{"id":"W4249906271","doi":"10.1109/visual.1995.480812","title":"Direct rendering of Laplacian pyramid compressed volume data","year":2002,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Volume rendering; Uncompressed video; Voxel; Rendering (computer graphics); Computer vision; Volume (thermodynamics); Ray tracing (physics); Artificial intelligence; Data compression; Computer graphics (images); Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001685882,0.0000853813,0.0001425685,0.0001240026,0.00005197597,0.00009756153,0.001707952,0.00003329608,0.00007788427],"category_scores_gemma":[0.00001329637,0.00008081755,0.00003065818,0.0004205896,0.00003396717,0.0004341181,0.0009419677,0.00004755886,0.00000909222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005293184,"about_ca_system_score_gemma":0.000006318664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004986311,"about_ca_topic_score_gemma":0.00001085904,"domain_scores_codex":[0.9991018,0.00003183788,0.000216696,0.0003226741,0.0001917297,0.0001352806],"domain_scores_gemma":[0.9984835,0.00003322775,0.00007724631,0.001297091,0.00006099431,0.00004788144],"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":[0.00000192725,0.0003544719,0.003283858,0.00007039942,0.00005657089,0.00001027968,0.0007989595,0.00005654164,0.001106126,0.8047435,0.1124818,0.07703546],"study_design_scores_gemma":[0.00008257229,0.00003564582,0.0005242448,0.00001466889,0.000001935809,0.000002049169,0.000002024867,0.9526448,0.005622766,0.0006414541,0.04031998,0.0001078987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003218641,0.0001202121,0.982785,0.0001548521,0.0001063803,0.00007002004,0.000006097593,0.0004832201,0.01595236],"genre_scores_gemma":[0.959768,0.00007913938,0.0393311,0.0001579538,0.00002399277,0.000002275531,0.000008711254,0.000006861046,0.0006219696],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9594461,"threshold_uncertainty_score":0.3295644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07693548584728324,"score_gpt":0.2907141629137341,"score_spread":0.2137786770664509,"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."}}