{"id":"W4388717807","doi":"10.48550/arxiv.2311.07704","title":"Cosmic-ray searches with the MATHUSLA detector","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Particle Detector Development and Performance","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; National Science Foundation","keywords":"Cosmic ray; Detector; Physics; Air shower; Large Hadron Collider; Measure (data warehouse); Range (aeronautics); Bar (unit); Nuclear physics; COSMIC cancer database; Optics; Astrophysics; Aerospace engineering; Meteorology; Computer science; Engineering","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":[0.001645902,0.0004344683,0.0005994827,0.00106969,0.0003719368,0.001323271,0.0008065338,0.000623018,0.002844942],"category_scores_gemma":[0.001356512,0.0002772411,0.000341608,0.000873582,0.0002228092,0.0010178,0.00092575,0.0003002159,0.001089242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000997665,"about_ca_system_score_gemma":0.0006170927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002125645,"about_ca_topic_score_gemma":0.003296174,"domain_scores_codex":[0.9991511,0.0002745073,0.00003556448,0.0002270203,0.0001972473,0.0001144993],"domain_scores_gemma":[0.9990032,0.0002291814,0.0001217445,0.0002441157,0.0002506034,0.0001512552],"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":[0.01116236,0.0007104643,0.243051,0.0005859042,0.0007744962,0.001258091,0.0005879618,0.05417281,0.4567514,0.02854971,0.02172903,0.1806667],"study_design_scores_gemma":[0.0007470384,0.003067835,0.1275971,0.00007194979,0.0003074914,0.001733259,0.0001891206,0.3818084,0.438183,0.001983921,0.04402051,0.0002904414],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9532151,0.0004503697,0.01595968,0.000225169,0.00003594321,0.000092127,0.003083109,0.006775841,0.02016273],"genre_scores_gemma":[0.9672346,0.00005913939,0.0265868,0.00006261012,0.00001587086,0.0000433118,0.002934762,0.00009867019,0.002964172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002844942,"threshold_uncertainty_score":0.009517252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08795504901004504,"score_gpt":0.1916670235757952,"score_spread":0.1037119745657501,"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."}}