{"id":"W1867747937","doi":"10.1109/ccece.2000.849637","title":"A VHDL implementation of a shearing unit for shear-warp factorization volume rendering","year":2002,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Rendering (computer graphics); Voxel; Path tracing; Shearing (physics); Volume rendering; Factorization; Computer graphics (images); Algorithm; Computer hardware; Computer vision; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000411541,0.000743054,0.0004532561,0.0005073243,0.0002976586,0.001172332,0.00157945,0.000484759,0.0180803],"category_scores_gemma":[0.001052257,0.0004283111,0.000343789,0.0002993545,0.0002637292,0.0006077416,0.0004882616,0.001003022,0.005276289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004928086,"about_ca_system_score_gemma":0.0006493435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001323595,"about_ca_topic_score_gemma":0.0007818905,"domain_scores_codex":[0.9996958,0.00003923079,0.00003278793,0.00004399405,0.000129535,0.00005871249],"domain_scores_gemma":[0.9996712,0.0000900872,0.00002393259,0.00007867203,0.0001082096,0.00002788938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001471948,0.0003037515,0.00279351,0.001066109,0.0001680315,0.0009703737,0.001023705,0.1060558,0.2551835,0.063386,0.03974856,0.5278288],"study_design_scores_gemma":[0.0003428888,0.0005423876,0.0009641022,0.00008889237,0.0001085849,0.000840225,0.00008332702,0.477884,0.341143,0.008201323,0.1696919,0.0001093442],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007714062,0.000107108,0.9601552,0.00006130668,0.00007945328,0.00012656,0.0003947003,0.02625504,0.005106584],"genre_scores_gemma":[0.3006243,0.0002996463,0.6747553,0.0002659902,0.00007846099,0.0005479112,0.001815068,0.00351757,0.01809588],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0180803,"threshold_uncertainty_score":0.06048459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06607782832901839,"score_gpt":0.3329814248342334,"score_spread":0.266903596505215,"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."}}