{"id":"W1865476398","doi":"","title":"Towards unbiased evaluation of uncertainty reasoning: The URREF ontology","year":2012,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Ontology; Computer science; Situation awareness; Sensor fusion; Representation (politics); Situation analysis; Knowledge representation and reasoning; Data mining; Ontology-based data integration; Data collection; Information retrieval; Data science; Artificial intelligence; Engineering; Semantic Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07722241,0.001552734,0.002025025,0.009284201,0.002899122,0.01561941,0.006010235,0.004026616,0.002144177],"category_scores_gemma":[0.08800412,0.001094695,0.003338628,0.007141335,0.008589114,0.02703483,0.009978929,0.005461888,0.000909809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006542041,"about_ca_system_score_gemma":0.01157535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01804419,"about_ca_topic_score_gemma":0.01135674,"domain_scores_codex":[0.9177967,0.04348657,0.007572165,0.004342042,0.02434077,0.002461736],"domain_scores_gemma":[0.9343625,0.02236636,0.004484892,0.01302691,0.02431199,0.001447335],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004203683,0.00007661585,0.0005799218,0.0001959291,0.00005880871,0.0001165998,0.00106632,0.009990348,0.0006517965,0.9209033,0.003146483,0.06317195],"study_design_scores_gemma":[0.00003894794,0.00005891461,0.0004110282,0.0006640462,0.00012493,0.0001980852,0.001157018,0.120462,0.003499483,0.8106828,0.0626002,0.0001025305],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002773585,0.0005141561,0.986046,0.001759303,0.00008488921,0.0002259536,0.000195005,0.000508268,0.007892825],"genre_scores_gemma":[0.06913987,0.0006822501,0.9267035,0.0004780099,0.0001212786,0.000466716,0.0009265133,0.0002896458,0.001192333],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.07722241,"threshold_uncertainty_score":0.408396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08811752557596993,"score_gpt":0.3427408974872192,"score_spread":0.2546233719112493,"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."}}