{"id":"W2054259981","doi":"10.1016/j.agrformet.2010.03.002","title":"Biometric and eddy-covariance based estimates of carbon fluxes in an age-sequence of temperate pine forests","year":2010,"lang":"en","type":"article","venue":"Agricultural and Forest Meteorology","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":119,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Eddy covariance; Primary production; Ecosystem respiration; Ecosystem; Environmental science; Temperate rainforest; Atmospheric sciences; Temperate forest; Productivity; Forest ecology; Temperate climate; Ecology; Forestry; Biology; Geography; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001296889,0.0001091517,0.0002001703,0.00008953395,0.00002823628,0.000009008631,0.0001025191,0.0001034629,0.0000155705],"category_scores_gemma":[0.00003471753,0.00006691792,0.00001720815,0.0003941438,0.0003189013,0.0001286753,0.00005681585,0.0001052377,6.928933e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000775744,"about_ca_system_score_gemma":0.000003871218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001396289,"about_ca_topic_score_gemma":0.009077916,"domain_scores_codex":[0.9993254,0.00002747278,0.0002044761,0.0002012313,0.0000815957,0.0001598507],"domain_scores_gemma":[0.9996849,0.00006281696,0.00008798845,0.00009585763,0.00001185309,0.00005657244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001318401,0.00003064437,0.642205,0.000008902056,0.000003315243,0.000004081734,0.00004760466,0.002495919,0.3542451,0.0003029289,8.176498e-7,0.0006424786],"study_design_scores_gemma":[0.0002799774,0.0002138148,0.9614034,0.000006335868,0.00001322314,0.00003065251,0.000006443052,0.03407338,0.002804494,0.001066008,0.000009333071,0.00009294573],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995635,0.00004981992,0.00002976373,0.00007299207,0.00004749741,0.0001095669,0.00001908312,0.000008281619,0.00009947146],"genre_scores_gemma":[0.9960422,0.0000216886,0.003840348,0.00001416534,0.000007258713,0.0000084055,0.0000466087,0.000003167548,0.00001614036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3514406,"threshold_uncertainty_score":0.506569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008984533089089758,"score_gpt":0.2143115830806387,"score_spread":0.205327049991549,"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."}}