{"id":"W2911102648","doi":"10.1016/j.envint.2018.12.049","title":"Exploring the arsenic removal potential of various biosorbents from water","year":2019,"lang":"en","type":"article","venue":"Environment International","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Agriculture, Faisalabad; Grand Challenges Canada; Higher Education Commission, Pakistan; International Foundation for Science","keywords":"Arsenic; Environmental chemistry; Environmental science; Water quality; Waste management; Chemistry; Environmental engineering; Ecology; Engineering; Biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002017294,0.000488432,0.0003131751,0.000389746,0.000166737,0.0003340651,0.0001888371,0.000284285,0.0007565558],"category_scores_gemma":[0.0002037676,0.0001401799,0.0003845079,0.0003407428,0.0001233241,0.0003084113,0.0002803016,0.0003131002,0.0003360582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001260821,"about_ca_system_score_gemma":0.0001689979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009084191,"about_ca_topic_score_gemma":0.001498667,"domain_scores_codex":[0.9998599,0.00001987274,0.00001057432,0.00002050723,0.00006611575,0.00002289829],"domain_scores_gemma":[0.9999448,0.00001574322,0.000008455555,0.000002767196,0.00002260822,0.000005516489],"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":[0.00003275771,0.00002106856,0.0002361567,0.0001019632,0.000007017432,0.00003492645,0.00001546179,0.000142245,0.9970651,0.00003475198,0.00001790347,0.002290513],"study_design_scores_gemma":[0.000006282015,0.0004329627,0.003303297,0.000009227759,0.00002267165,0.0001283853,0.00008430725,0.001708073,0.9926891,0.00006118865,0.001542769,0.00001164548],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938605,0.001096681,0.003191,0.00005347548,0.00001469391,0.00002927254,0.0001915144,0.00003806886,0.001524805],"genre_scores_gemma":[0.9886752,0.001727298,0.006293389,0.00005183289,0.000005324382,0.00004342769,0.0003549289,0.00001966267,0.002828882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009084191,"threshold_uncertainty_score":0.002530932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01520034503312395,"score_gpt":0.1877542132565128,"score_spread":0.1725538682233888,"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."}}