{"id":"W2050079933","doi":"10.1139/x02-165","title":"Belowground biomass dynamics in the Carbon Budget Model of the Canadian Forest Sector: recent improvements and implications for the estimation of NPP and NEP","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":249,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Primary production; Biomass (ecology); Environmental science; Temperate climate; Taiga; Tree allometry; Carbon sequestration; Ecosystem; Atmospheric sciences; Ecology; Biomass partitioning; Biology; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002025064,0.00007164489,0.00009936932,0.0001731896,0.0004382371,0.00003898308,0.0004142241,0.00006154535,0.00001283947],"category_scores_gemma":[0.0003082471,0.00004233324,0.00002914738,0.0003521227,0.0006656996,0.00008383559,0.00003795931,0.0002175195,2.331741e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007393784,"about_ca_system_score_gemma":0.0006638893,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.3628408,"about_ca_topic_score_gemma":0.9961132,"domain_scores_codex":[0.9989699,0.0001168859,0.0002541012,0.0001083988,0.0002054239,0.0003453295],"domain_scores_gemma":[0.99918,0.0001979485,0.000118205,0.0002411816,0.00007098982,0.0001917338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001324254,0.00002368525,0.8914022,0.00002868509,0.00002901103,0.000001907502,0.0006752377,0.01706763,0.00004847765,0.08743528,0.0007834058,0.002491225],"study_design_scores_gemma":[0.0003363664,0.0001375491,0.884719,0.00001564581,0.0000194326,0.00001280159,0.0004121429,0.05600831,0.00002542687,0.05718691,0.001074353,0.00005203185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897172,0.0001649385,0.0003080483,0.006718808,0.00006293448,0.0007569043,0.00004158194,4.45672e-7,0.002229068],"genre_scores_gemma":[0.9995674,0.00005143338,0.0001789151,0.00008851329,0.000006358764,0.00003256953,0.000002928933,0.0000066956,0.00006519381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6332724,"threshold_uncertainty_score":0.6414021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03352682070981912,"score_gpt":0.2864103015843583,"score_spread":0.2528834808745392,"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."}}