{"id":"W2552162027","doi":"10.1002/rem.21481","title":"Selectivity of Nano Zerovalent Iron in <i>In Situ</i> Chemical Reduction: Challenges and Improvements","year":2016,"lang":"en","type":"article","venue":"Remediation Journal","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Strategic Environmental Research and Development Program; Oak Ridge Institute for Science and Education; U.S. Department of Defense","keywords":"Zerovalent iron; Reduction (mathematics); In situ; Selectivity; Nano-; Chemistry; Nanotechnology; Biochemical engineering; Chemical engineering; Materials science; Engineering; Mathematics; Organic chemistry; Catalysis","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":[0.0007789529,0.0004650762,0.0004681781,0.0003491527,0.0002711621,0.0007320762,0.0007457813,0.0008093544,0.001094086],"category_scores_gemma":[0.000723256,0.0002660141,0.0003778026,0.0002092876,0.0003710445,0.0009313138,0.0004208668,0.000891788,0.0007546188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008473122,"about_ca_system_score_gemma":0.0002851417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001272067,"about_ca_topic_score_gemma":0.00186903,"domain_scores_codex":[0.9995973,0.00004680292,0.0000219477,0.0000990066,0.0001656195,0.00006938774],"domain_scores_gemma":[0.9996861,0.00008991945,0.00006275612,0.00001964601,0.0001096988,0.00003182057],"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":[0.00005937939,0.00004723347,0.0004110889,0.0007583642,0.00001382535,0.0001344508,0.00007476738,0.0005314637,0.9725011,0.001078817,0.0007338932,0.02365565],"study_design_scores_gemma":[0.00001044965,0.0004503959,0.001230025,0.00006337759,0.00003195322,0.0005536109,0.0001121309,0.00310779,0.957637,0.0005909168,0.03618796,0.00002439682],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7310283,0.1491723,0.07922051,0.00620538,0.001033252,0.000266905,0.0005102735,0.001127526,0.03143552],"genre_scores_gemma":[0.9261284,0.04300256,0.02261321,0.001045674,0.0002674681,0.0000855022,0.000375317,0.000133959,0.006347879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001272067,"threshold_uncertainty_score":0.006147683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009095453123644286,"score_gpt":0.2026970665219116,"score_spread":0.1936016133982673,"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."}}