{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003349694,0.0004668034,0.0003858044,0.001189755,0.0006430921,0.0007035612,0.0005942659,0.0002649273,0.00002735492],"category_scores_gemma":[0.000002236199,0.0004995573,0.0001893574,0.002194863,0.0001387706,0.0008666684,0.00002128586,0.0002367008,0.00001835105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006011021,"about_ca_system_score_gemma":0.00005994849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001437721,"about_ca_topic_score_gemma":0.00007692449,"domain_scores_codex":[0.9970821,0.0002383576,0.0007177389,0.0009138511,0.000617976,0.000429945],"domain_scores_gemma":[0.9984069,0.00009389253,0.0002522106,0.0006628422,0.0004142841,0.0001699021],"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":[0.00001067447,0.0004120466,0.0002211929,0.00004253548,0.00003456743,0.000005720427,0.0002290615,0.0003119279,0.00001785464,0.9926454,0.001099118,0.004969852],"study_design_scores_gemma":[0.0007594381,0.0003628738,0.002827724,0.000072286,0.0000314807,0.00002471831,0.000007304241,0.9800622,0.001001772,0.01125578,0.003018714,0.0005757002],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009597536,0.0001090646,0.9871171,0.00008307263,0.001042212,0.0004621912,0.00001986788,0.001517323,0.00005167275],"genre_scores_gemma":[0.9945003,0.0004584222,0.003156302,0.001302839,0.0001243519,0.00007192924,0.00006643224,0.00006283779,0.0002565607],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9849028,"threshold_uncertainty_score":0.9997456,"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."}}