{"id":"W2521691211","doi":"10.1080/09593330.2016.1239658","title":"Optimizing removal of arsenic, chromium, copper, pentachlorophenol and polychlorodibenzo-dioxins/furans from the 1–4 mm fraction of polluted soil using an attrition process","year":2016,"lang":"en","type":"article","venue":"Environmental Technology","topic":"Microbial bioremediation and biosurfactants","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pentachlorophenol; Arsenic; Chemistry; Chromium; Pulmonary surfactant; Soil contamination; Environmental chemistry; Fraction (chemistry); Environmental remediation; Contamination; Box–Behnken design; Chromatography; Response surface methodology; Organic chemistry","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":[0.0001210717,0.0001749911,0.0002333432,0.00006080918,0.0001173504,0.000006569119,0.0002575737,0.0002170365,0.0008762782],"category_scores_gemma":[0.00001785831,0.0001200291,0.00004517132,0.0001611668,0.0008802355,0.0002831019,0.0001541612,0.0001196267,0.0000316436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001970304,"about_ca_system_score_gemma":0.000008031815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002888207,"about_ca_topic_score_gemma":0.0001166891,"domain_scores_codex":[0.9987555,0.00005462347,0.0003496062,0.0003797936,0.0002365123,0.00022393],"domain_scores_gemma":[0.9993071,0.00002980511,0.0002891232,0.0003163356,0.000003150029,0.0000545077],"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":[0.0000600866,0.0001670489,0.01893678,0.000003417887,0.00001609543,0.000004110575,0.0001704652,0.0000284697,0.9719746,0.000005111822,0.00001536374,0.008618427],"study_design_scores_gemma":[0.001063042,0.0001640619,0.03016357,0.00003953398,0.00003745333,0.00006889684,0.001275653,0.0002638565,0.966292,0.0002259395,0.0002161436,0.0001898515],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982928,0.0001981608,0.0003586153,0.0005353863,0.00007054152,0.0002562414,0.0001906537,0.000040855,0.00005672341],"genre_scores_gemma":[0.9974961,0.0003074828,0.00203881,0.00007333357,0.0000268801,0.000003219186,0.00001947004,0.00001821844,0.000016523],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0112268,"threshold_uncertainty_score":0.9594632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370081215074596,"score_gpt":0.2263806725729912,"score_spread":0.2126798604222453,"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."}}