{"id":"W1968980241","doi":"10.1190/1.3575281","title":"Coherence and curvature attributes on preconditioned seismic data","year":2011,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"ARC Resources (Canada)","funders":"","keywords":"Coherence (philosophical gambling strategy); Noise (video); Curvature; Seismology; Geology; Footprint; Computer science; Seismic noise; Multiple; Data processing; Data mining; Algorithm; Artificial intelligence; Statistics; Mathematics; Image (mathematics); Geometry","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":[0.0006234347,0.0003563089,0.0002964377,0.001055166,0.0002145872,0.0008144388,0.000339305,0.0003809335,0.002147435],"category_scores_gemma":[0.005035337,0.0002491475,0.0003351219,0.001015342,0.0005603098,0.001100478,0.00058192,0.0007901369,0.0005510997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003220614,"about_ca_system_score_gemma":0.0006270841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00446425,"about_ca_topic_score_gemma":0.007351284,"domain_scores_codex":[0.999589,0.00008284386,0.00002944944,0.00007405975,0.0001725812,0.00005209343],"domain_scores_gemma":[0.9983974,0.0006007575,0.0001780781,0.0004302495,0.0003182075,0.00007524735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006475594,0.0001460188,0.01773121,0.0002439913,0.00005619618,0.0006938711,0.0004159555,0.5377842,0.1214732,0.0205659,0.003162878,0.297079],"study_design_scores_gemma":[0.00001747348,0.00007236881,0.01243049,0.000017202,0.000009161566,0.0001118864,0.00007327853,0.9602803,0.0188247,0.00612708,0.002000964,0.00003508225],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2906285,0.0001031577,0.70424,0.0001825663,0.00002558408,0.00006614144,0.001154192,0.00129354,0.002306381],"genre_scores_gemma":[0.7975706,0.0002851218,0.1978528,0.00003811538,0.00003174825,0.00004922685,0.002292691,0.0002967017,0.001583032],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00446425,"threshold_uncertainty_score":0.008876503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09758068051388094,"score_gpt":0.2534756411729354,"score_spread":0.1558949606590544,"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."}}