{"id":"W4384918873","doi":"10.48550/arxiv.2307.09689","title":"Electron-beam Calibration of Aerogel Tiles for the HELIX RICH Detector","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Nuclear Safety and Security Commission; McGill University; TRIUMF; National Aeronautics and Space Administration","keywords":"Cherenkov radiation; Detector; Particle identification; Physics; Cherenkov detector; Optics; Cosmic ray; Calibration; Nuclear physics; Radiator (engine cooling); Electron; Beam (structure); Refractive index; Range (aeronautics); Helix (gastropod); Aerogel; Materials science; Nanotechnology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001841059,0.0006966816,0.0005735434,0.0006768407,0.0006903394,0.0007470794,0.00150376,0.001031345,0.00370529],"category_scores_gemma":[0.002205942,0.000456264,0.0003802554,0.0009554392,0.0006550293,0.0006135526,0.001080332,0.0007117768,0.00148717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008201376,"about_ca_system_score_gemma":0.000626725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009597112,"about_ca_topic_score_gemma":0.002244722,"domain_scores_codex":[0.9985201,0.0001880994,0.00007816333,0.0003315076,0.0007695428,0.000112617],"domain_scores_gemma":[0.9984921,0.0002602548,0.000180008,0.0005550091,0.0004579487,0.00005470674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006104562,0.00009462769,0.01293126,0.0001222539,0.00004995458,0.0001866845,0.0003625097,0.004888405,0.9595449,0.004596707,0.001101987,0.01551022],"study_design_scores_gemma":[0.00002805453,0.0001515828,0.007325358,0.0000181003,0.00002055222,0.0001128893,0.00006252716,0.009300867,0.9702007,0.0003010373,0.01245368,0.00002470848],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6616619,0.0003869713,0.3112339,0.0001662149,0.0002713954,0.000871536,0.00325151,0.002818116,0.0193385],"genre_scores_gemma":[0.7151036,0.0003977944,0.2705144,0.0001307154,0.00002832274,0.0009702391,0.00209603,0.0005098067,0.010249],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00370529,"threshold_uncertainty_score":0.01239544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06071091202113287,"score_gpt":0.2017141528670213,"score_spread":0.1410032408458884,"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."}}