{"id":"W4385699008","doi":"10.1016/j.envpol.2023.122351","title":"Advances in green materials derived from wood for detecting and removing mercury ions in water","year":2023,"lang":"en","type":"review","venue":"Environmental Pollution","topic":"Carbon and Quantum Dots Applications","field":"Materials Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Mercury (programming language); Environmental remediation; Environmental science; Adsorption; Biochemical engineering; Environmentally friendly; Waste management; Environmental pollution; Hemicellulose; Environmental chemistry; Lignin; Pulp and paper industry; Chemistry; Nanotechnology; Materials science; Computer science; Contamination; Environmental protection; Engineering; Organic chemistry; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003109924,0.0007408702,0.0006420268,0.00117572,0.0001654936,0.000613315,0.0004855519,0.0007148065,0.002560133],"category_scores_gemma":[0.000253767,0.0002719279,0.0004034819,0.001536543,0.0002346125,0.0008483451,0.000475623,0.001110926,0.000957024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000365464,"about_ca_system_score_gemma":0.0004867272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005633453,"about_ca_topic_score_gemma":0.001615509,"domain_scores_codex":[0.9999063,0.00001027461,0.000007602761,0.00002112589,0.00003991799,0.00001467911],"domain_scores_gemma":[0.9999068,0.00004534455,0.00001351975,0.000004986481,0.00002203998,0.000007323399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005602707,0.0001061898,0.0001409218,0.01844009,0.00008663883,0.0002306098,0.00005992271,0.0007581171,0.03888162,0.01168312,0.01697101,0.9125857],"study_design_scores_gemma":[0.000008797648,0.0001317287,0.0006004329,0.001252408,0.00009588893,0.0005662092,0.0000422911,0.0003699205,0.01702622,0.002531258,0.9773507,0.00002409095],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001017623,0.994172,0.001013334,0.0002929288,0.00029892,0.000008010782,0.00004164396,0.00001548271,0.003140007],"genre_scores_gemma":[0.004185973,0.9920651,0.001109861,0.0001960854,0.0001322727,0.000009713734,0.00005134039,0.000004224569,0.002245421],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002560133,"threshold_uncertainty_score":0.008564532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03101406659110596,"score_gpt":0.2926634008853767,"score_spread":0.2616493342942708,"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."}}