{"id":"W4381248276","doi":"10.1103/physrevc.107.064311","title":"Experimental study of the <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"><mml:mmultiscripts><mml:mi mathvariant=\"normal\">S</mml:mi><mml:mprescripts/><mml:none/><mml:mn>38</mml:mn></mml:mmultiscripts></mml:math> excited level scheme","year":2023,"lang":"lv","type":"article","venue":"Physical review. C","topic":"Nuclear physics research studies","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"TRIUMF","funders":"Lawrence Berkeley National Laboratory; Argonne National Laboratory; U.S. Department of Energy; Laboratory Directed Research and Development; Lawrence Livermore National Laboratory; Nuclear Physics; Office of Science","keywords":"Physics; Yrast; Energy (signal processing); Machine learning; Neutron; Algorithm; Atomic physics; Nuclear physics; Computer science; Quantum mechanics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001123147,0.0006043735,0.0005420651,0.0003785486,0.001248354,0.0005947307,0.001145039,0.001089081,0.01327606],"category_scores_gemma":[0.001087206,0.0004584079,0.0002816224,0.001086492,0.0006223127,0.0006024581,0.0006980118,0.001486193,0.003237168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007075628,"about_ca_system_score_gemma":0.0006921408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001077498,"about_ca_topic_score_gemma":0.001762004,"domain_scores_codex":[0.9993025,0.00007135965,0.00002814989,0.0002327566,0.0001679703,0.0001973363],"domain_scores_gemma":[0.9991409,0.0002663057,0.0001043909,0.0001893177,0.0002029066,0.00009621387],"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":[0.0009682691,0.0003829763,0.0009830792,0.0004010573,0.00002900075,0.0002807944,0.0004394595,0.0009536585,0.9850723,0.002778827,0.002093149,0.005617289],"study_design_scores_gemma":[0.00004346169,0.001284648,0.001653543,0.0000191175,0.00001972816,0.00008447869,0.0001417015,0.001565299,0.9841285,0.000307016,0.01072257,0.00003006515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521372,0.0009482807,0.02237608,0.0004551162,0.0001935233,0.0003776914,0.003135847,0.0005874833,0.01978864],"genre_scores_gemma":[0.9654259,0.001208871,0.01740564,0.0002604547,0.00004039089,0.000402064,0.002355753,0.0004810202,0.01241995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01327606,"threshold_uncertainty_score":0.04441285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04758341240614775,"score_gpt":0.3116350159535201,"score_spread":0.2640516035473723,"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."}}