{"id":"W2148228104","doi":"10.1190/1.1444899","title":"Use of multiattribute transforms to predict log properties from seismic data","year":2001,"lang":"en","type":"article","venue":"Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":614,"is_retracted":false,"has_abstract":true,"ca_institutions":"Shell (Canada)","funders":"","keywords":"Artificial neural network; Computer science; Series (stratigraphy); Algorithm; Data mining; Probabilistic neural network; Nonlinear system; Pattern recognition (psychology); Mathematics; Artificial intelligence; Geology; Time delay neural network","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":[0.00009213186,0.0001127757,0.0001645753,0.00004400307,0.00006573259,0.00003713886,0.000441658,0.00004164262,0.0001521758],"category_scores_gemma":[0.00002848678,0.00008403717,0.00003564141,0.0001853285,0.00006705484,0.0006015904,0.00003411501,0.0000952256,0.0001822452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002670649,"about_ca_system_score_gemma":0.00003726815,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04905513,"about_ca_topic_score_gemma":0.0001366805,"domain_scores_codex":[0.9991158,0.00002535644,0.0001769363,0.0002591884,0.0002074399,0.000215232],"domain_scores_gemma":[0.9992585,0.00004977207,0.00004497017,0.0005228503,0.00004176379,0.00008217737],"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":[0.0001976177,0.00007831864,0.1038093,0.000030661,0.00006727368,0.00001538871,0.0009024048,0.003437115,0.001732839,0.000007503978,0.05032923,0.8393924],"study_design_scores_gemma":[0.0003588862,0.0002681228,0.06676566,0.0001273624,0.00005839482,0.000005946452,0.000203175,0.5445273,0.01774825,0.001299961,0.3682274,0.0004095676],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855425,0.0001281835,0.01063313,0.0008970946,0.0002145069,0.0002213114,0.001880337,0.0001457171,0.0003372103],"genre_scores_gemma":[0.9930272,0.0001315272,0.003474039,0.002028885,0.00009981874,7.913734e-7,0.0009493193,0.000004430555,0.0002840597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8389828,"threshold_uncertainty_score":0.9572773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07823300361671363,"score_gpt":0.2275285950517936,"score_spread":0.1492955914350799,"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."}}