{"id":"W2899742902","doi":"10.1016/j.nima.2018.10.209","title":"Development of a thick gas electron multiplier-based beta-ray detector","year":2018,"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":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; CANDU Owners Group","keywords":"Gas electron multiplier; Detector; Physics; Monte Carlo method; Electron multiplier; BETA (programming language); X-ray detector; Electron; Particle detector; Radiation; Beta particle; Optics; Nuclear physics; Nuclear engineering; Computational physics; Computer science; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002131381,0.0003545028,0.0005009969,0.0004799585,0.0006505547,0.0001707429,0.0002242065,0.0001384536,0.0002120768],"category_scores_gemma":[0.00003978831,0.0003356457,0.00009057747,0.001146433,0.0003210271,0.0002927576,0.0002106335,0.000688783,0.000007184859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003997466,"about_ca_system_score_gemma":0.0001814389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001452999,"about_ca_topic_score_gemma":0.00004660177,"domain_scores_codex":[0.9966877,0.0004739878,0.0006637799,0.0006125831,0.0006330943,0.0009288262],"domain_scores_gemma":[0.998879,0.00016791,0.0002695843,0.0002378512,0.0002081485,0.0002374688],"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":[0.0002234773,0.0004192267,0.04058874,0.00004761187,0.0002855822,7.50213e-7,0.002927981,0.000005052281,0.6893029,0.0002361734,0.00001781576,0.2659447],"study_design_scores_gemma":[0.002421094,0.0008124699,0.0360325,0.0001396775,0.00002659469,4.774739e-7,0.0006649224,0.005697252,0.9507796,0.0009512746,0.001962195,0.0005119774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971696,0.00002147472,0.001230644,0.00002502785,0.0002476638,0.0004904976,0.00001076528,0.00004577373,0.0007586117],"genre_scores_gemma":[0.9587914,0.00002192347,0.04083925,0.00002426801,0.0001729586,0.00006786043,0.00001054648,0.00004868466,0.00002305119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2654327,"threshold_uncertainty_score":0.9999096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05743565189150618,"score_gpt":0.3768231431673388,"score_spread":0.3193874912758326,"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."}}