{"id":"W2135758670","doi":"10.1111/j.1365-2486.2006.01298.x","title":"A method for deriving net primary productivity and component respiratory fluxes from tower‐based eddy covariance data: a case study using a 17‐year data record from a Douglas‐fir chronosequence","year":2006,"lang":"en","type":"article","venue":"Global Change Biology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Chronosequence; Eddy covariance; Ecosystem respiration; Primary production; Productivity; Environmental science; Atmospheric sciences; Ecosystem; Forestry; Mathematics; Ecology; Geography; Biology; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001897275,0.0005487028,0.0003152003,0.001206423,0.0005336702,0.00101251,0.0005207148,0.0004702041,0.000651633],"category_scores_gemma":[0.0034321,0.0003770314,0.0004676228,0.001385995,0.0001487363,0.000335722,0.0002597715,0.0003812232,0.0002811481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006670665,"about_ca_system_score_gemma":0.001398265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05644016,"about_ca_topic_score_gemma":0.1002582,"domain_scores_codex":[0.9995757,0.0001013842,0.00005446568,0.0001072143,0.0001374288,0.00002384484],"domain_scores_gemma":[0.9980425,0.000954829,0.0001846937,0.000185779,0.0005775903,0.00005467861],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00030124,0.0002756883,0.4656076,0.0002887764,0.0004702711,0.0005624492,0.000526269,0.09639082,0.03253013,0.001693326,0.001986279,0.3993671],"study_design_scores_gemma":[0.0001127265,0.0001282413,0.3427683,0.0000798421,0.0001267379,0.0006356522,0.0002491879,0.6317971,0.01728909,0.0006076587,0.006096384,0.0001090661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5612462,0.0004451988,0.4315185,0.0000919749,0.00003636937,0.0003113686,0.002558731,0.001307497,0.002484198],"genre_scores_gemma":[0.5047011,0.0001535112,0.4923357,0.00001850462,0.00001617651,0.0002183453,0.001490665,0.00009124707,0.0009745839],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05644016,"threshold_uncertainty_score":0.1122233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1236061372033682,"score_gpt":0.3332791753181903,"score_spread":0.2096730381148221,"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."}}