{"id":"W4388663278","doi":"10.1016/j.scitotenv.2023.168591","title":"Application of Diffusive Gradients in Thin-films (DGT) for assessing the heavy metals mobility in soil and prediction of their transfer to Russula virescens","year":2023,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Fungal Biology and Applications","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Ontario Ministry of Research, Innovation and Science; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Ministerul Cercetării, Inovării şi Digitalizării; Corporation for National and Community Service","keywords":"Bioaccumulation; Chemistry; Metal; Environmental chemistry; Heavy metals; Diffusive gradients in thin films; Soil contamination; Soil water; Environmental science; Soil science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001200914,0.00006130571,0.000138973,0.00004726515,0.0001017237,0.000002633934,0.0001937539,0.00002923929,0.000002424754],"category_scores_gemma":[0.00005430347,0.00002881184,0.0000439159,0.0003519855,0.0008766461,0.00004163953,0.00007371797,0.00007748416,8.750242e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003689356,"about_ca_system_score_gemma":0.00002325154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007022476,"about_ca_topic_score_gemma":0.000003736832,"domain_scores_codex":[0.9992548,0.00004940514,0.0002298047,0.0001735156,0.0001714901,0.0001209355],"domain_scores_gemma":[0.9994314,0.0001425129,0.00004646892,0.000345805,0.00001001863,0.00002373546],"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":[0.00004841651,0.0001712292,0.002275081,0.00002188325,0.000006887753,1.7426e-8,0.001716051,0.008041981,0.98657,0.0004711749,0.000002680074,0.0006745968],"study_design_scores_gemma":[0.0002513763,0.00008156206,0.6935542,0.00003037255,0.00002132691,0.000001240037,0.0007715387,0.01410362,0.2903309,0.000824417,0.000005930116,0.00002342101],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963717,0.00003871971,0.0002569851,0.002033282,0.00002485985,0.001177797,0.00004697446,0.000003679751,0.00004598454],"genre_scores_gemma":[0.9997203,0.00002708411,0.00004799394,0.00002004736,0.000005469855,0.0001475513,0.000002617288,0.000003009246,0.00002588793],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6962391,"threshold_uncertainty_score":0.3230039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01681362862000198,"score_gpt":0.2646228664935722,"score_spread":0.2478092378735702,"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."}}