{"id":"W4299715301","doi":"10.48550/arxiv.1208.2716","title":"Prediction and Computer Model Calibration Using Outputs From\\n Multi-fidelity Simulators","year":2012,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Fidelity; Computer science; Calibration; Sensitivity (control systems); Process (computing); Field (mathematics); Physical system; Simple (philosophy); Bayesian probability; Simulation; Artificial intelligence; Engineering; Programming language; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006055907,0.0002474518,0.0003192036,0.0002603432,0.0002522296,0.000195163,0.0004953565,0.0004005107,0.00005935064],"category_scores_gemma":[0.00007402975,0.0002517409,0.0001473297,0.0003759368,0.0001152252,0.0005864723,0.0008756027,0.0003353631,0.00001919435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001448793,"about_ca_system_score_gemma":0.00008657764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003389984,"about_ca_topic_score_gemma":0.0000251597,"domain_scores_codex":[0.9979327,0.0001578415,0.0004722335,0.0009998061,0.0002307178,0.0002066548],"domain_scores_gemma":[0.9978402,0.0003086677,0.0004256601,0.000923822,0.0003013451,0.0002002918],"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":[0.00001201185,0.00005796166,0.03688937,0.000004175723,0.00001821909,0.000001216458,0.000162882,0.9547542,0.00006295202,0.007252624,0.0001361525,0.00064825],"study_design_scores_gemma":[0.000250358,0.000005876019,0.00909762,0.00001907123,0.00006546901,3.648853e-7,0.00004044205,0.9307354,0.00007017175,0.05939715,0.0001074953,0.0002105712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4603407,0.00002030885,0.5389442,0.00001470225,0.0001333475,0.0002341053,0.0001540092,0.0001125914,0.00004594609],"genre_scores_gemma":[0.9823766,0.00002207275,0.016993,0.00008274178,0.0001591144,0.000001066002,0.00007793342,0.00001783267,0.0002696317],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5220359,"threshold_uncertainty_score":0.9999935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4036395460781888,"score_gpt":0.3097037159058391,"score_spread":0.09393583017234969,"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."}}