{"id":"W2374789931","doi":"","title":"The Research of Anti-aliasing Technology in Catmull Algorithm","year":2005,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Aliasing; Anti-aliasing; Algorithm; Pixel; Margin (machine learning); Process (computing); Artificial intelligence; Undersampling; Machine learning; Computer hardware","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001798799,0.00007204612,0.0001228981,0.0002630662,0.00009349388,0.00001397081,0.0003655964,0.00006068483,0.000002048741],"category_scores_gemma":[0.000001037833,0.00006073245,0.00003014029,0.001269329,0.0001365177,0.00004521398,0.00008820369,0.0002492488,0.00002871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008609742,"about_ca_system_score_gemma":0.0000075327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001089853,"about_ca_topic_score_gemma":0.00001386719,"domain_scores_codex":[0.9992769,0.00001727295,0.0002447739,0.0001454011,0.00008943327,0.0002261849],"domain_scores_gemma":[0.9994811,0.00009869907,0.00002264695,0.0002965473,0.00007943243,0.00002157096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[2.25866e-7,0.00003496858,0.0001103979,0.000005915269,0.000009721968,2.400544e-7,0.00004628496,0.003207637,0.04316782,0.001519296,0.0005203999,0.9513771],"study_design_scores_gemma":[0.00009328922,0.000008741456,0.0004048675,0.00001479384,0.000003994226,0.000005672318,0.00004424899,0.05905043,0.1800213,0.008951104,0.7512823,0.0001192297],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007343981,0.0007748919,0.9900624,0.0009677495,0.000002843783,0.0003222892,0.000003104318,0.0002581912,0.0002645167],"genre_scores_gemma":[0.3213053,0.0002133499,0.6780364,0.00001457289,0.00005833099,0.0003203677,0.000003467175,0.00001758929,0.00003056715],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9512579,"threshold_uncertainty_score":0.2476598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587561680985945,"score_gpt":0.3200108206840266,"score_spread":0.3041352038741672,"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."}}