{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":102,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":102,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"3957de1a4c68","filters":{"venue":"International Journal of Computer Vision"}},"results":[{"id":"W2151103935","doi":"10.1023/b:visi.0000029664.99615.94","title":"Distinctive Image Features from Scale-Invariant Keypoints","year":2004,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":55266,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Clutter; Affine transformation; Computer science; Cognitive neuroscience of visual object recognition; Computer vision; Invariant (physics); Hough transform; 3D single-object recognition; Scale-invariant feature transform; Feature extraction; Mathematics; Image (mathematics); Radar","retraction":null,"screen_n_in":null,"score":{"opus":0.003827422997516845,"gpt":0.2291002685932968,"spread":0.22527284559578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008167628,0.0001067855,0.0001385887,0.0001356301,0.0000242622,0.0001285687,0.0002545165,0.00005024378,0.00003269748],"category_scores_gemma":[0.00001331229,0.00009101976,0.00009546171,0.00005442045,0.00002153437,0.0002617332,0.00003730811,0.0001661477,0.00002329309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433183,"about_ca_system_score_gemma":0.00002137487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002092007,"about_ca_topic_score_gemma":0.000006992638,"domain_scores_codex":[0.9990767,0.00001848706,0.0003195708,0.00008715503,0.0004092634,0.00008877558],"domain_scores_gemma":[0.9993607,0.00004976856,0.0001051411,0.00007765751,0.0003368948,0.00006979987],"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":[0.0001036831,0.0001110361,0.0001661205,0.000005486478,0.0001829931,0.0003264895,0.0003928655,0.9598663,0.01230162,0.0008449435,0.002414422,0.02328398],"study_design_scores_gemma":[0.007851262,0.0008053505,0.1704838,0.001639626,0.0001043219,0.0009514456,0.00007998564,0.7067423,0.06960829,0.03632257,0.004561108,0.0008498534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1714287,0.00006331371,0.825494,0.0004795288,0.002178167,0.00003030651,0.00001281769,0.00002872376,0.0002844374],"genre_scores_gemma":[0.9408378,0.00004134146,0.05787215,0.0001340286,0.001069106,2.384608e-7,0.00002219767,0.00001701293,0.000006103325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7694091,"threshold_uncertainty_score":0.3711678,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999478155","doi":"10.1023/b:visi.0000022288.19776.77","title":"Efficient Graph-Based Image Segmentation","year":2004,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6198,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Science Foundation","keywords":"Image segmentation; Range segmentation; Segmentation; Pattern recognition (psychology); Artificial intelligence; Graph; Segmentation-based object categorization; Computer science; Minimum spanning tree-based segmentation; Scale-space segmentation; Connected-component labeling; Mathematics; Algorithm; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.008245873275068125,"gpt":0.3150820002371132,"spread":0.3068361269620451,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004850031,0.0001183693,0.0001421021,0.0004844327,0.00004398219,0.0002878529,0.001208653,0.00004028637,0.00003074914],"category_scores_gemma":[0.00003451726,0.00009951668,0.0001499152,0.0002129233,0.00005841059,0.0005082383,0.0001494435,0.0001673171,0.00002862835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001904897,"about_ca_system_score_gemma":0.0001385143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005364852,"about_ca_topic_score_gemma":3.031457e-7,"domain_scores_codex":[0.9978356,0.00006860991,0.0005681164,0.0001772577,0.001219958,0.0001304182],"domain_scores_gemma":[0.9983392,0.00008042147,0.0004566414,0.000178557,0.0008202904,0.0001249301],"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":[0.0001676604,0.001589502,0.0001156749,0.00002558369,0.0001789381,0.001231706,0.001124413,0.09481878,0.191792,0.008023568,0.004821243,0.696111],"study_design_scores_gemma":[0.007284361,0.001906127,0.00386867,0.0007229853,0.00002300871,0.0007605377,0.00002955678,0.1985994,0.7700717,0.01562306,0.0006290945,0.0004815022],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02032076,0.00002914937,0.9746338,0.003029103,0.001753111,0.000091102,0.000001321543,0.00007736126,0.00006433047],"genre_scores_gemma":[0.2614133,0.000007249773,0.7370275,0.001302013,0.0002357893,0.000001553739,0.00000309667,0.000006135914,0.000003366941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6956295,"threshold_uncertainty_score":0.4058172,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110764733","doi":"10.1007/s11263-007-0090-8","title":"LabelMe: A Database and Web-Based Tool for Image Annotation","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":4205,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Naval Research; Multidisciplinary University Research Initiative; National Geospatial-Intelligence Agency; National Science Foundation","keywords":"Computer science; Annotation; WordNet; Automatic image annotation; Object (grammar); Ground truth; Artificial intelligence; Variety (cybernetics); Image retrieval; Information retrieval; Image (mathematics); Cognitive neuroscience of visual object recognition; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.0115600212004847,"gpt":0.3456764815954525,"spread":0.3341164603949678,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000904926,0.00009819434,0.0001280068,0.0003077214,0.00005237404,0.0002151818,0.0006009986,0.00003435383,0.000002877425],"category_scores_gemma":[0.00009699734,0.00008373596,0.00007608074,0.0001223233,0.00003639465,0.001541943,0.0001453319,0.0001129445,0.000001694926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005076551,"about_ca_system_score_gemma":0.00006159512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.732627e-7,"about_ca_topic_score_gemma":4.260106e-7,"domain_scores_codex":[0.9988315,0.00002452248,0.0004193059,0.000162215,0.0004337987,0.0001286481],"domain_scores_gemma":[0.9981455,0.0003353159,0.0003175949,0.0001411032,0.0009955923,0.00006491339],"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":[0.0003432431,0.0001500699,0.0002361491,0.00001799717,0.00003487468,0.0002365246,0.00007446893,0.00004677384,0.04933739,0.007384428,0.003986086,0.938152],"study_design_scores_gemma":[0.009464905,0.003693207,0.01304406,0.0008921679,0.00003456667,0.001274482,0.00001264729,0.4730852,0.3272391,0.0269704,0.1435634,0.0007258712],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01759249,0.0001048481,0.9804872,0.0009581612,0.0006600268,0.0001105203,0.000008158467,0.00004434818,0.0000342233],"genre_scores_gemma":[0.2360451,0.00003719645,0.7628173,0.0007342271,0.0003409722,9.76563e-7,0.000006000741,0.000006524776,0.00001170366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9374261,"threshold_uncertainty_score":0.3414653,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2139047213","doi":"10.1007/s11263-007-0075-7","title":"Incremental Learning for Robust Visual Tracking","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3101,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Honda Research Institute, USA","keywords":"Computer science; Artificial intelligence; Tracking (education); Subspace topology; Computer vision; Representation (politics); Active appearance model; Principal component analysis; Pattern recognition (psychology); Eye tracking; Forgetting; Video tracking; Range (aeronautics); Object (grammar); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.03210245907793505,"gpt":0.3744627854844399,"spread":0.3423603264065048,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002852016,0.0001107188,0.0001770887,0.0003380679,0.00008486918,0.0002992575,0.0009198775,0.0000505094,0.000005881714],"category_scores_gemma":[0.00009460017,0.00009765982,0.000184869,0.000129668,0.0000193288,0.0008868171,0.000174955,0.0002311595,0.000004378018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000913077,"about_ca_system_score_gemma":0.00004709324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002796095,"about_ca_topic_score_gemma":0.000002164524,"domain_scores_codex":[0.9983312,0.00008266937,0.0005649367,0.0001711875,0.0006512151,0.000198777],"domain_scores_gemma":[0.9980605,0.0005409741,0.0004477596,0.00008668531,0.0007820068,0.00008212629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001342943,0.0001158177,0.003698545,0.000004801847,0.00008391163,0.0001257422,0.0002527789,0.008512973,0.00338441,0.001460825,0.0003454213,0.9818805],"study_design_scores_gemma":[0.005229582,0.003391676,0.1831437,0.0005553987,0.00002278643,0.002058253,0.00007597006,0.7362517,0.02746178,0.004656958,0.03652377,0.0006285042],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09432817,0.00006262348,0.901693,0.0004546409,0.003272872,0.00005312203,3.026188e-7,0.00003278243,0.0001025118],"genre_scores_gemma":[0.625546,0.000008683101,0.3731895,0.0001986142,0.001039793,2.707342e-7,0.000001218971,0.000006718237,0.000009206417],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.981252,"threshold_uncertainty_score":0.3982452,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2988916019","doi":"10.1007/s11263-019-01247-4","title":"Deep Learning for Generic Object Detection: A Survey","year":2019,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2776,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Oulun Yliopisto; National Natural Science Foundation of China","keywords":"Computer science; Object detection; Artificial intelligence; Deep learning; Field (mathematics); Representation (politics); Object (grammar); Context (archaeology); Feature (linguistics); Feature learning; Machine learning; Learning object; Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Geography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01823367031555728,"gpt":0.3036416907018827,"spread":0.2854080203863254,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004354201,0.0001045532,0.0001598701,0.0001977089,0.00006408443,0.0001647249,0.001112222,0.00004007921,0.000009185543],"category_scores_gemma":[0.00003798741,0.00009284267,0.0001340712,0.0002298764,0.00001266192,0.0006500165,0.0002091607,0.0001928804,0.00003502739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008426865,"about_ca_system_score_gemma":0.0000326393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002470294,"about_ca_topic_score_gemma":0.000004661978,"domain_scores_codex":[0.9987321,0.00009280875,0.0003946256,0.0002074763,0.0004329332,0.000140014],"domain_scores_gemma":[0.9979895,0.0004715857,0.0004348883,0.0001790939,0.0008593625,0.00006555356],"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":[0.0001223561,0.00007528369,0.001673338,0.000004458307,0.00007454799,0.00001265526,0.0001054955,0.2240762,0.004460838,0.001294059,0.0003798312,0.7677209],"study_design_scores_gemma":[0.000762417,0.0006193373,0.02847524,0.00003484274,0.000003499825,0.0002986819,0.000002305006,0.949137,0.001425526,0.001839847,0.01726849,0.0001328462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05862093,0.00009246796,0.9381599,0.0003566932,0.002565067,0.0001285756,5.341892e-7,0.00003411101,0.00004169315],"genre_scores_gemma":[0.8670601,0.00003530955,0.1319532,0.000235637,0.0006325651,0.000003715869,0.000003519433,0.00001005576,0.00006596272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8084391,"threshold_uncertainty_score":0.3786014,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2126060993","doi":"10.1007/s11263-006-0002-3","title":"Automatic Panoramic Image Stitching using Invariant Features","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2665,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Image stitching; Panorama; Artificial intelligence; Computer vision; Computer science; Invariant (physics); Matching (statistics); Orientation (vector space); Rotation (mathematics); Pattern recognition (psychology); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.009339479816242275,"gpt":0.3144595167045351,"spread":0.3051200368882928,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003619208,0.0001397091,0.0001989884,0.0003621576,0.00006639137,0.0005563427,0.001289146,0.00004825149,0.00001037448],"category_scores_gemma":[0.00003344365,0.0001136211,0.0001406228,0.0001791539,0.00003180857,0.002057379,0.0003501142,0.0002365811,0.000006236276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001352521,"about_ca_system_score_gemma":0.00007953893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002870791,"about_ca_topic_score_gemma":9.923376e-7,"domain_scores_codex":[0.9983677,0.00007214501,0.0005421105,0.0001724164,0.0006894812,0.0001561718],"domain_scores_gemma":[0.9985347,0.0001153015,0.0005131155,0.0001922449,0.0005918799,0.00005273591],"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":[0.00005381824,0.0003357581,0.0002887135,0.00001726513,0.0001089989,0.001682687,0.000231515,0.003677287,0.1000128,0.02040282,0.006095152,0.8670931],"study_design_scores_gemma":[0.001299542,0.0005666533,0.01775456,0.0008678814,0.00002305271,0.004414415,0.000008469582,0.8398236,0.0608897,0.07004416,0.003849494,0.0004584877],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07428827,0.0001638573,0.9236598,0.0005339494,0.001093418,0.0000552443,0.000001149024,0.00008276256,0.0001215762],"genre_scores_gemma":[0.4697511,0.00001466511,0.5294922,0.0001944184,0.0005281047,1.903309e-7,0.00000107905,0.000007026454,0.00001117953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8666347,"threshold_uncertainty_score":0.5364826,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2030536784","doi":"10.1023/b:visi.0000042934.15159.49","title":"Pictorial Structures for Object Recognition","year":2004,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2199,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Object (grammar); Artificial intelligence; Cognitive neuroscience of visual object recognition; Computer science; Computer vision; Image (mathematics); Pattern recognition (psychology); Active appearance model; 3D single-object recognition","retraction":null,"screen_n_in":null,"score":{"opus":0.01887257822172331,"gpt":0.3364786760714065,"spread":0.3176060978496832,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002361443,0.00009697919,0.0001386149,0.0002221778,0.00004316349,0.000206603,0.0008804226,0.00004864775,0.000004377018],"category_scores_gemma":[0.00006778732,0.00007897492,0.0001556646,0.00009363241,0.00001875212,0.0011821,0.0001254599,0.000125487,0.000003794724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001088624,"about_ca_system_score_gemma":0.00008150482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002035007,"about_ca_topic_score_gemma":3.41365e-7,"domain_scores_codex":[0.9988924,0.00002194602,0.0003757848,0.000142554,0.0004584984,0.0001087765],"domain_scores_gemma":[0.998424,0.0001012178,0.00032623,0.0001155684,0.0009778052,0.00005518024],"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":[0.0001955934,0.00007964768,0.000008423935,0.000004562859,0.00005387502,0.00005860545,0.0001519312,0.0003744731,0.003842329,0.01576498,0.001364199,0.9781014],"study_design_scores_gemma":[0.002652682,0.001888886,0.0009175911,0.0001981135,0.000009818089,0.0005593466,0.000003841992,0.001561585,0.1050227,0.8658638,0.02112027,0.0002013656],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006229209,0.00006410856,0.9878568,0.0007913745,0.004846103,0.00009481062,0.000004048531,0.00004899196,0.00006454137],"genre_scores_gemma":[0.4018739,0.00004106431,0.595556,0.0003352346,0.002178665,0.000001240493,0.000003769964,0.000006138632,0.000004073031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9779,"threshold_uncertainty_score":0.3220504,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2119300483","doi":"10.1007/s11263-006-7934-5","title":"Graph Cuts and Efficient N-D Image Segmentation","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1904,"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":"Segmentation; Cut; Image segmentation; Computer science; Artificial intelligence; Graph; Graph partition; Segmentation-based object categorization; Scale-space segmentation; Mathematics; Algorithm; Pattern recognition (psychology); Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.004883711073049776,"gpt":0.2907923018320399,"spread":0.2859085907589901,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003914639,0.00009670605,0.0001213674,0.0003507524,0.0000393351,0.0003281622,0.000625964,0.00003215728,0.00001677535],"category_scores_gemma":[0.00001454471,0.00008069628,0.00007048353,0.0001270149,0.00005578878,0.0007026026,0.0002014879,0.0001172839,0.000007789928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005791048,"about_ca_system_score_gemma":0.00003081253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001051052,"about_ca_topic_score_gemma":5.147807e-7,"domain_scores_codex":[0.998382,0.00006794229,0.0004808151,0.0001565952,0.0008101353,0.0001024533],"domain_scores_gemma":[0.9988475,0.00008973611,0.0003575287,0.0001108252,0.0005207721,0.00007357584],"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":[0.00006710381,0.0005606221,0.000660587,0.00001728924,0.00008520474,0.00049378,0.0005232713,0.001398308,0.1428287,0.008712319,0.03279069,0.8118621],"study_design_scores_gemma":[0.00705569,0.002157748,0.0632174,0.0008075425,0.00004891646,0.00395701,0.00006074159,0.44288,0.4275186,0.04644655,0.004919536,0.0009302964],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04138239,0.00008105438,0.955906,0.001309571,0.001019913,0.00006698618,0.000001090118,0.00004639797,0.0001866295],"genre_scores_gemma":[0.3076833,0.00003386745,0.6913729,0.0004876434,0.00038628,0.000001171123,0.000003675018,0.000005734038,0.00002545004],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8109318,"threshold_uncertainty_score":0.3290699,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2507296351","doi":"10.1007/s11263-018-1140-0","title":"Semantic Understanding of Scenes Through the ADE20K Dataset","year":2018,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1667,"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":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Samsung; National Science Foundation","keywords":"Computer science; Normalization (sociology); Parsing; Segmentation; Artificial intelligence; Variety (cybernetics); Object (grammar); Pixel; Context (archaeology); Pattern recognition (psychology); Computer vision; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.05449105503919781,"gpt":0.3589244139169077,"spread":0.3044333588777099,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002319145,0.00008713682,0.0001282172,0.0000981168,0.00009660288,0.0001087268,0.001963418,0.00002498383,0.0000116632],"category_scores_gemma":[0.00001603799,0.00005751299,0.00007196005,0.0002213941,0.0001432902,0.0008448106,0.0004454056,0.0001298079,0.00001378005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006318501,"about_ca_system_score_gemma":0.00003829635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003824347,"about_ca_topic_score_gemma":0.000002533511,"domain_scores_codex":[0.9986808,0.00005689774,0.0004378464,0.0001410018,0.0005731184,0.0001103024],"domain_scores_gemma":[0.9984736,0.000257677,0.0005117462,0.0003154278,0.0004060161,0.00003557027],"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":[0.0002364547,0.0004693929,0.0005345511,0.00001960697,0.0004257476,0.0001250145,0.002634394,0.01451747,0.006147951,0.6662045,0.1659968,0.1426882],"study_design_scores_gemma":[0.002314758,0.001523065,0.004013892,0.0007282593,0.00004366555,0.001872357,0.0001165688,0.4714196,0.01236776,0.335868,0.1693011,0.00043099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005051653,0.00007345829,0.9871348,0.006067066,0.00148191,0.00005839785,0.00001195885,0.00001243341,0.0001083626],"genre_scores_gemma":[0.9090503,0.00005700469,0.08922163,0.0007234388,0.0009280971,5.379886e-7,0.000007123556,0.000005475929,0.000006423328],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9039986,"threshold_uncertainty_score":0.3648551,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2099333815","doi":"10.1007/s11263-009-0273-6","title":"HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":1175,"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 New Brunswick","funders":"","keywords":"Initialization; Computer science; Artificial intelligence; Computer vision; Motion estimation; Pose; Motion (physics); Motion capture; Context (archaeology); Set (abstract data type); Baseline (sea); Resampling; Match moving; Ground truth; Structure from motion; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.02119264791420594,"gpt":0.3358728710878643,"spread":0.3146802231736583,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001337971,0.0001105132,0.0001792207,0.000312177,0.00007961965,0.0001585731,0.0002353103,0.00006274546,0.00001712657],"category_scores_gemma":[0.00004017563,0.00009758951,0.00005976748,0.00007708337,0.00002643326,0.001002506,0.00005509476,0.0001072221,0.000001058887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006284826,"about_ca_system_score_gemma":0.00002827431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004736786,"about_ca_topic_score_gemma":0.000001613545,"domain_scores_codex":[0.9983765,0.0001444696,0.0005262896,0.000196099,0.0006667273,0.00008987152],"domain_scores_gemma":[0.9978713,0.00008054863,0.0004961008,0.0001166602,0.001371595,0.00006375156],"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":[0.00003128627,0.0001691232,0.00001532836,0.000007315029,0.00005166292,0.000007418775,0.0001507232,0.0007093528,0.005042693,0.0005844259,0.001449822,0.9917809],"study_design_scores_gemma":[0.003596997,0.0005216305,0.008716076,0.0001781481,0.00006735629,0.0002561987,0.000008865184,0.9730827,0.003047033,0.009910139,0.000498637,0.0001162652],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1504804,0.0001282598,0.847966,0.0007812004,0.0004161735,0.0001671275,0.00004062719,0.00001239825,0.000007800059],"genre_scores_gemma":[0.9533789,0.00003103878,0.04570537,0.0002488488,0.00036924,0.000001589592,0.0002573485,0.000004499708,0.000003147309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9916646,"threshold_uncertainty_score":0.3979585,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1498915505","doi":"10.1023/a:1011183429707","title":"Face Recognition Using the Discrete Cosine Transform","year":2001,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":429,"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":"Discrete cosine transform; Normalization (sociology); Artificial intelligence; Robustness (evolution); Computer science; Facial recognition system; Pattern recognition (psychology); Feature extraction; Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.02842829422288995,"gpt":0.3140763903320113,"spread":0.2856480961091213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003653911,0.0001075493,0.0001238348,0.0001814015,0.00009677345,0.0003060725,0.0009905488,0.00004575736,0.00003958241],"category_scores_gemma":[0.00001250782,0.00006834643,0.0001229736,0.0001637183,0.00002969623,0.001317906,0.0001195877,0.0001952302,0.00002692914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005822113,"about_ca_system_score_gemma":0.0000412583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007808501,"about_ca_topic_score_gemma":0.000001413332,"domain_scores_codex":[0.9985705,0.00007640953,0.0004203947,0.0001349425,0.0006666535,0.0001310823],"domain_scores_gemma":[0.9988518,0.00009806642,0.0002956264,0.0001399126,0.0005476842,0.0000668658],"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":[0.0001517302,0.000123247,0.00006654504,0.000003348187,0.0000781629,0.0001708275,0.0006478533,0.004459004,0.005605749,0.0002058129,0.003241545,0.9852462],"study_design_scores_gemma":[0.002074538,0.0007460451,0.001706043,0.0009574544,0.00002761228,0.004439997,0.00009130784,0.9293692,0.009695189,0.01404473,0.03651999,0.0003279567],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1019775,0.00006457005,0.888983,0.006929953,0.001729128,0.00006919652,0.000002650388,0.00002072993,0.0002233748],"genre_scores_gemma":[0.9344623,0.0002032815,0.06344756,0.001008702,0.0008334335,9.595902e-7,0.000006477452,0.000008229742,0.0000290328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9849182,"threshold_uncertainty_score":0.2951464,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2146022472","doi":"10.1007/s11263-006-8614-1","title":"Semantic Modeling of Natural Scenes for Content-Based Image Retrieval","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":388,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Image retrieval; Semantic similarity; Categorization; Artificial intelligence; Ranking (information retrieval); Explicit semantic analysis; Information retrieval; Representation (politics); Rank (graph theory); Content-based image retrieval; Similarity (geometry); Visual Word; Pattern recognition (psychology); Image (mathematics); Automatic image annotation; Semantic computing; Mathematics; Semantic technology; Semantic Web","retraction":null,"screen_n_in":null,"score":{"opus":0.02323227829819488,"gpt":0.2984414559853364,"spread":0.2752091776871415,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004575964,0.0001108872,0.0002104821,0.00030534,0.00003892792,0.0001688454,0.001039339,0.00004518145,0.000003036115],"category_scores_gemma":[0.00004484082,0.00008906096,0.0002427378,0.000141608,0.00004011136,0.0006710739,0.00009662593,0.0001157041,0.000001541733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006293891,"about_ca_system_score_gemma":0.0000924811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000961782,"about_ca_topic_score_gemma":4.063252e-7,"domain_scores_codex":[0.9983302,0.00004149884,0.0006922487,0.0001540523,0.000661344,0.0001206135],"domain_scores_gemma":[0.9967119,0.0001402883,0.0005523308,0.0001561655,0.002403449,0.00003583142],"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":[0.001182861,0.0008419566,0.0003184812,0.0001022433,0.0001639952,0.00008822619,0.0001062453,0.004114045,0.8659737,0.03225368,0.001306904,0.09354772],"study_design_scores_gemma":[0.0007825828,0.0002348484,0.0004180597,0.0001588624,0.000007116778,0.00005208324,0.000003164471,0.7438281,0.251402,0.002776848,0.0002531527,0.00008315212],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03292788,0.0001677228,0.9638473,0.001701853,0.001192105,0.000101043,0.000003826814,0.00003655809,0.00002166835],"genre_scores_gemma":[0.6764003,0.000007648749,0.3231659,0.0001012668,0.0002933748,4.986392e-7,0.000004211553,0.000005553776,0.00002124807],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.739714,"threshold_uncertainty_score":0.36318,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2004586880","doi":"10.1007/s11263-007-0118-0","title":"Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields","year":2008,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":377,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Canadian Institute for Advanced Research","keywords":"Pooling; Pattern recognition (psychology); Artificial intelligence; Computer science; Object (grammar); Feature (linguistics); Position (finance); Matching (statistics); Cognitive neuroscience of visual object recognition; Class (philosophy); Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03865115523951647,"gpt":0.2939910873716738,"spread":0.2553399321321574,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001822989,0.0001045047,0.0001301198,0.0003285861,0.0001061435,0.0001410761,0.000277425,0.00006528642,0.00000937543],"category_scores_gemma":[0.0000186298,0.00008172553,0.00005855649,0.0002076372,0.0000452445,0.0009576621,0.00008545658,0.0001707435,0.000004917315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006206792,"about_ca_system_score_gemma":0.0000488875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001354669,"about_ca_topic_score_gemma":0.000005026339,"domain_scores_codex":[0.9988158,0.00009557606,0.0003030977,0.0001697473,0.0005229856,0.0000928116],"domain_scores_gemma":[0.998661,0.00005218306,0.0003192811,0.00008558797,0.0008149575,0.00006698449],"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":[0.001597311,0.0009770201,0.01008075,0.00003421124,0.0005421842,0.001302554,0.007239413,0.04471041,0.007679441,0.001341614,0.006099695,0.9183954],"study_design_scores_gemma":[0.003511599,0.004012668,0.07785964,0.0007880864,0.00003872999,0.01618264,0.00009172713,0.8835236,0.007701782,0.003629418,0.002147093,0.0005130618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3811273,0.00002763702,0.6177726,0.0002174033,0.000747135,0.0000381678,7.238499e-7,0.00001912667,0.00004995326],"genre_scores_gemma":[0.9595682,0.0001084346,0.03948484,0.0004868392,0.0003213992,4.766529e-7,0.000004531269,0.000006152853,0.00001913115],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9178823,"threshold_uncertainty_score":0.333267,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2292288263","doi":"10.1007/s11263-017-1013-y","title":"Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos","year":2017,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":365,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Moment (physics); Action (physics); Action recognition; Sequence (biology); Frame (networking); Pattern recognition (psychology); Ranging; Sequence labeling; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.04994238637893542,"gpt":0.353276334976413,"spread":0.3033339485974775,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002295232,0.00005689435,0.0001152271,0.0002443346,0.00005980142,0.000173031,0.0006873349,0.00002718339,0.00008365667],"category_scores_gemma":[0.00001741164,0.00005118857,0.00007245086,0.00003455368,0.0000218434,0.0007330199,0.0001335262,0.000110073,0.00003607525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000635923,"about_ca_system_score_gemma":0.00004191793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006034701,"about_ca_topic_score_gemma":0.000004947985,"domain_scores_codex":[0.9991053,0.00003043975,0.0003465963,0.00008385401,0.0003684275,0.00006540309],"domain_scores_gemma":[0.9988905,0.00004689597,0.0004572478,0.0001519657,0.000419903,0.0000335256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001814841,0.0009429416,0.007054058,0.00002868149,0.0003222597,0.000416797,0.0007684885,0.003150942,0.02328134,0.01187051,0.02140778,0.9305747],"study_design_scores_gemma":[0.006681409,0.001020293,0.4140552,0.001933092,0.00004470173,0.001133933,0.00005083923,0.4038622,0.01399831,0.02246195,0.1340946,0.0006633524],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2930673,0.00003840039,0.7007853,0.001642004,0.002837229,0.00006121281,0.00000374466,0.00001369302,0.001551121],"genre_scores_gemma":[0.9792399,0.00005008977,0.02021632,0.0001627146,0.0002761124,4.954672e-7,0.0000017061,0.00000324428,0.0000493843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9299114,"threshold_uncertainty_score":0.2087409,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3207758636","doi":"10.1007/s11263-021-01531-2","title":"Rescaling Egocentric Vision: Collection, Pipeline and Challenges for EPIC-KITCHENS-100","year":2021,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":364,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; University of Toronto; Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Computer science; Pipeline (software); Artificial intelligence; Action (physics); Task (project management); Anticipation (artificial intelligence); Adaptation (eye); EPIC; Action recognition; Pattern recognition (psychology); Computer vision; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.031786217904964,"gpt":0.3117536532749697,"spread":0.2799674353700057,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004274929,0.0001106468,0.0001850807,0.000319278,0.0001154412,0.0003181728,0.0003731217,0.00006187506,0.00003125642],"category_scores_gemma":[0.00006286278,0.000100331,0.0001293858,0.0001604951,0.0000183097,0.0007473679,0.0001721423,0.0001418942,0.000007037313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007061462,"about_ca_system_score_gemma":0.00008804622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001097375,"about_ca_topic_score_gemma":0.000002769198,"domain_scores_codex":[0.9985873,0.0000804333,0.0004970428,0.0002358041,0.000472763,0.0001266456],"domain_scores_gemma":[0.9975312,0.0002524052,0.0003129725,0.0001245943,0.001681636,0.0000971942],"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":[0.0001175459,0.0003169884,0.00005695279,0.00003316584,0.0001138647,0.0002235695,0.0005320786,0.0003581296,0.0009246318,0.00847641,0.01134544,0.9775012],"study_design_scores_gemma":[0.006692318,0.001739852,0.01078076,0.001131039,0.00006203519,0.006767965,0.000145521,0.546358,0.01703907,0.04530368,0.3633702,0.0006095172],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01507695,0.002179574,0.9633125,0.0158832,0.003220232,0.00007354817,0.000002532219,0.00002974418,0.0002216907],"genre_scores_gemma":[0.8013774,0.007342633,0.1856706,0.001574228,0.003667074,0.000004146611,0.00001400935,0.00002123851,0.0003287345],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9768917,"threshold_uncertainty_score":0.4091378,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1998270967","doi":"10.1007/s11263-009-0243-z","title":"Entropy Minimization for Shadow Removal","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":355,"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":"Artificial intelligence; Computer vision; Pixel; RGB color model; Invariant (physics); Mathematics; Computer science; Entropy (arrow of time); Grayscale; Pattern recognition (psychology); Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01077352914825799,"gpt":0.3196221034701442,"spread":0.3088485743218862,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022499,0.00009829101,0.0001445519,0.0002650372,0.00004916505,0.0002479938,0.001032775,0.00003011677,0.000008550733],"category_scores_gemma":[0.00004857787,0.00008257217,0.0001482233,0.0001087963,0.00001204712,0.001220487,0.00008773307,0.00009789058,0.000006537055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006430061,"about_ca_system_score_gemma":0.00004609347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.810549e-7,"about_ca_topic_score_gemma":5.771816e-8,"domain_scores_codex":[0.9987233,0.00002985732,0.0004409993,0.0001603556,0.0005166264,0.000128844],"domain_scores_gemma":[0.9985026,0.00009120508,0.0003645991,0.0001414562,0.0008213241,0.00007881282],"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":[0.00008820557,0.0001076535,0.0000183585,0.000001304264,0.00001892036,0.0000746288,0.00012118,0.002144307,0.00274138,0.03052853,0.007016996,0.9571385],"study_design_scores_gemma":[0.001622216,0.0007833836,0.001785222,0.0001414639,0.000004882832,0.0008515553,0.000004311974,0.8737279,0.001868706,0.03867261,0.08039718,0.0001405948],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001843743,0.00009215553,0.9874446,0.007881422,0.002483339,0.00006763775,9.973737e-7,0.00003052239,0.0001555879],"genre_scores_gemma":[0.2439471,0.00003773655,0.7529252,0.002180643,0.0008504445,3.112921e-7,0.000002781818,0.000004680875,0.00005106205],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9569979,"threshold_uncertainty_score":0.3367195,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2024276620","doi":"10.1007/s11263-011-0474-7","title":"Energy-Based Geometric Multi-model Fitting","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":314,"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":"RANSAC; Mathematics; Geometric data analysis; Regularization (linguistics); Algorithm; Mathematical optimization; Outlier; Data point; Computer science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.02462600032141308,"gpt":0.2704007078274928,"spread":0.2457747075060797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000209611,0.0000720876,0.00008448531,0.00017555,0.00004227912,0.0000349014,0.0003919048,0.00003340287,0.0001462139],"category_scores_gemma":[0.0000115543,0.00005995542,0.00009501797,0.0001490128,0.00003965189,0.00016609,0.00009574289,0.00009485371,0.00005278748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008619393,"about_ca_system_score_gemma":0.00001582491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005412211,"about_ca_topic_score_gemma":0.000003448692,"domain_scores_codex":[0.9990586,0.00002372837,0.0002919258,0.0001092213,0.0004267422,0.00008980231],"domain_scores_gemma":[0.999456,0.00004117765,0.0002478895,0.00009991789,0.00008399961,0.00007097637],"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":[0.00008177174,0.0005633727,0.004137474,0.000001581897,0.00004884763,0.00006383874,0.0003981686,0.09411592,0.005943349,0.0002064341,0.00673663,0.8877026],"study_design_scores_gemma":[0.0005157223,0.000106748,0.02155524,0.00003599713,0.000007520456,0.0000752392,0.00000449992,0.968272,0.003747475,0.0005507188,0.005037679,0.00009111263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1144473,0.0000108279,0.883203,0.0001854395,0.0003622297,0.00001721238,9.02852e-7,0.0000111942,0.001761928],"genre_scores_gemma":[0.7426068,0.000004667446,0.2568614,0.0003433579,0.000108846,1.089103e-7,0.000001247544,0.000005888754,0.00006767288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8876115,"threshold_uncertainty_score":0.2444911,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3010242131","doi":"10.1007/s11263-020-01385-0","title":"Image Matching Across Wide Baselines: From Paper to Practice","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":300,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Benchmarking; Benchmark (surveying); Embedding; Matching (statistics); Modular design; Code (set theory); Image (mathematics); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.01408368424989498,"gpt":0.3606898845706324,"spread":0.3466062003207375,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004077521,0.0001449302,0.0002093923,0.00008503976,0.00005225635,0.000559578,0.001770631,0.00004134715,0.00002483605],"category_scores_gemma":[0.0004843805,0.0001220495,0.0001347893,0.0002081109,0.00001792599,0.004136327,0.0007754725,0.0002871985,0.00009709562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006167701,"about_ca_system_score_gemma":0.00005097031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001797711,"about_ca_topic_score_gemma":6.05846e-7,"domain_scores_codex":[0.9980645,0.00009226492,0.0005785326,0.0002644965,0.0008361508,0.00016403],"domain_scores_gemma":[0.9974001,0.0005811944,0.0004099332,0.0001974148,0.001196002,0.000215337],"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":[0.0006306622,0.0002299283,0.00009504667,0.000008538747,0.0001784009,0.001962618,0.0062962,0.001270876,0.05706513,0.0007545913,0.03923929,0.8922687],"study_design_scores_gemma":[0.001896738,0.001882971,0.002107699,0.0006295458,0.00002390262,0.0005981454,0.0002210145,0.05363681,0.04376842,0.01846631,0.8761238,0.000644681],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00670843,0.0001034963,0.9545702,0.03719379,0.001196409,0.00006833329,0.000009971066,0.00008155857,0.00006784501],"genre_scores_gemma":[0.1073021,0.00007403194,0.8599312,0.03135629,0.0013137,7.097976e-7,0.000002999072,0.00001219585,0.000006764071],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.891624,"threshold_uncertainty_score":0.5396023,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1570514300","doi":"10.1023/a:1016376116653","title":"Hamilton-Jacobi Skeletons","year":2002,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Topological and Geometric Data Analysis","field":"Computer Science","cited_by":286,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Army Research Office; Air Force Office of Scientific Research; Natural Sciences and Engineering Research Council of Canada","keywords":"Eikonal equation; Mathematics; Gravitational singularity; Algorithm; Vector field; Image processing; Computer science; Artificial intelligence; Applied mathematics; Mathematical analysis; Geometry; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01543913469100953,"gpt":0.2710607166309451,"spread":0.2556215819399356,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002756567,0.000101802,0.0001911976,0.0005621068,0.00004790603,0.0002773727,0.002179488,0.000045332,0.0002668385],"category_scores_gemma":[0.00003930764,0.00007338943,0.0002193807,0.0004538503,0.00003431834,0.0008164287,0.0003182926,0.0001772593,0.000157883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004936816,"about_ca_system_score_gemma":0.00001059362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004885207,"about_ca_topic_score_gemma":4.817312e-7,"domain_scores_codex":[0.998391,0.00005862038,0.0004591594,0.0001750536,0.0007727593,0.000143433],"domain_scores_gemma":[0.9987625,0.0001767525,0.000295629,0.0002329246,0.0004130787,0.0001190742],"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":[0.000008996336,0.0004233969,0.0003854333,0.000001502959,0.0002109058,0.000532191,0.0002835887,0.001506868,0.0002043172,0.01487768,0.0565047,0.9250605],"study_design_scores_gemma":[0.001257834,0.0009817597,0.007867908,0.00007455522,0.00002472837,0.001313278,0.000009844228,0.4192359,0.00045959,0.008582793,0.5598717,0.0003201415],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0184056,0.0002639421,0.9728463,0.005147972,0.002028606,0.0000223398,0.000003336008,0.00003168391,0.001250199],"genre_scores_gemma":[0.9145252,0.0002939731,0.08241046,0.001265753,0.0009784795,4.099303e-7,0.000002158728,0.000004389265,0.000519219],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9247403,"threshold_uncertainty_score":0.4050066,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3135485589","doi":"10.1007/s11263-022-01606-8","title":"Countering Malicious DeepFakes: Survey, Battleground, and Horizon","year":2022,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":185,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; National Research Foundation Singapore; National Satellite of Excellence in Trustworthy Software Systems, National University of Singapore; National Postdoctoral Program for Innovative Talents; National Research Foundation; Natural Science Foundation of Hubei Province; Nvidia","keywords":"Computer science; Misinformation; Categorization; Data science; Limiting; Artificial intelligence; Computer security","retraction":null,"screen_n_in":null,"score":{"opus":0.01122297621897268,"gpt":0.2578077704744552,"spread":0.2465847942554825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001014947,0.0001057563,0.000165557,0.0002009206,0.0001424437,0.0003542338,0.000999252,0.00001931358,0.00003159181],"category_scores_gemma":[0.00002462553,0.00009577215,0.00007583158,0.0001247122,0.00002615666,0.0006717757,0.0007698049,0.0001947894,0.000002653246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021853,"about_ca_system_score_gemma":0.00004413486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003289184,"about_ca_topic_score_gemma":0.000006005213,"domain_scores_codex":[0.9983674,0.0002674113,0.0003953803,0.0001800051,0.0006648873,0.0001249301],"domain_scores_gemma":[0.9989036,0.0002062812,0.0003240218,0.0001329536,0.0003646783,0.00006846091],"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":[0.0003541565,0.000351178,0.005184389,0.000007239397,0.0003697764,0.000573915,0.001174128,0.07749646,0.004984866,0.00345484,0.01551038,0.8905387],"study_design_scores_gemma":[0.001752134,0.002761308,0.04834738,0.00008321418,0.00001530808,0.002932665,0.00006583692,0.8711752,0.0006284327,0.00170087,0.07013921,0.0003984085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1081694,0.0002169304,0.885599,0.0007960235,0.005103396,0.00003721922,0.000005707242,0.00001231383,0.00006006372],"genre_scores_gemma":[0.9628352,0.00005285171,0.03598735,0.0003508842,0.0007392521,0.000001092069,0.000003200762,0.000007435005,0.00002273275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8901402,"threshold_uncertainty_score":0.3905475,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2060223645","doi":"10.1007/s11263-009-0251-z","title":"Benchmarking Image Segmentation Algorithms","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":184,"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 New Brunswick","funders":"","keywords":"Benchmarking; Segmentation; Image segmentation; Segmentation-based object categorization; Scale-space segmentation; Artificial intelligence; Benchmark (surveying); Computer science; Ground truth; Pattern recognition (psychology); Precision and recall; Minimum spanning tree-based segmentation; Complement (music); Algorithm; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.009640851050672892,"gpt":0.3366817382973911,"spread":0.3270408872467181,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003589157,0.0001148693,0.0001495252,0.0003003697,0.00004696259,0.0003401225,0.001100958,0.0000377847,0.00001309056],"category_scores_gemma":[0.00001986736,0.00009735455,0.0001274398,0.0001688136,0.00001858556,0.002379709,0.0001444548,0.0001760208,0.000009069953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009900764,"about_ca_system_score_gemma":0.00003868243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.756815e-7,"about_ca_topic_score_gemma":6.182438e-8,"domain_scores_codex":[0.9984774,0.00004682225,0.0004654165,0.0001670504,0.0007130026,0.0001303056],"domain_scores_gemma":[0.998561,0.00006715195,0.0004099606,0.0001542225,0.0007350297,0.00007264226],"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":[0.00002359619,0.00007981337,0.00002202787,0.000001018787,0.00001817759,0.0001879055,0.0001174097,0.00009253372,0.01531741,0.001526836,0.001832769,0.9807805],"study_design_scores_gemma":[0.003721153,0.007122693,0.02601996,0.0009345375,0.00003045648,0.003725861,0.00002790809,0.3530314,0.4675198,0.09889115,0.03803089,0.0009441457],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003607967,0.00008933101,0.9926968,0.001836561,0.001387591,0.00005629119,7.60064e-7,0.00005990127,0.00026476],"genre_scores_gemma":[0.2168669,0.0001329864,0.7810909,0.0009716203,0.0009152844,3.005916e-7,0.000002407665,0.000004332749,0.00001519925],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9798363,"threshold_uncertainty_score":0.3970003,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400767051","doi":"10.1007/s11263-024-02181-w","title":"A Comprehensive Survey on Test-Time Adaptation Under Distribution Shifts","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":179,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Adaptation (eye); Pattern recognition (psychology); Distribution (mathematics); Test (biology); Statistics; Artificial intelligence; Computer science; Mathematics; Psychology; Biology; Neuroscience","retraction":null,"screen_n_in":null,"score":{"opus":0.01785575816386711,"gpt":0.3246482199964133,"spread":0.3067924618325462,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003140187,0.0001153803,0.0001099743,0.0001431978,0.00002965881,0.0001512526,0.0002893633,0.0001006554,0.00002856314],"category_scores_gemma":[0.00006618316,0.000102426,0.0001310491,0.0001009579,0.00002924531,0.0000158225,0.0001121744,0.0002075477,0.00005437438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001102117,"about_ca_system_score_gemma":0.0001489774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001105116,"about_ca_topic_score_gemma":0.000003625528,"domain_scores_codex":[0.9986432,0.0001256088,0.0002927906,0.000203105,0.000614777,0.000120545],"domain_scores_gemma":[0.9987944,0.0001375535,0.0001051367,0.0001150008,0.0007768639,0.00007109677],"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":[0.001624417,0.000311432,0.00001889878,0.00002119694,0.0005040456,0.0002722417,0.00007160578,0.04331077,0.8028445,0.0007727573,0.04884791,0.1014002],"study_design_scores_gemma":[0.004535499,0.01186006,0.06733475,0.001596143,0.00008264259,0.001102853,0.00003812603,0.3461362,0.3765782,0.00286702,0.1869147,0.0009538117],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.166226,0.0004405165,0.829659,0.001384654,0.001931465,0.0001120941,0.0001609143,0.00001429969,0.00007100873],"genre_scores_gemma":[0.997263,0.0001232923,0.0007643888,0.0004220817,0.0008170707,0.000001211089,0.0004913701,0.00001932242,0.00009826364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.831037,"threshold_uncertainty_score":0.417681,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2157431410","doi":"10.1007/s11263-006-0032-x","title":"A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":176,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University; University of Calgary","funders":"National Science Council; University of Kentucky; National Science Foundation","keywords":"Computer science; Graphics; Matching (statistics); Constraint (computer-aided design); Artificial intelligence; Computation; Process (computing); Time constraint; Pixel; Stereopsis; Speedup; Computer graphics; Computer vision; Algorithm; Computer graphics (images); Mathematics; Parallel computing","retraction":null,"screen_n_in":null,"score":{"opus":0.03976144217590096,"gpt":0.3290448712590217,"spread":0.2892834290831208,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000950411,0.0001633194,0.0002318759,0.0006691094,0.00005317908,0.0002287498,0.001027282,0.00003321609,0.000005751695],"category_scores_gemma":[0.00001374564,0.0001279687,0.00008189725,0.0001892549,0.00001685584,0.001157407,0.0002576224,0.0002805591,0.00001885019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189162,"about_ca_system_score_gemma":0.00002643989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004142743,"about_ca_topic_score_gemma":0.000003514443,"domain_scores_codex":[0.9977991,0.00009475016,0.000697298,0.000242472,0.0009700462,0.0001963552],"domain_scores_gemma":[0.9988173,0.0002224329,0.0004516095,0.0002129061,0.0002051344,0.0000906552],"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":[0.0002433693,0.001004783,0.02070623,0.000004390986,0.00003872138,0.0002223398,0.002852048,0.006038995,0.002421679,0.0004023959,0.00004939979,0.9660156],"study_design_scores_gemma":[0.002803778,0.001267089,0.4462259,0.0006112256,0.000004293806,0.0001685785,0.0001511546,0.5453936,0.002453781,0.0005538317,0.0001410514,0.000225682],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.559342,0.000004278115,0.4395446,0.000210538,0.0006196883,0.000109417,2.214647e-7,0.00001802029,0.0001512236],"genre_scores_gemma":[0.9703385,0.00001231455,0.02923848,0.0001057081,0.0002705891,0.000001185357,0.000001414023,0.000009921167,0.00002189923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.96579,"threshold_uncertainty_score":0.5218412,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2163387321","doi":"10.1023/a:1026135101267","title":"Multiscale Medial Loci and Their Properties","year":2003,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":175,"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":"Medial axis; Geometry; Boundary (topology); Mathematics; Object (grammar); Voronoi diagram; Computer science; Artificial intelligence; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.01557043381905846,"gpt":0.2618458004855453,"spread":0.2462753666664869,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000342749,0.0000849975,0.0001168734,0.0001414427,0.00003893738,0.0002151527,0.0006003331,0.00003631378,0.000008516525],"category_scores_gemma":[0.00003939421,0.00005486752,0.0000641354,0.00007454659,0.00005001499,0.0006378413,0.0001170259,0.0001180695,0.000004904914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003192134,"about_ca_system_score_gemma":0.00004697577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001107935,"about_ca_topic_score_gemma":2.073912e-7,"domain_scores_codex":[0.9990901,0.00007382673,0.0002991265,0.000117252,0.0003375901,0.0000821069],"domain_scores_gemma":[0.9990731,0.00005280843,0.0001991503,0.0001073343,0.0005045386,0.00006305872],"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":[0.00006149441,0.0002504519,0.0004226796,0.00001012119,0.00008622471,0.00006725368,0.001590062,0.00002463311,0.05120821,0.02947073,0.002777305,0.9140308],"study_design_scores_gemma":[0.002403399,0.001301535,0.008012692,0.0003863757,0.00001052324,0.003024337,0.00007970824,0.1214398,0.6715647,0.02584867,0.1654023,0.0005259701],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0294194,0.000326928,0.9674416,0.001642278,0.0009029183,0.000042487,5.474059e-7,0.00003108295,0.0001927461],"genre_scores_gemma":[0.9292687,0.0001242646,0.07006902,0.0002638458,0.0002210132,6.983151e-7,3.655724e-7,0.000004155222,0.00004794938],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9135048,"threshold_uncertainty_score":0.2237432,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2080622120","doi":"10.1007/s11263-010-0406-y","title":"Global Minimization for Continuous Multiphase Partitioning Problems Using a Dual Approach","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Norges Forskningsråd; Ministry of Education, India; National Science Foundation","keywords":"Smoothing; Mathematical optimization; Entropy maximization; Mathematics; Maximization; Minification; Potts model; Relaxation (psychology); Dual (grammatical number); Thresholding; Computer science; Algorithm; Principle of maximum entropy; Artificial intelligence; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.02105771506408064,"gpt":0.3430672733782631,"spread":0.3220095583141825,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005372661,0.0001153238,0.000173295,0.0001781789,0.0000690457,0.0003954584,0.0007389853,0.00007132177,0.00001121582],"category_scores_gemma":[0.0001167999,0.0001015633,0.0001229283,0.0001475957,0.00005098808,0.001111525,0.0001595922,0.0001544006,0.00000138031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007522388,"about_ca_system_score_gemma":0.0001022488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005847595,"about_ca_topic_score_gemma":0.000001146106,"domain_scores_codex":[0.9983274,0.00005561251,0.0006039905,0.0001996081,0.0006667775,0.0001466137],"domain_scores_gemma":[0.9979001,0.0001030527,0.0005636535,0.0001436637,0.001169878,0.0001196727],"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":[0.0001964334,0.001395329,0.001429836,0.00004774711,0.0002409589,0.0001348084,0.001041066,0.01991995,0.05842875,0.01183338,0.009044142,0.8962876],"study_design_scores_gemma":[0.001684481,0.0003286728,0.000757211,0.0001073527,0.00001214717,0.0008640773,0.000009829396,0.987606,0.0048577,0.002571958,0.001059141,0.000141452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02563744,0.00001519176,0.971356,0.0003609515,0.00231456,0.0002044968,0.000006545165,0.00005757505,0.00004718259],"genre_scores_gemma":[0.2631584,0.000003012407,0.735912,0.0002883996,0.000612767,0.000004251567,0.00000986235,0.000005652647,0.000005708605],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.967686,"threshold_uncertainty_score":0.4141632,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2041306713","doi":"10.1007/s11263-009-0279-0","title":"Contextual Part Analogies in 3D Objects","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":169,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Matching (statistics); Object (grammar); Hierarchy; Artificial intelligence; Context (archaeology); Similarity (geometry); Graph; Pattern recognition (psychology); Scene graph; Function (biology); Simple (philosophy); Shape analysis (program analysis); Information retrieval; Theoretical computer science; Image (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.009702834041186693,"gpt":0.2614141826444403,"spread":0.2517113486032536,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001623668,0.00007564907,0.0001525025,0.0003041544,0.000009476626,0.00006101223,0.0002433926,0.00003338124,0.00001958953],"category_scores_gemma":[0.00001207128,0.00006469361,0.00009052525,0.00008079814,0.00000764891,0.0001964197,0.00001571808,0.0001483965,0.000008939218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005574205,"about_ca_system_score_gemma":0.00001175404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002025766,"about_ca_topic_score_gemma":0.000003814025,"domain_scores_codex":[0.9991881,0.00001699257,0.0003560933,0.00005948386,0.0002918999,0.00008741656],"domain_scores_gemma":[0.9996462,0.00003819767,0.00006734393,0.00005304931,0.0001596083,0.00003559043],"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":[0.00002844367,0.00005452027,0.0002986784,0.000001937019,0.00007249568,0.0001787604,0.0002393402,0.7116597,0.000482807,0.00007792193,0.002760141,0.2841453],"study_design_scores_gemma":[0.0006684278,0.000195505,0.008010862,0.0002492379,0.000009842959,0.00009277515,0.00002543046,0.9875913,0.0003168883,0.0009776071,0.00174664,0.0001154716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6134952,0.0005183364,0.3824368,0.0006129541,0.001586056,0.00002317256,0.000002110676,0.00004962711,0.001275744],"genre_scores_gemma":[0.9949694,0.0001430969,0.004150243,0.0001870927,0.000530032,1.111288e-7,0.000002457679,0.000004746708,0.00001282441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3814742,"threshold_uncertainty_score":0.2638129,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2058056568","doi":"10.1007/s11263-007-0049-9","title":"A Theory of Refractive and Specular 3D Shape by Light-Path Triangulation","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":150,"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; Microsoft Research; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Specular reflection; Computer vision; Triangulation; Artificial intelligence; Computer science; Point (geometry); Path (computing); Specular highlight; Ray; Pixel; Plane (geometry); Optics; Structured light; Epipolar geometry; Computer graphics (images); Mathematics; Image (mathematics); Geometry; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.007237067827696615,"gpt":0.3007243201093985,"spread":0.2934872522817019,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009654005,0.00009702296,0.0001709788,0.0002875782,0.00003289821,0.00008828164,0.0005395194,0.00003631497,0.0000156881],"category_scores_gemma":[0.00005240995,0.000078565,0.00007943721,0.0001220701,0.00003050667,0.001040541,0.0001939809,0.0001499158,0.000002644574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005232021,"about_ca_system_score_gemma":0.00002729522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.026928e-7,"about_ca_topic_score_gemma":9.553472e-8,"domain_scores_codex":[0.9985641,0.00006832022,0.0005102875,0.0001546475,0.0005966861,0.0001059965],"domain_scores_gemma":[0.99844,0.0002256628,0.0005679723,0.0001249301,0.0005615419,0.00007995308],"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":[0.0002136779,0.0001182838,0.0001446885,0.000003079011,0.00005328768,0.0000829008,0.0005692253,0.00006878508,0.02349526,0.008285755,0.0006038962,0.9663612],"study_design_scores_gemma":[0.00738311,0.00234336,0.04538077,0.001223787,0.00004146436,0.001792479,0.000199653,0.7313271,0.07598488,0.07698427,0.05666371,0.0006753762],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05051632,0.0003454883,0.9470912,0.0009179826,0.0008200861,0.00004477937,0.000001107424,0.00001115157,0.0002518402],"genre_scores_gemma":[0.7608961,0.00005230978,0.2384356,0.0003391144,0.0002450095,1.108513e-7,0.000001169046,0.000005783008,0.00002472664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9656858,"threshold_uncertainty_score":0.3203788,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1977472541","doi":"10.1007/s11263-010-0330-1","title":"An Anisotropic Fourth-Order Diffusion Filter for Image Noise Removal","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Anisotropic diffusion; Edge-preserving smoothing; Mathematics; Filter (signal processing); Speckle noise; Noise (video); Nonlinear filter; Adaptive filter; Algorithm; Diffusion; Artificial intelligence; Computer vision; Computer science; Filter design; Image (mathematics); Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01198180678129496,"gpt":0.3212085976822605,"spread":0.3092267909009655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008032876,0.0001643147,0.0002214374,0.0003232213,0.0001010178,0.0006681948,0.001898215,0.00008835291,0.00004010808],"category_scores_gemma":[0.0000855469,0.0001315071,0.0002021238,0.0001269769,0.00004373892,0.001745486,0.0002434504,0.0003352488,0.00001392667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003407766,"about_ca_system_score_gemma":0.00009218106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005453646,"about_ca_topic_score_gemma":0.000002035403,"domain_scores_codex":[0.9981927,0.0001202463,0.0005250553,0.0002658921,0.0006924109,0.0002036615],"domain_scores_gemma":[0.9972631,0.0002276411,0.0003744347,0.0003558254,0.0016288,0.0001502467],"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":[0.0002484086,0.0003146512,0.0000916916,0.000006330851,0.00004955084,0.0005368487,0.0004083811,0.0003250803,0.5059719,0.002860996,0.0030099,0.4861763],"study_design_scores_gemma":[0.005117053,0.001879141,0.008308797,0.0001417932,0.00002431727,0.004732933,0.000007672109,0.8897069,0.03159505,0.0161908,0.04186866,0.0004269491],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1950264,0.00001508285,0.7977195,0.001455816,0.005587242,0.00007830671,0.000002646598,0.0000296265,0.00008535141],"genre_scores_gemma":[0.1709354,0.00000857706,0.8263668,0.0006100371,0.001990402,0.000001103293,0.000004080835,0.00001398206,0.00006964429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8893818,"threshold_uncertainty_score":0.6443418,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2030488300","doi":"10.1007/s11263-006-6993-y","title":"Object Recognition as Many-to-Many Feature Matching","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":145,"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":"Office of Naval Research; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Pattern recognition (psychology); Artificial intelligence; Embedding; Feature vector; Mathematics; Cognitive neuroscience of visual object recognition; Matching (statistics); Computer science; Feature extraction","retraction":null,"screen_n_in":null,"score":{"opus":0.006716534434750066,"gpt":0.2619409834550265,"spread":0.2552244490202764,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005111942,0.0001477812,0.0001742611,0.0004953267,0.00007928398,0.0004709812,0.00133878,0.00006293285,0.00002464578],"category_scores_gemma":[0.00001075179,0.0001255545,0.0001915268,0.0002230417,0.00001886986,0.0009945761,0.000273612,0.0002757153,0.0001394816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006553161,"about_ca_system_score_gemma":0.00004108154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001330286,"about_ca_topic_score_gemma":0.00000105549,"domain_scores_codex":[0.9983357,0.00009966923,0.0004075302,0.0002183256,0.0007664352,0.0001723079],"domain_scores_gemma":[0.9987253,0.0001140509,0.0003175562,0.0001688572,0.0005749357,0.0000993108],"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":[0.0002272644,0.0004413384,0.0001928028,0.00001006259,0.0001590873,0.001385461,0.0008489062,0.005879484,0.007479907,0.06375423,0.01616584,0.9034556],"study_design_scores_gemma":[0.00256366,0.00169806,0.02631174,0.001114836,0.00002805807,0.006371295,0.00003917145,0.0721544,0.01160772,0.8528182,0.02454844,0.0007444553],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1846332,0.00005079479,0.8085292,0.002687603,0.003297763,0.00005587229,0.00000274128,0.00004001657,0.0007028269],"genre_scores_gemma":[0.8118745,0.000009972769,0.1849952,0.001273435,0.001676402,9.684294e-7,0.000005979102,0.0000102803,0.0001532551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9027112,"threshold_uncertainty_score":0.5119963,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2122301224","doi":"10.1007/s11263-012-0587-7","title":"Modeling Coverage in Camera Networks: A Survey","year":2012,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":138,"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; Context (archaeology); Artificial intelligence; Computer vision; Network planning and design; Data mining; Computer network; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01389906773714351,"gpt":0.2573686605412988,"spread":0.2434695928041553,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000445472,0.00007524998,0.0001191885,0.0001872497,0.000009977811,0.0000544845,0.0001608477,0.00004754779,0.0000117321],"category_scores_gemma":[0.00001410853,0.00006964841,0.00004756539,0.00008130817,0.000004686284,0.0003249643,0.0000240485,0.0001657001,0.000004240243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009367012,"about_ca_system_score_gemma":0.000008971689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002053396,"about_ca_topic_score_gemma":0.000009217842,"domain_scores_codex":[0.9991556,0.00005250346,0.0003624554,0.00004286321,0.0002645331,0.0001220641],"domain_scores_gemma":[0.9995783,0.00006782684,0.00005333105,0.00004973032,0.0001916974,0.00005913199],"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":[0.00002555053,0.00003771843,0.006662063,0.000002051433,0.00002498071,0.00001347883,0.00008328658,0.9811399,0.00004798298,0.00007217193,0.0003074354,0.01158336],"study_design_scores_gemma":[0.0003622275,0.00003005069,0.01714132,0.00008350068,0.00000222634,0.00003849836,0.000002348884,0.9819989,0.00002307101,0.00004563987,0.0002035584,0.00006868599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3577239,0.0001926833,0.640215,0.00002332279,0.001786447,0.00001647165,0.000001022094,0.000007994076,0.00003309577],"genre_scores_gemma":[0.9960818,0.0001828528,0.002851024,0.0000878406,0.0007693568,1.75704e-7,0.00001153174,0.00001324573,0.00000219455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6383579,"threshold_uncertainty_score":0.284018,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167717741","doi":"10.1023/a:1008195307933","title":"Probabilistic Detection and Tracking of Motion Boundaries","year":2000,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":127,"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":"Motion estimation; Artificial intelligence; Boundary (topology); Mathematics; Computer vision; Discontinuity (linguistics); Generative model; Motion (physics); Motion field; Orientation (vector space); Probabilistic logic; Structure from motion; Pattern recognition (psychology); Computer science; Geometry; Mathematical analysis; Generative grammar","retraction":null,"screen_n_in":null,"score":{"opus":0.009339942711309097,"gpt":0.2862814246533634,"spread":0.2769414819420543,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245344,0.00007192553,0.000124006,0.0001791762,0.00006207673,0.0002875311,0.0003858287,0.00002337434,0.00002180614],"category_scores_gemma":[0.00002870514,0.00006070999,0.0000581239,0.00009448527,0.00006884114,0.001254787,0.0000737993,0.0001128845,0.000002738066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003646776,"about_ca_system_score_gemma":0.00002897215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000321424,"about_ca_topic_score_gemma":8.117183e-7,"domain_scores_codex":[0.9989731,0.00004227121,0.0003872241,0.000118336,0.0004055107,0.00007356443],"domain_scores_gemma":[0.9991125,0.00006221989,0.0002506707,0.00009226252,0.0004377891,0.0000445299],"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":[0.00003883583,0.00004546962,0.00006763663,0.000004594199,0.00001335495,0.00001307679,0.0002670734,0.001250365,0.002217579,0.001017942,0.0000262303,0.9950379],"study_design_scores_gemma":[0.00128067,0.000714871,0.03064854,0.0004075452,0.00001022796,0.00126003,0.00001410716,0.9173182,0.008626607,0.02474354,0.01479893,0.000176696],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1612458,0.00009654203,0.837244,0.0004704601,0.0008038819,0.00003201975,4.099156e-7,0.00001368742,0.00009311716],"genre_scores_gemma":[0.9266713,0.00004667978,0.0729871,0.0001010404,0.0001759516,2.110189e-7,2.782296e-7,0.000003638962,0.00001376157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9948611,"threshold_uncertainty_score":0.2772669,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2075563626","doi":"10.1007/s11263-012-0532-9","title":"Coupled Action Recognition and Pose Estimation from Multiple Views","year":2012,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":116,"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","keywords":"Pose; Benchmark (surveying); Computer science; Artificial intelligence; Action (physics); 3D pose estimation; Pattern recognition (psychology); Action recognition; Task (project management); Set (abstract data type); Estimation; Articulated body pose estimation; Machine learning; Computer vision; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.063600227003345,"gpt":0.3294617868814169,"spread":0.2658615598780719,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003852196,0.000112136,0.0001466449,0.0002737715,0.00006664648,0.000255296,0.000301314,0.00006248716,0.00006074741],"category_scores_gemma":[0.00004084978,0.00009954783,0.00008302906,0.00008229999,0.00001794378,0.003058462,0.0001033574,0.0001624493,0.0001032994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007401674,"about_ca_system_score_gemma":0.00002138073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001817933,"about_ca_topic_score_gemma":0.000003153635,"domain_scores_codex":[0.9987371,0.00009175252,0.0004461502,0.000135622,0.0004661551,0.0001232384],"domain_scores_gemma":[0.9986778,0.0001889583,0.0004727871,0.0001021566,0.0004387577,0.0001194729],"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":[0.00007858322,0.0002217899,0.0006332915,0.00000433339,0.0000697663,0.00001233733,0.000587428,0.0001320018,0.005927318,0.0001237529,0.001270123,0.9909393],"study_design_scores_gemma":[0.003104276,0.0005266948,0.07172141,0.0004408264,0.00004929013,0.0008634995,0.00004475987,0.8816778,0.01295684,0.0170833,0.01112788,0.0004033863],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4385912,0.00009493269,0.5580949,0.0003423385,0.002771418,0.00004584595,0.000003714947,0.00002358027,0.00003209706],"genre_scores_gemma":[0.9127836,0.0001232065,0.08503321,0.0003917635,0.001613163,0.00000155206,0.00003837945,0.000006776748,0.00000831531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905359,"threshold_uncertainty_score":0.4059442,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3084274556","doi":"10.1007/s11263-020-01366-3","title":"Rain Rendering for Evaluating and Improving Robustness to Bad Weather","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":112,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Rendering (computer graphics); Robustness (evolution); Global illumination; Object detection; Synthetic data; Object based","retraction":null,"screen_n_in":null,"score":{"opus":0.04708621936685208,"gpt":0.3068659415989364,"spread":0.2597797222320843,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005217122,0.00005528354,0.00009898416,0.00009654243,0.00004224549,0.0001254463,0.0001851191,0.00001528547,0.00009875811],"category_scores_gemma":[0.0001011442,0.00004422926,0.00005863662,0.00006102533,0.000006342385,0.000249675,0.00001820459,0.00005250385,0.000003610483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005025043,"about_ca_system_score_gemma":0.00002047069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008379965,"about_ca_topic_score_gemma":0.000009285651,"domain_scores_codex":[0.9991743,0.00003483445,0.000243382,0.00009688612,0.0003823781,0.00006821016],"domain_scores_gemma":[0.999341,0.0001068082,0.0001502643,0.00002464785,0.0002827278,0.00009453248],"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":[0.0001878581,0.000006791778,0.01197453,0.000008157195,0.00007295589,0.000006596063,0.0005994078,0.1950451,0.002265313,0.00001319015,0.0005027761,0.7893173],"study_design_scores_gemma":[0.0005193021,0.0004316094,0.02924458,0.00005975862,0.00001688233,0.00001189209,0.00004639604,0.9685594,0.0001395403,0.0001173201,0.0007861418,0.00006717485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.390667,0.00008089606,0.6054158,0.003367431,0.0003647812,0.0000464627,0.000004050552,0.000005118933,0.0000484858],"genre_scores_gemma":[0.8948734,0.000004821711,0.1039636,0.0006470751,0.0004954316,1.498553e-7,0.000004796731,0.000001941433,0.000008718453],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7892501,"threshold_uncertainty_score":0.1803617,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1597899558","doi":"10.1023/a:1008156202475","title":"Design and Use of Linear Models for Image Motion Analysis","year":2000,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","retraction":null,"screen_n_in":null,"score":{"opus":0.03034150820794095,"gpt":0.3301095661615995,"spread":0.2997680579536585,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003196878,0.00008204363,0.0001915504,0.0003927395,0.00003544356,0.000147195,0.0004580431,0.00002437752,0.0000136047],"category_scores_gemma":[0.000015648,0.00006817147,0.0001464646,0.0001902447,0.00002586666,0.002154429,0.0000800847,0.00007107761,0.000001237944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002279188,"about_ca_system_score_gemma":0.00001964293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002444935,"about_ca_topic_score_gemma":9.76279e-8,"domain_scores_codex":[0.9989099,0.00005576647,0.0004309777,0.0001467865,0.0003724175,0.00008418986],"domain_scores_gemma":[0.9986177,0.0002194918,0.0002758465,0.0001292615,0.0006986333,0.00005905872],"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":[0.00009759614,0.00007164117,0.00002596587,0.000002564092,0.0001441016,0.00001292803,0.0001777315,0.3686156,0.001090395,0.0007141492,0.0002278312,0.6288195],"study_design_scores_gemma":[0.0005200298,0.0001938604,0.0005582542,0.00004701044,0.00002824522,0.00006105272,0.000001395687,0.9913535,0.001289206,0.005076598,0.0008077034,0.00006321311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01190023,0.00004595267,0.9871992,0.0005389054,0.0002268467,0.00006570311,0.000002112163,0.00001084239,0.00001018041],"genre_scores_gemma":[0.2304127,0.00008867223,0.7692127,0.0001682416,0.00008785414,4.05889e-7,0.000001442308,0.000003939518,0.00002407803],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6287563,"threshold_uncertainty_score":0.2779952,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097184811","doi":"10.1007/s11263-013-0681-5","title":"Spectral Log-Demons: Diffeomorphic Image Registration with Very Large Deformations","year":2013,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":99,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Maxima and minima; Robustness (evolution); Computer science; Atlas (anatomy); Artificial intelligence; Feature matching; Image registration; Graph; Image (mathematics); Algorithm; Computer vision; Mathematics; Theoretical computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.008146136628583225,"gpt":0.2791274192474271,"spread":0.2709812826188439,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002685252,0.0001446307,0.0001768445,0.0002953405,0.00007799543,0.0005323083,0.001062823,0.00004767798,0.0000363647],"category_scores_gemma":[0.00003182338,0.0001048227,0.0001084876,0.0001922934,0.00004694835,0.004666208,0.0001825605,0.0002535222,0.00004883885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001125491,"about_ca_system_score_gemma":0.00007380843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006568197,"about_ca_topic_score_gemma":0.000002077607,"domain_scores_codex":[0.9983627,0.00005422551,0.000516045,0.000164928,0.0007135177,0.0001885847],"domain_scores_gemma":[0.9979839,0.00008902197,0.0005180885,0.0002360817,0.001073092,0.00009978509],"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":[0.0005934368,0.002507744,0.007405096,0.00007489822,0.0007753036,0.001981157,0.002196467,0.001889562,0.0358064,0.1218626,0.07008307,0.7548242],"study_design_scores_gemma":[0.008578574,0.00864618,0.1600469,0.001904153,0.00007479671,0.01199344,0.0001342259,0.466089,0.08848942,0.209837,0.0424329,0.001773482],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03013184,0.00005218311,0.9658866,0.002794404,0.0005249261,0.000131078,0.000002493653,0.00007608279,0.0004003803],"genre_scores_gemma":[0.6615151,0.00007340189,0.3375125,0.0004756715,0.0003666412,0.00000276236,0.000005450061,0.000008189213,0.00004031],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7530507,"threshold_uncertainty_score":0.5133061,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2075500716","doi":"10.1007/s11263-006-0021-0","title":"A Study of the Rao-Blackwellised Particle Filter for Efficient and Accurate Vision-Based SLAM","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":97,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Simultaneous localization and mapping; Particle filter; Computer science; Odometry; Computer vision; Artificial intelligence; Pose; Scalability; Filter (signal processing); Range (aeronautics); Monocular vision; Robotics; Mobile robot; Robot; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01248595908966754,"gpt":0.2817521895487849,"spread":0.2692662304591174,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003801378,0.00007950159,0.0001201554,0.00009618991,0.0000314073,0.00005222634,0.0001982548,0.00002822919,0.000004928004],"category_scores_gemma":[0.00002219576,0.00005430752,0.00007283652,0.00008009479,0.0000197809,0.0000631788,0.0000311001,0.00007307812,5.911639e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003527822,"about_ca_system_score_gemma":0.00001280129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001561435,"about_ca_topic_score_gemma":0.00000308928,"domain_scores_codex":[0.9990078,0.00002677261,0.0004385444,0.00007145893,0.0003649062,0.00009054223],"domain_scores_gemma":[0.9991181,0.0002144779,0.0001540633,0.00008989184,0.0003804014,0.00004299085],"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":[0.0001582677,0.0002223087,0.0008221307,0.00000948848,0.0000484728,0.000009413751,0.0003322391,0.9859791,0.003152748,0.00008905475,0.0003169707,0.008859783],"study_design_scores_gemma":[0.001914537,0.0004619312,0.0204155,0.000100237,0.00001617827,0.00001030358,0.00004240703,0.967591,0.009063547,0.00005214646,0.0002720109,0.00006023614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5630186,0.00001875625,0.4361064,0.0001102151,0.0006286619,0.0001027959,0.000001589366,0.000005432416,0.000007627731],"genre_scores_gemma":[0.9971681,0.000003477587,0.002566533,0.00007682588,0.0001684245,6.013737e-7,0.000001205584,0.00001102954,0.000003773724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4341495,"threshold_uncertainty_score":0.2214596,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2998360197","doi":"10.1007/s11263-020-01373-4","title":"A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Segmentation; Image segmentation; Pixel; Segmentation-based object categorization; Pattern recognition (psychology); Image (mathematics); Set (abstract data type); Scale-space segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.01786697280962874,"gpt":0.309480288127839,"spread":0.2916133153182103,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008714318,0.0001348185,0.0003742756,0.0005444588,0.00002338382,0.0000813674,0.001012505,0.00003151245,0.00001469281],"category_scores_gemma":[0.00001259845,0.0001122567,0.0002341163,0.0008913628,0.00003543046,0.0006216677,0.0003052328,0.0001665888,0.000004656952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007631214,"about_ca_system_score_gemma":0.00002499185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003802261,"about_ca_topic_score_gemma":0.000002694108,"domain_scores_codex":[0.9981494,0.0000998152,0.000754401,0.0002225298,0.0006553116,0.0001185584],"domain_scores_gemma":[0.9983986,0.000238478,0.0005530258,0.0001773512,0.0005333073,0.00009925315],"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":[0.00050663,0.001142377,0.01098492,0.00005935585,0.002444852,0.0004637138,0.005554332,0.3625714,0.3999666,0.00830888,0.001596781,0.2064002],"study_design_scores_gemma":[0.0009768469,0.0002731727,0.07634374,0.00006494723,0.0000673149,0.00002460108,0.00002791099,0.916357,0.004540972,0.0009461785,0.0002610942,0.0001162333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3689821,0.00003451957,0.6274615,0.003225137,0.0001961366,0.00007439091,0.000003266469,0.00001135099,0.00001159445],"genre_scores_gemma":[0.8832085,0.00005819844,0.1158714,0.0006658096,0.0001781344,0.000001823412,0.000008965993,0.000005889879,0.000001283205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5537856,"threshold_uncertainty_score":0.4577695,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1560959218","doi":"10.1023/a:1026502202780","title":"Probabilistic Models of Appearance for 3-D Object Recognition","year":2000,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":91,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Object (grammar); Feature (linguistics); Cognitive neuroscience of visual object recognition; Matching (statistics); Abstraction; Transformation (genetics); Range (aeronautics); Computer vision; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02662475631536259,"gpt":0.2982352884865427,"spread":0.2716105321711801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003822639,0.00007807532,0.0001514745,0.0001548959,0.00002568388,0.00008635024,0.0008301823,0.00003934025,0.00001786973],"category_scores_gemma":[0.0000206116,0.00006494168,0.0001452599,0.0001192718,0.0000319452,0.0008145958,0.00004335295,0.00008047185,0.00000635588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004282552,"about_ca_system_score_gemma":0.00006171442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001813312,"about_ca_topic_score_gemma":1.265295e-7,"domain_scores_codex":[0.9987975,0.00004127184,0.0005014206,0.000132357,0.0004428672,0.00008457736],"domain_scores_gemma":[0.9982945,0.00006819215,0.0003440301,0.000134844,0.001119818,0.00003860335],"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":[0.0001378654,0.0001471048,0.000003121082,0.00001577948,0.00002902219,0.000005031196,0.0001244092,0.0009357235,0.001653864,0.004221173,0.000410871,0.992316],"study_design_scores_gemma":[0.001063001,0.001058449,0.0005655155,0.0005805683,0.00001172606,0.0002310733,0.000003269707,0.7812368,0.02939407,0.1811013,0.004582878,0.0001712833],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01204617,0.00009960792,0.9862064,0.0007422445,0.0004365712,0.0001266431,0.000005204053,0.00002904854,0.0003081547],"genre_scores_gemma":[0.8074619,0.0001099927,0.1919782,0.0001458563,0.0002428643,0.0000034189,0.000002786629,0.00000534078,0.00004957499],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9921448,"threshold_uncertainty_score":0.2648245,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4378375546","doi":"10.1007/s11263-023-01808-8","title":"Instance Segmentation in the Dark","year":2023,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":86,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Segmentation; Upsampling; Convolutional neural network; Computer vision; Feature (linguistics); Pattern recognition (psychology); Deep learning; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01543978522500432,"gpt":0.3459689402436785,"spread":0.3305291550186741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006406002,0.00006468443,0.00008249204,0.0003526436,0.00003632528,0.0002135138,0.001371528,0.00001529646,0.000003544111],"category_scores_gemma":[0.00002506623,0.00004363253,0.00005849978,0.0004313608,0.00001596315,0.001111283,0.0001705749,0.0001542479,0.00003368834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004932571,"about_ca_system_score_gemma":0.00003126873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001258874,"about_ca_topic_score_gemma":8.204358e-7,"domain_scores_codex":[0.9986789,0.00008061554,0.0003409926,0.0001088371,0.0006877818,0.0001028399],"domain_scores_gemma":[0.9992593,0.0001597982,0.00020378,0.0001365976,0.0002140344,0.00002650352],"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":[0.00002534911,0.00006950678,0.0006833724,0.000002152499,0.0000132061,0.0003851812,0.001798557,0.0050747,0.001589318,0.006336065,0.01048202,0.9735405],"study_design_scores_gemma":[0.002171113,0.0003788588,0.08604915,0.000383015,0.000003082267,0.0007128646,0.0002405733,0.831311,0.001000117,0.03465564,0.04287266,0.0002218564],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03972399,0.00004706712,0.9503919,0.007407325,0.002038043,0.000047163,4.647364e-7,0.00002601075,0.0003179966],"genre_scores_gemma":[0.8894044,0.0001104807,0.1080853,0.002037573,0.0003295472,0.000001103307,0.000002055116,0.000004553322,0.00002500243],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9733187,"threshold_uncertainty_score":0.2548664,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2236404076","doi":"10.1007/s11263-015-0844-7","title":"Multi-Cue Illumination Estimation via a Tree-Structured Group Joint Sparse Representation","year":2015,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Standard illuminant; Artificial intelligence; Pattern recognition (psychology); RGB color model; Sparse approximation; Tree (set theory); Computer science; Histogram; Mathematics; Representation (politics); Computer vision; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.031464596383116,"gpt":0.3320183981772484,"spread":0.3005538017941324,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003073235,0.0001001771,0.0001361599,0.0002185722,0.0000520151,0.0001413956,0.0003211146,0.00002842748,0.00004512614],"category_scores_gemma":[0.00001513402,0.00008688876,0.000113002,0.0001627297,0.00003887521,0.00073915,0.0000787657,0.0001209086,0.00002834054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008966713,"about_ca_system_score_gemma":0.00005658488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004821865,"about_ca_topic_score_gemma":0.00000596021,"domain_scores_codex":[0.9986666,0.00004765999,0.0004624903,0.0001523659,0.0005736342,0.00009723258],"domain_scores_gemma":[0.9985029,0.00003619592,0.0004956161,0.000120917,0.0007342198,0.0001101189],"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":[0.00009650024,0.0004794283,0.002915147,0.000002114765,0.00009264561,0.000013733,0.001241419,0.04911727,0.01468256,0.003394945,0.003146821,0.9248174],"study_design_scores_gemma":[0.001784812,0.0002304125,0.04180617,0.00005502173,0.00002171127,0.00006303349,0.0001484261,0.9390891,0.003623455,0.01105251,0.001981607,0.000143729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2337025,0.000007705255,0.7643136,0.0007941045,0.0009209016,0.00009093648,0.00000401075,0.00001024132,0.0001559741],"genre_scores_gemma":[0.90867,0.000001233991,0.09041137,0.00006396171,0.0007620525,0.000004241112,0.00004479803,0.000006565151,0.00003577749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9246737,"threshold_uncertainty_score":0.3543221,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2026396026","doi":"10.1007/s11263-010-0349-3","title":"Plane-Based Calibration for Linear Cameras","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":56,"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":"Computer vision; Artificial intelligence; Planar; Calibration; Perpendicular; Orientation (vector space); Computer science; Object (grammar); Camera resectioning; Plane (geometry); Image sensor; Mathematics; Computer graphics (images); Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.02173648753408229,"gpt":0.3191176219938546,"spread":0.2973811344597723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003450889,0.00009242249,0.00012558,0.0002125612,0.00003387366,0.0002235959,0.001101914,0.00006213097,0.00002226383],"category_scores_gemma":[0.00005879442,0.00007262516,0.0001190656,0.00005997795,0.0000261239,0.00071653,0.00007507811,0.0002137546,0.00000467923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002544023,"about_ca_system_score_gemma":0.00008554504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002405866,"about_ca_topic_score_gemma":0.000002316267,"domain_scores_codex":[0.9988494,0.00002480959,0.0003852972,0.0001292325,0.0005056087,0.0001056685],"domain_scores_gemma":[0.9985291,0.0001429955,0.0002455098,0.0001449614,0.0008628945,0.00007451768],"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":[0.0006030104,0.0008692294,0.001389788,0.00002653556,0.0001716032,0.00006602854,0.0002891928,0.00115019,0.3981122,0.1379448,0.02715365,0.4322238],"study_design_scores_gemma":[0.001202906,0.001862693,0.001307736,0.0001546247,0.000008622133,0.00007691329,0.000001759796,0.7968627,0.1653243,0.008042381,0.02495197,0.0002034154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03318716,0.000005914546,0.959613,0.003567836,0.003312686,0.00008578313,0.000002274978,0.00004725575,0.0001781157],"genre_scores_gemma":[0.6685203,0.000001792855,0.3301882,0.0005241617,0.0007481836,0.000001682781,0.000003063835,0.000004327858,0.00000822048],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7957125,"threshold_uncertainty_score":0.2961568,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2913105986","doi":"10.1007/s11263-008-0164-2","title":"Confocal Stereo","year":2008,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Focus (optics); Aperture (computer memory); Computer science; Pixel; Lens (geology); Radiance; Optics; Confocal; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01213226992382694,"gpt":0.2790000625560536,"spread":0.2668677926322267,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005938654,0.00005321114,0.0000746797,0.00009778238,0.00002296538,0.00003381799,0.0002740881,0.00002476079,0.00002390914],"category_scores_gemma":[0.000002520142,0.0000462473,0.00005267916,0.00003828467,0.00002257249,0.0001860455,0.00003031583,0.0001089496,0.00001414221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003153694,"about_ca_system_score_gemma":0.00001436982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.642734e-7,"about_ca_topic_score_gemma":6.478605e-8,"domain_scores_codex":[0.9994614,0.000005073803,0.0002246899,0.00003843427,0.0002161447,0.00005431677],"domain_scores_gemma":[0.9995929,0.0000181757,0.0000606338,0.0000522599,0.0002423002,0.0000337107],"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":[0.00008030067,0.0002438296,0.0006957994,0.00002512878,0.0002280016,0.0006304032,0.0007095415,0.01839849,0.01874942,0.002621343,0.1682552,0.7893625],"study_design_scores_gemma":[0.001647448,0.0004395653,0.0119476,0.0004044806,0.0000193533,0.008046582,0.00001205597,0.5424005,0.03432241,0.004977224,0.3953471,0.0004357568],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09677643,0.0001060652,0.9010857,0.0003106229,0.0006767574,0.00002074045,0.000001384418,0.00007016146,0.000952086],"genre_scores_gemma":[0.9307036,0.0001011538,0.06853299,0.0001207658,0.0005034799,5.939529e-7,0.000001509226,0.000008310342,0.00002760819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8339272,"threshold_uncertainty_score":0.188591,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1993280531","doi":"10.1007/s11263-010-0325-y","title":"Learning Articulated Structure and Motion","year":2010,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":49,"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; National Science Foundation","keywords":"Feature (linguistics); Artificial intelligence; Motion (physics); Sequence (biology); Computer science; Probabilistic logic; Series (stratigraphy); Structure from motion; Algorithm; Parsing; Pattern recognition (psychology); Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.003954102044626396,"gpt":0.2779573757332545,"spread":0.2740032736886281,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017357,0.0000746959,0.00009452338,0.0001872196,0.00005111173,0.0002543406,0.0004590899,0.00003491905,0.00001900534],"category_scores_gemma":[0.00004362265,0.00005902845,0.00004375282,0.00008045897,0.00002453028,0.001005085,0.0001985445,0.0003916024,0.000003698986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001344408,"about_ca_system_score_gemma":0.00001643749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.054179e-7,"about_ca_topic_score_gemma":3.770095e-7,"domain_scores_codex":[0.99913,0.00003256499,0.0002575001,0.0001224341,0.0003734763,0.00008399349],"domain_scores_gemma":[0.9991162,0.00005362685,0.0002293528,0.00008943264,0.0004350398,0.00007635092],"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":[0.00001311092,0.00002434486,0.001118109,0.000001256518,0.0000176692,0.0000575801,0.0002266568,0.001605065,0.05547079,0.002530405,0.0001304044,0.9388046],"study_design_scores_gemma":[0.000782852,0.0002308452,0.03576362,0.00006838399,0.000003620054,0.001598734,0.000006685967,0.9381951,0.006742815,0.007566974,0.008918948,0.0001214393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3565599,0.00001650646,0.6405631,0.00104994,0.001752151,0.00001496445,1.758252e-7,0.00001819205,0.00002507585],"genre_scores_gemma":[0.804247,0.00001020698,0.1952146,0.0002075141,0.0003058784,5.229008e-8,6.643286e-7,0.000003562663,0.00001056726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9386832,"threshold_uncertainty_score":0.2452612,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2115578160","doi":"10.1007/s11263-007-0067-7","title":"Learning to Recognize Objects with Little Supervision","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Conditional random field; Pattern recognition (psychology); Computer science; Kernel (algebra); Context (archaeology); Machine learning; Segmentation; Object (grammar); Feature selection; Feature (linguistics); Bayesian probability; Multiple kernel learning; Class (philosophy); Kernel method; Support vector machine; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.009405553709298077,"gpt":0.3122572938147168,"spread":0.3028517401054188,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008313878,0.0001238574,0.0001689337,0.0004419721,0.00005518423,0.0002203112,0.001078733,0.00004337353,0.00001016251],"category_scores_gemma":[0.00008085104,0.00009194279,0.00008259621,0.0003225124,0.00001712083,0.001155255,0.0003229855,0.0002794736,0.00002352131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009043922,"about_ca_system_score_gemma":0.00004979531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000312836,"about_ca_topic_score_gemma":0.000001510935,"domain_scores_codex":[0.9983401,0.00004340899,0.0004242638,0.0002003849,0.000804128,0.0001876629],"domain_scores_gemma":[0.9981021,0.0002083648,0.0002514874,0.0001595018,0.001124044,0.0001544644],"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":[0.0002367524,0.00007676292,0.000429072,0.000002654185,0.00003053395,0.0004559679,0.0006627642,0.0004923047,0.005040145,0.0003762688,0.0006355022,0.9915613],"study_design_scores_gemma":[0.006269013,0.03124602,0.07124972,0.003799731,0.00003923279,0.008264289,0.0003331341,0.04122108,0.5208582,0.01525254,0.2996927,0.001774409],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0716069,0.00006072959,0.9262523,0.000769144,0.0007831545,0.00006578015,2.543081e-7,0.00006876899,0.0003929698],"genre_scores_gemma":[0.5712759,0.00003536289,0.4276866,0.0005354913,0.0004047222,2.53083e-7,6.773162e-7,0.000008059808,0.0000528943],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9897869,"threshold_uncertainty_score":0.3749318,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2059501997","doi":"10.1007/s11263-012-0551-6","title":"A Computational Learning Theory of Active Object Recognition Under Uncertainty","year":2012,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":46,"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":"Computational complexity theory; Object (grammar); Representation (politics); Noise (video); Mathematics; Constraint (computer-aided design); Cognitive neuroscience of visual object recognition; Class (philosophy); Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Time complexity; Algorithm; Computer science; Theoretical computer science; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01582512618309218,"gpt":0.3043202385338636,"spread":0.2884951123507715,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001045856,0.0001211103,0.000204017,0.0003399965,0.00005634343,0.0000846547,0.0006609364,0.00005097993,0.00005081189],"category_scores_gemma":[0.0000793107,0.0001005511,0.0001673204,0.0001588881,0.00004162714,0.0009472555,0.0002004001,0.0003757874,0.00002307857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008331094,"about_ca_system_score_gemma":0.00007902917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007623786,"about_ca_topic_score_gemma":1.893806e-7,"domain_scores_codex":[0.9981031,0.0003595444,0.0004725123,0.0001289228,0.0007738773,0.0001620071],"domain_scores_gemma":[0.9977211,0.0005733494,0.0006724974,0.00009174709,0.0008450453,0.00009629288],"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":[0.0001301592,0.0002338837,0.0005357696,0.000005452627,0.000173563,0.00001229693,0.001339717,0.2258395,0.0001418669,0.008571002,0.0002351803,0.7627816],"study_design_scores_gemma":[0.002180036,0.00113074,0.05387519,0.0004593511,0.00003337721,0.001084475,0.0001884374,0.8738649,0.0008085093,0.06182985,0.004187769,0.0003573384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1305904,0.00007751415,0.8665108,0.000516961,0.001828446,0.00003598043,0.000002283554,0.00002496408,0.0004126452],"genre_scores_gemma":[0.9052894,0.00001647372,0.09359047,0.0002165194,0.0008264962,4.354794e-7,0.00001045817,0.000007658276,0.00004210419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.774699,"threshold_uncertainty_score":0.4100356,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046606421","doi":"10.1007/s11263-005-4436-9","title":"Efficient Discriminant Viewpoint Selection for Active Bayesian Recognition","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":45,"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; Computer science; Pattern recognition (psychology); Classifier (UML); Bayesian probability; Selection (genetic algorithm); Machine learning; Mutual information; Discriminant; Linear discriminant analysis; Cognitive neuroscience of visual object recognition; Data mining; Object (grammar)","retraction":null,"screen_n_in":null,"score":{"opus":0.00862820358836279,"gpt":0.28551302835709,"spread":0.2768848247687272,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004032923,0.0001147049,0.0001531878,0.0002883606,0.00008523763,0.0002062243,0.0005413633,0.00004110458,0.000009208843],"category_scores_gemma":[0.00002407744,0.00009149596,0.0001760444,0.0001209534,0.00001523203,0.0002626733,0.00009402673,0.0001742109,0.000008401902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001151943,"about_ca_system_score_gemma":0.00004965653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002789811,"about_ca_topic_score_gemma":0.00000307803,"domain_scores_codex":[0.9986567,0.00007523315,0.0004365361,0.000189143,0.0005006524,0.000141745],"domain_scores_gemma":[0.9985288,0.0001262832,0.0004466117,0.00008172345,0.0007646266,0.000051946],"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":[0.0001173043,0.0003054048,0.00005802829,0.000007694728,0.00004390933,0.00002099626,0.0001745285,0.0392928,0.001366078,0.00341517,0.001917375,0.9532807],"study_design_scores_gemma":[0.0008521322,0.000612059,0.004418251,0.000154508,0.00001014694,0.0003954326,0.000004948711,0.9768534,0.002579374,0.009138257,0.004855288,0.0001261441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03826713,0.00002799644,0.9559815,0.002878622,0.002581618,0.00009327849,0.000003662326,0.00003270549,0.0001335119],"genre_scores_gemma":[0.7279604,0.00000502418,0.2700214,0.0002039638,0.001748847,0.000002957745,0.000009652587,0.00000873693,0.00003905591],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9531546,"threshold_uncertainty_score":0.3731097,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2074685565","doi":"10.1007/s11263-006-8892-7","title":"Pre-Attentive and Attentive Detection of Humans in Wide-Field Scenes","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":42,"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; Probabilistic logic; Computer vision; Bayesian probability; Pattern recognition (psychology); Gaze; Saccadic masking; Prior probability; Eye movement","retraction":null,"screen_n_in":null,"score":{"opus":0.009060323129259432,"gpt":0.3020425760075991,"spread":0.2929822528783397,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005855283,0.00008717198,0.0001821341,0.0003739825,0.00002468972,0.00009689499,0.0004575072,0.00004412311,0.000001999465],"category_scores_gemma":[0.00003540411,0.00007569761,0.00008308974,0.0001409249,0.00002875714,0.0005988475,0.0001479766,0.0001449582,8.812792e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003558807,"about_ca_system_score_gemma":0.00002622558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001007052,"about_ca_topic_score_gemma":0.00005025219,"domain_scores_codex":[0.99875,0.0001254506,0.0004762318,0.0001518242,0.0004014818,0.00009501723],"domain_scores_gemma":[0.9986947,0.0003344523,0.0003904167,0.000100329,0.0004544175,0.00002572048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002978962,0.0004360206,0.209478,0.00002470321,0.0001334913,0.0002329145,0.001105763,0.004186112,0.03309938,0.002729552,0.000291314,0.7479848],"study_design_scores_gemma":[0.0008639613,0.000480464,0.9472721,0.000268352,0.000004695244,0.0001170651,0.000009168114,0.02063394,0.02182039,0.008096785,0.0003327741,0.0001003431],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4684687,0.00009163314,0.5303398,0.0003199869,0.0006987079,0.00002662028,3.760117e-7,0.000005166591,0.0000490321],"genre_scores_gemma":[0.9605217,0.00003285285,0.039108,0.0001045098,0.000216903,4.939296e-7,4.653289e-7,0.000003944548,0.00001111129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7478845,"threshold_uncertainty_score":0.3086859,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3008728787","doi":"10.1007/s11263-020-01300-7","title":"Layout2image: Image Generation from Layout","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Generative Adversarial Networks and Image Synthesis","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":"Bank of Canada; Vector Institute; University of British Columbia; Business Development Bank of Canada; Royal Bank of Canada","funders":"","keywords":"Computer science; Artificial intelligence; Bounding overwatch; Embedding; Set (abstract data type); Pattern recognition (psychology); Image (mathematics); Object (grammar); Generative model; Representation (politics); Boosting (machine learning); Generative grammar","retraction":null,"screen_n_in":null,"score":{"opus":0.01976551630966746,"gpt":0.2679329790610798,"spread":0.2481674627514124,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000200704,0.0001345281,0.0002014827,0.00008728782,0.00005020277,0.0005476937,0.001307338,0.0000442873,0.00006824346],"category_scores_gemma":[0.00004975335,0.0001122124,0.0001642369,0.0001229202,0.00002600171,0.001563557,0.000329232,0.000178037,0.00006708781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003976634,"about_ca_system_score_gemma":0.00005850569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001095689,"about_ca_topic_score_gemma":9.731926e-7,"domain_scores_codex":[0.9984064,0.0001220168,0.0004782412,0.0002330995,0.0006400651,0.0001201759],"domain_scores_gemma":[0.9985318,0.00008202274,0.0003644188,0.0001528106,0.0007229993,0.0001460173],"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":[0.000214981,0.0002936452,0.0004111101,0.00000407473,0.0005284277,0.0009692094,0.002778298,0.05809256,0.2138584,0.005377285,0.182896,0.5345761],"study_design_scores_gemma":[0.0007777229,0.0003044233,0.001669996,0.00003189965,0.00001157631,0.00004016184,0.000007880074,0.9622287,0.01223506,0.0007280469,0.0218119,0.0001526544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01629869,0.0001167002,0.9645181,0.0160962,0.002722243,0.00004326785,0.000006635318,0.00002857328,0.0001696298],"genre_scores_gemma":[0.6425797,0.00002332277,0.3503142,0.002534607,0.004524129,3.458061e-7,0.000007074714,0.000007253708,0.000009350534],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9041361,"threshold_uncertainty_score":0.5281423,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110457125","doi":"10.1007/s11263-009-0276-3","title":"Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":40,"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":"Pattern recognition (psychology); Artificial intelligence; Embedding; Computer science; Cognitive neuroscience of visual object recognition; Benchmark (surveying); Feature vector; Feature (linguistics); Outlier; Maxima and minima; Range (aeronautics); Object (grammar); Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01910360717271248,"gpt":0.2913059001392245,"spread":0.272202292966512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002561346,0.000110942,0.0001721539,0.0002822606,0.00001953481,0.00009532076,0.0001617259,0.00005593083,0.00003205665],"category_scores_gemma":[0.0000138409,0.0001088536,0.00007869277,0.0001147404,0.000008888092,0.0003943169,0.00001458728,0.0001419271,0.00001326749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001710506,"about_ca_system_score_gemma":0.00001825371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009839715,"about_ca_topic_score_gemma":9.293537e-7,"domain_scores_codex":[0.9989529,0.00004651937,0.0004508107,0.00008836488,0.0003427755,0.0001186466],"domain_scores_gemma":[0.9994302,0.00005597344,0.0001351823,0.00006022118,0.0002701591,0.0000481951],"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":[0.00005441916,0.00004560742,0.0001519893,0.000004387413,0.0000211177,0.0001271606,0.0001035203,0.9366159,0.04366792,0.000009249373,0.000425934,0.01877275],"study_design_scores_gemma":[0.000696539,0.000119577,0.004725318,0.000386391,0.000009025778,0.0001385462,0.000006567441,0.9897311,0.003588532,0.0004210172,0.00005734675,0.0001199776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7420335,0.00004389104,0.2567801,0.0001264803,0.0007625083,0.00004478984,0.000003325837,0.00002835067,0.0001770323],"genre_scores_gemma":[0.9301792,0.0001326465,0.06919498,0.00005267857,0.0004066979,6.940426e-8,0.00001167538,0.00001500098,0.000007011748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1881458,"threshold_uncertainty_score":0.4438921,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2171120124","doi":"10.1007/s11263-011-0471-x","title":"Automatic Real-Time Video Matting Using Time-of-Flight Camera and Multichannel Poisson Equations","year":2011,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; Memorial University of Newfoundland","funders":"University of Kentucky; National Science Foundation","keywords":"Artificial intelligence; Computer vision; Computer science; Segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.02337258113584984,"gpt":0.3007975328614793,"spread":0.2774249517256294,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005972331,0.0001331313,0.0002298618,0.0004423305,0.00005567414,0.0001319592,0.0008790385,0.0000462423,0.00006961898],"category_scores_gemma":[0.00005143394,0.0001186782,0.00008666429,0.0001356071,0.00004386555,0.001204418,0.0003979123,0.0001178085,0.00001814744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008010313,"about_ca_system_score_gemma":0.00005598505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003563324,"about_ca_topic_score_gemma":2.577858e-7,"domain_scores_codex":[0.9983254,0.00009654902,0.0006800884,0.0001722863,0.0005839242,0.0001417155],"domain_scores_gemma":[0.9981143,0.0001955656,0.000752965,0.000191216,0.0006767379,0.00006914009],"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":[0.00008967436,0.000775644,0.0004102481,0.000062721,0.0004349992,0.0003348792,0.01133211,0.0007118839,0.4250592,0.005149082,0.002189257,0.5534503],"study_design_scores_gemma":[0.0003903748,0.0002862964,0.00142347,0.0005384316,0.00001566421,0.00022319,0.000007078665,0.9513202,0.04380845,0.001808162,0.00005007725,0.0001285848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2087384,0.00003156373,0.790282,0.000216488,0.0004274763,0.00007586982,0.000001170681,0.00005450076,0.000172581],"genre_scores_gemma":[0.430647,0.00001413145,0.5691084,0.00007075902,0.0001262887,5.973972e-7,8.587101e-7,0.000008245496,0.00002371804],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9506083,"threshold_uncertainty_score":0.4839558,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}