{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003278392,0.0007162201,0.0006381168,0.0006862751,0.0004343903,0.00076651,0.0008769404,0.0005715812,0.005357743],"category_scores_gemma":[0.001167958,0.0004328107,0.0005426759,0.0005233141,0.0001890761,0.0007488796,0.0004383589,0.0004082636,0.0004106835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004830603,"about_ca_system_score_gemma":0.001133687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001592048,"about_ca_topic_score_gemma":0.003228027,"domain_scores_codex":[0.9997998,0.00005259913,0.000007542044,0.00002812383,0.00005751323,0.00005446106],"domain_scores_gemma":[0.9996889,0.0001304536,0.00003941916,0.00003929252,0.00007774616,0.00002430027],"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":[0.000399996,0.0001720774,0.001223799,0.0001396928,0.00007372259,0.0001115125,0.00004246779,0.9079215,0.01668547,0.004683828,0.001066073,0.06747985],"study_design_scores_gemma":[0.00002919811,0.0002044646,0.0005176379,0.00001139128,0.00004626043,0.00004002327,0.00002875036,0.9901021,0.006949373,0.001243397,0.0008190709,0.000008415616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5265597,0.0009802524,0.4227105,0.00040986,0.0001553789,0.0001118912,0.0002731147,0.002002483,0.04679691],"genre_scores_gemma":[0.9536919,0.0001047593,0.04350101,0.00005516624,0.00001139819,0.00003472737,0.0001020827,0.0001209367,0.002378078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005357743,"threshold_uncertainty_score":0.01792341,"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."}}