{"id":"W1995935313","doi":"10.1002/jctb.2280","title":"Model for a solid‐liquid airlift two‐phase partitioning bioscrubber for the treatment of BTEX","year":2009,"lang":"en","type":"article","venue":"Journal of Chemical Technology & Biotechnology","topic":"Odor and Emission Control Technologies","field":"Chemical Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"BTEX; Airlift; Ethylbenzene; Chemistry; Toluene; Xylene; Volumetric flow rate; Bioreactor; Chromatography; Environmental engineering; Environmental science; Thermodynamics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005328112,0.000954874,0.0009113602,0.0005706637,0.0007858052,0.001129574,0.001363536,0.00231845,0.005289121],"category_scores_gemma":[0.0008553674,0.0005985159,0.001253169,0.0003552262,0.0006236551,0.0008470849,0.0007716189,0.001215236,0.0008905691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001813201,"about_ca_system_score_gemma":0.001951675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03052702,"about_ca_topic_score_gemma":0.01674579,"domain_scores_codex":[0.9997662,0.00003986766,0.00001276524,0.0000752316,0.00006438533,0.0000416029],"domain_scores_gemma":[0.9996272,0.0002032659,0.00004659163,0.000009695793,0.0001004967,0.00001271322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002767389,0.00002357222,0.0004489195,0.00005883041,0.00001361217,0.00005454,0.0000333334,0.9941955,0.002040154,0.001176664,0.0002175913,0.001709694],"study_design_scores_gemma":[0.000007830285,0.00001781345,0.0001352382,0.000005095914,0.000007639201,0.000006722939,0.000008429375,0.9988005,0.0004967762,0.0001988482,0.000310647,0.000004457083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2286583,0.0008226323,0.7276407,0.001354505,0.0002199773,0.0005178973,0.002068941,0.001529233,0.03718789],"genre_scores_gemma":[0.9520741,0.0003917443,0.02043033,0.000134025,0.00002415066,0.001164375,0.0006640867,0.00008206475,0.02503507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03052702,"threshold_uncertainty_score":0.06069869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02902077347769502,"score_gpt":0.325912674214365,"score_spread":0.29689190073667,"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."}}