{"id":"W3047568140","doi":"10.1190/tle39080593.1","title":"Adopting multispectral dip components for coherence and curvature attribute computations","year":2020,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Curvature; Classification of discontinuities; Coherence (philosophical gambling strategy); Computation; Covariance matrix; Covariance; Optics; Multispectral image; Mathematics; Algorithm; Geology; Geometry; Physics; Mathematical analysis; Remote sensing; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001303058,0.00007106313,0.00009242079,0.00001550676,0.0002712594,0.00005592577,0.000143648,0.00002637232,0.00002439285],"category_scores_gemma":[0.00004903403,0.00005020101,0.00002619,0.00009129153,0.00009533159,0.00008840472,0.00001124526,0.0001313311,0.00004898645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002135648,"about_ca_system_score_gemma":0.000007989782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001448277,"about_ca_topic_score_gemma":0.000004924012,"domain_scores_codex":[0.9995231,0.00003039117,0.00008561376,0.0001353159,0.00006909728,0.0001565046],"domain_scores_gemma":[0.9996178,0.0002118674,0.00003040155,0.00005858461,0.00002015022,0.0000612104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001317749,0.00002784811,0.4076961,0.0002183543,0.00009371463,0.00001088729,0.01071902,0.005685025,0.001442259,0.0007926817,0.3093444,0.263838],"study_design_scores_gemma":[0.000447511,0.0001393392,0.09721518,0.000068567,0.00003289023,0.00001380485,0.0002921385,0.7657864,0.001047063,0.0005996948,0.1341258,0.0002315413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8853819,0.002232322,0.06308493,0.0460321,0.000596069,0.0007706502,0.0002752554,0.0006303635,0.0009963505],"genre_scores_gemma":[0.9862112,0.00001611349,0.009671718,0.003843373,0.0001222436,0.000001126069,0.00006292379,0.000002767151,0.00006851023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7601014,"threshold_uncertainty_score":0.2086336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06270570447002224,"score_gpt":0.2588666133706176,"score_spread":0.1961609089005954,"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."}}