{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":1044,"total_is_capped":false,"direct_labels_cover":2,"predictions_cover":1044,"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":"22932fd4a33e","filters":{"topic":"Advanced Image and Video Retrieval Techniques"}},"results":[{"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":"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":"W1627400044","doi":"10.5220/0001787803310340","title":"FAST APPROXIMATE NEAREST NEIGHBORS WITH AUTOMATIC ALGORITHM CONFIGURATION","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":2602,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; k-nearest neighbors algorithm; Algorithm; Nearest-neighbor chain algorithm; Artificial intelligence; Cluster analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.00896403359212129,"gpt":0.2526739173602294,"spread":0.2437098837681081,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009318484,0.00013779,0.0001372812,0.0000710005,0.00009677377,0.0002155499,0.0003882119,0.00003852116,0.00002819897],"category_scores_gemma":[0.00001413474,0.0000973592,0.0000272385,0.0004063248,0.00003228578,0.001157636,0.00003312359,0.0000912839,0.00003418591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002593805,"about_ca_system_score_gemma":0.00003749669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006290564,"about_ca_topic_score_gemma":8.731123e-7,"domain_scores_codex":[0.9990759,0.00002162542,0.0001678089,0.0002783029,0.0002287026,0.0002276524],"domain_scores_gemma":[0.9993338,0.0000250052,0.00007278674,0.0004025282,0.00009066085,0.00007520358],"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":[0.000001795258,0.00003475241,0.000009778124,0.000003593689,0.000003146124,0.00001742535,0.00009813072,0.000009606908,0.0006626923,0.02456965,0.0002847156,0.9743047],"study_design_scores_gemma":[0.0005415948,0.001143652,0.002829672,0.00006415261,0.000008610038,0.00009640749,0.00005719198,0.7712486,0.1963902,0.02475296,0.002344941,0.0005219922],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002614657,0.00002804788,0.9856914,0.0006819281,0.00002749308,0.0002202378,6.82282e-7,0.001166118,0.01192259],"genre_scores_gemma":[0.1423333,0.00001378285,0.856043,0.0009969183,0.00003061588,0.000009449089,0.000004170552,0.000006889488,0.0005618694],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9737827,"threshold_uncertainty_score":0.3970193,"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":"W2086504823","doi":"10.1109/tpami.2014.2321376","title":"Scalable Nearest Neighbor Algorithms for High Dimensional Data","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1415,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Best bin first; k-nearest neighbors algorithm; Nearest-neighbor chain algorithm; Nearest neighbor search; Cover tree; Scalability; Matching (statistics); Cluster analysis; Artificial intelligence; Algorithm; Large margin nearest neighbor; Set (abstract data type); Data mining; Pattern recognition (psychology); Canopy clustering algorithm; Correlation clustering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0376312748953848,"gpt":0.3119817468991594,"spread":0.2743504720037746,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003772605,0.0002094454,0.0003174249,0.0002906653,0.0002671804,0.0001627923,0.0008867292,0.0000612137,0.00005758906],"category_scores_gemma":[0.00001910425,0.0001745575,0.0001308523,0.0007564833,0.0000678842,0.0005604748,0.0000243582,0.0001841943,0.00001558912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001807536,"about_ca_system_score_gemma":0.00001670183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006043136,"about_ca_topic_score_gemma":0.0002563949,"domain_scores_codex":[0.9983532,0.00005719427,0.0003280714,0.000750325,0.0002511917,0.0002599767],"domain_scores_gemma":[0.9982481,0.0003074105,0.00009576142,0.001107627,0.0001133338,0.0001277893],"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":[0.00001013958,0.00009781101,0.00007333171,0.000010127,0.0001488089,0.000001668934,0.00001770695,0.002736836,0.0001911505,0.0002685384,0.00006520654,0.9963787],"study_design_scores_gemma":[0.0001072332,0.0002266259,0.0002800614,0.00001725121,0.0002270793,0.000005425317,0.000002947681,0.7735525,0.2219163,0.002411032,0.00100105,0.0002524776],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001572635,0.00008056744,0.9984439,0.0006600358,0.0001605587,0.0001697522,0.0001654251,0.0001440218,0.00001846924],"genre_scores_gemma":[0.9323228,0.0001740084,0.06639393,0.0007361273,0.0000412089,0.00002743329,0.00004261047,0.00001333682,0.0002485691],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9961262,"threshold_uncertainty_score":0.711825,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2966661","doi":"10.1007/978-3-642-21735-7_6","title":"Transforming Auto-Encoders","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1323,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Scale-invariant feature transform; Artificial neural network; Feature (linguistics); Domain (mathematical analysis); Orientation (vector space); Representation (politics); Encoder; Computer vision; Pattern recognition (psychology); Feature extraction; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02486934333846073,"gpt":0.267167582133467,"spread":0.2422982387950063,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007058712,0.0005824945,0.0005442866,0.000870512,0.0002559634,0.0003380678,0.00397293,0.0003589269,0.00003779999],"category_scores_gemma":[0.00006798869,0.000527878,0.000188272,0.000645076,0.0007320545,0.001600022,0.0008368524,0.0009736067,0.00005396678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002604826,"about_ca_system_score_gemma":0.0004909675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000200391,"about_ca_topic_score_gemma":0.00001979073,"domain_scores_codex":[0.9962822,0.00002034502,0.0005399615,0.001537266,0.0008365551,0.000783709],"domain_scores_gemma":[0.9976878,0.0002254527,0.0002333582,0.001439883,0.0002147964,0.0001986775],"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":[0.000003625708,0.00001324908,0.000005603674,0.00002248931,0.000005390196,0.00009614247,0.0006123824,0.0002406927,0.0002644475,0.03349571,0.00001324712,0.965227],"study_design_scores_gemma":[0.0002254703,0.0003417132,0.00001692875,0.0005451411,0.00001027465,0.0001298265,1.206736e-7,0.04929345,0.04781064,0.8854082,0.01507144,0.001146749],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000003711019,0.0006797562,0.97912,0.0002636928,0.0008806547,0.000433314,0.000002659998,0.0005609202,0.01805533],"genre_scores_gemma":[0.03672412,0.0003741689,0.9598228,0.001758843,0.0003009053,0.00001390226,0.000002677322,0.0000590058,0.0009435512],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9640803,"threshold_uncertainty_score":0.9997173,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2913932916","doi":"10.1016/j.ijar.2008.11.006","title":"Semantic hashing","year":2008,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1272,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.02107195537136535,"gpt":0.2914815013127126,"spread":0.2704095459413473,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003590907,0.0000982733,0.0001640778,0.0002115953,0.00008093522,0.0001200023,0.001189469,0.00003393395,0.000008192279],"category_scores_gemma":[0.0002763814,0.00008326581,0.0001164508,0.0001654651,0.00004082766,0.001564129,0.0001840237,0.0002179704,0.000007099815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007805516,"about_ca_system_score_gemma":0.00008181171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004012095,"about_ca_topic_score_gemma":1.24596e-7,"domain_scores_codex":[0.9987246,0.00002909956,0.0003732709,0.0001294749,0.0005873526,0.0001562278],"domain_scores_gemma":[0.9987643,0.00007489939,0.0004013047,0.0001520217,0.00053529,0.00007221747],"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":[0.0002993896,0.0007018927,0.01983619,0.00005634617,0.000841768,0.01309864,0.007447652,0.001231093,0.03456909,0.2466208,0.006217294,0.6690798],"study_design_scores_gemma":[0.004320981,0.0009453102,0.01149123,0.002325885,0.00005400609,0.06801628,0.0003443131,0.1671411,0.6026452,0.09020378,0.05097472,0.001537245],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04467186,0.0003579984,0.9522019,0.0005008654,0.0004732097,0.00003468334,4.940885e-7,0.00007820561,0.00168076],"genre_scores_gemma":[0.6788814,0.0004334094,0.3202609,0.0001516217,0.0002081841,8.082742e-7,4.606864e-7,0.000007916512,0.00005526251],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6675426,"threshold_uncertainty_score":0.3395481,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2139427956","doi":"10.1109/cvpr.2007.383157","title":"Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1108,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Pattern recognition (psychology); Artificial intelligence; MNIST database; Computer science; Classifier (UML); Unsupervised learning; Invariant (physics); Convolutional neural network; Cognitive neuroscience of visual object recognition; Sigmoid function; Feature extraction; Mathematics; Deep learning; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.01925013140001985,"gpt":0.2800126297393185,"spread":0.2607624983392987,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002563287,0.0000829425,0.0001047671,0.0001641889,0.00008061036,0.00003398839,0.0002801591,0.00003556287,0.000007709783],"category_scores_gemma":[0.00004211675,0.00006293756,0.00002403217,0.0008517834,0.00002805742,0.0003255314,0.00008636162,0.0001393038,0.00001263488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001626914,"about_ca_system_score_gemma":0.00002862206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001547436,"about_ca_topic_score_gemma":0.00001352202,"domain_scores_codex":[0.9993163,0.0000206094,0.0001256781,0.000219472,0.0001558846,0.0001620298],"domain_scores_gemma":[0.9993875,0.0001026277,0.00005074112,0.0002493293,0.0001410775,0.00006874259],"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":[0.00006313885,0.00006728184,0.0004946478,0.00002979431,0.000015168,0.000009155115,0.0008433362,0.00002598827,0.040293,0.03236609,0.0002474906,0.9255449],"study_design_scores_gemma":[0.0003071877,0.0007623395,0.003412752,0.00008080669,0.000008439888,0.00003176366,0.0002145015,0.0004484869,0.9621152,0.01110583,0.02120408,0.0003086067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002300416,0.00003857129,0.9887176,0.000325092,0.000007597987,0.000321241,9.346866e-7,0.0002869282,0.008001664],"genre_scores_gemma":[0.2505112,0.00002149088,0.7486511,0.0002868375,0.00002285979,0.00002607225,0.000004800594,0.000006591865,0.0004690467],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9252363,"threshold_uncertainty_score":0.2566519,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107884096","doi":"10.1109/tpami.2007.70844","title":"A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":981,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Energy minimization; Belief propagation; Markov random field; Cut; Computer science; Minification; Artificial intelligence; Inpainting; Image stitching; Algorithm; Image segmentation; Computer vision; Segmentation; Image (mathematics); Decoding methods","retraction":null,"screen_n_in":null,"score":{"opus":0.03761726605168452,"gpt":0.3531680751970586,"spread":0.3155508091453741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001697672,0.0001824266,0.0004774558,0.000419188,0.000177068,0.00002753182,0.0002699674,0.00004483768,0.00001543915],"category_scores_gemma":[0.000005745375,0.0001384022,0.0001497708,0.001072237,0.00007384872,0.0001908645,0.000003269035,0.0001027127,1.687602e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001428674,"about_ca_system_score_gemma":0.00002701565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004183742,"about_ca_topic_score_gemma":0.0005947094,"domain_scores_codex":[0.9987666,0.000172552,0.0003589356,0.000396649,0.000169155,0.0001361133],"domain_scores_gemma":[0.9987411,0.0004754835,0.0001714319,0.0003661243,0.0001870712,0.00005872561],"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":[0.0003960389,0.0009340626,0.0007294727,0.00002204377,0.0006507314,0.000005864234,0.001797405,0.05434399,0.0001905371,0.00002254039,0.000004958417,0.9409024],"study_design_scores_gemma":[0.0006492564,0.001467713,0.0003531911,0.00001772278,0.0003778536,0.000004392184,0.0001304486,0.3899416,0.6066962,0.0001076442,0.00004443812,0.0002095594],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003818594,0.00007133991,0.9956319,0.00005092786,0.00003257958,0.0003125688,0.00000994765,0.00005843117,0.00001365746],"genre_scores_gemma":[0.881981,0.0001118735,0.1176446,0.0001105226,0.00000409068,0.00008523037,0.000003025966,0.000006570076,0.00005308341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9406928,"threshold_uncertainty_score":0.5643876,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159680539","doi":"10.1109/tpami.2007.1055","title":"Sharing Visual Features for Multiclass and Multiview Object Detection","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":707,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"National Geospatial-Intelligence Agency; National Science Foundation","keywords":"Artificial intelligence; Computer science; Classifier (UML); Computational complexity theory; Object detection; Pattern recognition (psychology); Computation; Cognitive neuroscience of visual object recognition; Machine learning; Contextual image classification; Detector; Computer vision; Object (grammar); Image (mathematics); Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.02232043985467503,"gpt":0.3305113970215057,"spread":0.3081909571668306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004192177,0.0001908572,0.0002524407,0.0004402237,0.0002529234,0.0001352578,0.0002117641,0.00006841607,0.000004043211],"category_scores_gemma":[0.00001431915,0.0001647209,0.0001682011,0.0006858997,0.00005132668,0.0003390532,0.000008207298,0.0002027745,0.000001316558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003082428,"about_ca_system_score_gemma":0.000005169593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003830806,"about_ca_topic_score_gemma":0.001943066,"domain_scores_codex":[0.9987648,0.00002378112,0.000285271,0.0005325191,0.0001514653,0.0002422219],"domain_scores_gemma":[0.9992191,0.0002576978,0.00008508421,0.0002519691,0.00008121241,0.0001049193],"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":[0.00002156267,0.00005780744,0.0001804693,0.00002151115,0.0001226852,0.000003359583,0.0001494757,0.0002658711,0.003666438,0.00002353385,4.245348e-7,0.9954869],"study_design_scores_gemma":[0.00009687617,0.0002153067,0.002099482,0.00002252925,0.0001742259,0.00001341995,0.00003229926,0.1211516,0.8755963,0.000329057,0.00006720044,0.000201757],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004517067,0.0003197956,0.9946232,0.00006043844,0.00008592004,0.0002289726,0.000009158191,0.0001421606,0.00001332885],"genre_scores_gemma":[0.9859296,0.0005778319,0.01313528,0.0002079207,0.00001879519,0.00002291428,0.000001383647,0.00001043595,0.00009581302],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9952851,"threshold_uncertainty_score":0.6717122,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2221852422","doi":"","title":"Minimal Loss Hashing for Compact Binary Codes","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":704,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Hash function; Binary code; Computer science; Binary number; Code (set theory); Algorithm; Hash table; Theoretical computer science; Similarity (geometry); Hinge loss; Artificial intelligence; Image (mathematics); Mathematics; Arithmetic; Set (abstract data type)","retraction":null,"screen_n_in":null,"score":{"opus":0.07442839610254194,"gpt":0.3188149895157965,"spread":0.2443865934132545,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001755603,0.0001027559,0.0001307504,0.00006144702,0.00009635244,0.00006113922,0.0006045552,0.00003702491,0.00002922509],"category_scores_gemma":[0.00004623278,0.00008283329,0.00006945008,0.0001524392,0.00004768212,0.0009152917,0.0001115286,0.00006085195,0.00001859564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001639975,"about_ca_system_score_gemma":0.00002320486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002268862,"about_ca_topic_score_gemma":0.000001655687,"domain_scores_codex":[0.9992585,0.00001484366,0.0001455487,0.0002431488,0.0001006791,0.0002372555],"domain_scores_gemma":[0.9993954,0.00009711727,0.00004908422,0.0003198835,0.00007140235,0.0000671166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002509371,0.0006187572,0.00771532,0.0001183049,0.00007787701,0.0001582202,0.003534086,0.000004952135,0.03266529,0.6797286,0.04583939,0.2292883],"study_design_scores_gemma":[0.0005127333,0.001053908,0.005848196,0.00004260984,0.000008834988,0.00004071342,0.00007536421,0.01162432,0.8776841,0.07477008,0.02784729,0.000491882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004481074,0.00007474355,0.9837203,0.0002019657,0.00007586514,0.000180567,0.000002491638,0.0005289894,0.01073398],"genre_scores_gemma":[0.5379956,0.00001063908,0.4611627,0.0003050832,0.00002485565,0.000006232693,9.618645e-7,0.000006262459,0.0004876956],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8450188,"threshold_uncertainty_score":0.3377844,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3152336969","doi":"10.1109/iccv.2003.1238630","title":"Recognising panoramas","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":681,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Panorama; Computer science; Computer vision; Artificial intelligence; Probabilistic logic; Matching (statistics); Invariant (physics); Object (grammar); Computer graphics (images); Noise (video); Image (mathematics); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01852197886323722,"gpt":0.2696540420565059,"spread":0.2511320631932687,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001335703,0.00005529596,0.00005709053,0.00003514521,0.00005279434,0.00008211887,0.0002227078,0.00002238347,0.00005502212],"category_scores_gemma":[0.0001024164,0.00004568878,0.00002606874,0.0002452616,0.00001180833,0.0005182153,0.00004091614,0.00005257352,0.00008086926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001126067,"about_ca_system_score_gemma":0.00001856565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001582807,"about_ca_topic_score_gemma":3.126133e-7,"domain_scores_codex":[0.9994976,0.00002567754,0.00008353531,0.000168378,0.0000836145,0.0001412011],"domain_scores_gemma":[0.9995922,0.00003420451,0.00002051164,0.0002739938,0.00003861135,0.00004047165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[6.198637e-7,0.00002139424,0.0002914816,0.000002412764,0.000002770761,0.00001237199,0.00004463705,5.726734e-7,0.002740633,0.5497957,0.0020701,0.4450173],"study_design_scores_gemma":[0.00008179441,0.00005452804,0.00007558939,0.000006091933,9.043413e-7,0.00002435462,0.000008949376,0.0002396859,0.6218294,0.1443987,0.2331376,0.0001424202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002465496,0.00008225774,0.8667154,0.0001063173,0.00006741565,0.00004165977,4.907689e-8,0.0004043763,0.132336],"genre_scores_gemma":[0.1855955,0.00003072426,0.8112388,0.0006948163,0.000009985863,0.000002635488,1.304806e-7,0.000004224224,0.002423268],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6190887,"threshold_uncertainty_score":0.1863135,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012778485","doi":"10.5244/c.16.23","title":"Invariant Features from Interest Point Groups","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":607,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Invariant (physics); Computer science; Mathematics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.03741037394625085,"gpt":0.2594839259170317,"spread":0.2220735519707809,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006833212,0.0001182709,0.0001217329,0.00005935421,0.00005832196,0.000166577,0.0007544113,0.00005153619,0.0004296794],"category_scores_gemma":[0.00006608007,0.00009076702,0.00005433036,0.0002136619,0.00002943169,0.0008539682,0.0003441187,0.0001528232,0.0002459098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002399959,"about_ca_system_score_gemma":0.000003999668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006877662,"about_ca_topic_score_gemma":0.00002313136,"domain_scores_codex":[0.9991845,0.00002875465,0.0001478217,0.000321231,0.0001237735,0.0001939339],"domain_scores_gemma":[0.9992049,0.00008195521,0.00004262642,0.0005542972,0.00003691811,0.00007934867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008086675,0.0001832993,0.0001677419,0.000006184229,0.00003042473,0.0001992642,0.001006343,9.708922e-7,0.0162583,0.4375454,0.1445913,0.4000027],"study_design_scores_gemma":[0.0007509635,0.0004560777,0.004714533,0.00009506528,0.0000117299,0.0001056957,0.0001171527,0.01151354,0.6589929,0.252974,0.06928954,0.0009788073],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00134564,0.0003301766,0.9767789,0.002179738,0.0001323238,0.00008918247,0.000002013266,0.0006588311,0.01848323],"genre_scores_gemma":[0.7269549,0.0001348525,0.2685555,0.002275712,0.00009436232,0.000006805934,0.00000192812,0.000008789289,0.001967069],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7256093,"threshold_uncertainty_score":0.4704688,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2557889580","doi":"10.1109/cvpr.2017.305","title":"Deep Watershed Transform for Instance Segmentation","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":555,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Watershed; Computer science; Artificial intelligence; Conditional random field; Segmentation; Deep learning; Image segmentation; Object (grammar); Convolutional neural network; Matching (statistics); Task (project management); Pattern recognition (psychology); Computer vision; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02710488122607905,"gpt":0.3232249328594106,"spread":0.2961200516333315,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009903568,0.0000654761,0.00007143688,0.00002325606,0.0002938504,0.0002223566,0.0006291451,0.00002455585,0.000005952425],"category_scores_gemma":[0.00002577493,0.000051591,0.00004083432,0.00002625086,0.00002719514,0.001741103,0.00004291374,0.0000291031,0.000006646053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002114483,"about_ca_system_score_gemma":0.000009561367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007578864,"about_ca_topic_score_gemma":0.00001526632,"domain_scores_codex":[0.9994919,0.000003758146,0.00009851521,0.0001782141,0.00008288363,0.0001447592],"domain_scores_gemma":[0.9994066,0.00001715867,0.00005276249,0.0004358044,0.00005517483,0.00003244864],"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":[0.0000130147,0.00001514138,0.00007072755,0.00001354547,0.000004622464,0.000001604318,0.0002356868,0.000002215184,0.006683224,0.03269173,0.0003170322,0.9599515],"study_design_scores_gemma":[0.0004093771,0.0001079287,0.0004791815,0.000006599527,0.000002267212,0.000001865146,0.00001698379,0.01018707,0.9161586,0.06255485,0.009948848,0.0001264553],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002815893,0.00002170051,0.9912105,0.00120377,0.00008019418,0.000302729,6.706375e-7,0.0002004857,0.006698378],"genre_scores_gemma":[0.3522202,0.00004407084,0.6464126,0.0002991408,0.00002665235,0.00004772139,0.000002163483,0.00000490756,0.0009425626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.959825,"threshold_uncertainty_score":0.2260089,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2104408738","doi":"10.1109/cvpr.2014.254","title":"Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts","year":2014,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":550,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Pascal (unit); Torso; Computer science; Artificial intelligence; Computer vision; Object (grammar); Low resolution; Representation (politics); Pattern recognition (psychology); High resolution","retraction":null,"screen_n_in":null,"score":{"opus":0.05712206809061373,"gpt":0.3150092895509254,"spread":0.2578872214603117,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004367293,0.000157962,0.0001911298,0.00009711477,0.0002685282,0.0006991921,0.0002114523,0.00005645111,9.008028e-7],"category_scores_gemma":[0.0003189504,0.0001425201,0.00002629708,0.0002165039,0.00005894831,0.002272886,0.0003628689,0.0001353329,4.359274e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002079343,"about_ca_system_score_gemma":0.00001702342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001431486,"about_ca_topic_score_gemma":0.00002149214,"domain_scores_codex":[0.9987132,0.00006962685,0.0002010624,0.0005207889,0.0001642912,0.0003310558],"domain_scores_gemma":[0.999096,0.0002114576,0.00009535942,0.0004109318,0.00006989654,0.0001162978],"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":[0.00001024036,0.00001571723,0.000735071,0.0001004378,0.00001941527,0.00003893856,0.001250442,0.0006807272,0.03178002,0.006812186,0.00001354523,0.9585432],"study_design_scores_gemma":[0.0001637692,0.00007009052,0.0001125926,0.0001125751,0.00001010278,0.0001187922,0.0001185373,0.7042067,0.2233534,0.07140018,0.00007103073,0.0002622489],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.096302,0.0005694189,0.9021387,0.00005726971,0.00007808609,0.0001261574,1.571062e-7,0.0002929146,0.0004352778],"genre_scores_gemma":[0.8383424,0.0001989404,0.1611724,0.0001797314,0.00004938511,0.000003666534,1.458539e-7,0.00001147362,0.00004192029],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.958281,"threshold_uncertainty_score":0.6742326,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963674285","doi":"10.1109/cvpr.2018.00282","title":"Learning to Find Good Correspondences","year":2018,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":546,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01651373649161346,"gpt":0.2680865692247527,"spread":0.2515728327331392,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005176099,0.0003782131,0.0003923126,0.0003230787,0.0004603708,0.0009525443,0.003791339,0.0002828595,0.00009001034],"category_scores_gemma":[0.00346872,0.0003951653,0.0001876925,0.0007956887,0.0001897926,0.0004246221,0.005047863,0.0008962394,0.0002169064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001174643,"about_ca_system_score_gemma":0.0003266238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002404436,"about_ca_topic_score_gemma":0.0001112417,"domain_scores_codex":[0.9937274,0.003509817,0.0005007031,0.001186962,0.0005999593,0.0004750932],"domain_scores_gemma":[0.9928229,0.001374461,0.0004621504,0.002670045,0.002398572,0.00027184],"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":[0.00003568068,0.0005719825,0.00251698,0.0002009891,0.0001011203,0.00003552752,0.03003827,0.0001864116,0.00999828,0.121276,0.009194857,0.8258439],"study_design_scores_gemma":[0.000540831,0.000009423158,0.006951251,0.00478827,0.00004323147,0.0000398283,0.0001987951,0.04322236,0.6681958,0.05844006,0.2156501,0.001920044],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0180744,0.0004761347,0.943218,0.005836632,0.0003275688,0.0004388982,0.00001044693,0.000883231,0.03073471],"genre_scores_gemma":[0.3452097,0.0004543705,0.6197701,0.0003509431,0.00006537129,0.00009757499,0.00006578947,0.00004910801,0.03393712],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8239239,"threshold_uncertainty_score":0.99985,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2113307832","doi":"","title":"Hamming Distance Metric Learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":518,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Computer science; Hamming distance; Binary code; Metric (unit); Binary number; Multilinear map; Ranking (information retrieval); Inference; Algorithm; Theoretical computer science; Similarity (geometry); Artificial intelligence; Mathematics; Deep learning; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.0165373512354428,"gpt":0.2859282439208606,"spread":0.2693908926854178,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002515723,0.0000738184,0.00008036601,0.00009230889,0.00009894247,0.00006089429,0.0003383035,0.00002412079,0.00002226187],"category_scores_gemma":[0.0001617376,0.00006093108,0.00003402576,0.0007152221,0.00001471765,0.001526592,0.0001590027,0.0001234512,0.00008390849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002795568,"about_ca_system_score_gemma":0.000007039737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000443487,"about_ca_topic_score_gemma":2.372798e-7,"domain_scores_codex":[0.9992873,0.00002585214,0.0001025459,0.000142145,0.0001491911,0.0002929368],"domain_scores_gemma":[0.9995298,0.00008269207,0.00004113677,0.0002287213,0.00003391208,0.00008375296],"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":[0.000001645181,0.00004030292,0.01238647,0.000007896762,0.00000536723,0.000003703638,0.000262236,0.000007579128,0.00130587,0.2263126,0.0009670294,0.7586993],"study_design_scores_gemma":[0.0001511815,0.00008943838,0.005109043,0.00001754286,0.00000441712,0.00002409332,0.00006044723,0.004359645,0.2385691,0.005225504,0.7459613,0.0004282125],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002874864,0.001186872,0.9779676,0.00008569937,0.0001022774,0.00004516331,5.342859e-8,0.0006258789,0.01969903],"genre_scores_gemma":[0.7046061,0.00004378877,0.2927443,0.0001612551,0.00005231201,0.000003594194,2.387582e-7,0.000004518384,0.002383825],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7582711,"threshold_uncertainty_score":0.2484698,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2604233003","doi":"10.1109/cvpr.2017.12","title":"Convolutional Neural Network Architecture for Geometric Matching","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":514,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Agence Nationale de la Recherche; Canadian Institute for Advanced Research","keywords":"Affine transformation; Computer science; Convolutional neural network; Artificial intelligence; Matching (statistics); Generalization; Pattern recognition (psychology); Transformation (genetics); Feature extraction; Spline (mechanical); Artificial neural network; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03670996608720355,"gpt":0.3223604438481787,"spread":0.2856504777609752,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004147908,0.0003222926,0.0003946319,0.0002322526,0.0004216938,0.0005869793,0.00261842,0.0002574783,0.00001113343],"category_scores_gemma":[0.0002032297,0.0002777632,0.0002961783,0.0001945715,0.00006971126,0.0003400902,0.002834603,0.0007288111,0.000007392124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006613717,"about_ca_system_score_gemma":0.0001430619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003358658,"about_ca_topic_score_gemma":0.000005444441,"domain_scores_codex":[0.9980569,0.00003738313,0.0003034307,0.0007789258,0.000310794,0.0005126047],"domain_scores_gemma":[0.9976803,0.0003099581,0.0003516949,0.001352962,0.0001961527,0.0001089456],"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":[0.00004655414,0.00006584916,0.0002254168,0.0003443342,0.000119326,0.00003065056,0.0001154504,0.04530467,0.00008388155,0.1203971,0.04520692,0.7880599],"study_design_scores_gemma":[0.0002167501,0.00008894403,0.0008073652,0.000113554,0.00001547131,0.00002860561,0.000001354859,0.0363747,0.000901101,0.9236192,0.03727518,0.0005578518],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001497657,0.001352767,0.9926789,0.001245318,0.001212141,0.000719019,0.0000241946,0.0007197264,0.001898142],"genre_scores_gemma":[0.06522109,0.0001088937,0.9310104,0.0007516866,0.001083396,0.000146579,0.00004732779,0.00002840914,0.001602268],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8032221,"threshold_uncertainty_score":0.9999675,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2105464770","doi":"10.1109/cvpr.2006.200","title":"Multiclass Object Recognition with Sparse, Localized Features","year":2006,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":459,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Pooling; Object (grammar); Cognitive neuroscience of visual object recognition; Pattern recognition (psychology); Feature (linguistics); Categorization; Matching (statistics); Position (finance); Class (philosophy); Task (project management); Computer vision; Scale (ratio); Feature extraction; Machine learning; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01419158525478288,"gpt":0.2502844115908242,"spread":0.2360928263360413,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008787839,0.0001210751,0.0001176939,0.00007052468,0.00007030821,0.0001149803,0.0002674111,0.0000466014,0.00001897493],"category_scores_gemma":[0.00002011354,0.00008424394,0.00003455459,0.0003629167,0.00004357241,0.000618936,0.00006180664,0.00009784545,0.00004639869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002631003,"about_ca_system_score_gemma":0.00002369095,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001667126,"about_ca_topic_score_gemma":0.00007023943,"domain_scores_codex":[0.9991584,0.00002917942,0.0001244404,0.000287235,0.0001913017,0.0002094807],"domain_scores_gemma":[0.999449,0.00005521086,0.00005092796,0.0002988951,0.0001071025,0.000038845],"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":[0.0001607659,0.0002589998,0.001094704,0.00003476125,0.00003014405,0.0002068039,0.000111214,0.0000797451,0.01624605,0.03567747,0.02952518,0.9165742],"study_design_scores_gemma":[0.000961507,0.0003332026,0.003546387,0.0000493065,0.000009725049,0.00009799704,0.00001758851,0.002498449,0.9129069,0.05846499,0.02067401,0.0004399798],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001763449,0.00008773788,0.9728583,0.0002280001,0.00003643485,0.0001702322,0.000001212081,0.000785972,0.02406864],"genre_scores_gemma":[0.2740569,0.00001888887,0.7231723,0.0004503108,0.0000543518,0.0000170358,0.000007630308,0.00001022705,0.002212385],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9161342,"threshold_uncertainty_score":0.3435368,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2122015342","doi":"10.1109/cvpr.2005.235","title":"Multi-Image Matching Using Multi-Scale Oriented Patches","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":401,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"RANSAC; Artificial intelligence; Pattern recognition (psychology); Invariant (physics); Computer science; Feature extraction; Scale space; Matching (statistics); Feature (linguistics); Computer vision; Mathematics; Haar wavelet; Outlier; Panorama; Feature vector; Wavelet transform; Wavelet; Image (mathematics); Discrete wavelet transform; Image processing","retraction":null,"screen_n_in":null,"score":{"opus":0.03801778359576503,"gpt":0.3286674344129686,"spread":0.2906496508172036,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002058632,0.0001890511,0.0001836202,0.0001035628,0.0001775823,0.000145673,0.000579127,0.00006623725,0.00002971562],"category_scores_gemma":[0.00003948332,0.0001631006,0.00008345074,0.000376582,0.00005111989,0.001991257,0.0003783773,0.0001760988,0.00007729512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008133006,"about_ca_system_score_gemma":0.00003220707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009225955,"about_ca_topic_score_gemma":0.00003889293,"domain_scores_codex":[0.9986571,0.00003986767,0.0002775276,0.0004435326,0.0002090721,0.0003728692],"domain_scores_gemma":[0.9991093,0.00004480448,0.00008667664,0.0005341586,0.0001122252,0.0001128312],"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":[0.00001045431,0.0004985449,0.00128174,0.00002721043,0.00002005165,0.00004032834,0.002304462,0.0001613084,0.7791232,0.001492792,0.0002703589,0.2147696],"study_design_scores_gemma":[0.0005716328,0.00003668791,0.0008533744,0.00003697365,0.000005640622,0.00002845941,0.0001354429,0.3788945,0.612195,0.0003776541,0.006480127,0.0003845204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01557235,0.0001003691,0.9824229,0.0002306583,0.00008679117,0.0001777798,0.000001804812,0.0009888965,0.0004183806],"genre_scores_gemma":[0.09490836,0.00002258725,0.9035814,0.0005260108,0.00006346695,0.00000540038,0.000001230089,0.00001667342,0.0008748962],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3787332,"threshold_uncertainty_score":0.6651048,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2396976214","doi":"","title":"Using very deep autoencoders for content-based image retrieval.","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":375,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Image retrieval; Binary code; Hash function; Pattern recognition (psychology); Content-based image retrieval; Set (abstract data type); Binary number; Image (mathematics); Matching (statistics); Deep learning; Binary image; Computer vision; Image processing; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1788960244118397,"gpt":0.3284254669151485,"spread":0.1495294425033087,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002702897,0.0001628159,0.0001848745,0.0001170785,0.0001292645,0.00008363757,0.0006552035,0.00006918038,0.00004106165],"category_scores_gemma":[0.0001858859,0.0001420719,0.0001355617,0.0003179868,0.00008285806,0.0011198,0.000113923,0.00008780991,0.00001300433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006204627,"about_ca_system_score_gemma":0.00008249083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003396866,"about_ca_topic_score_gemma":0.000002181705,"domain_scores_codex":[0.9987993,0.00002797052,0.0002469584,0.0003878418,0.0001780282,0.0003598901],"domain_scores_gemma":[0.9989197,0.0001009583,0.0001011153,0.0005274376,0.0002503211,0.0001004648],"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":[0.0009183745,0.0007551767,0.0009785065,0.0002596803,0.0001340526,0.000166026,0.00136841,0.00004262974,0.7655531,0.1430856,0.001977482,0.08476097],"study_design_scores_gemma":[0.0004591107,0.0002075706,0.00008387072,0.0000175968,0.000009695984,0.000005508083,0.00004477636,0.099011,0.8917726,0.007413324,0.0007404626,0.0002344524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006856215,0.00007719814,0.9958662,0.00007959113,0.0001732295,0.0003912898,0.000002305644,0.0006925174,0.002032005],"genre_scores_gemma":[0.02086742,0.00000711959,0.9778987,0.000949537,0.0000270512,0.000009905933,0.000001635905,0.00001720204,0.0002214301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1356723,"threshold_uncertainty_score":0.5793526,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2609825896","doi":"10.1109/cvpr.2017.477","title":"Annotating Object Instances with a Polygon-RNN","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":372,"is_retracted":false,"has_abstract":true,"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; “la Caixa” Foundation; Nvidia","keywords":"Computer science; Polygon (computer graphics); Segmentation; Artificial intelligence; Annotation; Object (grammar); Generalization; Ground truth; Factor (programming language); Matching (statistics); Process (computing); Pattern recognition (psychology); Image segmentation; Vertex (graph theory); Pixel; Computer vision; Graph; Theoretical computer science; Mathematics; Programming language","retraction":null,"screen_n_in":null,"score":{"opus":0.01881389897620761,"gpt":0.3009481754234627,"spread":0.2821342764472551,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001182539,0.00009952979,0.0001105702,0.00003841255,0.0004531396,0.0004658246,0.001032683,0.00002291737,0.000006167299],"category_scores_gemma":[0.00008632457,0.00006686088,0.00002472764,0.0000949788,0.00008150064,0.00192326,0.0002281383,0.00008424334,0.00001318321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001300126,"about_ca_system_score_gemma":0.00004277683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007980911,"about_ca_topic_score_gemma":0.00003925076,"domain_scores_codex":[0.9992486,0.00001099687,0.0001005753,0.0002644302,0.0001694529,0.0002059246],"domain_scores_gemma":[0.9989057,0.00003350632,0.0001262055,0.0008154711,0.00007008139,0.00004901361],"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":[0.00002172226,0.0000394452,0.007251092,0.00001649701,0.00001724363,0.0001063462,0.0004710644,0.000002552504,0.003582577,0.08311911,0.0009626191,0.9044097],"study_design_scores_gemma":[0.001292018,0.001268478,0.0357987,0.0003122801,0.00001177346,0.0001323308,0.0002231905,0.007827136,0.8509436,0.05218766,0.04876448,0.001238308],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004563815,0.00009130822,0.9298748,0.0004781872,0.00005304326,0.00008925029,4.566673e-7,0.0003801483,0.06446903],"genre_scores_gemma":[0.6350989,0.00002371137,0.3636575,0.0002380829,0.00002886496,0.000006315714,1.484579e-7,0.000004894612,0.0009416122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9031714,"threshold_uncertainty_score":0.4491958,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2067381981","doi":"10.1016/j.cviu.2013.04.005","title":"50 Years of object recognition: Directions forward","year":2013,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":351,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Cognitive neuroscience of visual object recognition; Inference; Machine learning; Object (grammar); Automation; Human–computer interaction; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0374214784470072,"gpt":0.2891256136729817,"spread":0.2517041352259745,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001427578,0.0001179419,0.0001821891,0.0002036609,0.0001331267,0.0002554524,0.0002299999,0.0000466829,0.00003926213],"category_scores_gemma":[0.00002548276,0.0001112139,0.00006982206,0.0003356757,0.000097795,0.001478269,0.000260717,0.0001103847,0.00002622882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005699628,"about_ca_system_score_gemma":0.00001551863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001348111,"about_ca_topic_score_gemma":5.903581e-7,"domain_scores_codex":[0.9990807,0.00004289687,0.000227673,0.0002978757,0.0001634762,0.0001873756],"domain_scores_gemma":[0.9993185,0.0001486057,0.00009236731,0.0002582003,0.00009108514,0.00009125467],"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":[0.000007714642,0.00006263874,0.00005119622,0.00004169045,0.00002241633,0.00000900273,0.0004247836,7.876759e-7,0.01116411,0.008527594,0.006321105,0.973367],"study_design_scores_gemma":[0.002863465,0.003012332,0.01095998,0.001210613,0.00005620008,0.0003833888,0.0009667212,0.09386765,0.07690986,0.7748762,0.03299959,0.001893967],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002219312,0.0001890534,0.995316,0.0002648831,0.0001843032,0.0001940054,0.000001842556,0.0002302274,0.001400361],"genre_scores_gemma":[0.3479234,0.0004979951,0.651139,0.0002714256,0.00006999147,0.000008412871,0.000003159755,0.0000130388,0.00007355421],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.971473,"threshold_uncertainty_score":0.453517,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148554573","doi":"10.1109/cvpr.2013.388","title":"Cartesian K-Means","year":2013,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":309,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Codebook; Cartesian product; Vector quantization; Quantization (signal processing); Hash function; Cluster analysis; Linde–Buzo–Gray algorithm; Computer science; Algorithm; Binary number; Outer product; Theoretical computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Discrete mathematics; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.008591707009253102,"gpt":0.2404465763594297,"spread":0.2318548693501766,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003683319,0.00005439976,0.00005379921,0.00003295406,0.00003739553,0.00009793712,0.0004047499,0.00001977607,0.0001666492],"category_scores_gemma":[0.00001566617,0.00004161969,0.00002561598,0.000172864,0.00001489004,0.0009195632,0.00009606343,0.00004555643,0.0006447008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009323383,"about_ca_system_score_gemma":0.000009486224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004403586,"about_ca_topic_score_gemma":0.000001215816,"domain_scores_codex":[0.9995378,0.000008965556,0.00007444372,0.000147517,0.0000865483,0.0001447412],"domain_scores_gemma":[0.9995271,0.00001958133,0.00001490411,0.0003305245,0.00005184902,0.00005602042],"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":[2.323524e-7,0.0000233528,0.000377319,0.000003111421,0.000003534313,0.000005892406,0.0001096775,9.545707e-7,0.005484215,0.2139202,0.03341071,0.7466608],"study_design_scores_gemma":[0.0002552716,0.0002482936,0.008144008,0.00001539596,0.000002556801,0.00003861337,0.00005472678,0.02774781,0.4431908,0.3286403,0.1910817,0.0005805736],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002234689,0.00003113382,0.9350533,0.001145809,0.00003693264,0.00009492494,6.727475e-8,0.0005746104,0.06283975],"genre_scores_gemma":[0.3192172,0.00001538216,0.6742241,0.001602879,0.00003122636,0.00002136426,2.98539e-7,0.000004928621,0.004882609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7460803,"threshold_uncertainty_score":0.8286539,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107640784","doi":"","title":"Contextual Models for Object Detection Using Boosted Random Fields","year":2004,"lang":"en","type":"article","venue":"DSpace@MIT (Massachusetts Institute of Technology)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":305,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia Hospital","funders":"Defense Advanced Research Projects Agency","keywords":"Conditional random field; Boosting (machine learning); Exploit; Computer science; Inference; Artificial intelligence; Object detection; Pixel; Graph; Random field; Pattern recognition (psychology); Cascade; Machine learning; Computer vision; Theoretical computer science; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.0291059072421215,"gpt":0.286198180272219,"spread":0.2570922730300976,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002608535,0.0003245355,0.000538143,0.0007379106,0.0002746924,0.00006069454,0.001073793,0.0005441703,0.000001379605],"category_scores_gemma":[0.0004086282,0.0003153897,0.0002203188,0.001144164,0.0003510957,0.001657259,0.0003136816,0.0003912045,0.000002730541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001989538,"about_ca_system_score_gemma":0.0001908212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007986428,"about_ca_topic_score_gemma":0.00009116553,"domain_scores_codex":[0.9980668,0.00001963799,0.0005061161,0.0006221979,0.0002804425,0.0005048286],"domain_scores_gemma":[0.9982656,0.00005332167,0.0003367073,0.00093116,0.0003364307,0.00007674761],"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":[0.0006943966,0.0005744527,0.0001725784,0.0002881574,0.000480423,0.0001331549,0.0008672946,0.02181263,0.2032848,0.2335289,0.0004409117,0.5377223],"study_design_scores_gemma":[0.004360413,0.0006400515,0.00001198907,0.0001840619,0.00005376466,0.0000891801,0.00004974593,0.03366085,0.8135309,0.1322144,0.01469144,0.0005132001],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02880297,0.0005975578,0.9657874,0.001841554,0.0004757924,0.0009353707,0.00002077113,0.001241993,0.0002966425],"genre_scores_gemma":[0.7003778,0.00006736516,0.2992302,0.0001556228,0.00003715215,0.00006568168,0.00000307041,0.00001961068,0.00004345288],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6715748,"threshold_uncertainty_score":0.9999298,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2130623086","doi":"10.1109/iccv.2007.4408853","title":"Non-metric affinity propagation for unsupervised image categorization","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":302,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Automatic summarization; Affinity propagation; Computer science; Metric (unit); Categorization; Cluster analysis; Similarity (geometry); Scale-invariant feature transform; Preprocessor; Matching (statistics); Feature extraction; Image (mathematics); Computer vision; Mathematics; Fuzzy clustering","retraction":null,"screen_n_in":null,"score":{"opus":0.0154490916039802,"gpt":0.2974285247303452,"spread":0.281979433126365,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005714605,0.0001025326,0.0001022012,0.0002379273,0.000114391,0.0001022099,0.0003647534,0.00005588086,0.000007582894],"category_scores_gemma":[0.0002109626,0.00008812333,0.0000490752,0.001178474,0.00001993187,0.001202501,0.00007424111,0.00006171649,0.00001839546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005299967,"about_ca_system_score_gemma":0.00003871881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001065168,"about_ca_topic_score_gemma":0.000003339355,"domain_scores_codex":[0.99908,0.000009156932,0.000220851,0.0002751175,0.0001726104,0.0002422881],"domain_scores_gemma":[0.9991291,0.0001217983,0.00007286789,0.0003022219,0.0003101279,0.00006386826],"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":[0.00005753606,0.0002372142,0.0009974863,0.00009542618,0.00001304152,0.0000101735,0.0003331435,0.00001177467,0.1581945,0.1020213,0.002621422,0.735407],"study_design_scores_gemma":[0.0003419049,0.0001982674,0.003631194,0.000005278911,0.000003711502,0.000002913431,0.00001365883,0.01733552,0.9626175,0.01404679,0.001625035,0.0001782295],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001700092,0.00001846091,0.9933349,0.0001507116,0.0001063738,0.0005887413,9.174072e-7,0.0004652392,0.00363453],"genre_scores_gemma":[0.4993528,0.00001179898,0.5000553,0.0001786345,0.00005628853,0.00001903711,0.000009821948,0.000007902073,0.0003084394],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.804423,"threshold_uncertainty_score":0.3593565,"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":"W2146020873","doi":"10.1109/crv.2012.60","title":"Fast Matching of Binary Features","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":291,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Binary number; Hamming distance; Matching (statistics); Binary search algorithm; Scale-invariant feature transform; Cluster analysis; Feature (linguistics); Pattern recognition (psychology); Binary search tree; Nearest neighbor search; Algorithm; Artificial intelligence; Binary tree; Search algorithm; Feature extraction; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01447656425421122,"gpt":0.2902956974137997,"spread":0.2758191331595884,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001152299,0.00005455504,0.00007566268,0.00004756838,0.00002805301,0.00001480572,0.0003130604,0.00002382036,0.00001504708],"category_scores_gemma":[0.000012653,0.00004116612,0.00003214836,0.0001687488,0.00001734279,0.0008921583,0.0001830761,0.00005895278,0.00001201444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000691583,"about_ca_system_score_gemma":0.000006654533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000113521,"about_ca_topic_score_gemma":2.304555e-7,"domain_scores_codex":[0.9995574,0.00001339473,0.00008704849,0.00008282249,0.0001053032,0.0001540512],"domain_scores_gemma":[0.999617,0.00003332246,0.00003691103,0.0002429414,0.000025958,0.00004390038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000008146435,0.0001769314,0.002630518,0.00004164969,0.00001437489,0.00000552974,0.001693435,0.000008915878,0.1830626,0.4394309,0.008087894,0.3648391],"study_design_scores_gemma":[0.00008772761,0.00008841189,0.0194104,0.00002398349,0.00000253849,0.00002616178,0.00007537047,0.0001118379,0.9592649,0.01496971,0.005769105,0.0001698198],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007207615,0.0004565232,0.9790395,0.0001210966,0.00008198142,0.00004456367,3.369247e-7,0.0002125678,0.01283579],"genre_scores_gemma":[0.6850719,0.00002072934,0.3140391,0.0001549756,0.00002903254,0.000001311615,2.400854e-7,0.000002778434,0.0006798869],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7762023,"threshold_uncertainty_score":0.1678706,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2122196799","doi":"10.1109/cvpr.2012.6248043","title":"Fast search in Hamming space with multi-index hashing","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":266,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Hash function; Hamming space; Hamming distance; Hamming code; Computer science; Substring; Linear code; Hash table; Binary code; Dynamic perfect hashing; Perfect hash function; Algorithm; Theoretical computer science; Binary number; Bit array; Hamming(7,4); Code (set theory); Data structure; Block code; Double hashing; Mathematics; Arithmetic; Decoding methods","retraction":null,"screen_n_in":null,"score":{"opus":0.03193554309429394,"gpt":0.3080545919139152,"spread":0.2761190488196212,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003609656,0.000104606,0.0001104813,0.0001287969,0.00006281373,0.00009958883,0.0003977107,0.00003745054,0.000009777959],"category_scores_gemma":[0.00002928449,0.00007845788,0.00001915422,0.0004892771,0.00003151494,0.001930257,0.0002277917,0.0002009049,0.00002573611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005241134,"about_ca_system_score_gemma":0.00002703874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000104491,"about_ca_topic_score_gemma":0.00004730455,"domain_scores_codex":[0.9989845,0.00003405112,0.0001102739,0.0002156891,0.0002132367,0.000442213],"domain_scores_gemma":[0.9994476,0.00005831852,0.00002592566,0.0003237162,0.00004264394,0.0001017404],"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":[0.00003471702,0.000404152,0.3919584,0.00004651037,0.00001581872,0.00008938874,0.006446618,0.0002638685,0.01143067,0.04079473,0.000279056,0.5482361],"study_design_scores_gemma":[0.001794955,0.0003144339,0.1033966,0.0002458474,0.000004548072,0.0001219081,0.001207116,0.1157661,0.7644141,0.0006361804,0.01093473,0.001163445],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00843255,0.00009777947,0.9880291,0.0002959754,0.00003366399,0.0001320938,1.268004e-7,0.0002663413,0.002712397],"genre_scores_gemma":[0.593012,0.000006770216,0.4061453,0.0001135447,0.00002124731,0.000004320116,1.568569e-7,0.000006109741,0.0006905667],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7529835,"threshold_uncertainty_score":0.319942,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2169561155","doi":"10.1109/cvpr.2009.5206839","title":"Picking the best DAISY","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":260,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Discriminative model; Normalization (sociology); Artificial intelligence; Scale-invariant feature transform; Pattern recognition (psychology); Byte; Computer vision; Feature extraction; Dimensionality reduction; Filter (signal processing)","retraction":null,"screen_n_in":null,"score":{"opus":0.0598398514029056,"gpt":0.3177254451712137,"spread":0.2578855937683081,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002101288,0.0002133387,0.0001742707,0.0001107573,0.0002375898,0.0004679363,0.0004618316,0.00007945832,0.00001866796],"category_scores_gemma":[0.000009523709,0.0001460909,0.00005794908,0.0001966367,0.0000460621,0.0005824739,0.00007151072,0.0002627234,0.0001452045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001593406,"about_ca_system_score_gemma":0.00001927188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000059012,"about_ca_topic_score_gemma":0.000001898964,"domain_scores_codex":[0.9986948,0.0001039628,0.0002378675,0.0004590198,0.0002517915,0.0002525396],"domain_scores_gemma":[0.9991442,0.0001044701,0.0001132174,0.0003916952,0.0001532846,0.00009310666],"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":[0.000004680593,0.00006067989,0.00003978893,0.00000391504,0.000002817161,0.00001597194,0.00009708987,0.000001594801,0.0006154938,0.0004576298,0.0008422877,0.997858],"study_design_scores_gemma":[0.004928937,0.02096315,0.04659412,0.004820337,0.00009755589,0.00082483,0.0001773425,0.5195312,0.1693029,0.1991498,0.02921069,0.004399107],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01798851,0.00006644145,0.9778264,0.002242073,0.0003685237,0.0002250234,0.000005341646,0.0002225609,0.001055105],"genre_scores_gemma":[0.9773641,0.0005196457,0.01602113,0.005793087,0.0002109641,0.000006980483,0.00001025409,0.00000757811,0.00006628766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9934589,"threshold_uncertainty_score":0.5957414,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2005107673","doi":"10.1109/cvpr.2007.383120","title":"Maximally Stable Colour Regions for Recognition and Matching","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":255,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Vetenskapsrådet","keywords":"Artificial intelligence; Pattern recognition (psychology); Pixel; Mathematics; Detector; Feature (linguistics); Piecewise; Affine transformation; Computer science; Cluster analysis; Euclidean distance; Computer vision; Matching (statistics)","retraction":null,"screen_n_in":null,"score":{"opus":0.05008673343903235,"gpt":0.3045064331028079,"spread":0.2544196996637756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003726493,0.00005978697,0.00006884021,0.00006523683,0.0001065047,0.00007502876,0.0001380091,0.00003259358,0.000004062788],"category_scores_gemma":[0.00004430352,0.00005356337,0.000021268,0.0001447479,0.00001617713,0.0006227988,0.00007177605,0.00004829175,0.000005488387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000167422,"about_ca_system_score_gemma":0.00001361469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001956778,"about_ca_topic_score_gemma":0.00001836155,"domain_scores_codex":[0.9994477,0.00000632206,0.0001144485,0.0001815635,0.00006589128,0.0001841006],"domain_scores_gemma":[0.9995564,0.0001378699,0.00003768113,0.0001347206,0.00008168939,0.00005158849],"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":[0.00004645371,0.00005481528,0.0000603825,0.0000374055,0.000009847807,0.00001560653,0.0003573227,6.988158e-7,0.0133413,0.2261573,0.003069659,0.7568492],"study_design_scores_gemma":[0.0002763197,0.0002111394,0.0003176535,0.00002572972,0.000004177922,0.00003741192,0.00008436004,0.0005292005,0.1490678,0.8242372,0.02502337,0.0001855762],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002920344,0.00004834331,0.9917463,0.0003676102,0.00003712785,0.0002145535,0.000002080606,0.0002990449,0.004364598],"genre_scores_gemma":[0.06795405,0.00007614065,0.9304926,0.0006825757,0.00002853549,0.00001133519,0.000003063834,0.000006205983,0.0007455122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7566636,"threshold_uncertainty_score":0.2184251,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1976591483","doi":"10.1109/iccvw.2009.5457541","title":"Better matching with fewer features: The selection of useful features in large database recognition problems","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":236,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Preprocessor; Matching (statistics); Set (abstract data type); Feature extraction; Feature selection; Selection (genetic algorithm); Image (mathematics); Graph; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01383430459890901,"gpt":0.2642036091326749,"spread":0.2503693045337659,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003050092,0.0001187117,0.000120743,0.0001110998,0.00008058742,0.00008239534,0.0003340305,0.00004832134,0.00001108498],"category_scores_gemma":[0.00002513944,0.000068569,0.00002833237,0.0005903292,0.000018628,0.001082536,0.00005726902,0.0002748329,0.000002747711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000240128,"about_ca_system_score_gemma":0.00002133403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005035488,"about_ca_topic_score_gemma":0.0002042819,"domain_scores_codex":[0.9990883,0.00005793533,0.0001592377,0.0002646096,0.0002143727,0.0002155146],"domain_scores_gemma":[0.9994334,0.0000534296,0.00008703188,0.0003105814,0.00009066402,0.00002490425],"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":[0.0003397328,0.001105065,0.01080007,0.0001669372,0.00006251328,0.00006538403,0.004629052,0.0001413386,0.09998964,0.01931943,0.01862214,0.8447587],"study_design_scores_gemma":[0.001407131,0.001101635,0.1380764,0.0006319329,0.00002202152,0.0002046671,0.0001847642,0.0009285632,0.7814266,0.07084088,0.00448739,0.0006880375],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03330841,0.0001018054,0.9632127,0.001583827,0.00002546781,0.0003633335,0.000004929794,0.0001970624,0.001202437],"genre_scores_gemma":[0.8080617,0.00006476665,0.1890115,0.002499559,0.00004396728,0.00001643352,0.00001645422,0.000008607901,0.0002770421],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8440707,"threshold_uncertainty_score":0.2796163,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2136811977","doi":"10.1109/tgrs.2007.892601","title":"ARRSI: Automatic Registration of Remote-Sensing Images","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":233,"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":"","keywords":"Computer science; Image registration; Artificial intelligence; Computer vision; Point set registration; Control point; Remote sensing; Matching (statistics); Overhead (engineering); Point (geometry); Image (mathematics); Geography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01815165148450653,"gpt":0.2835752449716854,"spread":0.2654235934871789,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008050456,0.0001738801,0.0002113947,0.0003068708,0.0003750507,0.00010607,0.0002104346,0.00008602865,0.000001032099],"category_scores_gemma":[0.00004241058,0.000158103,0.00007917183,0.0006555542,0.0002907132,0.0006998632,0.000005306819,0.0002213734,0.000003074632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004546236,"about_ca_system_score_gemma":0.00005878924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002680506,"about_ca_topic_score_gemma":0.00003471681,"domain_scores_codex":[0.9983587,0.00004251701,0.000406742,0.000448585,0.0003956499,0.0003478457],"domain_scores_gemma":[0.9989156,0.000178947,0.0001933595,0.0004709768,0.0001501479,0.00009101821],"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":[0.000005989115,0.000007919369,6.450739e-8,0.00001759154,0.000003202505,0.00002218058,0.0001929662,0.00001727474,0.04335541,0.00002730697,0.00000464951,0.9563454],"study_design_scores_gemma":[0.0001266641,0.0001329237,0.0001069236,0.0002083127,0.00001159755,0.0002036679,0.00008019932,0.3695039,0.6264364,0.002810891,0.0001952131,0.0001832924],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01133867,0.00006000314,0.9868805,0.0002717525,0.0003158101,0.0001568088,0.000001126946,0.0002614797,0.0007137954],"genre_scores_gemma":[0.4037895,0.0000799201,0.5958223,0.0001146806,0.00001383414,4.186349e-9,1.460885e-7,0.000006479982,0.0001731789],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9561622,"threshold_uncertainty_score":0.6447254,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4319300119","doi":"10.1109/wacv56688.2023.00301","title":"MixVPR: Feature Mixing for Visual Place Recognition","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Nature","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Margin (machine learning); Process (computing); Cascade; Pattern recognition (psychology); Latency (audio); Set (abstract data type); Scale (ratio); Feature extraction; Precision and recall; Computer vision; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.03833107397971237,"gpt":0.3512669887496743,"spread":0.3129359147699619,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004224528,0.0003287644,0.0004017331,0.0005356647,0.0002246711,0.0002447238,0.001299034,0.00018337,0.00002846764],"category_scores_gemma":[0.0000349979,0.0003167586,0.0002290291,0.00127577,0.00009926216,0.0006283874,0.0003435463,0.0002943901,0.0004048012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005763724,"about_ca_system_score_gemma":0.00009204435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004524079,"about_ca_topic_score_gemma":0.000002847609,"domain_scores_codex":[0.9976518,0.00007868405,0.0005261417,0.0008874897,0.0004220923,0.0004338135],"domain_scores_gemma":[0.9975351,0.0004452323,0.0003173036,0.0009114046,0.0006439961,0.0001469706],"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":[0.00009389,0.0002483899,0.00001171083,0.0001149647,0.00004371208,0.000006220147,0.0002196068,0.00007483337,0.0142,0.008062584,0.08099297,0.8959311],"study_design_scores_gemma":[0.002147814,0.003267005,0.0008509512,0.001266011,0.00005585745,0.0000374211,0.0001532821,0.5226959,0.2545435,0.0735863,0.1398208,0.001575237],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001951803,0.00003290585,0.9921804,0.002590337,0.0003612198,0.001287891,0.00007251774,0.0008343824,0.0006885477],"genre_scores_gemma":[0.3902042,0.000368916,0.6007606,0.001606671,0.0008675194,0.001682381,0.0004361471,0.0001081106,0.003965523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8943559,"threshold_uncertainty_score":0.9999285,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2951187577","doi":"10.2312/3dor/3dor11/071-078","title":"SHREC '11: Robust Feature Detection and Description Benchmark","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":209,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Benchmark (surveying); Feature (linguistics); Computer science; Artificial intelligence; Pattern recognition (psychology); Feature detection (computer vision); Variety (cybernetics); Feature extraction; Image (mathematics); Data mining; Image processing","retraction":null,"screen_n_in":null,"score":{"opus":0.08128406691134166,"gpt":0.1878718794608525,"spread":0.1065878125495109,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001801335,0.0002986508,0.0002575447,0.000263763,0.0001829616,0.0001484949,0.0008359658,0.0004093239,0.00001183228],"category_scores_gemma":[0.0000356243,0.00033402,0.0001222671,0.0004547933,0.0001024145,0.001049038,0.001372682,0.0006178743,0.000010913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001645013,"about_ca_system_score_gemma":0.00005574798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001437498,"about_ca_topic_score_gemma":0.0001039661,"domain_scores_codex":[0.9984113,0.00009105737,0.0001278067,0.00102117,0.00007201862,0.0002766301],"domain_scores_gemma":[0.9986288,0.00003443144,0.0002035434,0.0008345868,0.0001603431,0.0001383025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007857119,0.001004883,0.03532962,0.001679277,0.0007125536,0.003070786,0.003348679,0.02821221,0.01388915,0.5615408,0.007253115,0.3431731],"study_design_scores_gemma":[0.001303132,0.0005834843,0.02513361,0.0006078766,0.0003079443,0.0001144455,0.0001571955,0.301653,0.04802329,0.6102586,0.009048132,0.002809205],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05201057,0.0002101401,0.9450918,0.0000334509,0.0003886379,0.000278585,0.000004430502,0.0004892486,0.001493168],"genre_scores_gemma":[0.9752921,0.0008410062,0.02226649,0.0000679178,0.00006835475,0.000001378974,0.000007199907,0.00001628634,0.001439255],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9232816,"threshold_uncertainty_score":0.9999112,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2162808897","doi":"10.1109/iccv.2007.4409025","title":"Shape Descriptors for Maximally Stable Extremal Regions","year":2007,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Scale-invariant feature transform; Artificial intelligence; Pattern recognition (psychology); Affine transformation; Invariant (physics); Computer vision; Mathematics; Scale invariance; Computer science; Feature extraction; Feature (linguistics); Shape context; Image (mathematics); Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.0417754634320851,"gpt":0.3033740566339255,"spread":0.2615985932018404,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004202334,0.0001139443,0.0001171683,0.0001156272,0.0001330887,0.0000920797,0.0005761187,0.00005301076,0.00003207971],"category_scores_gemma":[0.00009212918,0.00009790578,0.00007688513,0.0003662698,0.00003431266,0.0008850248,0.0001387164,0.00007328298,0.00001527339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004372799,"about_ca_system_score_gemma":0.00003987436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001252795,"about_ca_topic_score_gemma":0.00001509169,"domain_scores_codex":[0.9989213,0.000007802745,0.0002013491,0.0003092701,0.0001523096,0.0004079771],"domain_scores_gemma":[0.9991866,0.0001229388,0.00005165817,0.0003931574,0.0001388316,0.0001068207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003848848,0.00008944638,0.0002773067,0.00001711129,0.00001125757,0.00002577403,0.0001359462,0.000001256625,0.01227074,0.5875246,0.01935289,0.3802552],"study_design_scores_gemma":[0.0004891625,0.0004512225,0.0008438521,0.00002713472,0.000008003325,0.000040835,0.00007957831,0.008893187,0.3976863,0.09200046,0.4989831,0.0004971446],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005129451,0.0001029775,0.9833599,0.00035183,0.0001295207,0.0002697502,0.000001201494,0.0006545579,0.01461728],"genre_scores_gemma":[0.08540802,0.00003140947,0.9094706,0.0008546212,0.00006930382,0.00001588303,0.000001683464,0.00001260728,0.004135846],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4955241,"threshold_uncertainty_score":0.3992482,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W60423602","doi":"10.1007/0-387-28831-7_5","title":"Graph Cuts in Vision and Graphics: Theories and Applications","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Graph; Graphics; Computer science; Cut; Theoretical computer science; Computer graphics; Algorithm; Artificial intelligence; Computer graphics (images); Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.008586180534336851,"gpt":0.2690897711654366,"spread":0.2605035906310997,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001335353,0.0002049142,0.0002118948,0.0003060157,0.00007274345,0.000104243,0.000270949,0.0001605243,0.0000104049],"category_scores_gemma":[0.000005957555,0.000167878,0.00003615655,0.00009123752,0.0001879282,0.0004620572,0.0002844819,0.000224209,0.000004498929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001109256,"about_ca_system_score_gemma":0.00001504433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006225599,"about_ca_topic_score_gemma":0.00003047335,"domain_scores_codex":[0.9990777,0.000007632728,0.0002030365,0.0004507965,0.0001320486,0.0001287748],"domain_scores_gemma":[0.9993227,0.00009017157,0.00007660132,0.000400069,0.00005025826,0.00006022054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000001212082,0.00000379339,0.00001368224,0.000008949803,0.000002479815,0.000001989837,0.00002172089,4.052514e-8,0.000008899129,0.7723361,0.00008458886,0.2275166],"study_design_scores_gemma":[0.00006288951,0.00004549645,0.00008130389,0.00005092413,0.000003444888,0.00001146951,0.000001798059,0.00004252734,0.0001882537,0.7730092,0.2263283,0.0001743843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.000002079598,0.003494162,0.7727175,0.0002483578,0.0000116611,0.0003019163,0.000003238747,0.0001899471,0.2230311],"genre_scores_gemma":[0.01274453,0.1267476,0.409075,0.003738214,0.0004482401,0.0002417791,0.00003282715,0.0001404654,0.4468313],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3636425,"threshold_uncertainty_score":0.6845866,"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":"W2127786001","doi":"10.1109/tip.2009.2024578","title":"$n$-SIFT: $n$-Dimensional Scale Invariant Feature Transform","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":180,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Scale-invariant feature transform; Artificial intelligence; Curse of dimensionality; Pattern recognition (psychology); Histogram; Feature extraction; Computer science; Invariant (physics); Matching (statistics); Salient; Computer vision; Feature (linguistics); Image registration; Mathematics; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01169971106401026,"gpt":0.2738951675905187,"spread":0.2621954565265084,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001886657,0.0002770055,0.0002373633,0.0002306781,0.0005299897,0.0003270312,0.0005498845,0.0001300951,0.00002201264],"category_scores_gemma":[0.000005411461,0.0002484094,0.0001430137,0.0007885174,0.00007916254,0.002491697,0.000002165159,0.0005384083,0.00003644353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008014536,"about_ca_system_score_gemma":0.0001320963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003781383,"about_ca_topic_score_gemma":0.000003399389,"domain_scores_codex":[0.998307,0.00003409958,0.0002633905,0.0005569884,0.0004152705,0.0004232433],"domain_scores_gemma":[0.9991427,0.00004141935,0.00008175647,0.0004083862,0.000171394,0.0001543341],"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":[0.00004270478,0.000235293,2.829475e-7,0.00002394037,0.000006376868,0.00003738966,0.0003365865,0.000165378,0.05950809,0.0001001819,0.0003324202,0.9392114],"study_design_scores_gemma":[0.0005528905,0.0003352574,0.00006441106,0.0001960999,0.00002212516,0.0001575868,0.00001904994,0.02687632,0.9610901,0.008592115,0.001629574,0.0004644308],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003246198,0.0002384501,0.9921453,0.004206271,0.0001426146,0.0002452097,0.000006909409,0.00083672,0.001853915],"genre_scores_gemma":[0.5808595,0.00004328557,0.416707,0.001664199,0.00004587799,0.00001841703,0.000001441387,0.00001840349,0.0006418832],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9387469,"threshold_uncertainty_score":0.9999968,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003115311","doi":"10.1109/cvpr.2012.6248111","title":"Local Naive Bayes Nearest Neighbor for image classification","year":2012,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":178,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Pooling; k-nearest neighbors algorithm; Computer science; Naive Bayes classifier; Contextual image classification; Merge (version control); Mathematics; Image (mathematics); Support vector machine","retraction":null,"screen_n_in":null,"score":{"opus":0.03453215885528536,"gpt":0.3230913751074366,"spread":0.2885592162521512,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001853692,0.00009683907,0.00009456934,0.00004791,0.00009575982,0.00008659918,0.0003813152,0.00004866177,0.00002365564],"category_scores_gemma":[0.00008471066,0.00007881002,0.00005537874,0.000187691,0.00005828504,0.001783168,0.0001000863,0.00006263306,0.00007290691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003968842,"about_ca_system_score_gemma":0.00002321059,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005860212,"about_ca_topic_score_gemma":7.80548e-7,"domain_scores_codex":[0.9992197,0.00001842161,0.0001430202,0.00020476,0.0001192381,0.0002949179],"domain_scores_gemma":[0.999247,0.0001230575,0.00005426303,0.0003527329,0.0001234427,0.00009952092],"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":[0.00001337883,0.0001026632,0.0004144721,0.000019287,0.000007569153,0.00000105622,0.000196259,5.545435e-7,0.01620008,0.435482,0.01410952,0.5334532],"study_design_scores_gemma":[0.0004130294,0.000268388,0.008403749,0.00001891604,0.00001050347,0.00001841844,0.000155398,0.04773802,0.7123975,0.03175761,0.1983449,0.0004735282],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000125742,0.000163336,0.9931861,0.0006296099,0.0001271738,0.0002587911,0.000002862639,0.0004225808,0.0050838],"genre_scores_gemma":[0.4856599,0.00002422476,0.5133532,0.0003909954,0.00009409046,0.00004709814,0.000004254465,0.000008090432,0.0004181641],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6961974,"threshold_uncertainty_score":0.3213779,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017950063","doi":"10.1109/tifs.2012.2190594","title":"Perceptual Image Hashing Based on Shape Contexts and Local Feature Points","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Scale-invariant feature transform; Hash function; Pattern recognition (psychology); Feature (linguistics); Computer vision; Feature detection (computer vision); Feature extraction; Image retrieval; Robustness (evolution); Image (mathematics); Image processing","retraction":null,"screen_n_in":null,"score":{"opus":0.009477938299568691,"gpt":0.2475128547851118,"spread":0.2380349164855431,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002175922,0.0001566523,0.0001353215,0.0001363618,0.0002833365,0.0001970129,0.0001132388,0.0001054058,0.00001364314],"category_scores_gemma":[0.00001100009,0.000138393,0.00004983728,0.0001590805,0.0001229928,0.003615308,0.000004840808,0.0003238137,0.00001564684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003764998,"about_ca_system_score_gemma":0.00001881394,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000676457,"about_ca_topic_score_gemma":0.000002861909,"domain_scores_codex":[0.9992059,0.00002947391,0.0001847628,0.0001295949,0.0002262354,0.0002240464],"domain_scores_gemma":[0.9993918,0.00008488662,0.00007011561,0.0002040105,0.0001014187,0.0001477856],"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":[0.00009884798,0.0001020311,0.00003775761,0.00006724671,0.00001499404,0.000001783586,0.006560388,0.0001121885,0.00009475358,0.0144145,0.001244702,0.9772508],"study_design_scores_gemma":[0.002535156,0.001107853,0.002444326,0.0002077329,0.00004397493,0.00009880345,0.00106036,0.8966433,0.05944803,0.01089213,0.02444964,0.001068672],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007582608,0.00002699886,0.9906354,0.0005495916,0.0001907291,0.0001753656,0.00003225505,0.0001595619,0.0006474961],"genre_scores_gemma":[0.9739828,0.00004764822,0.0241824,0.001736926,0.0000195256,0.000009148706,0.00000683047,0.000005360459,0.000009349915],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9761822,"threshold_uncertainty_score":0.5643503,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2041878876","doi":"10.1109/tpami.2013.231","title":"Fast Exact Search in Hamming Space With Multi-Index Hashing","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Networks of Centres of Excellence of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Hamming space; Hash function; Hamming distance; Substring; Hamming code; Hash table; Binary code; Computer science; Linear code; Dynamic perfect hashing; Perfect hash function; Algorithm; Binary number; Data structure; Theoretical computer science; Mathematics; Block code; Double hashing; Decoding methods; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.02335983520519338,"gpt":0.2957195390013335,"spread":0.2723597037961402,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004128734,0.0002237001,0.0003217057,0.000702791,0.0001682241,0.0001891886,0.0004386961,0.00005708665,0.00002483924],"category_scores_gemma":[0.000007434451,0.0001798809,0.0001160133,0.001469296,0.00006999265,0.000510786,0.000009338354,0.0004248084,0.000009215075],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005493412,"about_ca_system_score_gemma":0.0000219179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00170823,"about_ca_topic_score_gemma":0.003377047,"domain_scores_codex":[0.9984106,0.0001146707,0.000286215,0.0005864779,0.0002917253,0.0003102718],"domain_scores_gemma":[0.999067,0.0001725467,0.00007194266,0.0005017787,0.00006846092,0.0001182704],"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":[0.00001351354,0.0001124638,0.004733202,0.00001285879,0.00008289698,0.00001475973,0.0005459831,0.03310583,0.0004246995,0.00004421397,4.040462e-7,0.9609092],"study_design_scores_gemma":[0.0001509928,0.0001949155,0.003193934,0.0000635112,0.00006584536,0.00001168917,0.00007432889,0.6493129,0.3464979,0.00008686558,0.00005563151,0.0002915809],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003253542,0.00004119388,0.9960846,0.000281627,0.00003367278,0.0001230108,0.000004114484,0.0001200954,0.0000581931],"genre_scores_gemma":[0.9729754,0.0001413291,0.02643823,0.0002214721,0.000009663188,0.00001372791,0.000001104233,0.00001298263,0.0001861319],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9697218,"threshold_uncertainty_score":0.7335328,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2537480791","doi":"10.1109/iccv.2009.5459259","title":"Image sequence geolocation with human travel priors","year":2009,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":148,"is_retracted":false,"has_abstract":true,"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; Canadian Institute for Advanced Research; Microsoft Research; National Science Foundation","keywords":"Geolocation; Computer science; Artificial intelligence; Sequence (biology); Prior probability; Computer vision; Image (mathematics); Matching (statistics); Interval (graph theory); Pattern recognition (psychology); Bayesian probability; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.02133003570166049,"gpt":0.3090081754630035,"spread":0.287678139761343,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001016909,0.0001007628,0.00009137191,0.00006016395,0.0001000767,0.00009204931,0.0004627646,0.00002921137,0.00001191211],"category_scores_gemma":[0.00001283909,0.00007453161,0.00002180963,0.000318505,0.00003636095,0.001120633,0.00003251524,0.00008637597,0.00002010622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002925161,"about_ca_system_score_gemma":0.00002966768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001463468,"about_ca_topic_score_gemma":0.0000025382,"domain_scores_codex":[0.9992431,0.00001444587,0.0001212922,0.0002698443,0.0001674551,0.0001838416],"domain_scores_gemma":[0.9993803,0.00001223864,0.00004979997,0.0004050948,0.00009887057,0.00005367719],"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":[0.00001014197,0.0001150157,0.0001933818,0.000009403251,0.00000592695,0.00006279947,0.0004782792,0.000006464633,0.4057005,0.2731119,0.0006269383,0.3196792],"study_design_scores_gemma":[0.0003486195,0.001153956,0.01713261,0.00003908847,0.000005340522,0.00007774698,0.00004251416,0.005370706,0.9130793,0.06109304,0.001205412,0.0004517082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003743041,0.00001762186,0.9808229,0.0008101268,0.000009285589,0.0001388416,2.043206e-7,0.0004628764,0.01399514],"genre_scores_gemma":[0.5932105,0.00000651253,0.4055608,0.0006230838,0.00001324209,0.000003123413,0.0000011961,0.000002964401,0.0005785483],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5894675,"threshold_uncertainty_score":0.3039311,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3186910762","doi":"10.1109/tip.2022.3160602","title":"Scalable Image Coding for Humans and Machines","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":148,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Codec; Scalability; Artificial intelligence; Coding (social sciences); Computer vision; Machine vision; Machine learning; Task (project management); Computer hardware; Database; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02030060063684558,"gpt":0.3022955667161125,"spread":0.2819949660792669,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0002750657,0.0001647802,0.0001661921,0.0001923309,0.00157499,0.0003569352,0.0003798566,0.00002588149,0.00002955232],"category_scores_gemma":[0.00001081089,0.0001701839,0.00006997752,0.0004278802,0.00008125203,0.001553026,0.00001309622,0.0002773582,0.000002396094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000685732,"about_ca_system_score_gemma":0.00006184261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007420593,"about_ca_topic_score_gemma":0.000001687023,"domain_scores_codex":[0.9988074,0.00003811042,0.0002084656,0.0004430321,0.0002142422,0.0002887843],"domain_scores_gemma":[0.9994105,0.00009246545,0.0000857241,0.0002437574,0.0001027391,0.00006488057],"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":[0.00005443331,0.0001805498,0.000006110153,0.0001908588,0.00001305815,0.0000147058,0.0007625477,0.0002058085,0.1374791,0.0003532064,0.0003566754,0.860383],"study_design_scores_gemma":[0.001088189,0.0005420066,0.0000322362,0.00009466768,0.0000443454,0.000117572,0.000244026,0.2165877,0.7618147,0.01325014,0.005503143,0.0006812985],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006578437,0.0002348789,0.9972026,0.0005200668,0.0001427151,0.0003027382,0.00002440843,0.0004919647,0.0004227607],"genre_scores_gemma":[0.5093091,0.00004511134,0.4892448,0.0003908453,0.0000294392,0.0002631751,0.000001611139,0.00002950219,0.0006864642],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8597016,"threshold_uncertainty_score":0.9997248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2104431043","doi":"10.1109/tpami.2005.142","title":"Indexing hierarchical structures using graph spectra","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":140,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; University of Toronto","funders":"","keywords":"Directed acyclic graph; Search engine indexing; Computer science; Pattern recognition (psychology); Linear subspace; Theoretical computer science; Artificial intelligence; Algorithm; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02547224963115513,"gpt":0.308010740132767,"spread":0.2825384905016119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001713091,0.0002224889,0.000284542,0.0006633507,0.0002483045,0.0001590925,0.0004398177,0.00006845163,0.0001071387],"category_scores_gemma":[0.000004523815,0.0001902584,0.0002409189,0.001265355,0.00008796671,0.0004667123,0.000008241233,0.0003997505,0.000005819822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000443454,"about_ca_system_score_gemma":0.00001784915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002820394,"about_ca_topic_score_gemma":0.0003307311,"domain_scores_codex":[0.9985121,0.00006295722,0.0003387347,0.0005170056,0.0002721346,0.0002970149],"domain_scores_gemma":[0.999181,0.0000902684,0.00008628183,0.0004488436,0.00005077812,0.0001428241],"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":[0.000007222347,0.00005830836,0.0001573339,0.000005247693,0.0001676344,0.000008362475,0.0001610051,0.01809349,0.0011707,0.0004361974,0.000001725953,0.9797328],"study_design_scores_gemma":[0.00006648482,0.0001019297,0.0004808461,0.00001741552,0.000206683,0.00003478584,0.00001458953,0.2400658,0.7519903,0.006482999,0.0002069302,0.0003312851],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002576542,0.0001766475,0.9966049,0.0002540924,0.00006631044,0.00009010859,0.00001167619,0.0001656745,0.00005406775],"genre_scores_gemma":[0.9212177,0.0003590426,0.07782406,0.0005045031,0.00003682309,0.000004391269,0.000001289585,0.000009962474,0.00004226188],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9794015,"threshold_uncertainty_score":0.7758514,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963174348","doi":"10.14778/2994509.2994534","title":"LSH ensemble","year":2016,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":136,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Jaccard index; Computer science; Data mining; Domain (mathematical analysis); Locality-sensitive hashing; Data structure; Data set; Set (abstract data type); Hash function; Mathematics; Cluster analysis; Hash table; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01131773862150246,"gpt":0.2360180883857376,"spread":0.2247003497642351,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000185335,0.00009858549,0.0001151825,0.00004465561,0.00006265881,0.00003412601,0.001077037,0.00002741466,0.000007321221],"category_scores_gemma":[0.0001139504,0.00004764201,0.00008073931,0.000247322,0.0000565943,0.0005505309,0.0005385869,0.00004971574,0.00001444116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005022846,"about_ca_system_score_gemma":0.00001560587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000370667,"about_ca_topic_score_gemma":1.579966e-7,"domain_scores_codex":[0.9991205,0.000003087012,0.0001775863,0.0002209275,0.0002731289,0.000204796],"domain_scores_gemma":[0.999418,0.00003544148,0.0001481641,0.0002022782,0.0001529271,0.00004322637],"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":[0.000007125408,0.00004081479,0.0008684829,0.0000181603,0.00001025701,3.289275e-7,0.0001135073,6.117543e-8,0.678563,0.1562902,0.004964155,0.1591239],"study_design_scores_gemma":[0.0001654469,0.00006468075,0.0004327887,0.00007612592,0.00000323255,0.000007140899,0.000007675085,0.00001165163,0.9174836,0.06580421,0.01586788,0.00007553816],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04250883,0.0004496242,0.8830945,0.01294032,0.0005182425,0.0009562579,0.000004291896,0.0007452253,0.05878263],"genre_scores_gemma":[0.9593542,0.0001255179,0.03851346,0.0002420546,0.00003889057,0.00002536894,2.208931e-8,0.000007568619,0.001692888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9168454,"threshold_uncertainty_score":0.2001421,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1950112384","doi":"10.1109/cvpr.2015.7298913","title":"Real-time coarse-to-fine topologically preserving segmentation","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":135,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Segmentation; Markov random field; Computer science; Task (project management); Artificial intelligence; Image segmentation; Boundary (topology); Markov chain; Pattern recognition (psychology); Algorithm; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.04298823515020382,"gpt":0.3267948454851618,"spread":0.283806610334958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002829279,0.00008994959,0.0001079671,0.00006013018,0.00004301441,0.0000934167,0.0005919062,0.00003671159,0.00007760947],"category_scores_gemma":[0.0001761553,0.00007072121,0.00002435499,0.0003246633,0.00001683342,0.0006946042,0.0004285771,0.0000515239,0.0002532974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004950344,"about_ca_system_score_gemma":0.00003414772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005989422,"about_ca_topic_score_gemma":0.000003959838,"domain_scores_codex":[0.9991216,0.00004248673,0.0001589022,0.0002620338,0.0002228469,0.0001921257],"domain_scores_gemma":[0.9992026,0.00005277175,0.00004134076,0.0003903047,0.0001475147,0.0001654809],"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":[0.00009227092,0.0002287788,0.0008742306,0.00001777052,0.00002951664,0.00009329818,0.001454814,0.0002452772,0.4030556,0.08624084,0.1457141,0.3619535],"study_design_scores_gemma":[0.0007862531,0.001524893,0.001030407,0.00003458838,0.000008925713,0.0000316594,0.0001026309,0.02829075,0.8493307,0.09581789,0.02239931,0.0006419697],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00492168,0.0000127941,0.9484872,0.001289377,0.00005841672,0.0002044094,5.45133e-7,0.0007750203,0.04425052],"genre_scores_gemma":[0.01903963,0.00001530184,0.9732164,0.0006777667,0.00004871799,0.00001807197,0.000002651165,0.000006493767,0.006975014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4462751,"threshold_uncertainty_score":0.3255709,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1578197944","doi":"10.1007/11744023_27","title":"Learning and Incorporating Top-Down Cues in Image Segmentation","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":135,"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","keywords":"Computer science; Artificial intelligence; Conditional random field; Segmentation; Image segmentation; Classifier (UML); Pattern recognition (psychology); Image (mathematics); Object (grammar); Segmentation-based object categorization; Modular design; Exploit; Computer vision; Scale-space segmentation","retraction":null,"screen_n_in":null,"score":{"opus":0.01012384088604272,"gpt":0.2721488084768841,"spread":0.2620249675908414,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007292852,0.0003728659,0.000379983,0.0007803625,0.0001925977,0.0005497529,0.0009248946,0.0001865896,0.000003858238],"category_scores_gemma":[0.0001142251,0.0003597854,0.00004823689,0.0006045683,0.0004531879,0.00137755,0.0009285487,0.0008221763,0.000005252966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000227206,"about_ca_system_score_gemma":0.0001579825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004990142,"about_ca_topic_score_gemma":0.0000563074,"domain_scores_codex":[0.9974393,0.00004240869,0.0004754387,0.001101363,0.0005277941,0.0004136862],"domain_scores_gemma":[0.998766,0.0002999584,0.0003074107,0.0004143274,0.0001390645,0.00007326213],"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":[0.00000299635,0.00001189477,0.001099503,0.0000344787,0.000001719665,0.0001034953,0.0003526782,0.00234497,0.002518756,0.00226346,0.000008227118,0.9912578],"study_design_scores_gemma":[0.000582611,0.000590005,0.0009930155,0.001076861,0.000007488066,0.0001177023,0.000001701263,0.4383957,0.06877055,0.487133,0.0008926407,0.001438738],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004013912,0.0004657843,0.9955311,0.0001956509,0.0001973775,0.0003132658,0.000001104829,0.0002085156,0.002685782],"genre_scores_gemma":[0.09430722,0.0001075467,0.9047968,0.0003189257,0.0001623002,0.00000869719,0.000006678712,0.00002477485,0.000267103],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9898191,"threshold_uncertainty_score":0.9998854,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999378860","doi":"10.1145/1666420.1666446","title":"Using the forest to see the trees","year":2010,"lang":"en","type":"article","venue":"Communications of the ACM","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":129,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"Division of Information and Intelligent Systems; Office of Naval Research; Canadian Institute for Advanced Research; Multidisciplinary University Research Initiative; National Defense Science and Engineering Graduate; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Computer science; Object (grammar); Artificial intelligence; Context (archaeology); Cognitive neuroscience of visual object recognition; Probabilistic logic; Computer vision; Object detection; Space (punctuation); Machine learning; Pattern recognition (psychology); Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.09117393012537497,"gpt":0.3782707714526707,"spread":0.2870968413272957,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0003845635,0.00005777096,0.00006103422,0.0000231113,0.0005604567,0.00006453488,0.05063661,0.00002263786,0.00000180633],"category_scores_gemma":[0.003892839,0.00002607377,0.00006187389,0.000456204,0.0002724815,0.000181868,0.02523709,0.0002689068,0.000005906124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009037492,"about_ca_system_score_gemma":0.0000347114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005848306,"about_ca_topic_score_gemma":0.0002883437,"domain_scores_codex":[0.9994729,0.00009680312,0.0001325433,0.00008502771,0.0001158111,0.00009695071],"domain_scores_gemma":[0.9646829,0.0005175161,0.00009197684,0.0345833,0.0001057084,0.00001858075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009491006,0.0002144213,0.007590424,0.000008802017,0.00005533243,2.648359e-7,0.005301852,0.0004292171,0.1900968,0.64319,0.04378972,0.1093138],"study_design_scores_gemma":[0.00009770708,0.00003683212,0.02248546,0.00004381627,0.00002245137,0.00001949097,0.0001800053,0.01102727,0.09491303,0.6940816,0.1769245,0.0001678166],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04662608,0.0007834655,0.6003836,0.3463071,0.0003237065,0.0009156132,0.000006098995,0.0002029658,0.004451363],"genre_scores_gemma":[0.6014901,0.00003780783,0.3977953,0.0005715758,0.00001429652,0.000013396,1.901488e-7,0.00000371933,0.0000736642],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.554864,"threshold_uncertainty_score":0.9826466,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963513598","doi":"10.1109/cvpr.2016.323","title":"Learning Structured Inference Neural Networks with Label Relations","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":127,"is_retracted":false,"has_abstract":true,"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","keywords":"Computer science; Categorization; Abstraction; Artificial intelligence; Exploit; Semantics (computer science); Inference; Benchmark (surveying); Set (abstract data type); Encoding (memory); Multi-label classification; Machine learning; Image (mathematics); Artificial neural network; Pattern recognition (psychology); Contextual image classification; Deep learning","retraction":null,"screen_n_in":null,"score":{"opus":0.01460195490070895,"gpt":0.2736443086600053,"spread":0.2590423537592964,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006143733,0.0000926577,0.00008127664,0.00004279692,0.0001160536,0.00006677365,0.0003280362,0.00003966977,0.00003860604],"category_scores_gemma":[0.00008824215,0.00004866086,0.00001525983,0.0002995566,0.00004514972,0.000944076,0.0001231911,0.0001355017,0.0000109473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001886452,"about_ca_system_score_gemma":0.00001891747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004463446,"about_ca_topic_score_gemma":0.000006549951,"domain_scores_codex":[0.9993225,0.00003205309,0.0001045333,0.000225851,0.000121216,0.0001938791],"domain_scores_gemma":[0.9993752,0.0001590513,0.00005502178,0.000268103,0.00008650499,0.0000561704],"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":[0.00001268359,0.00001339294,0.01557852,0.00000158935,0.000009501406,0.00001286382,0.000088448,0.001504793,0.001834312,0.0640725,0.0003174224,0.916554],"study_design_scores_gemma":[0.002140193,0.001987523,0.03702322,0.0001659575,0.00001858674,0.00009355137,0.00003883609,0.8633492,0.03531693,0.04671977,0.01186091,0.001285322],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003288914,0.00003123092,0.9935976,0.0005677525,0.00003609363,0.00007453135,2.007877e-7,0.0006192254,0.001784442],"genre_scores_gemma":[0.8228143,0.00001706281,0.1749667,0.000132756,0.00002238125,0.000004728834,4.185551e-7,0.000005583177,0.002036094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9152687,"threshold_uncertainty_score":0.1984332,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}