{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003997609,0.0004242987,0.0005963615,0.0002498571,0.0002360615,0.0004803171,0.0004072692,0.0004445153,0.0004165713],"category_scores_gemma":[0.0003581811,0.0001979085,0.000486638,0.0002993628,0.0002165358,0.0003434237,0.0003327042,0.0003696055,0.0002416619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003041693,"about_ca_system_score_gemma":0.0004901759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001491511,"about_ca_topic_score_gemma":0.002727031,"domain_scores_codex":[0.9995838,0.00006625483,0.00003936453,0.0000805609,0.0001516198,0.00007854131],"domain_scores_gemma":[0.9998043,0.000053123,0.00006522741,0.00001092501,0.00005205717,0.00001435557],"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":[0.00009179088,0.00003220501,0.0004947628,0.0001023768,0.000009065052,0.00002649948,0.00002022007,0.0003132275,0.9962477,0.000009800293,0.000006807421,0.00264546],"study_design_scores_gemma":[0.00001010948,0.0004332034,0.002378966,0.000006042827,0.00002860115,0.00003784681,0.00004282013,0.001109388,0.9954349,0.00001010891,0.0005014565,0.000006529026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958022,0.0004017235,0.003504899,0.00002011632,0.000004582721,0.00002854011,0.00004656595,0.00001816694,0.0001732524],"genre_scores_gemma":[0.9839488,0.001047888,0.01329392,0.00003129367,0.000006891295,0.00007095501,0.0001630201,0.00001771919,0.001419487],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001491511,"threshold_uncertainty_score":0.002965629,"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."}}