{"id":"W2499995823","doi":"10.1177/1548512916660637","title":"Understanding and taxonomy of uncertainty in modeling, simulation, and risk profiling for border control automation","year":2016,"lang":"en","type":"article","venue":"The Journal of Defense Modeling and Simulation Applications Methodology Technology","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Grantová Agentura České Republiky; European Commission; North Atlantic Treaty Organization","keywords":"Computer science; Dempster–Shafer theory; Uncertainty reduction theory; Uncertainty quantification; Profiling (computer programming); Risk assessment; Risk analysis (engineering); Uncertainty analysis; Automation; Homeland security; Data science; Data mining; Computer security; Machine learning; Simulation; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003085379,0.0001349488,0.0003656287,0.0006540111,0.0002157878,0.00001002799,0.0001015343,0.0002540438,0.0000167105],"category_scores_gemma":[0.0008428469,0.00009276761,0.00004397016,0.0002144829,0.0001991351,0.0001223347,0.00002520414,0.0002285124,6.270736e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006383217,"about_ca_system_score_gemma":0.00003411541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001806359,"about_ca_topic_score_gemma":0.0000176981,"domain_scores_codex":[0.9980388,0.0005924475,0.0009189366,0.0002022358,0.00008625055,0.0001613743],"domain_scores_gemma":[0.9945494,0.004057276,0.0007349413,0.0002106238,0.0004075773,0.00004017001],"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":[0.000353992,0.00002854742,0.003280648,0.0000133913,0.00008109884,1.120152e-7,0.0006678696,0.9444454,0.0003062288,0.03567044,0.000002182503,0.01515013],"study_design_scores_gemma":[0.00210818,0.00009933962,0.0001688984,0.00003108081,0.0001158646,0.00002937609,0.001978302,0.9278482,0.0000151683,0.06733277,0.0001841341,0.00008867804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3214428,0.000271622,0.6769124,0.0007462933,0.00005432984,0.0005042903,0.000009804116,0.00003007971,0.00002839293],"genre_scores_gemma":[0.9714105,0.0001594427,0.02822199,0.00004299009,0.00004051931,0.00009314391,0.000001764681,0.0000139689,0.00001564982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6499677,"threshold_uncertainty_score":0.3782953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2323252631006779,"score_gpt":0.4348308585194478,"score_spread":0.2025055954187699,"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."}}