{"id":"W4243138559","doi":"10.22215/etd/2009-08744","title":"Resolution scalable image coding using rational wavelet transforms","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Wavelet; Computer science; Scalability; Artificial intelligence; Database","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":[0.0002051258,0.0003300794,0.0002807391,0.0005182645,0.0000965305,0.0006268979,0.000332604,0.000307775,0.003263294],"category_scores_gemma":[0.0007763632,0.0001545831,0.0003000016,0.0005538142,0.0002588276,0.0009082936,0.0004957953,0.0007201508,0.0009767546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002282584,"about_ca_system_score_gemma":0.0002746698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000694598,"about_ca_topic_score_gemma":0.001013089,"domain_scores_codex":[0.999861,0.00001538809,0.000006847597,0.00001675135,0.00008158883,0.00001843719],"domain_scores_gemma":[0.999782,0.00006064573,0.00001735736,0.00007573801,0.00005025766,0.0000138741],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002158729,0.00007859375,0.0002819023,0.000141209,0.00003646481,0.0003100149,0.00009356732,0.08927219,0.3155508,0.1484907,0.008137024,0.4373918],"study_design_scores_gemma":[0.00002935004,0.0000633304,0.0003594396,0.00002367521,0.00001460654,0.0002062484,0.00002674942,0.8867217,0.07564913,0.02655699,0.01032949,0.00001919719],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02842508,0.000518658,0.9614132,0.0001913147,0.00009597842,0.00002819175,0.0001040614,0.0008303112,0.008393271],"genre_scores_gemma":[0.3898729,0.001842293,0.585718,0.0001564718,0.000135789,0.00007234692,0.0006954388,0.0003632297,0.02114361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003263294,"threshold_uncertainty_score":0.01091683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0281425866956293,"score_gpt":0.3108964483395151,"score_spread":0.2827538616438858,"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."}}