{"id":"W2266357608","doi":"10.1002/2015gc006159","title":"Performance benchmarks for a next generation numerical dynamo model","year":2016,"lang":"en","type":"article","venue":"Geochemistry Geophysics Geosystems","topic":"Geomagnetism and Paleomagnetism Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":105,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Muscular Dystrophy Canada","funders":"Natural Environment Research Council; Sight Research UK; Leverhulme Trust; Science and Technology Facilities Council; National Science Foundation","keywords":"Dynamo; Dynamo theory; Computer science; Scaling; Computational science; Earth's magnetic field; Range (aeronautics); Observable; Statistical physics; Field (mathematics); Computational physics; Algorithm; Magnetic field; Physics; Aerospace engineering; Mathematics; Geometry","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":[0.0001477461,0.0003364412,0.0002824201,0.00002044444,0.0002331758,0.00004462545,0.0002827354,0.000231378,0.00003339468],"category_scores_gemma":[0.00005534812,0.0002703156,0.0001811701,0.00008109722,0.00008418097,0.00001837025,0.0001342192,0.00006519136,0.00002542988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002237699,"about_ca_system_score_gemma":0.0001005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001705097,"about_ca_topic_score_gemma":0.000009253135,"domain_scores_codex":[0.9982075,0.00002388141,0.000346831,0.0006616463,0.0002138323,0.0005462738],"domain_scores_gemma":[0.9988699,0.00002637045,0.0001556584,0.0005987911,0.000216313,0.0001330003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006255534,0.00005942607,0.001191893,0.0001599049,0.00005687867,8.951406e-7,0.00003762894,0.0005348238,0.9809626,0.00004771451,0.009307561,0.007578084],"study_design_scores_gemma":[0.004772807,0.00161091,0.002167992,0.0002513974,0.0001991835,0.00008612131,0.0001723676,0.2958765,0.6542196,0.0006735804,0.03750608,0.002463515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821535,0.0006700017,0.01274765,0.0002241861,0.000316963,0.0004251802,0.0002526797,0.0000328916,0.003176911],"genre_scores_gemma":[0.991576,0.0002328738,0.0008922472,0.00007250336,0.001396588,0.0003778061,0.0007690989,0.00003497865,0.004647938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3267431,"threshold_uncertainty_score":0.9999749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01827087323741481,"score_gpt":0.2228479689278133,"score_spread":0.2045770956903984,"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."}}