{"id":"W1981194772","doi":"10.1021/es011099s","title":"Interfacial Mass Transfer in Randomly Packed Towers:  A Confident Correlation for Environmental Applications","year":2001,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Water Systems and Optimization","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionless quantity; Schmidt number; Froude number; Mass transfer; Sherwood number; Reynolds number; Range (aeronautics); Chemistry; Mass transfer coefficient; Standard deviation; Transfer (computing); Mechanics; Thermodynamics; Tower; Correlation; Statistics; Mathematics; Chromatography; Physics; Computer science; Geometry; Engineering; Structural engineering; Aerospace 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":[0.0001508159,0.0001246185,0.0001339529,0.000326785,0.0001067178,0.00002268404,0.0002218649,0.0001197468,0.00008834659],"category_scores_gemma":[0.000003019589,0.0001282658,0.00003213414,0.0003020948,0.000370672,0.0002394126,0.00002521769,0.000102699,0.00005108465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003825481,"about_ca_system_score_gemma":0.000005729517,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003318866,"about_ca_topic_score_gemma":0.00002051327,"domain_scores_codex":[0.9990451,0.000006486649,0.0002437122,0.0002701195,0.0001449978,0.0002896203],"domain_scores_gemma":[0.9997556,0.00001031015,0.00001879917,0.000169993,0.000001476995,0.00004378738],"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":[0.0001072169,0.0002308512,0.04069486,0.00001805708,0.00002301531,0.000008506207,0.00134529,0.1364021,0.7726842,0.001647775,0.0001103951,0.04672772],"study_design_scores_gemma":[0.01392575,0.0007143046,0.02355353,0.00008977437,0.00007515947,0.0001745843,0.006294917,0.4589895,0.2935702,0.002892908,0.1978218,0.001897569],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4737203,0.0001402935,0.5240567,0.0001133282,0.000170701,0.0009640189,0.00002011714,0.0001317555,0.0006828415],"genre_scores_gemma":[0.9984552,0.0001090531,0.0006519783,0.00001253011,0.00002363228,0.0004371494,0.00002440161,0.00001494806,0.0002710718],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.524735,"threshold_uncertainty_score":0.5230529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003885598803216087,"score_gpt":0.1803257800184941,"score_spread":0.176440181215278,"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."}}