{"id":"W1990543026","doi":"10.1109/ccece.2008.4564533","title":"A computational engine for petroleum applications using Genetic Algorithms","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Correctness; Genetic algorithm; Computer science; Ray tracing (physics); Algorithm; Tracing; Process (computing); Software; Seismic migration; Computational science; Geology; Geophysics; Programming language; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0006148378,0.000596299,0.0003870396,0.0007306701,0.0005654261,0.0009535704,0.001125859,0.000830206,0.004199421],"category_scores_gemma":[0.002006674,0.0002656935,0.000709475,0.0006896448,0.0005998816,0.0007430724,0.0005581583,0.0006796777,0.001031475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006172242,"about_ca_system_score_gemma":0.001198462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005635882,"about_ca_topic_score_gemma":0.004439966,"domain_scores_codex":[0.9997441,0.00005835132,0.00001836213,0.00003663737,0.0001199303,0.00002266782],"domain_scores_gemma":[0.9995894,0.0002106236,0.00002429437,0.00006450774,0.00009635405,0.00001483353],"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":[0.00006637387,0.0000704535,0.0008516793,0.0001459712,0.00005991355,0.0001723079,0.0001214404,0.7060445,0.008205034,0.08246501,0.004811857,0.1969854],"study_design_scores_gemma":[0.00002545785,0.00003229956,0.0001014001,0.0000203909,0.00001714156,0.00005176623,0.000013419,0.9669409,0.003047083,0.01945928,0.01027996,0.00001100594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008613067,0.0001306502,0.9839303,0.0001570017,0.00004642086,0.00006891891,0.00007782192,0.002346057,0.004629807],"genre_scores_gemma":[0.1110303,0.0003028832,0.8841926,0.0001010128,0.00002691886,0.0002940867,0.0002100885,0.0002651177,0.003577111],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005635882,"threshold_uncertainty_score":0.01404846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02100389838846293,"score_gpt":0.199207184962923,"score_spread":0.1782032865744601,"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."}}