{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008463741,0.0005410797,0.0003293077,0.001901572,0.0002862771,0.001454994,0.0004256721,0.000545188,0.006492405],"category_scores_gemma":[0.004137259,0.0001939169,0.0003659786,0.002460968,0.0005519781,0.001039223,0.001214695,0.0006298243,0.0009355596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002439331,"about_ca_system_score_gemma":0.0001979644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001723721,"about_ca_topic_score_gemma":0.002049409,"domain_scores_codex":[0.9995486,0.0001345881,0.0000406544,0.00007304997,0.0001699046,0.00003319239],"domain_scores_gemma":[0.9985945,0.0006948502,0.00009284818,0.0003103463,0.0002483943,0.00005907055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004570392,0.0001496624,0.007654482,0.0003436152,0.00008973309,0.0005537849,0.0009435303,0.06081197,0.1242942,0.02953069,0.008410805,0.7667605],"study_design_scores_gemma":[0.00005336359,0.0002068926,0.01272671,0.0000689891,0.0000384632,0.000547956,0.0006704638,0.8607625,0.07206931,0.02291872,0.02986583,0.00007072568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07562209,0.0002899497,0.9100387,0.0003845337,0.00005348998,0.00009542506,0.0006508718,0.008527552,0.004337402],"genre_scores_gemma":[0.4832815,0.0006072071,0.5106538,0.0001432344,0.0001369855,0.00008883564,0.001175252,0.001222472,0.002690797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006492405,"threshold_uncertainty_score":0.02171928,"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."}}