{"id":"W4386977572","doi":"10.48550/arxiv.2309.11844","title":"Constructing the Hyper-Kamiokande Computing Model in the Build Up to Data Taking","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; European Commission; Institute for Cosmic Ray Research, University of Tokyo; Institut National de Physique Nucléaire et de Physique des Particules; Imperial College London; UK Research and Innovation","keywords":"Physics; Particle physics; Neutrino; Detector; Large Hadron Collider; Scalability; Neutrino oscillation; Super-Kamiokande; Monte Carlo method; Petabyte; Workflow; Computer science; Big data; Operating system; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002517762,0.000644374,0.0007079149,0.0009239162,0.002021327,0.005209731,0.003925183,0.001483898,0.006755946],"category_scores_gemma":[0.005596561,0.0009248816,0.001249687,0.002142678,0.002153552,0.006725566,0.004610703,0.002890957,0.004683438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003069421,"about_ca_system_score_gemma":0.005871055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01669766,"about_ca_topic_score_gemma":0.01174603,"domain_scores_codex":[0.9981969,0.0004687931,0.0001013599,0.0004406675,0.0004917391,0.0003005489],"domain_scores_gemma":[0.9977399,0.0004200659,0.0000811282,0.0008317943,0.0005053717,0.0004216905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005015532,0.0002164485,0.004614024,0.0002017029,0.00004685279,0.0007307517,0.000726731,0.2858747,0.004732924,0.6401222,0.02595742,0.03627481],"study_design_scores_gemma":[0.0001183143,0.000081843,0.0007468132,0.000075075,0.00002330075,0.0001765039,0.000335443,0.7269487,0.003321425,0.2011869,0.06691664,0.0000691583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07703528,0.0008171506,0.8268505,0.009754863,0.0005705532,0.001016163,0.003682662,0.006832777,0.07344001],"genre_scores_gemma":[0.2814069,0.001123267,0.6928977,0.001299281,0.0001398801,0.001524088,0.004067741,0.001399353,0.01614184],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01669766,"threshold_uncertainty_score":0.03320098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.210333578650108,"score_gpt":0.2641624902849221,"score_spread":0.0538289116348141,"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."}}