{"id":"W2111302207","doi":"","title":"Dempster-Shafer Theory: Combination of Information Using Contextual Knowledge","year":2009,"lang":"en","type":"article","venue":"viXra","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada; Thales (Canada)","funders":"","keywords":"Dempster–Shafer theory; Credibility; Reliability (semiconductor); Computer science; Process (computing); Sensor fusion; Information fusion; Information overload; Set (abstract data type); Data mining; Artificial intelligence; Machine learning","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":[0.003611004,0.0001314838,0.0002949162,0.0006069748,0.0001165322,0.000255104,0.0005693786,0.00009873602,0.0005006685],"category_scores_gemma":[0.003787299,0.0001014689,0.0001020931,0.0007738573,0.00006794417,0.001493257,0.0001026569,0.00009823464,0.000521067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005022692,"about_ca_system_score_gemma":0.00006467009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005381514,"about_ca_topic_score_gemma":0.000003005913,"domain_scores_codex":[0.9973572,0.0003351918,0.0009726225,0.0002073555,0.000935746,0.0001919241],"domain_scores_gemma":[0.9970896,0.001090994,0.000462157,0.0005035674,0.0007812422,0.00007239663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001941988,0.0001865272,0.001302998,0.000006523223,0.0000113145,0.000001802057,0.005388138,0.000274681,0.01081865,0.1034391,0.002981535,0.8753945],"study_design_scores_gemma":[0.005511508,0.0006204972,0.1429285,0.0002798148,0.00005488509,0.0000456119,0.005040852,0.3344228,0.01286789,0.4290079,0.0683552,0.0008645247],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8005997,0.00009570635,0.1850209,0.0001594913,0.00057055,0.0002397419,0.00001478535,0.0000437213,0.01325542],"genre_scores_gemma":[0.9958785,0.000001362025,0.003489215,0.000308927,0.00004168299,0.000001449207,0.000004884112,0.000005143762,0.0002687899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.87453,"threshold_uncertainty_score":0.6697436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1837051799305429,"score_gpt":0.4512865948637421,"score_spread":0.2675814149331991,"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."}}