{"id":"W3109881943","doi":"10.1016/j.jece.2020.104843","title":"Multicomponent column optimization of ternary adsorption based removal of phenolic compounds using modified activated carbon","year":2020,"lang":"en","type":"article","venue":"Journal of environmental chemical engineering","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adsorption; Chemistry; Desorption; Hydroquinone; Activated carbon; Resorcinol; Phenol; Ternary operation; Chromatography; Elution; Catechol; Organic chemistry","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":[0.00009908216,0.0001679271,0.0003187386,0.00006045115,0.00001382583,0.000005904305,0.0001772679,0.0001290708,0.0002826795],"category_scores_gemma":[0.0000274521,0.0001646654,0.0001580739,0.0001597066,0.0001085789,0.0001505985,0.00008531109,0.000260276,0.000001665805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002440274,"about_ca_system_score_gemma":0.000008731089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001136637,"about_ca_topic_score_gemma":4.417315e-8,"domain_scores_codex":[0.998535,0.00002488279,0.0006413224,0.0001518363,0.0004872652,0.0001597163],"domain_scores_gemma":[0.9992093,0.00002756081,0.0004899293,0.00009656655,0.000006344556,0.000170288],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001095273,0.00009885202,0.0003287714,0.00001497175,0.00001523835,0.000006271136,0.00004168906,0.4516098,0.5476885,8.071751e-7,0.000001861096,0.00008359857],"study_design_scores_gemma":[0.000689846,0.00007221509,0.0005983239,0.00003227238,0.00002747627,0.00006672993,0.00001967894,0.5708521,0.4275196,9.764631e-7,0.0000246215,0.00009613992],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835464,0.00002893859,0.01608247,0.00006463406,0.00006301775,0.00009983202,0.00001589751,0.00001357043,0.00008520095],"genre_scores_gemma":[0.9781643,0.00001052686,0.02169322,0.00004112944,0.00005690587,4.368182e-7,0.00000791882,0.00002216574,0.000003431303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.120169,"threshold_uncertainty_score":0.671486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446642536312485,"score_gpt":0.195637998599702,"score_spread":0.1811715732365771,"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."}}