{"id":"W2792042394","doi":"10.1190/int-2018-0006.1","title":"Coherence attribute applications on seismic data in various guises — Part 1","year":2018,"lang":"en","type":"article","venue":"Interpretation","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"ARC Resources (Canada)","funders":"","keywords":"Coherence (philosophical gambling strategy); Computer science; Computation; Seismic inversion; Classification of discontinuities; Amplitude; Bandwidth (computing); Algorithm; Geology; Seismology; Optics; Mathematics; Physics; Telecommunications; Statistics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002302778,0.00007388042,0.00007672124,0.00009169881,0.00007087247,0.00004400516,0.0003767563,0.0000368149,0.0005795271],"category_scores_gemma":[0.00006845815,0.00006641245,0.00001258497,0.0001760374,0.00008959586,0.000242968,0.00001980145,0.00009033885,0.001010227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007871376,"about_ca_system_score_gemma":0.000030891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002434721,"about_ca_topic_score_gemma":0.000312374,"domain_scores_codex":[0.9992664,0.00004843673,0.000161616,0.0002641517,0.0001253001,0.0001340215],"domain_scores_gemma":[0.999361,0.0001013402,0.00005839144,0.0004025656,0.0000422039,0.00003444084],"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":[0.00006521759,0.00003723404,0.05695243,0.00001208526,0.000009667988,0.00000365783,0.000566073,0.0003656858,0.00004569677,0.00007199134,0.06488836,0.8769819],"study_design_scores_gemma":[0.0001876436,0.0002551931,0.02497512,0.0001250471,0.00001055424,0.000008430156,0.0001353126,0.7326134,0.002036282,0.004072208,0.2353709,0.000209967],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2971403,0.0006889796,0.540444,0.006127228,0.002270693,0.001932381,0.001441087,0.001150793,0.1488045],"genre_scores_gemma":[0.994864,0.00001431597,0.001247458,0.003049029,0.00009843596,0.000005750323,0.0005488556,0.000002284888,0.0001698495],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8767719,"threshold_uncertainty_score":0.9997676,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03228016412159657,"score_gpt":0.2784457129596848,"score_spread":0.2461655488380883,"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."}}