{"id":"W4390607267","doi":"10.2139/ssrn.4685190","title":"Neutralnet: Development and Testing of a Machine Learning Solution for Pulse Shape Discrimination","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Pulse (music); Computer science; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002545876,0.001875223,0.0006462774,0.001340343,0.0006263694,0.001339349,0.003425426,0.002728852,0.009376313],"category_scores_gemma":[0.007160944,0.0005572995,0.0007281639,0.0009564647,0.000708739,0.002257142,0.001517342,0.001702401,0.004034355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009246053,"about_ca_system_score_gemma":0.001543322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006675533,"about_ca_topic_score_gemma":0.006057608,"domain_scores_codex":[0.9990863,0.0001802797,0.0000742033,0.0003374775,0.0002150746,0.0001066635],"domain_scores_gemma":[0.9975362,0.001337337,0.00007959317,0.0002685489,0.0006504845,0.0001279289],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002375961,0.00138267,0.005897888,0.0009817306,0.0003825655,0.0006308136,0.0002662734,0.2519882,0.02710764,0.01347828,0.05619997,0.639308],"study_design_scores_gemma":[0.0001423046,0.0001860269,0.000387858,0.00001961173,0.00002431088,0.00008294389,0.00005058247,0.9743819,0.01727346,0.003845831,0.003588449,0.00001658489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3531138,0.001369805,0.5355809,0.001341034,0.001932819,0.0006625961,0.005482522,0.08453744,0.01597912],"genre_scores_gemma":[0.4760297,0.0003665783,0.4999431,0.000630169,0.0001716022,0.0004470971,0.00904844,0.002305809,0.01105751],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009376313,"threshold_uncertainty_score":0.03136688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754207116074993,"score_gpt":0.2871491581157343,"score_spread":0.2596070869549844,"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."}}