{"id":"W4405092304","doi":"10.17615/0sha-aa90","title":"Assay-based background projection for the Majorana Demonstrator using Monte Carlo uncertainty propagation","year":2024,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Medical Imaging Techniques and Applications","field":"Medicine","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":"MAJORANA; Monte Carlo method; Projection (relational algebra); Statistical physics; Computer science; Physics; Algorithm; Nuclear physics; Statistics; Mathematics","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":[0.0002783573,0.0001096469,0.0001297392,0.00006646612,0.0002163796,0.0002117761,0.00007555151,0.00007146566,0.00003839995],"category_scores_gemma":[0.00009667187,0.00006672445,0.00008727692,0.000301674,0.0001823564,0.0002099572,0.00001656218,0.0001655508,0.000003780186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006068458,"about_ca_system_score_gemma":0.0004931368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000135981,"about_ca_topic_score_gemma":0.000008695,"domain_scores_codex":[0.9992329,0.00002708226,0.0001942483,0.0002138605,0.0001776784,0.0001542097],"domain_scores_gemma":[0.9993389,0.000274651,0.0000395304,0.0002180042,0.00006768884,0.00006123671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004305556,0.0002851609,0.001794848,0.002183135,0.0003386256,0.00002504678,0.0006460531,0.0004827657,0.03481301,0.7439117,0.1602924,0.0547967],"study_design_scores_gemma":[0.0002742825,0.00008629564,0.000076111,0.0002626777,0.0002149705,0.00001540003,0.0002121697,0.9123729,0.008177426,0.009720949,0.06848282,0.0001040347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0199297,0.0008931018,0.9385594,0.03652511,0.0002795358,0.002366131,0.00004232225,0.0008149574,0.0005898004],"genre_scores_gemma":[0.8306998,0.000006203972,0.1663259,0.001109095,0.0005474288,0.0005130702,0.00006659321,0.00004090952,0.0006910532],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9118901,"threshold_uncertainty_score":0.2720944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06733018219977402,"score_gpt":0.3349049247367596,"score_spread":0.2675747425369855,"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."}}