{"id":"W1965115805","doi":"10.13031/2013.32600","title":"Nebraska Water and Energy Flux Measurement, Modeling, and Research Network (NEBFLUX)","year":2010,"lang":"en","type":"article","venue":"Transactions of the ASABE","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"FluxNet; Environmental science; Microclimate; Ecosystem; Biosphere; Water vapor; Atmospheric sciences; Energy flux; Water cycle; Vegetation (pathology); Terrestrial ecosystem; Eddy covariance; Ecology; Hydrology (agriculture); Geography; Meteorology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005739889,0.00006686756,0.00007057298,0.00002344833,0.0003184621,0.00003038771,0.0001489735,0.00006182047,0.0002005383],"category_scores_gemma":[0.000003846244,0.00004141085,0.00002903531,0.0001091935,0.0002138909,0.0001474313,0.00004465331,0.0002360563,0.000006094625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001794469,"about_ca_system_score_gemma":0.000005766539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006350608,"about_ca_topic_score_gemma":0.001358642,"domain_scores_codex":[0.9991713,0.00005066262,0.0001161772,0.0001470939,0.0002941305,0.0002206652],"domain_scores_gemma":[0.9996701,0.00001556777,0.00001400541,0.0002303001,0.0000166371,0.00005336131],"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":[0.00006452369,0.0002036384,0.01378857,0.00002421694,0.0001023309,0.000001552356,0.0014744,0.5295686,0.4252963,0.002610616,0.0007438114,0.02612144],"study_design_scores_gemma":[0.0006377393,0.00009256973,0.005559141,0.00003465236,0.000093443,0.00005689905,0.00008245924,0.9383133,0.01395922,0.02329572,0.0175449,0.0003299663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9705288,0.00004137458,0.02428326,0.0004951684,0.0001485659,0.00007083671,0.000006733695,0.00001423268,0.004410983],"genre_scores_gemma":[0.9967696,0.00003742676,0.0004518132,0.00002457513,0.0000166532,0.000008201584,0.000001847001,0.000007530094,0.002682353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4113371,"threshold_uncertainty_score":0.2449386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02286992792411674,"score_gpt":0.2291380809368755,"score_spread":0.2062681530127587,"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."}}