{"id":"W4410023227","doi":"10.1088/1361-6471/add360","title":"Emulation of the calculations of final <i>r</i>-process abundance patterns with a neural network","year":2025,"lang":"en","type":"article","venue":"Journal of Physics G Nuclear and Particle Physics","topic":"Nuclear physics research studies","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of British Columbia; TRIUMF","funders":"Los Alamos National Laboratory; Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy; Lawrence Berkeley National Laboratory; National Science Foundation","keywords":"Emulation; Abundance (ecology); Artificial neural network; Process (computing); Computer science; Artificial intelligence; Psychology; Operating system; Biology; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008264324,0.0004586077,0.0003700635,0.0003702533,0.0003013035,0.0004399937,0.0008191574,0.0007252244,0.002128186],"category_scores_gemma":[0.002734031,0.0003422616,0.0004225721,0.0002591988,0.0004453388,0.0006169113,0.0004754016,0.0006610699,0.0002442721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008601692,"about_ca_system_score_gemma":0.0007691628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006038059,"about_ca_topic_score_gemma":0.003951458,"domain_scores_codex":[0.9998667,0.00004588523,0.000006551618,0.00002356985,0.00003807492,0.00001927638],"domain_scores_gemma":[0.9990222,0.0006146858,0.00007266966,0.00008558691,0.0001677946,0.00003707796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002037006,0.000009634844,0.0004229315,0.000008285976,0.00001004937,0.0000215756,0.000007205883,0.993315,0.0007739183,0.0009093463,0.00007668345,0.004424979],"study_design_scores_gemma":[7.017867e-7,0.000001740547,0.00003125997,5.919406e-7,4.818708e-7,0.000001389399,5.399486e-7,0.9994733,0.0003145313,0.0001539365,0.00002083038,6.609037e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2383074,0.00007591834,0.7554641,0.0002391996,0.00003943959,0.00004817819,0.0001402542,0.001378003,0.004307596],"genre_scores_gemma":[0.9046076,0.00003066205,0.09335053,0.00007818674,0.00001175692,0.00008261514,0.0001411776,0.0001145086,0.001583017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006038059,"threshold_uncertainty_score":0.01200587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02037608584025132,"score_gpt":0.2944735229454282,"score_spread":0.2740974371051769,"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."}}