{"id":"W3127904453","doi":"10.1016/j.seppur.2021.118407","title":"Development of a self-sustained model to predict the performance of direct contact membrane distillation","year":2021,"lang":"en","type":"article","venue":"Separation and Purification Technology","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Membrane distillation; Porosity; Materials science; Mass transfer; Membrane; Permeation; Distillation; Thermodynamics; Flux (metallurgy); Efficient energy use; Concentration polarization; Energy balance; Process engineering; Chemistry; Chromatography; Composite material; Desalination; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003179989,0.0005258151,0.0006962715,0.000291083,0.0006465948,0.0006268295,0.001260653,0.001961659,0.001507813],"category_scores_gemma":[0.00109359,0.000482992,0.0007037374,0.0002460925,0.0004808696,0.0009484912,0.0005040543,0.001034891,0.0003606404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008419289,"about_ca_system_score_gemma":0.001445116,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01649313,"about_ca_topic_score_gemma":0.006395692,"domain_scores_codex":[0.9999077,0.00001524135,0.000005150758,0.00002024029,0.00003402815,0.00001762462],"domain_scores_gemma":[0.9996375,0.0001764474,0.00003020355,0.0000293927,0.000100171,0.00002630875],"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":[0.00001544506,0.00004064686,0.0003902391,0.00002675905,0.00001011892,0.00005124928,0.00002458018,0.9910331,0.004003251,0.002137714,0.0001642474,0.002102637],"study_design_scores_gemma":[0.00000187977,0.000003762027,0.00003692407,5.608095e-7,0.000001047548,0.000002091177,0.000001030984,0.9993576,0.0003964754,0.0001421975,0.00005502998,0.000001369744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4538164,0.0005360085,0.5239989,0.0007278823,0.0001855985,0.0002491105,0.0004417874,0.0009883817,0.019056],"genre_scores_gemma":[0.9768891,0.0001560473,0.01518212,0.00006679463,0.00002086676,0.0002036916,0.0001559463,0.00009139936,0.007234044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01649313,"threshold_uncertainty_score":0.0327943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01271048435628138,"score_gpt":0.2510811672090304,"score_spread":0.238370682852749,"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."}}