{"id":"W2095527632","doi":"10.2118/152754-ms","title":"Building Trust in History Matching: The Role of Multidimensional Projection","year":2012,"lang":"en","type":"article","venue":"","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates - Technology Futures; CMG Reservoir Simulation Foundation","keywords":"Computer science; Projection (relational algebra); Population; Cluster analysis; Matching (statistics); Data mining; Artificial intelligence; Principal component analysis; Machine learning; Algorithm; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.009338657,0.0008357988,0.001430883,0.001631824,0.001034234,0.004407995,0.002042743,0.001397948,0.001584269],"category_scores_gemma":[0.05029772,0.0007178225,0.0007867672,0.001251101,0.002736598,0.00571417,0.006024955,0.002117839,0.0002834796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001437265,"about_ca_system_score_gemma":0.002486908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00548877,"about_ca_topic_score_gemma":0.002615406,"domain_scores_codex":[0.9950734,0.002721737,0.0002868212,0.0005126887,0.001180476,0.0002248522],"domain_scores_gemma":[0.9771164,0.01402995,0.002499984,0.00333598,0.00224156,0.0007760323],"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":[0.0001826924,0.0001054963,0.01068203,0.0001544774,0.000141907,0.0002008463,0.0008971311,0.8052558,0.002933559,0.04629901,0.001367303,0.1317797],"study_design_scores_gemma":[0.000007871973,0.00004142413,0.0006535656,0.00002402441,0.000009092406,0.00003027653,0.0001043861,0.9785538,0.001485347,0.01834576,0.0007200293,0.00002447992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07264569,0.0003087337,0.9231572,0.0007250035,0.00005151905,0.0000939345,0.00005617126,0.0009471048,0.002014634],"genre_scores_gemma":[0.7708281,0.0002101086,0.2278765,0.00008132657,0.00003248931,0.0001090497,0.00009366398,0.000210976,0.0005578345],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009338657,"threshold_uncertainty_score":0.04938817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747730784054677,"score_gpt":0.2573942981713745,"score_spread":0.2399169903308278,"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."}}