{"id":"W2147842609","doi":"10.1109/tvcg.2006.72","title":"HDR VolVis: high dynamic range volume visualization","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Research Institute for Applied Mechanics, Kyushu University; Peking University; Ryerson University; University of Minnesota","keywords":"Computer science; Tone mapping; Volume rendering; High dynamic range; Rendering (computer graphics); Visualization; Computer graphics (images); Pixel; Compositing; Parallel rendering; Data visualization; Image resolution; Artificial intelligence; Computer vision; Dynamic range; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009085728,0.0008480549,0.0006094876,0.001269606,0.0004172261,0.002195763,0.00178465,0.0007676383,0.00963383],"category_scores_gemma":[0.001852965,0.000458916,0.0007340575,0.0007064207,0.0004807207,0.001788945,0.002079439,0.001160778,0.00229958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000316634,"about_ca_system_score_gemma":0.0005060809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001879079,"about_ca_topic_score_gemma":0.001965249,"domain_scores_codex":[0.9992682,0.0001026285,0.00002562356,0.0000771281,0.0004610979,0.00006522577],"domain_scores_gemma":[0.9994482,0.0001623679,0.00003998305,0.0001241652,0.0001570512,0.00006813616],"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":[0.0006088978,0.0001817765,0.0021233,0.0006104395,0.0001873903,0.0006860182,0.0005599355,0.03842298,0.1269895,0.04585826,0.07639678,0.7073748],"study_design_scores_gemma":[0.0001731209,0.0002718704,0.002572063,0.0001198361,0.00007355584,0.00196147,0.0001287544,0.5676123,0.1388773,0.0199699,0.2679778,0.0002621317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005190751,0.0004631664,0.9554608,0.0001705463,0.00009558526,0.00008807692,0.0005794187,0.03236051,0.005591054],"genre_scores_gemma":[0.1281552,0.001304115,0.8494126,0.0003227487,0.0001990959,0.0002373053,0.004081656,0.006473755,0.009813511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00963383,"threshold_uncertainty_score":0.03222841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01011456950615502,"score_gpt":0.2614778141340798,"score_spread":0.2513632446279248,"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."}}