{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120397,0.0001759034,0.0002144473,0.0001588847,0.0001629894,0.00003835773,0.0004280191,0.0001503996,0.00005713144],"category_scores_gemma":[0.00002384777,0.0001572731,0.0002295078,0.000352181,0.00008713685,0.00007286697,0.0001855046,0.0002700228,0.00001501249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000050474,"about_ca_system_score_gemma":0.00009024263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001434692,"about_ca_topic_score_gemma":0.00003642917,"domain_scores_codex":[0.9991569,0.00002871591,0.0001805923,0.0003843913,0.00005495371,0.0001944293],"domain_scores_gemma":[0.9988983,0.000210479,0.0002719055,0.0004770128,0.0001087968,0.00003354156],"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":[0.0001150052,0.00009634022,0.02344673,0.0001135331,0.0007579066,0.000002697482,0.0002056894,0.896066,0.001329795,0.07164171,0.0040664,0.002158177],"study_design_scores_gemma":[0.001002057,0.0001585906,0.004827319,0.00006100002,0.000355705,3.598618e-7,0.001650087,0.8474258,0.07757696,0.06330968,0.002949395,0.0006831029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8953325,0.00003717499,0.1030203,0.0001361744,0.000421636,0.000542167,0.0001256656,0.0002459095,0.0001384667],"genre_scores_gemma":[0.9979603,0.00003645677,0.00004090518,0.000009126562,0.0001200702,0.00001033788,0.00001593662,0.00002227881,0.001784618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1029794,"threshold_uncertainty_score":0.6413413,"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."}}