{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":812,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":812,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"e41de119bc65","filters":{"topic":"Target Tracking and Data Fusion in Sensor Networks"}},"results":[{"id":"W2054091988","doi":"10.1109/tac.2009.2019800","title":"Cubature Kalman Filters","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":3320,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Nonlinear system; Kalman filter; Mathematics; Moment (physics); Filter (signal processing); Algorithm; State vector; Dimension (graph theory); Applied mathematics; Computer science; Statistics; Combinatorics","authors":[{"name":"Ienkaran Arasaratnam","is_ca":true},{"name":"S. Haykin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008948602436733833,"gpt":0.2293294026373995,"spread":0.2203808002006657,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001884483,0.001183435,0.001757498,0.0009527907,0.0007439609,0.002105096,0.00158305,0.001624836,0.005467952],"category_scores_gemma":[0.006429735,0.0007911421,0.001132818,0.001224176,0.0009332364,0.00238271,0.001396944,0.001849685,0.003463787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001483911,"about_ca_system_score_gemma":0.001655838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01235978,"about_ca_topic_score_gemma":0.01066496,"domain_scores_codex":[0.9985871,0.0003716777,0.00008434775,0.0004119051,0.0004399611,0.0001049433],"domain_scores_gemma":[0.9977751,0.001024837,0.0002224856,0.0003539373,0.0005642885,0.00005928332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002018649,0.00006140957,0.001264775,0.0002742706,0.0001894117,0.00006451362,0.0001400145,0.5957677,0.00590796,0.1132593,0.008851015,0.2740178],"study_design_scores_gemma":[0.00001026738,0.00002232463,0.0001752244,0.0000175583,0.00001521037,0.00002436721,0.000007681062,0.9783955,0.001650106,0.01026448,0.00939703,0.00002030185],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009678927,0.0002466301,0.9966387,0.0000777122,0.00007210539,0.0000157463,0.00006268228,0.0004354919,0.001483028],"genre_scores_gemma":[0.1587348,0.001546416,0.8182766,0.0004790553,0.0003051187,0.0003500392,0.0009916809,0.0004804152,0.01883591],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01235978,"threshold_uncertainty_score":0.02457565,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2161435744","doi":"10.1109/taes.2005.1561884","title":"Sequential monte carlo methods for multi-target filtering with random finite sets","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1236,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Monte Carlo method; Particle filter; Filter (signal processing); Algorithm; Moment (physics); Computer science; Bayesian probability; Convergence (economics); Finite set; Set (abstract data type); Probability density function; Posterior probability; Nonlinear system; Mathematics; Mathematical optimization; Applied mathematics; Artificial intelligence; Statistics","authors":[{"name":"Ba‐Ngu Vo","is_ca":false},{"name":"Sumeetpal S. Singh","is_ca":false},{"name":"A. Boucet","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02916473054150436,"gpt":0.306869947200782,"spread":0.2777052166592776,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00410476,0.001020101,0.001226928,0.001299164,0.000728075,0.001134088,0.001773649,0.001392902,0.003380106],"category_scores_gemma":[0.00995067,0.0009075463,0.001328567,0.001204954,0.001568152,0.001629069,0.001420352,0.002159383,0.0006842267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001616673,"about_ca_system_score_gemma":0.001594854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004608448,"about_ca_topic_score_gemma":0.004644951,"domain_scores_codex":[0.9983155,0.0008437029,0.00008151555,0.0001861871,0.0005064644,0.00006668198],"domain_scores_gemma":[0.9930467,0.005761614,0.0002971491,0.0003679286,0.0004381017,0.00008849876],"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":[0.00005957222,0.00003494877,0.0004054242,0.0001063838,0.00007360819,0.00006554707,0.00007484154,0.7639341,0.001028762,0.1918538,0.0006482468,0.04171492],"study_design_scores_gemma":[0.00001026615,0.00001350212,0.00004340007,0.000009915996,0.000007075845,0.00001647267,0.000003385506,0.9640406,0.0003068307,0.03454901,0.0009910522,0.000008493581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004777233,0.0001135371,0.9989348,0.00003365492,0.00001282717,0.00001676185,0.000007077898,0.00005666222,0.0003469999],"genre_scores_gemma":[0.1327192,0.001016743,0.86207,0.0001293624,0.000140632,0.0005530752,0.0001389549,0.0001357362,0.003096305],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004608448,"threshold_uncertainty_score":0.02170831,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2147953023","doi":"10.1007/978-3-540-24670-1_3","title":"A Boosted Particle Filter: Multitarget Detection and Tracking","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":1029,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"AdaBoost; Particle filter; Artificial intelligence; Tracking (education); Computer science; Computer vision; Filter (signal processing); Mixture model; Video tracking; Gaussian; Context (archaeology); Pattern recognition (psychology); Object detection; Object (grammar); Classifier (UML)","authors":[{"name":"Kenji Okuma","is_ca":true},{"name":"Ali Taleghani","is_ca":true},{"name":"Nando de Freitas","is_ca":true},{"name":"James J. Little","is_ca":true},{"name":"David Lowe","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01866135330152782,"gpt":0.2339072648257161,"spread":0.2152459115241882,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009959921,0.0009645462,0.001262349,0.000737978,0.0003834129,0.001038252,0.001659281,0.002330281,0.004540222],"category_scores_gemma":[0.002317689,0.0009248252,0.0007654772,0.001705568,0.0004851038,0.001620726,0.001155988,0.001994339,0.004431722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004577412,"about_ca_system_score_gemma":0.0006065857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002786384,"about_ca_topic_score_gemma":0.002851936,"domain_scores_codex":[0.9993594,0.0001151219,0.00002976954,0.0001504022,0.0003149961,0.00003030697],"domain_scores_gemma":[0.9993486,0.0002451505,0.00002567002,0.0001285932,0.0002219076,0.00003006609],"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":[0.0001805974,0.00008952418,0.0002475775,0.0002848349,0.0001212455,0.0001075319,0.00005598013,0.1180748,0.0181805,0.01801698,0.01677023,0.8278703],"study_design_scores_gemma":[0.0000205642,0.00004187238,0.0002278542,0.00001272569,0.00003543895,0.000114606,0.00000531016,0.9715014,0.006667161,0.00847392,0.01287812,0.00002113053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004777772,0.0009291917,0.9970675,0.00004991062,0.0001759971,0.00001277417,0.00002140316,0.000551958,0.00071351],"genre_scores_gemma":[0.03538112,0.002134716,0.9502252,0.0001793664,0.0003091478,0.0001027834,0.0002178184,0.0002067018,0.01124318],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004540222,"threshold_uncertainty_score":0.01518857,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2105905583","doi":"10.1109/taes.2007.4441756","title":"PHD filters of higher order in target number","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":972,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Filter (signal processing); Moment (physics); Mathematics; Probability density function; Order (exchange); Nonlinear system; Nonlinear filter; Algorithm; Computer science; Applied mathematics; Filter design; Statistics; Physics","authors":[{"name":"Ronald Mahler","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01105888183286928,"gpt":0.2424716310828631,"spread":0.2314127492499938,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002761508,0.0006142035,0.0007493207,0.0006571459,0.0004364117,0.001340897,0.0008490838,0.001253807,0.002781816],"category_scores_gemma":[0.01018784,0.0006185471,0.0008387447,0.0006337026,0.001413521,0.00294494,0.001365313,0.002125518,0.0009859006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643647,"about_ca_system_score_gemma":0.001172873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001856088,"about_ca_topic_score_gemma":0.002007,"domain_scores_codex":[0.9987506,0.0002938339,0.00005735439,0.000275599,0.0005048941,0.0001176603],"domain_scores_gemma":[0.9944008,0.003375161,0.0005361059,0.0006063274,0.0009495811,0.0001318573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002749571,0.00006949194,0.001948029,0.0002000516,0.00006339261,0.0002234555,0.0003134567,0.320136,0.02543046,0.5306076,0.002254885,0.1184781],"study_design_scores_gemma":[0.00001564705,0.00003521194,0.0003910136,0.0000133291,0.000008445667,0.00009163084,0.00001444661,0.9473168,0.005296757,0.0443917,0.0023996,0.00002537936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004522493,0.00009173492,0.9939745,0.0001493489,0.00002170455,0.00001146252,0.00003059857,0.0001026638,0.001095544],"genre_scores_gemma":[0.4095946,0.000887605,0.5690587,0.0005563454,0.0002130755,0.0001805487,0.0003023388,0.000163736,0.01904304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002781816,"threshold_uncertainty_score":0.01460445,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2144903371","doi":"10.1109/tsp.2010.2056923","title":"Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":572,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"University of Oxford; McMaster University","keywords":"Mathematics; Kalman filter; Nonlinear system; Filter (signal processing); Covariance; Context (archaeology); State vector; Applied mathematics; Curse of dimensionality; Gaussian; Algorithm; Computer science; Statistics","authors":[{"name":"Ienkaran Arasaratnam","is_ca":true},{"name":"S. Haykin","is_ca":true},{"name":"T. R. Hurd","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01305406023016089,"gpt":0.2595318407802652,"spread":0.2464777805501043,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001834372,0.0004424095,0.0009842142,0.0004641956,0.0004598472,0.0009824549,0.0006976876,0.001100643,0.002085767],"category_scores_gemma":[0.006497198,0.0004425605,0.0005714878,0.0007329824,0.001020866,0.001445317,0.0009093037,0.001369571,0.0003907081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028775,"about_ca_system_score_gemma":0.0008298695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01229401,"about_ca_topic_score_gemma":0.005282749,"domain_scores_codex":[0.9995402,0.0001999895,0.00002355508,0.00005669791,0.0001351716,0.00004436075],"domain_scores_gemma":[0.9963436,0.002860979,0.0001756311,0.0002298075,0.0003404765,0.00004941391],"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":[0.0000210611,0.00001068638,0.0003333467,0.00003097307,0.00001109398,0.00001951098,0.00003430965,0.9656965,0.0002590353,0.02431339,0.0002887632,0.008981386],"study_design_scores_gemma":[0.00000162184,0.000002988119,0.00002926664,0.000002343168,9.729047e-7,0.000003808956,0.00000183632,0.9964014,0.00005995139,0.003289558,0.0002042134,0.000001911195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01084991,0.0004077578,0.9848065,0.0002252005,0.0000433272,0.0000205332,0.00003312552,0.0002140472,0.003399581],"genre_scores_gemma":[0.7738658,0.00119592,0.2198993,0.000139568,0.00007163814,0.0002512672,0.0001603455,0.0001123621,0.004303771],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01229401,"threshold_uncertainty_score":0.02444494,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320498734","doi":"10.1088/1538-3873/acac53","title":"Performance of NIRCam on JWST in Flight","year":2023,"lang":"en","type":"article","venue":"Publications of the Astronomical Society of the Pacific","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":371,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Herzberg Institute of Astrophysics; University of Victoria; Université de Montréal; Canadian Institute for Theoretical Astrophysics; National Research Council Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Goddard Space Flight Center; National Aeronautics and Space Administration","keywords":"Environmental science; Astrobiology; Physics","authors":[{"name":"Marcia Rieke","is_ca":false},{"name":"Douglas Kelly","is_ca":false},{"name":"K. A. Misselt","is_ca":false},{"name":"John Stansberry","is_ca":false},{"name":"Martha L. Boyer","is_ca":false},{"name":"Thomas G. Beatty","is_ca":false},{"name":"Eiichi Egami","is_ca":false},{"name":"Michael Florian","is_ca":false},{"name":"Thomas P. Greene","is_ca":false},{"name":"Kevin Hainline","is_ca":false},{"name":"Jarron Leisenring","is_ca":false},{"name":"Thomas L. Roellig","is_ca":false},{"name":"Everett Schlawin","is_ca":false},{"name":"Fengwu Sun","is_ca":false},{"name":"Lee Tinnin","is_ca":false},{"name":"Christina C. Williams","is_ca":false},{"name":"Christopher N. A. Willmer","is_ca":false},{"name":"Debra Wilson","is_ca":false},{"name":"Charles R. Clark","is_ca":false},{"name":"Scott Rohrbach","is_ca":false},{"name":"Brian Brooks","is_ca":false},{"name":"Alicia Canipe","is_ca":false},{"name":"Matteo Correnti","is_ca":false},{"name":"Audrey DiFelice","is_ca":false},{"name":"Mario Gennaro","is_ca":false},{"name":"J. H. Girard","is_ca":false},{"name":"G. Hartig","is_ca":false},{"name":"B. Hilbert","is_ca":false},{"name":"Anton M. Koekemoer","is_ca":false},{"name":"Nikolay Nikolov","is_ca":false},{"name":"Nor Pirzkal","is_ca":false},{"name":"A. Rest","is_ca":false},{"name":"Massimo Robberto","is_ca":false},{"name":"Ben Sunnquist","is_ca":false},{"name":"Randal Telfer","is_ca":false},{"name":"Chi Rai Wu","is_ca":false},{"name":"Malcolm Ferry","is_ca":false},{"name":"Dan Lewis","is_ca":false},{"name":"Stefi A. Baum","is_ca":true},{"name":"Charles Beichman","is_ca":false},{"name":"René Doyon","is_ca":true},{"name":"Alan Dressler","is_ca":false},{"name":"Daniel J. Eisenstein","is_ca":false},{"name":"Laura Ferrarese","is_ca":true},{"name":"K. W. Hodapp","is_ca":false},{"name":"Scott Horner","is_ca":false},{"name":"D. T. Jaffe","is_ca":false},{"name":"Doug Johnstone","is_ca":true},{"name":"John Krist","is_ca":false},{"name":"P. G. Martin","is_ca":true},{"name":"Donald W. McCarthy","is_ca":false},{"name":"Michael R. Meyer","is_ca":false},{"name":"G. H. Rieke","is_ca":false},{"name":"John T. Trauger","is_ca":false},{"name":"Erick T. Young","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01558729364621754,"gpt":0.219635293979961,"spread":0.2040480003337435,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027108,0.0004108599,0.0006267304,0.0006263079,0.0006522497,0.0009614799,0.0007148869,0.0005761675,0.003575593],"category_scores_gemma":[0.001551719,0.0002151598,0.0002047559,0.0004780106,0.0004280772,0.0008812584,0.0008428282,0.0006401733,0.001027954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007141192,"about_ca_system_score_gemma":0.0006021323,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00954838,"about_ca_topic_score_gemma":0.01562864,"domain_scores_codex":[0.9993611,0.00004446241,0.00001847123,0.0001205919,0.0003676137,0.00008775751],"domain_scores_gemma":[0.9991632,0.0000988472,0.00003921969,0.00009185026,0.0004473465,0.0001595957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01288719,0.002618535,0.1326833,0.0006633171,0.0006637613,0.003421123,0.003421676,0.06092265,0.3959881,0.003650884,0.0941912,0.2888883],"study_design_scores_gemma":[0.000790019,0.00531554,0.2957163,0.0001007373,0.0002007919,0.001121338,0.002067681,0.5006368,0.1429323,0.0009846584,0.04994179,0.0001921257],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829252,0.0001078672,0.001895549,0.0003386965,0.0001550117,0.0000527757,0.001008228,0.0021836,0.0113331],"genre_scores_gemma":[0.9878181,0.00003907468,0.005045052,0.0001512726,0.00003305757,0.00001593101,0.002777067,0.0001044353,0.004015817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00954838,"threshold_uncertainty_score":0.01898557,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2118603200","doi":"10.1109/jproc.2003.823149","title":"Probabilistic Data Association Techniques for Target Tracking in Clutter","year":2004,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":254,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Clutter; Estimator; Data association; Tracking (education); Probabilistic logic; Radar; Radar tracker; Artificial intelligence; Low probability of intercept radar; Tracking system; Sonar; Statistical model; Computer vision; Kalman filter; Mathematics; Statistics; Radar engineering details","authors":[{"name":"T. Kirubarajan","is_ca":true},{"name":"Yaakov Bar‐Shalom","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03054598795343635,"gpt":0.2737829069069535,"spread":0.2432369189535172,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005591791,0.001203779,0.001822308,0.002131944,0.001067177,0.002045035,0.002421342,0.001911262,0.002243378],"category_scores_gemma":[0.02098612,0.000998109,0.001403905,0.005278787,0.001338134,0.003699707,0.003022943,0.003887511,0.00230194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007315738,"about_ca_system_score_gemma":0.001427862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00123297,"about_ca_topic_score_gemma":0.001018484,"domain_scores_codex":[0.9946058,0.001670816,0.0003871436,0.0007955005,0.002359136,0.0001815941],"domain_scores_gemma":[0.9893712,0.006616111,0.0008144793,0.00157616,0.001511829,0.00011022],"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":[0.0002296931,0.00009663843,0.0009568979,0.0004497052,0.0002012503,0.0002057114,0.0003158759,0.1629975,0.004388795,0.1289235,0.005066848,0.6961675],"study_design_scores_gemma":[0.00004411566,0.0001218601,0.0004823474,0.0001031414,0.00006881008,0.0005763804,0.00005595212,0.842415,0.004909387,0.11989,0.03124623,0.00008682777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003487746,0.0008177867,0.9981593,0.00007953345,0.00004638627,0.00001500524,0.00001624776,0.0002101355,0.0003067839],"genre_scores_gemma":[0.05314443,0.003852167,0.9394505,0.0002421148,0.0003981878,0.0003482395,0.0002633307,0.0001219899,0.002179058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005591791,"threshold_uncertainty_score":0.02957261,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2570372101","doi":"10.1016/j.sigpro.2017.01.001","title":"Gaussian filters for parameter and state estimation: A general review of theory and recent trends","year":2017,"lang":"en","type":"review","venue":"Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":224,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph; McMaster University","funders":"","keywords":"Computer science; Parametric statistics; Robustness (evolution); Gaussian; Gaussian noise; Kalman filter; Estimation theory; Nonlinear system; State (computer science); Noise (video); Control theory (sociology); Algorithm; Mathematics; Artificial intelligence; Control (management); Statistics","authors":[{"name":"Hamed Afshari","is_ca":true},{"name":"S. Andrew Gadsden","is_ca":true},{"name":"Saeid Habibi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08643494222221643,"gpt":0.3790179068213947,"spread":0.2925829645991783,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002127514,0.001332139,0.00205949,0.002848696,0.0003096782,0.001880429,0.002016393,0.002428451,0.003679072],"category_scores_gemma":[0.004158358,0.0005783932,0.0007356773,0.005505647,0.00142399,0.003381388,0.001034574,0.002331708,0.002685009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051252,"about_ca_system_score_gemma":0.002161172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002952078,"about_ca_topic_score_gemma":0.002493993,"domain_scores_codex":[0.9993197,0.0001103103,0.00008450016,0.0001261799,0.0003153533,0.00004408765],"domain_scores_gemma":[0.9971702,0.001852562,0.0001602014,0.0001087593,0.0006405842,0.00006765346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005573848,0.00008327914,0.0003996467,0.00857785,0.00009695483,0.0001082954,0.00007008107,0.001746661,0.001122195,0.02615467,0.02517854,0.936406],"study_design_scores_gemma":[0.00002918466,0.0001797025,0.001230142,0.004142486,0.0001801849,0.001374676,0.000111884,0.003424078,0.001836074,0.03595847,0.9514177,0.00011548],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002426352,0.9869748,0.009635862,0.0007820448,0.0005886597,0.00001351559,0.00005303784,0.00003676701,0.001672712],"genre_scores_gemma":[0.002300332,0.9899974,0.005197274,0.0004300212,0.001142337,0.00001799624,0.00007776434,0.00001282486,0.0008241441],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003679072,"threshold_uncertainty_score":0.0123077,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2755153992","doi":"10.1109/mcs.2017.2718830","title":"Unbiased Finite Impluse Response Filtering: An Iterative Alternative to Kalman Filtering Ignoring Noise and Initial Conditions","year":2017,"lang":"en","type":"article","venue":"IEEE Control Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":201,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Infinite impulse response; Kalman filter; Control theory (sociology); Estimator; Robustness (evolution); Computer science; Gaussian noise; White noise; Noise (video); Finite impulse response; Impulse noise; Noise measurement; Algorithm; Linear filter; Mathematics; Statistics; Filter (signal processing); Digital filter; Artificial intelligence; Noise reduction","authors":[],"retraction":null,"screen_n_in":null,"score":{"opus":0.04228007136065928,"gpt":0.3170828686242744,"spread":0.2748027972636152,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141543,0.0007143499,0.001272626,0.0009068145,0.0005144905,0.001561659,0.001875667,0.001685325,0.002707721],"category_scores_gemma":[0.004520765,0.0004012266,0.0008896285,0.001523825,0.0007412798,0.002025288,0.00125391,0.001416847,0.001779925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007000613,"about_ca_system_score_gemma":0.00150715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004617655,"about_ca_topic_score_gemma":0.004016188,"domain_scores_codex":[0.9985385,0.000317937,0.00006437856,0.0003287253,0.0006559409,0.00009454457],"domain_scores_gemma":[0.998751,0.0005336634,0.0001412656,0.0002569309,0.000280692,0.00003627102],"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":[0.0001846081,0.0001328673,0.001234956,0.000310383,0.0001453878,0.0002334768,0.0003863631,0.2528816,0.01464711,0.1596666,0.00414204,0.5660346],"study_design_scores_gemma":[0.00001232379,0.00005325024,0.0002556519,0.00004112231,0.00002656049,0.0001365582,0.00001785862,0.9618184,0.00339958,0.0259932,0.008208834,0.00003660062],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006645165,0.0001716755,0.9975001,0.00003421925,0.00002762275,0.00001096937,0.00001606532,0.0002702298,0.001304582],"genre_scores_gemma":[0.1373882,0.001115707,0.8543834,0.0002227476,0.0002732665,0.0001455385,0.0002566338,0.0002844337,0.005930041],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004617655,"threshold_uncertainty_score":0.009181559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164769497","doi":"10.1109/icma.2005.1626777","title":"Navigation with IMU/GPS/digital compass with unscented Kalman filter","year":2006,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Inertial measurement unit; Global Positioning System; Compass; Kalman filter; Computer science; GPS/INS; Noise (video); Precision Lightweight GPS Receiver; Assisted GPS; Computer vision; Artificial intelligence; Geography; Telecommunications; Gps receiver","authors":[{"name":"Pingrui Zhang","is_ca":true},{"name":"Jason Gu","is_ca":true},{"name":"Evangelos Milios","is_ca":true},{"name":"Paul Huynh","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007463086523565589,"gpt":0.2006033444370807,"spread":0.1931402579135151,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005945108,0.000818727,0.00090506,0.0007359291,0.0004453245,0.0006977744,0.0007914334,0.000581768,0.001569282],"category_scores_gemma":[0.001252728,0.000402041,0.0007315922,0.001021501,0.0003337487,0.001211769,0.0009127233,0.0006785771,0.001416005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004831313,"about_ca_system_score_gemma":0.001012366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01027797,"about_ca_topic_score_gemma":0.0105767,"domain_scores_codex":[0.9991079,0.0001595218,0.0000685939,0.0002111644,0.0003923306,0.00006042397],"domain_scores_gemma":[0.999626,0.00004336993,0.00005186218,0.00009697126,0.0001684959,0.00001324485],"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":[0.0004473041,0.0001767849,0.005871885,0.000501811,0.0003749743,0.0001675937,0.0002725889,0.28003,0.02764011,0.01133465,0.006135589,0.6670468],"study_design_scores_gemma":[0.0000705241,0.0002161772,0.002524644,0.00002864397,0.0001085011,0.0001328351,0.00003270773,0.9668651,0.01435473,0.00310222,0.01251762,0.00004629593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005959686,0.0002255147,0.9911323,0.00004214767,0.00006535863,0.0000504903,0.00008629314,0.001344806,0.001093346],"genre_scores_gemma":[0.3474131,0.0007145091,0.6438475,0.0001373778,0.0001181706,0.0003496595,0.0007078509,0.0001153037,0.00659655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01027797,"threshold_uncertainty_score":0.02043635,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2076924667","doi":"10.1109/maes.2005.1412121","title":"Online INS/GPS integration with a radial basis function neural network","year":2005,"lang":"en","type":"article","venue":"IEEE Aerospace and Electronic Systems Magazine","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":162,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary; Royal Military College of Canada","funders":"","keywords":"Global Positioning System; GPS/INS; Inertial navigation system; Artificial neural network; Perceptron; Computer science; Kalman filter; GPS signals; Multilayer perceptron; Radial basis function; Precision Lightweight GPS Receiver; Artificial intelligence; Differential GPS; Assisted GPS; Real-time computing; Inertial frame of reference; Telecommunications","authors":[{"name":"Rashad Sharaf","is_ca":true},{"name":"Aboelmagd Noureldin","is_ca":true},{"name":"Ahmed Osman","is_ca":true},{"name":"Naser El‐Sheimy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009299787054887237,"gpt":0.2113638583695505,"spread":0.2020640713146633,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005744398,0.0008436864,0.0007207704,0.0004665113,0.000354791,0.0005362764,0.0008292801,0.0007410962,0.001130567],"category_scores_gemma":[0.0009367815,0.0004842371,0.00061768,0.000449205,0.0002190543,0.0007256323,0.0006510377,0.0008050615,0.0007119855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004071986,"about_ca_system_score_gemma":0.0005224474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007248071,"about_ca_topic_score_gemma":0.005730412,"domain_scores_codex":[0.9996357,0.00005320104,0.00002332839,0.00009716281,0.0001548735,0.00003560057],"domain_scores_gemma":[0.9997537,0.00004523819,0.00003325364,0.00003780095,0.0001177379,0.0000123308],"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":[0.0004280963,0.0001931861,0.003989872,0.0001561379,0.0001592139,0.0002954497,0.0001567034,0.5102546,0.03778934,0.001918069,0.001619372,0.4430401],"study_design_scores_gemma":[0.000007772941,0.00005000588,0.0006416944,0.00000567945,0.00001790475,0.00002555025,0.000004319936,0.994321,0.004090511,0.0002084046,0.0006181184,0.000009045725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08063876,0.0004664768,0.9099472,0.0001238542,0.0001340283,0.00007472895,0.00005773207,0.004775182,0.003782077],"genre_scores_gemma":[0.7957696,0.0001871508,0.2006296,0.00007815017,0.00004193278,0.00008337238,0.0001469509,0.00007648827,0.002986655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007248071,"threshold_uncertainty_score":0.01441175,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102643044","doi":"10.1016/j.automatica.2014.04.004","title":"An event-triggered approach to state estimation with multiple point- and set-valued measurements","year":2014,"lang":"en","type":"article","venue":"Automatica","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Estimator; Event (particle physics); Covariance; Optimal estimation; Set (abstract data type); Covariance intersection; Algorithm; Covariance matrix; Computer science; Gaussian; Sensor fusion; Monte Carlo method; Mathematics; Mathematical optimization; Extended Kalman filter; Statistics; Artificial intelligence; Machine learning","authors":[{"name":"Dawei Shi","is_ca":true},{"name":"Tongwen Chen","is_ca":true},{"name":"Ling Shi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02307214856833501,"gpt":0.2565125328443505,"spread":0.2334403842760155,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001287948,0.0008578426,0.001089078,0.0005766642,0.0004066539,0.00157477,0.002501378,0.001234651,0.001889816],"category_scores_gemma":[0.00418515,0.0006450539,0.0009084273,0.0009224473,0.0007398722,0.001967879,0.001917584,0.001571935,0.0003686357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005005312,"about_ca_system_score_gemma":0.000592804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001464918,"about_ca_topic_score_gemma":0.001626356,"domain_scores_codex":[0.9986084,0.0003222293,0.00008809393,0.0003788936,0.0005132853,0.00008912784],"domain_scores_gemma":[0.9983451,0.001033473,0.000165063,0.0001870259,0.0002155095,0.00005380908],"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":[0.0008991878,0.0002709404,0.001381219,0.0002696964,0.000289525,0.0008452841,0.0002935865,0.623978,0.03020063,0.08239844,0.001783647,0.2573899],"study_design_scores_gemma":[0.00001087994,0.00003296311,0.0001335736,0.000004125321,0.0000110687,0.00005061914,0.000006452266,0.9893119,0.001802629,0.008202206,0.0004244206,0.000009243988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002196989,0.00004707941,0.9972571,0.00002942549,0.00002676851,0.00001348606,0.0000181833,0.0001221008,0.0002889026],"genre_scores_gemma":[0.6257721,0.0002865371,0.3703345,0.0001515704,0.0001525545,0.0001479813,0.0002036932,0.0001020051,0.002849138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002501378,"threshold_uncertainty_score":0.00681144,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2095925246","doi":"10.1109/taes.2008.4516991","title":"Multiple-model probability hypothesis density filter for tracking maneuvering targets","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Filter (signal processing); Monte Carlo method; Curse of dimensionality; Estimator; Nonlinear system; Moment (physics); Gaussian; Tracking (education); State space; Probability density function; Nonlinear filter; Control theory (sociology); Particle filter; Computer science; Algorithm; Mathematics; Artificial intelligence; Filter design; Statistics; Computer vision; Physics","authors":[{"name":"Kumaradevan Punithakumar","is_ca":true},{"name":"T. Kirubarajan","is_ca":true},{"name":"Abhijit Sinha","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0398802896818926,"gpt":0.2273746594614149,"spread":0.1874943697795223,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002283099,0.0005634148,0.001004398,0.0006716391,0.0003900992,0.0006915122,0.001111939,0.001183499,0.001633875],"category_scores_gemma":[0.007272431,0.0004918567,0.0008322647,0.0007530621,0.0005801064,0.001602486,0.0006577624,0.001249596,0.0005882583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009542069,"about_ca_system_score_gemma":0.001512817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006342893,"about_ca_topic_score_gemma":0.004032549,"domain_scores_codex":[0.9991475,0.0002704411,0.00003359765,0.0001534995,0.0003379804,0.00005694756],"domain_scores_gemma":[0.9979322,0.001437849,0.0001358191,0.0001435726,0.0003080353,0.0000425576],"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":[0.0002115653,0.00008047624,0.0009091123,0.0001204336,0.00008195158,0.0000939299,0.00008195181,0.7939802,0.003726157,0.02636953,0.001402891,0.1729417],"study_design_scores_gemma":[0.000007413553,0.00001824103,0.00009028904,0.000002823523,0.00000561137,0.0000149059,0.000002196361,0.996622,0.0006441256,0.002214108,0.0003730658,0.0000052374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002158503,0.00009108234,0.9972901,0.00004174422,0.00001694846,0.000009444218,0.0000125349,0.0001752461,0.0002044666],"genre_scores_gemma":[0.3889302,0.0007252893,0.6058651,0.000168142,0.0001497572,0.000232803,0.0003052866,0.00009902369,0.003524412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006342893,"threshold_uncertainty_score":0.01261193,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1966184693","doi":"10.1109/tsp.2007.914964","title":"Square-Root Quadrature Kalman Filtering","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"U.S. Department of Homeland Security","keywords":"Extended Kalman filter; Mathematics; Kalman filter; Clenshaw–Curtis quadrature; Gauss–Hermite quadrature; Fast Kalman filter; Invariant extended Kalman filter; Adaptive quadrature; Gauss–Kronrod quadrature formula; Ensemble Kalman filter; Square root; Quadrature (astronomy); Control theory (sociology); Covariance intersection; Alpha beta filter; Tanh-sinh quadrature; Applied mathematics; Covariance matrix; Algorithm; Gaussian quadrature; Computer science; Mathematical analysis; Statistics; Artificial intelligence; Engineering; Integral equation; Electronic engineering; Nyström method; Geometry; Moving horizon estimation","authors":[{"name":"Ienkaran Arasaratnam","is_ca":true},{"name":"S. Haykin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02674836540943531,"gpt":0.2465436623518149,"spread":0.2197952969423796,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001292046,0.0006060583,0.00125682,0.0004427432,0.0004379579,0.001306478,0.001192521,0.0009702736,0.003686765],"category_scores_gemma":[0.005906325,0.0003350514,0.0006215223,0.0009015245,0.0007213452,0.001616473,0.001100912,0.001059898,0.00189331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006047364,"about_ca_system_score_gemma":0.001199047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003404154,"about_ca_topic_score_gemma":0.002838412,"domain_scores_codex":[0.9988187,0.0002490113,0.00005496397,0.0002872448,0.0004884792,0.0001014242],"domain_scores_gemma":[0.9984749,0.0004917072,0.0001181538,0.0002832814,0.0005950792,0.00003672237],"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":[0.0001836722,0.00006944974,0.001689721,0.0003190559,0.0001415928,0.0001578988,0.0001834036,0.3887161,0.01406758,0.2117818,0.01243666,0.370253],"study_design_scores_gemma":[0.00002077744,0.00004076893,0.0003264131,0.00002516455,0.00002485556,0.00007324864,0.0000184686,0.9424977,0.003049626,0.04110988,0.01279142,0.00002163261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001262719,0.0002194682,0.9958488,0.00007072352,0.00006250294,0.00001572017,0.00004756946,0.0003354887,0.00213694],"genre_scores_gemma":[0.3624599,0.001950984,0.6200131,0.0004069428,0.0003290893,0.0001842073,0.0007917146,0.0003204453,0.01354364],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003686765,"threshold_uncertainty_score":0.01233345,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1965305830","doi":"10.1109/jstsp.2013.2256772","title":"A Multiple-Detection Joint Probabilistic Data Association Filter","year":2013,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":155,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Clutter; Computer science; Probabilistic logic; Data association; Filter (signal processing); Radar tracker; Artificial intelligence; Tracking (education); Multipath propagation; Algorithm; Radar; Pattern recognition (psychology); Computer vision; Telecommunications","authors":[{"name":"B. Habtemariam","is_ca":true},{"name":"Ratnasingham Tharmarasa","is_ca":true},{"name":"T. Thayaparan","is_ca":true},{"name":"Mahendra Mallick","is_ca":false},{"name":"T. Kirubarajan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03675506028158698,"gpt":0.252280432335527,"spread":0.21552537205394,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002663338,0.001134154,0.001860128,0.001220193,0.0009098983,0.001474835,0.002404047,0.002055086,0.002519203],"category_scores_gemma":[0.005436057,0.0007021818,0.001133176,0.001920108,0.0005618067,0.002318194,0.001291935,0.002042928,0.001784754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008320196,"about_ca_system_score_gemma":0.002362029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004277764,"about_ca_topic_score_gemma":0.003708808,"domain_scores_codex":[0.9971584,0.0004057666,0.0001862752,0.0009223454,0.001118583,0.0002086999],"domain_scores_gemma":[0.9975189,0.0009896677,0.0002045607,0.0002999884,0.0009148968,0.00007205017],"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":[0.0003710111,0.0001703363,0.002608282,0.0002072704,0.000186056,0.0001628677,0.0001176533,0.1223118,0.01534351,0.01072187,0.004765736,0.8430336],"study_design_scores_gemma":[0.00004753715,0.00009595417,0.0005880034,0.00001653844,0.00004294759,0.0002681423,0.00001368649,0.9804746,0.007848805,0.002564964,0.007998347,0.00004047011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001589214,0.0001572838,0.9972466,0.00004967236,0.0000549555,0.00002618473,0.00004092191,0.0004741254,0.0003611582],"genre_scores_gemma":[0.1242513,0.0005397764,0.8692929,0.0002847888,0.000155529,0.0002996759,0.000596982,0.00008905245,0.00449009],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004277764,"threshold_uncertainty_score":0.01408529,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W186403726","doi":"","title":"The Oxford Handbook of Nonlinear Filtering","year":2011,"lang":"en","type":"book","venue":"RePEc: Research Papers in Economics","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":153,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Mathematics; Filtering problem; Nonlinear system; Lipschitz continuity; Nonlinear filter; Applied mathematics; Particle filter; Brownian motion; Stochastic differential equation; Kalman filter; Discretization; Exponential stability; Control theory (sociology); Filter (signal processing); Mathematical analysis; Extended Kalman filter; Computer science; Filter design","authors":[{"name":"Dan Crisan","is_ca":false},{"name":"B. L. Rozovskiĭ","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03504787015040414,"gpt":0.2768372377849677,"spread":0.2417893676345636,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001456184,0.001874525,0.001787276,0.003558772,0.000831074,0.003382833,0.001570634,0.002985178,0.1165892],"category_scores_gemma":[0.005193027,0.0008155315,0.0009500482,0.004300696,0.001376271,0.004151299,0.001819192,0.003925854,0.08565515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001642659,"about_ca_system_score_gemma":0.002814602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004174928,"about_ca_topic_score_gemma":0.003383003,"domain_scores_codex":[0.9978876,0.0002703513,0.0002370829,0.0004313674,0.001070623,0.0001030324],"domain_scores_gemma":[0.9976456,0.0009995714,0.0001365129,0.0003411376,0.0007474374,0.0001297074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004581348,0.00002874726,0.0002506815,0.001341482,0.0000427084,0.0001211149,0.0001807936,0.002288741,0.000690273,0.08700503,0.4565996,0.451405],"study_design_scores_gemma":[0.000003758287,0.00001205648,0.0001710475,0.0002843565,0.000005580187,0.0001489986,0.00002880732,0.000649116,0.0001017865,0.02143199,0.9771458,0.00001676124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"review","genre_gemma":"other","genre_scores_codex":[0.0009398835,0.392327,0.180655,0.01187524,0.02031982,0.0001845632,0.005177167,0.004066224,0.3844551],"genre_scores_gemma":[0.01552899,0.3510305,0.1036341,0.005756956,0.01450669,0.000460498,0.008990427,0.001986817,0.498105],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1165892,"threshold_uncertainty_score":0.3900297,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135621383","doi":"10.1109/tits.2010.2052805","title":"Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":150,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"Odometer; Inertial navigation system; Gyroscope; Kalman filter; Global Positioning System; Inertial measurement unit; Control theory (sociology); GPS/INS; Computer science; Vibrating structure gyroscope; Extended Kalman filter; Engineering; Inertial frame of reference; Assisted GPS; Artificial intelligence; Aerospace engineering; Physics","authors":[{"name":"Jacques Georgy","is_ca":true},{"name":"Aboelmagd Noureldin","is_ca":true},{"name":"Michael J. Korenberg","is_ca":true},{"name":"M.M. Bayoumi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02428573833753502,"gpt":0.2609835330453157,"spread":0.2366977947077807,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004110983,0.000611685,0.0004131678,0.0003055405,0.0002792328,0.0003716665,0.0006405343,0.0006217969,0.0004690748],"category_scores_gemma":[0.0009345431,0.000447283,0.0004962329,0.0003961131,0.0004088105,0.0006007081,0.0004324836,0.0007411159,0.0001619975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005845036,"about_ca_system_score_gemma":0.0008737959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01900811,"about_ca_topic_score_gemma":0.01344403,"domain_scores_codex":[0.9996731,0.00004461209,0.0000137163,0.00008777068,0.0001477548,0.00003298725],"domain_scores_gemma":[0.9998387,0.0000536607,0.00004653793,0.00001441606,0.00003963324,0.000007007667],"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":[0.00004902408,0.00001895941,0.002040191,0.00004870653,0.0000309701,0.00009607624,0.00006363553,0.9698544,0.006983923,0.005275209,0.0002833782,0.01525553],"study_design_scores_gemma":[0.000002034323,0.00001845712,0.0007914712,0.000002113835,0.000007977955,0.00001397095,0.000002958452,0.9977179,0.0007759037,0.0003898335,0.0002728312,0.000004607982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07231392,0.0004530227,0.9243467,0.0001172825,0.00009516605,0.00005177134,0.0001124856,0.0003340941,0.002175566],"genre_scores_gemma":[0.9647275,0.0005525874,0.03125634,0.00003442955,0.00004356684,0.00007761314,0.000131973,0.00003296468,0.003143081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01900811,"threshold_uncertainty_score":0.03779495,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3118132944","doi":"10.1007/978-3-030-58523-5_29","title":"RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":131,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Radar; Computer vision; Artificial intelligence; Exploit; Lidar; Context (archaeology); Radar engineering details; Sensor fusion; Doppler radar; Noise (video); Radar tracker; Radar imaging; Perception; Remote sensing; Geography; Image (mathematics); Telecommunications","authors":[{"name":"Bin Yang","is_ca":false},{"name":"Runsheng Benson Guo","is_ca":true},{"name":"Ming Liang","is_ca":false},{"name":"Sergio Casas","is_ca":false},{"name":"Raquel Urtasun","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02282137749275062,"gpt":0.2411671041178171,"spread":0.2183457266250664,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003624641,0.0009207161,0.0005345418,0.0005784208,0.000151202,0.001008512,0.001064184,0.0008145234,0.01024866],"category_scores_gemma":[0.0003871649,0.0004103315,0.0003413707,0.0005205014,0.0004334949,0.0016093,0.0008439895,0.0006503102,0.005124694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002378021,"about_ca_system_score_gemma":0.0002599533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001026793,"about_ca_topic_score_gemma":0.001519296,"domain_scores_codex":[0.9998159,0.00001765123,0.00000590665,0.00004071381,0.0001023821,0.00001745009],"domain_scores_gemma":[0.999863,0.00004531499,0.00001004088,0.00002978545,0.00003871574,0.00001308138],"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":[0.0003609555,0.000116105,0.0003504976,0.0003689995,0.00008228451,0.0002306265,0.0001001974,0.04963896,0.1308375,0.03678682,0.07427786,0.7068492],"study_design_scores_gemma":[0.0000662455,0.0002259409,0.0008881043,0.00007860202,0.00005678032,0.0007756359,0.00003937193,0.670512,0.09090447,0.03238468,0.2039987,0.00006951246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006758982,0.001891469,0.9580917,0.0001913687,0.0004183666,0.0000475889,0.0004703195,0.01292169,0.01920863],"genre_scores_gemma":[0.1117989,0.003670854,0.8298062,0.0004880063,0.0004293161,0.0001405224,0.00313512,0.002542009,0.04798914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01024866,"threshold_uncertainty_score":0.03428519,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165996196","doi":"10.1117/12.478508","title":"<title>Comparison of EKF, pseudomeasurement, and particle filters for a bearing-only target tracking problem</title>","year":2002,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Extended Kalman filter; Particle filter; Linearization; Tracking (education); Control theory (sociology); Computer science; Invariant extended Kalman filter; Estimator; Nonlinear system; Kalman filter; Filter (signal processing); Monte Carlo method; Radar tracker; Nonlinear filter; Bearing (navigation); Filter design; Mathematics; Computer vision; Artificial intelligence; Radar; Physics; Telecommunications","authors":[{"name":"Xiangdong Lin","is_ca":false},{"name":"Thiagalingam Kirubarajan","is_ca":true},{"name":"Yaakov Bar‐Shalom","is_ca":false},{"name":"Simon Maskell","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0249738202700299,"gpt":0.2402039904374721,"spread":0.2152301701674423,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001753942,0.0004329242,0.0007514335,0.0003984325,0.0002170774,0.0007153313,0.0004734299,0.0009848225,0.0009920261],"category_scores_gemma":[0.005672339,0.00015481,0.0003670646,0.0004170666,0.0002865529,0.001180034,0.0002666319,0.0004540267,0.0003656724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004032356,"about_ca_system_score_gemma":0.0006563172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005280795,"about_ca_topic_score_gemma":0.003425837,"domain_scores_codex":[0.9995013,0.0001842734,0.00003672382,0.00007387873,0.000167467,0.00003624804],"domain_scores_gemma":[0.9974551,0.001864822,0.00008857361,0.0001525623,0.0004088469,0.00003019402],"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":[0.0008030009,0.0001558514,0.003339401,0.0003084988,0.0001516428,0.00008706643,0.00008792371,0.4408315,0.009471581,0.009778976,0.001902051,0.5330825],"study_design_scores_gemma":[0.00003572813,0.00009499583,0.001090277,0.00001459642,0.00001992893,0.00004419883,0.0000121674,0.99161,0.00497341,0.001076979,0.001011214,0.00001663737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0402358,0.001494584,0.9556448,0.0002448726,0.0001044723,0.00003933164,0.00005055999,0.0005357024,0.001649752],"genre_scores_gemma":[0.3848858,0.001691702,0.6103628,0.0001366357,0.00008002342,0.0001105198,0.0002957702,0.0001305162,0.002306323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005280795,"threshold_uncertainty_score":0.01050013,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2082827047","doi":"10.1109/taes.2005.1541438","title":"Multisensor-multitarget bias estimation for general asynchronous sensors","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Cramér–Rao bound; Computer science; Covariance; Asynchronous communication; Algorithm; Monte Carlo method; Upper and lower bounds; Estimation theory; Statistics; Mathematics; Telecommunications","authors":[{"name":"Xiangdong Lin","is_ca":false},{"name":"Yaakov Bar‐Shalom","is_ca":false},{"name":"T. Kirubarajan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01725603855674469,"gpt":0.2496030889403237,"spread":0.2323470503835789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009630664,0.000787425,0.0007368079,0.0005733105,0.0003459827,0.0007314814,0.0006823433,0.0008266207,0.0009958826],"category_scores_gemma":[0.003521803,0.0003966376,0.0005957201,0.0007467868,0.0003666448,0.00126847,0.001270587,0.0006750003,0.0003690381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005611823,"about_ca_system_score_gemma":0.0005548011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008751541,"about_ca_topic_score_gemma":0.001036278,"domain_scores_codex":[0.999364,0.0001528546,0.00003113451,0.0001678696,0.0002437035,0.0000405417],"domain_scores_gemma":[0.999249,0.000333513,0.0001266764,0.0001043378,0.0001658084,0.00002058545],"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":[0.0002428534,0.00005066855,0.00198206,0.0003590145,0.0001510488,0.0002926929,0.0001722506,0.5065591,0.05604712,0.08229271,0.0018363,0.3500142],"study_design_scores_gemma":[0.00001783989,0.00004629112,0.0004824839,0.00001274427,0.00002615772,0.0001188984,0.0000133179,0.9724246,0.008542796,0.0160501,0.002245264,0.00001952781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003221076,0.0001560084,0.9961553,0.00002681729,0.00002257932,0.000007454097,0.00001430527,0.00006331893,0.0003331565],"genre_scores_gemma":[0.4187766,0.001121279,0.5744988,0.0001174891,0.0001638515,0.0001734675,0.0001647948,0.0001059558,0.004877787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0009958826,"threshold_uncertainty_score":0.005093217,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2162488823","doi":"10.1109/taes.2007.4285352","title":"PCRLB-based multisensor array management for multitarget tracking","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":124,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Clutter; Computer science; Upper and lower bounds; Cramér–Rao bound; Algorithm; Radar tracker; Statistical power; Sensor fusion; Artificial intelligence; Mathematics; Estimation theory; Radar; Statistics; Telecommunications","authors":[{"name":"Ratnasingham Tharmarasa","is_ca":true},{"name":"T. Kirubarajan","is_ca":true},{"name":"Marcel L. Hernandez","is_ca":false},{"name":"Abhijit Sinha","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01496891488172391,"gpt":0.2493935887544965,"spread":0.2344246738727726,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002409086,0.001169177,0.001079251,0.0007468822,0.0005422044,0.001460418,0.00163806,0.001340416,0.002618553],"category_scores_gemma":[0.005688714,0.0007158431,0.0005894323,0.001007045,0.0009802828,0.002082305,0.00167352,0.001353852,0.0009764885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134603,"about_ca_system_score_gemma":0.001699095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003226086,"about_ca_topic_score_gemma":0.002889212,"domain_scores_codex":[0.9982579,0.0004403428,0.00006987382,0.0003740423,0.0007085999,0.0001491681],"domain_scores_gemma":[0.9980541,0.001009189,0.0002939039,0.000174936,0.0004031566,0.00006465687],"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":[0.00006623966,0.0000669585,0.0002725961,0.0001523613,0.00003493889,0.00007866274,0.0001097126,0.889501,0.009293932,0.01547836,0.001592725,0.08335257],"study_design_scores_gemma":[0.000005241265,0.00003344097,0.00005591504,0.000009060759,0.000007271822,0.00002603725,0.000009172234,0.9930977,0.001635427,0.004251756,0.0008609149,0.000008008057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001102032,0.000201412,0.9975641,0.00006436827,0.00001423134,0.00001984893,0.00001104518,0.0002456031,0.000777383],"genre_scores_gemma":[0.4520871,0.001040542,0.5401897,0.0004210198,0.0001750445,0.0003351276,0.0001865543,0.0002628298,0.005302014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003226086,"threshold_uncertainty_score":0.01274061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2615536673","doi":"10.1109/tac.2017.2704442","title":"Variational Bayesian Adaptive Cubature Information Filter Based on Wishart Distribution","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Wishart distribution; Inverse-Wishart distribution; Mathematics; Recursive Bayesian estimation; Posterior probability; Bayesian probability; Noise (video); Algorithm; Computer science; Artificial intelligence; Statistics; Multivariate statistics","authors":[{"name":"Peng Dong","is_ca":false},{"name":"Zhongliang Jing","is_ca":false},{"name":"Henry Leung","is_ca":true},{"name":"Kai Shen","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00969140815505092,"gpt":0.2236731113282009,"spread":0.21398170317315,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00201409,0.0007957815,0.00168611,0.0008465673,0.0007276505,0.00172882,0.001798593,0.001507344,0.002631949],"category_scores_gemma":[0.004715324,0.0006984876,0.001125219,0.001154134,0.0009746686,0.002664894,0.001278658,0.001544617,0.0007293306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467976,"about_ca_system_score_gemma":0.001782094,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009480831,"about_ca_topic_score_gemma":0.00522154,"domain_scores_codex":[0.9987774,0.0003840725,0.00006215546,0.0002593731,0.0004032549,0.0001137012],"domain_scores_gemma":[0.9984818,0.000795626,0.0001149954,0.00009984559,0.0004474073,0.00006032044],"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":[0.0002790348,0.00007491196,0.001363317,0.0001997321,0.0001732083,0.0001452174,0.0002419887,0.6195301,0.0104543,0.1867023,0.004654844,0.176181],"study_design_scores_gemma":[0.000007824427,0.00001545591,0.00007363682,0.000005356123,0.000009388103,0.00002468892,0.000004725126,0.9904536,0.0009831033,0.007485153,0.0009227156,0.00001441374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00140648,0.0001129421,0.9978145,0.00006229067,0.00001586306,0.000009609499,0.00001943201,0.00008507542,0.0004737878],"genre_scores_gemma":[0.3200743,0.001243241,0.6666797,0.0005366553,0.0001878256,0.0003340488,0.0006690927,0.0003063108,0.009968739],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009480831,"threshold_uncertainty_score":0.01885128,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3128732602","doi":"10.1109/tac.2021.3056343","title":"An Adaptive Kalman Filter With Inaccurate Noise Covariances in the Presence of Outliers","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":119,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Outlier; Kalman filter; Noise (video); Extended Kalman filter; Computer science; Ensemble Kalman filter; Algorithm; Gaussian; Noise measurement; Probability density function; Gaussian noise; Gaussian process; Mathematics; Control theory (sociology); Artificial intelligence; Statistics; Noise reduction","authors":[{"name":"Guorui Zhang","is_ca":false},{"name":"Yongfu Li","is_ca":false},{"name":"Henry Leung","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01673102254433105,"gpt":0.245087297664296,"spread":0.228356275119965,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001669997,0.00073183,0.001160642,0.0006507192,0.0007192111,0.001002501,0.00215043,0.001278122,0.0006698208],"category_scores_gemma":[0.004555011,0.0008554315,0.0007809087,0.0009612344,0.0007594039,0.002153442,0.001196777,0.00172811,0.0002791497],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007753278,"about_ca_system_score_gemma":0.002364341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02316216,"about_ca_topic_score_gemma":0.01365814,"domain_scores_codex":[0.9986095,0.0002381776,0.00009740728,0.0003597377,0.0005560248,0.0001391126],"domain_scores_gemma":[0.9989415,0.0003779218,0.0001541591,0.0000957568,0.0003911138,0.00003954261],"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":[0.000203759,0.00004402082,0.003276001,0.0002008052,0.0001804581,0.0001923449,0.0002523633,0.7741938,0.012947,0.02857882,0.001871387,0.1780592],"study_design_scores_gemma":[0.00001228096,0.00002173665,0.0002956375,0.000008847899,0.00001940541,0.00004654245,0.000009376364,0.9950169,0.001513379,0.001953114,0.001086781,0.00001594409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003656039,0.0001620089,0.9956354,0.00005216773,0.00003106409,0.000008405541,0.00001510522,0.0001305452,0.0003092417],"genre_scores_gemma":[0.6133612,0.0008724531,0.3812324,0.0002170915,0.0001277791,0.0001280751,0.0003445814,0.0001198756,0.003596572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02316216,"threshold_uncertainty_score":0.04605466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2915945464","doi":"10.1109/tim.2019.2895495","title":"Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Khalifa University of Science, Technology and Research","keywords":"Overfitting; Computer science; Artificial intelligence; Artificial neural network; Estimator; Noise (video); Kalman filter; Inertial measurement unit; Deep learning; Dropout (neural networks); Noise measurement; Machine learning; Stability (learning theory); Noise reduction","authors":[{"name":"Mohammad Al-Sharman","is_ca":true},{"name":"Yahya Zweiri","is_ca":false},{"name":"Mohammad A. Jaradat","is_ca":false},{"name":"Raghad Al-Husari","is_ca":false},{"name":"Dongming Gan","is_ca":false},{"name":"Lakmal Seneviratne","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02971224321442426,"gpt":0.2686895848397728,"spread":0.2389773416253486,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006509831,0.0006169719,0.0003396413,0.0002933815,0.0001743614,0.0002849181,0.0005096124,0.0005604469,0.0008048],"category_scores_gemma":[0.001677889,0.0002289166,0.000272388,0.0004447951,0.0002694452,0.0005361788,0.0005899621,0.0008396764,0.0001884947],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000449523,"about_ca_system_score_gemma":0.0005360745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007839669,"about_ca_topic_score_gemma":0.008313471,"domain_scores_codex":[0.9998354,0.0000338074,0.00001312333,0.00004062807,0.00004512322,0.00003185952],"domain_scores_gemma":[0.9995281,0.0002070333,0.00005248103,0.0000469211,0.0001472822,0.00001833347],"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":[0.0001229101,0.0001218436,0.001845152,0.0001091133,0.00004497165,0.0000846627,0.00008029612,0.6122384,0.01413702,0.00215884,0.001458422,0.3675984],"study_design_scores_gemma":[0.000001601377,0.0000121307,0.0001450518,0.000002769334,0.000002497218,0.000004655399,0.000002378038,0.9975141,0.001873284,0.0002724035,0.0001670657,0.000001941918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06092064,0.0008190291,0.934131,0.0002674682,0.00007451448,0.00003919588,0.00008350893,0.001303092,0.002361601],"genre_scores_gemma":[0.8519073,0.0003960934,0.144691,0.0001273538,0.00003240971,0.00004505336,0.0002011255,0.00004990321,0.002549737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007839669,"threshold_uncertainty_score":0.0155881,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2092391127","doi":"10.1139/f07-064","title":"State–space model for light-based tracking of marine animals","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"National Marine Fisheries Service; NOAA Research; University of Hawai'i","keywords":"Drifter; Geolocation; Buoy; Mooring; Global Positioning System; Tracking (education); Meteorology; Geographic coordinate system; Sea state; Longitude; Geodesy; Trajectory; Latitude; Environmental science; Remote sensing; Computer science; Geography; Geology; Mathematics; Physics; Lagrangian; Oceanography","authors":[{"name":"Anders Nielsen","is_ca":false},{"name":"John Sibert","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03176606931588955,"gpt":0.2478827858182999,"spread":0.2161167165024104,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001206653,0.001209193,0.001371579,0.0008357004,0.0008077984,0.001792779,0.002446644,0.001920331,0.004707164],"category_scores_gemma":[0.00251227,0.0006520051,0.00104041,0.001260133,0.00107019,0.001667039,0.001204339,0.002199637,0.00160593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001406733,"about_ca_system_score_gemma":0.001161134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02614141,"about_ca_topic_score_gemma":0.01731908,"domain_scores_codex":[0.9994295,0.0001366651,0.00003291641,0.0001942158,0.000119258,0.0000873977],"domain_scores_gemma":[0.9990371,0.0005079643,0.0001610688,0.00005524134,0.0002005532,0.00003805683],"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":[0.00004050135,0.00001878128,0.0005610628,0.00002569999,0.00003061581,0.00004185619,0.0000362843,0.9821572,0.0003103024,0.01119319,0.0004410885,0.005143321],"study_design_scores_gemma":[0.000006607876,0.00001017527,0.00009891158,0.000002203912,0.000006611728,0.000005329433,0.000002788662,0.9973464,0.00004282302,0.002222676,0.0002505179,0.000004932917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01207739,0.0002713389,0.9826813,0.0002973335,0.00008190017,0.00004778931,0.0004950493,0.0005640539,0.003483827],"genre_scores_gemma":[0.9016317,0.0008208834,0.0722354,0.0002129946,0.0002008872,0.0009370696,0.001704134,0.0001267956,0.02213015],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02614141,"threshold_uncertainty_score":0.05197847,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1979659209","doi":"10.1109/taes.2014.110768","title":"Kalman and smooth variable structure filters for robust estimation","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Extended Kalman filter; Kalman filter; Control theory (sociology); Robustness (evolution); Estimator; Invariant extended Kalman filter; Unscented transform; Computer science; Alpha beta filter; Fast Kalman filter; Variable (mathematics); Mathematics; Moving horizon estimation; Artificial intelligence; Statistics","authors":[{"name":"S. Andrew Gadsden","is_ca":true},{"name":"Saeid Habibi","is_ca":true},{"name":"T. Kirubarajan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008034039809377082,"gpt":0.2057363428000091,"spread":0.197702302990632,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001378382,0.00105689,0.001258795,0.001181019,0.0003210152,0.001455577,0.0007976936,0.001438848,0.004770592],"category_scores_gemma":[0.005829105,0.0005473408,0.001080705,0.001819099,0.0009336692,0.001792866,0.0009474972,0.002030667,0.002201289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007358691,"about_ca_system_score_gemma":0.0009607304,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004205553,"about_ca_topic_score_gemma":0.002290021,"domain_scores_codex":[0.99866,0.0002815905,0.00008889797,0.0003633912,0.0005246041,0.0000814815],"domain_scores_gemma":[0.9986559,0.0006951471,0.000142502,0.0001920304,0.0002877279,0.00002669244],"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":[0.0001376533,0.00004814389,0.0006207637,0.0006292284,0.0002100556,0.0001854802,0.0001257278,0.2751225,0.006528676,0.3129025,0.01075331,0.392736],"study_design_scores_gemma":[0.0000361825,0.0001065501,0.0008825193,0.0001335407,0.00006643217,0.000181308,0.00002859213,0.7667035,0.003561176,0.1655968,0.06261264,0.0000907049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008300634,0.004489677,0.9912349,0.0001845648,0.0001961387,0.00002274092,0.0001075405,0.0003380792,0.002596271],"genre_scores_gemma":[0.1940379,0.02004934,0.7581288,0.0005225966,0.001706547,0.0003823541,0.0009632143,0.0003614457,0.02384786],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004770592,"threshold_uncertainty_score":0.01595926,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2523112045","doi":"10.1109/tsp.2017.2723348","title":"A Multisensor Multi-Bernoulli Filter","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Public Works and Government Services Canada; Defence Research and Development Canada","keywords":"Bernoulli's principle; Filter (signal processing); Computer science; Filtering theory; Control theory (sociology); Mathematics; Algorithm; Artificial intelligence; Computer vision; Engineering","authors":[{"name":"Augustin-Alexandru Saucan","is_ca":true},{"name":"Mark Coates","is_ca":true},{"name":"Michael Rabbat","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05146927237224522,"gpt":0.2957671963388285,"spread":0.2442979239665833,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001435602,0.0006512055,0.0008559109,0.0006508848,0.0004615973,0.0008428507,0.001377761,0.001411161,0.002053863],"category_scores_gemma":[0.0033119,0.0004462103,0.0007426462,0.0006925566,0.0006091162,0.001596749,0.001065177,0.001328271,0.00115462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008099974,"about_ca_system_score_gemma":0.001280519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002342263,"about_ca_topic_score_gemma":0.002062002,"domain_scores_codex":[0.9988216,0.0002075063,0.00005336642,0.000307534,0.0005333203,0.00007676143],"domain_scores_gemma":[0.9991997,0.0003524564,0.00007182223,0.0001183015,0.0002198131,0.00003793336],"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":[0.0002164178,0.0000727391,0.001438352,0.0002338681,0.000107163,0.0002252511,0.0001856598,0.5272334,0.02821684,0.1020831,0.003509208,0.3364781],"study_design_scores_gemma":[0.000009468848,0.00003353773,0.0001966514,0.00001250188,0.00001216295,0.000128463,0.000005029327,0.9864359,0.004041562,0.006145298,0.002964164,0.00001520257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007674618,0.00006196411,0.998625,0.00003451399,0.00002682403,0.000009215546,0.00001548763,0.00007029649,0.0003892781],"genre_scores_gemma":[0.15297,0.0005686223,0.8411276,0.0002900341,0.0001436247,0.0001579022,0.0002246241,0.00007451486,0.004443201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002342263,"threshold_uncertainty_score":0.00759232,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002910452","doi":"10.1016/j.sigpro.2013.08.015","title":"Combined cubature Kalman and smooth variable structure filtering: A robust nonlinear estimation strategy","year":2013,"lang":"en","type":"article","venue":"Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Ford Motor Company (Canada); McMaster University","funders":"","keywords":"Kalman filter; Robustness (evolution); Control theory (sociology); Nonlinear system; Mathematics; Gaussian; State variable; Algorithm; Computer science; Statistics; Artificial intelligence","authors":[{"name":"S. Andrew Gadsden","is_ca":true},{"name":"Mohammad Al‐Shabi","is_ca":false},{"name":"Ienkaran Arasaratnam","is_ca":true},{"name":"Saeid Habibi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0132410885447598,"gpt":0.2223789842309562,"spread":0.2091378956861964,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001363591,0.0009331155,0.001701834,0.000745696,0.000487905,0.001189202,0.001135942,0.001378389,0.001729796],"category_scores_gemma":[0.002623198,0.0008029125,0.0008622179,0.0009087758,0.0006079519,0.00165229,0.001438853,0.0009829177,0.000687803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004884453,"about_ca_system_score_gemma":0.0009427417,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003844512,"about_ca_topic_score_gemma":0.003660144,"domain_scores_codex":[0.9993279,0.0001902622,0.0000407372,0.0001521536,0.0002320074,0.00005688407],"domain_scores_gemma":[0.9989893,0.000413908,0.00009779288,0.0001332998,0.0003316684,0.00003402567],"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":[0.0004544135,0.0001105,0.0007524614,0.0002236857,0.0002495172,0.0001143097,0.0001267193,0.599638,0.02601657,0.04214068,0.002709376,0.3274638],"study_design_scores_gemma":[0.000008644774,0.00003731771,0.00009797687,0.000004093867,0.00001527625,0.00002057172,0.000003545191,0.9944978,0.00172018,0.002679172,0.0009040879,0.00001136209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002298207,0.0001378279,0.9969262,0.00004279171,0.00002910901,0.000007870162,0.000009872618,0.0001074328,0.0004406556],"genre_scores_gemma":[0.3838682,0.0005628553,0.6065543,0.0001728253,0.000177186,0.0001742109,0.0002276323,0.0001962613,0.008066553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003844512,"threshold_uncertainty_score":0.007644236,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1941377048","doi":"10.1137/15m100955x","title":"Multilevel ensemble Kalman filtering","year":2016,"lang":"en","type":"article","venue":"SIAM Journal on Numerical Analysis","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Kootenay Association for Science & Technology","funders":"Oak Ridge National Laboratory; Laboratory Directed Research and Development; King Abdullah University of Science and Technology","keywords":"Kalman filter; Mathematics; Ensemble Kalman filter; Monte Carlo method; Stochastic differential equation; Applied mathematics; Statistical physics; Algorithm; Mathematical optimization; Extended Kalman filter; Statistics; Physics","authors":[{"name":"Håkon Hoel","is_ca":true},{"name":"Kody J. H. Law","is_ca":false},{"name":"Raúl Tempone","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01889284729905682,"gpt":0.2550497568181999,"spread":0.2361569095191431,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009922528,0.0004817202,0.001248805,0.0006285006,0.0005084404,0.000902331,0.001374388,0.0009325049,0.002408629],"category_scores_gemma":[0.004349775,0.0003656069,0.0009567514,0.0007761823,0.000498154,0.00180089,0.002159399,0.001422241,0.0006676373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005641583,"about_ca_system_score_gemma":0.0008834995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005976709,"about_ca_topic_score_gemma":0.005588844,"domain_scores_codex":[0.9989893,0.0002493977,0.00005747861,0.0002006019,0.0003987079,0.0001044935],"domain_scores_gemma":[0.9982687,0.0007160898,0.0001667019,0.0004019628,0.0003787985,0.00006773846],"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":[0.0001193371,0.00003799862,0.002128038,0.0001045689,0.000143419,0.00008739403,0.00009989605,0.8031263,0.004814558,0.06137845,0.001919463,0.1260405],"study_design_scores_gemma":[0.000003231595,0.00001334233,0.0001519077,0.00000554232,0.000007416182,0.00001283545,0.000004281585,0.9925337,0.0005592519,0.005871589,0.0008307786,0.000006130326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005662564,0.0001926303,0.9922116,0.00007294163,0.00003969832,0.00001141232,0.00007028133,0.0002070419,0.001531939],"genre_scores_gemma":[0.5599588,0.0007429267,0.4348974,0.0001696827,0.0001489833,0.000140739,0.0005593078,0.0001111393,0.003271073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005976709,"threshold_uncertainty_score":0.01188385,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2041348998","doi":"10.1016/j.sigpro.2012.07.036","title":"Kalman filtering strategies utilizing the chattering effects of the smooth variable structure filter","year":2012,"lang":"en","type":"article","venue":"Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Control theory (sociology); Kalman filter; Robustness (evolution); Extended Kalman filter; Invariant extended Kalman filter; Unscented transform; Nonlinear system; Alpha beta filter; Fast Kalman filter; Computer science; Mathematics; Moving horizon estimation; Artificial intelligence; Physics","authors":[{"name":"Mohammad Al‐Shabi","is_ca":false},{"name":"S. Andrew Gadsden","is_ca":true},{"name":"Saeid Habibi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0129132641325198,"gpt":0.2297087900840749,"spread":0.2167955259515551,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000890881,0.0006467162,0.0007784112,0.0005520441,0.0004214529,0.000799592,0.0006959716,0.0007436324,0.001600216],"category_scores_gemma":[0.003271839,0.0003682586,0.000568665,0.0005277679,0.0006571956,0.001216951,0.0007636558,0.0009292512,0.0005426655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003498768,"about_ca_system_score_gemma":0.0007013558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002347902,"about_ca_topic_score_gemma":0.003358163,"domain_scores_codex":[0.9996256,0.00009834201,0.00002983111,0.00006908228,0.0001493752,0.00002775637],"domain_scores_gemma":[0.9987912,0.0006726006,0.00009794988,0.0001482938,0.0002657703,0.00002422095],"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":[0.000230791,0.000080394,0.001018735,0.0003637285,0.0002270462,0.0001364907,0.0003725988,0.5587904,0.03120331,0.1015608,0.001929449,0.3040862],"study_design_scores_gemma":[0.00001833771,0.00009197201,0.000267344,0.00001343112,0.00003124973,0.00003675214,0.00001388045,0.9821119,0.004259073,0.01156339,0.001572553,0.00002009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002836596,0.000144348,0.9963542,0.00002417338,0.00003284085,0.000008137012,0.000005259811,0.00009310559,0.0005014057],"genre_scores_gemma":[0.5410053,0.0008757049,0.4506588,0.0001203541,0.0001232725,0.0001184033,0.0001016438,0.0001252704,0.006871323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002347902,"threshold_uncertainty_score":0.005353212,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152670243","doi":"10.1109/tsp.2007.907883","title":"An EM Algorithm for Nonlinear State Estimation With Model Uncertainties","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":85,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Extended Kalman filter; Particle filter; Expectation–maximization algorithm; State vector; Kalman filter; Estimation theory; Algorithm; Ensemble Kalman filter; Mathematics; Nonlinear system; A priori and a posteriori; Markov chain Monte Carlo; Mathematical optimization; Monte Carlo method; Computer science; Statistics","authors":[{"name":"Amin Zia","is_ca":true},{"name":"T. Kirubarajan","is_ca":true},{"name":"J.P. Reilly","is_ca":true},{"name":"Derek Yee","is_ca":true},{"name":"Kumaradevan Punithakumar","is_ca":true},{"name":"Shahram Shirani","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02664566972613973,"gpt":0.2653166356978732,"spread":0.2386709659717335,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00126405,0.001397684,0.001320588,0.000837153,0.0006109428,0.0009539706,0.001719489,0.001615614,0.003727859],"category_scores_gemma":[0.003637015,0.0007909438,0.00106045,0.001295252,0.0007583578,0.001658531,0.001640449,0.002406551,0.001807304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006777185,"about_ca_system_score_gemma":0.001414997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004363631,"about_ca_topic_score_gemma":0.003604699,"domain_scores_codex":[0.9993124,0.0002334028,0.00004523858,0.0001814376,0.0001887366,0.0000387923],"domain_scores_gemma":[0.9992948,0.0004248073,0.00005635352,0.0000632907,0.000144697,0.00001608852],"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":[0.00008611389,0.00004231305,0.0004700343,0.0001903453,0.0001114143,0.0001308569,0.0001071279,0.6662533,0.002284804,0.0545126,0.006045651,0.2697655],"study_design_scores_gemma":[0.00001335319,0.00001620451,0.00008691853,0.00001683232,0.000009135627,0.00005130992,0.000009078626,0.9801899,0.0007195732,0.01371966,0.005154217,0.00001383764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003481304,0.0001645867,0.998607,0.00004717219,0.00002140332,0.00001387766,0.00002434528,0.000217822,0.0005556276],"genre_scores_gemma":[0.05145692,0.0007812248,0.9418129,0.0001222085,0.0001130479,0.0003013231,0.0004459626,0.0001869151,0.004779585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004363631,"threshold_uncertainty_score":0.0124709,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2994954803","doi":"10.1016/j.sysarc.2019.101694","title":"Efficient drone hijacking detection using two-step GA-XGBoost","year":2019,"lang":"en","type":"article","venue":"Journal of Systems Architecture","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"China Scholarship Council; Northeastern University; Shenyang Institute of Automation; National Natural Science Foundation of China","keywords":"Drone; Computer science; Correctness; Global Positioning System; Spoofing attack; Inertial measurement unit; Real-time computing; Artificial intelligence; Upload; Computer security; Algorithm; Operating system","authors":[{"name":"Zhiwei Feng","is_ca":false},{"name":"Nan Guan","is_ca":false},{"name":"Mingsong Lv","is_ca":false},{"name":"Wenchen Liu","is_ca":false},{"name":"Qingxu Deng","is_ca":false},{"name":"Xue Liu","is_ca":true},{"name":"Wang Yi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0104882875912265,"gpt":0.2323840885458052,"spread":0.2218958009545787,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005854191,0.001232184,0.001601057,0.0008153862,0.0006711797,0.0008184049,0.001954768,0.001613122,0.002116954],"category_scores_gemma":[0.001022506,0.000516448,0.0007665257,0.0006046696,0.0004836233,0.0007451806,0.001017211,0.001032057,0.0007636194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005744795,"about_ca_system_score_gemma":0.001798898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01426068,"about_ca_topic_score_gemma":0.01545235,"domain_scores_codex":[0.9995199,0.00006836392,0.00001883085,0.0001164799,0.0001242412,0.0001520675],"domain_scores_gemma":[0.9996731,0.0001006692,0.00003361706,0.00003485608,0.0001287809,0.00002890646],"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":[0.0007645031,0.0004503367,0.003507202,0.0001057394,0.0002382965,0.0001542016,0.00007803622,0.4924501,0.01773362,0.002098999,0.005320702,0.4770983],"study_design_scores_gemma":[0.0000161313,0.00006939698,0.0005262814,0.00000514712,0.00001559781,0.00002949861,0.00001032959,0.9971595,0.001507104,0.0003477084,0.000306129,0.000007219541],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07721574,0.0006618458,0.9147286,0.0002219957,0.0002352153,0.000151891,0.00007430706,0.003038194,0.003672362],"genre_scores_gemma":[0.7262176,0.0001629647,0.2638533,0.0003804723,0.00005795324,0.0001997036,0.0002892773,0.0001528418,0.008685885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01426068,"threshold_uncertainty_score":0.02835536,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108371319","doi":"10.1109/tits.2004.828169","title":"Simultaneous Registration and Fusion of Multiple Dissimilar Sensors for Cooperative Driving","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Kalman filter; Sensor fusion; Platoon; Computer vision; Fuse (electrical); Computer science; Global Positioning System; Artificial intelligence; Key (lock); Inertial measurement unit; Intelligent transportation system; Real-time computing; Radar; Engineering; Telecommunications","authors":[{"name":"Wenjie Li","is_ca":true},{"name":"Henry Leung","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02097660468860363,"gpt":0.2555294403562073,"spread":0.2345528356676037,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001421084,0.0005702287,0.0007514435,0.000449816,0.0003988928,0.0005943159,0.0008184802,0.0009437739,0.0004123061],"category_scores_gemma":[0.00423798,0.0004813356,0.0006597642,0.0005585297,0.0007441856,0.001907109,0.001582731,0.0007728448,0.0002568372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004507034,"about_ca_system_score_gemma":0.0007517629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002294851,"about_ca_topic_score_gemma":0.001936051,"domain_scores_codex":[0.9988666,0.0003288976,0.00006474481,0.0002510324,0.0003837919,0.0001050152],"domain_scores_gemma":[0.9988596,0.0003812006,0.00020401,0.0003168544,0.0001984048,0.00004000312],"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":[0.0003633734,0.00009119812,0.001989496,0.0001436491,0.00008953532,0.0002585605,0.0004334456,0.6482397,0.06188259,0.01684172,0.0009544711,0.2687123],"study_design_scores_gemma":[0.00001185481,0.00008065194,0.0005022897,0.000004651474,0.00001626957,0.00009102355,0.00004382532,0.9809994,0.01182711,0.005107551,0.001296755,0.00001866591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02182127,0.000121709,0.9773667,0.00006072045,0.00001921338,0.00001239572,0.000009279444,0.0001487231,0.000439828],"genre_scores_gemma":[0.8282886,0.0001751281,0.1702666,0.00003924228,0.00003295596,0.00004585442,0.00007557232,0.00003387787,0.001042053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002294851,"threshold_uncertainty_score":0.00751549,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2060702310","doi":"10.1109/radar.2010.5494383","title":"Cognitive tracking radar","year":2010,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Waveform; Computer science; Radar; Radar tracker; Transmitter; Tracking (education); Low probability of intercept radar; Radar engineering details; Fire-control radar; Real-time computing; Dynamic programming; Tracking error; Selection (genetic algorithm); Algorithm; Artificial intelligence; Radar imaging; Telecommunications; Channel (broadcasting)","authors":[{"name":"S. Haykin","is_ca":true},{"name":"Amin Zia","is_ca":true},{"name":"Ienkaran Arasaratnam","is_ca":true},{"name":"Yanbo Xue","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01715741580953993,"gpt":0.2585445770460868,"spread":0.2413871612365469,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006239958,0.000437245,0.0004942177,0.0002628351,0.0004068354,0.001230583,0.001037807,0.000997397,0.001921259],"category_scores_gemma":[0.001619568,0.0001698211,0.000393763,0.000351785,0.0006628853,0.001120512,0.0009966936,0.0009543761,0.0008137624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004661906,"about_ca_system_score_gemma":0.0006417781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008412075,"about_ca_topic_score_gemma":0.0008941801,"domain_scores_codex":[0.9993598,0.0001286935,0.00001910712,0.0001334899,0.0002867188,0.0000722363],"domain_scores_gemma":[0.999332,0.0001866093,0.00007782879,0.0001299863,0.0002251552,0.00004844362],"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":[0.0003320954,0.0001359124,0.001158069,0.0003709839,0.0001179344,0.0001800417,0.000200025,0.07984509,0.04655372,0.2455292,0.01332487,0.612252],"study_design_scores_gemma":[0.000107005,0.0007816607,0.001217867,0.0001289939,0.0001623119,0.00140839,0.000123397,0.6327991,0.04925183,0.08144914,0.2324207,0.0001496247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007843125,0.002345888,0.9680313,0.0006953313,0.0005185017,0.00003773159,0.00002800972,0.0002794221,0.02022064],"genre_scores_gemma":[0.5362824,0.003708783,0.4336182,0.001676076,0.001228562,0.0001158216,0.0001534395,0.00006192406,0.02315475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001921259,"threshold_uncertainty_score":0.006427288,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1968476015","doi":"10.1109/jstsp.2012.2233459","title":"Detection of Anomalous Trajectory Patterns in Target Tracking via Stochastic Context-Free Grammars and Reciprocal Process Models","year":2012,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"University of Adelaide","keywords":"Computer science; Trajectory; Reciprocal; Markov chain; Context (archaeology); Markov process; Hidden Markov model; Random walk; Bayesian probability; Stochastic process; Markov model; Point process; Artificial intelligence; Algorithm; Machine learning; Mathematics; Statistics","authors":[{"name":"Mustafa Fanaswala","is_ca":true},{"name":"Vikram Krishnamurthy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02167600160328232,"gpt":0.2485832474212066,"spread":0.2269072458179243,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002812045,0.000585196,0.0009789206,0.001496291,0.0005143461,0.001426212,0.001415294,0.001441479,0.0007099241],"category_scores_gemma":[0.01489086,0.0006639542,0.001395044,0.001132551,0.00186403,0.002439651,0.001331524,0.001512485,0.0002556542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001334255,"about_ca_system_score_gemma":0.00127328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01052252,"about_ca_topic_score_gemma":0.00879428,"domain_scores_codex":[0.9984043,0.0006135485,0.0000973685,0.0004447579,0.000313061,0.0001269479],"domain_scores_gemma":[0.9912697,0.006789756,0.0007870961,0.0005779681,0.0004149395,0.0001605524],"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":[0.00009222094,0.0000545107,0.004390266,0.00005886569,0.00006269383,0.0003108041,0.0004069501,0.8539903,0.002300974,0.1050108,0.0005248356,0.0327969],"study_design_scores_gemma":[0.000003367407,0.000007489795,0.0002274804,0.000003151384,0.000006141972,0.00002299573,0.00001038026,0.9680181,0.0002257444,0.0313411,0.0001253994,0.000008589205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08407287,0.0001549923,0.9141521,0.0003001348,0.00001306268,0.00002578494,0.0001513339,0.0004176056,0.0007121232],"genre_scores_gemma":[0.8760737,0.0002748871,0.1217827,0.000104759,0.00003250056,0.00009161265,0.0004090517,0.00009545797,0.001135462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01052252,"threshold_uncertainty_score":0.02092254,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2121538634","doi":"10.1109/jproc.2003.823143","title":"Bayesian sequential state estimation for MIMO wireless communications","year":2004,"lang":"en","type":"article","venue":"Proceedings of the IEEE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Particle filter; MIMO; Computer science; Kalman filter; Channel state information; Narrowband; Channel (broadcasting); Wireless; Algorithm; Control theory (sociology); Electronic engineering; Telecommunications; Artificial intelligence; Engineering","authors":[{"name":"S. Haykin","is_ca":true},{"name":"Kurt Huber","is_ca":true},{"name":"Zhe Chen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02408847085964534,"gpt":0.2681437588991853,"spread":0.24405528803954,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001722008,0.0007986067,0.0009729276,0.0005460739,0.0003986026,0.0009221252,0.0008196852,0.001084695,0.001764249],"category_scores_gemma":[0.008497769,0.0005646287,0.000488551,0.0008496046,0.0008801503,0.001717891,0.0008269878,0.001552559,0.0004769396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009643316,"about_ca_system_score_gemma":0.001007352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005140572,"about_ca_topic_score_gemma":0.004586001,"domain_scores_codex":[0.9991198,0.0003261149,0.00004362176,0.0001591117,0.0002935106,0.00005786493],"domain_scores_gemma":[0.9969343,0.002372337,0.0001969648,0.0001933011,0.0002609444,0.00004209257],"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":[0.0001060109,0.00004761456,0.0006653412,0.0001463115,0.00007629661,0.00007822401,0.00009598795,0.747171,0.002156893,0.1264292,0.002047138,0.1209799],"study_design_scores_gemma":[0.00001058641,0.0000314315,0.0002068022,0.00001223971,0.00001073422,0.00002336753,0.000006812557,0.9613787,0.0004912759,0.03624881,0.001566689,0.00001249671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001478864,0.0004554651,0.9971494,0.0001114247,0.00003493896,0.00001007514,0.0000197129,0.000090093,0.0006499272],"genre_scores_gemma":[0.4939247,0.004684827,0.4932392,0.0002969209,0.000495033,0.0002981415,0.0003442428,0.0001079538,0.006608944],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005140572,"threshold_uncertainty_score":0.0102213,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994597642","doi":"10.1115/1.4006628","title":"A New Robust Filtering Strategy for Linear Systems","year":2012,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Smoothing; Kalman filter; Control theory (sociology); Robustness (evolution); Covariance; Parametric statistics; Mathematics; Linear system; Gaussian; Computer science; Mathematical optimization; Algorithm; Applied mathematics; Statistics; Mathematical analysis","authors":[{"name":"S. Andrew Gadsden","is_ca":false},{"name":"Saeid Habibi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05275953804249949,"gpt":0.251346866601759,"spread":0.1985873285592595,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001622013,0.0013698,0.001181216,0.000880764,0.000388639,0.001340287,0.001047475,0.001176718,0.003567294],"category_scores_gemma":[0.002750351,0.0004155996,0.001075301,0.000565809,0.0007867328,0.001320149,0.001191178,0.00139294,0.0009170234],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000757219,"about_ca_system_score_gemma":0.0007215305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003227478,"about_ca_topic_score_gemma":0.001821282,"domain_scores_codex":[0.9988779,0.0003035815,0.0000868451,0.0002648627,0.0004079767,0.00005883815],"domain_scores_gemma":[0.9992035,0.0003723064,0.00008426373,0.00006775522,0.0002525617,0.00001971586],"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":[0.00009526675,0.00005313155,0.0001890408,0.0003497996,0.000155746,0.0002323997,0.0001951798,0.6748632,0.01234477,0.1356588,0.00315892,0.1727038],"study_design_scores_gemma":[0.00001013026,0.0000674858,0.00004233287,0.00001310081,0.0000160417,0.00002585778,0.000006063606,0.9844135,0.001013234,0.01162833,0.00275279,0.00001118888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009110087,0.0001710874,0.9970865,0.00005831381,0.00004193666,0.00001667665,0.00001178707,0.0001231121,0.001579669],"genre_scores_gemma":[0.4843893,0.00140724,0.4960864,0.0004235216,0.0004924398,0.0005299885,0.0002709552,0.0001742666,0.01622588],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003567294,"threshold_uncertainty_score":0.01193386,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2065935304","doi":"10.1109/jsen.2014.2388153","title":"A Kalman Filter-Based Framework for Enhanced Sensor Fusion","year":2015,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Kalman filter; Covariance intersection; Robustness (evolution); Initialization; Sensor fusion; Fast Kalman filter; Covariance; Computer science; Extended Kalman filter; Noise (video); Fusion; Noise measurement; Ensemble Kalman filter; Invariant extended Kalman filter; Control theory (sociology); Engineering; Artificial intelligence; Mathematics; Noise reduction; Statistics","authors":[{"name":"Akbar Assa","is_ca":true},{"name":"Farrokh Janabi‐Sharifi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0475242517804953,"gpt":0.2953507278275746,"spread":0.2478264760470793,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009195292,0.0008633721,0.0008840275,0.0006007685,0.0004411347,0.0008360596,0.001280438,0.001025775,0.002263247],"category_scores_gemma":[0.001410052,0.0003547729,0.000884132,0.000814023,0.0004782494,0.001663903,0.001171539,0.001140301,0.0008246959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006302227,"about_ca_system_score_gemma":0.001164942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006602752,"about_ca_topic_score_gemma":0.004973472,"domain_scores_codex":[0.9992961,0.0001489588,0.00005323599,0.0001655394,0.0002820169,0.00005416016],"domain_scores_gemma":[0.9997116,0.00008092019,0.00003273418,0.00003878441,0.0001224043,0.00001352567],"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":[0.0001084519,0.00005796841,0.0003749494,0.000259438,0.0001120243,0.0002281802,0.0001592784,0.6444386,0.01810583,0.1178853,0.004244382,0.2140256],"study_design_scores_gemma":[0.00001102968,0.0000487722,0.0001443602,0.00001606866,0.00001931921,0.00006840964,0.00001104083,0.9765086,0.002736291,0.01169148,0.008716528,0.00002810872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003433553,0.0002288831,0.9985452,0.00002722356,0.00003159691,0.00001116504,0.00001869668,0.0001523828,0.0006416003],"genre_scores_gemma":[0.2171923,0.001897989,0.7738203,0.000155161,0.0002166192,0.000228706,0.00031284,0.0001344216,0.006041669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006602752,"threshold_uncertainty_score":0.01312864,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2116697437","doi":"10.1109/taes.2012.6178056","title":"Track Quality Based Multitarget Tracking Approach for Global Nearest-Neighbor Association","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada; McMaster University; Raytheon Technologies (Canada); AUG Signals (Canada)","funders":"","keywords":"Track (disk drive); Computer science; Probabilistic logic; False alarm; Unobservable; Tracking (education); Artificial intelligence; Measure (data warehouse); Association (psychology); Computer vision; Data mining; Mathematics","authors":[{"name":"Abhijit Sinha","is_ca":true},{"name":"Zhen Ding","is_ca":true},{"name":"T. Kirubarajan","is_ca":true},{"name":"M. Farooq","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02584021715594951,"gpt":0.2779965019342499,"spread":0.2521562847783003,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001556561,0.0007219848,0.00112191,0.001157138,0.0005000848,0.001176247,0.001579968,0.0007940627,0.001725941],"category_scores_gemma":[0.003524274,0.0002682869,0.0008533559,0.001448049,0.0005176389,0.001836092,0.001452214,0.001098073,0.0005462973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009578528,"about_ca_system_score_gemma":0.0008860299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003535144,"about_ca_topic_score_gemma":0.002460648,"domain_scores_codex":[0.9983682,0.0002144042,0.00008075606,0.0004945403,0.0007453996,0.00009668341],"domain_scores_gemma":[0.998634,0.0003827165,0.0002010518,0.0002264715,0.0004998644,0.00005579621],"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":[0.0001850101,0.0001302469,0.003698627,0.0002561315,0.0001583859,0.0001808713,0.0002489201,0.4223576,0.01273808,0.02317161,0.002389092,0.5344855],"study_design_scores_gemma":[0.000009100022,0.00009449734,0.000939786,0.00001361872,0.00004222725,0.0001656577,0.00002212452,0.9885046,0.003192657,0.004510794,0.002482574,0.00002239305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003498828,0.0003663514,0.9949775,0.00003560269,0.00004299511,0.00002558271,0.00001916778,0.0001833574,0.0008505547],"genre_scores_gemma":[0.5049857,0.001097049,0.4879209,0.000187894,0.0001992156,0.0001803009,0.0003411757,0.000148565,0.004939271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003535144,"threshold_uncertainty_score":0.008231997,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2130828767","doi":"10.1109/tsp.2013.2245123","title":"Distributed Particle Filter Implementation With Intermittent/Irregular Consensus Convergence","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Particle filter; Algorithm; Fisher information; Gaussian; Convergence (economics); Computer science; Estimator; Filter (signal processing); Control theory (sociology); Sensor fusion; Mathematical optimization; Mathematics; Artificial intelligence; Statistics; Computer vision; Physics; Machine learning","authors":[{"name":"Arash Mohammadi","is_ca":true},{"name":"Amir Asif","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01833047543619573,"gpt":0.2532243351472778,"spread":0.2348938597110821,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00129226,0.0004311107,0.0006285069,0.0003648328,0.0004213688,0.0006881272,0.001316234,0.000985835,0.001845174],"category_scores_gemma":[0.004511401,0.0002770463,0.0004532369,0.000431606,0.0005453842,0.0008897209,0.0008693803,0.001149682,0.000527109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005933354,"about_ca_system_score_gemma":0.001181445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003661885,"about_ca_topic_score_gemma":0.003376441,"domain_scores_codex":[0.9992712,0.0001566668,0.0000426191,0.0001413559,0.0003311504,0.00005695728],"domain_scores_gemma":[0.9984466,0.0006122792,0.0001261263,0.0003236164,0.0004468841,0.00004442892],"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":[0.00008622667,0.00005529374,0.000671259,0.0000648873,0.00003469463,0.00008777301,0.00009968662,0.8706456,0.005199243,0.03000683,0.001345195,0.09170334],"study_design_scores_gemma":[0.00001143293,0.00001946726,0.00005381392,0.000001956015,0.000002559332,0.0000129259,0.000003794457,0.9957184,0.001137062,0.002614538,0.0004210037,0.000003058496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003864001,0.0000241555,0.9950079,0.00005207273,0.00002038387,0.0000118185,0.000008214757,0.0002470788,0.0007644902],"genre_scores_gemma":[0.5422503,0.00009152492,0.4548524,0.00007662173,0.00005199047,0.0001807066,0.00008535806,0.00006296808,0.002348162],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003661885,"threshold_uncertainty_score":0.007281125,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2503670596","doi":"10.1109/tsp.2017.2703684","title":"Particle Filtering With Invertible Particle Flow","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Particle filter; Auxiliary particle filter; Degeneracy (biology); Flow (mathematics); Algorithm; Particle (ecology); Computer science; Mathematical optimization; Mathematics; Filter (signal processing); Applied mathematics; Ensemble Kalman filter; Kalman filter; Artificial intelligence; Extended Kalman filter; Computer vision; Geometry","authors":[{"name":"Yunpeng Li","is_ca":true},{"name":"Mark Coates","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03414034921326445,"gpt":0.2588743290496557,"spread":0.2247339798363913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001925474,0.0007566559,0.0006892551,0.0006398497,0.0004866658,0.0009548877,0.0008872485,0.001620041,0.001766254],"category_scores_gemma":[0.007467172,0.0004949216,0.0009698846,0.0007223726,0.0008897365,0.001277156,0.001140653,0.001796049,0.0004806506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006977734,"about_ca_system_score_gemma":0.00141295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006613306,"about_ca_topic_score_gemma":0.003361506,"domain_scores_codex":[0.9990817,0.0002762778,0.00005330417,0.000198338,0.0003238111,0.00006640001],"domain_scores_gemma":[0.9984937,0.0009736227,0.0001175194,0.0001732996,0.0002098337,0.00003202016],"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":[0.00007158903,0.00004761024,0.0005592531,0.0001100031,0.00004422341,0.00009518502,0.0001067982,0.8103133,0.002924038,0.06184948,0.001387116,0.1224914],"study_design_scores_gemma":[0.00001071413,0.00001609326,0.00008666793,0.000004896203,0.000004108858,0.00001927362,0.000004190018,0.9899754,0.0008192415,0.007950216,0.001102652,0.000006580844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001408146,0.00004005959,0.9978572,0.00005180117,0.00002381592,0.00001988737,0.00001126968,0.0001216981,0.0004662106],"genre_scores_gemma":[0.229684,0.000410774,0.7650962,0.0001853473,0.0001265887,0.0002731739,0.0001640036,0.00008574752,0.003974145],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006613306,"threshold_uncertainty_score":0.01314962,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2108777121","doi":"10.1109/tsp.2007.908949","title":"Signal Interpretation of Multifunction Radars: Modeling and Statistical Signal Processing With Stochastic Context Free Grammar","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Statistical signal processing; Radar; Estimator; Artificial intelligence; SIGNAL (programming language); Signal processing; Speech recognition; Algorithm; Pattern recognition (psychology); Mathematics; Statistics","authors":[{"name":"A. Wang","is_ca":true},{"name":"Vikram Krishnamurthy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02222943704889509,"gpt":0.2373248540300424,"spread":0.2150954169811473,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001343468,0.0007030743,0.0007439652,0.001034433,0.0005662835,0.001833123,0.001714598,0.00166798,0.001255134],"category_scores_gemma":[0.004730612,0.0005819743,0.001714315,0.0007041916,0.002212791,0.001707701,0.0008907885,0.001679742,0.000379143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00176883,"about_ca_system_score_gemma":0.001657495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01493465,"about_ca_topic_score_gemma":0.007592352,"domain_scores_codex":[0.9987988,0.0004274254,0.00007230793,0.0002849766,0.0003180269,0.00009849523],"domain_scores_gemma":[0.9961705,0.003050558,0.0002703207,0.0001709424,0.0002885595,0.00004919122],"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":[0.00003246702,0.00004413018,0.001493008,0.00007135137,0.00003912011,0.0004679901,0.0005448599,0.7971749,0.003315363,0.1781144,0.0008132663,0.01788911],"study_design_scores_gemma":[0.000002898055,0.0000061987,0.0001287165,0.000005206991,0.000006529075,0.00002554345,0.00001213024,0.962632,0.000390013,0.03630506,0.000478708,0.000006923629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02455915,0.0002197217,0.9723831,0.0004394647,0.00002789228,0.00004321817,0.0001778427,0.0005153839,0.001634225],"genre_scores_gemma":[0.7368634,0.0004833142,0.2575335,0.0002187733,0.0001149291,0.0002306371,0.0005697354,0.0003099241,0.003675769],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01493465,"threshold_uncertainty_score":0.02969545,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971518541","doi":"10.1117/12.883988","title":"Information fusion measures of effectiveness (MOE) for decision support","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Computer science; Robustness (evolution); Decision support system; Decision tree; Decision quality; Sensor fusion; Data mining; Machine learning; Artificial intelligence; Information gain ratio; Knowledge management","authors":[{"name":"Erik Blasch","is_ca":true},{"name":"Richard Breton","is_ca":true},{"name":"Pierre Valin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01945416248394658,"gpt":0.232131545899073,"spread":0.2126773834151264,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05167939,0.003055663,0.003840972,0.01134306,0.001383262,0.007825576,0.002613012,0.004204364,0.004784486],"category_scores_gemma":[0.1931212,0.0007205846,0.003033049,0.00899895,0.003887941,0.01330895,0.004436483,0.004417361,0.001542663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003720328,"about_ca_system_score_gemma":0.002270498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001145147,"about_ca_topic_score_gemma":0.0006933883,"domain_scores_codex":[0.9293478,0.03777976,0.006123043,0.0042892,0.02136243,0.001097741],"domain_scores_gemma":[0.7762113,0.18325,0.01273238,0.01424802,0.01258401,0.0009744386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009021814,0.0003971257,0.01301003,0.003083817,0.001842774,0.0001774818,0.0008539486,0.08046626,0.003051039,0.4802945,0.01071796,0.4052029],"study_design_scores_gemma":[0.0001343346,0.001267726,0.01029935,0.001635239,0.000776225,0.0006746182,0.0009952887,0.2209937,0.009529623,0.7054018,0.04781715,0.0004749378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0120057,0.01372019,0.9513751,0.002688443,0.000486489,0.0006501693,0.001053226,0.0005260807,0.01749462],"genre_scores_gemma":[0.3871156,0.004991172,0.6004738,0.001224067,0.0009950424,0.001992758,0.001113205,0.0002811218,0.00181324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05167939,"threshold_uncertainty_score":0.27331,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2833602261","doi":"10.1109/tac.2018.2854897","title":"Multiple-Model State Estimation Based on Variational Bayesian Inference","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Automatic Control","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Bayesian inference; Computer science; Algorithm; Gaussian; State space; Bayesian probability; State-space representation; Extension (predicate logic); Approximate inference; Mixture model; Mathematical optimization; Mathematics; Applied mathematics; Artificial intelligence; Statistics","authors":[{"name":"Yanjun Ma","is_ca":true},{"name":"Shunyi Zhao","is_ca":true},{"name":"Biao Huang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01361277080319994,"gpt":0.2556674653651453,"spread":0.2420546945619454,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001593451,0.0009497305,0.001632851,0.001074039,0.0005503076,0.00119329,0.002567398,0.001161784,0.001838861],"category_scores_gemma":[0.004700754,0.001052733,0.001477319,0.001132998,0.0008567378,0.002453934,0.001680096,0.002230942,0.0004906693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009498286,"about_ca_system_score_gemma":0.00138447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008547446,"about_ca_topic_score_gemma":0.006933363,"domain_scores_codex":[0.9989563,0.0003318546,0.00004879009,0.0002220506,0.0003690655,0.00007190313],"domain_scores_gemma":[0.998647,0.0009384828,0.0001250295,0.00009891154,0.0001533432,0.0000372326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003948918,0.00003848656,0.000569333,0.0001048922,0.0001149696,0.00008702857,0.00008990255,0.8765698,0.001604225,0.05631625,0.0008609553,0.06360476],"study_design_scores_gemma":[0.00000265664,0.000004643344,0.00003486975,0.000003579601,0.000005645339,0.00001215674,0.000002469759,0.9916497,0.0001804848,0.007795671,0.0003022839,0.000005706782],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009092641,0.0001017666,0.9984967,0.00004296331,0.00001075193,0.000007895748,0.00001229448,0.00009275164,0.0003257509],"genre_scores_gemma":[0.371544,0.0009079094,0.6225349,0.000212817,0.0001408593,0.0002192215,0.0004017017,0.0002372399,0.003801483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008547446,"threshold_uncertainty_score":0.01699543,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2050563854","doi":"10.1109/tsp.2015.2407322","title":"A Multiple-Detection Probability Hypothesis Density Filter","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Filter (signal processing); Computer science; Probability density function; Recursion (computer science); Algorithm; Clutter; Radar tracker; Tracking (education); Finite set; Gaussian; Set (abstract data type); Matched filter; Radar; Statistical power; Multipath propagation; Mathematics; Statistics; Computer vision","authors":[{"name":"Xu Tang","is_ca":false},{"name":"Xin Chen","is_ca":true},{"name":"Michael McDonald","is_ca":true},{"name":"Ronald Mahler","is_ca":false},{"name":"Ratnasingham Tharmarasa","is_ca":true},{"name":"Thiagalingam Kirubarajan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05900416336028731,"gpt":0.2445620846247452,"spread":0.1855579212644579,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001961353,0.0006527384,0.001238196,0.0006942476,0.0004408931,0.001009626,0.002095044,0.00181206,0.003953914],"category_scores_gemma":[0.005014524,0.0005418212,0.00082631,0.001105279,0.0006769155,0.002051102,0.0009644562,0.001538688,0.0014806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009031774,"about_ca_system_score_gemma":0.001474946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00335219,"about_ca_topic_score_gemma":0.002295814,"domain_scores_codex":[0.9988183,0.0002229829,0.00005891482,0.0003761279,0.000438798,0.00008479139],"domain_scores_gemma":[0.998664,0.000729841,0.00007919444,0.000132867,0.0003559661,0.00003818066],"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":[0.0002865763,0.0001324957,0.001584027,0.0002904841,0.0001169214,0.0002268283,0.0001167634,0.3716321,0.01359043,0.07309157,0.005215166,0.5337166],"study_design_scores_gemma":[0.00001759196,0.00004455572,0.000216931,0.000008906903,0.00001659191,0.000110632,0.000005470001,0.9890099,0.002676876,0.004965755,0.002911308,0.00001555526],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008277106,0.00008657972,0.9983016,0.00006113999,0.0000298614,0.00002069455,0.00003260864,0.0001665654,0.0004731124],"genre_scores_gemma":[0.1989618,0.0008228399,0.7894169,0.0004131844,0.0002834214,0.0002897264,0.00051194,0.00007978146,0.009220393],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003953914,"threshold_uncertainty_score":0.01322711,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2294783111","doi":"10.1109/taes.2015.140574","title":"A practical bias estimation algorithm for multisensor-multitarget tracking","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Aerospace and Electronic Systems","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; McMaster University","funders":"","keywords":"Fusion center; Computer science; Filter (signal processing); Tracking (education); Sensor fusion; Algorithm; Transmission (telecommunications); Sampling (signal processing); Tracking system; Radar tracker; Real-time computing; Artificial intelligence; Computer vision; Radar; Telecommunications; Wireless","authors":[{"name":"Ehsan Taghavi","is_ca":true},{"name":"Ratnasingham Tharmarasa","is_ca":true},{"name":"T. Kirubarajan","is_ca":true},{"name":"Yaakov Bar‐Shalom","is_ca":false},{"name":"Michael McDonald","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03823993947948046,"gpt":0.2898215399782648,"spread":0.2515816004987843,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001009569,0.0006965466,0.0006805744,0.0006212125,0.0005819849,0.0006347839,0.0009188614,0.00120728,0.001679652],"category_scores_gemma":[0.003166629,0.0003715301,0.0005007471,0.0008002185,0.0004465469,0.001532921,0.001143494,0.001324119,0.001062131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007554164,"about_ca_system_score_gemma":0.001265107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001400622,"about_ca_topic_score_gemma":0.002092988,"domain_scores_codex":[0.9993927,0.00007351665,0.00003484431,0.0001622617,0.0002965157,0.0000401256],"domain_scores_gemma":[0.9992441,0.000230917,0.00008781596,0.0001201381,0.0002912313,0.00002588352],"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":[0.0001622142,0.00005809665,0.00111581,0.0001319016,0.00006940048,0.00009632069,0.0001354575,0.1875494,0.03218397,0.02634366,0.002792275,0.7493614],"study_design_scores_gemma":[0.00004423424,0.00009875054,0.0005386675,0.00002239659,0.00002323149,0.0002702658,0.00001833872,0.9679937,0.01323858,0.01026368,0.007455942,0.00003219613],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009265362,0.00007981723,0.9986039,0.00002142364,0.00002235022,0.00001148458,0.000006306394,0.0001450689,0.0001830977],"genre_scores_gemma":[0.06365746,0.0002423097,0.9340456,0.00006482431,0.00005071947,0.00009703147,0.00008171181,0.00005312892,0.00170726],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001679652,"threshold_uncertainty_score":0.005618989,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2156715130","doi":"10.1109/lcomm.2007.070842","title":"Lognormal Sum Approximation with Type IV Pearson Distribution","year":2007,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Cape Breton University","funders":"","keywords":"Kurtosis; Mathematics; Log-normal distribution; Heavy-tailed distribution; Pearson product-moment correlation coefficient; Skewness; Statistics; Random variable; Ratio distribution; Compound probability distribution; Type (biology); Distribution (mathematics); Probability distribution; Distribution fitting; Asymptotic distribution; Mathematical analysis","authors":[{"name":"Hong Nie","is_ca":false},{"name":"Shaohua Chen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02576260187283272,"gpt":0.2594602796849786,"spread":0.2336976778121459,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003593082,0.001430045,0.001130373,0.001585542,0.0006343905,0.001688594,0.002444268,0.001447061,0.003106648],"category_scores_gemma":[0.01618984,0.0005973713,0.001377138,0.002393215,0.001411402,0.004429265,0.001604049,0.002491459,0.002182278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001360762,"about_ca_system_score_gemma":0.0009819205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003142141,"about_ca_topic_score_gemma":0.001863894,"domain_scores_codex":[0.9970041,0.0009708578,0.0001300202,0.0004819741,0.001122947,0.0002901487],"domain_scores_gemma":[0.993457,0.003277826,0.0006454964,0.0009461901,0.001504572,0.0001689569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003103003,0.00007377647,0.005109787,0.0002769106,0.0001538631,0.0008639977,0.000324788,0.6119588,0.005675296,0.267636,0.008157987,0.09945844],"study_design_scores_gemma":[0.00001110154,0.00003885478,0.0004354806,0.00002333069,0.0000208292,0.0005067423,0.0000393315,0.9612462,0.001147818,0.0339478,0.002547847,0.00003463923],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004085814,0.0002888554,0.9933848,0.00009811682,0.00006675161,0.00002065645,0.00004462325,0.0003263059,0.001684013],"genre_scores_gemma":[0.6087354,0.003642703,0.3690534,0.001166906,0.0005346182,0.0004065726,0.0008155943,0.0006846425,0.01496014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003593082,"threshold_uncertainty_score":0.01900226,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136739364","doi":"","title":"Performance measures for multiple target tracking problems","year":2011,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Tracking (education); Artificial intelligence; Machine learning; Data mining; Statistical classification; Pattern recognition (psychology)","authors":[{"name":"A. A. Gorji","is_ca":true},{"name":"Ratnasingham Tharmarasa","is_ca":true},{"name":"T. Kirubarajan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08252286723461763,"gpt":0.2607177616495735,"spread":0.1781948944149559,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01212783,0.00352472,0.002059198,0.004946704,0.0008238613,0.003412458,0.00242582,0.002447535,0.002292515],"category_scores_gemma":[0.04759647,0.0003669546,0.001442824,0.004616175,0.001568991,0.005296154,0.002292482,0.002774176,0.001292555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001716418,"about_ca_system_score_gemma":0.001095716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008563191,"about_ca_topic_score_gemma":0.0003582165,"domain_scores_codex":[0.9849415,0.006367313,0.001514852,0.001495102,0.005121894,0.0005593296],"domain_scores_gemma":[0.9606207,0.02678501,0.003936649,0.002677729,0.005308553,0.0006712783],"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":[0.0004727439,0.0003559301,0.006948098,0.002814872,0.0005962873,0.0002545129,0.0002803077,0.4930818,0.01029343,0.1299256,0.008910556,0.3460659],"study_design_scores_gemma":[0.00002734554,0.001058763,0.004851082,0.0003404874,0.0001760593,0.0007056776,0.0001775327,0.8929292,0.006377662,0.07935011,0.01385843,0.0001477179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006219654,0.009466725,0.9791365,0.0003079387,0.000240239,0.0001464967,0.000260213,0.0004060854,0.003816222],"genre_scores_gemma":[0.4999852,0.01419909,0.4747508,0.0005009161,0.0015942,0.00138151,0.002230694,0.0006331676,0.004724431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01212783,"threshold_uncertainty_score":0.06413889,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2884043659","doi":"10.1109/tcyb.2018.2850368","title":"Robust Consensus Nonlinear Information Filter for Distributed Sensor Networks With Measurement Outliers","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Outlier; Filter (signal processing); Computer science; Nonlinear system; Consensus; Gaussian; Estimator; Divergence (linguistics); Convergence (economics); Nonlinear filter; Information filtering system; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Machine learning; Statistics; Filter design; Multi-agent system","authors":[{"name":"Peng Dong","is_ca":false},{"name":"Zhongliang Jing","is_ca":false},{"name":"Henry Leung","is_ca":true},{"name":"Kai Shen","is_ca":false},{"name":"Minzhe Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03414656309257745,"gpt":0.2254839410680587,"spread":0.1913373779754813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00170973,0.0007025067,0.00126643,0.0007375561,0.0006389235,0.000855429,0.00139459,0.001297993,0.001016018],"category_scores_gemma":[0.005633766,0.0003255314,0.0007486459,0.001028793,0.0009032001,0.001666102,0.001095924,0.001227953,0.00030622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431307,"about_ca_system_score_gemma":0.001633387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005731806,"about_ca_topic_score_gemma":0.002974864,"domain_scores_codex":[0.9986182,0.0002681846,0.0000730783,0.0003731695,0.0005644466,0.0001029557],"domain_scores_gemma":[0.9986038,0.0006438189,0.0002272962,0.0001035099,0.0003904698,0.00003115412],"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":[0.0001198773,0.0000313557,0.0004988167,0.0001278419,0.00005620515,0.0001073225,0.0001502896,0.8545257,0.00469731,0.03996167,0.0013325,0.09839113],"study_design_scores_gemma":[0.000008340661,0.00002219165,0.00007211357,0.000003615309,0.000005722347,0.00001569675,0.000006902831,0.993579,0.0006857851,0.005051498,0.000541461,0.000007618189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002651989,0.00009744256,0.9966151,0.00006012544,0.00001675065,0.0000122149,0.00001267019,0.0001118752,0.0004217724],"genre_scores_gemma":[0.7507346,0.0008244761,0.2418109,0.0001891189,0.0001373177,0.0003630981,0.0002462916,0.00008708041,0.005607226],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005731806,"threshold_uncertainty_score":0.01139694,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3109815911","doi":"10.1016/j.dsp.2020.102912","title":"The smooth variable structure filter: A comprehensive review","year":2020,"lang":"en","type":"review","venue":"Digital Signal Processing","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; American University of Sharjah","keywords":"Robustness (evolution); Kalman filter; Control theory (sociology); Mean squared error; Variable (mathematics); Mathematics; Computer science; Statistics; Control (management); Artificial intelligence","authors":[{"name":"Mohammad Avzayesh","is_ca":false},{"name":"Mamoun F. Abdel–Hafez","is_ca":false},{"name":"Mohammad Al‐Shabi","is_ca":true},{"name":"S. Andrew Gadsden","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03400262015253255,"gpt":0.2843117136819469,"spread":0.2503090935294144,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006791416,0.001006153,0.001274179,0.002447212,0.0002262067,0.0008795839,0.0008008826,0.001171535,0.003976827],"category_scores_gemma":[0.001226319,0.0004320611,0.0007017677,0.00283672,0.0003634481,0.00159791,0.0005353555,0.000948624,0.002681083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004505777,"about_ca_system_score_gemma":0.001258894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002005242,"about_ca_topic_score_gemma":0.00146735,"domain_scores_codex":[0.9997643,0.00002652028,0.00003543057,0.00005504219,0.00009915317,0.00001956902],"domain_scores_gemma":[0.9994216,0.0002816266,0.00006026141,0.00002202257,0.0001897591,0.00002466716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005318406,0.00006233449,0.000207078,0.01146142,0.00008760703,0.00009225937,0.00003222999,0.001975021,0.001476501,0.005857354,0.01685113,0.961844],"study_design_scores_gemma":[0.00001506845,0.000250727,0.001107666,0.003104264,0.0002217318,0.0008645629,0.00005170562,0.002226121,0.001880273,0.003923935,0.986291,0.00006293345],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003891453,0.9899839,0.005405257,0.0002982723,0.0003801954,0.00001371818,0.0000592633,0.00004690627,0.003423376],"genre_scores_gemma":[0.003624518,0.9902579,0.003453176,0.0001844087,0.0004223578,0.00002109774,0.0001422115,0.00001282892,0.001881433],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003976827,"threshold_uncertainty_score":0.01330382,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}