{"id":"W4390137248","doi":"10.48550/arxiv.2312.14110","title":"Global Characterization of a Laser-Generated Neutron Source","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Laser-Plasma Interactions and Diagnostics","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Lawrence Berkeley National Laboratory; Centre National de la Recherche Scientifique; Institut de Radioprotection et de SÛreté Nucléaire; Agence Nationale de la Recherche; Lawrence Livermore National Laboratory; U.S. Department of Energy","keywords":"Neutron; Laser; Inertial confinement fusion; Characterization (materials science); Neutron source; Nuclear physics; Neutron imaging; Optics; Titan (rocket family); Neutron detection; Detector; Physics; Materials science; Nuclear engineering","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":[0.0004849776,0.0003967713,0.0005382952,0.001006509,0.0003842381,0.0005828686,0.0004044164,0.0005005866,0.001719528],"category_scores_gemma":[0.0004230812,0.0001610595,0.0002284993,0.0006054297,0.0004775715,0.0003931769,0.0006323179,0.0004102204,0.0005695449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006220698,"about_ca_system_score_gemma":0.0002080027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000772051,"about_ca_topic_score_gemma":0.001093325,"domain_scores_codex":[0.9996426,0.0000365067,0.00001123282,0.0001152072,0.0001393779,0.00005498314],"domain_scores_gemma":[0.9995577,0.0001104914,0.00007950857,0.00004407536,0.0001707414,0.00003749681],"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":[0.0002624566,0.00002859647,0.005888277,0.00005162206,0.00001995316,0.00009934132,0.00008662845,0.001233626,0.9884284,0.0001887119,0.00007760689,0.003634827],"study_design_scores_gemma":[0.000009458716,0.000289925,0.01976907,0.000009391231,0.00002310731,0.0001471304,0.0000857854,0.003489925,0.9741289,0.00007967336,0.001954399,0.00001331003],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816251,0.0004536735,0.01251315,0.00002678977,0.000008428948,0.00004310732,0.000822888,0.0001835052,0.004323415],"genre_scores_gemma":[0.9908645,0.0002317117,0.005581,0.00003408351,0.000006533009,0.00005678465,0.0008442273,0.0001025726,0.002278663],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001719528,"threshold_uncertainty_score":0.005752444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04133285579553711,"score_gpt":0.1943611947040973,"score_spread":0.1530283389085602,"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."}}