{"id":"W4382286187","doi":"10.1142/s0217751x23500884","title":"Comparison between some machine learning algorithms on predicting the spectra of quark–anti-quark bound states","year":2023,"lang":"en","type":"article","venue":"International Journal of Modern Physics A","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alchemy (Canada)","funders":"","keywords":"Random forest; Machine learning; Physics; Artificial intelligence; Quark; Algorithm; Regression; Linear regression; Ridge; Particle physics; Support vector machine; Regression analysis; Computer science; Mathematics; Statistics","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":[0.00424577,0.0009008311,0.0009224631,0.00180635,0.0004453704,0.0009063209,0.0009801834,0.001121395,0.0006756117],"category_scores_gemma":[0.007630228,0.000213462,0.0007480098,0.001237686,0.0002831322,0.001087056,0.0005073806,0.0009037989,0.0003563975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004335356,"about_ca_system_score_gemma":0.0007856449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004389504,"about_ca_topic_score_gemma":0.002761103,"domain_scores_codex":[0.9986351,0.0006482974,0.0001126113,0.0002014003,0.0003021809,0.0001004712],"domain_scores_gemma":[0.9941338,0.004368362,0.0002696383,0.0003080298,0.0008219526,0.00009820644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007253891,0.0002898535,0.009280071,0.0004345489,0.0004501059,0.00009153189,0.0001012764,0.7041473,0.002536054,0.002942256,0.001445119,0.2775566],"study_design_scores_gemma":[0.00001466502,0.0001274151,0.001767086,0.00002890721,0.0000392176,0.00002430026,0.00003283525,0.9947773,0.002075185,0.0007424587,0.0003576327,0.00001294534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5613647,0.01523174,0.4086328,0.0009770067,0.0003742135,0.000135876,0.0006980768,0.003022112,0.009563359],"genre_scores_gemma":[0.829585,0.003524828,0.1640451,0.00015216,0.0001465952,0.00007705748,0.001011687,0.0001730277,0.001284528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004389504,"threshold_uncertainty_score":0.02245408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03026776591092997,"score_gpt":0.3328647356424317,"score_spread":0.3025969697315018,"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."}}