{"id":"W96008780","doi":"","title":"Flow Control in Optimistic Simulation","year":2003,"lang":"en","type":"article","venue":"","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Rollback; Flow control (data); Control (management); Distributed computing; Stability (learning theory); Control flow; Flow (mathematics); Granularity; Latency (audio); Parallel computing; Real-time computing; Database transaction; Artificial intelligence; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009942623,0.0000518924,0.000100181,0.0001373578,0.00004675267,0.00007023285,0.0001460752,0.0000397346,0.002384435],"category_scores_gemma":[0.001589511,0.00003872039,0.0000349885,0.0005412797,0.00001765442,0.000106291,0.000006674646,0.0000438225,0.0003149634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001958735,"about_ca_system_score_gemma":0.00001967292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006713669,"about_ca_topic_score_gemma":0.000008987548,"domain_scores_codex":[0.9989404,0.00007768905,0.0003496825,0.0002012729,0.0003321067,0.00009885613],"domain_scores_gemma":[0.9982104,0.001271207,0.00005252567,0.0002999767,0.0001253519,0.00004055993],"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":[0.000002899769,0.00002790332,0.005254417,1.812902e-7,6.723179e-7,3.706509e-7,0.00002362401,0.9346557,0.00005872734,0.04723788,0.0008130816,0.01192455],"study_design_scores_gemma":[0.000274972,0.000006935211,0.003266101,8.995405e-7,0.000001018857,3.389029e-7,0.00003272861,0.8857938,0.0001019049,0.07732137,0.03314923,0.00005071824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007965331,0.000009028615,0.9296963,0.0002638153,0.00002992175,0.0002025062,0.000002343723,0.00005517471,0.06177558],"genre_scores_gemma":[0.9805697,5.596718e-7,0.01678565,0.0004179623,0.000009133019,0.0000202896,9.078439e-7,0.00000354472,0.002192314],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9726043,"threshold_uncertainty_score":0.9985275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1337458277115225,"score_gpt":0.4503044750310333,"score_spread":0.3165586473195109,"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."}}