{"id":"W2021188143","doi":"10.1016/j.ins.2014.03.015","title":"Performance analysis of statistical optimal data fusion algorithms","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Ministry of Education of the People's Republic of China; National Natural Science Foundation of China","keywords":"Covariance intersection; Fusion; Sensor fusion; Algorithm; Robustness (evolution); Computer science; Intersection (aeronautics); Pattern recognition (psychology); Artificial intelligence; Covariance matrix; Estimation of covariance matrices","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":[0.01033791,0.0008464161,0.001120931,0.001851642,0.0006836782,0.001731561,0.0009282018,0.001262551,0.001798322],"category_scores_gemma":[0.04069381,0.0004581105,0.000789404,0.001866492,0.001021566,0.002244866,0.001640622,0.0007969334,0.0004077978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553222,"about_ca_system_score_gemma":0.002623353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004154796,"about_ca_topic_score_gemma":0.002316228,"domain_scores_codex":[0.993946,0.002852309,0.0003987823,0.0005124581,0.001915858,0.0003745375],"domain_scores_gemma":[0.9706932,0.02330771,0.000884687,0.001420288,0.003500842,0.000193318],"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":[0.002197822,0.0001551263,0.003886804,0.0002486132,0.0002389795,0.00006136554,0.0001170876,0.7673144,0.007495895,0.02644974,0.001709826,0.1901244],"study_design_scores_gemma":[0.00001905758,0.0001142493,0.001013865,0.000008785515,0.00002852692,0.00005131351,0.00002317471,0.9888383,0.004053395,0.005546456,0.0002837269,0.00001919508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1537171,0.00288007,0.8378779,0.0006950912,0.0001210349,0.00009100543,0.0002514939,0.0008906765,0.003475614],"genre_scores_gemma":[0.7787278,0.0009210798,0.217877,0.0001144992,0.0001129973,0.0001138278,0.0007091284,0.0001942034,0.00122943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01033791,"threshold_uncertainty_score":0.05467272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02251820317720614,"score_gpt":0.3001401519316516,"score_spread":0.2776219487544455,"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."}}