{"id":"W2913224464","doi":"10.2312/egsh.20161018","title":"Texel Shading","year":2016,"lang":"en","type":"article","venue":"Eurographics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Advanced Micro Devices (Canada)","funders":"","keywords":"Texel; Shading; Computer science; Computer graphics (images); Pixel; Computer vision; Texture (cosmology); Artificial intelligence; Frame rate; Speedup; Texture filtering; Image texture; Segmentation; Image segmentation; Image (mathematics); Parallel computing","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":[0.0004680951,0.0006527898,0.0007278908,0.0006658619,0.0007183446,0.00154588,0.001489208,0.0004960239,0.04792215],"category_scores_gemma":[0.002094922,0.0004562004,0.0007247159,0.0007651245,0.0004687383,0.001209337,0.002089881,0.001209857,0.008187383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004779994,"about_ca_system_score_gemma":0.0008533389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001911441,"about_ca_topic_score_gemma":0.001922958,"domain_scores_codex":[0.9993111,0.00005101703,0.00003328726,0.00009460491,0.0004124354,0.00009769022],"domain_scores_gemma":[0.9990948,0.000196534,0.00003329453,0.0002866567,0.0002813053,0.0001073048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001055908,0.0002118346,0.003088929,0.0007532092,0.0001072786,0.0006032108,0.0007236372,0.0445942,0.1778001,0.07286817,0.1435388,0.5546548],"study_design_scores_gemma":[0.0003809061,0.0003589572,0.00256501,0.00007687455,0.00004643702,0.001004907,0.0001136385,0.3183511,0.1457112,0.0252218,0.5060285,0.0001405895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03689928,0.0006959942,0.794589,0.0004150697,0.0006812881,0.0002440974,0.003139324,0.0875819,0.07575414],"genre_scores_gemma":[0.3325261,0.0008476984,0.5762041,0.0005983979,0.0002239687,0.0003589843,0.008298038,0.0293749,0.05156783],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04792215,"threshold_uncertainty_score":0.1603156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176525736755954,"score_gpt":0.2745674896647747,"score_spread":0.2528022322972152,"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."}}