{"id":"W1997864786","doi":"10.1061/(asce)cp.1943-5487.0000149","title":"Dempster-Shafer Theory for Handling Conflict in Hydrological Data: Case of Snow Water Equivalent","year":2012,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of British Columbia; Okanagan University College; University of British Columbia, Okanagan Campus","funders":"National Oceanic and Atmospheric Administration","keywords":"Dempster–Shafer theory; Vagueness; Probabilistic logic; Ambiguity; Data mining; Computer science; Data quality; Conflict resolution; Artificial intelligence; Engineering; Metric (unit)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002865027,0.0001066641,0.0002685178,0.0001077736,0.0000347401,0.000006437106,0.0002320308,0.00005390274,0.00004001206],"category_scores_gemma":[0.0001733528,0.00007578966,0.00004652722,0.00006778564,0.00004589122,0.0002309226,0.0004325908,0.0001976022,0.000002778989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005299138,"about_ca_system_score_gemma":0.000001741385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001091638,"about_ca_topic_score_gemma":0.00002219398,"domain_scores_codex":[0.9989198,0.00005229395,0.0004564858,0.0001141607,0.0001000241,0.0003572784],"domain_scores_gemma":[0.9992788,0.0004311021,0.0001050029,0.0001335486,0.000006045934,0.00004549824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008700549,0.0001332035,0.09791368,0.00007245215,0.00005923977,0.0001945276,0.002902045,0.8958257,0.001601669,0.0001063287,0.00007504397,0.001029054],"study_design_scores_gemma":[0.004333262,0.0004999961,0.1006375,0.0006341546,0.0001616604,0.001677335,0.0005092521,0.8784544,0.006272034,0.0005482548,0.005601031,0.0006710811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9420656,0.0001642695,0.05713063,0.00008550721,0.0002911425,0.00009017,9.658173e-7,0.000006453251,0.0001652923],"genre_scores_gemma":[0.9979342,0.00001358679,0.001901028,0.00003970229,0.00009330182,0.000001207403,0.000001085441,0.000008247712,0.000007592802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05586868,"threshold_uncertainty_score":0.3090613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03727190579765491,"score_gpt":0.2767487533078907,"score_spread":0.2394768475102358,"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."}}