{"id":"W2896204203","doi":"10.1021/acsomega.8b01871","title":"Graphene Oxide–Chitosan Composite Material for Treatment of a Model Dye Effluent","year":2018,"lang":"en","type":"article","venue":"ACS Omega","topic":"Graphene and Nanomaterials Applications","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Chitosan; Oxide; Composite number; Effluent; Materials science; Chemical engineering; Composite material; Nanotechnology; Waste management; Engineering; Metallurgy","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":[0.00002435805,0.0001371522,0.0002164295,0.00007749051,0.00005829777,0.00001893878,0.0001015645,0.0000557806,0.000004090758],"category_scores_gemma":[0.000001099933,0.0001233713,0.00007272368,0.0001038658,0.00004440632,0.00004756474,0.00001229193,0.000007477875,0.000007833783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001675978,"about_ca_system_score_gemma":0.000008515735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003389837,"about_ca_topic_score_gemma":0.000008789923,"domain_scores_codex":[0.999386,0.000005462476,0.000227274,0.000134456,0.00006089171,0.0001858994],"domain_scores_gemma":[0.9996063,0.00001514305,0.00003556401,0.0002642265,0.00002929018,0.00004953447],"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":[0.00004484544,0.00006800659,0.0001042691,0.00002679111,0.00006118665,1.543362e-7,0.0001145807,0.0008883365,0.9969237,0.001195715,0.0001823195,0.0003901323],"study_design_scores_gemma":[0.0006311936,0.0001787594,0.0009160619,0.000007939996,0.0000504078,0.000001528312,0.000005030961,0.004464113,0.9913969,0.001412788,0.0008121651,0.0001231033],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944074,0.00006014051,0.004186031,0.00002012264,0.0002107853,0.0003826075,0.0003228227,0.0001352183,0.0002749244],"genre_scores_gemma":[0.992303,0.00006058524,0.007094567,0.00001385413,0.0001308245,0.0002702513,0.00008444084,0.00003121875,0.00001133306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005526754,"threshold_uncertainty_score":0.5030935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01570994788705355,"score_gpt":0.2409270616717694,"score_spread":0.2252171137847159,"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."}}