{"id":"W2168870036","doi":"10.1016/j.ecolmodel.2008.06.021","title":"Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an ensemble Kalman filter","year":2008,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":127,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Eddy covariance; Ecosystem respiration; Environmental science; Atmospheric sciences; Biosphere model; Ecosystem; Leaf area index; Data assimilation; Photosynthetic capacity; Soil respiration; Carbon cycle; Stomatal conductance; Photosynthesis; Soil science; Ecology; Biosphere; Botany; Soil water; Biology; Meteorology; Physics","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":[0.0002005908,0.000139346,0.0002029133,0.00001618452,0.0001902983,0.00001818716,0.0003540536,0.0001082368,0.0001363059],"category_scores_gemma":[0.00001064054,0.0001054996,0.0000250318,0.0001328435,0.00007822181,0.0006460702,0.0001632538,0.0001341101,0.00001287232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006454339,"about_ca_system_score_gemma":0.00001386861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004524938,"about_ca_topic_score_gemma":0.00006098028,"domain_scores_codex":[0.998745,0.00005875807,0.0002962573,0.0004344463,0.0002260026,0.0002395419],"domain_scores_gemma":[0.9992672,0.00006481574,0.0001497694,0.0004435998,0.00001212943,0.00006244193],"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":[0.00002319058,0.00008529843,0.001588999,0.000004160521,0.000007825404,0.000009509145,0.0001841805,0.997812,0.00009396976,0.0001235285,0.00001996897,0.00004741825],"study_design_scores_gemma":[0.0001868837,0.0001093271,0.00007656925,0.00001416566,0.0000158004,0.00002256983,0.00001412845,0.9986863,0.00007128855,0.0006299628,0.00001688155,0.000156186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4260547,0.000003068531,0.5722926,0.00001474606,0.00001508467,0.0001096658,0.00003375637,0.00002695016,0.001449396],"genre_scores_gemma":[0.573076,0.00001465025,0.4266426,0.00004137388,0.000005768828,0.000005838993,0.0001288293,0.000007743354,0.00007729128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1470212,"threshold_uncertainty_score":0.4302148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07384850583724742,"score_gpt":0.239073172807476,"score_spread":0.1652246669702286,"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."}}