{"id":"W2559340517","doi":"10.1038/nplants.2016.187","title":"Major losses of nutrients following a severe drought in a boreal forest","year":2016,"lang":"en","type":"article","venue":"Nature Plants","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Ouranos","funders":"Ministère des Forêts, de la Faune et des Parcs","keywords":"Throughfall; Environmental science; Nutrient; Ecosystem; Canopy; Leaching (pedology); Forest ecology; Taiga; Nutrient cycle; Tree canopy; Potassium; Soil water; Agronomy; Ecology; Biology; Soil science; Chemistry","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":[0.0003570052,0.0002159097,0.0002844748,0.0002711277,0.001113652,0.000520493,0.0003500369,0.0006830365,0.0006827569],"category_scores_gemma":[0.000593844,0.0001495589,0.0002618631,0.0002982097,0.0006095864,0.0004841097,0.0004591225,0.0005680767,0.00007184642],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006523373,"about_ca_system_score_gemma":0.0004332832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01819492,"about_ca_topic_score_gemma":0.03770908,"domain_scores_codex":[0.9999142,0.00001045027,0.000008763454,0.00001805951,0.00001131543,0.00003718021],"domain_scores_gemma":[0.9996129,0.00004522707,0.0001082151,0.00002278858,0.00005023727,0.0001606932],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005658978,0.0006465573,0.8944128,0.00009721894,0.0003896191,0.002196318,0.0007771484,0.002705059,0.07842892,0.0002856923,0.00101179,0.01338999],"study_design_scores_gemma":[0.00001200245,0.00009732641,0.9983687,0.000001813002,0.00001682146,0.0001550716,0.0002808702,0.0004715509,0.0003896994,0.00006928567,0.0001313318,0.000005562454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996561,0.00002870439,0.00003379011,0.00005154392,0.000006406,0.000001832509,0.00006176989,0.000003019278,0.000156883],"genre_scores_gemma":[0.9997645,0.00002026089,0.00003904819,0.00002276123,0.00000672116,0.000001970139,0.00007561053,9.398922e-7,0.00006832876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01819492,"threshold_uncertainty_score":0.03617799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003653955915270285,"score_gpt":0.212450381687867,"score_spread":0.2087964257725967,"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."}}