{"id":"W2928557548","doi":"10.2136/sssaj2018.08.0306","title":"Effects of Biomass Removal Levels on Soil Carbon and Nutrient Reserves in Conifer‐Dominated, Coarse‐Textured Sites in Northern Ontario: 20‐Year Results","year":2019,"lang":"en","type":"article","venue":"Soil Science Society of America Journal","topic":"Forest ecology and management","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; Ministry of Energy, Northern Development and Mines; Ministry of Natural Resources and Forestry","funders":"Natural Resources Canada; Ministry of Natural Resources","keywords":"Nutrient; Forest floor; Biomass (ecology); Environmental science; Soil water; Agronomy; Animal science; Ecology; Biology; Soil science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001157373,0.0001522493,0.0003121193,0.0001085954,0.0001136386,0.00002475291,0.0004213692,0.00008457034,0.00004847952],"category_scores_gemma":[0.0001026805,0.0001289894,0.00009271415,0.0007797155,0.001783843,0.0002498228,0.0002778062,0.0003492217,0.00001379268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005251414,"about_ca_system_score_gemma":0.0001410884,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01279985,"about_ca_topic_score_gemma":0.06551883,"domain_scores_codex":[0.9980662,0.00009807511,0.0003952637,0.0003872625,0.0006125467,0.0004406616],"domain_scores_gemma":[0.999145,0.0001423208,0.0003719944,0.0001974137,0.00002961681,0.0001136303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003931945,0.0004999286,0.851136,0.00007547008,0.00003325714,0.00004074272,0.009288511,0.00473053,0.1309027,0.00004629511,0.001092974,0.00176047],"study_design_scores_gemma":[0.002927528,0.0008690587,0.9794397,0.000125447,0.0000123976,0.00001413514,0.00212881,0.001953054,0.01117736,0.0007024442,0.0004522829,0.0001977194],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962268,0.00004786991,0.000009569984,0.0005440429,0.0001348315,0.0002560406,0.000002656916,0.000004836978,0.002773372],"genre_scores_gemma":[0.9985766,0.00007257478,0.0007149223,0.0001050484,0.000008183089,0.000003282854,8.768551e-7,0.000006376368,0.0005121768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1283038,"threshold_uncertainty_score":0.993774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006063265695911107,"score_gpt":0.214579703284657,"score_spread":0.2085164375887459,"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."}}