{"id":"W4415651676","doi":"10.1029/2024ms004733","title":"Parameter Estimation in Land Surface Models: Challenges and Opportunities With Data Assimilation and Machine Learning","year":2025,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Inversa Systems (Canada); Western University","funders":"H2020 Marie Skłodowska-Curie Actions; Lawrence Berkeley National Laboratory; Lawrence Livermore National Laboratory; Horizon 2020 Framework Programme; Academy of Finland; U.S. Department of Energy; European Commission; National Aeronautics and Space Administration; National Centre for Earth Observation; Pacific Northwest National Laboratory; UK Research and Innovation; HORIZON EUROPE Framework Programme; National Center for Atmospheric Research; National Science Foundation","keywords":"Leverage (statistics); Data assimilation; Earth system science; Climate change; Estimation theory; Climate model; Ecosystem model; Earth observation","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":[],"consensus_categories":[],"category_scores_codex":[0.0005829282,0.00006497178,0.0001463427,0.0000760261,0.00002835857,0.00003224038,0.00006724253,0.00003174896,5.342046e-7],"category_scores_gemma":[0.00001617672,0.0000489182,0.000004762447,0.00004654426,0.00002537268,0.001026761,0.0000483154,0.0001488921,7.659319e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001991508,"about_ca_system_score_gemma":0.000008509103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006734552,"about_ca_topic_score_gemma":0.0005263024,"domain_scores_codex":[0.9993527,0.0000661572,0.0002614337,0.0001122205,0.000132839,0.00007468381],"domain_scores_gemma":[0.9997028,0.00006626824,0.0001225237,0.00007766348,0.00000906019,0.00002165592],"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":[0.00002337856,0.000007041775,0.02991275,0.00002805601,0.00000360128,0.000005890859,0.0002399902,0.9591448,0.000004763301,0.00009647221,1.554326e-7,0.01053311],"study_design_scores_gemma":[0.0002803835,0.00003258598,0.0009756652,0.0003381751,0.00000703795,0.00003509669,0.0001727298,0.9968646,5.083072e-7,0.001137377,0.0001026458,0.00005317126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8489382,0.01961938,0.1307364,0.0001129093,0.0000459283,0.00007634061,0.0000050606,0.000004331767,0.0004614453],"genre_scores_gemma":[0.9732779,0.02115061,0.005517835,0.000005237472,0.000003690365,6.780933e-7,0.000007074597,0.000003448321,0.00003353819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1252186,"threshold_uncertainty_score":0.1994826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0659667731173006,"score_gpt":0.2611015898883512,"score_spread":0.1951348167710507,"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."}}