{"id":"W2097364769","doi":"10.5555/2431518.2431693","title":"Panel discussion: integrating data from multiple simulation models of different fidelity","year":2011,"lang":"en","type":"article","venue":"Winter Simulation Conference","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Extrapolation; Computational model; Fidelity; Markov chain Monte Carlo; Context (archaeology); Calibration; Presentation (obstetrics); Computation; Bayesian probability; Markov model; Field (mathematics); Markov chain; Machine learning; Artificial intelligence; Algorithm; Statistics; Mathematics","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":[0.0008176108,0.0002159109,0.0003881046,0.0001269592,0.00008052548,0.00009685166,0.001294025,0.0001138522,0.001038494],"category_scores_gemma":[0.003826242,0.0001213979,0.00008191998,0.0002135752,0.00009706987,0.0008925593,0.0004756933,0.0001682853,0.00004617783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002900689,"about_ca_system_score_gemma":0.00005267048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001271318,"about_ca_topic_score_gemma":0.00007831894,"domain_scores_codex":[0.9971262,0.0002076531,0.001041561,0.0006844537,0.0007488478,0.0001912942],"domain_scores_gemma":[0.9948102,0.002484979,0.000398791,0.001598746,0.0005957702,0.0001114879],"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":[0.0001046473,0.0001128196,0.007888566,0.000007521458,0.00002292347,0.000001130745,0.003069053,0.9661387,0.0003597684,0.001605982,0.00008220033,0.02060668],"study_design_scores_gemma":[0.0002888757,0.00003190383,0.01826064,0.00008142066,0.00001736772,1.471116e-7,0.0003730153,0.9173619,0.0001744962,0.06318112,0.00007061646,0.0001584669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06544289,0.00001641483,0.9328489,0.00004792625,0.0003490792,0.0002143756,0.0001629494,0.00006522831,0.000852193],"genre_scores_gemma":[0.9885415,0.000001152115,0.01103854,0.00002557181,0.00006561624,0.000004731666,0.0001077571,0.00001255499,0.0002026052],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9230986,"threshold_uncertainty_score":0.9998747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5161449601405311,"score_gpt":0.3836969725789227,"score_spread":0.1324479875616085,"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."}}