{"id":"W2461745581","doi":"10.1021/acs.energyfuels.6b00108","title":"Role of Preconditioning Cationic Zetag Flocculant in Enhancing Mature Fine Tailings Flocculation","year":2016,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Flocculation; Tailings; Turbidity; Dewatering; Oil sands; Environmental science; Filtration (mathematics); Water treatment; Cationic polymerization; Suspended solids; Waste management; Pulp and paper industry; Chemistry; Environmental engineering; Wastewater; Materials science; Geology; Engineering; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0001451635,0.0004463911,0.000220238,0.0002106632,0.0001225352,0.0003015829,0.0002029539,0.0003920668,0.0006425875],"category_scores_gemma":[0.0002950347,0.000132809,0.0002281118,0.00008911789,0.0002138537,0.0003124674,0.0002213193,0.0004200325,0.000222651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000223046,"about_ca_system_score_gemma":0.0003029688,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001224374,"about_ca_topic_score_gemma":0.001716252,"domain_scores_codex":[0.9999194,0.000009514994,0.000007113187,0.00002005821,0.00002050628,0.00002335873],"domain_scores_gemma":[0.999869,0.0000217278,0.00003976217,0.000007072225,0.00002508209,0.00003732867],"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":[0.00004923616,0.0000409462,0.0000483425,0.00006366998,0.000002538947,0.00003009353,0.00001058793,0.0001526412,0.9972355,0.00004278752,0.00002533653,0.002298469],"study_design_scores_gemma":[0.00001122974,0.0004104754,0.000624083,0.00001007682,0.00001099138,0.00003412016,0.000009130727,0.001151566,0.99696,0.00001325808,0.0007592891,0.000005909132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938698,0.001016684,0.003657984,0.00007913436,0.00004816041,0.00009455285,0.00006929164,0.0001252698,0.001039163],"genre_scores_gemma":[0.993364,0.000763805,0.005022113,0.00006576575,0.00001444081,0.00002796033,0.00005185627,0.00001959722,0.0006704598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001224374,"threshold_uncertainty_score":0.002434552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005346203243226824,"score_gpt":0.2219813993025566,"score_spread":0.2166351960593298,"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."}}