{"id":"W2898363363","doi":"10.1088/1748-9326/aaeaeb","title":"Quantifying the effect of forest age in annual net forest carbon balance","year":2018,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McMaster University","funders":"Biological and Environmental Research; Natural Sciences and Engineering Research Council of Canada; Canadian Foundation for Climate and Atmospheric Sciences; Natural Resources Canada; U.S. Department of Energy","keywords":"Environmental science; Eddy covariance; Forest ecology; Biosphere; Abiotic component; Temporal scales; Atmospheric sciences; Ecosystem; Ecology; Climate change; Forest management; Physical geography; Geography; Agroforestry; Biology","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":[0.001358463,0.0001427392,0.0001516469,0.00008111799,0.0001631119,0.00002409144,0.0004509538,0.00006026757,0.0001251194],"category_scores_gemma":[0.00004074323,0.0001001177,0.00005041129,0.0002657835,0.001395656,0.0001384829,0.0004400558,0.0003761467,0.0001583045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002850612,"about_ca_system_score_gemma":0.000003047697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009608781,"about_ca_topic_score_gemma":0.002453689,"domain_scores_codex":[0.9977683,0.0003612256,0.0002177722,0.0003160997,0.0007447557,0.0005917773],"domain_scores_gemma":[0.999235,0.0002761586,0.0000507442,0.0003627519,0.000001058562,0.0000743219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005990481,0.00002666514,0.9564838,0.000005356596,0.000006302127,0.00004287778,0.0003787196,0.00414153,0.03794806,0.00001189077,0.0002191777,0.0006756669],"study_design_scores_gemma":[0.0004822043,0.000358459,0.9770738,0.00002548909,0.000004958108,0.000009239012,0.0000742359,0.01762487,0.002793672,0.00004905677,0.001366274,0.0001377167],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974465,0.00003261894,0.00002500408,0.0004083235,0.00006326658,0.0004036381,0.00002314051,0.000008798364,0.00158868],"genre_scores_gemma":[0.9995504,0.0000250059,0.00005020104,0.00008920822,0.00004811463,0.00003972195,0.0000239899,0.00001768397,0.0001556715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03515439,"threshold_uncertainty_score":0.5142351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567241187576608,"score_gpt":0.2703113979566713,"score_spread":0.2546389860809052,"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."}}