{"id":"W2885605551","doi":"10.1080/09593330.2018.1513078","title":"Application of recycling waste products for <i>ex situ</i> and <i>in situ</i> water treatment methods","year":2018,"lang":"en","type":"article","venue":"Environmental Technology","topic":"Arsenic contamination and mitigation","field":"Environmental Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Effluent; Dispersion (optics); Slag (welding); Adsorption; Chemistry; Column (typography); Analytical Chemistry (journal); Environmental science; Waste management; Materials science; Chromatography; Environmental engineering; Metallurgy; Mathematics; Physics","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":[0.0002141612,0.000112572,0.0001439148,0.00006943184,0.0000635746,0.000002837399,0.00008857784,0.0001061078,0.00005313894],"category_scores_gemma":[0.00001385044,0.00009311717,0.00002176881,0.00009470751,0.0004395471,0.0001072608,0.0001183534,0.00004567676,0.00005451655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001756054,"about_ca_system_score_gemma":0.000002058309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001437444,"about_ca_topic_score_gemma":0.00006018673,"domain_scores_codex":[0.9991385,0.00003175452,0.0002195692,0.0003528548,0.00006951113,0.0001878499],"domain_scores_gemma":[0.9996518,0.00002584359,0.00007216669,0.0002247065,0.000002004595,0.00002341116],"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":[0.00001721467,0.00007705019,0.003479637,0.000003224425,0.00000403279,2.297085e-7,0.000270709,0.000005174065,0.8255607,0.00008604159,0.00001382301,0.1704821],"study_design_scores_gemma":[0.0005639733,0.0002442411,0.002076843,0.00000353198,0.00001283462,0.000005896139,0.0003282063,0.000425972,0.9749102,0.001175708,0.02015194,0.0001006546],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824367,0.00004772174,0.01480243,0.001051685,0.00004505994,0.0006593798,0.00000415985,0.00003215036,0.0009207086],"genre_scores_gemma":[0.9738502,0.00004645038,0.02538277,0.00007580974,0.00002277184,0.0001232835,0.00002171764,0.00001179157,0.0004652315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1703815,"threshold_uncertainty_score":0.3797208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008323021843525714,"score_gpt":0.2619701742672012,"score_spread":0.2536471524236754,"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."}}