{"id":"W2951963561","doi":"10.48550/arxiv.1606.00548","title":"Large-scale Reservoir Simulations on IBM Blue Gene/Q","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":0,"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":"Scalability; IBM; Workstation; Computer science; Grid; Scale (ratio); Simulation; Parallel computing; Operating system; Geology","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":[0.0004657273,0.0005468693,0.0005664291,0.0003298853,0.0005665146,0.000458975,0.00123591,0.0005924375,0.004410417],"category_scores_gemma":[0.001575673,0.0003638767,0.000332833,0.00100232,0.0005695517,0.0008272921,0.0007084993,0.0009033427,0.0005486536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009362873,"about_ca_system_score_gemma":0.001515767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02621749,"about_ca_topic_score_gemma":0.01696537,"domain_scores_codex":[0.9997426,0.0000706822,0.000006902881,0.00003248562,0.00009435476,0.00005296296],"domain_scores_gemma":[0.9994352,0.0002684345,0.00003665107,0.00005933944,0.0001224331,0.00007783585],"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":[0.0002131787,0.000135769,0.004451937,0.0000761323,0.00004200713,0.0002418021,0.0001979582,0.9641443,0.00848593,0.00653385,0.006077856,0.009399322],"study_design_scores_gemma":[0.00009672353,0.00006131867,0.001157197,0.000006529266,0.000008414894,0.00002247668,0.00004178389,0.9890866,0.003232586,0.002340462,0.003933635,0.0000122309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8834117,0.0005845343,0.07497061,0.001378415,0.0001117811,0.0001216264,0.002437049,0.005775345,0.03120913],"genre_scores_gemma":[0.9351618,0.0003703016,0.05653136,0.0001404529,0.00001679685,0.0001608212,0.0018448,0.0007130701,0.005060537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02621749,"threshold_uncertainty_score":0.05212981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06073133752084314,"score_gpt":0.2193129452602557,"score_spread":0.1585816077394126,"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."}}