{"id":"W2885750789","doi":"","title":"An intercomparison eddy correlation system for the Fluxnet-Canada Research Network","year":2004,"lang":"en","type":"article","venue":"26th Agricultural and Forest Meteorology/13th Air Pollution/5th Urban Environment/16th Biometeorology and Aerobiology","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"FluxNet; Environmental science; Eddy covariance; Meteorology; Remote sensing; Climatology; Geography; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004688989,0.001713975,0.001319395,0.003724811,0.002745863,0.00213784,0.003149522,0.001123155,0.01139153],"category_scores_gemma":[0.004908404,0.000895832,0.0008230841,0.006550541,0.0005259461,0.002340226,0.001317098,0.00111584,0.005203401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008358557,"about_ca_system_score_gemma":0.02634306,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9170357,"about_ca_topic_score_gemma":0.9170184,"domain_scores_codex":[0.9983916,0.0002089957,0.000100787,0.0003165392,0.0007494244,0.0002326302],"domain_scores_gemma":[0.9937665,0.0002791582,0.0003171747,0.0008042867,0.004203358,0.0006295639],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001922331,0.0008419525,0.09930752,0.0004157696,0.0007740711,0.0002015663,0.0007311836,0.09302713,0.01251274,0.008593928,0.5407521,0.2409198],"study_design_scores_gemma":[0.001542762,0.0001466162,0.1662315,0.000253744,0.0005904657,0.00008473662,0.0004013922,0.4722794,0.01311916,0.007327422,0.3375224,0.0005004013],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1401104,0.001236295,0.1222173,0.001308174,0.001000244,0.00203259,0.6158429,0.07060144,0.0456507],"genre_scores_gemma":[0.2059368,0.000764074,0.2170028,0.0005106463,0.0002421322,0.001548339,0.5424585,0.007189663,0.02434707],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0829643,"threshold_uncertainty_score":0.1669058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009836940173711927,"score_gpt":0.2044185465011866,"score_spread":0.1945816063274746,"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."}}