{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004523168,0.001763237,0.001109857,0.001175469,0.001340193,0.001247259,0.001892164,0.001982505,0.003507651],"category_scores_gemma":[0.009413745,0.0004347824,0.0008480125,0.001300285,0.001085844,0.001471298,0.001967718,0.001983644,0.003156833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334073,"about_ca_system_score_gemma":0.001415018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01434063,"about_ca_topic_score_gemma":0.02089213,"domain_scores_codex":[0.9970746,0.00128777,0.00009752914,0.0006276005,0.0006297186,0.0002827515],"domain_scores_gemma":[0.9943187,0.002663918,0.0001737461,0.001494759,0.0009795021,0.0003693314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003931091,0.003178635,0.02591401,0.001070527,0.0007544651,0.0006761389,0.001745122,0.09690408,0.01855828,0.005405364,0.1736665,0.6681957],"study_design_scores_gemma":[0.0005904736,0.001587222,0.019666,0.0001661265,0.0002504048,0.0003305132,0.001968251,0.8489051,0.03604029,0.01957054,0.07080228,0.0001228377],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7705664,0.005565048,0.1357406,0.003452032,0.001225819,0.0007606108,0.006716686,0.04118069,0.03479193],"genre_scores_gemma":[0.7115138,0.0007486312,0.2307739,0.001342515,0.0002930353,0.0003658562,0.0279134,0.002549136,0.02449975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01434063,"threshold_uncertainty_score":0.02851433,"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."}}