{"id":"W4399886612","doi":"10.3723/ljrn1924","title":"Improving Driveability Predictions for Offshore Piles Using Bayesian Optimisation","year":2023,"lang":"en","type":"article","venue":"","topic":"Oil and Gas Production Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Submarine pipeline; Marine engineering; Computer science; Bayesian probability; Environmental science; Geology; Engineering; Oceanography; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000839598,0.0008448182,0.0006764089,0.0008246137,0.0003005232,0.0009261303,0.0007364275,0.0008292727,0.001788114],"category_scores_gemma":[0.003399475,0.0007270496,0.0006198068,0.000522664,0.0004338597,0.0008407251,0.0006818394,0.0008252925,0.0006537891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006980524,"about_ca_system_score_gemma":0.001002638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052686,"about_ca_topic_score_gemma":0.01285237,"domain_scores_codex":[0.9996575,0.00007960027,0.00001840211,0.00006017524,0.0001436389,0.00004081115],"domain_scores_gemma":[0.9989183,0.0006399761,0.0001318417,0.0000676195,0.0002069583,0.00003530457],"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":[0.00001749792,0.00001022578,0.0004610092,0.00001722457,0.000005606465,0.00001150944,0.00000938157,0.9905424,0.0008877512,0.000283966,0.00009505559,0.007658428],"study_design_scores_gemma":[0.000002829004,0.000009917306,0.0003306086,0.000004989346,0.000001899019,0.000004816968,0.000006709291,0.9983711,0.0005565849,0.0005677178,0.0001365086,0.000006330469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08782026,0.0001239228,0.9068332,0.0001155644,0.00001548505,0.00006025976,0.0002786442,0.0007102649,0.00404231],"genre_scores_gemma":[0.8802466,0.0001440189,0.1163141,0.00004477575,0.00001258488,0.000115638,0.0007145779,0.0001735044,0.002234258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01052686,"threshold_uncertainty_score":0.02093118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101437665316956,"score_gpt":0.253447944001807,"score_spread":0.2324335673486375,"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."}}