{"id":"W3031674256","doi":"10.1016/j.nima.2020.164807","title":"The GlueX beamline and detector","year":2020,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Regina","funders":"Comisión Nacional de Investigación Científica y Tecnológica; Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; GSI Helmholtzzentrum für Schwerionenforschung; China Scholarship Council; Forschungszentrum Jülich; National Natural Science Foundation of China; U.S. Department of Energy; Office of Science; Thomas Jefferson National Accelerator Facility; Russian Foundation for Basic Research; National Science Foundation","keywords":"Beamline; Detector; Physics; Nuclear physics; Optics; Beam (structure)","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":[0.001329379,0.0002569257,0.000309425,0.0001165025,0.0009017002,0.0004578694,0.0001656903,0.00007987006,0.00006608306],"category_scores_gemma":[0.00007673493,0.0002047899,0.00005359997,0.0007541963,0.0002404933,0.0002985746,0.0003065129,0.0007401246,0.000003530686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001341329,"about_ca_system_score_gemma":0.00003976316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001400752,"about_ca_topic_score_gemma":0.0000128889,"domain_scores_codex":[0.9975935,0.0004529702,0.000375273,0.0004947602,0.000399665,0.0006838109],"domain_scores_gemma":[0.9990888,0.0002611676,0.0001268441,0.0001400921,0.00007382123,0.000309244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002300926,0.0001407566,0.09021919,0.00003921722,0.0003235525,0.000002426607,0.002999482,0.000004280622,0.1912745,0.001061297,0.0001123493,0.7135928],"study_design_scores_gemma":[0.01521954,0.005174745,0.3357702,0.0003779918,0.000184099,0.00001028207,0.01221078,0.07462964,0.4771681,0.0257363,0.05016238,0.00335601],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981624,0.0001076216,0.0002602293,0.0003321059,0.0001855132,0.0003952725,0.00001094347,0.00004136439,0.0005045834],"genre_scores_gemma":[0.9967968,0.0003722283,0.002398317,0.00009314178,0.0002278052,0.00004424093,0.000004187618,0.00003520655,0.00002800015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7102368,"threshold_uncertainty_score":0.8351091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05799438388462064,"score_gpt":0.3680706108973527,"score_spread":0.3100762270127321,"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."}}