{"id":"W2947847260","doi":"10.1007/978-3-030-19781-0_29","title":"Numerical Simulation of a Water Jet Impacting a Titanium Target","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in mechanical engineering","topic":"Erosion and Abrasive Machining","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Water jet; Jet (fluid); Titanium alloy; Smoothed-particle hydrodynamics; Displacement (psychology); Materials science; Mechanics; Finite element method; Computer simulation; Coupling (piping); Position (finance); Titanium; Particle (ecology); Mechanical engineering; Alloy; Structural engineering; Engineering; Composite material; Physics; Geology; Metallurgy","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000209152,0.0003127362,0.0004358539,0.0000801011,0.00002233806,0.00001384862,0.0001894763,0.0003677401,0.003261483],"category_scores_gemma":[0.0002425368,0.0002366846,0.0001459831,0.00005013837,0.00001663763,0.00006594441,0.0001795027,0.0007283698,0.0001915431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001674711,"about_ca_system_score_gemma":0.000007120846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002389652,"about_ca_topic_score_gemma":0.000005773095,"domain_scores_codex":[0.9986129,0.00001384058,0.0003710357,0.0003640221,0.0003240191,0.0003141694],"domain_scores_gemma":[0.9992588,0.0003262124,0.00008865757,0.0002409162,0.000007526031,0.00007795873],"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":[0.00001289556,0.000008460562,0.0001139132,0.00004000621,0.00001021682,0.00000787138,0.0000817805,0.9347395,0.06299848,0.0007228635,0.00000252936,0.001261469],"study_design_scores_gemma":[0.0003020393,0.0001100282,0.00007615951,0.0003109924,0.00002091777,0.000006524575,0.000001064492,0.9490831,0.04289884,0.003690281,0.002993769,0.0005062154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003140569,0.00008812713,0.9810361,0.0001143888,0.0004970513,0.0003886942,0.00001024753,0.0001058362,0.01461904],"genre_scores_gemma":[0.9926545,0.000003439567,0.006465166,0.00009626855,0.00007697118,0.000002333418,0.00001819331,0.00007156884,0.0006115289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9895139,"threshold_uncertainty_score":0.9976497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008664099759938801,"score_gpt":0.2228987420919336,"score_spread":0.2142346423319948,"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."}}