{"id":"W2220131553","doi":"10.1038/nplants.2015.160","title":"Woody biomass production lags stem-girth increase by over one month in coniferous forests","year":2015,"lang":"en","type":"article","venue":"Nature Plants","topic":"Plant Water Relations and Carbon Dynamics","field":"Environmental Science","cited_by":408,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; Université du Québec à Chicoutimi","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Institut National de la Recherche Agronomique; Agence Nationale de la Recherche; Austrian Science Fund; Javna Agencija za Raziskovalno Dejavnost RS; Russian Science Foundation; Chinese Academy of Sciences; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Biomass (ecology); Environmental science; Woody plant; Carbon sequestration; Taiga; Biomass partitioning; Crown (dentistry); Ecosystem; Eddy covariance; Carbon cycle; Ecology; Atmospheric sciences; Biology; Carbon dioxide","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002328759,0.0001454541,0.000246066,0.0002340026,0.0003084489,0.0004303815,0.0001759475,0.0002917503,0.00172168],"category_scores_gemma":[0.0005507381,0.0001374783,0.0001296394,0.0002634899,0.000232636,0.0004707118,0.0002448024,0.0002723052,0.0004421704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003574372,"about_ca_system_score_gemma":0.0004140011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01171825,"about_ca_topic_score_gemma":0.02899375,"domain_scores_codex":[0.9999243,0.000008413651,0.000004516388,0.00002322352,0.000009180769,0.00003032734],"domain_scores_gemma":[0.9994751,0.000124995,0.000144743,0.00003906847,0.00005845312,0.0001576754],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009896293,0.0001549242,0.9212158,0.00007931975,0.00005538953,0.0001937774,0.000274448,0.0008053659,0.05800556,0.0002616604,0.0008389952,0.01712508],"study_design_scores_gemma":[0.000001679044,0.00002456637,0.998281,0.000001598034,0.000004088398,0.00003670999,0.00006948788,0.0003888045,0.0008441184,0.00007005453,0.00027598,0.000001848832],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987491,0.0002211466,0.0002345406,0.00004401267,0.00000724706,0.000001102456,0.0002450145,0.00002929472,0.000468484],"genre_scores_gemma":[0.999216,0.00005451429,0.000060512,0.00001632965,0.000005445892,0.000001594516,0.0003342155,0.000004076269,0.0003073384],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01171825,"threshold_uncertainty_score":0.02330005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007624998649740135,"score_gpt":0.2079503340756461,"score_spread":0.2003253354259059,"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."}}