{"id":"W3012594226","doi":"10.1007/s11356-020-08347-6","title":"Evaluation of a nanoscale zero-valent iron amendment as a potential tool to reduce mobility, toxicity, and bioaccumulation of arsenic and mercury from wetland sediments","year":2020,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Intrinsik (Canada); Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada","keywords":"Environmental chemistry; Bioaccumulation; Mercury (programming language); Arsenic; Sediment; Zerovalent iron; Environmental science; Ecotoxicology; Wetland; Amendment; Slurry; Environmental remediation; Contamination; Chemistry; Environmental engineering; Ecology; Geology; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001883952,0.0001051487,0.0001424544,0.0001256423,0.000116353,0.00003397813,0.0001005675,0.00005251418,0.000120758],"category_scores_gemma":[0.0001020956,0.0001040093,0.00001430153,0.0002142261,0.0005496009,0.0002688159,0.0002553064,0.00006918007,0.00001378616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002763548,"about_ca_system_score_gemma":0.00003518289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007084986,"about_ca_topic_score_gemma":0.000002756913,"domain_scores_codex":[0.9972757,0.0001374826,0.0002815265,0.0003335293,0.001756751,0.0002150732],"domain_scores_gemma":[0.9995762,0.00002662817,0.00005065597,0.0001287011,0.00002055997,0.0001972988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000394794,0.00005930983,0.002454036,0.00002135074,0.000008269335,2.784349e-7,0.0006909725,0.001725769,0.9861321,0.000006317296,0.00002877733,0.00883335],"study_design_scores_gemma":[0.0007472601,0.0002817328,0.1892819,0.00002274171,0.00002634131,0.000001493264,0.0003108669,0.01344938,0.7955681,0.0001525127,0.00005392895,0.0001037656],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983048,0.0003611975,0.00006843409,0.0003316107,0.00005998262,0.0007129913,0.0001017603,0.0000096345,0.00004957122],"genre_scores_gemma":[0.9992474,0.0004135426,0.0002250049,0.00003562267,0.00002521698,0.0000270428,0.00001197132,0.000008120886,0.000006059704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.190564,"threshold_uncertainty_score":0.4241378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0446734275948004,"score_gpt":0.3223240345823656,"score_spread":0.2776506069875652,"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."}}