{"id":"W2810718050","doi":"10.7566/jpscp.22.011032","title":"Monte Carlo Simulations for the Optimization and Data Analysis of Experiments with Ultracold Neutrons","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"TRIUMF","funders":"Science and Technology Facilities Council; Deutsche Forschungsgemeinschaft; University of Sussex","keywords":"Ultracold neutrons; Physics; Neutron; Monte Carlo method; Dipole; Neutron electric dipole moment; Nuclear physics; Computational physics; Electric dipole moment; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001303178,0.0001530603,0.0003118808,0.00009279771,0.000144859,0.00006981121,0.0004971476,0.00004515714,0.0001627129],"category_scores_gemma":[0.00000374513,0.00009862583,0.00008264922,0.0002091316,0.0001328448,0.00009953164,0.0005679852,0.000131053,3.468338e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001923434,"about_ca_system_score_gemma":0.0001879515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008912482,"about_ca_topic_score_gemma":0.00005337574,"domain_scores_codex":[0.9990303,0.00002897898,0.0002132703,0.0004098667,0.0001621411,0.0001554794],"domain_scores_gemma":[0.9983179,0.000255025,0.0001606808,0.001037462,0.0001822117,0.00004670203],"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":[0.00003158991,0.00007586121,0.01452596,0.00001282014,0.003423447,2.517591e-8,0.0003926862,0.9789265,0.00003698853,0.001222218,0.0003766213,0.000975227],"study_design_scores_gemma":[0.0002546974,0.000009135175,0.000423818,0.000008225134,0.001252014,1.16703e-8,0.0005263287,0.9968903,0.0002555229,0.0002032052,0.00006657284,0.0001101634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09948023,0.00005206749,0.8965645,0.00007933678,0.00003233342,0.0007613648,0.002355776,0.000009803346,0.0006645145],"genre_scores_gemma":[0.9939213,0.00000644583,0.004584323,0.000008696644,0.0001046633,0.00007253526,0.0009450649,0.00001740588,0.000339529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8944411,"threshold_uncertainty_score":0.4021845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07658991855600372,"score_gpt":0.3844386585448786,"score_spread":0.3078487399888749,"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."}}