{"id":"W4401252992","doi":"10.1103/physrevd.110.034001","title":"Event generator for jet tomography in electron-ion collisions","year":2024,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"High-Energy Particle Collisions Research","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Los Alamos National Laboratory; National Nuclear Security Administration; Office of Energy Research and Development; Nuclear Physics; Laboratory Directed Research and Development; Basic and Applied Basic Research Foundation of Guangdong Province; Chinese University of Hong Kong; Shenzhen University; National Natural Science Foundation of China; High Energy Physics; U.S. Department of Energy; National Science Foundation","keywords":"Physics; Parton; Gluon; Hadron; Electron; Nuclear physics; Particle physics; Deep inelastic scattering; Quark–gluon plasma; Jet (fluid); Quantum chromodynamics; Generator (circuit theory); Scattering; Inelastic scattering; Quantum mechanics","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":[0.001668895,0.0008513388,0.0004833086,0.0006045532,0.0004141734,0.001013152,0.002453783,0.0006740405,0.009692517],"category_scores_gemma":[0.003398451,0.0004116558,0.0006808735,0.0005466369,0.0004397677,0.001129554,0.001458057,0.001080752,0.002008012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004431747,"about_ca_system_score_gemma":0.0005547268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004185849,"about_ca_topic_score_gemma":0.0003715174,"domain_scores_codex":[0.9993899,0.0002121286,0.00003871096,0.00007301754,0.0002314508,0.00005479976],"domain_scores_gemma":[0.999034,0.0004582763,0.0000774373,0.0002006377,0.0001508628,0.00007878505],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002548103,0.0004406779,0.01291499,0.0005303771,0.0002281285,0.003018749,0.0007174161,0.2425796,0.04947777,0.3480243,0.0519674,0.2875524],"study_design_scores_gemma":[0.0001700068,0.000106713,0.0006732607,0.00002341631,0.00001827592,0.0004099347,0.00003088931,0.9053509,0.03270667,0.03514638,0.02530165,0.00006190853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009096924,0.00005984576,0.9738359,0.00008463769,0.00008568831,0.000227587,0.0006799484,0.01182419,0.004105358],"genre_scores_gemma":[0.2843728,0.0001812603,0.7014596,0.0002297086,0.0001290329,0.0007894525,0.003092926,0.004497378,0.005247743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009692517,"threshold_uncertainty_score":0.03242469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837112832302101,"score_gpt":0.4506260734729193,"score_spread":0.4322549451498983,"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."}}