{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008782492,0.00004783324,0.0001094071,0.00002880956,0.00006765919,0.00002611635,0.0002106319,0.00001643143,0.0001793775],"category_scores_gemma":[0.00003483075,0.00003406459,0.00003440857,0.00009298744,0.0003765847,0.0003082055,0.00003110047,0.00002321089,0.00003199173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003462385,"about_ca_system_score_gemma":0.00002845486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007706843,"about_ca_topic_score_gemma":7.444269e-7,"domain_scores_codex":[0.9989955,0.00004441998,0.0003161407,0.00008314291,0.0004516684,0.0001091539],"domain_scores_gemma":[0.9994652,0.00004234659,0.0003176718,0.00006659132,0.00001105383,0.00009716992],"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":[0.000009702122,0.0001073817,0.002729265,7.287485e-7,0.000001227368,0.000001744784,0.000101509,0.0001686272,0.9943945,0.0001671534,0.0005410679,0.001777131],"study_design_scores_gemma":[0.0002028816,0.0002252577,0.005366036,0.000009065535,0.000009515679,0.00008392518,0.0004702962,0.0001009025,0.9908053,0.0008661523,0.001814159,0.00004645742],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987708,0.0004516153,0.00007487456,0.0002584138,0.00006174757,0.00003609176,0.000007077241,0.000002831912,0.0003364928],"genre_scores_gemma":[0.9969234,0.00001988542,0.0029608,0.00002362126,0.00003041547,7.631309e-7,1.727065e-7,0.000002321901,0.00003863644],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003589111,"threshold_uncertainty_score":0.1964058,"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."}}