{"id":"W2963483495","doi":"10.1016/j.cageo.2020.104522","title":"Recursive convolutional neural networks in a multiple-point statistics framework","year":2020,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Convolutional neural network; Sensitivity (control systems); Strengths and weaknesses; Point (geometry); Image (mathematics); Artificial intelligence; Data mining; Artificial neural network; Machine learning; Algorithm; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009066975,0.0006292852,0.0008805924,0.0004697841,0.0003018375,0.001080217,0.00180266,0.001459428,0.001763745],"category_scores_gemma":[0.002867735,0.0007202763,0.0006306578,0.0008906344,0.0008057614,0.001991792,0.001231146,0.001559641,0.0006022453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000971099,"about_ca_system_score_gemma":0.001319462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01716374,"about_ca_topic_score_gemma":0.01994426,"domain_scores_codex":[0.9996521,0.00009963161,0.00002207509,0.00009279525,0.0000817555,0.00005158808],"domain_scores_gemma":[0.998853,0.0006982263,0.00009393757,0.0001271544,0.0001871494,0.00004050884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005973893,0.00002731724,0.0004056873,0.00004376633,0.00004724666,0.00005673915,0.00003157479,0.8727748,0.002385921,0.07011663,0.0009843636,0.05306626],"study_design_scores_gemma":[0.000001446811,0.000004352269,0.00004862408,0.000001529701,0.000003348227,0.000003616587,0.000001073238,0.9938374,0.0002111209,0.005711542,0.0001732468,0.000002688001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0111994,0.0003194438,0.9869105,0.0002024433,0.00003113084,0.000007436369,0.00007902867,0.0003935736,0.0008568977],"genre_scores_gemma":[0.5393481,0.001081318,0.4444284,0.0001675554,0.0002296503,0.0001034723,0.0006263212,0.0003076721,0.01370749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01716374,"threshold_uncertainty_score":0.03412765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02299412323387093,"score_gpt":0.2220331684298788,"score_spread":0.1990390451960078,"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."}}