{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000511428,0.0002274544,0.000270497,0.0001008236,0.00008329008,0.00004596411,0.0002812041,0.000133463,0.0004769114],"category_scores_gemma":[0.000008805753,0.0002691384,0.000186977,0.0004272711,0.00005046857,0.0001162506,0.0003811046,0.0002179388,0.0003211178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000871755,"about_ca_system_score_gemma":0.0001274187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551076,"about_ca_topic_score_gemma":0.00006287837,"domain_scores_codex":[0.9990003,0.00005762749,0.0002113787,0.0004664857,0.00005311671,0.0002111645],"domain_scores_gemma":[0.9989312,0.0000582441,0.0003182701,0.0004138646,0.0001838368,0.00009453721],"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":[0.00009543185,0.0004529188,0.133548,0.00009802719,0.0003814997,0.00003571922,0.00007022882,0.8387521,0.001342011,0.02364049,0.001242101,0.0003414744],"study_design_scores_gemma":[0.004734882,0.0003985429,0.2487875,0.001179758,0.001985045,0.000003632517,0.001204057,0.6604303,0.02574252,0.03308975,0.01923467,0.003209382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9534456,5.694876e-7,0.04289328,0.00003169627,0.0006614614,0.0001877555,0.0009309725,0.00009451208,0.001754148],"genre_scores_gemma":[0.9921487,0.00001386694,0.00003028732,0.00001149277,0.0001831386,0.000001891319,0.001180628,0.00002388367,0.006406138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1783218,"threshold_uncertainty_score":0.9999761,"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."}}