{"id":"W2591705736","doi":"10.1039/c7ra00615b","title":"Experimental and computational modeling studies on silica-embedded NiO/MgO nanoparticles for adsorptive removal of organic pollutants from wastewater","year":2017,"lang":"en","type":"article","venue":"RSC Advances","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"University of Calgary","keywords":"Non-blocking I/O; Adsorption; Wastewater; Pollutant; Nanoparticle; Materials science; Chemical engineering; Chemistry; Nanotechnology; Environmental science; Organic chemistry; Environmental engineering; Catalysis; Engineering","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":[0.000107393,0.0001660194,0.0002373101,0.00002451705,0.0003452784,0.00003553527,0.0001766472,0.00004436261,0.0002146361],"category_scores_gemma":[0.00007680643,0.0001304328,0.00005913897,0.00003430595,0.0004007751,0.0004934997,0.0001594364,0.00005019653,0.00004043863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006187538,"about_ca_system_score_gemma":0.000007586831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001217299,"about_ca_topic_score_gemma":0.00002724339,"domain_scores_codex":[0.9988776,0.00002653785,0.0002795575,0.0003547734,0.0002678703,0.0001936089],"domain_scores_gemma":[0.9994223,0.00007176366,0.0002191649,0.0001944181,0.00001802053,0.00007431104],"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":[0.0002685478,0.00009549191,0.0004407972,0.000007079547,0.00003856136,0.000004356098,0.001176111,0.007157145,0.9868195,0.000328927,0.00001812877,0.003645362],"study_design_scores_gemma":[0.00203598,0.0004286426,0.002272165,0.00008906439,0.00003650523,0.00003096689,0.004596889,0.09983306,0.8759561,0.01385173,0.0004498411,0.0004190092],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979085,0.0005591976,0.0005549891,0.0002161763,0.0001783664,0.0002202975,0.0001268947,0.00002660582,0.0002090259],"genre_scores_gemma":[0.9928171,0.00004900177,0.006769177,0.00008240606,0.00005543705,0.000009238257,0.000005729137,0.00001363751,0.0001982873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1108634,"threshold_uncertainty_score":0.5318894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0362314751118226,"score_gpt":0.308582764979939,"score_spread":0.2723512898681164,"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."}}