{"id":"W4404456642","doi":"10.1016/j.jobab.2024.11.003","title":"Remediation and resource utilization of Cr(Ⅲ), Al(Ⅲ) and Zr(Ⅳ)-containing tannery effluent based on chitosan-carboxymethyl cellulose aerogel","year":2024,"lang":"en","type":"article","venue":"Journal of Bioresources and Bioproducts","topic":"Pigment Synthesis and Properties","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Quanzhou City Science and Technology Program; National Natural Science Foundation of China; Science and Technology Projects of Fujian Province","keywords":"Aerogel; Carboxymethyl cellulose; Chitosan; Cellulose; Environmental remediation; Effluent; Waste management; Chemistry; Nuclear chemistry; Pulp and paper industry; Materials science; Chemical engineering; Organic chemistry; Nanotechnology; Contamination; Engineering; Sodium","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":[],"consensus_categories":[],"category_scores_codex":[0.0005708062,0.0001825992,0.00029138,0.0002007233,0.00008063622,0.0001232182,0.00008059989,0.00009946563,0.00002656003],"category_scores_gemma":[0.0001922352,0.0001250047,0.00005865967,0.0001353121,0.0001362103,0.0001175501,0.00003984715,0.0001881517,3.221112e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001524251,"about_ca_system_score_gemma":0.00003327798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001099945,"about_ca_topic_score_gemma":0.000001084947,"domain_scores_codex":[0.9987165,0.00005001005,0.0004627991,0.000277567,0.0003352966,0.0001578481],"domain_scores_gemma":[0.9992562,0.0001448182,0.0002965743,0.0001385296,0.00005597209,0.0001079322],"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":[0.0006214084,0.00009203577,0.007333992,0.0008254771,0.00009665425,0.00002387301,0.001467306,0.00004777133,0.910245,0.00005490447,0.0003524205,0.07883916],"study_design_scores_gemma":[0.0006720104,0.000558566,0.003832487,0.001364811,0.0001520544,0.00005093024,0.0008402249,0.005307306,0.955817,0.00004050597,0.03113077,0.0002333642],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973557,0.02398371,0.00001485715,0.001826644,0.00008720427,0.00005708249,0.00001136675,0.00001668885,0.0004454456],"genre_scores_gemma":[0.99804,0.001188795,0.0001710777,0.0001283972,0.0003466258,0.000001409941,0.000005685377,0.00002017063,0.0000978779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0786058,"threshold_uncertainty_score":0.5097544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02194836520442568,"score_gpt":0.2364813966808375,"score_spread":0.2145330314764118,"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."}}