{"id":"W2085978235","doi":"10.1103/physrevd.63.053011","title":"Unweighted event generation in hadronic<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mi>WZ</mml:mi></mml:math>production at order<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>α</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">S</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:math>","year":2001,"lang":"lv","type":"article","venue":"Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Event (particle physics); Algorithm; Monte Carlo method; Physics; Mathematics; Computer science; 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":[0.002693023,0.001723169,0.001127245,0.002427661,0.001443346,0.00274864,0.004139598,0.00092578,0.03311208],"category_scores_gemma":[0.006006636,0.001058374,0.002346286,0.002195605,0.0009153212,0.0024923,0.004426011,0.001627854,0.008926958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001202451,"about_ca_system_score_gemma":0.001657904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002846045,"about_ca_topic_score_gemma":0.003862173,"domain_scores_codex":[0.9979576,0.0003937929,0.000148997,0.0004633819,0.0007607813,0.0002754676],"domain_scores_gemma":[0.9970682,0.001155356,0.0001817436,0.0007510444,0.000700413,0.0001431698],"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":[0.002554246,0.0004416321,0.006094313,0.0006604276,0.0003236807,0.001113466,0.0008490607,0.04707892,0.01985687,0.1667659,0.03486498,0.7193966],"study_design_scores_gemma":[0.0003792126,0.0002422476,0.001605914,0.00008951603,0.0001492309,0.0007883825,0.0002257461,0.5438429,0.07790118,0.3306908,0.04386183,0.0002230455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01140702,0.0001111176,0.9697798,0.0001197077,0.00009507032,0.0002261009,0.0008141095,0.01254608,0.004900993],"genre_scores_gemma":[0.1227922,0.00009933898,0.8570992,0.0001326233,0.00005100129,0.0003815957,0.004123192,0.003854059,0.01146681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03311208,"threshold_uncertainty_score":0.1107709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01728021969423522,"score_gpt":0.277293093055299,"score_spread":0.2600128733610638,"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."}}