{"id":"W4255067050","doi":"10.5194/nhess-2020-199","title":"Tailings-flow runout analysis: Examining the applicability of a semi-physical area–volume relationship using a novel database","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Tailings Management and Properties","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Queen's University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Suncor Energy Incorporated","keywords":"Tailings; Geology; Volume (thermodynamics); Tailings dam; Volcano; Flow (mathematics); Mining engineering; STREAMS; Scale (ratio); Environmental science; Geotechnical engineering; Hydrology (agriculture); Geography; Computer science","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":[0.003411299,0.0004579037,0.0005094582,0.004967489,0.000253125,0.001734139,0.0009821838,0.0006321646,0.0008774331],"category_scores_gemma":[0.01114585,0.0001597275,0.0004705197,0.003178813,0.0002951349,0.001592152,0.0009334952,0.0003526525,0.0004752809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005441632,"about_ca_system_score_gemma":0.0003934329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00611248,"about_ca_topic_score_gemma":0.00451623,"domain_scores_codex":[0.9976556,0.0005718232,0.0003365601,0.000710515,0.0006154468,0.0001099993],"domain_scores_gemma":[0.9854875,0.008865077,0.001660113,0.001864505,0.001849315,0.0002735223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007324506,0.0004941046,0.787396,0.0002080318,0.00028294,0.0002299301,0.0003994002,0.05543089,0.00714917,0.001625289,0.001960539,0.1440912],"study_design_scores_gemma":[0.00003430261,0.0004128668,0.3699767,0.00002744167,0.00006923663,0.000407047,0.0004448513,0.6157588,0.008468508,0.001009462,0.003338438,0.00005229735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9507845,0.0001816052,0.03837193,0.00004516253,0.00001780385,0.0001123668,0.008466784,0.0007603181,0.001259525],"genre_scores_gemma":[0.9562881,0.00007537337,0.03104219,0.00001256534,0.00001494755,0.0001028183,0.012099,0.00004265468,0.0003224131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00611248,"threshold_uncertainty_score":0.01804084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09556409311086587,"score_gpt":0.2622549768601392,"score_spread":0.1666908837492733,"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."}}