{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0117943,0.001042357,0.001106089,0.001521314,0.000921316,0.0019297,0.002150145,0.0008799151,0.00128882],"category_scores_gemma":[0.035216,0.0009775101,0.001639754,0.00112553,0.001199743,0.001869083,0.001793215,0.00167549,0.0002732278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001881717,"about_ca_system_score_gemma":0.002427576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01842313,"about_ca_topic_score_gemma":0.01209595,"domain_scores_codex":[0.996783,0.001555034,0.0001594085,0.0006275258,0.0006427038,0.0002322307],"domain_scores_gemma":[0.984124,0.01205832,0.001364835,0.001047512,0.001102674,0.0003026906],"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":[0.0009295118,0.0002029404,0.03264586,0.000158037,0.0005444502,0.0002375555,0.0004195694,0.8156205,0.00620575,0.07187413,0.001328511,0.06983312],"study_design_scores_gemma":[0.00004490202,0.00002797903,0.004579593,0.00001121113,0.00005916703,0.00002025474,0.00001381699,0.9735034,0.001346727,0.01996381,0.0003991964,0.00003001003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.254769,0.0003416999,0.7401891,0.0003408256,0.00002557273,0.0001089255,0.0007675607,0.000563892,0.002893473],"genre_scores_gemma":[0.9085158,0.0002106805,0.0870478,0.000142149,0.00005255433,0.0001085148,0.00227137,0.0002042806,0.00144682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01842313,"threshold_uncertainty_score":0.06237495,"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."}}