{"id":"W2600013803","doi":"10.1061/9780784480472.029","title":"Evaluation of Geotextile Performance for the Filtration of Fine-Grained Tailings","year":2017,"lang":"en","type":"article","venue":"Geotechnical Frontiers 2017","topic":"Grouting, Rheology, and Soil Mechanics","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"CTT Group (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Shell Canada; Canadian Natural Resources Limited","keywords":"Geotextile; Tailings; Clogging; Filtration (mathematics); Geotechnical engineering; Oil sands; Hydraulic conductivity; Environmental science; Materials science; Geology; Composite material; Asphalt; Metallurgy; Soil science; Soil water","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":[0.0004838537,0.0003958502,0.0002605596,0.0004611425,0.0003350909,0.0003793871,0.0002008025,0.0004034556,0.0007906691],"category_scores_gemma":[0.001017785,0.00009883197,0.0003319381,0.0003187961,0.0002190693,0.000518599,0.0002796241,0.0003239827,0.0002202295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002515405,"about_ca_system_score_gemma":0.0002028552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001778507,"about_ca_topic_score_gemma":0.003053964,"domain_scores_codex":[0.9997033,0.0000358688,0.00002698301,0.00004347097,0.0001319541,0.00005846507],"domain_scores_gemma":[0.9994001,0.0001528846,0.0001279661,0.00004670765,0.0002059091,0.00006647947],"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":[0.0001992274,0.0000796501,0.002185618,0.00008049551,0.000008107452,0.00005787759,0.00009148159,0.0006393059,0.9907041,0.00003657029,0.0000595541,0.005857994],"study_design_scores_gemma":[0.000005105531,0.0009104566,0.01153988,0.00000690885,0.00001783957,0.00004581216,0.0001191934,0.001862181,0.9849532,0.00001796126,0.0005093441,0.00001225188],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973074,0.0001877922,0.002047086,0.00001064616,0.0000099343,0.00002453441,0.0001057385,0.00003413023,0.0002726669],"genre_scores_gemma":[0.9972913,0.000217819,0.001590704,0.00001246159,0.000004129484,0.00001691184,0.000119286,0.00001005337,0.000737285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001778507,"threshold_uncertainty_score":0.003536284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0338203509554122,"score_gpt":0.2678982871311301,"score_spread":0.2340779361757179,"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."}}