{"id":"W2030925581","doi":"10.1002/cjce.21623","title":"Studies of extraction of methylene blue from synthetic waste water using liquid emulsion membrane technology","year":2012,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Diluent; Extraction (chemistry); Emulsion; Kerosene; Methylene blue; Membrane; Permeation; Chromatography; Wastewater; Pulmonary surfactant; Phase (matter); Chemistry; Materials science; Waste management; Nuclear chemistry; Organic chemistry; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001949978,0.0001040649,0.0002543071,0.0002340656,0.00002681534,0.00000600574,0.0001257708,0.00009942472,0.00004024472],"category_scores_gemma":[0.0001489222,0.00007319316,0.00006086684,0.0001493972,0.00005657921,0.0001927978,0.000008291793,0.000255704,0.000001369339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097993,"about_ca_system_score_gemma":0.00002954797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009732236,"about_ca_topic_score_gemma":0.00002924031,"domain_scores_codex":[0.9992303,0.00000975743,0.0004013032,0.00004283807,0.0001173802,0.0001984637],"domain_scores_gemma":[0.9994424,0.00009306584,0.00009158255,0.0001025898,0.0001422471,0.0001281344],"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":[0.000008501176,0.000004369977,0.000007302189,0.00006573818,0.00009098904,0.000001516408,0.0006952437,0.1210968,0.877876,0.00003820492,0.0000106613,0.0001046397],"study_design_scores_gemma":[0.00009934262,0.00001506477,0.000001936115,0.0001362786,0.000061738,0.00005728053,0.0002445779,0.008186627,0.9905908,0.00002888841,0.0005024487,0.00007505252],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931026,0.004000051,0.00218968,0.0001197186,0.0005076655,0.00003256878,0.000003550564,0.00001590038,0.0000282379],"genre_scores_gemma":[0.9988608,0.00004465329,0.0009165524,0.000005105893,0.0001463345,0.000001001367,8.495543e-7,0.00001892702,0.000005734124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1129102,"threshold_uncertainty_score":0.298473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02221958806660982,"score_gpt":0.2549319529624283,"score_spread":0.2327123648958185,"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."}}