{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":962,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":962,"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"},"query_hash":"deb88c3382a2","filters":{"topic":"Video Surveillance and Tracking Methods"}},"results":[{"id":"W2139047213","doi":"10.1007/s11263-007-0075-7","title":"Incremental Learning for Robust Visual Tracking","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3101,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Honda Research Institute, USA","keywords":"Computer science; Artificial intelligence; Tracking (education); Subspace topology; Computer vision; Representation (politics); Active appearance model; Principal component analysis; Pattern recognition (psychology); Eye tracking; Forgetting; Video tracking; Range (aeronautics); Object (grammar); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03210245907793505,"gpt":0.3744627854844399,"spread":0.3423603264065048,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002852016,0.0001107188,0.0001770887,0.0003380679,0.00008486918,0.0002992575,0.0009198775,0.0000505094,0.000005881714],"category_scores_gemma":[0.00009460017,0.00009765982,0.000184869,0.000129668,0.0000193288,0.0008868171,0.000174955,0.0002311595,0.000004378018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000913077,"about_ca_system_score_gemma":0.00004709324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002796095,"about_ca_topic_score_gemma":0.000002164524,"domain_scores_codex":[0.9983312,0.00008266937,0.0005649367,0.0001711875,0.0006512151,0.000198777],"domain_scores_gemma":[0.9980605,0.0005409741,0.0004477596,0.00008668531,0.0007820068,0.00008212629],"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":[0.0001342943,0.0001158177,0.003698545,0.000004801847,0.00008391163,0.0001257422,0.0002527789,0.008512973,0.00338441,0.001460825,0.0003454213,0.9818805],"study_design_scores_gemma":[0.005229582,0.003391676,0.1831437,0.0005553987,0.00002278643,0.002058253,0.00007597006,0.7362517,0.02746178,0.004656958,0.03652377,0.0006285042],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09432817,0.00006262348,0.901693,0.0004546409,0.003272872,0.00005312203,3.026188e-7,0.00003278243,0.0001025118],"genre_scores_gemma":[0.625546,0.000008683101,0.3731895,0.0001986142,0.001039793,2.707342e-7,0.000001218971,0.000006718237,0.000009206417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.981252,"threshold_uncertainty_score":0.3982452,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2052524720","doi":"10.1109/cvprw.2012.6238919","title":"Changedetection.net: A new change detection benchmark dataset","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":814,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Benchmarking; Benchmark (surveying); Change detection; Artificial intelligence; Ranking (information retrieval); Ground truth; Frame (networking); Shadow (psychology); Machine learning; Data mining; Information retrieval","retraction":null,"screen_n_in":null,"score":{"opus":0.09362766237507576,"gpt":0.3200240891011569,"spread":0.2263964267260811,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007953466,0.0001267549,0.0001202558,0.0001241963,0.0001367952,0.00009681088,0.0003757076,0.00006794873,0.0001065403],"category_scores_gemma":[0.00004882033,0.0001104086,0.00004306073,0.0005555013,0.00001240877,0.001476167,0.0001592708,0.0001157262,0.0002858805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000321368,"about_ca_system_score_gemma":0.0000155009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000551219,"about_ca_topic_score_gemma":0.0002968602,"domain_scores_codex":[0.9988797,0.0001193787,0.0001362529,0.000258165,0.0001995983,0.0004069621],"domain_scores_gemma":[0.9990722,0.00007513683,0.00005816421,0.0005685376,0.00002343484,0.0002025292],"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":[0.000004519874,0.00004231468,0.004252096,0.000007921102,0.00001491754,0.000001651501,0.0005828342,8.323696e-7,0.00130324,0.001354009,0.004867019,0.9875686],"study_design_scores_gemma":[0.0005767414,0.0002034632,0.2428415,0.00001699462,0.00001435479,0.0001432453,0.00003675384,0.003571072,0.03614413,0.001870793,0.7139322,0.0006487537],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00240566,0.0006552983,0.9918595,0.0007226348,0.002054467,0.000186729,0.00001643966,0.0003285166,0.001770788],"genre_scores_gemma":[0.9271915,0.00005213862,0.06875416,0.001660348,0.001905596,0.0000462336,0.00004714174,0.00001309159,0.0003297746],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9869199,"threshold_uncertainty_score":0.4502333,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2130026429","doi":"10.1109/iccvw.2015.79","title":"The Visual Object Tracking VOT2015 Challenge Results","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":705,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Javna Agencija za Raziskovalno Dejavnost RS; European Commission","keywords":"BitTorrent tracker; Computer science; Benchmark (surveying); Artificial intelligence; Eye tracking; Object (grammar); Computer vision; Video tracking; Frame (networking); Tracking (education); Bounding overwatch; Term (time); Annotation; Visualization; Minimum bounding box; Object detection; Pattern recognition (psychology); Image (mathematics); Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.09512999012652176,"gpt":0.3695303862370754,"spread":0.2744003961105537,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007697237,0.0004153761,0.0004641561,0.0001141099,0.0003232784,0.001166501,0.003055484,0.000365144,0.000002625105],"category_scores_gemma":[0.0009399195,0.0002705545,0.00023278,0.0002644409,0.00009519888,0.0002822124,0.002674103,0.001079063,0.0001226851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001180447,"about_ca_system_score_gemma":0.0005831289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001924997,"about_ca_topic_score_gemma":0.0004613816,"domain_scores_codex":[0.9960251,0.0007551227,0.0006694419,0.001104608,0.000808815,0.0006368399],"domain_scores_gemma":[0.9957953,0.001025482,0.0003801549,0.002098449,0.0005057772,0.0001948185],"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":[0.00007667296,0.000151396,0.0001256179,0.00006622326,0.0001720624,0.00009200544,0.002972297,0.0009546215,0.00001766274,0.02450656,0.02311761,0.9477473],"study_design_scores_gemma":[0.003270759,0.0008414474,0.009481434,0.0005884544,0.00006421869,0.0001044756,0.0004576247,0.1645068,0.001702947,0.3484056,0.4670433,0.003532944],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003192656,0.006860345,0.7412917,0.01865667,0.01253106,0.001011727,0.00002537612,0.002062366,0.2143681],"genre_scores_gemma":[0.9226553,0.001042545,0.0712861,0.0003504001,0.001388648,0.00007839705,0.00002396674,0.00005872721,0.003115852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9442143,"threshold_uncertainty_score":0.9999747,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994634851","doi":"10.1109/tip.2014.2378053","title":"SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":651,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Computer science; Artificial intelligence; Pixel; Segmentation; Background subtraction; Change detection; Computer vision; Noise (video); Sensitivity (control systems); Image segmentation; Frame (networking); Adaptation (eye); Frame rate; Analytics; Fidelity; Object detection; Pattern recognition (psychology); Image (mathematics); Data mining","retraction":null,"screen_n_in":null,"score":{"opus":0.02707902773261169,"gpt":0.2795606137100687,"spread":0.2524815859774571,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009869662,0.0002179807,0.0002288219,0.0002066181,0.0004458554,0.0001814398,0.0001659189,0.00008524246,0.000002913422],"category_scores_gemma":[0.000008357397,0.0001948786,0.00007176258,0.0008128394,0.0001168623,0.001325608,0.000002775443,0.0003669318,0.00001716488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009182678,"about_ca_system_score_gemma":0.00006868586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001531103,"about_ca_topic_score_gemma":0.0003712278,"domain_scores_codex":[0.998124,0.0005654758,0.0001499857,0.0005346447,0.0002940205,0.0003318931],"domain_scores_gemma":[0.9989626,0.0002884037,0.0001045227,0.0003283709,0.0002130772,0.0001030721],"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":[0.00008539109,0.00006688095,0.00000536312,0.00002688635,0.00001747278,0.00003294165,0.000739172,0.002227929,0.007350781,0.00004408103,7.307147e-7,0.9894024],"study_design_scores_gemma":[0.0005792085,0.0003299275,0.0005496033,0.00009964441,0.00003082776,0.0002186335,0.0001369597,0.7748713,0.2225111,0.0002332141,0.000109373,0.0003302374],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001849262,0.00001700774,0.9967939,0.0002505658,0.0002166079,0.0001520739,0.000002446812,0.0004066957,0.0003113919],"genre_scores_gemma":[0.660217,0.000002349047,0.3395346,0.0001451205,0.00004721167,0.00001514986,1.848125e-7,0.00001597346,0.00002240874],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9890721,"threshold_uncertainty_score":0.794692,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2169671170","doi":"10.1109/cvpr.2007.383134","title":"Detecting Pedestrians by Learning Shapelet Features","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":512,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); AdaBoost; Pedestrian detection; Pattern recognition (psychology); Pedestrian; Set (abstract data type); Feature (linguistics); Machine learning; Computer vision; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0137160163036321,"gpt":0.287990266333755,"spread":0.2742742500301228,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001830942,0.0001024966,0.0001133551,0.00006762241,0.0001931548,0.0001660318,0.0004281302,0.00006350959,0.00002294794],"category_scores_gemma":[0.0002267308,0.00008912719,0.00005134731,0.0002869991,0.00001590524,0.0002375672,0.0000938041,0.000278141,0.00004635178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001837808,"about_ca_system_score_gemma":0.00001768145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008211529,"about_ca_topic_score_gemma":0.00007030284,"domain_scores_codex":[0.9989109,0.00009177437,0.0001566211,0.0002873378,0.0001909158,0.0003624568],"domain_scores_gemma":[0.999126,0.0004718599,0.00005837694,0.0002281911,0.00003764454,0.00007792538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000561016,0.00002088932,0.01595645,0.000006110454,0.00001315691,0.00003419324,0.000436538,0.00007808875,0.0166068,0.004191275,0.001860577,0.9607903],"study_design_scores_gemma":[0.002651773,0.0008589776,0.328941,0.00008187872,0.00002062242,0.000587397,0.001082406,0.02156003,0.3884647,0.008834927,0.2443909,0.002525405],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04343883,0.0001513525,0.9304949,0.0002937551,0.0002202846,0.0000437264,1.408174e-7,0.0004455274,0.02491149],"genre_scores_gemma":[0.8645682,0.000006245407,0.1332791,0.0003365453,0.00008577652,9.726641e-7,7.14753e-7,0.000007958807,0.001714475],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9582649,"threshold_uncertainty_score":0.3634501,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3109991383","doi":"10.1007/978-3-030-58536-5_36","title":"V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":449,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Viewpoints; Perception; Artificial intelligence; Bandwidth (computing); Joint (building); Feature (linguistics); Computer vision; Range (aeronautics); Real-time computing; Telecommunications; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03757137983663022,"gpt":0.2797818468397802,"spread":0.24221046700315,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001334681,0.000310116,0.0003810195,0.0003719519,0.0003261643,0.000455119,0.001327341,0.0002150112,0.000003646775],"category_scores_gemma":[0.0001585287,0.0003036453,0.00008474098,0.0003822255,0.0002603733,0.000504288,0.0008317792,0.000465117,0.00001654306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001688403,"about_ca_system_score_gemma":0.0001520465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000170983,"about_ca_topic_score_gemma":0.00004600069,"domain_scores_codex":[0.9975603,0.00007216194,0.0004126235,0.001126008,0.0004790527,0.0003498482],"domain_scores_gemma":[0.9980151,0.0004133444,0.0001789003,0.0009926973,0.0002218678,0.0001780873],"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":[0.00001125253,0.0000127949,0.0002456715,0.00004968681,0.000007588989,0.000003329414,0.001363811,0.006183483,0.001879103,0.005329166,0.00003804236,0.9848761],"study_design_scores_gemma":[0.000356425,0.000380366,0.008784026,0.0002948899,0.000008380564,0.00001936954,5.233405e-7,0.8699014,0.0006515146,0.1170536,0.002115377,0.0004341925],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005648541,0.0002277497,0.9931122,0.004254713,0.0005989858,0.0006260231,0.00001161818,0.0001883939,0.000415438],"genre_scores_gemma":[0.2981871,0.00007655157,0.6989749,0.002333331,0.0003132256,0.00002652683,0.0000113531,0.0000267707,0.00005023821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9844419,"threshold_uncertainty_score":0.9999416,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2525668722","doi":"10.1016/j.patrec.2016.09.014","title":"Interactive deep learning method for segmenting moving objects","year":2016,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":336,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Ground truth; Artificial intelligence; Segmentation; Convolutional neural network; Computer vision; Margin (machine learning); Pixel; Market segmentation; Code (set theory); Deep learning; Pattern recognition (psychology); Machine learning; Set (abstract data type)","retraction":null,"screen_n_in":null,"score":{"opus":0.02705768264026516,"gpt":0.3007710899321639,"spread":0.2737134072918987,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001137127,0.0001735151,0.0001942601,0.0001655177,0.0001890759,0.0001655335,0.0003150306,0.00004867935,0.00003528008],"category_scores_gemma":[0.0003964092,0.0001390553,0.0001372785,0.0001581452,0.00001861888,0.0008024055,0.0001124663,0.0001482691,0.0001058059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007089201,"about_ca_system_score_gemma":0.000009831632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002075828,"about_ca_topic_score_gemma":0.00001327881,"domain_scores_codex":[0.9981902,0.0004420632,0.0002674207,0.0005165717,0.0001789623,0.0004048258],"domain_scores_gemma":[0.997598,0.001781462,0.0002244323,0.0002179539,0.0001105718,0.00006755839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008195969,0.00001230674,0.003457889,0.00002152022,0.00003734826,0.00000763968,0.0006456478,0.00002164022,0.04907664,0.000005411373,0.00005509349,0.9466507],"study_design_scores_gemma":[0.01313121,0.0009202748,0.05882057,0.003179024,0.0001968384,0.0003924212,0.001155858,0.2382273,0.6534703,0.01498618,0.01063851,0.00488148],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03681544,0.00001312269,0.9589749,0.002920398,0.0006176006,0.0002145952,0.00000492315,0.0002506777,0.0001883592],"genre_scores_gemma":[0.5687392,0.000007787426,0.4267294,0.004110809,0.0002424011,0.0001050816,0.00001036049,0.00002485167,0.00003010069],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9417692,"threshold_uncertainty_score":0.5670511,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158403369","doi":"10.1109/icpr.2008.4760998","title":"Review and Evaluation of Commonly-Implemented Background Subtraction Algorithms","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":309,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Computer vision; Jitter; Motion detection; Noise (video); Probabilistic logic; Process (computing); Frame (networking); Algorithm; Subtraction; Motion estimation; Motion (physics); Pixel; Mathematics; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.2051079295448833,"gpt":0.4140804321586002,"spread":0.2089725026137169,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002438903,0.00006821196,0.0001688259,0.00004449355,0.00007228721,0.00001327519,0.0001527307,0.00002218373,0.0000558082],"category_scores_gemma":[0.00004994608,0.00005815805,0.00002969219,0.0002571235,0.00003044487,0.0003565351,0.00005162143,0.00005246093,0.000005909138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001973995,"about_ca_system_score_gemma":0.00005256592,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009232217,"about_ca_topic_score_gemma":0.00002967268,"domain_scores_codex":[0.998877,0.0003035642,0.000232472,0.0001803457,0.000303099,0.0001035389],"domain_scores_gemma":[0.999191,0.0001088697,0.0001063716,0.0002914881,0.0002687464,0.00003346492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000004064404,0.0001516154,0.01614317,0.0004334337,0.00007468034,0.000003803458,0.0002548116,0.00002259916,0.0007174498,0.005914584,0.007748802,0.968531],"study_design_scores_gemma":[0.002818676,0.0003907628,0.8412035,0.0005394668,0.0002713719,0.0006087375,0.00007515835,0.107831,0.007229198,0.00689982,0.03141049,0.0007218283],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1137313,0.02739085,0.8491281,0.002046039,0.0003322493,0.0007459337,0.00000349661,0.0001549824,0.006467044],"genre_scores_gemma":[0.8363821,0.01109859,0.1513013,0.001063324,0.00003493481,0.00002260712,0.000009675365,0.000007118343,0.00008033066],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9678091,"threshold_uncertainty_score":0.2371616,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3034326629","doi":"10.1007/s40747-020-00161-4","title":"Overview and methods of correlation filter algorithms in object tracking","year":2020,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":305,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"Science and Technology Program of Hunan Province; State Key Laboratory of Computer Aided Design and Computer Graphics; Zhejiang University","keywords":"Tracking (education); Video tracking; Computer science; Artificial intelligence; Computer vision; Eye tracking; Object (grammar); Tracking system; Reliability (semiconductor); Filter (signal processing); Presentation (obstetrics)","retraction":null,"screen_n_in":null,"score":{"opus":0.1935927057548633,"gpt":0.3925816649300978,"spread":0.1989889591752345,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00167458,0.0001722747,0.0004955888,0.0001231936,0.00004503781,0.0001116682,0.0004604609,0.00007211474,0.00001568321],"category_scores_gemma":[0.00020836,0.0001595496,0.00008491088,0.0005796658,0.00004237367,0.000267822,0.0001668863,0.0001693666,0.00001390758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004320896,"about_ca_system_score_gemma":0.00003642775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001453911,"about_ca_topic_score_gemma":0.00001272686,"domain_scores_codex":[0.997566,0.0008702598,0.0006709926,0.0004298973,0.0002316977,0.0002311829],"domain_scores_gemma":[0.9986451,0.000579151,0.0002356424,0.0003438875,0.00009829755,0.00009786592],"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":[0.00003361296,0.0001004912,0.04028954,0.001333106,0.0001183583,0.00004217431,0.0125299,0.00960979,0.00867881,0.03465873,0.0005727505,0.8920327],"study_design_scores_gemma":[0.0002842021,0.0001503998,0.0320993,0.0003068335,0.000009530703,0.00003501599,0.0002033615,0.9517611,0.003787506,0.001196029,0.009870145,0.0002966147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003861696,0.005095717,0.9893257,0.000208083,0.0005966516,0.000320168,0.000003412578,0.00007805272,0.0005105476],"genre_scores_gemma":[0.8419746,0.0002060476,0.1574684,0.0001988676,0.00009867744,0.00001494555,0.000004920447,0.00001403946,0.00001951518],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9421513,"threshold_uncertainty_score":0.6506245,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110671801","doi":"","title":"Incremental Learning for Visual Tracking","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":288,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Particle filter; Artificial intelligence; Computer science; Eye tracking; Tracking (education); Video tracking; Computer vision; Subspace topology; Representation (politics); Inference; Active appearance model; Markov chain Monte Carlo; Task (project management); Pattern recognition (psychology); Object (grammar); Kalman filter; Bayesian probability; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.0371558157009693,"gpt":0.3422921101543846,"spread":0.3051362944534153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006024801,0.00007754237,0.00009637442,0.00005095037,0.0001595192,0.0001360958,0.0002671733,0.00003111475,0.000007511542],"category_scores_gemma":[0.0000867609,0.00006986619,0.00006268961,0.0001554695,0.00001309928,0.0003810593,0.00006809441,0.00008427424,0.00002249643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003467845,"about_ca_system_score_gemma":0.00003802488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002868287,"about_ca_topic_score_gemma":0.00001503786,"domain_scores_codex":[0.9992504,0.00003727084,0.0001285086,0.0002300555,0.0001294586,0.0002243099],"domain_scores_gemma":[0.999646,0.0001107616,0.00003787804,0.000119978,0.00004215568,0.00004324652],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000152148,0.0001393504,0.01133062,0.00002683637,0.00003713056,0.00001406682,0.0009488062,0.005802914,0.02044513,0.1645513,0.00008248779,0.7966062],"study_design_scores_gemma":[0.009673377,0.002268277,0.1238059,0.0001526432,0.00002479045,0.0001622625,0.0005722881,0.09233105,0.6130012,0.1231051,0.03289128,0.002011765],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.071942,0.00002616047,0.9252001,0.0003694594,0.0002005749,0.00009459876,1.069792e-7,0.0002496431,0.001917376],"genre_scores_gemma":[0.7349279,0.000002048726,0.2647013,0.0002187083,0.00006340683,0.000008223399,8.891161e-7,0.000005435086,0.00007209822],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7945944,"threshold_uncertainty_score":0.2849061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313013512","doi":"10.1109/cvpr52688.2022.00716","title":"Part-based Pseudo Label Refinement for Unsupervised Person Re-identification","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":278,"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":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Smoothing; Context (archaeology); Cluster analysis; Similarity (geometry); Noise (video); Exploit; Machine learning; Pattern recognition (psychology); Feature (linguistics); Identification (biology); Code (set theory); Source code; Feature learning; Task (project management); Data mining; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.1351141458386117,"gpt":0.3274952700900095,"spread":0.1923811242513978,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001518312,0.0003415011,0.0003897618,0.0003211492,0.0007749685,0.0005537642,0.0006607831,0.00008794845,0.0006485864],"category_scores_gemma":[0.0000254018,0.0003422551,0.0001471722,0.0003973841,0.00004786167,0.0003231951,0.0001690612,0.000358567,0.00007245215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008052568,"about_ca_system_score_gemma":0.00009866225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002867592,"about_ca_topic_score_gemma":0.00002388608,"domain_scores_codex":[0.9965976,0.0007031315,0.0005387986,0.001086178,0.0006517505,0.0004225563],"domain_scores_gemma":[0.9981435,0.0004028693,0.000316991,0.0006714729,0.0002954255,0.0001697465],"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":[0.0001048834,0.0004020462,0.0003086676,0.00008986588,0.00003330417,0.0000167803,0.0004792975,0.000170951,0.002269788,0.0003235237,0.01064674,0.9851542],"study_design_scores_gemma":[0.004531231,0.002642364,0.004220663,0.000239534,0.00003773866,0.00002691245,0.0002041547,0.9662951,0.004187609,0.002377238,0.01426967,0.0009678077],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07965615,0.0000454791,0.9099608,0.006020615,0.002678294,0.0007979684,0.0002432289,0.0002803469,0.0003171322],"genre_scores_gemma":[0.9566188,0.00008716028,0.03319826,0.008010814,0.0003686273,0.0006885924,0.0006625543,0.00004687432,0.0003183606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9841864,"threshold_uncertainty_score":0.999903,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2101956459","doi":"10.1145/2557642.2563678","title":"YawDD","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":270,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Dash; Video camera; Benchmark (surveying); Front (military); Computer graphics (images); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01198600600739313,"gpt":0.2561424033236177,"spread":0.2441563973162246,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004621777,0.00003387659,0.00004895777,0.00002103324,0.00003054042,0.0000520516,0.0003240308,0.0000142559,0.00002013108],"category_scores_gemma":[0.00005007462,0.00002606049,0.00002056292,0.0001077589,0.000007172887,0.0001273143,0.00005403708,0.00002960988,0.0002247156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002470083,"about_ca_system_score_gemma":0.000005762663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007522157,"about_ca_topic_score_gemma":0.000003795818,"domain_scores_codex":[0.9995933,0.00005885351,0.00005266333,0.0001221012,0.00007327746,0.00009983272],"domain_scores_gemma":[0.9995396,0.00009267339,0.00001160182,0.0003093299,0.00001673152,0.00003010678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[1.978042e-7,0.000006408553,0.00278431,0.000001296011,0.000001670279,6.815172e-7,0.00003218239,0.00001103407,0.0001901564,0.541764,0.001359652,0.4538484],"study_design_scores_gemma":[0.000439302,0.0001150279,0.1133477,0.000007754891,0.000001575997,0.00002345734,0.000004462425,0.1141693,0.01328093,0.1927431,0.565489,0.0003784856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001538536,0.000007286259,0.8677977,0.0007395202,0.0001952974,0.00001137695,1.76577e-8,0.0001794487,0.1295308],"genre_scores_gemma":[0.6627537,0.000001007835,0.3355404,0.0009107601,0.00004461366,9.755008e-7,7.599934e-8,0.000001594814,0.0007468613],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6612152,"threshold_uncertainty_score":0.288834,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2984040540","doi":"10.1109/iccv.2019.00379","title":"Batch DropBlock Network for Person Re-Identification and Beyond","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":255,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Feature (linguistics); Computer science; Artificial intelligence; Margin (machine learning); Feature learning; Salient; Pattern recognition (psychology); Metric (unit); Representation (politics); Identification (biology); Feature extraction; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02527074253156034,"gpt":0.2827099289710358,"spread":0.2574391864394754,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007959581,0.00007016079,0.0001071181,0.00002764744,0.00008174712,0.0001555181,0.0002355506,0.0000417659,0.00001661384],"category_scores_gemma":[0.00002727994,0.00006138776,0.00003487906,0.000156164,0.00001151426,0.0002544877,0.00004182394,0.00004409519,0.00004171575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009050645,"about_ca_system_score_gemma":0.00001507151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001349604,"about_ca_topic_score_gemma":0.00001629081,"domain_scores_codex":[0.999245,0.0000484432,0.0001126924,0.0003098025,0.0001001427,0.0001838789],"domain_scores_gemma":[0.9992859,0.000209974,0.00005127791,0.0003591256,0.00005536955,0.00003836943],"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":[0.00003605479,0.00007536644,0.1216507,0.000154278,0.00007648944,0.000002717006,0.002454566,0.0009874046,0.006868143,0.2741,0.05050128,0.543093],"study_design_scores_gemma":[0.001922235,0.0004619878,0.3128065,0.00005152742,0.00002358831,0.00002654069,0.0004290699,0.5266579,0.00798289,0.06792448,0.08073872,0.0009744982],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05241206,0.000168928,0.9375139,0.002091137,0.0007864255,0.0002906625,0.000001019406,0.0001230848,0.006612807],"genre_scores_gemma":[0.7471268,0.00001362392,0.247453,0.0004428922,0.0001166763,0.00001421097,0.000003245611,0.000006995582,0.00482258],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6947147,"threshold_uncertainty_score":0.250332,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2799107345","doi":"10.1007/978-3-030-01261-8_12","title":"Domain Adaptation Through Synthesis for Unsupervised Person Re-identification","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":245,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Identification (biology); Artificial intelligence; Domain adaptation; Adaptation (eye); Domain (mathematical analysis); Scale (ratio); Training set; Pattern recognition (psychology); Machine learning; Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06365457729492724,"gpt":0.2993547881583341,"spread":0.2357002108634069,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00269558,0.0004688634,0.000520874,0.0004981332,0.0004379962,0.0007048568,0.002589457,0.0003484421,0.00002681439],"category_scores_gemma":[0.0003277254,0.000445091,0.000203467,0.000577451,0.0005329589,0.0009934666,0.0002810875,0.0003403361,0.00005258447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002745912,"about_ca_system_score_gemma":0.0003950987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000268188,"about_ca_topic_score_gemma":0.0001245762,"domain_scores_codex":[0.9961648,0.0001097108,0.0005506723,0.001741921,0.0008359666,0.0005969534],"domain_scores_gemma":[0.9958919,0.00158642,0.0004107167,0.001524949,0.0004918187,0.0000942143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002678856,0.00004091392,0.00004378043,0.0001286594,0.00003279429,0.00001694973,0.009973039,0.007607608,0.0007794814,0.02750707,0.00009766888,0.9537452],"study_design_scores_gemma":[0.0003899555,0.0002346462,0.000441171,0.0005238852,0.00002182663,0.00002553182,0.00000520441,0.4300094,0.007977718,0.5536184,0.005760617,0.000991601],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001499439,0.0002535372,0.9939338,0.001280313,0.002199418,0.0006543737,0.00001316515,0.0001967773,0.001318668],"genre_scores_gemma":[0.05418838,0.00003693206,0.9440222,0.0006886595,0.0007278235,0.00006049899,0.00001113689,0.00004276688,0.0002216053],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9527537,"threshold_uncertainty_score":0.9998001,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2149454580","doi":"10.1109/crv.2006.3","title":"A feature-based tracking algorithm for vehicles in intersections","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":226,"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":"Feature (linguistics); Computer science; Tracking (education); Intelligent transportation system; Track (disk drive); Computer vision; Algorithm; Field (mathematics); Artificial intelligence; Extension (predicate logic); Vehicle tracking system; Engineering; Transport engineering; Mathematics; Segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.02344892529384042,"gpt":0.2932022787556262,"spread":0.2697533534617857,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004483532,0.00008596892,0.0001150581,0.0001535159,0.00006966454,0.0001489756,0.0002645577,0.00005070824,0.000002582838],"category_scores_gemma":[0.00002520516,0.00007749884,0.0000760755,0.000367621,0.00001626739,0.0002286384,0.00002341523,0.00009078144,0.000003740586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003397489,"about_ca_system_score_gemma":0.00003582892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001884476,"about_ca_topic_score_gemma":0.0005256712,"domain_scores_codex":[0.9992629,0.00005508798,0.0001202388,0.0002525416,0.00009072238,0.0002185146],"domain_scores_gemma":[0.9994162,0.0002620657,0.00003449406,0.000213198,0.00005156299,0.00002248634],"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":[0.000003865056,0.0001032875,0.006326188,0.000009484173,0.000005088199,0.000009605753,0.00006104536,0.001050398,0.0008535809,0.01379576,0.001804779,0.9759769],"study_design_scores_gemma":[0.001256051,0.00009849057,0.06727836,0.0000394561,0.000003273091,0.00001131197,0.00002387658,0.8803529,0.01692718,0.01853324,0.0151693,0.000306563],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003027199,0.00005401646,0.9936989,0.001379624,0.0002775661,0.0001401231,0.00000211635,0.0001760419,0.001244401],"genre_scores_gemma":[0.3856295,4.186697e-7,0.6137369,0.0002584972,0.00007316314,0.0000314459,0.000002646642,0.000005726075,0.0002616823],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9756703,"threshold_uncertainty_score":0.3160311,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4322096648","doi":"10.1007/978-3-031-26351-4_20","title":"Cluster Contrast for Unsupervised Person Re-identification","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":192,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Softmax function; Artificial intelligence; Pattern recognition (psychology); Feature learning; Contrast (vision); Feature (linguistics); Cluster analysis; Unsupervised learning; Feature vector; Identification (biology); Artificial neural network; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.05271762679865026,"gpt":0.2996299347600211,"spread":0.2469123079613708,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00304688,0.0004775627,0.0005688373,0.000796173,0.0003495207,0.0008451508,0.002898778,0.0003550986,0.000008154303],"category_scores_gemma":[0.0003811779,0.0004511855,0.0002249355,0.0006833363,0.0003789963,0.0006018614,0.0004432943,0.0005222201,0.00008390881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002062214,"about_ca_system_score_gemma":0.0003634585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001715537,"about_ca_topic_score_gemma":0.0001443638,"domain_scores_codex":[0.9960315,0.00007440132,0.0005667835,0.001774105,0.0008285582,0.0007247034],"domain_scores_gemma":[0.995903,0.001727357,0.0003126705,0.001505391,0.0004089032,0.0001426902],"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":[0.00001736838,0.00002369664,0.00008824573,0.0001253133,0.00002616149,0.00002837317,0.001475035,0.01312959,0.000486555,0.01597713,0.000197715,0.9684248],"study_design_scores_gemma":[0.0007863788,0.0001876643,0.001198193,0.0004365257,0.00001640474,0.00002534254,8.557078e-7,0.8104825,0.00205306,0.1819579,0.001897669,0.0009575441],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004723454,0.0001659484,0.991317,0.002428632,0.003919789,0.000802902,0.00001716997,0.0003702717,0.0009310822],"genre_scores_gemma":[0.1408091,0.00008877474,0.8507567,0.003175934,0.001552132,0.0001105813,0.00004799227,0.000144025,0.003314755],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9674672,"threshold_uncertainty_score":0.999794,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165927991","doi":"10.1109/avss.2008.19","title":"Evaluation of Background Subtraction Algorithms with Post-Processing","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":189,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Computer science; Segmentation; Popularity; Artificial intelligence; Video processing; Computer vision; Image processing; Algorithm; Image segmentation; Image (mathematics); Pixel","retraction":null,"screen_n_in":null,"score":{"opus":0.1206932855900337,"gpt":0.3496764280950583,"spread":0.2289831425050246,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002018232,0.00007302679,0.0001061745,0.00007371543,0.0001013937,0.00003280597,0.000188532,0.00003246398,0.00002106543],"category_scores_gemma":[0.00004486447,0.0000549894,0.0000248859,0.000351128,0.00004025036,0.0007792668,0.00002202196,0.00006117898,0.000008971184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003878208,"about_ca_system_score_gemma":0.0002772845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008962696,"about_ca_topic_score_gemma":0.00003800987,"domain_scores_codex":[0.998576,0.0001707784,0.000147983,0.0002084774,0.0007599312,0.0001367597],"domain_scores_gemma":[0.9985689,0.00006145641,0.0001035865,0.0002475535,0.0009850899,0.00003341333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000112251,0.0000950481,0.005484592,0.00001439911,0.00002352576,0.000007219105,0.0006155309,0.001122606,0.002391471,0.0009583563,0.00004483314,0.9892312],"study_design_scores_gemma":[0.001216853,0.0003650956,0.5661801,0.000044277,0.00004194996,0.0003816866,0.000189106,0.4041198,0.02497721,0.001884749,0.0002809582,0.0003182162],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2724534,0.00008747957,0.7224883,0.0001721479,0.00009025435,0.00006995609,2.356213e-7,0.00006721257,0.004570995],"genre_scores_gemma":[0.7725759,0.000006592523,0.2272709,0.00006202835,0.00002708436,0.000003867871,0.00000121561,0.00000392762,0.00004847371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.988913,"threshold_uncertainty_score":0.2242402,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167462877","doi":"10.1007/11744085_9","title":"Robust Visual Tracking for Multiple Targets","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":188,"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":"Computer science; Computer vision; Particle filter; Artificial intelligence; Clutter; Tracking (education); Video tracking; Tracking system; Frame (networking); Filter (signal processing); Radar; Video processing","retraction":null,"screen_n_in":null,"score":{"opus":0.04229944082357135,"gpt":0.291790605628949,"spread":0.2494911648053777,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001981864,0.0006312515,0.0007145942,0.0007618074,0.0004131144,0.0007711952,0.002848069,0.0004048478,0.000006717238],"category_scores_gemma":[0.0002999556,0.0005974433,0.0002727591,0.0005352899,0.0004342334,0.000745482,0.0007034605,0.0006863765,0.00001749321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002439053,"about_ca_system_score_gemma":0.0004302063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003712926,"about_ca_topic_score_gemma":0.0001914313,"domain_scores_codex":[0.9955165,0.00005592862,0.0006579207,0.001918629,0.0008669955,0.000984016],"domain_scores_gemma":[0.9962715,0.001719837,0.0003629707,0.001101991,0.0003901849,0.0001535301],"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":[0.000008971563,0.00004555836,0.0003958062,0.00006297392,0.00001302242,0.00005638485,0.000197618,0.1507365,0.0001776792,0.003528029,0.000123538,0.8446539],"study_design_scores_gemma":[0.0005905078,0.0002198927,0.0009067415,0.0002772849,0.000008695538,0.00004686295,6.96249e-8,0.9029019,0.003357716,0.08447652,0.006225551,0.0009882785],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000489788,0.0006296519,0.9942458,0.000338303,0.002271543,0.0006059477,0.000008679112,0.0002806316,0.001570455],"genre_scores_gemma":[0.1258585,0.00001539947,0.8718325,0.0007988816,0.001053278,0.00002391897,0.00001637258,0.00006051589,0.0003406328],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8436657,"threshold_uncertainty_score":0.9996477,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1558051366","doi":"10.1371/journal.pone.0151984","title":"Expectation-Maximization Binary Clustering for Behavioural Annotation","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":172,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Fundación Biodiversidad; Universitat de Barcelona; Ministerio de Ciencia e Innovación; Institució Catalana de Recerca i Estudis Avançats; University of Toronto; Emory University; Lunds Universitet; Consejo Superior de Investigaciones Científicas; Vetenskapsrådet","keywords":"Cluster analysis; Expectation–maximization algorithm; Computer science; Annotation; Binary number; Computational biology; Artificial intelligence; Biology; Mathematics; Statistics; Maximum likelihood","retraction":null,"screen_n_in":null,"score":{"opus":0.1087564612498383,"gpt":0.2849939109235662,"spread":0.1762374496737279,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002215882,0.00006629551,0.00009484875,0.00007450145,0.0001038395,0.00005251802,0.0001554259,0.00003252682,0.00000612588],"category_scores_gemma":[0.0001437582,0.00005281526,0.00002881516,0.0001416046,0.00001184025,0.0005802195,0.00003920027,0.00002236647,0.00001109608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002925295,"about_ca_system_score_gemma":0.0000160515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005584478,"about_ca_topic_score_gemma":0.00001127797,"domain_scores_codex":[0.9993142,0.00005503955,0.0001456361,0.0002050933,0.0001394057,0.00014057],"domain_scores_gemma":[0.9993161,0.0002272839,0.00007277322,0.0002102158,0.0001417219,0.00003193249],"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":[0.0001027357,0.001676607,0.1247334,0.0002377454,0.0002277132,0.0001359018,0.004914879,0.0004433785,0.3321915,0.007530232,0.0002268246,0.5275791],"study_design_scores_gemma":[0.005120968,0.001243939,0.2160718,0.0009330173,0.000137261,0.000143993,0.0001983089,0.5228664,0.2376359,0.01418533,0.00009435009,0.00136871],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3137164,0.00001530308,0.685239,0.0006441589,0.00007435938,0.0001441319,0.000002644505,0.0001184141,0.00004562959],"genre_scores_gemma":[0.6753343,0.000005513917,0.3244247,0.00003567523,0.00004707341,0.00005078486,0.000003290744,0.000006575388,0.00009211789],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5262104,"threshold_uncertainty_score":0.2153744,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2121180856","doi":"10.1109/iembs.2006.260829","title":"Monocular 3D Head Tracking to Detect Falls of Elderly People","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":170,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Montréal","funders":"","keywords":"Monocular; Computer science; Computer vision; Head (geology); Tracking (education); Trajectory; Artificial intelligence; Elderly people; Population; Physical medicine and rehabilitation; Medicine; Psychology; Gerontology","retraction":null,"screen_n_in":null,"score":{"opus":0.02226613173337876,"gpt":0.2909532916902942,"spread":0.2686871599569154,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000639989,0.0001259463,0.0002442681,0.0001238627,0.00006009909,0.00009381236,0.0005791161,0.00005261495,0.00001567968],"category_scores_gemma":[0.00005232852,0.0001128918,0.00009439978,0.000595348,0.00001242174,0.0002664968,0.0001001638,0.00007671533,0.00004158575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001670729,"about_ca_system_score_gemma":0.00003333343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913047,"about_ca_topic_score_gemma":0.00204498,"domain_scores_codex":[0.9987132,0.0001042049,0.0002922908,0.0003374121,0.000256651,0.00029625],"domain_scores_gemma":[0.9990507,0.0001620657,0.00006722778,0.000556494,0.0001001918,0.00006326751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00000548321,0.00004545866,0.01332495,0.00002134635,0.00001321242,0.00001169192,0.0003380462,0.001907103,0.01714515,0.004261454,0.000260187,0.9626659],"study_design_scores_gemma":[0.001320664,0.001033409,0.5585017,0.0001198565,0.00001940755,0.00006272494,0.00005866566,0.03072132,0.3587286,0.02279437,0.02543576,0.001203548],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.216496,0.0001247375,0.7790288,0.0002502629,0.0001416674,0.0001045282,6.970716e-7,0.0001348181,0.003718485],"genre_scores_gemma":[0.599634,0.000002511323,0.4000366,0.0001089419,0.00003685499,0.000004715436,3.939779e-7,0.00000633078,0.0001696915],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9614624,"threshold_uncertainty_score":0.4603593,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003683977","doi":"10.1016/j.cviu.2011.10.006","title":"An iterative integrated framework for thermal–visible image registration, sensor fusion, and people tracking for video surveillance applications","year":2011,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; RANSAC; Tracking (education); Affine transformation; Video tracking; Image registration; Geometric transformation; Transformation (genetics); Matching (statistics); Image fusion; Pixel; Image sensor; Sensor fusion; Trajectory; Tracking system; Object (grammar); Image (mathematics); Kalman filter; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06477169842145275,"gpt":0.3341036869069383,"spread":0.2693319884854855,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001117491,0.0002756365,0.0003479913,0.0001455446,0.0007467411,0.0009663326,0.0003646376,0.000103623,0.000007124858],"category_scores_gemma":[0.0001038768,0.0002389036,0.00008103635,0.0003802287,0.0001293063,0.001359926,0.0001042713,0.0001640858,0.000001301034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005690814,"about_ca_system_score_gemma":0.00004801788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001298484,"about_ca_topic_score_gemma":0.0000271544,"domain_scores_codex":[0.9981219,0.0001880365,0.0003965441,0.0007839535,0.0001521904,0.0003574369],"domain_scores_gemma":[0.9978306,0.0009268835,0.0002270517,0.000512938,0.0003402393,0.0001623069],"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":[0.0005067303,0.0005542992,0.008812809,0.0006414122,0.0001725877,0.00002134406,0.02300607,0.00004592598,0.0515339,0.6451598,0.00179299,0.2677521],"study_design_scores_gemma":[0.002802302,0.001698879,0.02721569,0.0004038267,0.00003614154,0.00009972361,0.002170201,0.6450326,0.007223304,0.310021,0.001936866,0.001359472],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006243539,0.0001369576,0.991746,0.0003880246,0.0002165527,0.0009014552,0.00002613399,0.0002188146,0.0001224857],"genre_scores_gemma":[0.374156,0.0000358534,0.6253631,0.0002373324,0.0001047843,0.00005654851,0.00001821314,0.00002010475,0.000008130062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6449867,"threshold_uncertainty_score":0.9742206,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078459001","doi":"10.1109/wacv.2014.6836059","title":"Improving background subtraction using Local Binary Similarity Patterns","year":2014,"lang":"en","type":"article","venue":"IEEE Winter Conference on Applications of Computer Vision","topic":"Video Surveillance and Tracking Methods","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":"Polytechnique Montréal","funders":"","keywords":"Pixel; Background subtraction; Computer science; Artificial intelligence; Binary number; Similarity (geometry); Subtraction; Pattern recognition (psychology); Computer vision; Component (thermodynamics); Image (mathematics); Mathematics; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.04789635418718716,"gpt":0.3338327951447669,"spread":0.2859364409575798,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006943998,0.0002338892,0.000308681,0.0002377915,0.0001644555,0.0002178245,0.001000532,0.0001240475,0.0000123436],"category_scores_gemma":[0.000006724975,0.0002255722,0.0001245622,0.0003210258,0.00009528314,0.0005832504,0.0002088603,0.0002748144,0.00003776572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007045487,"about_ca_system_score_gemma":0.00006636502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009063194,"about_ca_topic_score_gemma":0.00001428408,"domain_scores_codex":[0.9979988,0.0002485305,0.0004702985,0.0006520234,0.0003518292,0.000278482],"domain_scores_gemma":[0.9978861,0.000266679,0.0003194018,0.001117503,0.000302939,0.0001073335],"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":[0.0000226168,0.0002686041,0.0005983796,0.00006738136,0.00001689091,0.000001823183,0.00009597471,0.002698401,0.01655198,0.01254076,0.00006711107,0.9670701],"study_design_scores_gemma":[0.0003531766,0.0004026294,0.009281664,0.0001522322,0.000009403745,0.00001814124,0.00001504068,0.9736031,0.01232149,0.002755477,0.0008048432,0.0002828185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07467772,0.000006466529,0.923888,0.0002905168,0.000486237,0.0002590332,0.000004645803,0.000146318,0.000241011],"genre_scores_gemma":[0.8510277,0.000005513186,0.1484826,0.0002297072,0.0002016192,0.00002053672,0.000006326805,0.00001398749,0.00001207631],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9709047,"threshold_uncertainty_score":0.9198566,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2922282711","doi":"10.1109/wacv.2019.00141","title":"Crowd Counting Using Scale-Aware Attention Networks","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","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":"University of Manitoba","funders":"","keywords":"Benchmark (surveying); Computer science; Scale (ratio); Artificial intelligence; Pixel; Image (mathematics); Focus (optics); Computer vision; Pattern recognition (psychology); Geography; Cartography","retraction":null,"screen_n_in":null,"score":{"opus":0.0456069500258801,"gpt":0.3181265693827793,"spread":0.2725196193568992,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001562031,0.0003095316,0.0004355596,0.0001375254,0.0001509636,0.0007615475,0.001225097,0.0003929767,0.00002061943],"category_scores_gemma":[0.000026519,0.0002972352,0.0002375479,0.0002653578,0.00003105763,0.0003566799,0.001921212,0.0007157158,0.00005062214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009859904,"about_ca_system_score_gemma":0.0001497362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002375255,"about_ca_topic_score_gemma":0.00003411065,"domain_scores_codex":[0.9975855,0.0002442832,0.0004213329,0.0009096585,0.0003791076,0.0004601503],"domain_scores_gemma":[0.9979151,0.0001525623,0.0003324102,0.00130709,0.0002236557,0.00006914107],"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":[0.000007643506,0.00007670842,0.197064,0.0004455141,0.0001412574,0.00003104769,0.0001867777,0.6980073,0.0003269787,0.002435343,0.0009977247,0.1002796],"study_design_scores_gemma":[0.0001280999,0.000008802082,0.02241696,0.0002716566,0.00001320678,0.00001418816,0.000008003428,0.9751451,0.00005872105,0.001203274,0.0003362967,0.0003957139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03263377,0.0002301151,0.959327,0.0001439894,0.004588651,0.0002627916,0.000001988365,0.0004107617,0.002400917],"genre_scores_gemma":[0.7649247,0.00003757848,0.2337445,0.0002878879,0.0004701217,0.000007113998,0.00001762109,0.00002913194,0.0004813443],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7322909,"threshold_uncertainty_score":0.999948,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3203857058","doi":"10.1109/iccv48922.2021.00971","title":"High-Performance Discriminative Tracking with Transformers","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; Artificial intelligence; Robustness (evolution); Video tracking; Minimum bounding box; Pattern recognition (psychology); Computer vision; BitTorrent tracker; Encoder; Transformer; Object detection; Eye tracking; Object (grammar); Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.04434457477481431,"gpt":0.3145527255423552,"spread":0.2702081507675409,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005166256,0.0004043473,0.0004249389,0.0002560942,0.0002369304,0.000778367,0.001225987,0.0001146614,0.0003515074],"category_scores_gemma":[0.00002601624,0.0003368496,0.0001410109,0.00056508,0.00009303298,0.001309406,0.0001766533,0.0004884604,0.0001583572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001531843,"about_ca_system_score_gemma":0.000290454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002715215,"about_ca_topic_score_gemma":0.00004573845,"domain_scores_codex":[0.9966545,0.0002583713,0.0005014484,0.00104798,0.001072172,0.0004655258],"domain_scores_gemma":[0.9977819,0.0002643322,0.0002076703,0.0005890611,0.0009898118,0.0001672752],"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":[0.0001519155,0.0004370266,0.0008037746,0.00004302646,0.000217858,0.000454957,0.001119596,0.004761705,0.003878125,0.08194441,0.0009940537,0.9051936],"study_design_scores_gemma":[0.003561457,0.002102875,0.0433658,0.001618147,0.00003899848,0.0003874241,0.0002253543,0.834169,0.1025617,0.004432013,0.005813807,0.00172345],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1226324,0.00002345202,0.8618357,0.005380659,0.003179347,0.0001639571,0.00001785117,0.0001432026,0.006623443],"genre_scores_gemma":[0.9019622,0.0001644193,0.09590646,0.0007924469,0.0003913446,0.00002263531,0.00004359828,0.00002534341,0.0006915238],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9034701,"threshold_uncertainty_score":0.9999083,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164734564","doi":"10.1109/tpami.2007.1039","title":"Learning and Removing Cast Shadows through a Multidistribution Approach","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Robustness (evolution); Computer vision; Pixel; Shadow (psychology); Foreground detection; Mixture model; Statistical model; Gaussian; Gaussian process; Background subtraction; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.02983047505234876,"gpt":0.3092939914860411,"spread":0.2794635164336924,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001084281,0.0002048062,0.0002981107,0.0002858139,0.0003668442,0.0001615306,0.0002128924,0.0000787767,0.00001554419],"category_scores_gemma":[0.00001575382,0.0001832976,0.000150542,0.001036639,0.00007106142,0.000302296,0.000008403516,0.0003921315,0.000005118349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003227425,"about_ca_system_score_gemma":0.00001023615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00131522,"about_ca_topic_score_gemma":0.0008380668,"domain_scores_codex":[0.9983853,0.0001492415,0.0003527489,0.0005854976,0.0002283424,0.0002988759],"domain_scores_gemma":[0.9991549,0.0002748363,0.0001120748,0.000285504,0.00006681566,0.0001059284],"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":[0.0000103693,0.00008415624,0.007800667,0.00001883711,0.0001966798,0.00001381306,0.0009252456,0.00786534,0.0003676727,0.0001194313,5.629181e-7,0.9825972],"study_design_scores_gemma":[0.0003154413,0.0003073935,0.02441251,0.00005591376,0.0004708039,0.0001628007,0.0006200321,0.8647041,0.1071806,0.0005626068,0.0003996716,0.0008080833],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01256249,0.0001879575,0.9866477,0.00009117523,0.0001016066,0.00009004673,0.000008673734,0.0001070104,0.0002033264],"genre_scores_gemma":[0.9643845,0.0003808631,0.03501235,0.0001145467,0.00001964475,0.000005591754,0.000005685911,0.000008596556,0.00006820476],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9817891,"threshold_uncertainty_score":0.7474661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2811025443","doi":"10.1109/icra.2018.8462884","title":"End-to-end Learning of Multi-sensor 3D Tracking by Detection","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Lidar; Tracking (education); Artificial intelligence; Matching (statistics); Computer vision; End-to-end principle; Remote sensing","retraction":null,"screen_n_in":null,"score":{"opus":0.05085731598797066,"gpt":0.3260732844474822,"spread":0.2752159684595116,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001862489,0.0003211694,0.0005027658,0.0002701587,0.0001481636,0.0002273803,0.001033277,0.0003155502,0.00006968248],"category_scores_gemma":[0.0004588837,0.0003086316,0.0001864274,0.000355226,0.00006585049,0.0002037165,0.0009950368,0.0007537543,0.00007207178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006641138,"about_ca_system_score_gemma":0.00007925818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004387661,"about_ca_topic_score_gemma":0.0001012319,"domain_scores_codex":[0.9971752,0.0004992468,0.0005309898,0.0009498317,0.0004398525,0.0004048974],"domain_scores_gemma":[0.9979478,0.0003299812,0.000379655,0.0009020676,0.000314102,0.0001264224],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002117484,0.0001083579,0.004390392,0.0001627351,0.0001043191,0.000008834783,0.001421102,0.005110902,0.03871098,0.0001599216,0.000190348,0.9496109],"study_design_scores_gemma":[0.000803823,0.000463737,0.02474033,0.0004074635,0.00004287649,0.00003561728,0.0000929768,0.3552012,0.5998241,0.0009776341,0.01594413,0.001466196],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03694594,0.0001306489,0.9590937,0.000107166,0.001436732,0.0002502298,0.000005711801,0.0003930917,0.001636783],"genre_scores_gemma":[0.6105356,0.0000331433,0.3886457,0.00007713111,0.0001414887,0.00001499246,0.000004735165,0.00002276597,0.000524447],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9481447,"threshold_uncertainty_score":0.9999366,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2164202775","doi":"10.1109/cvpr.2004.1315181","title":"An unsupervised, online learning framework for moving object detection","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Object detection; Background subtraction; Computer vision; Online learning; Labeled data; Machine learning; Pattern recognition (psychology); Pixel; Multimedia","retraction":null,"screen_n_in":null,"score":{"opus":0.03004087987215893,"gpt":0.3306314444655524,"spread":0.3005905645933935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006003687,0.0001107404,0.0001312307,0.00008389213,0.0002349081,0.0001684906,0.0003990296,0.00009094205,0.000005340265],"category_scores_gemma":[0.0002812867,0.000100768,0.00007177431,0.0003322356,0.0000135464,0.0005204648,0.0000462351,0.000218781,0.000008964154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004151541,"about_ca_system_score_gemma":0.00004013296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008866117,"about_ca_topic_score_gemma":0.0001326701,"domain_scores_codex":[0.9989908,0.00009144931,0.0001588324,0.0003557081,0.0001391795,0.000264004],"domain_scores_gemma":[0.9991925,0.0002443754,0.00004867269,0.000364161,0.00007720717,0.00007304895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001480701,0.0001219053,0.002643716,0.00002155428,0.00001652705,0.000005125585,0.0008852519,0.03207433,0.008452297,0.04432941,0.000001288288,0.9114338],"study_design_scores_gemma":[0.002086601,0.001848516,0.0599829,0.0001508757,0.00001769202,0.00006164624,0.0006002485,0.3689033,0.103437,0.4592769,0.002559129,0.00107511],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1484527,0.00003625072,0.8503014,0.0002255011,0.0003100985,0.0001054913,5.379597e-7,0.0004613582,0.0001066107],"genre_scores_gemma":[0.5173392,0.000005143709,0.4823172,0.0001875402,0.0001176697,0.00000669482,0.000001680784,0.000007549193,0.00001723952],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9103587,"threshold_uncertainty_score":0.4109201,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155067688","doi":"10.1109/crv.2006.66","title":"Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":128,"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":"Action recognition; Artificial intelligence; Computer science; Computer vision; Tracking (education); Pattern recognition (psychology); Action (physics); Principal component analysis; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.09271310548961603,"gpt":0.3160339430992911,"spread":0.2233208376096751,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000478286,0.0000881036,0.00008375827,0.00004478294,0.0002425704,0.0002807405,0.0001596879,0.00004332348,0.000007572613],"category_scores_gemma":[0.00007528527,0.00006168577,0.00003043676,0.0002045633,0.00003258009,0.0004509264,0.00004243151,0.00009507416,0.000007096598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002550381,"about_ca_system_score_gemma":0.00001639754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003209309,"about_ca_topic_score_gemma":0.0001444001,"domain_scores_codex":[0.9991906,0.000133472,0.0001421412,0.0002261879,0.0001342122,0.0001733826],"domain_scores_gemma":[0.9992889,0.0003585087,0.00006011319,0.000198055,0.0000706987,0.00002371905],"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":[0.000007240682,0.00003225592,0.002097226,0.00001122881,0.000008890125,0.0000177292,0.0001736344,0.001066554,0.01611058,0.001407981,0.00009846743,0.9789682],"study_design_scores_gemma":[0.0006389209,0.00009817304,0.01753982,0.00005887711,0.00002660041,0.0004229192,0.0001913155,0.8822394,0.04382356,0.04987571,0.004530574,0.0005541499],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3227612,0.00008428866,0.6756569,0.0002071625,0.0002807569,0.00006967222,4.690647e-7,0.0001235697,0.0008159408],"genre_scores_gemma":[0.8734738,0.00001144404,0.1261057,0.0001822352,0.0001457413,0.000001941552,0.00000110924,0.000005979338,0.00007200113],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9784141,"threshold_uncertainty_score":0.2707187,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386071639","doi":"10.1109/cvpr52729.2023.00148","title":"Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Clothing; Computer science; Identification (biology); Debiasing; Artificial intelligence; Causality (physics); Causal inference; Discriminative model; Representation (politics); Inference; Machine learning; Psychology; Social psychology; Mathematics; Econometrics; Law","retraction":null,"screen_n_in":null,"score":{"opus":0.1007287493549492,"gpt":0.3521748838833079,"spread":0.2514461345283587,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003072107,0.0001678269,0.000220189,0.000304834,0.0003975229,0.0003635692,0.0005648685,0.00009572523,0.00001714146],"category_scores_gemma":[0.0002737233,0.0001645943,0.0001438806,0.001318501,0.0000283148,0.0005839003,0.0001270827,0.00009721723,0.0001258774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006372355,"about_ca_system_score_gemma":0.00005505956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001834339,"about_ca_topic_score_gemma":0.0000679476,"domain_scores_codex":[0.997996,0.0001599608,0.0003509916,0.0006503331,0.0003101835,0.0005325176],"domain_scores_gemma":[0.9984555,0.0004111945,0.0001559736,0.0007515262,0.0001417086,0.00008413099],"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":[0.00005530116,0.0002062313,0.0309164,0.0004410321,0.0002224922,0.00003753165,0.03351784,0.0004299977,0.03495544,0.1234458,0.0427048,0.7330672],"study_design_scores_gemma":[0.002356053,0.0002768044,0.2171461,0.0002137624,0.00006213313,0.00002644716,0.002983046,0.5393454,0.131233,0.03085968,0.07362805,0.001869492],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03367002,0.00007791224,0.9567025,0.004509535,0.001457982,0.0003657465,0.00001120498,0.00116339,0.002041761],"genre_scores_gemma":[0.8883427,0.00002901152,0.1057608,0.0009926646,0.0002403875,0.00007577903,0.00002396659,0.0000333731,0.004501236],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8546727,"threshold_uncertainty_score":0.671196,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1965594729","doi":"10.1109/wacv.2014.6836010","title":"Urban Tracker: Multiple object tracking in urban mixed traffic","year":2014,"lang":"en","type":"article","venue":"IEEE Winter Conference on Applications of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Computer vision; Computer science; Artificial intelligence; Feature (linguistics); Object detection; Tracking (education); Video tracking; Object (grammar); Pattern recognition (psychology); Pixel","retraction":null,"screen_n_in":null,"score":{"opus":0.0272669572319792,"gpt":0.3038622739867174,"spread":0.2765953167547382,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009833358,0.0003053707,0.0004851789,0.0004540912,0.0001045542,0.0002171675,0.001586591,0.0001409177,0.000007731005],"category_scores_gemma":[0.00003196691,0.0002922711,0.0001583597,0.0006791794,0.0001033826,0.0004404505,0.0001267385,0.0003367613,0.00007850501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004894304,"about_ca_system_score_gemma":0.00006505798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000147192,"about_ca_topic_score_gemma":0.00004916125,"domain_scores_codex":[0.9972956,0.0003791584,0.0007089571,0.000827515,0.0003956052,0.0003932212],"domain_scores_gemma":[0.9974403,0.0006120005,0.000284302,0.001308339,0.0002338895,0.0001211851],"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":[0.00002920884,0.0005261394,0.003198919,0.00004522177,0.00001775854,0.000002885857,0.001086857,0.002041238,0.003067324,0.01327613,0.001438727,0.9752696],"study_design_scores_gemma":[0.001883578,0.0007633698,0.06612378,0.0004786191,0.00001027291,0.0000165654,0.0000285325,0.903953,0.01254744,0.002660546,0.01082233,0.0007120381],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1153493,0.000027229,0.8823484,0.0004714036,0.000418963,0.0004545735,0.000004322919,0.0002024088,0.0007234396],"genre_scores_gemma":[0.9220545,0.00001037925,0.07737958,0.0002005121,0.0001981053,0.00008768032,0.000008674915,0.00002097853,0.00003953945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9745576,"threshold_uncertainty_score":0.999953,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1863704471","doi":"10.1109/iccv.2015.496","title":"FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computation; Computer science; Bounded function; Online algorithm; Tracking (education); Inference; Minimum-cost flow problem; Algorithm; Mathematical optimization; Flow network; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.06428688319598616,"gpt":0.3213269967043053,"spread":0.2570401135083191,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008053926,0.0001451625,0.0001915753,0.00009156873,0.0001014368,0.0002536825,0.0002328538,0.00004423822,0.00000216277],"category_scores_gemma":[0.00006117877,0.0001092949,0.00002902125,0.0003457642,0.00005252142,0.0002584774,0.00008822295,0.0001049836,0.000009482814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003800746,"about_ca_system_score_gemma":0.00010612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004962614,"about_ca_topic_score_gemma":0.000162118,"domain_scores_codex":[0.9987266,0.0001243119,0.0001857421,0.0003798898,0.0003368297,0.0002466215],"domain_scores_gemma":[0.9991767,0.0001546274,0.00007068177,0.0002663876,0.0001700175,0.0001616259],"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":[0.00004861894,0.0002495985,0.005058857,0.00002506249,0.00004357718,0.00007855306,0.002519442,0.04518129,0.0001580529,0.001706276,0.0005221306,0.9444085],"study_design_scores_gemma":[0.002147562,0.0002484838,0.02431922,0.00004258307,0.00001246712,0.0001268866,0.0002169905,0.9686167,0.001024932,0.001948715,0.0009369284,0.0003585502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2280216,0.0001132193,0.7695019,0.0004701113,0.000248357,0.0001598845,0.000001655056,0.0001673175,0.001315996],"genre_scores_gemma":[0.6276525,0.000002156994,0.3719591,0.0002375584,0.00004418333,0.000003831434,0.000006695722,0.000007762652,0.00008621421],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.94405,"threshold_uncertainty_score":0.4456917,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2247229935","doi":"10.1109/iccvw.2015.86","title":"The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"BitTorrent tracker; Computer vision; Computer science; Artificial intelligence; Tracking (education); Benchmark (surveying); Video tracking; Infrared; Object (grammar); Term (time); Thermal infrared; Visualization; Eye tracking; Physics; Optics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.07255331517948924,"gpt":0.3342199512109393,"spread":0.26166663603145,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004452717,0.000181357,0.0001863402,0.00005929706,0.0002435406,0.0004232641,0.001190125,0.00009562751,0.000002847397],"category_scores_gemma":[0.0007854128,0.0001127755,0.00008576403,0.0003498334,0.00006271491,0.0006056329,0.0003008707,0.0002499176,0.0001251545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004873747,"about_ca_system_score_gemma":0.0001781423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005150046,"about_ca_topic_score_gemma":0.00009182926,"domain_scores_codex":[0.9976479,0.0006047083,0.0003553591,0.0004222349,0.0005031013,0.0004667406],"domain_scores_gemma":[0.9978009,0.0009328381,0.0001147199,0.0007771919,0.000205799,0.0001685092],"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":[0.0001392099,0.0001399865,0.001415425,0.000007610912,0.00006555701,0.00007041327,0.008362149,0.0003069447,0.0002590747,0.02919281,0.007115644,0.9529251],"study_design_scores_gemma":[0.009988957,0.002535723,0.1500081,0.0001301521,0.00003138042,0.0001745959,0.002320482,0.158567,0.01302366,0.05693196,0.6036832,0.002604851],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.07832276,0.002478603,0.4240988,0.01639596,0.005006751,0.0006835791,0.000005333513,0.001972781,0.4710354],"genre_scores_gemma":[0.9733744,0.00005529435,0.02394925,0.0003070107,0.0003653031,0.00001325187,0.000001929374,0.00001644076,0.001917173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9503203,"threshold_uncertainty_score":0.459885,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3209993199","doi":"10.1109/iccvw54120.2021.00305","title":"The Ninth Visual Object Tracking VOT2021 Challenge Results","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":112,"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":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Ninth; Computer vision; Artificial intelligence; Object (grammar); Video tracking; Eye tracking","retraction":null,"screen_n_in":null,"score":{"opus":0.03822439981751592,"gpt":0.3268587849193556,"spread":0.2886343851018397,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001774276,0.0001378108,0.0001651312,0.00003233938,0.0003979825,0.0005234944,0.0006727225,0.00006084254,0.00001397956],"category_scores_gemma":[0.0006531128,0.00009281762,0.0001125146,0.0004846488,0.00004393717,0.0003162098,0.0002881481,0.0002134202,0.00007502199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000235797,"about_ca_system_score_gemma":0.0001543434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002275101,"about_ca_topic_score_gemma":0.0003154757,"domain_scores_codex":[0.9980366,0.0004129671,0.0003053156,0.0005076037,0.0003415184,0.0003960516],"domain_scores_gemma":[0.9977741,0.001084874,0.00007852331,0.0007863289,0.0002002145,0.00007596256],"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":[0.00001512609,0.00008950779,0.0002907383,0.00000784549,0.00004441689,0.0002009324,0.0009246438,0.00004071897,0.000643209,0.06189284,0.001499615,0.9343504],"study_design_scores_gemma":[0.004156977,0.0007146296,0.09077169,0.0002117876,0.00003044881,0.0004984679,0.001744725,0.07322613,0.1440141,0.04533382,0.6372313,0.002065923],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01038058,0.003290732,0.7951543,0.02597724,0.002919686,0.0001838539,0.000003237735,0.0006962466,0.1613941],"genre_scores_gemma":[0.9519188,0.0004443375,0.04384217,0.0005016484,0.0002603943,0.000008508161,0.000002862633,0.00001283634,0.003008381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9415383,"threshold_uncertainty_score":0.5048068,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2114508695","doi":"10.1109/tits.2008.915647","title":"Multilevel Framework to Detect and Handle Vehicle Occlusion","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","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 Windsor","funders":"","keywords":"Inter frame; Occlusion; Artificial intelligence; Computer vision; Computer science; Tracking (education); Cluster analysis; Emphasis (telecommunications); Frame (networking); Reference frame","retraction":null,"screen_n_in":null,"score":{"opus":0.04673748613794341,"gpt":0.2950810844161124,"spread":0.248343598278169,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003547535,0.000224729,0.0002769433,0.0002676562,0.0004736543,0.00008523101,0.0003076128,0.0001426077,0.00001660475],"category_scores_gemma":[0.000009220871,0.0002163944,0.0001094633,0.0005065153,0.00004703118,0.000303125,8.114081e-7,0.0002529951,0.0001080519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004969454,"about_ca_system_score_gemma":0.00004424826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002082483,"about_ca_topic_score_gemma":0.00008133621,"domain_scores_codex":[0.9981321,0.0001325253,0.0004932086,0.0005341026,0.0004194895,0.0002885634],"domain_scores_gemma":[0.99878,0.0003316379,0.00008390591,0.0004452015,0.0001419132,0.0002173087],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003774616,0.000509405,0.00371469,0.0003359919,0.0002926466,0.0003369222,0.03851588,0.4219044,0.01613845,0.005212528,0.0003209402,0.5123407],"study_design_scores_gemma":[0.00284316,0.002493566,0.1070307,0.001822259,0.0001366436,0.0005031777,0.001091645,0.1842072,0.6773536,0.003534215,0.0154664,0.003517446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1195942,0.0001293479,0.8780013,0.0001156142,0.00143638,0.0004047118,0.00002548193,0.0002571247,0.00003579616],"genre_scores_gemma":[0.9477614,0.0001758847,0.05153568,0.000180781,0.00004353818,0.00007502244,0.000001985522,0.00002136992,0.0002043518],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8281671,"threshold_uncertainty_score":0.8824308,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226294111","doi":"10.1109/tmm.2022.3163847","title":"Spatial-Channel Enhanced Transformer for Visible-Infrared Person Re-Identification","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Artificial intelligence; Feature learning; Pattern recognition (psychology); Embedding; Transformer; Feature extraction; Feature (linguistics); Feature vector; Computer vision; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03848727033689459,"gpt":0.2986878252806538,"spread":0.2602005549437592,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000801403,0.0002145204,0.0002405396,0.0003023644,0.0007816197,0.00009917049,0.0005956286,0.00007606031,0.0001523362],"category_scores_gemma":[0.0000180001,0.0002356315,0.000253803,0.0005367722,0.00003904973,0.0003943446,0.000001708984,0.0003284674,0.00004165337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001305501,"about_ca_system_score_gemma":0.00009655816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008594681,"about_ca_topic_score_gemma":0.00008278113,"domain_scores_codex":[0.9979736,0.0002080944,0.0003477156,0.0006247245,0.0004575138,0.000388416],"domain_scores_gemma":[0.9986126,0.0004387761,0.0001228467,0.000600703,0.0001133133,0.0001117824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002340872,0.0005593326,0.000004121796,0.00005482556,0.0001092094,0.000005745349,0.01040205,0.05121644,0.06858233,0.00005414427,0.000657547,0.8681202],"study_design_scores_gemma":[0.002286863,0.0005224825,0.0004842492,0.00001936871,0.00004455742,0.00000999422,0.0005779073,0.4762807,0.5157511,0.000858834,0.002605213,0.0005587337],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001979487,0.00003826725,0.9926465,0.0007852361,0.003040789,0.0007328321,0.0001185433,0.0003173267,0.0003410558],"genre_scores_gemma":[0.9529465,0.00002286996,0.04423252,0.0002524943,0.00007671475,0.0009854155,0.00002072236,0.00003226495,0.00143044],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9509671,"threshold_uncertainty_score":0.9608772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155213743","doi":"10.1109/cvpr.2005.233","title":"Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Shadow (psychology); Gaussian; Shadow mapping; Computer vision; Computer science; Mixture model; Artificial intelligence; Gaussian process; Computer graphics (images); Physics; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.02016693290918421,"gpt":0.2666959264177416,"spread":0.2465289935085574,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004754777,0.0001774852,0.0001833149,0.00009823219,0.0001641031,0.0002383395,0.0005952001,0.0001192157,0.00005085709],"category_scores_gemma":[0.00005271341,0.0001536432,0.00009354446,0.0003066118,0.00001980941,0.0007868159,0.0001543677,0.0002242315,0.0001234064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000614273,"about_ca_system_score_gemma":0.00004651539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006449952,"about_ca_topic_score_gemma":0.002384757,"domain_scores_codex":[0.998546,0.0001169497,0.0002209623,0.0005228635,0.0002569443,0.0003362582],"domain_scores_gemma":[0.9990227,0.0001130464,0.00007013029,0.0006248069,0.00005197496,0.0001173967],"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":[0.000007559796,0.00004933308,0.0006658214,0.000005382701,0.00002251519,0.00001324213,0.0008019227,0.02157714,0.008784208,0.0043153,0.0003768684,0.9633807],"study_design_scores_gemma":[0.0002189146,0.00001887121,0.003332434,0.00001340426,0.000004241379,0.0000157795,0.00001418265,0.9764201,0.01068767,0.007697219,0.001347354,0.0002298389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01413902,0.0001268367,0.9738145,0.001498082,0.0002482536,0.00008575764,0.000003856941,0.000424027,0.009659686],"genre_scores_gemma":[0.6353665,0.00000676168,0.3632456,0.0007379092,0.0001924252,0.000006400415,0.000001573792,0.0000095119,0.0004333348],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9631509,"threshold_uncertainty_score":0.6265387,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4255802862","doi":"10.1007/978-3-540-24671-8_37","title":"Adaptive Probabilistic Visual Tracking with Incremental Subspace Update","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Probabilistic logic; Computer vision; Subspace topology; Tracking (education); A priori and a posteriori; Prior probability; Affine transformation; Video tracking; Object (grammar); Bayesian probability; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0242091433944153,"gpt":0.277435982169378,"spread":0.2532268387749627,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001559981,0.0007389038,0.0007106658,0.000685872,0.0003497007,0.000754466,0.002675086,0.0002847074,0.00002167542],"category_scores_gemma":[0.00008669482,0.0006152437,0.000130052,0.0008507661,0.000983806,0.0009978298,0.000919715,0.001068525,0.0000425878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006897432,"about_ca_system_score_gemma":0.001211235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005507653,"about_ca_topic_score_gemma":0.0003532202,"domain_scores_codex":[0.9950356,0.00009163636,0.0005225856,0.002042545,0.001383392,0.0009242727],"domain_scores_gemma":[0.9974041,0.0004730468,0.0003912738,0.001171916,0.0003413954,0.0002182622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005736953,0.0001082836,0.0004304946,0.00008509769,0.00005317682,0.0006349212,0.001291682,0.09052288,0.0001802779,0.06634653,0.000003623521,0.8402857],"study_design_scores_gemma":[0.002223331,0.002338968,0.002468852,0.002828922,0.00005422035,0.0006815571,0.000001562979,0.3648027,0.007747196,0.6126807,0.0005215711,0.00365039],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000465947,0.0003080304,0.995267,0.0004903808,0.0008667993,0.0005696173,0.000005273103,0.000264499,0.001762426],"genre_scores_gemma":[0.5083268,0.00002120152,0.4906169,0.0006622158,0.0002622203,0.00001065935,0.000004430722,0.00004351572,0.00005201047],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8366353,"threshold_uncertainty_score":0.9996299,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2166943865","doi":"10.1109/tip.2010.2087764","title":"Real-Time Discriminative Background Subtraction","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","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":"University of Alberta; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Background subtraction; Discriminative model; Markov random field; Artificial intelligence; Robustness (evolution); Graphics processing unit; Inference; Pixel; Maximum a posteriori estimation; A priori and a posteriori; Pattern recognition (psychology); Image segmentation; Computer vision; Segmentation; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02556292082399655,"gpt":0.3174708054092379,"spread":0.2919078845852414,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005783881,0.0002092784,0.0001929284,0.0002007438,0.0005319647,0.0005329007,0.0004414103,0.0001055317,0.00007697692],"category_scores_gemma":[0.00001378171,0.0001974192,0.0001010169,0.0005390379,0.0001177876,0.002248578,0.000002715113,0.0006011122,0.0001956929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004526669,"about_ca_system_score_gemma":0.0001219225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005109568,"about_ca_topic_score_gemma":0.00004012401,"domain_scores_codex":[0.998495,0.0001085624,0.0002519983,0.0005049873,0.0003047738,0.0003347032],"domain_scores_gemma":[0.9989164,0.0002015917,0.0001260626,0.000450783,0.0001969953,0.0001081621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001791284,0.0001949503,0.000007650578,0.00003614682,0.00001351812,0.00001496227,0.0007078266,0.0001029558,0.4524307,0.0001687098,0.00003812899,0.5462666],"study_design_scores_gemma":[0.0009638799,0.0002169044,0.003713201,0.0001361577,0.00005802899,0.0002118596,0.0002335622,0.1198536,0.867999,0.004917372,0.0007085994,0.0009878806],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03885106,0.000007153386,0.9540564,0.0004579502,0.0008685387,0.0001160971,0.000003930119,0.0004608199,0.005177986],"genre_scores_gemma":[0.7300585,0.00001360324,0.2692186,0.00006051877,0.00007565859,0.00002473121,9.766831e-7,0.00002126221,0.0005261746],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6912075,"threshold_uncertainty_score":0.8050521,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124082557","doi":"10.1109/tcsvt.2007.906935","title":"Statistical background subtraction using spatial cues","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Pixel; Computer vision; Frame (networking); Statistical model; Series (stratigraphy); Pattern recognition (psychology); Noise (video); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.05991418926184172,"gpt":0.3315918211826575,"spread":0.2716776319208157,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001134966,0.0001942183,0.0003276161,0.0005249179,0.0003911938,0.0001413982,0.0002660793,0.0002608828,0.000003348861],"category_scores_gemma":[0.00002344986,0.0001883789,0.00006612498,0.0004275503,0.0001226513,0.0002931044,0.000002739824,0.0002786963,0.000006953201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009221467,"about_ca_system_score_gemma":0.00005584352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003285604,"about_ca_topic_score_gemma":0.0002411334,"domain_scores_codex":[0.9983181,0.00007469728,0.0004374457,0.0005175878,0.0002025969,0.0004495376],"domain_scores_gemma":[0.9986369,0.0005864072,0.00013317,0.0004079392,0.0001408371,0.00009474636],"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":[0.00005306784,0.0002510721,0.0009140067,0.0002028133,0.0001486325,0.0000599746,0.0001763382,0.001542978,0.02259529,0.06735714,0.00005329529,0.9066454],"study_design_scores_gemma":[0.01082061,0.006340195,0.01711779,0.0009950079,0.0005014185,0.007084594,0.002753366,0.6029723,0.2375325,0.06206078,0.04679928,0.005022154],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06484894,0.0002031578,0.9319412,0.0001132625,0.002047666,0.0003889267,0.00002748623,0.0003584516,0.00007093925],"genre_scores_gemma":[0.9858666,0.00002862101,0.01387491,0.00004745412,0.00008832166,0.00003762551,0.000001180955,0.00002022736,0.00003506853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9210176,"threshold_uncertainty_score":0.7681867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2981657066","doi":"10.1109/tii.2019.2949347","title":"Edge Coordinated Query Configuration for Low-Latency and Accurate Video Analytics","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Video Surveillance and Tracking Methods","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":"University of Waterloo","funders":"Natural Science Foundation of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Video tracking; Analytics; Cloud computing; Latency (audio); Video quality; Edge computing; Low latency (capital markets); Video processing; Enhanced Data Rates for GSM Evolution; Real-time computing; Video post-processing; Uncompressed video; Artificial intelligence; Computer network; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.05528549721993517,"gpt":0.2933357789928377,"spread":0.2380502817729025,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007387358,0.0002029356,0.0003034335,0.0002399957,0.0002017309,0.0003017509,0.0003138546,0.0002648999,0.00002431304],"category_scores_gemma":[0.00004546196,0.0001885963,0.00009926658,0.0005099308,0.0000426515,0.001276652,0.000003517686,0.0003691723,0.00006163479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005780921,"about_ca_system_score_gemma":0.0001654114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001912496,"about_ca_topic_score_gemma":0.000006537593,"domain_scores_codex":[0.9985833,0.00007516405,0.0006587333,0.0001817441,0.000205187,0.0002958862],"domain_scores_gemma":[0.9984716,0.0005448943,0.0002526178,0.0004212537,0.0002022355,0.0001073545],"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":[0.0005724136,0.0004497724,0.0005108576,0.0004775328,0.0005451035,0.00000552594,0.005216619,0.167349,0.001937201,0.01122293,0.004257119,0.807456],"study_design_scores_gemma":[0.00536753,0.0008675334,0.0001730289,0.0002081213,0.00007568813,0.00002426134,0.0003710159,0.9413597,0.04417126,0.001180736,0.005459522,0.0007416217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03985683,0.000006073982,0.9563008,0.0002544522,0.002028754,0.0006711078,0.0000362532,0.0001496673,0.0006960658],"genre_scores_gemma":[0.9875485,0.00003149472,0.01155322,0.0003069975,0.00007936965,0.00003794548,0.000009983836,0.00001362184,0.0004188911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9476917,"threshold_uncertainty_score":0.7690733,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3045524274","doi":"10.1007/978-3-030-58583-9_10","title":"Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Benchmark (surveying); Metric (unit); Pattern recognition (psychology); Domain adaptation; Domain (mathematical analysis); Feature vector; Feature (linguistics); Gradient descent; Adaptation (eye); Representation (politics)","retraction":null,"screen_n_in":null,"score":{"opus":0.06383244040056965,"gpt":0.2974700550426327,"spread":0.2336376146420631,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003414813,0.0003631701,0.0003982504,0.0003912962,0.0002862925,0.0007144585,0.002934974,0.0002309157,0.00000293538],"category_scores_gemma":[0.000324916,0.0002892935,0.0001450355,0.0008220018,0.0002777368,0.0005582653,0.0002315179,0.0006476615,0.000008267848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001813357,"about_ca_system_score_gemma":0.0003217532,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003335309,"about_ca_topic_score_gemma":0.0003551031,"domain_scores_codex":[0.9967641,0.0001888035,0.0004464401,0.001341431,0.0008213089,0.0004379202],"domain_scores_gemma":[0.9970747,0.001282217,0.0002711529,0.001121364,0.0001702823,0.00008023807],"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":[0.0000419349,0.00007288303,0.0004984224,0.0001807131,0.00001739101,0.00007936737,0.02848027,0.03807909,0.000574154,0.1039233,0.00007539684,0.8279771],"study_design_scores_gemma":[0.000416564,0.0001457161,0.004358448,0.0001854508,0.000007374769,0.00001400219,0.000009943979,0.6895567,0.0004151596,0.3029895,0.001398516,0.0005026304],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001355527,0.0002221988,0.9844931,0.01263245,0.0009216718,0.0008200638,0.000009043365,0.00008649308,0.0006794469],"genre_scores_gemma":[0.3741241,0.00002364896,0.6233575,0.001995141,0.000368561,0.00004318752,0.00001865118,0.00002459945,0.00004467533],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8274745,"threshold_uncertainty_score":0.9999559,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2605089300","doi":"10.1109/tvcg.2017.2734599","title":"Deep 6-DOF Tracking","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Computer vision; Video tracking; Deep learning; Tracking (education); Visualization; Object (grammar)","retraction":null,"screen_n_in":null,"score":{"opus":0.04166425316061544,"gpt":0.3295033618752505,"spread":0.2878391087146351,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003700416,0.0001774402,0.0001855227,0.0002179414,0.001174217,0.0008888986,0.0005887373,0.0001001238,0.000007073524],"category_scores_gemma":[0.000006351739,0.0001759738,0.00009550063,0.0002147194,0.0001039368,0.0007980699,0.000007786748,0.0001745325,0.000007287386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001106855,"about_ca_system_score_gemma":0.00002257791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001786501,"about_ca_topic_score_gemma":0.00004349753,"domain_scores_codex":[0.9987574,0.0001256852,0.0002292577,0.0004222825,0.0002466077,0.0002187929],"domain_scores_gemma":[0.9987836,0.0001054614,0.0001447859,0.0007317053,0.0001258557,0.0001085371],"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":[0.0000116042,0.000198831,0.0009942946,0.00003309505,0.00006172137,0.00001430445,0.0009415548,0.0008480438,0.00002451024,0.6769472,0.00005520567,0.3198696],"study_design_scores_gemma":[0.0004943233,0.0001294304,0.01099245,0.0000407037,0.00001163347,0.00002433375,0.000007283181,0.9820687,0.001861706,0.003193357,0.0009132776,0.0002627656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004267468,0.00003585029,0.9937921,0.0001304326,0.001251251,0.0001097703,0.000001501679,0.0002401659,0.000171529],"genre_scores_gemma":[0.9897724,0.0002216644,0.00904951,0.0008111253,0.00008039801,0.00001007805,0.000001147032,0.00001585881,0.00003777477],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.985505,"threshold_uncertainty_score":0.9031249,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2244252896","doi":"10.1109/iccvw.2015.80","title":"Scalable Kernel Correlation Filter with Sparse Feature Integration","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Kernel (algebra); BitTorrent tracker; Eye tracking; Video tracking; Scalability; Benchmark (surveying); Correlation; Filter (signal processing); Pattern recognition (psychology); Computer vision; Mathematics; Object (grammar)","retraction":null,"screen_n_in":null,"score":{"opus":0.04202676590144399,"gpt":0.2770659564973058,"spread":0.2350391905958618,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004777363,0.00008889309,0.00009276321,0.00004933685,0.00004250919,0.0001508408,0.0002242692,0.00005594275,0.0000105365],"category_scores_gemma":[0.00005522162,0.00005798784,0.00001953671,0.0003005823,0.00001769183,0.0006740539,0.00004232525,0.0001156221,0.0001193028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000288271,"about_ca_system_score_gemma":0.00006119697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004417742,"about_ca_topic_score_gemma":0.00009000963,"domain_scores_codex":[0.9992585,0.00007899172,0.00008249946,0.0002229315,0.000216435,0.0001405975],"domain_scores_gemma":[0.9993439,0.00004860334,0.00004644351,0.0003281211,0.0001541164,0.00007879174],"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":[0.0001539723,0.0002344752,0.1475255,0.000019174,0.00006476384,0.00007138329,0.004072138,0.01139826,0.0008106086,0.1920947,0.1471768,0.4963783],"study_design_scores_gemma":[0.001853829,0.0005361445,0.08677763,0.00008633708,0.00001400906,0.0001436387,0.0001962121,0.8541722,0.008058473,0.01726307,0.03025387,0.0006446208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006443074,0.0000332167,0.970162,0.001222701,0.0003091557,0.00007861315,3.530488e-7,0.0001806763,0.02157026],"genre_scores_gemma":[0.5758908,0.000001647532,0.4181165,0.000380977,0.00005766574,0.000005611842,0.000006477985,0.000005667795,0.005534702],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8427739,"threshold_uncertainty_score":0.2364675,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2099438908","doi":"10.1109/tpami.2008.150","title":"Learning to Detect Moving Shadows in Dynamic Environments","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Artificial intelligence; Exploit; Set (abstract data type); Computer vision; Feature vector; Feature (linguistics); Pattern recognition (psychology); Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02022844312142072,"gpt":0.2801350885240175,"spread":0.2599066454025968,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004054811,0.0001873432,0.0002966431,0.0006993728,0.0002138608,0.0000561573,0.0003671224,0.00005136067,0.00004246601],"category_scores_gemma":[0.000009928233,0.0001786028,0.0001530593,0.00121284,0.00003526827,0.0001881142,0.000008782457,0.0003404669,0.00004531303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000533643,"about_ca_system_score_gemma":0.00001092955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008107916,"about_ca_topic_score_gemma":0.002147663,"domain_scores_codex":[0.9984244,0.0001856227,0.0003271121,0.000551891,0.0002392051,0.0002717885],"domain_scores_gemma":[0.9992692,0.0001663494,0.0000629336,0.0003722193,0.00001254363,0.0001167276],"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":[0.000005199276,0.00004254139,0.007618487,0.000003480088,0.00008408506,0.00003187342,0.0007800763,0.2030284,0.0007402226,0.000001482943,2.845827e-7,0.7876639],"study_design_scores_gemma":[0.0001903638,0.0003338361,0.1095368,0.00004439928,0.0001091419,0.00005033942,0.00008269645,0.814854,0.07382535,0.0001907423,0.0001703198,0.0006119573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08542461,0.00005500928,0.9141142,0.000155598,0.00008381216,0.00008554354,0.000003295384,0.00004870818,0.00002926345],"genre_scores_gemma":[0.9894018,0.0004613492,0.009613403,0.0002717855,0.000005316092,0.00002004301,0.000001065823,0.00001013427,0.0002150625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9045008,"threshold_uncertainty_score":0.7283212,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2778775889","doi":"10.1109/iccv.2017.265","title":"Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Science North","funders":"Army Research Office; National Science Foundation","keywords":"Metric (unit); Computer science; Matching (statistics); Adaptation (eye); Contrast (vision); Identification (biology); Set (abstract data type); Artificial intelligence; Margin (machine learning); Similarity (geometry); Cover (algebra); Machine learning; Mathematical optimization; Algorithm; Mathematics; Image (mathematics); Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1233692533169497,"gpt":0.3578340000265187,"spread":0.234464746709569,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009745809,0.0001071116,0.0001466686,0.0001454586,0.0005174359,0.0003393435,0.0007035176,0.00005094257,0.000006823945],"category_scores_gemma":[0.0006952336,0.00009245004,0.00008278548,0.0002022309,0.0000664389,0.000265977,0.00006695612,0.00006430003,0.00001418179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005404679,"about_ca_system_score_gemma":0.00004242838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003527334,"about_ca_topic_score_gemma":0.000353666,"domain_scores_codex":[0.998884,0.00008761803,0.0001996384,0.0003970493,0.0002330747,0.0001986388],"domain_scores_gemma":[0.99829,0.0004759024,0.000253738,0.0007286264,0.0001976624,0.00005407129],"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":[0.00001852253,0.0001239403,0.0005459026,0.0000205578,0.00002539399,0.000001251881,0.001281168,0.01502779,0.0008588538,0.01084744,0.0001832819,0.9710659],"study_design_scores_gemma":[0.0003442142,0.00006529981,0.08090744,0.000008988533,0.000007517859,0.000001564377,0.0003271389,0.910842,0.004899037,0.002223377,0.0002413714,0.0001320646],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01610441,0.00004183121,0.9815636,0.001064045,0.0005113524,0.0002685004,0.000009586665,0.0001068326,0.0003298427],"genre_scores_gemma":[0.6622266,0.000002746696,0.3374552,0.00005835214,0.00005946144,0.00001679964,0.000008804393,0.000005266772,0.000166726],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9709339,"threshold_uncertainty_score":0.3979751,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2033229116","doi":"10.1109/itsc.2007.4357793","title":"Probabilistic Collision Prediction for Vision-Based Automated Road Safety Analysis","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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 British Columbia","funders":"","keywords":"Collision; Computer science; Probabilistic logic; Traffic conflict; Process (computing); Computation; Motion (physics); Artificial intelligence; Track (disk drive); Collision avoidance; Traffic analysis; Real-time computing; Machine learning; Data mining; Floating car data; Transport engineering; Computer security; Traffic congestion; Algorithm; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01650654352623438,"gpt":0.3273080428498818,"spread":0.3108014993236474,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003325281,0.0001326199,0.0002461654,0.0003403863,0.0002066743,0.0001170218,0.0003791318,0.00009408841,0.00001341117],"category_scores_gemma":[0.0003263049,0.0001071508,0.0001944818,0.002007178,0.00002953163,0.0002447094,0.00004499232,0.00006229837,0.000009088003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008890469,"about_ca_system_score_gemma":0.00009799616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005481176,"about_ca_topic_score_gemma":0.0001136473,"domain_scores_codex":[0.9983872,0.0001086202,0.0004254032,0.0004660034,0.0003006666,0.0003120854],"domain_scores_gemma":[0.9981459,0.0008158291,0.000109784,0.0005649051,0.0002611681,0.0001023592],"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":[0.0004887035,0.0008136715,0.03654445,0.0001461252,0.0006791007,0.00002384921,0.000381107,0.4409628,0.001942611,0.01875341,0.004190715,0.4950735],"study_design_scores_gemma":[0.0004005181,0.0001258088,0.1909361,0.000008465262,0.0000470825,7.942157e-7,0.000003491645,0.8054734,0.001017177,0.0005089568,0.001372568,0.0001055675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01529826,0.00001518508,0.9812726,0.000289302,0.0003162749,0.0003974172,0.00001702301,0.00137027,0.001023715],"genre_scores_gemma":[0.6813908,0.000001239879,0.3182245,0.0001630618,0.0000386357,0.0000147812,0.00004227912,0.000006718705,0.0001180159],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6660926,"threshold_uncertainty_score":0.4369482,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2076866895","doi":"10.1109/tvcg.2013.168","title":"Interactive Exploration of Surveillance Video through Action Shot Summarization and Trajectory Visualization","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Computer science; Automatic summarization; Computer vision; Visualization; Artificial intelligence; Video tracking; Trajectory; Object (grammar); Movement (music); Timeline; Shot (pellet); Representation (politics)","retraction":null,"screen_n_in":null,"score":{"opus":0.06168984370974633,"gpt":0.3344722220536929,"spread":0.2727823783439465,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003453927,0.0002498037,0.0002908258,0.000392117,0.0002649625,0.0002502055,0.0001714016,0.0001532706,0.00001350196],"category_scores_gemma":[0.00001275573,0.0002548688,0.0000718461,0.0009822374,0.0001091806,0.00299935,0.000007893391,0.0001589987,0.000004026148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002968185,"about_ca_system_score_gemma":0.00003639863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001093966,"about_ca_topic_score_gemma":0.00007738339,"domain_scores_codex":[0.9980221,0.0004475942,0.0004876957,0.0005214866,0.0003237695,0.0001974011],"domain_scores_gemma":[0.9986269,0.0003022202,0.0002724761,0.0003025115,0.000414705,0.00008116524],"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":[0.000182035,0.001349949,0.005916819,0.0005750635,0.0003855458,0.000003717441,0.01836723,0.01006893,0.004045788,0.7304115,0.0004727438,0.2282207],"study_design_scores_gemma":[0.0008173091,0.000450166,0.01244565,0.0001001812,0.00001986697,0.00001317757,0.0001583607,0.9645231,0.01587614,0.004920331,0.0002714798,0.0004041863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03537197,0.00005541778,0.9630934,0.0000798884,0.000730738,0.0003997405,0.000005990599,0.0002151474,0.00004769152],"genre_scores_gemma":[0.9928126,0.001029126,0.005566486,0.0004154657,0.00005340335,0.00005570257,0.00002321367,0.00002440023,0.00001963444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9575269,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1980508392","doi":"10.1109/tip.2012.2214049","title":"Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","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":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Video tracking; Motion compensation; Computer science; Motion estimation; Markov random field; Block-matching algorithm; Block (permutation group theory); Markov chain; Pixel; Pattern recognition (psychology); Object (grammar); Mathematics; Image segmentation; Image (mathematics); Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.03635832405567782,"gpt":0.3198126644037912,"spread":0.2834543403481133,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002270517,0.0002423059,0.0002897743,0.0002418062,0.0005405831,0.0005324852,0.0006454346,0.0001079927,0.00001852949],"category_scores_gemma":[0.00002464005,0.0001920506,0.0001354679,0.0008520366,0.00008169827,0.002055384,0.000004487501,0.000547093,0.000008201688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006521888,"about_ca_system_score_gemma":0.00009990213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001185375,"about_ca_topic_score_gemma":0.0001068531,"domain_scores_codex":[0.9976283,0.0006337052,0.0004179885,0.0003585664,0.0004094991,0.0005519758],"domain_scores_gemma":[0.9987109,0.0005232143,0.0001475418,0.0004600489,0.00008150453,0.00007679114],"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":[0.0004123611,0.001220626,0.007358451,0.0003821137,0.00007339899,0.00009681267,0.03197553,0.01789975,0.01298585,0.0001057329,0.00009274974,0.9273966],"study_design_scores_gemma":[0.01324267,0.0003219378,0.02566617,0.001613018,0.000178157,0.0008080485,0.002493865,0.7839524,0.1610807,0.005874521,0.001779516,0.002988933],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07057287,0.0002681607,0.9274549,0.0004068553,0.0005342409,0.0002581057,0.000002414288,0.0001362532,0.0003661863],"genre_scores_gemma":[0.8146611,0.000007963583,0.1847858,0.000382859,0.0001012698,0.00003132744,9.938093e-7,0.00001783364,0.00001080226],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9244077,"threshold_uncertainty_score":0.7831594,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2128027845","doi":"","title":"Adaptive Discriminative Generative Model and Its Applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Generative model; Artificial intelligence; Generative grammar; Computer science; Context (archaeology); Pattern recognition (psychology); Active appearance model; Video tracking; Generative Design; Probabilistic logic; Object (grammar); Machine learning; Computer vision; Image (mathematics); Engineering; Metric (unit)","retraction":null,"screen_n_in":null,"score":{"opus":0.06194467764540884,"gpt":0.3174880210298427,"spread":0.2555433433844339,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001581686,0.00008822652,0.000094615,0.00004224489,0.0001292178,0.00005647494,0.0002253221,0.00002759231,0.000001470044],"category_scores_gemma":[0.00001696327,0.00007109263,0.00002194108,0.0001782171,0.00003112743,0.0003909112,0.0001134751,0.00006805279,0.00001664987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002529902,"about_ca_system_score_gemma":0.00005636689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009802655,"about_ca_topic_score_gemma":0.00002642533,"domain_scores_codex":[0.9993434,0.0000362423,0.00009176156,0.0002908869,0.0001025146,0.0001352045],"domain_scores_gemma":[0.9995663,0.00006608029,0.0000323151,0.0001869301,0.000087749,0.00006064464],"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":[0.000001092202,0.00002643842,0.000008668636,0.00000194106,0.000008393748,0.000001163684,0.001161503,0.008877588,0.0005687698,0.9671155,0.00001312474,0.02221584],"study_design_scores_gemma":[0.000388341,0.00007097556,0.0009538946,0.000007839607,0.000005016213,0.000009633643,0.0001148211,0.4941057,0.01904723,0.4849332,0.0001326063,0.0002307121],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002163224,0.0001731528,0.9878806,0.001106435,0.00002170172,0.0001767874,0.000003027993,0.000099333,0.008375767],"genre_scores_gemma":[0.5974651,0.00002331768,0.4020005,0.000245751,0.0000159064,0.00005264128,5.686999e-7,0.000002947903,0.0001933261],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5953019,"threshold_uncertainty_score":0.2899073,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2973617321","doi":"10.1109/access.2019.2941978","title":"IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Testbed; Guard (computer science); Scalability; Computer security; Edge computing; Internet of Things; Real-time computing; Computer network; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.01782531240764275,"gpt":0.3110033456493996,"spread":0.2931780332417568,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001624879,0.0002995024,0.0005078042,0.0001812937,0.0001591024,0.0003556516,0.002336679,0.0001162716,0.0000180713],"category_scores_gemma":[0.00002618423,0.0002849108,0.0002265617,0.000578344,0.00002981501,0.0005306203,0.0002507345,0.0001311788,0.0002491707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001485387,"about_ca_system_score_gemma":0.00008061408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009338345,"about_ca_topic_score_gemma":0.00003634802,"domain_scores_codex":[0.9971907,0.0003448934,0.0004688871,0.0008984656,0.000486386,0.0006107185],"domain_scores_gemma":[0.9973852,0.0005304972,0.0002985449,0.001456399,0.0002014971,0.0001278657],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001225888,0.001785437,0.4076165,0.01724304,0.002054389,0.0007367471,0.001856882,0.2026213,0.02254987,0.06803672,0.06519188,0.2090814],"study_design_scores_gemma":[0.007443925,0.0005376079,0.1146039,0.0008755289,0.00006638934,0.00003251495,0.00005305005,0.8055836,0.03998227,0.005530732,0.02283814,0.002452372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2329302,0.00006569386,0.7544039,0.0004423718,0.004023589,0.001701688,0.000043607,0.0008244117,0.005564516],"genre_scores_gemma":[0.9765694,0.00001205284,0.02250679,0.000222888,0.000188559,0.0001575812,0.00001254561,0.00003514171,0.0002950588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7436391,"threshold_uncertainty_score":0.9999603,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}