{"id":"W2126032443","doi":"10.1109/icecs.2001.957721","title":"An SIMD architecture for texture mapping","year":2002,"lang":"en","type":"article","venue":"","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"SIMD; Image warping; Computer science; Affine transformation; Transformation (genetics); Architecture; Texture (cosmology); Computer graphics (images); Pixel; Texture mapping; Computer vision; Artificial intelligence; Parallel computing; Image (mathematics); Mathematics; Geometry; 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":[0.00009666997,0.0000866191,0.00007937465,0.0001385111,0.0001041457,0.000196265,0.0006190678,0.00004809681,0.00002500785],"category_scores_gemma":[0.000005903765,0.00007226258,0.00005450082,0.0003127576,0.00001203168,0.0002039893,0.00007031346,0.00005662026,0.000003751754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005127069,"about_ca_system_score_gemma":0.000004218697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003085897,"about_ca_topic_score_gemma":0.000004966239,"domain_scores_codex":[0.999347,0.00001887693,0.000114217,0.000266071,0.00009995607,0.0001539336],"domain_scores_gemma":[0.9993947,0.00003489435,0.00003211638,0.0004109526,0.00006283907,0.00006449291],"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":[3.38478e-7,0.0000556866,0.000053538,0.000009908868,0.000004716896,6.202534e-7,0.0009874471,0.00002869029,0.0002133474,0.781841,0.01738276,0.1994219],"study_design_scores_gemma":[0.00008477522,0.0001123115,0.00007039154,0.00000580618,6.855127e-7,0.000004513789,0.000006561791,0.8593812,0.0005560255,0.03642724,0.1032322,0.0001183296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001916332,0.00005088301,0.9961817,0.0006688031,0.00008328191,0.0001824142,0.000001184612,0.0006876243,0.001952442],"genre_scores_gemma":[0.6194013,0.00001502521,0.3772706,0.002682209,0.0001338849,0.00003310624,0.000004092291,0.00001114103,0.000448623],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8593525,"threshold_uncertainty_score":0.2946782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03151189621405295,"score_gpt":0.2859050615554288,"score_spread":0.2543931653413759,"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."}}