{"id":"W2103170539","doi":"10.5194/bg-5-433-2008","title":"Quality control of CarboEurope flux data – Part 1: Coupling footprint analyses with flux data quality assessment to evaluate sites in forest ecosystems","year":2008,"lang":"en","type":"article","venue":"Biogeosciences","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":258,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Office of Science; European Commission; U.S. Department of Energy","keywords":"Eddy covariance; Footprint; Data quality; Terrain; Environmental science; Flux (metallurgy); Data set; Remote sensing; Data mining; Computer science; Geography; Cartography; Engineering; Ecosystem; Artificial intelligence; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00575557,0.0005226865,0.0006108799,0.002371934,0.0004621683,0.001164052,0.000725772,0.0004713462,0.000763269],"category_scores_gemma":[0.01260293,0.0002105821,0.0005217959,0.002358219,0.0005088593,0.0009122864,0.0009313942,0.0002376018,0.0001343643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000768398,"about_ca_system_score_gemma":0.0008831338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0225754,"about_ca_topic_score_gemma":0.02644403,"domain_scores_codex":[0.9972573,0.001109082,0.000227909,0.0005097478,0.0007481397,0.0001479005],"domain_scores_gemma":[0.9924096,0.002964292,0.000930525,0.001368074,0.002111712,0.0002156855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006566763,0.0002354482,0.7026255,0.0002022057,0.0002849247,0.0002023251,0.0004176591,0.09549018,0.015829,0.0009523177,0.001043099,0.1820607],"study_design_scores_gemma":[0.0001035837,0.0003089849,0.6359335,0.00004142864,0.00009808678,0.0002111028,0.0004826709,0.3333727,0.02568788,0.001179367,0.002511224,0.00006936469],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9002694,0.000141806,0.09507177,0.00004672754,0.00000835468,0.0002830421,0.002589347,0.0004880577,0.001101474],"genre_scores_gemma":[0.9282698,0.00003676996,0.06753267,0.0000115888,0.000006033845,0.0001404454,0.003654308,0.00005698563,0.0002913853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0225754,"threshold_uncertainty_score":0.04488802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1860835733960208,"score_gpt":0.3743128855437335,"score_spread":0.1882293121477127,"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."}}