{"id":"W1796935811","doi":"10.1109/icecs.1996.582934","title":"Lossless compression of bathymetric data using an improved nonlinear discrete wavelet transform","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Lossless compression; Bathymetry; Data compression; Wavelet transform; Wavelet; Compression (physics); Computer science; Discrete wavelet transform; Algorithm; Image compression; Nonlinear system; Artificial intelligence; Computer vision; Mathematics; Image processing; Geology; Image (mathematics); Materials science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001415349,0.0002436602,0.0002788194,0.00028997,0.0001223385,0.0002736489,0.0002937285,0.0002475013,0.0006582378],"category_scores_gemma":[0.0008397175,0.00007755053,0.0001515168,0.0004583234,0.0002934464,0.0005393667,0.0002835936,0.0002916332,0.0002650713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001497463,"about_ca_system_score_gemma":0.0001464398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001097647,"about_ca_topic_score_gemma":0.001146592,"domain_scores_codex":[0.9998991,0.000009256866,0.000005418248,0.0000077722,0.00007127463,0.000007211345],"domain_scores_gemma":[0.9998204,0.00007547903,0.00001767535,0.00003135297,0.00005007983,0.000005042544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005384748,0.00007131065,0.0009939168,0.0002267162,0.00002757876,0.0007535552,0.0001788914,0.1111925,0.4580993,0.008773432,0.002079062,0.4170653],"study_design_scores_gemma":[0.00003594224,0.0001920427,0.004578917,0.00002528795,0.00003425619,0.001071251,0.00006730102,0.818683,0.1650347,0.003257573,0.006986236,0.00003343719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.308713,0.0008083581,0.6848227,0.0004724433,0.000153683,0.00006918294,0.0002092921,0.0003918942,0.004359298],"genre_scores_gemma":[0.7555835,0.001556955,0.2337274,0.0001010048,0.0001937678,0.00006693573,0.0006305763,0.00006909937,0.008070847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001097647,"threshold_uncertainty_score":0.002202034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1035841402016856,"score_gpt":0.3350786273324924,"score_spread":0.2314944871308068,"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."}}