{"id":"W2971569609","doi":"10.3846/jeelm.2019.10792","title":"WOOD-DERIVED BIOCHAR INFLUENCES NUTRIENT USE EFFICIENCY OF HEAVY METALS IN SPINACH (SPINACIA OLERACEA) UNDER GROUNDWATER AND WASTEWATER IRRIGATION","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Engineering and Landscape Management","topic":"Wastewater Treatment and Reuse","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Biochar; Amendment; Wastewater; Irrigation; Environmental science; Agronomy; Biomass (ecology); Rhizosphere; Nutrient; Spinach; Groundwater; Chemistry; Environmental engineering; Pyrolysis; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002045977,0.0001781192,0.0002487713,0.0001558327,0.00002852922,0.0000402531,0.000101362,0.000046138,0.000209835],"category_scores_gemma":[0.000001660341,0.0001264638,0.00005311292,0.00008675958,0.0000583727,0.0003715018,0.0001687851,0.00009170919,0.00001632709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007501864,"about_ca_system_score_gemma":0.000001137983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001704871,"about_ca_topic_score_gemma":0.000001777839,"domain_scores_codex":[0.9989341,0.00002386913,0.0003675345,0.0001971219,0.0002762425,0.0002011442],"domain_scores_gemma":[0.9996386,0.00001703606,0.000139047,0.0001262501,0.000001746296,0.00007734942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002694114,0.0009443004,0.2591663,0.0002586675,0.000313483,0.00008614436,0.001890366,0.1126008,0.6230306,0.0001656262,0.0000322838,0.001242043],"study_design_scores_gemma":[0.003521306,0.001130964,0.9157686,0.0002908226,0.0001539729,0.00007015228,0.001549015,0.002892694,0.07331105,0.00007519525,0.0007885603,0.0004476914],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989768,0.0004636343,0.00009033312,0.00007892283,0.00009798727,0.0002213353,0.000002121333,0.000005203854,0.00006369426],"genre_scores_gemma":[0.998629,0.0003981436,0.0007034473,0.00001915566,0.0000112614,0.00000398565,0.000002981004,0.00001284581,0.0002192094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6566023,"threshold_uncertainty_score":0.5157045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005067970064678355,"score_gpt":0.177477112690821,"score_spread":0.1724091426261427,"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."}}