{"id":"W2762664148","doi":"10.1088/1741-4326/aa90c0","title":"ERO modeling and sensitivity analysis of locally enhanced beryllium erosion by magnetically connected antennas","year":2017,"lang":"en","type":"article","venue":"Nuclear Fusion","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Oak Ridge National Laboratory; H2020 Euratom; Euratom Research and Training Programme; Office of Science; U.S. Department of Energy; Advanced Scientific Computing Research; EUROfusion; UT-Battelle; Institute of Circulatory and Respiratory Health; Fusion Energy Sciences; Tekes; Battelle","keywords":"Limiter; Beryllium; Plasma; Atomic physics; Erosion; Materials science; Impurity; Ion; Sensitivity (control systems); Magnetic field; Computational physics; Jet (fluid); Flux (metallurgy); Plasma parameters; Physics; Mechanics; Nuclear physics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004264253,0.0006041198,0.0004911796,0.0003174101,0.0002548139,0.0006179717,0.0007521739,0.0009235866,0.001164407],"category_scores_gemma":[0.001320694,0.0002676201,0.000596619,0.0002979687,0.0003941788,0.0004615722,0.0005633278,0.0006437481,0.0001472408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007131473,"about_ca_system_score_gemma":0.0002836391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009748364,"about_ca_topic_score_gemma":0.002660619,"domain_scores_codex":[0.9998216,0.00004599799,0.000008385233,0.00004321367,0.00003929804,0.00004150455],"domain_scores_gemma":[0.9992409,0.0004854728,0.0000783162,0.00007786385,0.00009037603,0.00002713676],"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":[0.00008124701,0.00002545404,0.001682177,0.00004293118,0.000019271,0.00008708962,0.00002882027,0.9905917,0.006065323,0.0003519049,0.00008940326,0.000934669],"study_design_scores_gemma":[0.00001190097,0.0000475817,0.0008845369,0.000003973331,0.00001039272,0.00001600501,0.00001942389,0.9940211,0.004698571,0.0001110994,0.0001679817,0.000007448235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9831721,0.0001403686,0.01097245,0.0001117702,0.00001764573,0.00003131994,0.0005288306,0.0003965505,0.004628898],"genre_scores_gemma":[0.9978303,0.00004084092,0.001449537,0.00001569525,0.000002416072,0.0000150747,0.0001329136,0.00003781251,0.000475398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009748364,"threshold_uncertainty_score":0.01938325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01058081288628184,"score_gpt":0.2504447652444375,"score_spread":0.2398639523581556,"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."}}