{"id":"W2948307065","doi":"10.1039/c9sc01441a","title":"Iron detection and remediation with a functionalized porous polymer applied to environmental water samples","year":2019,"lang":"en","type":"article","venue":"Chemical Science","topic":"Covalent Organic Framework Applications","field":"Materials Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Basic Energy Sciences; Lawrence Berkeley National Laboratory; National Institute of General Medical Sciences; Canadian Institute for Advanced Research; Laboratory Directed Research and Development; U.S. Department of Energy; Office of Science; Howard Hughes Medical Institute; National Institutes of Health; National Science Foundation","keywords":"Environmental remediation; Porosity; Polymer; Zerovalent iron; Environmental science; Porous medium; Environmental chemistry; Chemical engineering; Waste management; Chemistry; Contamination; Engineering; Organic chemistry; Adsorption; Ecology","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":[0.0001325007,0.0003586715,0.0001560783,0.0002457441,0.0001473868,0.0001437122,0.0001882871,0.0003583304,0.0005757323],"category_scores_gemma":[0.0002262416,0.0001771273,0.0001757442,0.0001707658,0.0002075987,0.0001928802,0.0001708392,0.0002687469,0.0001658965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001548437,"about_ca_system_score_gemma":0.000141265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009475423,"about_ca_topic_score_gemma":0.001268218,"domain_scores_codex":[0.9998703,0.00001236215,0.000006119592,0.00004480429,0.00003934886,0.00002714202],"domain_scores_gemma":[0.9999293,0.00001680731,0.00002322182,0.00000552292,0.00001782365,0.000007212629],"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":[0.00001831979,0.000007646269,0.00009480143,0.00002221651,0.00000193929,0.00003799864,0.00001175131,0.0000792905,0.9984899,0.00002180373,0.00001032392,0.001204083],"study_design_scores_gemma":[0.000002221237,0.0001152616,0.0008480112,0.000002662858,0.000003438896,0.00006464195,0.00001363052,0.0008025962,0.997564,0.00001380762,0.0005657666,0.000003962156],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844354,0.0005895187,0.0133984,0.00008041747,0.0000210249,0.00005163734,0.0001417208,0.0001463173,0.001135539],"genre_scores_gemma":[0.9811936,0.0005186901,0.01614316,0.00006222942,0.000009504354,0.00004820011,0.0001333587,0.0000170383,0.001874257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009475423,"threshold_uncertainty_score":0.001926005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005379392826797304,"score_gpt":0.191850051609593,"score_spread":0.1864706587827957,"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."}}