{"id":"W1964188326","doi":"10.1016/j.jconhyd.2007.07.012","title":"Performance evaluation of granular iron for removing hexavalent chromium under different geochemical conditions","year":2007,"lang":"en","type":"article","venue":"Journal of Contaminant Hydrology","topic":"Environmental remediation with nanomaterials","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Water Network","keywords":"Carbonate; Hexavalent chromium; Chemistry; Hydroxide; Chromium; Carbonate minerals; Inorganic chemistry; Mineralogy","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001278221,0.0001197733,0.0003249834,0.0001468399,0.00003154873,0.000004196655,0.0001055263,0.0001157993,0.00008199858],"category_scores_gemma":[0.00007132951,0.000103824,0.00009196483,0.00003774269,0.00006295299,0.000111578,0.00001416916,0.00009791546,0.0000025852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002653908,"about_ca_system_score_gemma":0.00002380131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.641691e-7,"about_ca_topic_score_gemma":0.00000538225,"domain_scores_codex":[0.9986126,0.0000474666,0.0007134757,0.00008030308,0.0003334504,0.0002127118],"domain_scores_gemma":[0.9992149,0.0001816813,0.0003279471,0.0001074022,0.0001013807,0.00006668534],"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":[0.00008968937,0.00005709808,0.004632798,0.00006267868,0.00004212753,0.000002349235,0.000098404,0.01606543,0.977716,0.00003261367,0.00005912042,0.001141716],"study_design_scores_gemma":[0.002500632,0.0004458876,0.2624376,0.00006277338,0.0002465868,0.0001059164,0.0000449037,0.03990015,0.6937128,0.0001417617,0.0002716539,0.0001293082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927295,0.0003054622,0.005872573,0.00005925209,0.0006816431,0.0002547739,0.000009805125,0.00001014084,0.00007689351],"genre_scores_gemma":[0.999355,0.0001391559,0.0002599299,0.00002792172,0.0001681411,0.000008001592,0.00001572175,0.00001790481,0.000008182088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2840031,"threshold_uncertainty_score":0.4233821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348623485676673,"score_gpt":0.2543446555712247,"score_spread":0.2408584207144579,"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."}}