{"id":"W4386113750","doi":"10.48550/arxiv.2308.10856","title":"Majorana Demonstrator Data Release for AI/ML Applications","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pacific Northwest National Laboratory; Natural Sciences and Engineering Research Council of Canada; Nuclear Physics; Los Alamos National Laboratory; L'Oreal USA; Oak Ridge National Laboratory; South Dakota Board of Regents; Lawrence Berkeley National Laboratory; Laboratory Directed Research and Development; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Metadata; MAJORANA; Section (typography); Computer science; Calibration; Information retrieval; Event (particle physics); Artificial intelligence; Database; World Wide Web; Operating system; Particle physics; Physics","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":[0.002403983,0.002229973,0.001563402,0.002158722,0.001478515,0.002873454,0.00398546,0.002190167,0.05842663],"category_scores_gemma":[0.006871411,0.0007691489,0.001445524,0.00348057,0.0006197946,0.002514479,0.003333586,0.003311201,0.1254757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008584371,"about_ca_system_score_gemma":0.001599065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006674683,"about_ca_topic_score_gemma":0.01170986,"domain_scores_codex":[0.9976889,0.0003352179,0.0001617778,0.0005647253,0.000992535,0.0002569041],"domain_scores_gemma":[0.9936839,0.0007713393,0.0003576712,0.00347407,0.001239481,0.000473483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001193406,0.00007315289,0.000793989,0.0001370253,0.00002536863,0.00003875676,0.00002766885,0.0005057224,0.0006301469,0.0008763946,0.991392,0.005380469],"study_design_scores_gemma":[0.0002442338,0.00006612723,0.005026156,0.00007000859,0.00002108275,0.0001486566,0.0000916698,0.003354609,0.00299063,0.003724991,0.9842111,0.00005070494],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.002378742,0.0001217341,0.003269465,0.0003999964,0.0002506613,0.0001457716,0.968083,0.01652392,0.008826633],"genre_scores_gemma":[0.001993826,0.00002573836,0.003129802,0.0000968356,0.00004963196,0.0001893672,0.9917863,0.001051232,0.001677219],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05842663,"threshold_uncertainty_score":0.1954566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1419208102886759,"score_gpt":0.2338360210289953,"score_spread":0.09191521074031944,"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."}}