{"id":"W2739444546","doi":"10.1039/c7ra06187k","title":"Carbon nanosphere–iron oxide nanocomposites as high-capacity adsorbents for arsenic removal","year":2017,"lang":"en","type":"article","venue":"RSC Advances","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Barrick Gold (Canada); Concordia University; Laurentian University; University of Sudbury","funders":"Natural Sciences and Engineering Research Council of Canada; Goldcorp","keywords":"Nanocomposite; Arsenic; Adsorption; Iron oxide; Carbon fibers; Oxide; Chemical engineering; Graphene; Materials science; Chemistry; Nanotechnology; Composite number; Metallurgy; Organic chemistry; Composite material","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":[0.0002014429,0.0004875473,0.0002399672,0.000453386,0.0003355897,0.0003089109,0.0002541363,0.0005202075,0.0007994435],"category_scores_gemma":[0.0001721313,0.0002117997,0.0002523948,0.0002525843,0.0002059962,0.0003215015,0.0001908357,0.0003721726,0.0002127342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004025577,"about_ca_system_score_gemma":0.0002297743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002082702,"about_ca_topic_score_gemma":0.008124612,"domain_scores_codex":[0.9998596,0.00001707269,0.000007256293,0.00002500039,0.00006318362,0.00002794126],"domain_scores_gemma":[0.9999282,0.00001811675,0.000007566746,0.000005329438,0.00002705637,0.00001379925],"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":[0.00006768564,0.00007403701,0.0001618007,0.0001224537,0.0000175585,0.00004997999,0.00002785193,0.0006098156,0.9922491,0.0002504322,0.0002082583,0.006161141],"study_design_scores_gemma":[0.000007531629,0.0001185349,0.0007085072,0.000003262958,0.00001684008,0.00003487466,0.00001347623,0.003648109,0.9936336,0.00003872559,0.001767637,0.000008883633],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819509,0.003950376,0.008535925,0.0002630178,0.0001122482,0.00007487133,0.0001315911,0.0002985306,0.00468257],"genre_scores_gemma":[0.9874167,0.001254748,0.005404086,0.00004310118,0.0000252221,0.00001341351,0.0001118659,0.00002358427,0.005707256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002082702,"threshold_uncertainty_score":0.004141152,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01119000926879268,"score_gpt":0.2519524898251509,"score_spread":0.2407624805563583,"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."}}