{"id":"W4251202774","doi":"10.1190/int-2018-0007.1","title":"Coherence attribute applications on seismic data in various guises — Part 2","year":2018,"lang":"en","type":"article","venue":"Interpretation","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Virtual Materials Group (Canada)","funders":"","keywords":"Coherence (philosophical gambling strategy); Amplitude; Computer science; Bandwidth (computing); Inversion (geology); Seismic inversion; Optics; Algorithm; Geology; Physics; Mathematics; Seismology; 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":[0.00022901,0.00007385673,0.00007674019,0.00009163826,0.0000708777,0.0000441015,0.0003765448,0.00003681511,0.0005685193],"category_scores_gemma":[0.00006762475,0.00006639054,0.00001258251,0.0001758994,0.00008959461,0.000243136,0.00001979864,0.00009031787,0.0009993667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000787365,"about_ca_system_score_gemma":0.00003075233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00245941,"about_ca_topic_score_gemma":0.0003142383,"domain_scores_codex":[0.9992667,0.00004842018,0.0001616891,0.000263952,0.0001252685,0.0001339718],"domain_scores_gemma":[0.9993633,0.0000998953,0.00005847373,0.0004017852,0.00004210914,0.00003443567],"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":[0.00006381585,0.0000366229,0.05426933,0.00001199952,0.000009512521,0.000003698342,0.0005574389,0.0003615461,0.00004339137,0.00007209041,0.06278814,0.8817824],"study_design_scores_gemma":[0.0001881971,0.0002542329,0.02447632,0.0001230054,0.00001047435,0.000008415806,0.0001333931,0.7258464,0.002015558,0.004088713,0.2426466,0.0002086988],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2980909,0.0007070039,0.5345264,0.006155542,0.002306617,0.001947976,0.001422642,0.001161883,0.153681],"genre_scores_gemma":[0.9948863,0.00001469852,0.001219967,0.003049496,0.0000999164,0.000005639665,0.0005512582,0.000002281887,0.0001704314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8815737,"threshold_uncertainty_score":0.9997784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03115566324815306,"score_gpt":0.2778675280447863,"score_spread":0.2467118647966332,"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."}}