{"id":"W2004015652","doi":"10.1142/s0129183109013492","title":"WAVELET LOSSY COMPRESSION OF RANDOM DATA","year":2009,"lang":"en","type":"article","venue":"International Journal of Modern Physics C","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lossy compression; Wavelet; Compression (physics); Data compression; Data compression ratio; Computer science; Algorithm; Mathematics; Image compression; Artificial intelligence; Physics","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":[0.0005712491,0.00008282771,0.0002062894,0.00007815126,0.00002446086,0.00009128354,0.002616442,0.00002629877,0.000004296614],"category_scores_gemma":[0.00007658207,0.00006672154,0.00009567502,0.00007961762,0.00002570483,0.001019039,0.0002481527,0.000163218,0.000002669032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002006088,"about_ca_system_score_gemma":0.00007975804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002573931,"about_ca_topic_score_gemma":8.86803e-8,"domain_scores_codex":[0.998545,0.00009594842,0.0003989507,0.0001359723,0.0007298577,0.00009426501],"domain_scores_gemma":[0.9983655,0.0001478104,0.0004630465,0.0003885939,0.0005884265,0.00004665292],"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":[0.0002412047,0.0002568687,0.00003631171,0.000003409974,0.00009248811,0.00008557395,0.0003908565,0.001931104,0.07576533,0.007059655,0.00186327,0.9122739],"study_design_scores_gemma":[0.004514148,0.00017975,0.0009982428,0.0002301979,0.00002357644,0.0002009561,0.000004801359,0.5000593,0.07687599,0.4155193,0.00122591,0.0001678104],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008529938,0.0002153552,0.9888385,0.0009804617,0.0005770463,0.00002676463,0.000008206737,0.000008366348,0.0008153629],"genre_scores_gemma":[0.8702003,0.0000327727,0.1289539,0.0003245888,0.000431954,8.113949e-8,0.000005502583,0.000003792196,0.00004711965],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9121061,"threshold_uncertainty_score":0.4862044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05371141656267368,"score_gpt":0.345137486875855,"score_spread":0.2914260703131814,"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."}}