{"id":"W2124436712","doi":"10.1117/12.596031","title":"Efficient visualization of volume data sets with region of interest and wavelets","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Wavelet; Visualization; Computer vision; Artificial intelligence; Rendering (computer graphics); Volume rendering; Preprocessor; Data visualization; Wavelet transform; Context (archaeology); 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":[],"consensus_categories":[],"category_scores_codex":[0.0005220457,0.0001989531,0.000320756,0.0001640201,0.00004283777,0.00007867066,0.001430682,0.0001019748,0.000001353714],"category_scores_gemma":[0.0001540504,0.0001608511,0.0001476491,0.0004629043,0.0002048269,0.0004929337,0.0005752752,0.0001175525,1.055308e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000040539,"about_ca_system_score_gemma":0.00002933879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000781174,"about_ca_topic_score_gemma":3.242323e-7,"domain_scores_codex":[0.9983456,3.062451e-8,0.0006195492,0.000387682,0.0004552188,0.0001919118],"domain_scores_gemma":[0.9975886,0.00006982096,0.0005487166,0.0001328277,0.00158936,0.0000706115],"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":[0.00003241654,0.0001686912,0.000706954,0.0004189993,0.000143521,5.679039e-8,0.0003636972,0.0001160126,0.03749626,0.9579821,0.0009935608,0.0015777],"study_design_scores_gemma":[0.0005672607,0.0004360641,0.00129793,0.0004721938,0.00005113261,0.0000223604,0.0001349445,0.9261741,0.06879376,0.0009368815,0.0009018004,0.0002115904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965387,0.00007138789,0.03305153,0.0008627097,0.0000568604,0.000328987,0.00001896243,0.00006959288,0.0001528994],"genre_scores_gemma":[0.8378251,0.00007302786,0.1619328,0.00003917959,0.00006607255,0.00001673083,0.000008207453,0.00002107027,0.00001777618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9570453,"threshold_uncertainty_score":0.6559318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02741005209696529,"score_gpt":0.2676987123519723,"score_spread":0.2402886602550071,"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."}}