{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001420145,0.0002027283,0.0002100255,0.00008888832,0.0001982143,0.00005444285,0.0009018022,0.00009161008,0.00008927107],"category_scores_gemma":[0.000004395266,0.00024349,0.0001038677,0.0002789354,0.00005783105,0.000190259,0.0007153224,0.0002744167,0.0003163129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003735438,"about_ca_system_score_gemma":0.0002239102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001172219,"about_ca_topic_score_gemma":0.00003812482,"domain_scores_codex":[0.9987659,0.0000207011,0.0001698113,0.0007198942,0.00004366189,0.0002799834],"domain_scores_gemma":[0.9984981,0.00006660613,0.0001394253,0.001087662,0.00007977963,0.0001284571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004605099,0.0007314295,0.6185821,0.0008850541,0.002118414,0.00005586752,0.0003433205,0.06128819,0.0009312517,0.241577,0.04260554,0.03042137],"study_design_scores_gemma":[0.001717797,0.00003405735,0.01281864,0.0001222951,0.000511787,3.648344e-7,0.0002638395,0.8462364,0.001598623,0.08851584,0.04684661,0.001333736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4241512,0.00002266734,0.5705822,0.0001372946,0.0003302434,0.001100073,0.002164591,0.0002236372,0.001288017],"genre_scores_gemma":[0.9960103,0.00001552844,0.0004210316,0.00002542161,0.0002703866,0.00002100614,0.001321486,0.00002862923,0.001886202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7849482,"threshold_uncertainty_score":0.9929234,"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."}}