{"id":"W2226213745","doi":"","title":"The use of a large-strain consolidation model to optimise multilift tailing deposits","year":2015,"lang":"en","type":"article","venue":"Research Repository (Delft University of Technology)","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shell Canada","keywords":"Tailings; Consolidation (business); Deposition (geology); Environmental science; Geotechnical engineering; Shrinkage; Geology; Materials science; Metallurgy; Composite material","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006720785,0.00007365181,0.0001200245,0.0002222495,0.0003510233,0.00002020197,0.0004501533,0.0001323062,0.000009391664],"category_scores_gemma":[0.0003563199,0.00006918289,0.00004009795,0.0005547288,0.0005716233,0.0002349712,0.0003636463,0.0002018771,0.00001976008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001550585,"about_ca_system_score_gemma":0.00007396966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002833872,"about_ca_topic_score_gemma":0.0001717168,"domain_scores_codex":[0.9987373,0.0001307172,0.0001866721,0.0002232974,0.0004830194,0.0002390267],"domain_scores_gemma":[0.9990663,0.00008481245,0.00009938855,0.0003880297,0.0002347104,0.0001267407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003014652,0.000263699,0.005020975,0.00001629393,0.00003332099,0.00003492417,0.001746938,0.06745047,0.8910147,0.007689936,0.01928225,0.007145041],"study_design_scores_gemma":[0.0007950345,0.0004029406,0.001267854,0.00005211616,0.00001631428,0.00001467137,0.003627975,0.5177094,0.4288223,0.0006665607,0.04640172,0.0002231186],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9717254,0.000021675,0.02444323,0.0009999151,0.00001714319,0.0004328615,0.00001095641,0.0001081584,0.002240681],"genre_scores_gemma":[0.9567949,0.00002643031,0.03879318,0.0000135462,0.000003310879,0.000002392391,0.000003068652,0.000006055117,0.00435714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4621924,"threshold_uncertainty_score":0.2821196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1227043466649544,"score_gpt":0.3375590124180141,"score_spread":0.2148546657530597,"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."}}