{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.003089132,0.001358199,0.001748199,0.001681867,0.0005746221,0.0005492488,0.003473765,0.0006461487,0.0008265611],"category_scores_gemma":[0.0002104099,0.001420541,0.0005787488,0.003756626,0.0003567437,0.005609186,0.001488198,0.0008056893,0.0004348181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005487467,"about_ca_system_score_gemma":0.0004617353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007170119,"about_ca_topic_score_gemma":0.001062553,"domain_scores_codex":[0.9886479,0.001043456,0.003114529,0.00191709,0.002281431,0.002995564],"domain_scores_gemma":[0.9933727,0.0003345559,0.001565809,0.002963785,0.0006290776,0.001134093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000132335,0.004844828,0.6161262,0.0007886359,0.0004500124,0.00006450467,0.03372444,0.0005451193,0.008890118,0.2950944,0.02998541,0.009353953],"study_design_scores_gemma":[0.01993212,0.001495268,0.5839323,0.001504247,0.0007588199,0.0002329161,0.022015,0.07097325,0.06590719,0.005945696,0.2186207,0.008682482],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8260091,0.001619238,0.1538249,0.001313838,0.003032775,0.003131576,0.0007580823,0.001430879,0.008879581],"genre_scores_gemma":[0.9859192,0.0002909288,0.008694625,0.001876495,0.0005988374,0.0001767857,0.001129628,0.00021866,0.001094855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2891487,"threshold_uncertainty_score":0.9999169,"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."}}