{"id":"W4410401006","doi":"10.1103/physrevc.111.054913","title":"Bayesian inference analysis of jet quenching using inclusive jet and hadron suppression measurements","year":2025,"lang":"en","type":"article","venue":"Physical review. C","topic":"High-Energy Particle Collisions Research","field":"Physics and Astronomy","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Regina","funders":"European Research Council; Nuclear Physics; Natural Sciences and Engineering Research Council of Canada; Office of the Vice President for Research, Wayne State University; Office of Science; Academy of Finland; National Natural Science Foundation of China; Canada Research Chairs; U.S. Department of Energy; San Diego Supercomputer Center; Fundação de Amparo à Pesquisa do Estado de São Paulo; University of Regina; National Energy Research Scientific Computing Center; University of Texas at Austin; Wayne State University; National Science Foundation","keywords":"Hadron; Jet quenching; Particle physics; Jet (fluid); Inference; Bayesian probability; Nuclear physics; Physics; Bayesian inference; Quenching (fluorescence); Computer science; Artificial intelligence; Mechanics; Quark–gluon plasma; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002993583,0.0001547893,0.0005685046,0.0001648166,0.0001518414,0.00003301421,0.0002008602,0.00001813387,0.00008139662],"category_scores_gemma":[0.0001479276,0.0001181457,0.0001826048,0.00135715,0.00007035844,0.0001561418,0.0003183964,0.000158259,0.000003796617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004557841,"about_ca_system_score_gemma":0.0001032678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005619464,"about_ca_topic_score_gemma":0.00001752477,"domain_scores_codex":[0.9985763,0.0002311414,0.000307783,0.0002916573,0.0003638995,0.0002292088],"domain_scores_gemma":[0.9990323,0.0002185577,0.0001183386,0.0003575514,0.0001728407,0.0001004417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000372097,0.0008810159,0.7174103,0.000799913,0.002114079,0.000002232827,0.0004655878,0.01460836,0.1675345,0.07021376,0.0002849125,0.02564806],"study_design_scores_gemma":[0.0006331755,0.00006112356,0.03632919,0.003915798,0.002646353,1.03959e-7,0.00007170206,0.8936833,0.05449523,0.006617486,0.001123539,0.0004230611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785541,0.0009759075,0.01796169,0.0001670757,0.00002404123,0.0002405974,0.00002319309,0.000009193081,0.002044247],"genre_scores_gemma":[0.9993425,0.0001250296,0.0003999876,0.00002092554,0.0000318792,0.00003220046,0.00001829892,0.000006955822,0.0000222425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8790749,"threshold_uncertainty_score":0.4817844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03869568798346931,"score_gpt":0.4256283966406312,"score_spread":0.3869327086571619,"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."}}