{"id":"W2047065658","doi":"10.1046/j.1365-2486.2002.00492.x","title":"An initial intercomparison of micrometeorological and ecological inventory estimates of carbon exchange in a mid‐latitude deciduous forest","year":2002,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":129,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biological and Environmental Research; Indiana Department of Natural Resources; U.S. Forest Service; Manitoba Medical Service Foundation; U.S. Department of Agriculture; U.S. Department of Energy","keywords":"Eddy covariance; Environmental science; Deciduous; Atmospheric sciences; Biomass (ecology); Canopy; Carbon cycle; Ecology; Primary production; Soil respiration; Ecosystem; Hydrology (agriculture); Soil water; Soil science; Biology; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001230733,0.0001131617,0.0002629424,0.00004579986,0.00001449497,0.000003404561,0.0001553083,0.0001992044,0.0001831118],"category_scores_gemma":[0.00002343904,0.00009109075,0.00002784807,0.0001398189,0.0003514173,0.00005258351,0.0001659194,0.0000766954,0.000004176616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007313191,"about_ca_system_score_gemma":0.000001522218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651293,"about_ca_topic_score_gemma":0.003510181,"domain_scores_codex":[0.9991661,0.00009130986,0.0002411935,0.0002263634,0.00005942967,0.0002155433],"domain_scores_gemma":[0.9996997,0.00003356114,0.0000911267,0.0001086108,0.000005054629,0.00006194426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002891478,0.0002142905,0.9944769,0.000007629989,0.000005652095,0.000009880383,0.0002212381,0.00003120302,0.001458993,0.00009728154,0.000005386328,0.003442693],"study_design_scores_gemma":[0.0004497668,0.000931216,0.9472132,0.00001130494,0.00001335548,0.00003390414,0.00002226778,0.04913093,0.00007630265,0.001902564,0.00008770659,0.0001275235],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983421,0.000422997,0.00003711681,0.00003579217,0.00006901313,0.0001543702,0.00006111716,0.00001254527,0.00086494],"genre_scores_gemma":[0.9989712,0.0001048113,0.000803892,0.00004979114,0.00001751695,0.00001912528,0.00002946714,0.000002819345,0.00000134609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04909973,"threshold_uncertainty_score":0.3714573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04181007111899869,"score_gpt":0.2767424358697598,"score_spread":0.2349323647507611,"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."}}