{"id":"W4400958534","doi":"10.1111/cgf.15153","title":"Lossless Basis Expansion for Gradient‐Domain Rendering","year":2024,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Rendering (computer graphics); Pixel; Basis (linear algebra); Lossless compression; Algorithm; Mathematics; Computer science; Basis function; Computer vision; Geometry; Mathematical analysis; Data compression","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005432278,0.0003349414,0.0002959466,0.0009224826,0.0003603346,0.00101794,0.001268151,0.0001555318,0.000002914545],"category_scores_gemma":[0.000006359409,0.0003254857,0.0003948025,0.001607905,0.00007922377,0.000722774,0.0006980579,0.000228178,0.000007120914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004385211,"about_ca_system_score_gemma":0.00007638419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000944387,"about_ca_topic_score_gemma":0.00000862343,"domain_scores_codex":[0.9975323,0.00006436427,0.0004733032,0.0009482103,0.0003707884,0.0006110207],"domain_scores_gemma":[0.9984942,0.0002137655,0.00008142462,0.0008539324,0.0001829819,0.0001737143],"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":[0.000002927757,0.00004760876,0.0001401066,0.0001161724,0.00004552027,0.00001812948,0.0004188465,0.000009407629,0.00004754793,0.9366031,0.01437694,0.0481737],"study_design_scores_gemma":[0.0001902392,0.0002052647,0.0001279484,0.0001799366,0.000008408155,0.0000315365,0.00001052528,0.6520095,0.0009727665,0.2605285,0.08537162,0.0003636939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002572668,0.000900141,0.9900198,0.001337552,0.002646377,0.0004752602,0.00001453321,0.001939152,0.00009449592],"genre_scores_gemma":[0.7333989,0.0003468401,0.2630636,0.002125812,0.0005912597,0.000237331,0.00005473305,0.0001012763,0.00008025069],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7308262,"threshold_uncertainty_score":0.9999197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02373354552653736,"score_gpt":0.2873006643893486,"score_spread":0.2635671188628113,"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."}}