{"id":"W2016851993","doi":"10.1016/j.anucene.2011.11.005","title":"Utilization of reduced fuelling ripple set in ROP detector layout optimization","year":2012,"lang":"en","type":"article","venue":"Annals of Nuclear Energy","topic":"Nuclear reactor physics and engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Atomic Energy (Canada)","funders":"","keywords":"Detector; Computer science; Set (abstract data type); Bayesian optimization; Speedup; Process (computing); Simulated annealing; Probabilistic logic; Ripple; Point (geometry); Algorithm; Mathematical optimization; Real-time computing; Simulation; Parallel computing; Mathematics; Electrical engineering; Engineering; Artificial intelligence; Operating system; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007644645,0.00009425598,0.0001588093,0.0001124333,0.00001040839,0.000006069624,0.00007633625,0.0000614839,0.00005725654],"category_scores_gemma":[0.00001193049,0.0001064873,0.00004286587,0.0002351955,0.00001092649,0.0001947968,0.00001770752,0.0000529914,0.000002894746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001120272,"about_ca_system_score_gemma":0.000003186354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006950449,"about_ca_topic_score_gemma":0.000002713385,"domain_scores_codex":[0.9994202,0.00001086364,0.000215302,0.00006695159,0.00009656069,0.0001901205],"domain_scores_gemma":[0.9997195,0.0000128562,0.00004474519,0.0001324992,0.00003974486,0.00005066234],"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":[0.00000982849,0.00003974884,0.00007893614,0.00007565859,0.00002814301,3.073489e-7,0.0005080878,0.9209214,0.06577433,0.007461318,0.0002860939,0.004816149],"study_design_scores_gemma":[0.0002000709,0.00004120186,0.001132914,0.00009796221,0.00001001898,0.000001218705,0.0001254153,0.9097759,0.07168284,0.0001161941,0.01657394,0.0002423319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9841524,0.0006876063,0.008195976,0.00001998667,0.0003097223,0.00005325466,0.00001491793,0.000145353,0.006420803],"genre_scores_gemma":[0.99865,0.0003683982,0.0008205585,0.00001433028,0.00006704668,0.000001152182,0.00001506223,0.00005773893,0.000005675415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01628784,"threshold_uncertainty_score":0.4342425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05870023705985476,"score_gpt":0.2575161374441199,"score_spread":0.1988159003842651,"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."}}