{"id":"W2910120656","doi":"10.3968/7504","title":"Numerical Simulation of Submarine Pipeline Self-Buried on Sediment Seabed","year":2015,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Offshore Engineering and Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Submarine pipeline; Seabed; Pipeline (software); Marine engineering; Submarine; Subsea; Pipeline transport; Flow (mathematics); Geology; Petroleum engineering; Petroleum; Engineering; Geotechnical engineering; Oceanography; Mechanical engineering; Paleontology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002407034,0.0007082492,0.0006971864,0.0007548803,0.001092373,0.0008416498,0.0006863065,0.00151119,0.002506325],"category_scores_gemma":[0.0008398044,0.0003103616,0.0008689679,0.000529485,0.0009834259,0.0006760181,0.001005064,0.0007247922,0.0001702923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000683776,"about_ca_system_score_gemma":0.0009188281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02391275,"about_ca_topic_score_gemma":0.00949314,"domain_scores_codex":[0.9997911,0.00003332765,0.00001321446,0.00003424146,0.00006034506,0.00006779681],"domain_scores_gemma":[0.9996991,0.0001000892,0.00004607577,0.00001883699,0.00007328267,0.00006252997],"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":[0.00005047915,0.00004573299,0.005820872,0.0000508688,0.00002130868,0.0003065515,0.00009225163,0.9875237,0.001919345,0.001524988,0.0005289905,0.00211485],"study_design_scores_gemma":[0.00001179078,0.0000227111,0.0009154758,0.000007146541,0.000007918878,0.00002306551,0.00007106135,0.9978381,0.0003850709,0.0003157271,0.0003904066,0.00001154978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.877342,0.0009592123,0.08235482,0.001102421,0.0004287176,0.0001139992,0.001139369,0.0008549398,0.0357046],"genre_scores_gemma":[0.9886931,0.0002376408,0.006256827,0.00005363529,0.0000196589,0.00006700206,0.0002756784,0.00004243148,0.004353989],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02391275,"threshold_uncertainty_score":0.04754716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01965573559605447,"score_gpt":0.2479121932362776,"score_spread":0.2282564576402231,"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."}}