{"id":"W2089585358","doi":"10.1117/12.785658","title":"Statistical simulation of deformations using wavelet independent component analysis","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Independent component analysis; Computer science; Wavelet; Artificial intelligence; Regularization (linguistics); Segmentation; Component (thermodynamics); Pattern recognition (psychology); Statistical model; Random variable; Generalization; Algorithm; Mathematics; Statistics","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":[0.000681098,0.0002229205,0.0004343333,0.000288986,0.0001114708,0.00008117461,0.00101866,0.0001468217,0.000007355947],"category_scores_gemma":[0.0003159897,0.000195767,0.0005349124,0.0008153367,0.0001943138,0.000937562,0.0002358044,0.0002352379,6.466506e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001551128,"about_ca_system_score_gemma":0.00005801581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002125884,"about_ca_topic_score_gemma":2.192531e-7,"domain_scores_codex":[0.9975915,4.740695e-8,0.0008929374,0.0003119892,0.0009432529,0.0002602804],"domain_scores_gemma":[0.9970798,0.000223706,0.0005803258,0.00009118317,0.001926351,0.0000985707],"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":[0.00003054447,0.0002010821,0.0009710686,0.0001602032,0.0007715977,1.185959e-7,0.0009889466,0.03535139,0.1205481,0.8405474,0.000264046,0.0001654333],"study_design_scores_gemma":[0.0004104359,0.0001324821,0.003927971,0.00005358414,0.0001807345,0.0000118245,0.0002435753,0.9442905,0.0486605,0.001696047,0.0001850921,0.0002072693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8471184,0.0000159931,0.1514917,0.0004424805,0.00006083367,0.0003245674,0.00003880285,0.00008785211,0.0004193611],"genre_scores_gemma":[0.6464496,0.00001527955,0.3533926,0.00004267407,0.00004050354,0.0000228951,0.000009318675,0.00001313635,0.00001403751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9089391,"threshold_uncertainty_score":0.7983147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02220579160631072,"score_gpt":0.2635232787924489,"score_spread":0.2413174871861381,"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."}}