{"id":"W4281732312","doi":"10.1029/2021ms002836","title":"Toward Efficient Calibration of Higher‐Resolution Earth System Models","year":2022,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Climate variability and models","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Microsoft","keywords":"Computer science; Calibration; Resolution (logic); Convolutional neural network; Process (computing); Baseline (sea); Algorithm; Earth system science; Machine learning; Artificial intelligence; Mathematics; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00169656,0.0001265378,0.0003745491,0.0001344303,0.000119079,0.00002181111,0.0002672162,0.00004861828,0.00005401291],"category_scores_gemma":[0.00001718107,0.0001152621,0.000109171,0.0003009371,0.00004474006,0.000599262,0.0001414403,0.000271426,0.000001884367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002595737,"about_ca_system_score_gemma":0.00003754451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002320392,"about_ca_topic_score_gemma":0.00001677573,"domain_scores_codex":[0.9973306,0.0003014512,0.001062437,0.0002083271,0.0008672639,0.0002299167],"domain_scores_gemma":[0.9990188,0.0000630486,0.0006033526,0.000199868,0.00004147115,0.00007344531],"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":[0.00009574321,0.00009590619,0.0003120551,0.0001313708,0.000005465988,0.000010555,0.0006434218,0.9959636,0.0009154778,0.001674953,0.000006437297,0.0001450187],"study_design_scores_gemma":[0.0004541427,0.0001619328,0.00002066599,0.0001543892,0.00001204019,0.00007994429,0.001075035,0.9971114,0.00005024235,0.0004027189,0.0003688089,0.0001087022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6874888,0.002314282,0.3074821,0.00003955105,0.001243578,0.0002815651,0.00002034006,0.00001806948,0.001111727],"genre_scores_gemma":[0.9979881,0.00008961524,0.001771322,0.00001121177,0.00006774568,0.00001310346,0.000002458232,0.00001240764,0.00004403535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3104993,"threshold_uncertainty_score":0.4700251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03123660941020859,"score_gpt":0.2515298965500946,"score_spread":0.220293287139886,"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."}}