{"id":"W3169701557","doi":"","title":"Cognitive Identity Management: Risks, Trust and Decisions using Heterogeneous Sources","year":2019,"lang":"en","type":"article","venue":"IEEE Conference Proceedings","topic":"Cognitive Computing and Networks","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Identity (music); Computer science; Inference; Biometrics; Cognition; Process (computing); Identity management; Probabilistic logic; Artificial intelligence; Psychology","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":[0.0003095056,0.0002282027,0.0002429616,0.0001785672,0.0002671264,0.0008532828,0.0006314834,0.00008424759,0.00002411736],"category_scores_gemma":[0.00005062822,0.000226442,0.00006157105,0.0004298897,0.00009762155,0.0008850932,0.0005641488,0.0002144691,0.00008657053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000262708,"about_ca_system_score_gemma":0.00002997534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002562668,"about_ca_topic_score_gemma":0.000003662429,"domain_scores_codex":[0.9982787,0.00001773513,0.0002356527,0.0007210975,0.0003342816,0.0004125706],"domain_scores_gemma":[0.9989955,0.0001417513,0.0001536332,0.0001429358,0.0004177079,0.0001484472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001637617,0.0003705137,0.2151892,0.0003679842,0.0005282788,0.0001426892,0.01195051,0.0002949604,0.004560928,0.06338552,0.0004054763,0.7026402],"study_design_scores_gemma":[0.002130701,0.0003199444,0.04041228,0.001988594,0.0001607872,0.0003253954,0.00153439,0.9151347,0.005141558,0.03094358,0.0005783154,0.001329747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7649174,0.0001620217,0.2312205,0.00003373894,0.0003243813,0.0002631766,0.00000234167,0.0001429913,0.002933451],"genre_scores_gemma":[0.9949282,0.0001459888,0.004455132,0.000192476,0.0000788081,0.000009173597,8.695945e-7,0.00001368088,0.0001756623],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9148397,"threshold_uncertainty_score":0.9234036,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06815269028137452,"score_gpt":0.306804426647157,"score_spread":0.2386517363657824,"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."}}