,0.2974373,0.2649963,0.0165359,0.4144182,0.0002895201],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0227967,0.0001235993,0.6469353,0.0002686197,0.0001237485,0.0002329883,0.002480239,0.3058876,0.02115119],"genre_scores_gemma":[0.3050132,0.0003670701,0.5764208,0.0005929403,0.0001002728,0.0007107382,0.01398336,0.04752578,0.05528577],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.03013056,"threshold_uncertainty_score":0.1007968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01721737328424072,"score_gpt":0.3307440633212132,"score_spread":0.3135266900369725,"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."}}