{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003227894,0.003004135,0.003260938,0.004430922,0.005032497,0.003545304,0.0052148,0.004720049,0.2514186],"category_scores_gemma":[0.002843449,0.002481828,0.001563241,0.003410339,0.001193202,0.002983032,0.005357373,0.003816221,0.1638383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004847544,"about_ca_system_score_gemma":0.005805517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004265355,"about_ca_topic_score_gemma":0.005597877,"domain_scores_codex":[0.995218,0.0006307709,0.0001694523,0.001293023,0.00196169,0.0007271034],"domain_scores_gemma":[0.9968745,0.0002582038,0.0003170414,0.0009636661,0.001052051,0.0005345349],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004579172,0.0005538311,0.008819874,0.001306513,0.0002980077,0.0005459248,0.0003696022,0.001888046,0.1864502,0.05294343,0.6513965,0.09084892],"study_design_scores_gemma":[0.0007833894,0.0006883645,0.009246161,0.0003043106,0.0001875838,0.0007435211,0.0001484904,0.007089177,0.1668481,0.006255403,0.8074577,0.0002476456],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07224454,0.004381584,0.2352724,0.006104842,0.004588421,0.006061553,0.2138556,0.09795905,0.359532],"genre_scores_gemma":[0.2235817,0.001524269,0.3628726,0.005856427,0.000971422,0.01008812,0.170351,0.0135382,0.2112163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2514186,"threshold_uncertainty_score":0.841079,"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."}}