{"id":"W2211911353","doi":"10.1016/j.jes.2015.10.001","title":"Removal of nanoparticles by coagulation","year":2015,"lang":"en","type":"article","venue":"Journal of Environmental Sciences","topic":"Nanoparticles: synthesis and applications","field":"Materials Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Alberta Health; Alberta Innovates; Alberta Innovates - Health Solutions; Natural Sciences and Engineering Research Council of Canada; Alberta Health Services","keywords":"Environmental remediation; Phytoremediation; Brassica; Ecosystem; Nanomaterials; Wetland; Environmental chemistry; Chemistry; Contamination; Horticulture; Heavy metals; Biology; Materials science; Ecology; Nanotechnology","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":[0.0001651284,0.0003146281,0.0002309315,0.0002371916,0.000229415,0.0003672246,0.0001880119,0.0004778673,0.001837226],"category_scores_gemma":[0.0002223568,0.00020816,0.0003936854,0.0001104833,0.0001801443,0.0002194345,0.0002589784,0.0003752852,0.0007794467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004164071,"about_ca_system_score_gemma":0.0003270476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001235668,"about_ca_topic_score_gemma":0.001462136,"domain_scores_codex":[0.9997974,0.00001725926,0.00001135122,0.00004460211,0.00007362558,0.00005575589],"domain_scores_gemma":[0.9999335,0.00001925125,0.00001054294,0.000009158606,0.00002016517,0.000007527682],"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":[0.00006210241,0.00001204085,0.00008449407,0.00002956876,0.000004507122,0.0000373523,0.00002805758,0.0001233364,0.9971231,0.0001225956,0.0001286313,0.002244268],"study_design_scores_gemma":[0.000005308189,0.00003944457,0.0003765034,0.000001695111,0.000004141014,0.00003289277,0.0000058129,0.000561339,0.9979474,0.00002978409,0.0009935102,0.000002131322],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745551,0.001517075,0.009640355,0.0002164672,0.000114226,0.00006541754,0.0001028831,0.0001874856,0.01360099],"genre_scores_gemma":[0.98638,0.0005337656,0.002413149,0.00009261321,0.00001958933,0.00002042582,0.0001183441,0.00004850848,0.01037348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001837226,"threshold_uncertainty_score":0.006146133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02988272943582971,"score_gpt":0.2573924973268702,"score_spread":0.2275097678910405,"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."}}