{"id":"W2129810778","doi":"10.1007/s10069-000-0003-2","title":"Looking at the Inside of the Earth with 3-D Wavelets: A Pair of New Glasses for Geoscientists","year":2000,"lang":"en","type":"article","venue":"Visual Geosciences","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Wavelet; Curse of dimensionality; Synthetic data; Computer science; A priori and a posteriori; Algorithm; Spectral line; Plume; Pattern recognition (psychology); Wavelet transform; Artificial intelligence; Geology; Physics; Meteorology","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":[0.0004833452,0.0001023757,0.0001400558,0.00005418471,0.0003921373,0.00004334981,0.0004943874,0.00002794717,0.0008105973],"category_scores_gemma":[0.00005944215,0.00004590302,0.00007240275,0.0005575433,0.0009345285,0.0001899678,0.00002428662,0.00005947856,0.00001050398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002762243,"about_ca_system_score_gemma":0.0001690462,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01118206,"about_ca_topic_score_gemma":0.001221379,"domain_scores_codex":[0.9988019,0.00005368642,0.000201589,0.0002326068,0.0004541326,0.0002561036],"domain_scores_gemma":[0.9993504,0.0001984211,0.0001455535,0.0002025576,0.00004782924,0.00005521324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001370123,0.0000518431,0.3946466,0.00004833378,0.00001778875,0.000001417516,0.001403623,0.0005479782,0.001004011,0.0001114386,0.01511418,0.5869157],"study_design_scores_gemma":[0.0006013766,0.001313468,0.4952405,0.0002288757,0.00004544418,0.00004878614,0.001104733,0.03657372,0.1247778,0.00148778,0.3382364,0.0003411034],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959167,0.0002309161,0.0002373202,0.0009457157,0.0001683154,0.0002354555,0.00003941839,0.00002845763,0.002197694],"genre_scores_gemma":[0.991142,0.00003210515,0.001280903,0.0007814787,0.0000340215,0.000001273437,0.000004827162,0.000002195138,0.006721186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5865746,"threshold_uncertainty_score":0.9954026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01394552667679242,"score_gpt":0.2387153843418461,"score_spread":0.2247698576650537,"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."}}