{"id":"W2274967703","doi":"10.1016/j.nuclphysa.2016.01.014","title":"New insights from 3D simulations of heavy ion collisions","year":2016,"lang":"en","type":"article","venue":"Nuclear Physics A","topic":"High-Energy Particle Collisions Research","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"RIKEN; Natural Sciences and Engineering Research Council of Canada; Office of Science; U.S. Department of Energy; Fonds Québécois de la Recherche sur la Nature et les Technologies; McGill University; Canada Foundation for Innovation","keywords":"Rapidity; Physics; Observable; Heavy ion; Large Hadron Collider; Harmonics; Shear viscosity; Elliptic flow; Shear (geology); Nuclear physics; Range (aeronautics); Volume viscosity; Ion; Viscosity; Mechanics; Statistical physics; Thermodynamics; Materials science; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005461419,0.001460693,0.001533356,0.0009833323,0.001260967,0.00286839,0.002301771,0.003078818,0.008915432],"category_scores_gemma":[0.004064648,0.001077241,0.001077679,0.001123271,0.001784489,0.00213769,0.001579768,0.001999295,0.0009060567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001085598,"about_ca_system_score_gemma":0.001128908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01548967,"about_ca_topic_score_gemma":0.009810735,"domain_scores_codex":[0.9995832,0.0001019055,0.00001899064,0.00003925649,0.0001776514,0.00007902203],"domain_scores_gemma":[0.998795,0.0005978275,0.0001191877,0.0001368684,0.0001855381,0.0001655695],"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":[0.0001005673,0.00009358889,0.001732337,0.0001229796,0.00005592025,0.0001879445,0.0001795321,0.9654492,0.001379858,0.02523805,0.001885828,0.003574184],"study_design_scores_gemma":[0.00003507778,0.00001007308,0.0004830252,0.00001769364,0.000009117615,0.00002422504,0.00005171697,0.9829041,0.0002062285,0.01493896,0.001300682,0.00001908465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6159323,0.00546566,0.1707753,0.007590495,0.001928029,0.0002302844,0.00492335,0.002463996,0.1906905],"genre_scores_gemma":[0.9757707,0.00151458,0.01471456,0.0008509812,0.0003252131,0.0001279301,0.0009096678,0.0007240634,0.005062274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01548967,"threshold_uncertainty_score":0.03079903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189436153491039,"score_gpt":0.2729424990200285,"score_spread":0.2539988836709247,"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."}}