{"id":"W2120567641","doi":"10.1016/j.ecolmodel.2005.11.031","title":"A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO2 and H2O vapor exchange","year":2005,"lang":"en","type":"article","venue":"Ecological Modelling","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":216,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Environmental science; Phenology; Eddy covariance; Leaf area index; Vegetation (pathology); Atmospheric sciences; Growing season; Deciduous; Soil respiration; Carbon cycle; Primary production; Canopy; Biometeorology; Ecosystem respiration; Ecosystem; Hydrology (agriculture); Ecology; Soil water; Soil science; Biology","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":[0.0002496629,0.000149065,0.0001729775,0.00003099193,0.0001323371,0.00002261608,0.00006657984,0.0001897328,0.00004514852],"category_scores_gemma":[0.00002393727,0.0001258537,0.00002883712,0.0001067698,0.000164681,0.0002087162,0.0001017682,0.000110754,0.000008021971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003020639,"about_ca_system_score_gemma":0.0000117435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004696947,"about_ca_topic_score_gemma":0.008437392,"domain_scores_codex":[0.9988698,0.00006015248,0.0002398142,0.0003398407,0.0002693373,0.0002210966],"domain_scores_gemma":[0.9996223,0.00003965255,0.0001259021,0.0001092383,0.00002939861,0.00007351395],"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":[0.00002194255,0.00003781315,0.02302671,0.00001314403,0.000009672784,5.327331e-7,0.0002178135,0.9707352,0.002096747,0.0002274164,0.000001620423,0.003611388],"study_design_scores_gemma":[0.0005341889,0.00006017773,0.1114467,0.000009357976,0.00005722876,0.000008167412,0.000008067531,0.8725788,0.00002767396,0.01514454,0.000003175333,0.0001219143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9881084,0.0005237462,0.01046051,0.00005517834,0.00002544731,0.0003486,0.00003130136,0.00002576631,0.0004210274],"genre_scores_gemma":[0.9896076,0.0001254703,0.01005703,0.00006184614,0.00001244275,0.00002294455,0.00008415091,0.000007611104,0.00002091648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09815639,"threshold_uncertainty_score":0.5132164,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02361015365281416,"score_gpt":0.2454505966822772,"score_spread":0.221840443029463,"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."}}