{"id":"W4387617806","doi":"10.3390/psf2023008063","title":"Machine Learning Techniques to Enhance Event Reconstruction in Water Cherenkov Detectors","year":2023,"lang":"en","type":"article","venue":"","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"TRIUMF","funders":"","keywords":"Cherenkov radiation; Event (particle physics); Detector; Event reconstruction; Cherenkov detector; Computer science; Artificial intelligence; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001459882,0.0000811363,0.00009440757,0.0002698356,0.00006251434,0.00002542888,0.00006971876,0.00003833863,0.0008700031],"category_scores_gemma":[0.00001062989,0.00006180715,0.00003822661,0.0003787175,0.00001255565,0.00007254608,0.00003929862,0.0001524388,0.0003545752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002977528,"about_ca_system_score_gemma":0.000004920679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001902843,"about_ca_topic_score_gemma":0.00004367382,"domain_scores_codex":[0.9993973,0.00002240009,0.0001637243,0.0001740337,0.0000664637,0.000176058],"domain_scores_gemma":[0.9998123,0.00001210213,0.00002482508,0.0001038375,0.00001769547,0.00002922275],"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":[0.000007935297,0.00001256789,0.09587073,0.000003687247,0.00001203205,6.437328e-7,0.0002461184,0.001529747,0.05435773,0.0003053955,0.0002462214,0.8474072],"study_design_scores_gemma":[0.00005669238,0.00003839723,0.003252215,0.000009975429,0.000001178758,5.926635e-7,0.0004246106,0.003095617,0.9746753,0.0009044129,0.01741225,0.0001287408],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935595,0.000002588809,0.001775973,0.0005095539,0.0001189103,0.0001573708,9.150324e-7,0.0007391553,0.00313597],"genre_scores_gemma":[0.9960209,0.000003843155,0.0005059215,0.00001997064,0.00004068196,0.00006898485,0.000002048547,0.000009036228,0.003328616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9203176,"threshold_uncertainty_score":0.9525923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008032604056067821,"score_gpt":0.2636795274443993,"score_spread":0.2556469233883314,"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."}}