{"id":"W4393336127","doi":"10.48550/arxiv.2403.19496","title":"Calibrating the Scintillation Timing in SNO+ using In-Situ Backgrounds","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radiation Detection and Scintillator Technologies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Compute Canada; Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; Queen's University; Canadian Institute for Advanced Research; Fundação para a Ciência e a Tecnologia; U.S. Department of Energy; National Science Foundation","keywords":"In situ; Calibration; Scintillation; Environmental science; Remote sensing; Computer science; Materials science; Physics; Geography; Optics; Meteorology; Detector","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":[0.001029696,0.0006558503,0.0004471974,0.0007247736,0.000468881,0.001341191,0.0006168378,0.0006148785,0.001688062],"category_scores_gemma":[0.001037073,0.000386739,0.0002797057,0.0009716354,0.0002060062,0.0005627968,0.0006906497,0.0005104481,0.0007867691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000549163,"about_ca_system_score_gemma":0.0003615601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004245456,"about_ca_topic_score_gemma":0.01417425,"domain_scores_codex":[0.9996858,0.00004846803,0.00001436718,0.0001369976,0.00006817777,0.00004620276],"domain_scores_gemma":[0.9996234,0.00008252474,0.0000590626,0.00006551189,0.0001273453,0.00004197578],"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":[0.003904163,0.00056589,0.248145,0.0003535845,0.0004740343,0.0006130684,0.0005764897,0.03842053,0.5931233,0.004309499,0.00632637,0.1031881],"study_design_scores_gemma":[0.0002893336,0.0007590288,0.2303773,0.00008613306,0.0004981057,0.0007669754,0.0002986504,0.3277884,0.4101859,0.003287181,0.02552346,0.0001394644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9422482,0.0004975371,0.03809596,0.0002538636,0.0002582211,0.00004995968,0.002896487,0.001588431,0.01411119],"genre_scores_gemma":[0.9843029,0.0001312005,0.01127446,0.00008124553,0.00004061968,0.00001556698,0.001993262,0.000294385,0.001866399],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004245456,"threshold_uncertainty_score":0.008441508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09657653052578191,"score_gpt":0.2215817775834311,"score_spread":0.1250052470576492,"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."}}