{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":91,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":91,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"64f54ff57109","filters":{"venue":"Computer Vision and Image Understanding"}},"results":[{"id":"W2161308290","doi":"10.1016/j.cviu.2005.05.005","title":"A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition","year":2005,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Face recognition and analysis","field":"Computer Science","cited_by":975,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Research Council Canada; U.S. Department of Justice; National Science Foundation","keywords":"Facial recognition system; Modal; Matching (statistics); Computer science; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Machine learning; Computer vision; Mathematics; Statistics","authors":[{"name":"Kevin W. Bowyer","is_ca":false},{"name":"Kyong Chang","is_ca":false},{"name":"Patrick J. Flynn","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2677904166779405,"gpt":0.2995705412304389,"spread":0.0317801245524984,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00199981,0.0009250582,0.002268708,0.002594965,0.0009553487,0.003738243,0.002798222,0.001691104,0.004385687],"category_scores_gemma":[0.002365136,0.0008226842,0.001211463,0.004962973,0.0008784148,0.003849665,0.001446621,0.001107228,0.003039391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005077481,"about_ca_system_score_gemma":0.001111013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003813325,"about_ca_topic_score_gemma":0.005524612,"domain_scores_codex":[0.9984115,0.0002494154,0.0001428405,0.0003102983,0.0008043824,0.00008152169],"domain_scores_gemma":[0.9984205,0.0006783523,0.00007744452,0.0002167807,0.0005452221,0.00006165877],"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":[0.00004782691,0.00009100656,0.001239123,0.001398605,0.00005805276,0.00006835213,0.0001128122,0.005531119,0.006243087,0.008480677,0.006251268,0.970478],"study_design_scores_gemma":[0.00005257782,0.0005028413,0.008419775,0.001473414,0.0003526326,0.004125704,0.001853568,0.4514417,0.03792568,0.09238048,0.4011208,0.0003508269],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.005569058,0.08918772,0.8966624,0.001082877,0.000305718,0.0001027461,0.0001633608,0.001141902,0.005784229],"genre_scores_gemma":[0.05383759,0.1765151,0.7573295,0.001008844,0.0009910068,0.0002518883,0.001338409,0.0002555261,0.008472187],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004385687,"threshold_uncertainty_score":0.01467156,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2067381981","doi":"10.1016/j.cviu.2013.04.005","title":"50 Years of object recognition: Directions forward","year":2013,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":351,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Cognitive neuroscience of visual object recognition; Inference; Machine learning; Object (grammar); Automation; Human–computer interaction; Engineering","authors":[{"name":"Alexander Andreopoulos","is_ca":false},{"name":"John K. Tsotsos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0374214784470072,"gpt":0.2891256136729817,"spread":0.2517041352259745,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007997318,0.001547506,0.002987328,0.003359336,0.001212435,0.007212745,0.003034192,0.004164167,0.01789816],"category_scores_gemma":[0.01338403,0.0008004149,0.00166877,0.003605557,0.004570192,0.01204398,0.003721541,0.003821952,0.008277311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002412736,"about_ca_system_score_gemma":0.002983807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005194109,"about_ca_topic_score_gemma":0.004395082,"domain_scores_codex":[0.9977875,0.0006374646,0.0001725986,0.000453975,0.000679487,0.0002688879],"domain_scores_gemma":[0.9905559,0.005226479,0.0004800956,0.0009133746,0.001998545,0.0008255856],"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":[0.0004559942,0.0003501402,0.003597456,0.003167023,0.0001857486,0.0001582725,0.0004470045,0.001281182,0.003070903,0.07359069,0.1034291,0.8102664],"study_design_scores_gemma":[0.00004659923,0.0002321668,0.004871989,0.001844875,0.0001557809,0.0006115282,0.0006818442,0.003215758,0.001506109,0.08930463,0.8974311,0.00009770297],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.005064107,0.9067326,0.03223661,0.03093451,0.005825122,0.00006961313,0.0004179976,0.0005380946,0.01818145],"genre_scores_gemma":[0.07778317,0.775882,0.07631592,0.02674226,0.01624739,0.0001946804,0.001881619,0.0004098501,0.02454314],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01789816,"threshold_uncertainty_score":0.05987537,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2010792042","doi":"10.1006/cviu.1999.0822","title":"Watershed-Based Segmentation and Region Merging","year":2000,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":311,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Watershed; Computer science; Mathematical morphology; Image (mathematics); Image segmentation; Replicate; Process (computing); Segmentation; Artificial intelligence; Computer vision; Image processing; Mathematics; Statistics","authors":[{"name":"André Bleau","is_ca":true},{"name":"L.J. Leon","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0308797987098624,"gpt":0.2844545071042837,"spread":0.2535747083944213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001277539,0.0008792916,0.001753934,0.002636427,0.0008258129,0.001682614,0.002023963,0.001973311,0.004996753],"category_scores_gemma":[0.003495402,0.001296601,0.001331425,0.004049467,0.001160064,0.002584969,0.001640937,0.001630558,0.002657113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007266371,"about_ca_system_score_gemma":0.001175926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002332234,"about_ca_topic_score_gemma":0.003103915,"domain_scores_codex":[0.9987889,0.000176504,0.00007129225,0.0003298256,0.0005296633,0.0001038004],"domain_scores_gemma":[0.9991479,0.0003170945,0.00006489746,0.0002008763,0.0002357995,0.00003333811],"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":[0.0002656697,0.0001163174,0.000796446,0.0004655521,0.0001867827,0.0003430923,0.0004521938,0.05503458,0.1815518,0.04622414,0.004247747,0.7103158],"study_design_scores_gemma":[0.00008098299,0.0001498628,0.002343429,0.00005025217,0.0001640742,0.001136897,0.0001185457,0.7038086,0.2203895,0.04036672,0.03129262,0.0000985929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003306337,0.0003275968,0.9942742,0.0000528586,0.00003136967,0.00006259819,0.00002947728,0.00081832,0.001097219],"genre_scores_gemma":[0.05506742,0.0006911654,0.9391504,0.00005734748,0.00004944322,0.0001137715,0.0002156981,0.0005395645,0.004115168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004996753,"threshold_uncertainty_score":0.01671576,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2789834341","doi":"10.1016/j.cviu.2018.01.007","title":"Biometric recognition by gait: A survey of modalities and features","year":2018,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Gait Recognition and Analysis","field":"Engineering","cited_by":271,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Gait; Modalities; Biometrics; Computer science; Modality (human–computer interaction); Wearable computer; Gait analysis; Artificial intelligence; Accelerometer; Ground reaction force; Computer vision; Pattern recognition (psychology); Physical medicine and rehabilitation; Kinematics","authors":[{"name":"Patrick C. Connor","is_ca":false},{"name":"Arun Ross","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04066106400993056,"gpt":0.2570447062482641,"spread":0.2163836422383335,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160357,0.0008385502,0.001955906,0.00382981,0.0002716333,0.00222842,0.000895995,0.0008863339,0.00211616],"category_scores_gemma":[0.002754017,0.0003962525,0.0009539883,0.004831283,0.000694106,0.002816906,0.0007854193,0.0007451618,0.00144905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003795226,"about_ca_system_score_gemma":0.0006681653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001004746,"about_ca_topic_score_gemma":0.001160686,"domain_scores_codex":[0.9986946,0.0002461195,0.00016777,0.0003315267,0.0005059277,0.00005410764],"domain_scores_gemma":[0.998138,0.000775695,0.0002334787,0.000161926,0.0006221434,0.00006881704],"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":[0.0001215759,0.0001350779,0.00778429,0.002190696,0.0001254461,0.000103856,0.0001127815,0.0008541191,0.009171282,0.002857742,0.00556936,0.9709738],"study_design_scores_gemma":[0.000114436,0.002832934,0.1531728,0.007776342,0.001382244,0.02129074,0.002426427,0.0621714,0.06461616,0.05690559,0.626386,0.0009249243],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.03585796,0.6653932,0.2750475,0.002551519,0.0007890025,0.0003319571,0.002509186,0.001204069,0.01631565],"genre_scores_gemma":[0.1613061,0.5697203,0.2484457,0.002279762,0.003207687,0.0005251961,0.003498218,0.0002784516,0.01073862],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00382981,"threshold_uncertainty_score":0.008480608,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2113096211","doi":"10.1016/j.cviu.2009.06.008","title":"Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process","year":2010,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Psychiatry, Mental Health, Neuroscience","field":"Computer Science","cited_by":224,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Partially observable Markov decision process; Computer science; Heuristic; Markov decision process; Artificial intelligence; Process (computing); Machine learning; Hidden Markov model; Bayesian probability; Key (lock); Computer vision; Markov process; Markov model; Markov chain; Statistics; Mathematics; Computer security","authors":[{"name":"Jesse Hoey","is_ca":false},{"name":"Pascal Poupart","is_ca":true},{"name":"Axel von Bertoldi","is_ca":true},{"name":"Tammy Craig","is_ca":true},{"name":"Craig Boutilier","is_ca":true},{"name":"Alex Mihailidis","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03804501443123539,"gpt":0.3180536484682332,"spread":0.2800086340369978,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008121062,0.0004828667,0.0007287074,0.0005185471,0.0003683306,0.0006550908,0.0005217882,0.001028006,0.0009849455],"category_scores_gemma":[0.00255215,0.000395753,0.0006156241,0.0003022504,0.0003453844,0.0007282551,0.0004961324,0.0006411957,0.0001086933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007887261,"about_ca_system_score_gemma":0.001236003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02864407,"about_ca_topic_score_gemma":0.02018218,"domain_scores_codex":[0.9997004,0.00008909334,0.00002097661,0.0000803474,0.00005263585,0.00005649422],"domain_scores_gemma":[0.9981434,0.001465909,0.0001488821,0.00002682897,0.0001518074,0.00006310605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001490117,0.0005147938,0.01186594,0.0001375574,0.0001549565,0.0003631116,0.0001948516,0.8980224,0.006156893,0.003498874,0.0006593336,0.07694115],"study_design_scores_gemma":[0.00001214693,0.00005489212,0.001112438,0.000003060019,0.00001254258,0.00001279692,0.00001001443,0.9975568,0.0003658756,0.0008225809,0.00003044108,0.000006477796],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6125327,0.0003589091,0.3847071,0.0005232179,0.00005943543,0.00008773756,0.0001959792,0.0002781384,0.001256763],"genre_scores_gemma":[0.987008,0.00006923881,0.01217682,0.00002998806,0.00001393189,0.00002922892,0.00007474324,0.000004672227,0.0005934323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02864407,"threshold_uncertainty_score":0.05695474,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963780738","doi":"10.1016/j.cviu.2017.09.003","title":"Haze visibility enhancement: A Survey and quantitative benchmarking","year":2017,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":172,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Foundation Singapore; Nvidia","keywords":"Benchmark (surveying); Visibility; Benchmarking; Ground truth; Computer science; Haze; Artificial intelligence; Image (mathematics); Filter (signal processing); Computer vision; Remote sensing; Pattern recognition (psychology); Optics; Geography; Cartography; Physics","authors":[{"name":"Yu Li","is_ca":false},{"name":"Shaodi You","is_ca":false},{"name":"Michael S. Brown","is_ca":true},{"name":"Robby T. Tan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08099546624211718,"gpt":0.350093309666092,"spread":0.2690978434239748,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004185434,0.001641427,0.001924869,0.007127811,0.0005122354,0.002339505,0.00194443,0.001292992,0.002375081],"category_scores_gemma":[0.01101841,0.0006729109,0.0008541828,0.006470962,0.0008296102,0.003317313,0.001143572,0.0007441728,0.0008437583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007094919,"about_ca_system_score_gemma":0.0007976172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001720278,"about_ca_topic_score_gemma":0.001842281,"domain_scores_codex":[0.9961665,0.0005989192,0.000272299,0.0008494282,0.001953208,0.0001595453],"domain_scores_gemma":[0.9901873,0.004731954,0.001279534,0.001042908,0.002575548,0.0001826549],"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":[0.0003248532,0.0003614876,0.009792144,0.005357168,0.0003292538,0.00005119568,0.0001657285,0.01971981,0.02495816,0.003909853,0.004323388,0.9307069],"study_design_scores_gemma":[0.0001294152,0.004108801,0.06768648,0.003779387,0.001623629,0.00408166,0.001516506,0.4702968,0.3072453,0.01859065,0.120423,0.0005186028],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.07916511,0.193684,0.6995978,0.0005169997,0.0002480389,0.0005257392,0.00177098,0.003400727,0.02109065],"genre_scores_gemma":[0.5171887,0.1445234,0.3273012,0.0002774318,0.0004024436,0.0002745753,0.003295845,0.000978844,0.005757668],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007127811,"threshold_uncertainty_score":0.02213496,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2170932168","doi":"10.1016/j.cviu.2013.06.007","title":"An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions","year":2013,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":169,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Codebook; Artificial intelligence; Background subtraction; Computer vision; Video tracking; Probabilistic logic; Line (geometry); Pattern recognition (psychology); Tracking (education); Video processing; Pixel; Mathematics","authors":[{"name":"Mehrsan Javan","is_ca":true},{"name":"Martin D. Levine","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04988297504113703,"gpt":0.345789801825941,"spread":0.295906826784804,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007988951,0.00132087,0.00130761,0.002291639,0.000546244,0.0009121117,0.002057999,0.0009660269,0.001856269],"category_scores_gemma":[0.00190474,0.0004001689,0.0007631727,0.001758273,0.0004690036,0.001395043,0.001095407,0.00133679,0.001316825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000561039,"about_ca_system_score_gemma":0.001123223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005619978,"about_ca_topic_score_gemma":0.008249215,"domain_scores_codex":[0.9990544,0.00008112073,0.00006184932,0.0003153839,0.0004009788,0.00008634879],"domain_scores_gemma":[0.9988905,0.0002314746,0.0001653801,0.0001457581,0.000456623,0.0001103241],"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":[0.0003827597,0.0003001773,0.00283702,0.00007552383,0.00007956446,0.0001316736,0.00005801718,0.01967208,0.04675816,0.001371309,0.00268377,0.92565],"study_design_scores_gemma":[0.00001696818,0.0001001912,0.001414665,0.000006954209,0.00003652506,0.0002108974,0.00002757597,0.9765836,0.01852346,0.00119573,0.001867398,0.0000160722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02187944,0.0002314598,0.9742851,0.00007683596,0.00007978803,0.00008695784,0.0001639156,0.002496187,0.0007004356],"genre_scores_gemma":[0.2558469,0.0003606422,0.737817,0.0001165454,0.000154654,0.0001560836,0.0008978524,0.0002602871,0.00439008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005619978,"threshold_uncertainty_score":0.0111745,"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","authors":[{"name":"Atousa Torabi","is_ca":true},{"name":"Guillaume Massé","is_ca":true},{"name":"Guillaume-Alexandre Bilodeau","is_ca":true}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001354393,0.0009077287,0.001148445,0.000864278,0.0006165706,0.001149073,0.002583124,0.001226921,0.002292219],"category_scores_gemma":[0.002307307,0.0007551493,0.001738211,0.001050493,0.0006147016,0.001389205,0.001890461,0.001483959,0.0009598031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007527816,"about_ca_system_score_gemma":0.002054305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01102157,"about_ca_topic_score_gemma":0.01586017,"domain_scores_codex":[0.9989972,0.0001901106,0.00005214264,0.0002004815,0.0004645572,0.00009549493],"domain_scores_gemma":[0.99936,0.0001797514,0.00006360598,0.00008443526,0.0002705772,0.00004164434],"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":[0.0002267344,0.0001805345,0.0008120051,0.0001423794,0.0002266477,0.0001388867,0.0002466743,0.4475741,0.03367294,0.02806183,0.002672373,0.4860448],"study_design_scores_gemma":[0.000005181948,0.00002498495,0.0001110612,0.000003513776,0.00001413954,0.00003271022,0.000007680702,0.9939195,0.002668289,0.002301325,0.0009006298,0.00001094281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006609818,0.00002671962,0.9989989,0.000008603842,0.00000638233,0.000008597289,0.000005250302,0.0001428665,0.0001417231],"genre_scores_gemma":[0.05361614,0.0001123882,0.9439921,0.00003360384,0.00003261704,0.0001150595,0.000102561,0.0001327553,0.001862891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01102157,"threshold_uncertainty_score":0.02191478,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2155806937","doi":"10.1016/j.cviu.2014.10.006","title":"A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries","year":2014,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Army Research Office; National Research Foundation; Texas State University; McGill University; National Science Foundation","keywords":"Benchmark (surveying); Computer science; Sketch; CONTEST; NIST; Information retrieval; Image retrieval; Scale (ratio); Data mining; Artificial intelligence; Image (mathematics); Natural language processing; Algorithm","authors":[{"name":"Bo Li","is_ca":false},{"name":"Yijuan Lu","is_ca":false},{"name":"Chunyuan Li","is_ca":false},{"name":"Afzal Godil","is_ca":false},{"name":"Tobias Schreck","is_ca":false},{"name":"Masaki Aono","is_ca":false},{"name":"Martin Burtscher","is_ca":false},{"name":"Qiang Chen","is_ca":false},{"name":"Nihad Karim Chowdhury","is_ca":false},{"name":"Bin Fang","is_ca":false},{"name":"Hongbo Fu","is_ca":false},{"name":"Takahiko Furuya","is_ca":false},{"name":"Haisheng Li","is_ca":false},{"name":"Jianzhuang Liu","is_ca":false},{"name":"Henry Johan","is_ca":false},{"name":"Ryuichi Kosaka","is_ca":false},{"name":"Hitoshi Koyanagi","is_ca":false},{"name":"Ryutarou Ohbuchi","is_ca":false},{"name":"Atsushi Tatsuma","is_ca":false},{"name":"Chaoli Zhang","is_ca":false},{"name":"Changqing Zou","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04338948242728724,"gpt":0.3787148915335746,"spread":0.3353254091062873,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002168469,0.002080785,0.002442648,0.004595851,0.0007703717,0.00216618,0.002711296,0.002184904,0.006810208],"category_scores_gemma":[0.008752638,0.000421881,0.001326524,0.004226782,0.000432245,0.002181036,0.001641139,0.0006424639,0.003709259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008140497,"about_ca_system_score_gemma":0.001173899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01309288,"about_ca_topic_score_gemma":0.01409205,"domain_scores_codex":[0.997035,0.0003614186,0.0002770358,0.0004637901,0.001622904,0.0002399015],"domain_scores_gemma":[0.9960561,0.001513059,0.0001529928,0.0007518886,0.001317334,0.0002087135],"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":[0.003635488,0.0009188061,0.004009279,0.001585852,0.0006585967,0.0002263477,0.0001273603,0.05586126,0.04057454,0.00123445,0.03275903,0.8584091],"study_design_scores_gemma":[0.0004862474,0.002476322,0.02185575,0.0001351502,0.0003712258,0.001225989,0.0005082767,0.9008839,0.05066215,0.002398631,0.01878823,0.000208237],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6066276,0.02732938,0.2707485,0.001112442,0.001723412,0.001277471,0.01865786,0.04354451,0.02897883],"genre_scores_gemma":[0.6810576,0.004188038,0.2465464,0.000459389,0.0003222893,0.0003439808,0.0531098,0.002311674,0.01166082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01309288,"threshold_uncertainty_score":0.02603334,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167737187","doi":"10.1006/cviu.1999.0825","title":"Robustly Estimating Changes in Image Appearance","year":2000,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Specular reflection; Computer science; Image (mathematics); Optical flow; Probabilistic logic; Object (grammar); Generative model; Generative grammar; Brightness; Motion (physics)","authors":[{"name":"Michael J. Black","is_ca":false},{"name":"David J. Fleet","is_ca":true},{"name":"Yaser Yacoob","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02669768456184459,"gpt":0.2878823937279926,"spread":0.261184709166148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007020895,0.001087368,0.001367371,0.001618111,0.000267375,0.001366745,0.0009761704,0.001423947,0.001140815],"category_scores_gemma":[0.00338028,0.0008689082,0.001006774,0.001277468,0.0005546974,0.001581683,0.001181069,0.00194095,0.00141991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004314807,"about_ca_system_score_gemma":0.0004853637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002926524,"about_ca_topic_score_gemma":0.003680437,"domain_scores_codex":[0.9990897,0.0001090036,0.00003807938,0.0003262025,0.0003314737,0.0001055021],"domain_scores_gemma":[0.9987547,0.0003092447,0.0002166974,0.0003065226,0.0003504673,0.00006232392],"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":[0.0006094523,0.0001997931,0.002791517,0.0001889375,0.0002052415,0.0001907552,0.00007977404,0.04807041,0.3784176,0.001279377,0.002460107,0.5655069],"study_design_scores_gemma":[0.00002402153,0.0001698479,0.009317896,0.000016927,0.0001508876,0.0005577296,0.00004525108,0.8786814,0.1056993,0.00213167,0.003169945,0.00003505882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08562072,0.00109442,0.9092443,0.0002154184,0.000231214,0.00005359166,0.0001865634,0.002140122,0.001213759],"genre_scores_gemma":[0.5725171,0.001587283,0.41642,0.000237539,0.0002257485,0.00005949806,0.0009392296,0.0009050327,0.007108576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002926524,"threshold_uncertainty_score":0.005818963,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2039708910","doi":"10.1016/j.cviu.2010.11.021","title":"Local shape descriptor selection for object recognition in range data","year":2010,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Similarity (geometry); Pattern recognition (psychology); Object (grammar); Range (aeronautics); Selection (genetic algorithm); Computer vision; Cognitive neuroscience of visual object recognition; Point cloud; Computer science; Mathematics; Shape analysis (program analysis); Geometric shape; Similitude; Image (mathematics); Geometry","authors":[{"name":"Babak Taati","is_ca":true},{"name":"Michael Greenspan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06529521957399807,"gpt":0.2686573316324827,"spread":0.2033621120584846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000616271,0.0003602514,0.001284375,0.001222363,0.0003335823,0.0007032362,0.0009907127,0.000484335,0.002213767],"category_scores_gemma":[0.001723425,0.0002652862,0.0006028546,0.001618903,0.0004091915,0.0007834421,0.0006850077,0.0005866768,0.001347871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003685484,"about_ca_system_score_gemma":0.0006647168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002677785,"about_ca_topic_score_gemma":0.003046201,"domain_scores_codex":[0.9995039,0.00008849135,0.00003553604,0.0000974565,0.0002158098,0.00005894828],"domain_scores_gemma":[0.9991713,0.0002612193,0.00006994856,0.0001730553,0.0002717829,0.00005270878],"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":[0.000550845,0.0001787849,0.001450258,0.0001873493,0.00006924999,0.0001190484,0.00005776234,0.03410601,0.1020785,0.0037,0.006133238,0.851369],"study_design_scores_gemma":[0.00005073493,0.0001463778,0.002711341,0.00001247528,0.0000511748,0.0001989183,0.00007241859,0.9486701,0.04119115,0.004329525,0.002531446,0.00003430472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02952112,0.0005510303,0.9677817,0.00008141319,0.00004286002,0.00004236732,0.0001749143,0.001244926,0.000559625],"genre_scores_gemma":[0.5110174,0.0007810955,0.4797572,0.0001875283,0.0001306288,0.0002073025,0.001874806,0.0004292456,0.005614951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002677785,"threshold_uncertainty_score":0.007405818,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2127441648","doi":"10.1016/j.cviu.2003.10.013","title":"Selection weighted vector directional filters","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Arthur Vining Davis Foundations","keywords":"Smoothing; Weighting; Robustness (evolution); Computer science; Algorithm; Noise (video); Adaptive filter; Mathematics; Selection (genetic algorithm); Artificial intelligence; Mathematical optimization; Pattern recognition (psychology); Image (mathematics); Computer vision","authors":[{"name":"Rastislav Lukàč","is_ca":true},{"name":"Bogdan Smołka","is_ca":false},{"name":"Konstantinos N. Plataniotis","is_ca":true},{"name":"A.N. Venetsanopoulos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03362167272070989,"gpt":0.2829604046123393,"spread":0.2493387318916294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008202091,0.0007837247,0.000839186,0.0009841631,0.0003364216,0.001111742,0.0005751139,0.0008491635,0.005579293],"category_scores_gemma":[0.001204015,0.0003660066,0.0006153373,0.0009000427,0.0003680059,0.0009501437,0.0008001857,0.0006929378,0.002244402],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002814587,"about_ca_system_score_gemma":0.0005788261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007754678,"about_ca_topic_score_gemma":0.001781022,"domain_scores_codex":[0.9994498,0.00012864,0.00003047666,0.0001274092,0.0002160158,0.00004765704],"domain_scores_gemma":[0.9993948,0.0001083142,0.00005337322,0.0001155025,0.0002878367,0.00004014711],"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":[0.0006936045,0.0001115089,0.0009060659,0.0001909881,0.0001128936,0.00004856302,0.00005164825,0.02143685,0.1315491,0.04852192,0.007896849,0.7884799],"study_design_scores_gemma":[0.00008105861,0.0002921086,0.00294806,0.00004962235,0.000235717,0.0005389706,0.0000714489,0.8070428,0.1256481,0.02073125,0.04229299,0.00006795086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01037013,0.0004555233,0.9860108,0.0001073659,0.0001021497,0.00002509231,0.00007424175,0.0002871347,0.002567573],"genre_scores_gemma":[0.1764246,0.001257966,0.7977973,0.000247356,0.0001732898,0.0001168729,0.0007039236,0.0002725594,0.0230061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005579293,"threshold_uncertainty_score":0.0186646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2069552341","doi":"10.1016/j.cviu.2009.06.010","title":"Visual search for an object in a 3D environment using a mobile robot","year":2010,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Object (grammar); Limit (mathematics); Mobile robot; Robot; Computer vision; Visual search; Space (punctuation); Optimization problem; Mechanism (biology); Mathematics; Algorithm","authors":[{"name":"Ksenia Shubina","is_ca":true},{"name":"John K. Tsotsos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06650966170180576,"gpt":0.3477666385531639,"spread":0.2812569768513581,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002319293,0.0006242652,0.0007634367,0.0006805293,0.000647124,0.0008212094,0.0007678516,0.001521858,0.001455341],"category_scores_gemma":[0.000810246,0.0005929549,0.0007706386,0.0006811357,0.0006660278,0.001129597,0.001276802,0.000495124,0.000256738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004302619,"about_ca_system_score_gemma":0.0005373012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0057892,"about_ca_topic_score_gemma":0.005433299,"domain_scores_codex":[0.9998775,0.00001646124,0.000005242555,0.0000470434,0.00003889353,0.00001487912],"domain_scores_gemma":[0.9998491,0.00005923151,0.0000274439,0.00001891871,0.00002046682,0.00002477709],"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":[0.0009558316,0.0001951298,0.003607973,0.0002559401,0.0002200397,0.00120039,0.0007085077,0.5305512,0.132393,0.01383237,0.002228572,0.313851],"study_design_scores_gemma":[0.00003865079,0.0001460102,0.0007338244,0.00001234541,0.00003374462,0.0002230082,0.00008213879,0.9870306,0.005973898,0.00478609,0.0009212014,0.00001862953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1678742,0.000372256,0.8280104,0.0002634889,0.00003941386,0.00006979377,0.00004860245,0.0006993526,0.002622584],"genre_scores_gemma":[0.753579,0.000214621,0.2441696,0.00006942094,0.00002533537,0.00006734877,0.00004895855,0.00006546632,0.001760259],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0057892,"threshold_uncertainty_score":0.01151097,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2092489829","doi":"10.1016/j.cviu.2006.11.014","title":"Quaternion color texture segmentation","year":2007,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":90,"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":"Quaternion; Artificial intelligence; Computer vision; Color image; Pattern recognition (psychology); Mathematics; Texture (cosmology); Image texture; Texel; Basis (linear algebra); Computer science; Texture filtering; Bidirectional texture function; Segmentation; Color quantization; Principal component analysis; Image segmentation; Image processing; Image (mathematics); Geometry","authors":[{"name":"Lilong Shi","is_ca":true},{"name":"Brian Funt","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03066791098324887,"gpt":0.3019388743470515,"spread":0.2712709633638026,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002225625,0.0005784348,0.0004792337,0.002136,0.0004212788,0.0016607,0.0004718497,0.0005086237,0.01431339],"category_scores_gemma":[0.0005079517,0.0002971658,0.000450597,0.001477315,0.0003944566,0.0008132937,0.0005408523,0.000416887,0.00363043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006882127,"about_ca_system_score_gemma":0.0007013926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004491238,"about_ca_topic_score_gemma":0.005018576,"domain_scores_codex":[0.9998066,0.00001600793,0.000008529736,0.00004725571,0.00007111372,0.00005047495],"domain_scores_gemma":[0.9997768,0.00001762672,0.00001753858,0.00005176657,0.0001143906,0.00002195116],"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":[0.000465051,0.00007458677,0.001704991,0.0001302353,0.00004333022,0.0001553785,0.0001051608,0.01946954,0.2560035,0.01554501,0.00806533,0.6982379],"study_design_scores_gemma":[0.00007619322,0.0002212478,0.01122094,0.00005864254,0.00009062756,0.0007586097,0.0002306003,0.588237,0.3222088,0.01601025,0.06080978,0.00007739199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05383297,0.0005561209,0.9216177,0.000384314,0.0001996041,0.0001457633,0.0006088921,0.004499739,0.01815495],"genre_scores_gemma":[0.4689985,0.0009272707,0.5017818,0.0003000929,0.0001190809,0.00007381187,0.001512242,0.001307547,0.02497973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01431339,"threshold_uncertainty_score":0.04788309,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2117962700","doi":"10.1016/j.cviu.2007.04.006","title":"Performance characterization in computer vision: A guide to best practices","year":2007,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Variety (cybernetics); Novelty; Computer science; Field (mathematics); Domain (mathematical analysis); Artificial intelligence; Data science; Human–computer interaction; Machine learning; Management science; Emphasis (telecommunications); Mathematics; Psychology; Engineering","authors":[{"name":"Neil A. Thacker","is_ca":false},{"name":"Adrian F. Clark","is_ca":false},{"name":"John A. Barron","is_ca":true},{"name":"J. Ross Beveridge","is_ca":false},{"name":"Patrick Courtney","is_ca":false},{"name":"William R. Crum","is_ca":false},{"name":"Visvanathan Ramesh","is_ca":false},{"name":"Christine M. Clark","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05104033449093394,"gpt":0.364675211314213,"spread":0.3136348768232791,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00950563,0.003422937,0.002462071,0.01100123,0.00165122,0.008731027,0.00574609,0.003156252,0.01340616],"category_scores_gemma":[0.02634398,0.002193812,0.001595573,0.0116096,0.002891575,0.009113891,0.002258567,0.00451271,0.01510324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003008442,"about_ca_system_score_gemma":0.002735897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006466905,"about_ca_topic_score_gemma":0.007556229,"domain_scores_codex":[0.9902819,0.002297299,0.001758968,0.0009392351,0.004383406,0.0003391015],"domain_scores_gemma":[0.9805267,0.009420648,0.0006954552,0.002375634,0.006615316,0.0003662212],"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":[0.00008217648,0.0003545113,0.001282124,0.00208163,0.0001017237,0.0001317476,0.000464478,0.008018461,0.005638295,0.07280852,0.1085596,0.8004767],"study_design_scores_gemma":[0.00006332075,0.0004163268,0.004324373,0.002930846,0.0001783407,0.002116456,0.001139905,0.07006267,0.0222303,0.2190059,0.6770974,0.0004342383],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001849425,0.06059016,0.8959701,0.003599712,0.0006837457,0.0005208123,0.001378638,0.006112336,0.02929509],"genre_scores_gemma":[0.01982077,0.02956595,0.9311312,0.0007777373,0.0004975718,0.0007331442,0.001335196,0.001450712,0.01468765],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01340616,"threshold_uncertainty_score":0.05027115,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2059403017","doi":"10.1006/cviu.2002.0970","title":"Range Flow Estimation","year":2002,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Regularization (linguistics); Computation; Algorithm; Range (aeronautics); Grid; Computer science; Constraint (computer-aided design); Optical flow; Mathematics; Vector field; Motion estimation; Displacement field; Flow (mathematics); Displacement (psychology); Least-squares function approximation; Mathematical optimization; Artificial intelligence; Image (mathematics); Geometry","authors":[{"name":"Hagen Spies","is_ca":false},{"name":"Bernd Jähne","is_ca":false},{"name":"John A. Barron","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04409873601557305,"gpt":0.2770092737312213,"spread":0.2329105377156482,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004383942,0.00117002,0.00104695,0.00212475,0.0006515597,0.001450586,0.0009412855,0.00114181,0.01416208],"category_scores_gemma":[0.001834738,0.0006159082,0.0007694939,0.001234094,0.0003928186,0.002153744,0.001185262,0.001123748,0.008648506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003799353,"about_ca_system_score_gemma":0.0008740737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00257744,"about_ca_topic_score_gemma":0.002208054,"domain_scores_codex":[0.9994025,0.00005661214,0.00001992098,0.0002203998,0.000224197,0.00007637149],"domain_scores_gemma":[0.9996161,0.00006240469,0.00003904501,0.0001087704,0.0001506074,0.00002296371],"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":[0.0001323753,0.0000646362,0.0008921978,0.0001088372,0.00004639655,0.00006062404,0.00004377686,0.01875263,0.04867106,0.0117455,0.008437088,0.9110449],"study_design_scores_gemma":[0.00005571876,0.00017723,0.004814795,0.0001007862,0.0001297647,0.001084612,0.00008324,0.7600037,0.1543805,0.0220492,0.05704006,0.00008038353],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003377051,0.0004292882,0.9897605,0.000116063,0.00009526114,0.00005047801,0.0001613267,0.001356358,0.004653671],"genre_scores_gemma":[0.1634077,0.001624027,0.8041214,0.000287744,0.0003091588,0.0001619404,0.001544069,0.0005467636,0.02799717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01416208,"threshold_uncertainty_score":0.04737681,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097842599","doi":"10.1016/s1077-3142(03)00004-3","title":"An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; McGill University","funders":"","keywords":"Artificial intelligence; Computer vision; Image registration; Segmentation; Computer science; Voxel; Transformation (genetics); Rigid transformation; Image (mathematics)","authors":[{"name":"Michel Audette","is_ca":false},{"name":"Kaleem Siddiqi","is_ca":true},{"name":"Frank P. Ferrie","is_ca":true},{"name":"Terry M. Peters","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04743858791097781,"gpt":0.3291403473260618,"spread":0.281701759415084,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007172662,0.0006558082,0.0009592235,0.0006820685,0.0003308535,0.0008463495,0.001596898,0.001168623,0.001258045],"category_scores_gemma":[0.001032887,0.0005614063,0.0008702489,0.0006633517,0.0005503815,0.00118864,0.0008600025,0.0007979468,0.0005736275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003475795,"about_ca_system_score_gemma":0.001007806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002669051,"about_ca_topic_score_gemma":0.004332823,"domain_scores_codex":[0.9994842,0.00009996536,0.00002481746,0.00008961026,0.000264836,0.0000365704],"domain_scores_gemma":[0.9997306,0.00008043539,0.00004024415,0.00005007376,0.00007867454,0.00001988921],"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":[0.000277634,0.0002064569,0.0007189263,0.0002027227,0.0001645687,0.0001808013,0.000145271,0.2343751,0.2191517,0.01676099,0.002247713,0.5255681],"study_design_scores_gemma":[0.00001540164,0.0001187959,0.0007791466,0.00000888943,0.00005145124,0.0002480357,0.00001574335,0.9682883,0.02419007,0.004298068,0.001950568,0.00003545972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003778268,0.0001470791,0.9952955,0.00003909679,0.000009898462,0.00001634469,0.00001608117,0.0004633495,0.0002343714],"genre_scores_gemma":[0.1625649,0.0003715276,0.8348017,0.00008995969,0.00006007639,0.0001191186,0.0001274756,0.0001871546,0.001678118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002669051,"threshold_uncertainty_score":0.005307019,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056176311","doi":"10.1006/cviu.2000.0899","title":"Depth from Defocus Estimation in Spatial Domain","year":2001,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":60,"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":"Mathematics; Image (mathematics); Polynomial; Computer vision; Computation; Artificial intelligence; Hermite polynomials; Algorithm; Function (biology); Domain (mathematical analysis); Computer science; Mathematical analysis","authors":[{"name":"Djemel Ziou","is_ca":true},{"name":"F. Deschênes","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02023899713695293,"gpt":0.2635687435274636,"spread":0.2433297463905106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002420645,0.0004598323,0.0004836651,0.0008011818,0.0001871701,0.0005837871,0.0004044256,0.0005165546,0.00149885],"category_scores_gemma":[0.001361495,0.0003179498,0.0002831268,0.0008133135,0.000287148,0.001271417,0.0008785726,0.0005858607,0.0004590122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002739705,"about_ca_system_score_gemma":0.0004886767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002408637,"about_ca_topic_score_gemma":0.002180251,"domain_scores_codex":[0.9997301,0.00003809583,0.00001184184,0.00004338073,0.0001442243,0.00003241379],"domain_scores_gemma":[0.9995663,0.0001385758,0.00005027831,0.00005509409,0.000168438,0.00002129462],"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":[0.0004959241,0.00006367843,0.003043683,0.0004546709,0.00008398005,0.0001597932,0.0002686485,0.06443489,0.2576081,0.013376,0.003158994,0.6568516],"study_design_scores_gemma":[0.00003888427,0.0001278846,0.00558637,0.00004465295,0.00005382791,0.0008102267,0.0001460244,0.8328916,0.1429146,0.01054654,0.00678518,0.00005415734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03019359,0.0008359845,0.9669771,0.00009906693,0.00003042223,0.00002150644,0.0001016261,0.0002980786,0.001442659],"genre_scores_gemma":[0.4841975,0.00213681,0.5094434,0.0001050594,0.00007274134,0.00004564774,0.0004467687,0.00009758721,0.003454497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002408637,"threshold_uncertainty_score":0.005014122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135536726","doi":"10.1016/j.cviu.2010.12.011","title":"Bone graphs: Medial shape parsing and abstraction","year":2011,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; University of New Brunswick; University of Toronto; University of Ottawa","funders":"","keywords":"Parsing; Medial axis; Silhouette; Computer science; Artificial intelligence; Topological skeleton; Abstraction; Representation (politics); Graph; Computer vision; Pattern recognition (psychology); Object (grammar); Mathematics; Theoretical computer science; Active shape model; Segmentation","authors":[{"name":"Diego Macrini","is_ca":true},{"name":"Sven Dickinson","is_ca":true},{"name":"David J. Fleet","is_ca":true},{"name":"Kaleem Siddiqi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06984198816493613,"gpt":0.2934510386235782,"spread":0.2236090504586421,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006201226,0.001626917,0.001594638,0.00333532,0.0008138776,0.002393683,0.00284004,0.001618525,0.01290664],"category_scores_gemma":[0.002900903,0.001464677,0.002227543,0.002977257,0.0009208645,0.002769175,0.002712522,0.002310373,0.006012118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005668711,"about_ca_system_score_gemma":0.001218065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007560711,"about_ca_topic_score_gemma":0.01467605,"domain_scores_codex":[0.9994224,0.00007872675,0.00004296945,0.0001790708,0.0002064082,0.00007046045],"domain_scores_gemma":[0.9990264,0.0002985695,0.00007513423,0.0003471252,0.0001839477,0.00006874219],"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":[0.0005228556,0.0001669439,0.001259371,0.0004421231,0.000133263,0.000367209,0.0003471811,0.0470864,0.02460933,0.05539141,0.03624156,0.8334323],"study_design_scores_gemma":[0.0001042198,0.00009815949,0.001095033,0.0001089898,0.0001126412,0.0004505219,0.0002261688,0.7186442,0.02138955,0.2324794,0.02521331,0.00007777542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004899596,0.000298671,0.9842948,0.0001041427,0.00004916845,0.00007885539,0.0007217789,0.008092958,0.00146009],"genre_scores_gemma":[0.09804892,0.0007258595,0.8887577,0.0001519083,0.0000798917,0.0001414936,0.004165171,0.002794062,0.005135083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01290664,"threshold_uncertainty_score":0.04317701,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1968734870","doi":"10.1016/j.cviu.2012.11.003","title":"A learning framework for the optimization and automation of document binarization methods","year":2012,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Set (abstract data type); Feature (linguistics); Support vector machine; Image (mathematics); Automation; Grid; Feature vector; Data mining; Mathematics","authors":[{"name":"Mohamed Cheriet","is_ca":true},{"name":"Reza Farrahi Moghaddam","is_ca":true},{"name":"Rachid Hedjam","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0398230236819171,"gpt":0.3547683067541055,"spread":0.3149452830721884,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002029217,0.001057362,0.001677909,0.001001925,0.0007256143,0.00128537,0.00276648,0.001519565,0.003379094],"category_scores_gemma":[0.004941566,0.0008460637,0.001194046,0.001271566,0.001043297,0.001668708,0.002366543,0.002671036,0.001340926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008857695,"about_ca_system_score_gemma":0.001937022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006915428,"about_ca_topic_score_gemma":0.007776345,"domain_scores_codex":[0.9988106,0.0002601403,0.00009154924,0.0003136203,0.0004047351,0.0001193616],"domain_scores_gemma":[0.998318,0.0006660217,0.0001406522,0.0002766673,0.0005134018,0.00008531974],"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":[0.0001385442,0.0001484346,0.0004029995,0.0001585139,0.00008817924,0.00006579275,0.00008066634,0.4124598,0.01300359,0.04804087,0.004076627,0.521336],"study_design_scores_gemma":[0.0000118076,0.00003642806,0.00008269907,0.000008116803,0.000008722395,0.00001906806,0.000004858111,0.9860324,0.001848954,0.01050011,0.001436658,0.00001004768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004965641,0.00004389871,0.9990916,0.00001555839,0.000007364896,0.000009728785,0.00001119806,0.0002191856,0.0001048615],"genre_scores_gemma":[0.04497512,0.0002022925,0.9525088,0.00006284264,0.00008078888,0.0001314884,0.0001890923,0.0002194315,0.001630121],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006915428,"threshold_uncertainty_score":0.01375031,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2149498267","doi":"10.1016/j.cviu.2007.12.006","title":"Adaptive image retrieval based on the spatial organization of colors","year":2008,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":38,"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 science; Image retrieval; Artificial intelligence; Earth mover's distance; Matching (statistics); Image (mathematics); Pattern recognition (psychology); Segmentation; Computer vision; Mathematics","authors":[{"name":"Thomas Hurtut","is_ca":true},{"name":"Yann Gousseau","is_ca":false},{"name":"Francis Schmitt","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04143257408863407,"gpt":0.2496745138906994,"spread":0.2082419398020653,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002681937,0.0002671879,0.0004225194,0.0009253068,0.0002708595,0.0006461978,0.0006602093,0.0002662067,0.001518629],"category_scores_gemma":[0.001061168,0.0002140489,0.0003331914,0.001029068,0.0005812756,0.001018943,0.000417499,0.0004064241,0.0003692552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004624523,"about_ca_system_score_gemma":0.0003604519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001922688,"about_ca_topic_score_gemma":0.003098577,"domain_scores_codex":[0.9998428,0.00003406409,0.000005231316,0.00004243244,0.00005511428,0.00002038367],"domain_scores_gemma":[0.9995337,0.000151843,0.00004561618,0.00009229577,0.0001508659,0.00002577956],"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":[0.000637557,0.00009979725,0.001617726,0.0001878423,0.00009707094,0.00007998374,0.0001616989,0.05305473,0.4119157,0.03731961,0.003653146,0.4911751],"study_design_scores_gemma":[0.00009623662,0.0001875768,0.005278416,0.00001801374,0.0001306275,0.0004229847,0.00007148725,0.8737546,0.08602162,0.0263929,0.007556246,0.00006940594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06449649,0.001004297,0.9299311,0.0001634248,0.00007952483,0.00003567951,0.00007749203,0.0005434136,0.003668686],"genre_scores_gemma":[0.5201057,0.001253384,0.4727855,0.000152395,0.0001451606,0.00006967632,0.0001953839,0.0001986632,0.005094155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001922688,"threshold_uncertainty_score":0.005080342,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1972835213","doi":"10.1016/j.cviu.2006.05.001","title":"The representation and matching of categorical shape","year":2006,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":34,"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":"Directed acyclic graph; Categorical variable; Mathematics; Pattern recognition (psychology); ENCODE; Graph; Artificial intelligence; Directed graph; Matching (statistics); Theoretical computer science; Computer science; Algorithm","authors":[{"name":"Ali Shokoufandeh","is_ca":false},{"name":"Lars Bretzner","is_ca":false},{"name":"Diego Macrini","is_ca":true},{"name":"M. Fatih Demirci","is_ca":false},{"name":"Clas Jönsson","is_ca":false},{"name":"Sven Dickinson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02892958825507913,"gpt":0.284457069232792,"spread":0.2555274809777128,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082489,0.0002707715,0.0008703724,0.002255414,0.0005636277,0.00234353,0.002389885,0.001192972,0.003354667],"category_scores_gemma":[0.006179672,0.0004245423,0.0009627384,0.003173221,0.001705514,0.003716915,0.001728535,0.001162321,0.00127867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008435397,"about_ca_system_score_gemma":0.0008381108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005228373,"about_ca_topic_score_gemma":0.003600156,"domain_scores_codex":[0.9991488,0.0002203585,0.00005414196,0.0002339663,0.0002709771,0.00007167878],"domain_scores_gemma":[0.9984188,0.0003888792,0.0001581889,0.0006094733,0.0003340348,0.00009053641],"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":[0.000204622,0.0000641611,0.001716512,0.0002616414,0.0000345031,0.00009552277,0.0004596739,0.02275217,0.02572589,0.2745471,0.006866324,0.6672719],"study_design_scores_gemma":[0.00002662457,0.0001070409,0.002689049,0.00005315377,0.00003507131,0.0004276102,0.0003289497,0.504611,0.009578809,0.4688781,0.01320972,0.00005492533],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02457774,0.0007352465,0.9710981,0.0002632872,0.00006679702,0.00004493638,0.0003338657,0.0007048551,0.002175032],"genre_scores_gemma":[0.4113453,0.001094173,0.5817367,0.0001920455,0.0001073466,0.0001307979,0.001368573,0.0002782074,0.00374688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005228373,"threshold_uncertainty_score":0.01122248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4206077751","doi":"10.1016/j.cviu.2021.103352","title":"Cross-modal distillation for RGB-depth person re-identification","year":2022,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"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":"Artificial intelligence; Computer science; RGB color model; Computer vision; Pattern recognition (psychology); Identification (biology); Modality (human–computer interaction); Deep learning; Modal; Feature (linguistics); Distillation; Machine learning","authors":[{"name":"Frank M. Hafner","is_ca":false},{"name":"Amran Bhuyian","is_ca":true},{"name":"Julian F. P. Kooij","is_ca":false},{"name":"Éric Granger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09468000872548839,"gpt":0.3613157230118719,"spread":0.2666357142863836,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007400397,0.001264199,0.001194265,0.001141094,0.0006162525,0.0007937654,0.001351201,0.001104491,0.006723798],"category_scores_gemma":[0.00167019,0.0004625917,0.000923694,0.001447858,0.0004220359,0.00180742,0.002632339,0.001509758,0.004354174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003952766,"about_ca_system_score_gemma":0.001052394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005725062,"about_ca_topic_score_gemma":0.01365554,"domain_scores_codex":[0.9991652,0.0001131697,0.00002992454,0.0002605239,0.0002597303,0.000171519],"domain_scores_gemma":[0.9994642,0.00009576826,0.00003739635,0.0001805837,0.0001906098,0.00003136294],"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":[0.0006201846,0.0002941148,0.001074278,0.0001722501,0.0001018345,0.0001066918,0.00009561869,0.0205788,0.06925009,0.003349882,0.009021434,0.8953348],"study_design_scores_gemma":[0.00002577731,0.0001891147,0.004076967,0.00004469826,0.00007048284,0.0003888019,0.0001136119,0.9026337,0.07557058,0.006252679,0.01057244,0.00006123148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03502154,0.001007987,0.9545423,0.0002059922,0.0002795622,0.0001048891,0.0008629521,0.003748098,0.004226724],"genre_scores_gemma":[0.4041464,0.001039788,0.5722945,0.0005068311,0.0001901115,0.000185947,0.003324166,0.0004693138,0.01784295],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006723798,"threshold_uncertainty_score":0.0224933,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2025160301","doi":"10.1016/j.cviu.2004.07.015","title":"Panoramic stereo reconstruction using non-SVP optics","year":2005,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Catadioptric system; Computer vision; Artificial intelligence; Hough transform; Computer science; Conic section; Line (geometry); Projection (relational algebra); Map projection; Radial line; Optics; Field of view; Lens (geology); Mathematics; Geometry; Physics; Image (mathematics); Algorithm","authors":[{"name":"Mark A. Fiala","is_ca":true},{"name":"Anup Basu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02972044626360654,"gpt":0.2770824404047078,"spread":0.2473619941411012,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002737365,0.0007846066,0.0006043498,0.00102868,0.0003852109,0.00125349,0.0004746604,0.0004877225,0.008597113],"category_scores_gemma":[0.0007771701,0.0007414225,0.0007153395,0.001296446,0.0003972449,0.001144369,0.001054011,0.0009498455,0.002055961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003407524,"about_ca_system_score_gemma":0.0008633718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340724,"about_ca_topic_score_gemma":0.00365425,"domain_scores_codex":[0.9995449,0.0000431467,0.00001656611,0.00005843686,0.0002988276,0.00003805541],"domain_scores_gemma":[0.999643,0.0000487358,0.00002795578,0.0001446519,0.0001158164,0.00001983715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004640007,0.0001046198,0.001775291,0.000412253,0.0001395911,0.0002075523,0.0002651201,0.02197938,0.4761727,0.02293229,0.005313622,0.4702338],"study_design_scores_gemma":[0.0001288608,0.0002299406,0.008537915,0.00008356721,0.0001333068,0.002628966,0.0002492461,0.4475839,0.4882142,0.01326179,0.0388321,0.0001161439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02350684,0.0001549309,0.9654112,0.0000867534,0.00006932513,0.00005072781,0.0002399687,0.0008787825,0.009601367],"genre_scores_gemma":[0.1422731,0.0003946677,0.8495283,0.00005578482,0.00003373461,0.00004711636,0.0005032872,0.0003339031,0.006830188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008597113,"threshold_uncertainty_score":0.02876019,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2059583577","doi":"10.1016/j.cviu.2012.01.003","title":"Human attributes from 3D pose tracking","year":2012,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University; University of New Brunswick; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Computer vision; Sadness; Inference; Motion (physics); Tracking (education); Task (project management); Viewpoints; Pose; Video tracking; Motion capture; Machine learning; Psychology; Video processing","authors":[{"name":"Micha Livne","is_ca":true},{"name":"Leonid Sigal","is_ca":true},{"name":"Nikolaus F. Troje","is_ca":true},{"name":"David J. Fleet","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07834890582789548,"gpt":0.3036616463225474,"spread":0.2253127404946519,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004475039,0.0009744639,0.001309405,0.002078852,0.00032329,0.001307963,0.0006275705,0.0009694643,0.002566409],"category_scores_gemma":[0.001953884,0.0005221532,0.0008209496,0.002955987,0.0004646921,0.001238353,0.001333744,0.0009828864,0.003444509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003007304,"about_ca_system_score_gemma":0.0004763476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003287631,"about_ca_topic_score_gemma":0.005304335,"domain_scores_codex":[0.999398,0.000077579,0.00002472678,0.0002092355,0.0002241122,0.0000663636],"domain_scores_gemma":[0.9992022,0.0001241253,0.0001262069,0.0002505445,0.0002116927,0.00008527852],"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":[0.000990927,0.0003690968,0.02887292,0.0002769543,0.0002318911,0.0004934754,0.0002810003,0.04326804,0.09842942,0.002709887,0.009930778,0.8141456],"study_design_scores_gemma":[0.00005043115,0.0004276244,0.1111884,0.0001105026,0.0002005034,0.002229496,0.0004668077,0.7977927,0.05334905,0.0221776,0.0118825,0.0001244718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1507588,0.001655853,0.8329048,0.0002834746,0.0003802368,0.0001467929,0.004113774,0.00334731,0.006408923],"genre_scores_gemma":[0.8802458,0.001966685,0.1056225,0.0001931305,0.0002293305,0.00009518604,0.004989821,0.0003285477,0.006329053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003287631,"threshold_uncertainty_score":0.008585453,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2806994641","doi":"10.1016/j.cviu.2018.05.006","title":"Structure preserving image denoising based on low-rank reconstruction and gradient histograms","year":2018,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":25,"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":"Noise reduction; Histogram; Artificial intelligence; Pattern recognition (psychology); Computer science; Thresholding; Regularization (linguistics); Mathematics; Computer vision; Rank (graph theory); Matrix norm; Benchmark (surveying); Image (mathematics)","authors":[{"name":"Mingli Zhang","is_ca":true},{"name":"Christian Desrosiers","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02555056672241709,"gpt":0.2748242930027613,"spread":0.2492737262803442,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006171265,0.0006363349,0.0008500913,0.0009773402,0.0002929751,0.000958615,0.0006509115,0.0008743965,0.001661624],"category_scores_gemma":[0.002217469,0.0003201517,0.0006539558,0.0009054306,0.0006626996,0.001362899,0.0006300473,0.001177847,0.0007669532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002872279,"about_ca_system_score_gemma":0.0006057497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110952,"about_ca_topic_score_gemma":0.001602735,"domain_scores_codex":[0.9996536,0.00007518913,0.00002029335,0.0000640137,0.0001540151,0.00003297217],"domain_scores_gemma":[0.9994143,0.000164939,0.00008511396,0.0001176811,0.0001809916,0.00003703116],"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":[0.0005400056,0.0002190144,0.001528462,0.000494052,0.0001457964,0.0002600094,0.0002066673,0.1093637,0.2144331,0.0786982,0.004979789,0.5891311],"study_design_scores_gemma":[0.00001869428,0.00008907144,0.000708138,0.00001900926,0.00004074921,0.0003647516,0.00002985592,0.9303202,0.04974828,0.01578019,0.002847364,0.00003361504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008138605,0.0002469131,0.9905793,0.0001108632,0.00002692133,0.00001637428,0.00003257189,0.0001901358,0.0006582247],"genre_scores_gemma":[0.2031266,0.001229732,0.7896492,0.0001316096,0.0001207152,0.00005611564,0.0002861689,0.0002154713,0.005184383],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001661624,"threshold_uncertainty_score":0.005558729,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137651406","doi":"10.1016/j.cviu.2012.05.002","title":"MDS-based segmentation model for the fusion of contour and texture cues in natural images","year":2012,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Computer vision; Image segmentation; Pattern recognition (psychology); Scale-space segmentation; Image texture","authors":[{"name":"Max Mignotte","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04039792862173171,"gpt":0.3236877231092853,"spread":0.2832897944875536,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004085351,0.0004781233,0.0007454079,0.001009023,0.0002656871,0.0006261288,0.001010517,0.0007221131,0.001729914],"category_scores_gemma":[0.001011378,0.0003850486,0.0007996286,0.001079085,0.0005427294,0.0008879944,0.000665357,0.0006220725,0.0006276129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000197,"about_ca_system_score_gemma":0.0009506435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00859322,"about_ca_topic_score_gemma":0.00824991,"domain_scores_codex":[0.9997838,0.00003549809,0.00001425656,0.00006722467,0.00007943036,0.00001975966],"domain_scores_gemma":[0.9997539,0.00007040212,0.0000382515,0.00003424555,0.0000876783,0.00001551638],"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":[0.0002454606,0.00004804919,0.0009334477,0.0001558406,0.00008605464,0.0001003983,0.0001451607,0.7629853,0.0335112,0.03151211,0.001895957,0.1683809],"study_design_scores_gemma":[0.000002817501,0.00001346846,0.0001514762,0.000004750677,0.000008686106,0.00002272317,0.000005501499,0.9947343,0.001669778,0.002706655,0.0006733597,0.000006471089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01066024,0.0001735895,0.988,0.00007310951,0.00002053147,0.0000236788,0.0001178226,0.0003478529,0.0005831949],"genre_scores_gemma":[0.5349904,0.0008262912,0.4551304,0.0001264107,0.0000816383,0.0002438428,0.0009396305,0.0003099014,0.007351464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00859322,"threshold_uncertainty_score":0.01708645,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078679182","doi":"10.1016/j.cviu.2006.06.009","title":"Face detection in gray scale images using locally linear embeddings","year":2006,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Support vector machine; Computer science; Face (sociological concept); Computer vision; Face detection; Facial expression; Facial recognition system; Dimensionality reduction; Embedding; Rotation (mathematics); Mathematics","authors":[{"name":"Samuel Kadoury","is_ca":true},{"name":"Martin D. Levine","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02115941015040386,"gpt":0.2685617894855427,"spread":0.2474023793351389,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003487795,0.0005619001,0.0006607434,0.0008964002,0.0002539481,0.0008856349,0.0005699008,0.0005571332,0.002794948],"category_scores_gemma":[0.001303535,0.0003558594,0.0005921182,0.0008187824,0.0004163593,0.001506591,0.0008486332,0.0006502833,0.001413812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003162077,"about_ca_system_score_gemma":0.0003297926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001470258,"about_ca_topic_score_gemma":0.002079694,"domain_scores_codex":[0.9997155,0.0000645525,0.00001407207,0.00007871491,0.00008392131,0.00004321773],"domain_scores_gemma":[0.9995771,0.0001499616,0.00005565914,0.00009593504,0.00009216151,0.00002920498],"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":[0.0005028538,0.0002272468,0.002267246,0.000166609,0.0001008617,0.0001251881,0.0001310888,0.04585874,0.178856,0.006651171,0.00328648,0.7618265],"study_design_scores_gemma":[0.00001951977,0.0001667362,0.002710406,0.00001995136,0.00003875567,0.0002095255,0.00009046861,0.9409869,0.04748251,0.006892263,0.001361367,0.00002150521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0700536,0.0002713138,0.927069,0.000161282,0.00004109412,0.00004371623,0.0001096075,0.001144909,0.001105399],"genre_scores_gemma":[0.565978,0.0005247297,0.4272842,0.0001407444,0.00007042889,0.0001011862,0.0004364765,0.0002378768,0.005226426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002794948,"threshold_uncertainty_score":0.009350061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2038626130","doi":"10.1016/j.cviu.2005.05.001","title":"Segmentation of tissue boundary evolution from brain MR image sequences using multi-phase level sets","year":2005,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Institutes of Health","keywords":"Artificial intelligence; Computer science; Segmentation; Level set (data structures); Computer vision; Boundary (topology); Pattern recognition (psychology); Image segmentation; Level set method; Image registration; Consistency (knowledge bases); Image (mathematics); Mathematics","authors":[{"name":"Corina Drapaca","is_ca":true},{"name":"Valerie A. Cardenas","is_ca":false},{"name":"Colin Studholme","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09846050931042288,"gpt":0.390749975877398,"spread":0.2922894665669751,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000631299,0.000495285,0.0005172567,0.001519392,0.0003757153,0.001057745,0.00077506,0.001110696,0.0009943589],"category_scores_gemma":[0.00246777,0.0006348257,0.0007388875,0.0008802037,0.0004469966,0.0009239775,0.0005563065,0.001139398,0.0003833144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005073746,"about_ca_system_score_gemma":0.0008686433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00172702,"about_ca_topic_score_gemma":0.002042315,"domain_scores_codex":[0.9998522,0.00002851391,0.00001237886,0.0000266318,0.00006287833,0.0000172688],"domain_scores_gemma":[0.9995035,0.0002314104,0.00007139981,0.00006507925,0.00009948103,0.00002912082],"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":[0.000546894,0.0001649254,0.002161372,0.0004404131,0.0001158042,0.0006477751,0.0006844943,0.2371732,0.3709176,0.01309805,0.001632428,0.3724171],"study_design_scores_gemma":[0.00001931314,0.00008082807,0.002043514,0.00003710187,0.00004132997,0.0004665956,0.00005607705,0.916611,0.07105216,0.007796777,0.001768742,0.0000266452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0639793,0.0004198929,0.9338482,0.0001834201,0.00003023751,0.0001051435,0.00008309144,0.0005485018,0.000802321],"genre_scores_gemma":[0.3031492,0.0006625702,0.6938425,0.00008414057,0.00002988992,0.000134073,0.0003232197,0.0003677569,0.00140659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00172702,"threshold_uncertainty_score":0.003681242,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2120866333","doi":"10.1016/j.cviu.2009.09.001","title":"On the sensitivity analysis of camera calibration from images of spheres","year":2009,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":22,"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":"Conic section; Ellipse; Calibration; Sensitivity (control systems); Artificial intelligence; Computer vision; Parametric statistics; SPHERES; Ambiguity; Orientation (vector space); Mathematics; Camera resectioning; Computer science; Geometry; Physics; Statistics; Engineering","authors":[{"name":"Yan Lu","is_ca":true},{"name":"Shahram Payandeh","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04697909440152147,"gpt":0.2828737016542496,"spread":0.2358946072527282,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001359881,0.0009347826,0.0008197188,0.001425633,0.0003405941,0.00102929,0.0008150456,0.0006992485,0.002045783],"category_scores_gemma":[0.008377173,0.0004717782,0.001074946,0.001124184,0.001099576,0.001288395,0.001209907,0.001061103,0.000430273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008799541,"about_ca_system_score_gemma":0.0004875689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003627871,"about_ca_topic_score_gemma":0.001598773,"domain_scores_codex":[0.9985678,0.0004352427,0.00005029452,0.0002064982,0.0006581577,0.00008189066],"domain_scores_gemma":[0.9969348,0.002386169,0.0001479409,0.0002277451,0.000271239,0.00003218176],"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":[0.0002968224,0.00009090882,0.001440487,0.0006810129,0.0002899435,0.0003933033,0.0004278405,0.5095041,0.09801827,0.1310814,0.003730924,0.254045],"study_design_scores_gemma":[0.000004032198,0.00003491389,0.001577106,0.00002903803,0.00004447602,0.0002068504,0.00002451374,0.951988,0.01762976,0.02660087,0.001824379,0.00003596767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01367305,0.0008298469,0.9809538,0.0001200952,0.00004601844,0.00003848062,0.00003355056,0.0002308265,0.004074293],"genre_scores_gemma":[0.7846735,0.004242234,0.1999381,0.0003806881,0.0002901638,0.0001369854,0.0003314739,0.0006223373,0.009384516],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003627871,"threshold_uncertainty_score":0.007213473,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058611086","doi":"10.1016/j.cviu.2003.07.001","title":"A volumetric approach for interactive 3D modeling","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"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":"Robustness (evolution); Computer science; Image registration; Computer vision; Range (aeronautics); Artificial intelligence; Surface reconstruction; Representation (politics); Matching (statistics); Surface (topology); Algorithm; Process (computing); Mathematics; Image (mathematics); Geometry","authors":[{"name":"D. Tubic","is_ca":true},{"name":"P. Hébert","is_ca":true},{"name":"Denis Laurendeau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0361106434153572,"gpt":0.2458339268577926,"spread":0.2097232834424353,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000367708,0.001204275,0.0008638716,0.001681601,0.0006709373,0.002075192,0.002799934,0.001120428,0.006926098],"category_scores_gemma":[0.001294986,0.001185065,0.001649874,0.001565327,0.0009840553,0.001428214,0.003035397,0.00154174,0.002323409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006273219,"about_ca_system_score_gemma":0.0007647266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004484639,"about_ca_topic_score_gemma":0.006705842,"domain_scores_codex":[0.9993209,0.0000676369,0.00002370943,0.00008562263,0.000442593,0.00005958572],"domain_scores_gemma":[0.9996337,0.0001071584,0.00002175347,0.0001086212,0.00009649387,0.00003220454],"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":[0.0001329942,0.0001248466,0.0005291378,0.0004779152,0.0001434178,0.0004760751,0.0006244107,0.2723009,0.07242153,0.2182828,0.01283843,0.4216475],"study_design_scores_gemma":[0.00001843,0.00003816589,0.0001774062,0.00006297943,0.00004340575,0.0005475797,0.0001048887,0.8731065,0.0227413,0.05978927,0.04331114,0.00005905389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007382797,0.00005995281,0.996867,0.00003274867,0.00001243982,0.00002172188,0.00006148926,0.0008922704,0.001314159],"genre_scores_gemma":[0.09847945,0.0006386219,0.8945379,0.0001026697,0.00003772419,0.0001564122,0.0006029047,0.001126985,0.004317264],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006926098,"threshold_uncertainty_score":0.02317005,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362672512","doi":"10.1016/j.cviu.2023.103690","title":"SCA-Net: Spatial and channel attention-based network for 3D point clouds","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Point cloud; Computer science; Pooling; Artificial intelligence; Upsampling; Geometric networks; Channel (broadcasting); Geometric modeling; Key (lock); Geometric data analysis; Data mining; Point (geometry); Mathematics; Complex network; Image (mathematics); Geometry","authors":[{"name":"Xikai Tang","is_ca":true},{"name":"Karim G. Habashy","is_ca":true},{"name":"Fangzheng Huang","is_ca":true},{"name":"Chao Li","is_ca":false},{"name":"Dayan Ban","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02736372862404576,"gpt":0.2469127396543635,"spread":0.2195490110303178,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004820767,0.00104265,0.001034842,0.001638152,0.0008744294,0.0008752788,0.002303938,0.001089977,0.006537527],"category_scores_gemma":[0.001804943,0.0005967407,0.0007833424,0.001687536,0.0004479231,0.0014823,0.001697393,0.001129392,0.00163676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001466154,"about_ca_system_score_gemma":0.001501663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04326618,"about_ca_topic_score_gemma":0.06519758,"domain_scores_codex":[0.9997576,0.00002490383,0.000009670496,0.00007627668,0.00009074932,0.00004093105],"domain_scores_gemma":[0.9994879,0.0001429934,0.00003530894,0.000115991,0.000158156,0.0000597735],"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":[0.0005881591,0.0002533334,0.003131966,0.0001808211,0.0002074138,0.0002152774,0.0001186842,0.3482997,0.0103161,0.01023884,0.05972794,0.5667217],"study_design_scores_gemma":[0.00001269566,0.00002279022,0.0002761877,0.000005029772,0.00001200554,0.00002375125,0.00001061669,0.9913775,0.001921977,0.004347991,0.001981701,0.000007734152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02411444,0.0003731595,0.9443431,0.0002897076,0.0001907567,0.0002057364,0.003182768,0.02416278,0.003137413],"genre_scores_gemma":[0.4623566,0.0005417022,0.5130806,0.0004024368,0.0002077176,0.0004725554,0.01085257,0.001114833,0.01097098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04326618,"threshold_uncertainty_score":0.0860287,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999363260","doi":"10.1016/j.cviu.2003.10.003","title":"Detection and characterization of junctions in a 2D image","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"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":"Curvature; Characterization (materials science); Process (computing); Position (finance); Feature (linguistics); Artificial intelligence; Image (mathematics); Computer science; Pattern recognition (psychology); Constant (computer programming); Computer vision; Algorithm; Mathematics; Geometry; Physics; Optics","authors":[{"name":"Robert Bergevin","is_ca":true},{"name":"Annie Bubel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02469754101588828,"gpt":0.27651847325959,"spread":0.2518209322437017,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004429859,0.0006242135,0.000968852,0.002759875,0.0007076343,0.001936567,0.0009755647,0.001666361,0.001425607],"category_scores_gemma":[0.001797861,0.0005937379,0.0004661368,0.001173105,0.0007310292,0.00123056,0.0007038997,0.0009237956,0.0009010058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003413238,"about_ca_system_score_gemma":0.0008391513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001633317,"about_ca_topic_score_gemma":0.002329991,"domain_scores_codex":[0.9995267,0.00003633742,0.00002762187,0.0001250399,0.0002212641,0.00006293602],"domain_scores_gemma":[0.99907,0.0002320958,0.0001602501,0.0001522029,0.0002781155,0.0001073699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005791606,0.0002420241,0.01089445,0.0003379788,0.00007171762,0.0009537985,0.0006414159,0.01151293,0.6933632,0.0097224,0.002149967,0.269531],"study_design_scores_gemma":[0.00005108192,0.0002613897,0.03215773,0.00008241,0.0001591577,0.003230451,0.0005098577,0.4295037,0.5125633,0.009522032,0.0118618,0.00009687056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.192481,0.0006211741,0.8018221,0.0002037885,0.00006968476,0.00016189,0.0003151576,0.001633574,0.002691633],"genre_scores_gemma":[0.4329627,0.0005315364,0.5632815,0.00006968105,0.00006843334,0.0001048261,0.0006344935,0.0002820649,0.002064653],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002759875,"threshold_uncertainty_score":0.004769087,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W583340156","doi":"10.1016/j.cviu.2015.06.001","title":"Image segmentation via multi-scale stochastic regional texture appearance models","year":2015,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Canada Research Chairs","keywords":"Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Image texture; Image segmentation; Texture (cosmology); Computer vision; Scale (ratio); Scale-space segmentation; Image (mathematics); Geography","authors":[{"name":"R. S. Medeiros","is_ca":false},{"name":"Jacob Scharcanski","is_ca":false},{"name":"Alexander Wong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08912123015414102,"gpt":0.3274671731226516,"spread":0.2383459429685106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006402376,0.0005200357,0.001032797,0.0008491429,0.0002381016,0.00097192,0.001015496,0.001042213,0.0008223295],"category_scores_gemma":[0.002214647,0.0007804909,0.001295695,0.0008773768,0.0006490323,0.001224553,0.0007258118,0.0008516135,0.0004417891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008445418,"about_ca_system_score_gemma":0.0005026548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004278793,"about_ca_topic_score_gemma":0.00505875,"domain_scores_codex":[0.9996156,0.00009023566,0.00001642456,0.0001085445,0.0001254696,0.0000437228],"domain_scores_gemma":[0.9992962,0.0002919484,0.0001565257,0.00009673139,0.0001199073,0.00003865363],"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":[0.0001937842,0.00004410003,0.0007836571,0.0000831807,0.0000881536,0.00009201523,0.00006587016,0.8710467,0.02767371,0.009901608,0.0006584388,0.08936884],"study_design_scores_gemma":[0.000001893647,0.000005615535,0.0001013243,0.000001186853,0.000004577688,0.00001196895,0.000001376207,0.9981877,0.0006556179,0.0009533179,0.0000725214,0.000002864824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01283522,0.00008865209,0.9864829,0.00005466752,0.00001017389,0.000009618382,0.00002461852,0.000245322,0.0002487934],"genre_scores_gemma":[0.6885945,0.0005079883,0.3072357,0.0001147889,0.00006598846,0.00007361348,0.0002305034,0.0003034152,0.002873452],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004278793,"threshold_uncertainty_score":0.008507729,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167595698","doi":"10.1016/j.cviu.2015.03.010","title":"Collaborative part-based tracking using salient local predictors","year":2015,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Salient; Tracking (education); Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Machine learning; Psychology","authors":[{"name":"Wassim Bouachir","is_ca":true},{"name":"Guillaume-Alexandre Bilodeau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09344043918016402,"gpt":0.3352520775643212,"spread":0.2418116383841572,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001716064,0.001250928,0.002619872,0.001556151,0.0009943476,0.001547295,0.002481425,0.00196176,0.001847306],"category_scores_gemma":[0.004211571,0.001512564,0.00139222,0.002304866,0.0008099241,0.002334989,0.002375939,0.00162548,0.001556116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004405126,"about_ca_system_score_gemma":0.001067976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003445989,"about_ca_topic_score_gemma":0.004871498,"domain_scores_codex":[0.9988444,0.0002194413,0.00005035261,0.000437294,0.0003331431,0.0001153792],"domain_scores_gemma":[0.9973412,0.00110044,0.0002360342,0.0006682985,0.0004926327,0.0001613834],"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":[0.0008568836,0.0003314639,0.003081464,0.0001641028,0.0003389108,0.0002956852,0.0002681382,0.3144338,0.05798039,0.007408835,0.004531369,0.610309],"study_design_scores_gemma":[0.00001070654,0.000059669,0.0005667352,0.000005814024,0.00003576733,0.00008175519,0.0000112595,0.9908149,0.005204746,0.002548185,0.0006457107,0.00001489003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00854515,0.0001720664,0.9902906,0.00003457625,0.00003582885,0.00001828927,0.00002568788,0.0004749006,0.000402877],"genre_scores_gemma":[0.4333231,0.0004934904,0.5592043,0.0001864894,0.0001488482,0.0001185807,0.0004952595,0.0003518228,0.005678025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003445989,"threshold_uncertainty_score":0.009075522,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2050560110","doi":"10.1016/j.cviu.2014.03.011","title":"Hybrid structural and texture distinctiveness vector field convolution for region segmentation","year":2014,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Optimal distinctiveness theory; Artificial intelligence; Computer science; Pattern recognition (psychology); Convolution (computer science); Computer vision; Segmentation; Texture (cosmology); Convergence (economics); Texture filtering; Feature (linguistics); Image texture; Image segmentation; Image (mathematics); Artificial neural network","authors":[{"name":"Khalil Fergani","is_ca":true},{"name":"Dorothy Lui","is_ca":true},{"name":"Christian Scharfenberger","is_ca":true},{"name":"Alexander Wong","is_ca":true},{"name":"D.A. Clausi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02420147694611768,"gpt":0.2931098703540574,"spread":0.2689083934079397,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006832581,0.0004315519,0.0007223017,0.0009441975,0.0002418225,0.0007850197,0.0005803478,0.0005886856,0.002025228],"category_scores_gemma":[0.0008944977,0.0002725529,0.0005933524,0.001012703,0.0003233042,0.001075585,0.0006169551,0.000539903,0.0005576502],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004281029,"about_ca_system_score_gemma":0.0008089897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001918558,"about_ca_topic_score_gemma":0.002902079,"domain_scores_codex":[0.9996796,0.0000486281,0.00002046214,0.00006772289,0.0001315428,0.00005201509],"domain_scores_gemma":[0.99958,0.0001068241,0.00003739562,0.00007957216,0.0001616251,0.00003463999],"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":[0.0004572502,0.0001371461,0.001379441,0.0001552048,0.00008739686,0.00007165763,0.0000820648,0.04003874,0.3002603,0.009603337,0.001607907,0.6461195],"study_design_scores_gemma":[0.00001601065,0.0001199653,0.002014262,0.00001053015,0.00004540405,0.0002533456,0.00002798148,0.9038893,0.08718697,0.003920611,0.002493983,0.0000215976],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02264589,0.0002124865,0.9759083,0.00005201449,0.00002065142,0.00002640359,0.00005178379,0.0004480841,0.000634474],"genre_scores_gemma":[0.3080545,0.000386356,0.6872447,0.0001119458,0.00005207723,0.00005558792,0.0002990781,0.0001837567,0.003611963],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002025228,"threshold_uncertainty_score":0.006775022,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014895678","doi":"10.1016/j.cviu.2010.10.014","title":"Sampled medial loci for 3D shape representation","year":2010,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Medial axis; Representation (politics); Polyhedron; Mathematics; Boundary (topology); Surface (topology); Computation; Point (geometry); Topological skeleton; Geometry; Curvature; Boundary representation; Set (abstract data type); Algorithm; Artificial intelligence; Computer science; Computer vision; Mathematical analysis; Segmentation; Active shape model","authors":[{"name":"Svetlana Stolpner","is_ca":true},{"name":"Sue Whitesides","is_ca":true},{"name":"Kaleem Siddiqi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03820562661920025,"gpt":0.2857793422561336,"spread":0.2475737156369333,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008021348,0.0007010368,0.0009311297,0.002300711,0.0004243133,0.001664655,0.001785969,0.001468627,0.005622611],"category_scores_gemma":[0.005051893,0.0007259059,0.0008203514,0.001613892,0.001144079,0.002116783,0.002135501,0.001796953,0.002297387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006628465,"about_ca_system_score_gemma":0.0005049365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001415399,"about_ca_topic_score_gemma":0.001492285,"domain_scores_codex":[0.9993446,0.000129798,0.00003361531,0.0001279878,0.0003307732,0.00003310388],"domain_scores_gemma":[0.9986993,0.0005439969,0.0001524511,0.0002750928,0.0002364274,0.00009263327],"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":[0.0004665023,0.0001227432,0.0009468363,0.0003850131,0.00009875331,0.0003116608,0.000497097,0.1912464,0.03985911,0.2917039,0.004575212,0.4697867],"study_design_scores_gemma":[0.00001922275,0.00003988399,0.0001827537,0.00002245144,0.000008846816,0.0001465475,0.00004499739,0.8940595,0.004009166,0.09809912,0.0033449,0.00002250649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004574709,0.0001237014,0.9943954,0.00003550843,0.00001200415,0.00001679894,0.00007064518,0.0003496751,0.0004215671],"genre_scores_gemma":[0.25726,0.0005464755,0.7359185,0.00009188098,0.00009023181,0.0001339865,0.0007514368,0.0004825896,0.004724886],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005622611,"threshold_uncertainty_score":0.01880956,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068777304","doi":"10.1006/cviu.2002.0965","title":"The Multimodal Neighborhood Signature for Modeling Object Color Appearance and Applications in Object Recognition and Image Retrieval","year":2002,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Simon Fraser University; Ministerstvo Školství, Mládeže a Tělovýchovy","keywords":"Artificial intelligence; Computer science; Computer vision; Robustness (evolution); Rendering (computer graphics); Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Clutter; Segmentation; Image retrieval; Object (grammar); Image (mathematics)","authors":[{"name":"Jiřı́ Matas","is_ca":false},{"name":"D. Koubaroulis","is_ca":false},{"name":"Josef Kittler","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04682453710997326,"gpt":0.2744083467820979,"spread":0.2275838096721247,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003395179,0.0003579209,0.0005621725,0.0007449694,0.0003340521,0.0004074251,0.0006614393,0.000515041,0.001324298],"category_scores_gemma":[0.001338049,0.0001879441,0.0004394387,0.000811233,0.0003495116,0.001025632,0.0003717614,0.0004172151,0.0004608307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003994837,"about_ca_system_score_gemma":0.000370281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002746841,"about_ca_topic_score_gemma":0.003370769,"domain_scores_codex":[0.9998177,0.00005112493,0.000006532468,0.00004120426,0.00006778581,0.00001563496],"domain_scores_gemma":[0.9996755,0.0000622387,0.00004937937,0.00007135662,0.0001171871,0.00002447801],"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":[0.0002911989,0.0001246383,0.002999829,0.0001802661,0.00008055844,0.0002734262,0.0001859483,0.2576259,0.126751,0.1254251,0.004998497,0.4810636],"study_design_scores_gemma":[0.000003714916,0.00003810464,0.0006724608,0.000005751639,0.00001673536,0.0001585129,0.00001377441,0.9817355,0.006335472,0.009073422,0.001932808,0.00001374905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03100126,0.0003478473,0.9665787,0.00005827055,0.00002997377,0.00002659295,0.00009103211,0.0002949902,0.001571328],"genre_scores_gemma":[0.6976513,0.0008065108,0.2943581,0.0000842286,0.00009733245,0.0001342198,0.0002562723,0.0002425565,0.006369353],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002746841,"threshold_uncertainty_score":0.005461693,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001410840","doi":"10.1016/j.cviu.2009.07.003","title":"Dynamic edge tracing: Boundary identification in medical images","year":2009,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"McGill University; Massachusetts General Hospital","keywords":"Tracing; Computer vision; Artificial intelligence; Boundary (topology); Identification (biology); Computer science; Enhanced Data Rates for GSM Evolution; Edge detection; Computer graphics (images); Pattern recognition (psychology); Image processing; Mathematics; Image (mathematics); Mathematical analysis; Biology","authors":[{"name":"Daniel Withey","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Z.J. Koles","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02199240663742755,"gpt":0.3258861672043246,"spread":0.303893760566897,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008772643,0.0005843767,0.0009596322,0.002217363,0.0005354566,0.001455704,0.001276683,0.001600549,0.002873936],"category_scores_gemma":[0.003040965,0.0007085328,0.0005800521,0.001978677,0.0007720168,0.001527399,0.001070445,0.00129134,0.001170354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004642286,"about_ca_system_score_gemma":0.0008544062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001659782,"about_ca_topic_score_gemma":0.001625589,"domain_scores_codex":[0.9995492,0.00007471476,0.00002405511,0.0000984903,0.000202195,0.00005130425],"domain_scores_gemma":[0.9990913,0.0004290972,0.0000969291,0.0001621912,0.0001647898,0.00005563771],"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":[0.0004329991,0.0001272788,0.001101884,0.0003303449,0.00004172092,0.0003741717,0.0002734472,0.0271135,0.1739674,0.01438014,0.003158028,0.778699],"study_design_scores_gemma":[0.00005108136,0.0001541485,0.002376348,0.00008160926,0.00006549257,0.001456388,0.0001306445,0.7860662,0.1798929,0.01915983,0.01052031,0.00004505146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01578908,0.0004982048,0.9818586,0.000131689,0.00003138968,0.00004862703,0.0000422978,0.0008668784,0.0007332981],"genre_scores_gemma":[0.1139896,0.001031416,0.8816464,0.00009255305,0.00005499624,0.00006554138,0.0001738988,0.0004400009,0.002505628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002873936,"threshold_uncertainty_score":0.009614229,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2024909582","doi":"10.1006/cviu.2000.0859","title":"Polyhedral Representation and Adjacency Graph in n-dimensional Digital Images","year":2000,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Digital Image Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Lethbridge","funders":"","keywords":"Adjacency list; Digital topology; Euclidean geometry; Mathematics; Digital geometry; Combinatorics; Digital image; Graph; Representation (politics); Adjacency matrix; Euclidean distance; Topology (electrical circuits); Computer science; Discrete mathematics; Image processing; Image (mathematics); Topological space; Artificial intelligence; General topology; Geometry; Extension topology","authors":[{"name":"Mohammed Khachan","is_ca":false},{"name":"Patrick Chenin","is_ca":false},{"name":"Hafsa Deddi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02395372269932123,"gpt":0.2807965282528208,"spread":0.2568428055534995,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001332882,0.0003462376,0.0004573676,0.001917023,0.0005678185,0.001348366,0.0008619628,0.0006561836,0.00430134],"category_scores_gemma":[0.00119382,0.0002879839,0.0003710494,0.002375111,0.0006750134,0.001331096,0.0005977258,0.0007744782,0.0008816121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004411484,"about_ca_system_score_gemma":0.0003904717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005377029,"about_ca_topic_score_gemma":0.006093443,"domain_scores_codex":[0.9996791,0.00006273993,0.00002056852,0.0001016859,0.0001093539,0.00002653563],"domain_scores_gemma":[0.9995969,0.0001351532,0.0000877424,0.00005154746,0.00009558014,0.00003321106],"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":[0.000145761,0.0001431689,0.001461298,0.0002602997,0.00002336595,0.0003882468,0.0003567763,0.193749,0.01228423,0.5042586,0.007865579,0.2790636],"study_design_scores_gemma":[0.00001216797,0.00004129476,0.001357449,0.00003518747,0.00001718766,0.0003342151,0.0002354803,0.7059991,0.002482635,0.2776967,0.01175942,0.00002916298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05254106,0.0003369852,0.9362974,0.0003088607,0.00009788627,0.00008000597,0.0006145434,0.000333515,0.009389705],"genre_scores_gemma":[0.5973217,0.001182097,0.3838293,0.0002228339,0.0001366983,0.0002893467,0.001576929,0.0001951535,0.0152459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005377029,"threshold_uncertainty_score":0.0143894,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2102596245","doi":"10.1016/j.cviu.2010.03.003","title":"An automatic calibration method for stereo-based 3D distributed smart camera networks","year":2010,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Computer vision; Stereo camera; Artificial intelligence; Camera resectioning; Smart camera; Calibration; Computer graphics (images); Camera auto-calibration; Mathematics","authors":[{"name":"Aaron Mavrinac","is_ca":true},{"name":"Xiang Chen","is_ca":true},{"name":"Kemal Tepe","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01726319659717841,"gpt":0.2690912756839399,"spread":0.2518280790867615,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004489603,0.0007471222,0.0006879462,0.001199459,0.0006470618,0.0006692924,0.001205837,0.0008013838,0.003527967],"category_scores_gemma":[0.001342351,0.0005614533,0.0005486751,0.00101801,0.0003972889,0.00104401,0.0009209917,0.0007462384,0.001379139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007085946,"about_ca_system_score_gemma":0.001016296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004452123,"about_ca_topic_score_gemma":0.007646742,"domain_scores_codex":[0.9992266,0.000103148,0.00002260892,0.0001374993,0.0004627226,0.00004742246],"domain_scores_gemma":[0.9994673,0.00007306736,0.00004907401,0.00009092738,0.0003001212,0.00001952849],"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":[0.0001255791,0.00006544997,0.000611976,0.0001093418,0.00005882529,0.00008832861,0.0001160259,0.07359772,0.0745697,0.008790588,0.00615855,0.8357078],"study_design_scores_gemma":[0.00002911049,0.00005140991,0.0008722927,0.00001462644,0.00002825072,0.0002393186,0.00003467109,0.9549491,0.03183372,0.00299922,0.008914347,0.00003399349],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002515931,0.00006157697,0.9958975,0.00001852526,0.00003379772,0.00001709582,0.00001525645,0.0007454134,0.0006948944],"genre_scores_gemma":[0.1586867,0.000218721,0.8371629,0.00006867634,0.00005184417,0.0001143709,0.000159911,0.0001892796,0.003347754],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004452123,"threshold_uncertainty_score":0.01180226,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403635079","doi":"10.1016/j.cviu.2024.104213","title":"A GCN and Transformer complementary network for skeleton-based action recognition","year":2024,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Transformer; Artificial intelligence; Computer science; Action recognition; Skeleton (computer programming); Pattern recognition (psychology); Engineering; Voltage","authors":[{"name":"Xuezhi Xiang","is_ca":false},{"name":"Xiaoheng Li","is_ca":false},{"name":"Xuzhao Liu","is_ca":false},{"name":"Yulong Qiao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09234589976460437,"gpt":0.3236472678315014,"spread":0.2313013680668971,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004082696,0.0006704447,0.0007666061,0.000676506,0.0003534413,0.0004883584,0.001283721,0.0009239968,0.004335537],"category_scores_gemma":[0.0007474063,0.0003158353,0.0005539345,0.0009794062,0.0004079677,0.000761574,0.00076936,0.0009635676,0.001354516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000737462,"about_ca_system_score_gemma":0.00147889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0276146,"about_ca_topic_score_gemma":0.03063135,"domain_scores_codex":[0.9997094,0.00002159243,0.00001142136,0.000124465,0.00008453392,0.00004859641],"domain_scores_gemma":[0.9997414,0.00005134109,0.00001426384,0.00004247729,0.0001169031,0.00003362904],"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":[0.0003106674,0.0001808247,0.0008758403,0.0001029585,0.0000728623,0.0001567858,0.00003724773,0.05668147,0.05903518,0.006047841,0.008001115,0.8684973],"study_design_scores_gemma":[0.00001338848,0.00007243673,0.0006880288,0.00001062787,0.00003329772,0.000106737,0.00001330503,0.9821322,0.01190998,0.002941711,0.002061362,0.00001701862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02318124,0.0007310822,0.9665673,0.0001774347,0.0003475294,0.000110946,0.0004351832,0.003591299,0.004857855],"genre_scores_gemma":[0.4953899,0.0008448494,0.4904578,0.0004216025,0.0001656076,0.0001700575,0.001238755,0.0002539684,0.01105745],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0276146,"threshold_uncertainty_score":0.05490774,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4327559110","doi":"10.1016/j.cviu.2023.103682","title":"Grow-push-prune: Aligning deep discriminants for effective structural network compression","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Computer science; MNIST database; Pruning; Artificial intelligence; Deep learning; Machine learning; Construct (python library); Residual; Set (abstract data type); Algorithm","authors":[{"name":"Qing Tian","is_ca":true},{"name":"Tal Arbel","is_ca":true},{"name":"James J. Clark","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0348156777220367,"gpt":0.3205194251396037,"spread":0.285703747417567,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008887165,0.001495959,0.0009380722,0.001525864,0.0006420182,0.0008870732,0.001489927,0.001407239,0.005972534],"category_scores_gemma":[0.004315522,0.0007104232,0.0005721053,0.001217719,0.0007123812,0.002214734,0.001944025,0.002814653,0.002717865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005561376,"about_ca_system_score_gemma":0.001070426,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002845275,"about_ca_topic_score_gemma":0.008525753,"domain_scores_codex":[0.9995665,0.00007589433,0.00002435455,0.0001222674,0.0001487873,0.00006218487],"domain_scores_gemma":[0.9989218,0.0003589696,0.0000714799,0.000299305,0.0002606699,0.00008785314],"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":[0.0004571758,0.0002174865,0.0009148964,0.00009612225,0.00004555014,0.000152606,0.0001273836,0.05054125,0.03795861,0.01230099,0.01459524,0.8825926],"study_design_scores_gemma":[0.00004110327,0.0001149725,0.0003080882,0.00001793916,0.00001943232,0.000113913,0.00006159226,0.9552854,0.02156666,0.01828235,0.004172092,0.00001661297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0484342,0.0005849947,0.9403723,0.0004215939,0.0002135855,0.00009688109,0.000412612,0.006586368,0.002877456],"genre_scores_gemma":[0.3268782,0.0003902694,0.6610301,0.0003335026,0.0001400196,0.0001492973,0.001640096,0.001404129,0.008034521],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005972534,"threshold_uncertainty_score":0.01998013,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4399070826","doi":"10.1016/j.cviu.2024.104034","title":"DHBSR: A deep hybrid representation-based network for blind image super resolution","year":2024,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; University of Toronto","funders":"","keywords":"Artificial intelligence; Representation (politics); Image (mathematics); Computer science; Computer vision; Resolution (logic); Pattern recognition (psychology)","authors":[{"name":"Alireza Esmaeilzehi","is_ca":true},{"name":"Farshid Nooshi","is_ca":false},{"name":"Hossein Zaredar","is_ca":false},{"name":"M. Omair Ahmad","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04692993383634957,"gpt":0.3382567293562341,"spread":0.2913267955198845,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007310758,0.001131222,0.001134833,0.0007273143,0.0003973523,0.0007377898,0.002256892,0.00124591,0.004696129],"category_scores_gemma":[0.001311847,0.0005443876,0.0006858782,0.0007436742,0.0004987321,0.001425661,0.001728761,0.001718581,0.00240058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006361312,"about_ca_system_score_gemma":0.001145935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008240348,"about_ca_topic_score_gemma":0.01600748,"domain_scores_codex":[0.9996965,0.00005742646,0.0000110888,0.00006891162,0.000116121,0.00005002771],"domain_scores_gemma":[0.9996356,0.00009860446,0.00002714044,0.00009249358,0.0001009711,0.00004525554],"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":[0.0005868554,0.0002426135,0.0006541899,0.000227298,0.0002761445,0.0001674103,0.0000639276,0.1098102,0.04325132,0.01138603,0.02974665,0.8035873],"study_design_scores_gemma":[0.00002126618,0.00006262853,0.0001769555,0.0000125024,0.00002974178,0.00007440287,0.000009690983,0.9786204,0.01142146,0.005937874,0.003615626,0.00001747899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007870845,0.000824037,0.9846809,0.0002740243,0.0001412585,0.00006788658,0.0005340254,0.003981554,0.001625579],"genre_scores_gemma":[0.1885806,0.001153518,0.789407,0.0008398421,0.0001800169,0.0001790808,0.002439246,0.0006247369,0.01659592],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008240348,"threshold_uncertainty_score":0.01638478,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2011314809","doi":"10.1016/j.cviu.2005.05.003","title":"Detecting and removing specularities in facial images","year":2005,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Specularity; Artificial intelligence; Computer vision; Computer science; Boundary (topology); Color constancy; Luminance; Shadow (psychology); Mathematics; Image (mathematics); Specular reflection; Optics","authors":[{"name":"Martin D. Levine","is_ca":true},{"name":"Jisnu Bhattacharyya","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02014486696117292,"gpt":0.2758931667107556,"spread":0.2557482997495827,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003408614,0.0005090083,0.0006903125,0.0007845295,0.0003719979,0.0006375657,0.0003558102,0.0005771459,0.001736273],"category_scores_gemma":[0.0009909401,0.0003175173,0.000559922,0.0003962221,0.0003549972,0.0005078415,0.0004377344,0.0006851494,0.0008700615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003117551,"about_ca_system_score_gemma":0.0007489492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002317524,"about_ca_topic_score_gemma":0.003473568,"domain_scores_codex":[0.9997861,0.00001956376,0.000007383931,0.00004280843,0.00008721626,0.00005691221],"domain_scores_gemma":[0.9996911,0.00006571782,0.00003134784,0.00004620847,0.0001423347,0.00002329471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004195742,0.00009709314,0.004956697,0.0001516374,0.00007428184,0.0003488619,0.0001723541,0.004628344,0.5127015,0.001422651,0.002102099,0.4729249],"study_design_scores_gemma":[0.00004913744,0.0003199894,0.05848679,0.00006824401,0.0003371855,0.003348838,0.0005802677,0.2376806,0.6876221,0.003851599,0.007602845,0.00005241429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6329139,0.0009291881,0.3572004,0.0003622699,0.0001730182,0.0001047343,0.0002351097,0.0009122121,0.007169087],"genre_scores_gemma":[0.7594412,0.001768789,0.2310283,0.000182452,0.00008066172,0.00005446707,0.0005004702,0.0002160378,0.006727621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002317524,"threshold_uncertainty_score":0.005808473,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2024351211","doi":"10.1016/s1077-3142(03)00099-7","title":"Object-level structured contour map extraction","year":2003,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"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":"Curvature; Artificial intelligence; Computer vision; Graph; Active contour model; Computer science; Mathematics; Contour line; Object (grammar); Process (computing); Pattern recognition (psychology); Algorithm; Topology (electrical circuits); Image (mathematics); Geometry; Image segmentation; Theoretical computer science; Combinatorics","authors":[{"name":"Robert Bergevin","is_ca":true},{"name":"Annie Bubel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05317088986267293,"gpt":0.3200865482859827,"spread":0.2669156584233097,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002706461,0.0007302779,0.0007400037,0.002243502,0.0003560757,0.001223728,0.0008095654,0.001058367,0.004717066],"category_scores_gemma":[0.001317404,0.0005962614,0.0006187689,0.001792261,0.0003135845,0.001067454,0.0007372729,0.000712703,0.003067204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002863311,"about_ca_system_score_gemma":0.0008091122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001103315,"about_ca_topic_score_gemma":0.001773924,"domain_scores_codex":[0.9998013,0.00001495897,0.00001068548,0.00004272296,0.0001040206,0.00002619971],"domain_scores_gemma":[0.9995787,0.00009655612,0.00003456665,0.0001016103,0.0001635834,0.00002505759],"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":[0.0001830987,0.00008700419,0.0009750058,0.0002088072,0.00004459366,0.0002633478,0.00008691214,0.0134874,0.187786,0.004776921,0.004727037,0.787374],"study_design_scores_gemma":[0.0000427507,0.0001578376,0.005582481,0.00006617406,0.00009429515,0.001170584,0.00009123219,0.6732587,0.2882436,0.01424455,0.01700229,0.0000454573],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01215949,0.0001902126,0.9830061,0.00008789659,0.00003991927,0.0001011583,0.0001950092,0.002491193,0.001729174],"genre_scores_gemma":[0.1290163,0.0005118784,0.8639961,0.0001192159,0.00005048398,0.0001103414,0.0009172212,0.0005524094,0.004726192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004717066,"threshold_uncertainty_score":0.01578009,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002950286","doi":"10.1016/j.cviu.2013.01.016","title":"A LSS-based registration of stereo thermal–visible videos of multiple people using belief propagation","year":2013,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":8,"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; Belief propagation; Computer science; Image registration; Computer graphics (images); Algorithm; Image (mathematics)","authors":[{"name":"Atousa Torabi","is_ca":true},{"name":"Guillaume-Alexandre Bilodeau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03782924075788956,"gpt":0.2842711752231392,"spread":0.2464419344652496,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000759803,0.0007612108,0.001048731,0.001075288,0.0004201917,0.0009136177,0.001262069,0.00108074,0.002299629],"category_scores_gemma":[0.002047765,0.000810285,0.001154378,0.001276659,0.0006750906,0.001275611,0.001458376,0.00133184,0.001425414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005225978,"about_ca_system_score_gemma":0.001346325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009234984,"about_ca_topic_score_gemma":0.01041726,"domain_scores_codex":[0.999312,0.000109965,0.00003020916,0.0002130835,0.000248004,0.00008665743],"domain_scores_gemma":[0.9994824,0.0001063802,0.00006792405,0.0001025977,0.0001907675,0.00004998486],"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":[0.0007607261,0.0002285381,0.001975418,0.0001712421,0.0001997297,0.0001714955,0.0002650075,0.219124,0.06481969,0.005474486,0.003430088,0.7033796],"study_design_scores_gemma":[0.00001290886,0.00004542558,0.000754291,0.000009922524,0.00002156963,0.00005696106,0.00002766707,0.9878957,0.008531055,0.001757173,0.0008697764,0.00001750571],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01365933,0.00008692089,0.9845523,0.00008279176,0.00005635735,0.00002919362,0.00005438178,0.0007382331,0.0007405548],"genre_scores_gemma":[0.4254764,0.0002156492,0.566556,0.000140723,0.0000951472,0.0001038463,0.0005777867,0.0002542983,0.006580148],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009234984,"threshold_uncertainty_score":0.01836246,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385687748","doi":"10.1016/j.cviu.2023.103803","title":"Improving sparse graph attention for feature matching by informative keypoints exploration","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pooling; Artificial intelligence; Pattern recognition (psychology); Matching (statistics); Feature (linguistics); Graph; Focus (optics); Mathematics; Theoretical computer science","authors":[{"name":"Xingyu Jiang","is_ca":false},{"name":"Shihua Zhang","is_ca":true},{"name":"Xiao–Ping Zhang","is_ca":true},{"name":"Jiayi Ma","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04132610778898841,"gpt":0.3054216512712523,"spread":0.2640955434822639,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000368538,0.001418572,0.002483763,0.002330368,0.0006139217,0.0007570988,0.002238832,0.00174573,0.006470435],"category_scores_gemma":[0.002842565,0.0006412306,0.001018255,0.002901622,0.0005863009,0.002277893,0.002118588,0.001481652,0.0018334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005442195,"about_ca_system_score_gemma":0.001065555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00905008,"about_ca_topic_score_gemma":0.01322988,"domain_scores_codex":[0.9993561,0.00009245246,0.00002536115,0.0002285459,0.0001888595,0.0001087258],"domain_scores_gemma":[0.9989806,0.0004928769,0.00007243436,0.0002264851,0.0001597507,0.00006794559],"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":[0.0007962467,0.000433037,0.00126667,0.0002526051,0.0001539818,0.0002296959,0.000130742,0.09942496,0.05333538,0.007278778,0.0156591,0.8210388],"study_design_scores_gemma":[0.00003517484,0.0001078314,0.0004070385,0.000007493119,0.00003418932,0.0001111555,0.00003173387,0.9818859,0.007073647,0.008891696,0.001402641,0.00001151796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03568132,0.0008781656,0.9575053,0.0002016242,0.0001136172,0.00007561093,0.0002234251,0.003745864,0.001575239],"genre_scores_gemma":[0.6482001,0.0007259776,0.3403928,0.0005887158,0.0002250492,0.0001312275,0.001665681,0.000797654,0.007272845],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00905008,"threshold_uncertainty_score":0.02164578,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4323322511","doi":"10.1016/j.cviu.2023.103664","title":"Weakly supervised multi-class semantic video segmentation for road scenes","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; National Research Foundation of Korea; Information Technology Research Centre; Ministry of Science, ICT and Future Planning","keywords":"Computer science; Segmentation; Artificial intelligence; Computer vision; Pixel; Feature (linguistics); Class (philosophy); Object (grammar); Key (lock); Computation; Pattern recognition (psychology); Image segmentation","authors":[{"name":"Mehwish Awan","is_ca":false},{"name":"Jitae Shin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0966125858195375,"gpt":0.351806053681381,"spread":0.2551934678618435,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007908873,0.001262373,0.001708423,0.002465412,0.0009400477,0.001442337,0.001649452,0.001692269,0.001998207],"category_scores_gemma":[0.001689595,0.0004933479,0.001470656,0.001600904,0.0008122675,0.001810738,0.001165445,0.001357924,0.001596303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009105231,"about_ca_system_score_gemma":0.001742743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0072156,"about_ca_topic_score_gemma":0.01615715,"domain_scores_codex":[0.9990487,0.0001336528,0.00004172318,0.0003883797,0.000194487,0.0001931136],"domain_scores_gemma":[0.9991376,0.0002010625,0.0001114578,0.0002177959,0.0002544954,0.00007747303],"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":[0.001351217,0.0006940707,0.0053223,0.0004164652,0.0002183798,0.0002381247,0.0002092839,0.09114067,0.126315,0.007973228,0.008960814,0.7571605],"study_design_scores_gemma":[0.00001468861,0.00008420952,0.002301963,0.00002029649,0.00004552039,0.0001091634,0.00008297104,0.9645845,0.02463982,0.006053475,0.002049532,0.00001385815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07712126,0.0004746722,0.9164695,0.0001725951,0.00006928622,0.0001319994,0.0006496594,0.002552783,0.002358325],"genre_scores_gemma":[0.610958,0.0004259704,0.3742732,0.000197147,0.0001586325,0.0001810331,0.005814626,0.0007275256,0.007263835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0072156,"threshold_uncertainty_score":0.0143472,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055007934","doi":"10.1016/j.cviu.2015.01.001","title":"A framework for estimating relative depth in video","year":2015,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Epipolar geometry; A priori and a posteriori; Computer science; Computer vision; Artificial intelligence; Frame (networking); Constraint (computer-aided design); Fundamental matrix (linear differential equation); Tracking (education); Algorithm; Mathematics; Image (mathematics)","authors":[{"name":"Richard Rzeszutek","is_ca":true},{"name":"Dimitrios Androutsos","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1024635207726565,"gpt":0.3679117615586517,"spread":0.2654482407859953,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000901375,0.001050584,0.001145302,0.001728089,0.0005422692,0.001490501,0.002400159,0.001539636,0.001873506],"category_scores_gemma":[0.002746922,0.0007865409,0.001225621,0.00178764,0.0007848779,0.002358866,0.002048655,0.001963678,0.0007869371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007983391,"about_ca_system_score_gemma":0.001225519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01415116,"about_ca_topic_score_gemma":0.01016983,"domain_scores_codex":[0.9992484,0.0001259204,0.00003353152,0.0001964978,0.0003104305,0.00008522897],"domain_scores_gemma":[0.9995314,0.0001406184,0.00005934419,0.00007978972,0.0001524302,0.0000364817],"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":[0.000164333,0.0001056026,0.0008743545,0.0002875323,0.0001268077,0.0001486584,0.0001905678,0.3194071,0.04013574,0.09779499,0.004187061,0.5365773],"study_design_scores_gemma":[0.00001119622,0.00006084521,0.0003254635,0.00002429109,0.0000278596,0.0001258385,0.00002484258,0.9709519,0.005450599,0.01898211,0.003984242,0.00003077319],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000859982,0.0001622177,0.9986325,0.00001954033,0.00001111172,0.000009144955,0.00002421045,0.0001264057,0.0001549925],"genre_scores_gemma":[0.09144719,0.001132768,0.9049287,0.00007166484,0.0001153566,0.000100342,0.0002844755,0.0001328625,0.001786596],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01415116,"threshold_uncertainty_score":0.02813756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}