{"id":"W2914353428","doi":"10.1016/j.nima.2019.01.071","title":"Distillation and stripping pilot plants for the JUNO neutrino detector: Design, operations and reliability","year":2019,"lang":"en","type":"article","venue":"Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment","topic":"Neutrino Physics Research","field":"Physics and Astronomy","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Snolab","funders":"Institute of High Energy Physics; Nuclear Physics; Instituto Nazionale di Fisica Nucleare; Università degli Studi di Ferrara","keywords":"Scintillator; Stripping (fiber); Distillation; Environmental science; Reliability (semiconductor); Detector; Process engineering; Nuclear engineering; Materials science; Physics; Engineering; Chemistry; Mechanical engineering; Chromatography; Optics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002336302,0.0002796213,0.0003766192,0.0002455529,0.0008561945,0.0005977161,0.0001539989,0.00008452675,0.00004141091],"category_scores_gemma":[0.00003666804,0.0002305802,0.00005462084,0.0005183737,0.0002184626,0.0004205832,0.0003285458,0.0007233071,0.000001282566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002734595,"about_ca_system_score_gemma":0.00006288886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006190197,"about_ca_topic_score_gemma":0.00001751261,"domain_scores_codex":[0.9970871,0.0007599153,0.0003760301,0.0006852237,0.0004336406,0.0006581442],"domain_scores_gemma":[0.9982561,0.001049987,0.0001075697,0.0002564924,0.0001389054,0.0001909105],"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":[0.00072679,0.0006547663,0.0720555,0.0001798082,0.0005650953,9.829821e-7,0.002068308,0.0004666629,0.4914312,0.007406735,0.00004347752,0.4244006],"study_design_scores_gemma":[0.0195501,0.00933608,0.2904587,0.0005811273,0.0002406165,0.00001040621,0.008235102,0.1951968,0.4337865,0.03678323,0.003113744,0.002707583],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926625,0.00006102608,0.004667183,0.00009703096,0.0002052993,0.001953226,0.00004566609,0.0000279422,0.0002801623],"genre_scores_gemma":[0.9957136,0.0003029998,0.003565565,0.00002085473,0.0001389506,0.0001579647,0.00001074896,0.00004436655,0.00004496844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4216931,"threshold_uncertainty_score":0.9402786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07333702027756261,"score_gpt":0.3877259884902228,"score_spread":0.3143889682126602,"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."}}