{"id":"W2902347092","doi":"10.5194/gmd-11-4739-2018","title":"Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)","year":2018,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Oak Ridge National Laboratory; Natural Resources Canada; Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; Microsoft Research; Canadian Foundation for Climate and Atmospheric Sciences; University of Virginia; U.S. Department of Energy; National Science Foundation","keywords":"Algorithm; Data assimilation; Simulated annealing; Smoothing; Mathematical optimization; Data set; Mean squared error; Covariance; Mathematics; Computer science; Statistics; Meteorology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003899032,0.0009792374,0.000974403,0.0009592221,0.0003077187,0.0006532808,0.001108705,0.001515475,0.0006266306],"category_scores_gemma":[0.00713515,0.0004078224,0.0007806639,0.0007034862,0.0003828864,0.0008054986,0.0005971475,0.0008192722,0.0002064062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005595811,"about_ca_system_score_gemma":0.0006882715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009027349,"about_ca_topic_score_gemma":0.008238427,"domain_scores_codex":[0.999218,0.0004920659,0.0000458527,0.0001144287,0.00008778889,0.00004183641],"domain_scores_gemma":[0.9965484,0.002718424,0.000148905,0.0001980282,0.0003318059,0.00005459998],"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":[0.000350984,0.0002723261,0.00395928,0.0001748861,0.0002684717,0.00004555568,0.0001041607,0.9297509,0.002958915,0.00122446,0.0006287748,0.0602612],"study_design_scores_gemma":[0.00006720208,0.0001106457,0.001062692,0.00001128907,0.00001923887,0.00001115866,0.00002109962,0.9970849,0.001139202,0.0002519253,0.0002117393,0.000008968749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7559443,0.001937425,0.2352089,0.0003255265,0.00007255347,0.0002634901,0.0002852219,0.001837107,0.004125621],"genre_scores_gemma":[0.7824086,0.0002435159,0.2157361,0.0001216114,0.00001547138,0.0001987943,0.0004103112,0.0002905326,0.0005749957],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009027349,"threshold_uncertainty_score":0.02062035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1327265161500007,"score_gpt":0.327834031581097,"score_spread":0.1951075154310963,"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."}}