{"id":"W2101038922","doi":"10.1109/tcsvt.2005.854234","title":"Feature extraction on 3-D TexMesh using scale-space analysis and perceptual evaluation","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Bandwidth (computing); Feature extraction; Preprocessor; Visualization; Data mining; Artificial intelligence; Computer vision; Pattern recognition (psychology); Computer network","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":[0.0005094973,0.0002020762,0.0003085838,0.001615635,0.0004297116,0.0002104176,0.0002132981,0.000280153,0.000003001785],"category_scores_gemma":[0.000007757645,0.0001946195,0.0001078624,0.001315513,0.0000794223,0.000339217,0.000004357554,0.0002238356,0.000001220989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001042863,"about_ca_system_score_gemma":0.00003910943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003807959,"about_ca_topic_score_gemma":0.00009550061,"domain_scores_codex":[0.998552,0.00008396852,0.0002533248,0.0006008844,0.0002803962,0.0002294452],"domain_scores_gemma":[0.9989993,0.0001041849,0.0001482872,0.000429503,0.0002468766,0.00007186681],"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":[0.0000278056,0.0004465153,0.0007139536,0.0001284037,0.0006498007,0.000002977108,0.00117727,0.01695788,0.01350836,0.1053826,0.0004864135,0.860518],"study_design_scores_gemma":[0.0004457012,0.0003392347,0.0002963526,0.00005982534,0.0002385462,0.00007233113,0.0001399431,0.9866701,0.008022314,0.0006525909,0.002803931,0.0002591059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1018006,0.0003013024,0.8958116,0.0007731587,0.0002688554,0.0006286857,0.00001495665,0.0003759496,0.00002484035],"genre_scores_gemma":[0.9959558,0.0001193952,0.003559213,0.0000924122,0.00004897859,0.0001391714,0.000002527652,0.0000145938,0.00006786268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9697123,"threshold_uncertainty_score":0.7936354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03576780378886826,"score_gpt":0.3227079545360652,"score_spread":0.2869401507471969,"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."}}