{"id":"W4296175740","doi":"10.3389/fmats.2022.971816","title":"Multimodal data augmentation for digital twining assisted by artificial intelligence in mechanics of materials","year":2022,"lang":"en","type":"article","venue":"Frontiers in Materials","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Safran Electronics (Canada)","funders":"Safran","keywords":"Computer science; Finite element method; Component (thermodynamics); Representation (politics); Relation (database); Artificial intelligence; Algorithm; Data mining; Structural engineering; Engineering","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":[0.001121891,0.0008792128,0.00123366,0.001288841,0.0004633614,0.0008950871,0.001422936,0.001059836,0.002053594],"category_scores_gemma":[0.00428721,0.0005224628,0.001179837,0.001088937,0.0007021701,0.001724493,0.001799174,0.001259544,0.0008088763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004065892,"about_ca_system_score_gemma":0.0006842986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002816224,"about_ca_topic_score_gemma":0.003397846,"domain_scores_codex":[0.999343,0.000107983,0.000057018,0.0002355603,0.0002059266,0.00005047258],"domain_scores_gemma":[0.9985768,0.0005254135,0.0001369004,0.000357684,0.000345368,0.00005789009],"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":[0.0005520149,0.0003014091,0.004910441,0.0002897993,0.000103389,0.0002985649,0.0004672425,0.1907976,0.05267471,0.005014618,0.003715184,0.7408749],"study_design_scores_gemma":[0.000007702148,0.00009978301,0.001252156,0.00001607232,0.00002802397,0.00008366668,0.00007218133,0.9780322,0.01431499,0.003830041,0.00224527,0.00001785142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05351524,0.000387785,0.9421999,0.0002025998,0.00005841855,0.00008082174,0.0003033689,0.002330818,0.0009209299],"genre_scores_gemma":[0.502934,0.0004264364,0.4913351,0.0002098459,0.00007196134,0.0002625382,0.002211849,0.0002557343,0.002292524],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002816224,"threshold_uncertainty_score":0.006869912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04308739828618125,"score_gpt":0.2939572304284447,"score_spread":0.2508698321422635,"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."}}