{"id":"W2077328709","doi":"10.1016/j.chemosphere.2003.09.032","title":"Remediating dicamba-contaminated water with zerovalent iron","year":2003,"lang":"en","type":"article","venue":"Chemosphere","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Dicamba; Chemistry; Zerovalent iron; Water treatment; Degradation (telecommunications); Adsorption; Environmental chemistry; Reductive dechlorination; Mineralization (soil science); Environmental engineering; Organic chemistry; Biodegradation; Agronomy","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":[0.0001705341,0.0002905345,0.0002444902,0.0001995764,0.0002294021,0.0002540161,0.0003148204,0.0003734113,0.000844376],"category_scores_gemma":[0.0002992201,0.0001313661,0.0001857181,0.00008511471,0.000176945,0.0001773164,0.0002396425,0.0002400178,0.0002351026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002753143,"about_ca_system_score_gemma":0.0003544322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003786576,"about_ca_topic_score_gemma":0.005269221,"domain_scores_codex":[0.9999067,0.00001242994,0.000007364299,0.00001849952,0.00002263089,0.00003242233],"domain_scores_gemma":[0.9999483,0.00001079194,0.000007995973,0.000006917991,0.00001952175,0.000006542886],"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":[0.0001879872,0.0000243764,0.00028651,0.00005531969,0.000005853272,0.00005925435,0.00003553602,0.0001665642,0.9937046,0.00009944524,0.00009513451,0.005279458],"study_design_scores_gemma":[0.000007318897,0.0001125214,0.0003026249,0.000002555617,0.00000830108,0.0000354432,0.00001416426,0.0005552032,0.9982377,0.00003355061,0.0006882855,0.000002223164],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938489,0.0004584597,0.003671704,0.0001313252,0.00003350375,0.00002683502,0.00006508434,0.00007498587,0.001689199],"genre_scores_gemma":[0.9934753,0.0002708453,0.002908055,0.00003264432,0.000006073498,0.000008428549,0.00008889542,0.00001229044,0.003197559],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003786576,"threshold_uncertainty_score":0.00752902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00378305732297151,"score_gpt":0.1617882515151234,"score_spread":0.1580051941921519,"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."}}