{"id":"W3130951332","doi":"10.22323/1.358.0678","title":"Efficient Label Gathering for Machine Training:Results from Muon Hunter 2","year":2019,"lang":"en","type":"article","venue":"Proceedings of 36th International Cosmic Ray Conference — PoS(ICRC2019)","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Energy Research Scientific Computing Center; U.S. Department of Energy; Office of Science; Smithsonian Institution; National Science Foundation","keywords":"Crowdsourcing; Bottleneck; Convolutional neural network; Computer science; Cluster analysis; Muon; Class (philosophy); Event (particle physics); Artificial intelligence; Machine learning; World Wide Web; Particle physics; Embedded system; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002564956,0.0002246144,0.0002822279,0.0001034367,0.00005635686,0.0001005509,0.0004740226,0.00004630168,0.0005129464],"category_scores_gemma":[0.00003253776,0.0002116971,0.00009635045,0.00008557456,0.00004303682,0.0001895912,0.00009021318,0.0001557258,0.00009658156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003965792,"about_ca_system_score_gemma":0.00007462276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001122485,"about_ca_topic_score_gemma":0.000001836385,"domain_scores_codex":[0.9984831,0.000003541966,0.0004959552,0.0003943345,0.0003103124,0.0003127428],"domain_scores_gemma":[0.9989229,0.00009982097,0.0003380838,0.00009416092,0.000464061,0.00008094583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002161927,0.0005234563,0.2153821,0.0001182878,0.0007366222,5.781745e-7,0.01218511,0.0006800012,0.6914487,0.03136865,0.0009568332,0.04443775],"study_design_scores_gemma":[0.01243313,0.00040958,0.03555847,0.0007097603,0.00008319524,0.000002304758,0.002118337,0.6254925,0.3014075,0.00644029,0.01412565,0.001219278],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9794551,0.00001585887,0.0009400303,0.0002367279,0.0005988418,0.0003738323,0.0005130007,0.0000384126,0.01782827],"genre_scores_gemma":[0.9960828,0.000003103641,0.002187121,0.00005671809,0.0002313362,0.00004632081,0.0001429505,0.00002387173,0.001225711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6248125,"threshold_uncertainty_score":0.8632756,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03335105051901816,"score_gpt":0.2635042913640815,"score_spread":0.2301532408450633,"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."}}