{"id":"W4245789967","doi":"10.1007/s12665-020-09014-2","title":"Solutions to estimate the excess PWP, settlement and volume of draining water after slurry deposition. Part II: pervious base","year":2020,"lang":"en","type":"article","venue":"Environmental Earth Sciences","topic":"Tailings Management and Properties","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Université du Québec en Abitibi-Témiscamingue","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Fonds de recherche du Québec – Nature et technologies; Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Slurry; Impervious surface; Consolidation (business); Deposition (geology); Environmental science; Volume (thermodynamics); Environmental engineering; Geotechnical engineering; Settlement (finance); Tailings; Pervious concrete; Waste management; Hydrology (agriculture); Engineering; Geology; Materials science; Composite material","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":[0.000745111,0.00083382,0.0007187346,0.001602564,0.0004023987,0.001154678,0.001506872,0.001389715,0.004488321],"category_scores_gemma":[0.002529725,0.0006791836,0.0005978312,0.0008778425,0.0002212149,0.001078504,0.001054814,0.0006907515,0.001664961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009340388,"about_ca_system_score_gemma":0.001192364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009973094,"about_ca_topic_score_gemma":0.008859142,"domain_scores_codex":[0.999621,0.00005400568,0.00002952457,0.00009630005,0.0001679631,0.00003102966],"domain_scores_gemma":[0.9996649,0.0001299908,0.0000353385,0.00003734274,0.0001212525,0.00001112586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000204272,0.0001873043,0.02453575,0.00103626,0.0001796464,0.000215338,0.000315111,0.3623319,0.06512672,0.008826028,0.004795026,0.5322466],"study_design_scores_gemma":[0.00006959302,0.0003699744,0.02390127,0.0002225448,0.0001237064,0.0003291421,0.0006628528,0.8556498,0.08108877,0.02028931,0.01718227,0.0001106807],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08207179,0.001753325,0.9038591,0.0006211626,0.00009359665,0.0002510518,0.002067927,0.001246113,0.008035995],"genre_scores_gemma":[0.5306872,0.003443637,0.4363102,0.0002011112,0.00008847757,0.0009714623,0.002846472,0.0002235469,0.02522791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009973094,"threshold_uncertainty_score":0.01983011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01431812244233709,"score_gpt":0.1971071472238909,"score_spread":0.1827890247815538,"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."}}