{"id":"W4412982073","doi":"10.1016/j.jobab.2025.07.004","title":"Corrigendum to “Remediation and resource utilization of Cr(Ⅲ), Al(Ⅲ) and Zr(Ⅳ)-containing tannery effluent based on chitosan-carboxymethyl cellulose aerogel” [Journal of Bioresources and Bioproducts, 10 (2025) 77-91]","year":2025,"lang":"en","type":"erratum","venue":"Journal of Bioresources and Bioproducts","topic":"Neurological Disease Mechanisms and Treatments","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Quanzhou City Science and Technology Program; National Natural Science Foundation of China","keywords":"Bioproducts; Carboxymethyl cellulose; Chitosan; Environmental remediation; Aerogel; Effluent; Chemistry; Cellulose; Pulp and paper industry; Nuclear chemistry; Chemical engineering; Waste management; Materials science; Nanotechnology; Organic chemistry; Biofuel; Engineering; Contamination; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009974729,0.000741134,0.00132421,0.001377608,0.0003042912,0.0002805712,0.0004167875,0.0004262228,0.00002734908],"category_scores_gemma":[0.002136797,0.000526797,0.0002032792,0.000855186,0.0004669591,0.0002012355,0.0002887041,0.0008333464,8.171069e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004533218,"about_ca_system_score_gemma":0.0002199898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002043813,"about_ca_topic_score_gemma":0.000003520577,"domain_scores_codex":[0.995242,0.0004702888,0.001479132,0.001112798,0.001168709,0.0005270888],"domain_scores_gemma":[0.995873,0.0002748671,0.00243945,0.000432929,0.0003711442,0.0006086528],"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":[0.01390673,0.002950466,0.009533585,0.003329401,0.000685895,0.001483941,0.002813404,0.0001954333,0.7799925,0.0003473003,0.08364682,0.1011145],"study_design_scores_gemma":[0.008433202,0.02521323,0.03680826,0.004730126,0.002281907,0.0006540233,0.0009037177,0.001725734,0.6286731,0.00045385,0.2881999,0.001922975],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745271,0.01715115,0.00003863331,0.003340681,0.003722239,0.0007257279,0.0002059756,0.00002382351,0.0002647377],"genre_scores_gemma":[0.9887529,0.00513523,0.0003788683,0.001638913,0.001270068,0.000006815021,0.00003329324,0.00007144293,0.002712477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2045531,"threshold_uncertainty_score":0.9997184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03789947998779634,"score_gpt":0.2672586607057949,"score_spread":0.2293591807179985,"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."}}