{"id":"W4240260428","doi":"10.1109/wsc.1999.823145","title":"Quasi-Monte Carlo via linear shift-register sequences","year":2003,"lang":"en","type":"article","venue":"WSC'99. 1999 Winter Simulation Conference Proceedings. 'Simulation - A Bridge to the Future' (Cat. No.99CH37038)","topic":"Mathematical Approximation and Integration","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Citation; Monte Carlo method; Library science; Computer science; Computer graphics (images); Art history; Art; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001932521,0.0009983372,0.0009826599,0.0004343824,0.0007215042,0.0008607712,0.0009675554,0.0004942945,0.001712434],"category_scores_gemma":[0.00274974,0.0007517983,0.0004483158,0.00101302,0.0001980387,0.001555724,0.000177874,0.0005829304,0.00147606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004210496,"about_ca_system_score_gemma":0.0002424062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001283227,"about_ca_topic_score_gemma":0.0002412355,"domain_scores_codex":[0.9940494,0.0002614643,0.001988761,0.00124006,0.001440742,0.00101955],"domain_scores_gemma":[0.9943772,0.0008280732,0.0010632,0.0008775283,0.002335157,0.0005188669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002037099,0.0040322,0.006351105,0.003016142,0.001081921,0.00001753961,0.09672868,0.09701172,0.004031729,0.7217633,0.04563102,0.01829759],"study_design_scores_gemma":[0.0009357342,0.0003675817,0.0007666153,0.0003131795,0.0001465019,0.00001382206,0.0007203541,0.921253,0.0005076933,0.02704169,0.04700517,0.0009286427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3648233,0.00005602061,0.6122543,0.004958716,0.002121555,0.004280903,0.00009295736,0.000941388,0.01047087],"genre_scores_gemma":[0.9602804,0.00001308144,0.03298386,0.001472508,0.001591756,0.0002569991,0.00006455728,0.0001391457,0.003197734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8242413,"threshold_uncertainty_score":0.9994933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07285582614427846,"score_gpt":0.335712069209705,"score_spread":0.2628562430654265,"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."}}