{"id":"W3093354651","doi":"10.1051/matecconf/202032111093","title":"Advanced Titanium Alloy Fatigue Modeling","year":2020,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Fatigue and fracture mechanics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Forming Technologies (Canada)","funders":"","keywords":"Shot peening; Peening; Titanium alloy; Low-cycle fatigue; Fatigue testing; Titanium; Materials science; Computer science; Supply chain; Shot (pellet); Alloy; Mechanical engineering; Metallurgy; Engineering; Composite material","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":[0.000267152,0.0006973366,0.0006071204,0.0007063606,0.0003392211,0.0007247425,0.001918792,0.001292665,0.008142304],"category_scores_gemma":[0.0003931665,0.0003495469,0.001231217,0.0007117359,0.0002018885,0.0006238932,0.0004955206,0.00058779,0.003487015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008181265,"about_ca_system_score_gemma":0.000837088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0141513,"about_ca_topic_score_gemma":0.0144916,"domain_scores_codex":[0.9998386,0.00001639872,0.000008500076,0.00002518074,0.00008998696,0.00002131255],"domain_scores_gemma":[0.9998821,0.00002162163,0.000008455743,0.0000210027,0.0000601459,0.000006589806],"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":[0.00003148759,0.00003775492,0.0008761041,0.0001340168,0.00003823164,0.0000909468,0.00006107947,0.9366794,0.009520479,0.01521674,0.005646994,0.03166675],"study_design_scores_gemma":[0.000003968767,0.00001812466,0.0002940412,0.000009278675,0.000005502563,0.00002618562,0.000007303852,0.9875486,0.0006916692,0.001792051,0.009596006,0.000007259062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07437485,0.002456835,0.7812469,0.0004844223,0.0002971289,0.0002974527,0.007367293,0.002951086,0.130524],"genre_scores_gemma":[0.6457855,0.004232202,0.2285503,0.0002387726,0.0002174628,0.0007942155,0.01048235,0.001232977,0.1084662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0141513,"threshold_uncertainty_score":0.02813786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2847359754145329,"score_gpt":0.5001469231851997,"score_spread":0.2154109477706668,"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."}}