{"id":"W4406183978","doi":"10.1021/acssusresmgt.4c00451","title":"Experimental, Machine-Learning, and Computational Studies of the Sequestration of Pharmaceutical Mixtures Using Lignin-Derived Magnetic Activated Carbon","year":2025,"lang":"en","type":"article","venue":"ACS Sustainable Resource Management","topic":"Adsorption and biosorption for pollutant removal","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lignin; Carbon sequestration; Activated carbon; Carbon fibers; Materials science; Chemistry; Chemical engineering; Biochemical engineering; Organic chemistry; Engineering; Composite material; Carbon dioxide","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":[0.000179094,0.0001168684,0.0001375905,0.00007099627,0.0001541813,0.0000154216,0.0001158601,0.00003593971,0.00005492947],"category_scores_gemma":[0.00001963699,0.0000891368,0.00003742054,0.0003291056,0.0003672734,0.00004550151,0.0004798053,0.00008762197,4.692295e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001659834,"about_ca_system_score_gemma":0.000009625942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007675723,"about_ca_topic_score_gemma":8.945348e-7,"domain_scores_codex":[0.9990202,0.0001291916,0.0002278579,0.0001896452,0.0002613529,0.0001718184],"domain_scores_gemma":[0.9996995,0.00003286203,0.000120336,0.00009794303,0.00002216445,0.00002724497],"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":[0.0001803881,0.0003165069,0.01085212,0.000534593,0.0002333855,0.00001969885,0.001431917,0.03807728,0.9339244,0.01126484,0.000382572,0.002782283],"study_design_scores_gemma":[0.001962687,0.0002542855,0.009583607,0.0001594562,0.0002497033,0.00001141782,0.03201262,0.03866511,0.8913255,0.002048101,0.0233832,0.0003443513],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988629,0.0007730435,0.00005989628,0.0002941125,0.00002147324,0.0004062373,0.000001026649,0.00001954,0.009795686],"genre_scores_gemma":[0.9939982,0.00004900337,0.0003388773,0.00009121395,0.000004335981,0.000006294382,0.000002346148,0.000006125873,0.005503545],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04259896,"threshold_uncertainty_score":0.3634893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534307754869581,"score_gpt":0.2910628324764455,"score_spread":0.2757197549277497,"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."}}