{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001464741,0.0005121515,0.0004374889,0.0006358478,0.0003823628,0.0009721215,0.00106681,0.0008204303,0.001478978],"category_scores_gemma":[0.008075609,0.0003984223,0.000484059,0.0006025404,0.0006356582,0.001193259,0.0007486388,0.001104159,0.0002308842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001298888,"about_ca_system_score_gemma":0.001365042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008988716,"about_ca_topic_score_gemma":0.006139814,"domain_scores_codex":[0.9993893,0.0001894297,0.00003971078,0.0001047401,0.0002077884,0.0000690481],"domain_scores_gemma":[0.9967793,0.001783044,0.0003075887,0.000598871,0.0004541136,0.00007710603],"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":[0.00001499558,0.0000172796,0.001123713,0.00001064719,0.000007180371,0.00001138039,0.0000143221,0.9926387,0.0005058289,0.002736147,0.0001062699,0.002813519],"study_design_scores_gemma":[0.000002946056,0.000005491509,0.0001387059,0.000002206746,0.000001565417,0.000002300951,0.000003318559,0.9981604,0.000561853,0.0009925574,0.0001258868,0.000002794152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3057655,0.0001428507,0.6842951,0.0005090358,0.00006906439,0.0001415251,0.0007025718,0.001323176,0.007051158],"genre_scores_gemma":[0.937049,0.00009903157,0.06150917,0.00003386278,0.00001029352,0.0001161745,0.0004229703,0.00007912218,0.0006803725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008988716,"threshold_uncertainty_score":0.01787275,"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."}}