{"id":"W2108550173","doi":"10.1007/s10546-006-9139-4","title":"The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods","year":2006,"lang":"en","type":"article","venue":"Boundary-Layer Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":233,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"University of Hong Kong; City University of Hong Kong; National Science Foundation","keywords":"Eddy covariance; Sensible heat; Latent heat; Instrumentation (computer programming); Anemometer; Heat flux; Turbulence; Flux (metallurgy); Covariance; Meteorology; Energy balance; Environmental science; Mechanics; Physics; Mathematics; Heat transfer; Statistics; Thermodynamics; Chemistry; Computer science","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":[0.0007039272,0.0001842601,0.0002679776,0.00003642785,0.0004266149,0.00007272546,0.0005157792,0.000127834,0.000109564],"category_scores_gemma":[0.00005036085,0.0001371172,0.00003486447,0.0001539396,0.0004196564,0.0002308645,0.0006577886,0.0001644756,0.000005424282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006028597,"about_ca_system_score_gemma":0.00004433697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002163073,"about_ca_topic_score_gemma":0.000789254,"domain_scores_codex":[0.9983072,0.0002657511,0.0004191847,0.0004669742,0.0001861011,0.0003547827],"domain_scores_gemma":[0.9988912,0.0002187795,0.0002014829,0.0006171488,0.00001712367,0.00005422525],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001120964,0.0008223058,0.04403692,0.00006633079,0.0002847367,0.00006058194,0.003544311,0.02041392,0.4955691,0.01510354,0.01457413,0.4044031],"study_design_scores_gemma":[0.0007279695,0.0004524999,0.00737563,0.0000298584,0.00009442731,0.0001686875,0.0001384693,0.2789148,0.02694377,0.00463992,0.6800144,0.0004994858],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9286546,0.003597982,0.0598186,0.00188673,0.0005160322,0.0002127147,0.00007214318,0.00005678659,0.005184382],"genre_scores_gemma":[0.9622032,0.0001458314,0.0359791,0.0003936974,0.00006525811,0.00002278802,0.00006887143,0.00001776203,0.001103508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6654403,"threshold_uncertainty_score":0.5591478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0173588156784595,"score_gpt":0.2814687122797057,"score_spread":0.2641098966012462,"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."}}