{"id":"W2028885889","doi":"10.1139/x05-151","title":"Effects of climate, disturbance, and species on forest biomass across Russia","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Forest ecology and management","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"National Aeronautics and Space Administration","keywords":"Disturbance (geology); Biomass (ecology); Environmental science; Climate change; Forest inventory; Productivity; Ecology; Forestry; Physical geography; Forest management; Atmospheric sciences; Geography; Agroforestry; Biology; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0007782399,0.0002775566,0.0003805522,0.00113883,0.0002892339,0.0006544542,0.0002289631,0.0001387586,0.0004863075],"category_scores_gemma":[0.001153895,0.0002387306,0.0006367132,0.001014196,0.000330658,0.000252519,0.0006913007,0.0002038118,0.00009233463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004496807,"about_ca_system_score_gemma":0.000256564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01461031,"about_ca_topic_score_gemma":0.01824204,"domain_scores_codex":[0.9996136,0.00009822733,0.00004312107,0.0001214796,0.00006027287,0.00006331814],"domain_scores_gemma":[0.9990181,0.0004069332,0.0002756389,0.0001285815,0.00009585413,0.00007492695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006060893,0.0000139566,0.9934232,0.000007416551,0.00008998629,0.00004550018,0.000111776,0.002408141,0.001552289,0.00004568044,0.00002112998,0.002220488],"study_design_scores_gemma":[8.066529e-7,0.000009794513,0.9984661,0.000001480617,0.00001050102,0.00002352568,0.00002834742,0.001264334,0.0001177289,0.0000163544,0.00005945186,0.000001690171],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9994134,0.00003619241,0.0001851321,0.000002496243,7.430798e-7,0.000002255735,0.0002004579,0.000005818314,0.0001535426],"genre_scores_gemma":[0.999311,0.00003192807,0.0001494806,0.000001305518,8.539287e-7,0.000004523663,0.0004436853,0.000002310759,0.00005500402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01461031,"threshold_uncertainty_score":0.02905053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01489836706063483,"score_gpt":0.279452466188994,"score_spread":0.2645540991283591,"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."}}