{"id":"W4409307408","doi":"10.1016/j.seppur.2025.132941","title":"Arsenic (III) and (V) remediation in water using a particulate photocatalytic carbon nitride (CNx) system","year":2025,"lang":"en","type":"article","venue":"Separation and Purification Technology","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Mitacs; Canada Foundation for Innovation; University of Alberta","keywords":"Particulates; Carbon nitride; Environmental remediation; Arsenic; Photocatalysis; Environmental chemistry; Nitride; Environmental science; Carbon fibers; Groundwater remediation; Materials science; Chemistry; Waste management; Nanotechnology; Metallurgy; Contamination; Composite number; Composite material; 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":[0.0002557113,0.00009129506,0.0001141083,0.0002446673,0.00009539076,0.00002238126,0.00005317103,0.0001432863,0.00002094791],"category_scores_gemma":[0.00002763123,0.00008379808,0.00001094117,0.0003740942,0.0001208301,0.0001306556,0.00004688968,0.00008894641,0.00001581221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001640669,"about_ca_system_score_gemma":0.0000135229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001826373,"about_ca_topic_score_gemma":0.0003095219,"domain_scores_codex":[0.9991772,0.00004391579,0.0002867583,0.0002774169,0.00007473204,0.0001400488],"domain_scores_gemma":[0.9996996,0.00001684867,0.00007328895,0.0001668165,0.00001683091,0.00002659731],"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":[0.00006995868,0.0000874178,0.07107664,0.00008292719,0.00002075288,0.000005105518,0.002375991,0.0007548391,0.8441918,0.02837485,0.00007856049,0.05288111],"study_design_scores_gemma":[0.002113766,0.00005738221,0.04399633,0.0000962051,0.00007277184,0.00004004392,0.003117494,0.5367867,0.4028066,0.006296451,0.004222238,0.0003939467],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9944639,0.00007886401,0.00214069,0.00106436,0.00006637729,0.0004604899,5.346508e-7,0.00009010079,0.001634676],"genre_scores_gemma":[0.9992014,0.00003878064,0.0002148173,0.00006496648,0.000005936853,0.00006597609,0.0000200645,0.00000456891,0.0003834573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5360319,"threshold_uncertainty_score":0.3417186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007577748397652574,"score_gpt":0.2473528649582759,"score_spread":0.2397751165606233,"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."}}