{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001705168,0.0002366254,0.0001908768,0.0000764475,0.0002210544,0.00007678346,0.0005619087,0.00006692769,0.0001710604],"category_scores_gemma":[0.000002009127,0.0001799023,0.0001048031,0.0003385752,0.0001334148,0.00009638118,0.0004564366,0.000504058,0.0005182236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005299198,"about_ca_system_score_gemma":0.000155965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001980467,"about_ca_topic_score_gemma":0.000059214,"domain_scores_codex":[0.9989632,0.00005837395,0.00009661044,0.000451474,0.0000824934,0.0003478601],"domain_scores_gemma":[0.9991404,0.00009482531,0.0001095044,0.0004980164,0.0000660479,0.00009119681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005987084,0.0002284304,0.6932009,0.0003410156,0.001898039,0.0003569004,0.003270365,0.2599542,0.001192543,0.02825389,0.006465161,0.004239907],"study_design_scores_gemma":[0.004634631,0.0003275961,0.5188508,0.0007633769,0.0007110544,0.000003274753,0.004810022,0.395517,0.01689614,0.03703548,0.01626117,0.004189492],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894016,0.000009012784,0.007925116,0.0001105408,0.000153259,0.0002516794,0.00004539989,0.0001189338,0.001984473],"genre_scores_gemma":[0.9935309,0.00001076369,0.00005410645,0.00001873197,0.0001455842,0.000004715803,0.00002813536,0.00002998795,0.00617706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1743501,"threshold_uncertainty_score":0.7336201,"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."}}