{"id":"W4390305853","doi":"10.48550/arxiv.2312.15104","title":"A demonstrator for a real-time AI-FPGA-based triggering system for sPHENIX at RHIC","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"Los Alamos National Laboratory; Laboratory Directed Research and Development; U.S. Department of Energy","keywords":"Field-programmable gate array; Detector; Data acquisition; Computer science; Electronics; Momentum (technical analysis); Bandwidth (computing); Embedded system; Physics; Computer hardware; Electrical engineering; Operating system; Engineering; 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":[0.0006221816,0.00064475,0.0002980414,0.0003704,0.0002718722,0.0009506987,0.001442768,0.0004797676,0.01353916],"category_scores_gemma":[0.0007587294,0.0002480912,0.0001604427,0.0002456194,0.0002756042,0.000766054,0.0004242439,0.0006791286,0.003742607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005227092,"about_ca_system_score_gemma":0.0006065886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001313319,"about_ca_topic_score_gemma":0.001180304,"domain_scores_codex":[0.9995887,0.00006353566,0.00002540143,0.0001064858,0.0001352372,0.00008073897],"domain_scores_gemma":[0.9995951,0.00007345647,0.00004326754,0.00009658083,0.0001009869,0.00009070222],"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":[0.007363237,0.0007898007,0.01714395,0.0009911674,0.000269175,0.002923789,0.0009138628,0.02120004,0.4586103,0.03287386,0.07760894,0.3793119],"study_design_scores_gemma":[0.001081871,0.002925032,0.0111601,0.0002329049,0.0001603323,0.002309442,0.0001910497,0.2360216,0.5205477,0.003904828,0.2212785,0.0001866583],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3086378,0.001348825,0.5115249,0.001481065,0.001242244,0.001317628,0.002746521,0.08258299,0.08911794],"genre_scores_gemma":[0.8044271,0.0002143648,0.1685593,0.0007540288,0.0001306207,0.0002624682,0.001446898,0.0009353714,0.02326985],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01353916,"threshold_uncertainty_score":0.04529303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05884659971942133,"score_gpt":0.2146456650823022,"score_spread":0.1557990653628809,"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."}}