{"id":"W4410985615","doi":"10.1007/s40996-025-01849-8","title":"Comparison Between the Efficiencies of Reverse Osmosis Membrane and Carboxyl Multi-Walled Carbon Nanotubes in Copper Ions (Cu2+) Removal from Synthesized Wastewater","year":2025,"lang":"en","type":"article","venue":"Iranian Journal of Science and Technology Transactions of Civil Engineering","topic":"Membrane Separation Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; Natural Sciences and Engineering Research Council of Canada","funders":"","keywords":"Reverse osmosis; Copper; Wastewater; Membrane; Carbon nanotube; Ion; Osmosis; Materials science; Chemistry; Chemical engineering; Inorganic chemistry; Nanotechnology; Metallurgy; Organic chemistry; Environmental engineering; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0006822628,0.0001292218,0.0003944892,0.0007099033,0.0001248193,0.00001490299,0.0004618598,0.0001282944,0.00001629305],"category_scores_gemma":[0.0002834368,0.0000966962,0.00003992246,0.00151788,0.001916619,0.0002161455,0.00005118319,0.000318557,2.556363e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006378606,"about_ca_system_score_gemma":0.00004866242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001518708,"about_ca_topic_score_gemma":0.0002567155,"domain_scores_codex":[0.9987532,0.00002189971,0.0005394029,0.0001922227,0.0002907696,0.0002025403],"domain_scores_gemma":[0.9993033,0.0002168055,0.0001631526,0.0002251885,0.00005575962,0.00003580703],"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":[0.00001796537,0.00006398337,0.02223244,0.00003251958,0.00003832727,0.00000447147,0.0009557967,0.01434549,0.9589978,0.00005928937,0.000003508741,0.00324839],"study_design_scores_gemma":[0.0009861887,0.0001452338,0.02126489,0.0003133627,0.0001118879,0.00003625207,0.003366308,0.01630452,0.9569656,0.0001774771,0.0001673025,0.0001609463],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955729,0.0004284652,0.002554663,0.001101359,0.00008195243,0.0001308418,0.00000453917,0.00002966796,0.00009557749],"genre_scores_gemma":[0.9957736,0.0001821268,0.004016197,0.000004997316,0.000002683561,0.000003772613,7.780875e-8,0.000005025828,0.0000114916],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003087443,"threshold_uncertainty_score":0.7061862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120936322845704,"score_gpt":0.2329719368400845,"score_spread":0.2217625736116274,"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."}}