{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":245,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":245,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"4443e9ccd956","filters":{"venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence"}},"results":[{"id":"W2163922914","doi":"10.1109/tpami.2013.50","title":"Representation Learning: A Review and New Perspectives","year":2013,"lang":"en","type":"review","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13002,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Feature learning; Computer science; Machine learning; Representation (politics); Inference; Nonlinear dimensionality reduction; Unsupervised learning; Deep learning; Prior probability; External Data Representation; Probabilistic logic; Feature (linguistics); Domain knowledge; Active learning (machine learning); Semi-supervised learning; Bayesian probability; Dimensionality reduction","authors":[{"name":"Yoshua Bengio","is_ca":true},{"name":"Aaron Courville","is_ca":true},{"name":"P. M. Durai Raj Vincent","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06847167730100265,"gpt":0.3494132673853828,"spread":0.2809415900843801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001428543,0.001025348,0.001633368,0.004018942,0.0004863272,0.002493162,0.001689021,0.001880801,0.005719485],"category_scores_gemma":[0.002940873,0.0005683017,0.0006376241,0.006765103,0.001501645,0.004687501,0.001196421,0.002924564,0.003371411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001347856,"about_ca_system_score_gemma":0.00189876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002309308,"about_ca_topic_score_gemma":0.002579062,"domain_scores_codex":[0.9995494,0.0001002836,0.00006005402,0.00009186578,0.0001634734,0.00003489334],"domain_scores_gemma":[0.9978435,0.001358258,0.0001342456,0.0000748744,0.0004922036,0.00009686271],"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":[0.00005237017,0.00009529666,0.0002865162,0.0122111,0.0000921124,0.0001586456,0.0001465576,0.001020401,0.0004518745,0.03710653,0.07957305,0.8688056],"study_design_scores_gemma":[0.000009100431,0.00004935389,0.0004517437,0.003671211,0.00004673909,0.0007002932,0.00009836286,0.0003231022,0.0001498568,0.01561453,0.9788563,0.00002934029],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00006736899,0.9967295,0.0006929357,0.0009510136,0.000266763,0.000003263698,0.00001631599,0.00000897669,0.001263936],"genre_scores_gemma":[0.0007938418,0.9971471,0.0005402283,0.0003552082,0.0007208217,0.000006189723,0.00003221697,0.000004151709,0.0004002836],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005719485,"threshold_uncertainty_score":0.01913357,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963420686","doi":"10.1109/tpami.2019.2913372","title":"Squeeze-and-Excitation Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Force Microscopy Techniques and Applications","field":"Physics and Astronomy","cited_by":12510,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Key Research and Development Program of China; Engineering and Physical Sciences Research Council; Canadian Institute for Advanced Research; Chinese Academy of Sciences; Universidade de Macau; National Natural Science Foundation of China; University of Manchester","keywords":"Computer science; Artificial intelligence","authors":[{"name":"Jie Hu","is_ca":false},{"name":"Li Shen","is_ca":false},{"name":"Samuel Albanie","is_ca":false},{"name":"Gang Sun","is_ca":false},{"name":"Enhua Wu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008529432180569783,"gpt":0.2667022275782162,"spread":0.2581727953976464,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007783719,0.001600426,0.001101088,0.0004898725,0.000451743,0.0009983216,0.002183612,0.001445318,0.008831217],"category_scores_gemma":[0.003105957,0.0007599566,0.001026419,0.0005499112,0.001103401,0.003442608,0.002755399,0.002400953,0.002345877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007967319,"about_ca_system_score_gemma":0.0009094251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002592351,"about_ca_topic_score_gemma":0.0076508,"domain_scores_codex":[0.9995599,0.00007390146,0.00002182909,0.0001476304,0.0001219955,0.00007465866],"domain_scores_gemma":[0.9994129,0.000242165,0.00005187773,0.0001553702,0.00009097821,0.00004678572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001024955,0.0002051509,0.001986135,0.0003951104,0.0002543289,0.0004789997,0.0001998129,0.4482591,0.05078336,0.06856023,0.0216033,0.4062495],"study_design_scores_gemma":[0.00003622296,0.0001112804,0.0003596495,0.00003570442,0.00005464608,0.0001372354,0.00002819449,0.9477742,0.01461175,0.02882206,0.007993943,0.00003515305],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0650686,0.001834294,0.9075657,0.001173491,0.0003673941,0.0001355021,0.001099359,0.008757467,0.01399833],"genre_scores_gemma":[0.6795006,0.001234558,0.2839606,0.001464579,0.0001900651,0.0003050201,0.002832015,0.000853483,0.02965908],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008831217,"threshold_uncertainty_score":0.02954334,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2113137767","doi":"10.1109/tpami.2004.60","title":"An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":4600,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"University of Tsukuba","keywords":"Algorithm; Computer science; Maximum flow problem; Minimum cut; Benchmark (surveying); Maximum cut; Minification; Time complexity; Image segmentation; Segmentation; Running time; Cut; Energy minimization; Artificial intelligence; Graph; Mathematics; Theoretical computer science; Mathematical optimization","authors":[{"name":"Yuri Boykov","is_ca":true},{"name":"Vladimir Kolmogorov","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02598367102043963,"gpt":0.331384632381408,"spread":0.3054009613609683,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005013449,0.001557334,0.001155712,0.002968841,0.0009585991,0.001292781,0.002321465,0.001932131,0.006197018],"category_scores_gemma":[0.01629268,0.0004500189,0.000809575,0.002927233,0.0009229313,0.002789922,0.001122103,0.001299721,0.001114461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001758149,"about_ca_system_score_gemma":0.001184198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003793901,"about_ca_topic_score_gemma":0.005303922,"domain_scores_codex":[0.9967982,0.0009868479,0.0003367943,0.0005772703,0.001000068,0.0003008442],"domain_scores_gemma":[0.9861584,0.009247513,0.0003796449,0.00179915,0.002105523,0.0003097714],"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":[0.005115846,0.001792146,0.002672757,0.00173471,0.00037823,0.000138019,0.0001705026,0.3601791,0.01843242,0.01101096,0.01823635,0.5801391],"study_design_scores_gemma":[0.0003724523,0.001127107,0.00311863,0.00007147415,0.00008906324,0.0002211204,0.0001718206,0.9445285,0.0367625,0.008579639,0.004903288,0.00005440755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4158967,0.006478608,0.5283273,0.001506391,0.001007199,0.001017494,0.002699827,0.01392605,0.02914035],"genre_scores_gemma":[0.516904,0.001278093,0.4716573,0.0002481318,0.00009848143,0.0004917685,0.003844542,0.001519328,0.003958256],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006197018,"threshold_uncertainty_score":0.02651399,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2142069714","doi":"10.1109/34.824821","title":"Online and off-line handwriting recognition: a comprehensive survey","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":2489,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Handwriting; Computer science; Handwriting recognition; Intelligent character recognition; Preprocessor; Speech recognition; Artificial intelligence; Character (mathematics); Natural language processing; Reading (process); Word (group theory); Word recognition; Right-to-left; Character recognition; Feature extraction; Image (mathematics); Linguistics","authors":[{"name":"Réjean Plamondon","is_ca":true},{"name":"Sargur N. Srihari","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04721076647622582,"gpt":0.2951678601617646,"spread":0.2479570936855387,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007755436,0.0008278011,0.001011636,0.003712537,0.0002534249,0.001742535,0.001076217,0.0009461842,0.006052617],"category_scores_gemma":[0.001684943,0.0003042401,0.0004630556,0.004270205,0.0002956133,0.002709674,0.0004718433,0.0004029768,0.005434469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002125048,"about_ca_system_score_gemma":0.0006832832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001762656,"about_ca_topic_score_gemma":0.001736272,"domain_scores_codex":[0.9989493,0.0001250072,0.0001101961,0.0002277533,0.0005226229,0.00006506029],"domain_scores_gemma":[0.9983656,0.0006761624,0.000140656,0.0001420409,0.0005905624,0.00008501038],"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":[0.00005135636,0.00008047101,0.001311864,0.0008505632,0.00001995895,0.00004593394,0.00003455653,0.0005126355,0.001526301,0.0003794965,0.003856217,0.9913306],"study_design_scores_gemma":[0.00005533845,0.001251124,0.04237399,0.002592395,0.000319521,0.005382294,0.0009743952,0.0233943,0.02774277,0.005441503,0.8903303,0.0001419841],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.04333854,0.7616311,0.1253567,0.0009657855,0.0006796888,0.0002320323,0.001054751,0.002127352,0.06461404],"genre_scores_gemma":[0.1401248,0.7132107,0.06995171,0.001222943,0.001826556,0.0001968012,0.004304655,0.0004011039,0.06876069],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006052617,"threshold_uncertainty_score":0.020248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3030364939","doi":"10.1109/tpami.2021.3057446","title":"A continual learning survey: Defying forgetting in classification tasks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1593,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Huawei Technologies (Canada)","funders":"Huawei Technologies; Fonds Wetenschappelijk Onderzoek; Departament d'Innovació, Universitats i Empresa, Generalitat de Catalunya; Generalitat de Catalunya","keywords":"Forgetting; Computer science; Artificial intelligence; Machine learning; Task (project management); Artificial neural network; Task analysis; Cognitive psychology","authors":[{"name":"Matthias Delange","is_ca":false},{"name":"Rahaf Aljundi","is_ca":false},{"name":"Marc Masana","is_ca":false},{"name":"Sarah Parisot","is_ca":true},{"name":"Xu Jia","is_ca":true},{"name":"Aleš Leonardis","is_ca":true},{"name":"Greg Slabaugh","is_ca":true},{"name":"Tinne Tuytelaars","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04391655240961902,"gpt":0.2955440514744518,"spread":0.2516274990648328,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00545173,0.001718669,0.001672667,0.001538326,0.0007846461,0.002381514,0.005618012,0.00216272,0.002587389],"category_scores_gemma":[0.01931461,0.0009331173,0.001099846,0.001812657,0.00172595,0.006567425,0.002345612,0.003905404,0.001783939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001475654,"about_ca_system_score_gemma":0.001928293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004677658,"about_ca_topic_score_gemma":0.004157724,"domain_scores_codex":[0.9981622,0.0004376188,0.000167718,0.0006848259,0.0004267254,0.0001208566],"domain_scores_gemma":[0.9885445,0.006914604,0.0004860082,0.002113295,0.001615061,0.0003265404],"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":[0.0002395537,0.0002376863,0.00252827,0.001495284,0.0001506036,0.00003612838,0.0002414275,0.0305282,0.002028508,0.009295843,0.008775068,0.9444435],"study_design_scores_gemma":[0.0001594576,0.001955999,0.006696444,0.001626534,0.0003812376,0.00102423,0.0004528899,0.8004175,0.01734244,0.07945719,0.09026116,0.0002248766],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0519502,0.2482882,0.6747572,0.005044659,0.001146222,0.0003562361,0.0003444862,0.005570966,0.01254163],"genre_scores_gemma":[0.5629001,0.1008252,0.3138021,0.002851579,0.002393612,0.0005160731,0.001595691,0.001273687,0.013842],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005618012,"threshold_uncertainty_score":0.02883184,"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","authors":[{"name":"Marius Muja","is_ca":false},{"name":"David Lowe","is_ca":true}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002565212,0.001118496,0.002325685,0.002579625,0.001424583,0.001995156,0.003213601,0.001893063,0.004164001],"category_scores_gemma":[0.01156938,0.000837005,0.001420963,0.005025299,0.0008416918,0.004624987,0.003442186,0.001920601,0.002897039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313892,"about_ca_system_score_gemma":0.001843372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008225355,"about_ca_topic_score_gemma":0.009342107,"domain_scores_codex":[0.996648,0.0005922002,0.0002578518,0.0007669153,0.001574107,0.0001609832],"domain_scores_gemma":[0.9970856,0.0009589641,0.0002496922,0.001006475,0.0006194036,0.00007981759],"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":[0.000244973,0.0001730079,0.001567101,0.0003621948,0.0001905093,0.000179926,0.000228572,0.2733735,0.005073566,0.04003407,0.01817051,0.6604021],"study_design_scores_gemma":[0.00005318721,0.00005042887,0.0003933558,0.00002983982,0.00001911085,0.0001772469,0.00009226167,0.9249101,0.002366625,0.06402604,0.007850635,0.00003124134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0032917,0.0008230411,0.9929643,0.0001286522,0.00008412618,0.00008705188,0.0001757487,0.001460722,0.0009846146],"genre_scores_gemma":[0.06571697,0.0008029672,0.929535,0.0001327589,0.0001212749,0.0003481178,0.00120536,0.0002200117,0.001917513],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008225355,"threshold_uncertainty_score":0.01635498,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2992005611","doi":"10.1109/tpami.2020.2992934","title":"Normalizing Flows: An Introduction and Review of Current Methods","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":1209,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Collège Boréal","funders":"","keywords":"Current (fluid); Computer science; Context (archaeology); Generative grammar; Sampling (signal processing); Artificial intelligence; Machine learning; Data science; Geography; Engineering","authors":[{"name":"Ivan Kobyzev","is_ca":true},{"name":"Simon J. D. Prince","is_ca":true},{"name":"Marcus A. Brubaker","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04449516924581683,"gpt":0.3316204651828682,"spread":0.2871252959370514,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003210918,0.001837751,0.001809065,0.003825817,0.0007695551,0.003784249,0.002535975,0.002591492,0.008262563],"category_scores_gemma":[0.008792655,0.001368019,0.001153766,0.004985807,0.002269256,0.006237093,0.002138786,0.004919115,0.004744036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001526616,"about_ca_system_score_gemma":0.001847192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002631088,"about_ca_topic_score_gemma":0.001987605,"domain_scores_codex":[0.9984792,0.0003805193,0.0001565632,0.0003978118,0.0005073396,0.00007861884],"domain_scores_gemma":[0.9953902,0.003596007,0.000185162,0.0002729238,0.0004729902,0.00008277515],"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":[0.00007393509,0.0001101712,0.0007657005,0.004564983,0.00009432933,0.0001500411,0.0002565879,0.01647856,0.00108107,0.2575713,0.04436079,0.6744926],"study_design_scores_gemma":[0.00001796044,0.0001001041,0.0007906698,0.003320373,0.00009827632,0.001047102,0.00014607,0.04767242,0.001799076,0.3212426,0.6236039,0.0001614728],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001151081,0.6134999,0.3628081,0.003650018,0.00172061,0.0001128905,0.0005005523,0.0006897472,0.01586709],"genre_scores_gemma":[0.02100102,0.7969996,0.1638813,0.00221943,0.006269027,0.0002759506,0.0008646361,0.0005245744,0.007964562],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008262563,"threshold_uncertainty_score":0.02764106,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2119531662","doi":"10.1109/tpami.2009.96","title":"TurboPixels: Fast Superpixels Using Geometric Flows","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1140,"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":"Compact space; Constraint (computer-aided design); Image (mathematics); Limiting; Speedup; Artificial intelligence; Computer science; Computer vision; Algorithm; Pattern recognition (psychology); Mathematics; Geometry","authors":[{"name":"Alex Levinshtein","is_ca":true},{"name":"A. Stere","is_ca":true},{"name":"Kiriakos N. Kutulakos","is_ca":true},{"name":"David J. Fleet","is_ca":true},{"name":"Sven Dickinson","is_ca":true},{"name":"Kaleem Siddiqi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02889710000689543,"gpt":0.3010075387534626,"spread":0.2721104387465672,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009615738,0.001322593,0.001195727,0.002318457,0.0005946358,0.001388468,0.002248148,0.00122048,0.008854086],"category_scores_gemma":[0.002060491,0.0009245168,0.001119937,0.002031884,0.0008435273,0.00233446,0.001443871,0.001133777,0.002526538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009871738,"about_ca_system_score_gemma":0.001268937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005334985,"about_ca_topic_score_gemma":0.006940949,"domain_scores_codex":[0.9994082,0.00008399326,0.00003151022,0.0001292241,0.0002716437,0.0000754505],"domain_scores_gemma":[0.9992864,0.0002522252,0.00007704789,0.0001674254,0.0001634647,0.00005345764],"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":[0.000409045,0.00008855952,0.000698266,0.0003289946,0.0001086983,0.0002070235,0.0002216366,0.1489505,0.04379576,0.03014211,0.02132817,0.7537211],"study_design_scores_gemma":[0.00004790037,0.00006170764,0.0003107689,0.00002495766,0.00001492562,0.0002021605,0.00002642128,0.9408036,0.02603452,0.01709515,0.01535041,0.00002748269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00421593,0.0002349088,0.9894403,0.00005493635,0.00004871168,0.00006410324,0.0001472213,0.004719207,0.001074737],"genre_scores_gemma":[0.03900016,0.0002233676,0.957416,0.00006890419,0.00003773698,0.0000890732,0.0005596881,0.0009361596,0.001668839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008854086,"threshold_uncertainty_score":0.02961987,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2135957668","doi":"10.1109/tpami.2007.1115","title":"Weighted Graph Cuts without Eigenvectors A Multilevel Approach","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":1038,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"National Science Foundation","keywords":"Cluster analysis; Correlation clustering; Spectral clustering; Rand index; Computer science; Pattern recognition (psychology); Kernel (algebra); Hierarchical clustering; Canopy clustering algorithm; Clustering coefficient; Graph partition; Computation; CURE data clustering algorithm; Graph; Algorithm; Mathematics; Artificial intelligence; Theoretical computer science; Combinatorics","authors":[{"name":"Inderjit S. Dhillon","is_ca":false},{"name":"Yuqiang Guan","is_ca":false},{"name":"Brian Kulis","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01933690194146513,"gpt":0.2806329191327215,"spread":0.2612960171912564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399093,0.0009733629,0.001366768,0.002620845,0.0008793129,0.002153759,0.002391144,0.001866609,0.004093243],"category_scores_gemma":[0.006044087,0.0009262172,0.001455589,0.002984571,0.0009259749,0.002433773,0.002331845,0.002253755,0.001557278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00103297,"about_ca_system_score_gemma":0.001233578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003551642,"about_ca_topic_score_gemma":0.005957366,"domain_scores_codex":[0.9979588,0.0005057623,0.0001025097,0.0004936717,0.0007886507,0.0001505712],"domain_scores_gemma":[0.9978731,0.0007660075,0.0002100492,0.0004625826,0.000594443,0.00009383351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001339509,0.00009699613,0.001057726,0.0003317848,0.0002040432,0.0001489448,0.0002457633,0.4057469,0.01228174,0.1805843,0.01017566,0.3889922],"study_design_scores_gemma":[0.00002253362,0.00003825585,0.0003461943,0.00002531342,0.0000362782,0.00009619355,0.00005091077,0.9068319,0.00282739,0.08259688,0.007104221,0.00002400442],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001706839,0.00008825695,0.9969894,0.00007215147,0.00001796225,0.0000241334,0.00005167327,0.000196053,0.0008535358],"genre_scores_gemma":[0.05602477,0.0002482013,0.9400432,0.0001085676,0.00006009703,0.0001686519,0.0003648048,0.0002659188,0.002715849],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004093243,"threshold_uncertainty_score":0.01369321,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2124609748","doi":"10.1109/tpami.2007.1167","title":"Gaussian Process Dynamical Models for Human Motion","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":1038,"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":"Ontario Ministry of Research and Innovation; Research and Innovation Foundation; Alfred P. Sloan Foundation; National Science Foundation","keywords":"Gaussian process; Artificial intelligence; Latent variable; Computer science; Prior probability; Dynamical systems theory; Gaussian; Latent variable model; Representation (politics); Nonlinear system; Motion capture; Motion (physics); Machine learning; Algorithm; Physics","authors":[{"name":"Jonathan M. Wang","is_ca":true},{"name":"David J. Fleet","is_ca":true},{"name":"Aaron Hertzmann","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0235016921053867,"gpt":0.2974668141293074,"spread":0.2739651220239206,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001462844,0.001168374,0.001025818,0.001173976,0.0004167195,0.001250285,0.001986222,0.001837767,0.003860865],"category_scores_gemma":[0.007449452,0.0005029009,0.001272185,0.001592341,0.001198765,0.002328544,0.001281998,0.002239667,0.001145089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009535899,"about_ca_system_score_gemma":0.0008282603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00768911,"about_ca_topic_score_gemma":0.006062367,"domain_scores_codex":[0.9993043,0.000238812,0.00003120976,0.0001704887,0.000190165,0.00006507354],"domain_scores_gemma":[0.9985093,0.0008988005,0.0002019677,0.0001325252,0.000203223,0.00005408857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002945817,0.00003116588,0.001188059,0.0001030449,0.0000585142,0.00009791943,0.0001532474,0.571583,0.001041383,0.3944942,0.003999949,0.02722],"study_design_scores_gemma":[0.000006757498,0.00000897561,0.0002383937,0.00001082565,0.000007516459,0.00003264942,0.00000907189,0.8806589,0.000085566,0.1159318,0.002997291,0.00001226401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003776786,0.0006413195,0.9926547,0.0004961216,0.00007467567,0.00002444768,0.0003086949,0.0001850486,0.00183814],"genre_scores_gemma":[0.5954967,0.00595125,0.3760703,0.0007938353,0.0006505058,0.0008732405,0.002293634,0.0003145973,0.01755602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00768911,"threshold_uncertainty_score":0.01528871,"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","authors":[{"name":"Rick Szeliski","is_ca":false},{"name":"Ramin Zabih","is_ca":false},{"name":"Daniel Scharstein","is_ca":false},{"name":"Olga Veksler","is_ca":true},{"name":"Vladimir Kolmogorov","is_ca":false},{"name":"A. Agarwala","is_ca":false},{"name":"Martha Tappen","is_ca":false},{"name":"Carsten Rother","is_ca":false}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01065338,0.001418084,0.001879941,0.002845884,0.000797811,0.001555098,0.002052411,0.002976024,0.002781325],"category_scores_gemma":[0.02947901,0.0009321718,0.001736858,0.002297137,0.001318875,0.003971019,0.001607558,0.002496616,0.0005382249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002484913,"about_ca_system_score_gemma":0.001661642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006716287,"about_ca_topic_score_gemma":0.006704298,"domain_scores_codex":[0.9969705,0.001526827,0.0001619882,0.0003178025,0.0008700479,0.0001527134],"domain_scores_gemma":[0.9748459,0.02154148,0.000644974,0.001145744,0.001543965,0.0002778402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000405691,0.0001891675,0.001543049,0.0004450054,0.000209881,0.0000543897,0.0001627679,0.7349299,0.002010954,0.0678495,0.002098092,0.1901017],"study_design_scores_gemma":[0.00002454241,0.00006449314,0.0004073939,0.00004850444,0.00002424672,0.000048876,0.00002259588,0.9830308,0.0008956115,0.01425302,0.001157184,0.00002275637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0256098,0.006300654,0.9619586,0.001066279,0.0001188719,0.00008578777,0.00008070598,0.0005979002,0.004181363],"genre_scores_gemma":[0.250213,0.006220839,0.7370043,0.0003751342,0.0002253534,0.0003035387,0.0004925124,0.001118999,0.004046297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01065338,"threshold_uncertainty_score":0.05634111,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2125238156","doi":"10.1109/tpami.2007.61","title":"Supervised Learning of Semantic Classes for Image Annotation and Retrieval","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":870,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Google; National Science Foundation","keywords":"Artificial intelligence; Computer science; Image retrieval; Automatic image annotation; Pattern recognition (psychology); Feature (linguistics); Probabilistic logic; Annotation; Contextual image classification; Class (philosophy); Machine learning; Image (mathematics)","authors":[{"name":"Gustavo Carneiro","is_ca":false},{"name":"Antoni B. Chan","is_ca":false},{"name":"Pedro J. Moreno","is_ca":false},{"name":"Nuno Vasconcelos","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0217029757067044,"gpt":0.2927894438965103,"spread":0.2710864681898059,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003192492,0.001010847,0.001675418,0.002873654,0.001078045,0.002322891,0.00341018,0.002219779,0.002263857],"category_scores_gemma":[0.01319276,0.000671068,0.001572619,0.002640933,0.001910496,0.005022069,0.002425962,0.002336129,0.001225721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001860365,"about_ca_system_score_gemma":0.00220727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002996069,"about_ca_topic_score_gemma":0.004654783,"domain_scores_codex":[0.9960758,0.001396891,0.0001881568,0.0009524437,0.001151173,0.0002354081],"domain_scores_gemma":[0.9956667,0.002029781,0.0005995745,0.0008850411,0.0006928361,0.000126052],"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":[0.0003521472,0.0004188356,0.002310609,0.0003353205,0.0001796799,0.0001202871,0.0003488754,0.1593385,0.008505964,0.1277971,0.01476138,0.6855314],"study_design_scores_gemma":[0.00002492676,0.0000520071,0.0005393306,0.0000235708,0.00002743259,0.0001019519,0.00005066306,0.8947626,0.00348716,0.09720274,0.003700872,0.00002662964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003351935,0.000164556,0.9947973,0.0001711929,0.00002348898,0.00006781858,0.0001000282,0.0005367721,0.0007869283],"genre_scores_gemma":[0.2821203,0.0004935022,0.710963,0.0003450335,0.0003220995,0.0007707432,0.00162039,0.0003071615,0.00305781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00341018,"threshold_uncertainty_score":0.01688373,"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","authors":[{"name":"Antonio Torralba","is_ca":false},{"name":"Kevin Murphy","is_ca":true},{"name":"William T. Freeman","is_ca":false}],"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001594377,0.0007738818,0.001486402,0.001113627,0.0005410157,0.0009319533,0.001800593,0.00108526,0.001699273],"category_scores_gemma":[0.004965509,0.0005446437,0.0007606217,0.001192636,0.0008442203,0.002312602,0.001747745,0.0007932928,0.0005080228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006996568,"about_ca_system_score_gemma":0.0005287346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002223498,"about_ca_topic_score_gemma":0.002613268,"domain_scores_codex":[0.9990134,0.0001876139,0.00003260608,0.0003223987,0.0003014265,0.0001425308],"domain_scores_gemma":[0.9981954,0.000701176,0.0002099888,0.0005754903,0.0002040838,0.0001138729],"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":[0.0005120527,0.000327273,0.002845066,0.0001807086,0.0001838404,0.0002604367,0.0001611885,0.270159,0.0377405,0.01767144,0.003537301,0.6664212],"study_design_scores_gemma":[0.00001725949,0.00006437155,0.0008329222,0.000004338072,0.00001985922,0.000086033,0.0000210948,0.9679356,0.006773551,0.02336146,0.0008698734,0.00001364218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03570441,0.0003099795,0.962466,0.00009789015,0.00003353361,0.00003867709,0.0000677147,0.000627716,0.0006540949],"genre_scores_gemma":[0.6123041,0.0002153362,0.3851278,0.000109828,0.000101551,0.0001034954,0.0003036618,0.0001194911,0.001614794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002223498,"threshold_uncertainty_score":0.008431971,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2166502676","doi":"10.1109/tpami.2006.18","title":"On the removal of shadows from images","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":660,"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":"Artificial intelligence; Computer vision; Standard illuminant; Shadow (psychology); Pixel; Shadow mapping; Computer science; Representation (politics); Image (mathematics); Grayscale; Image restoration; Mathematics; Pattern recognition (psychology); Image processing","authors":[{"name":"Graham D. Finlayson","is_ca":false},{"name":"S. D. Hordley","is_ca":false},{"name":"Cheng Lu","is_ca":true},{"name":"Mark S. Drew","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01466433899117026,"gpt":0.2657386000601439,"spread":0.2510742610689737,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002935312,0.0004972073,0.0003876352,0.0005667355,0.0003137295,0.0009083824,0.0005565563,0.0004196465,0.002343004],"category_scores_gemma":[0.001706468,0.0002970134,0.0006127313,0.0005074817,0.0008831287,0.001320576,0.0009863535,0.0008533566,0.0009669266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003142449,"about_ca_system_score_gemma":0.0002491798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006315585,"about_ca_topic_score_gemma":0.0006853218,"domain_scores_codex":[0.9996897,0.00004225216,0.00001264984,0.00005391933,0.0001717749,0.00002978361],"domain_scores_gemma":[0.9994332,0.0001484285,0.00005221741,0.0002547106,0.00009040713,0.00002099954],"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":[0.0001776268,0.00005522247,0.0004247414,0.0004127195,0.00005832182,0.0004858055,0.0005620827,0.08013725,0.199716,0.2789191,0.003727937,0.4353232],"study_design_scores_gemma":[0.00002889379,0.00019577,0.001873627,0.00009014773,0.00004642421,0.001647805,0.0001473289,0.6201972,0.1672791,0.1631163,0.04528883,0.00008863678],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0189192,0.0002947835,0.9749322,0.0000942645,0.00005318676,0.00002246412,0.00006332579,0.0003721311,0.005248371],"genre_scores_gemma":[0.3528732,0.001713316,0.6308876,0.000238299,0.0001630965,0.00007096574,0.0003908006,0.0003722964,0.01329038],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002343004,"threshold_uncertainty_score":0.00783813,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2106097225","doi":"10.1109/tpami.2005.173","title":"Canny edge detection enhancement by scale multiplication","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":601,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Multiplication (music); Edge detection; Thresholding; Scale (ratio); Canny edge detector; Mathematics; Enhanced Data Rates for GSM Evolution; Scalar multiplication; Pattern recognition (psychology); Artificial intelligence; Deriche edge detector; Computer science; Image processing; Image (mathematics)","authors":[{"name":"Paul Bao","is_ca":false},{"name":"Lei Zhang","is_ca":true},{"name":"Xiaolin Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009654834140319035,"gpt":0.2536779626350946,"spread":0.2440231284947756,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001153825,0.001123397,0.0007938932,0.002336905,0.0004313477,0.0009113555,0.0008477935,0.001000913,0.004577509],"category_scores_gemma":[0.003087305,0.0004809961,0.0007434787,0.001860553,0.0006448095,0.001774502,0.001142217,0.0009388654,0.002305528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004450349,"about_ca_system_score_gemma":0.0004355265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008135976,"about_ca_topic_score_gemma":0.0008241812,"domain_scores_codex":[0.9990687,0.000114324,0.00003798207,0.0001545923,0.0005493991,0.00007501919],"domain_scores_gemma":[0.9988458,0.0002838427,0.00008797053,0.0001539011,0.0005953988,0.00003308583],"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":[0.0003574632,0.00009356993,0.0007063557,0.0005049012,0.0000960668,0.0002920996,0.0001364551,0.01626562,0.3174918,0.03059129,0.008891487,0.6245729],"study_design_scores_gemma":[0.00007586097,0.0004776111,0.005177041,0.00007580277,0.0001770942,0.00207782,0.00007087149,0.4931659,0.4186716,0.0182582,0.06158609,0.0001860942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01118,0.0007618419,0.981126,0.0001334316,0.0001324613,0.00009227359,0.00005412631,0.001832828,0.004687068],"genre_scores_gemma":[0.1176187,0.001293648,0.8722956,0.0002033109,0.0001828055,0.0001153626,0.0001842363,0.0003473781,0.007758963],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004577509,"threshold_uncertainty_score":0.01531333,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2152233525","doi":"10.1109/tpami.2012.147","title":"Visual Saliency Based on Scale-Space Analysis in the Frequency Domain","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":574,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Kadir–Brady saliency detector; Salient; Computer science; Frequency domain; Pattern recognition (psychology); Computer vision; Fourier transform; Detector; Spatial frequency; Convolution (computer science); Kernel (algebra); Scale space; Visualization; Entropy (arrow of time); Image processing; Mathematics; Image (mathematics); Saliency map; Optics; Artificial neural network; Physics","authors":[{"name":"Jian Li","is_ca":false},{"name":"Martin D. Levine","is_ca":true},{"name":"Xiangjing An","is_ca":false},{"name":"Xin Xu","is_ca":false},{"name":"Hangen He","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01777406914844843,"gpt":0.2915349400361138,"spread":0.2737608708876654,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003456527,0.0004138302,0.0004778785,0.001359994,0.000185468,0.0007463863,0.0004249905,0.0004023323,0.00128458],"category_scores_gemma":[0.001536677,0.0001887975,0.0005805616,0.0007509445,0.0005584962,0.001015219,0.0004222194,0.0003816083,0.0002726273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003839058,"about_ca_system_score_gemma":0.0001829321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001209803,"about_ca_topic_score_gemma":0.0007461865,"domain_scores_codex":[0.9998533,0.00002561446,0.000006559482,0.00003724786,0.00005911506,0.00001817297],"domain_scores_gemma":[0.9995643,0.0002032572,0.00007058237,0.00004886953,0.00008973889,0.00002325523],"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":[0.0003340128,0.00012962,0.005204442,0.0004653696,0.0002269049,0.0005349123,0.000467805,0.1840469,0.2873807,0.06828742,0.003913373,0.4490087],"study_design_scores_gemma":[0.00001189429,0.0001295645,0.006442041,0.00001486307,0.00004596285,0.0002965473,0.0000382139,0.9424988,0.01899296,0.02961019,0.001880662,0.00003824978],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04166299,0.0003982451,0.9557388,0.0001102811,0.00003938859,0.00003009537,0.00004957688,0.0002812632,0.001689286],"genre_scores_gemma":[0.8052297,0.000608346,0.1925437,0.00006130457,0.0001758213,0.00004628933,0.000113917,0.00007108199,0.001149816],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001359994,"threshold_uncertainty_score":0.004297316,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100968369","doi":"10.1109/tpami.2005.220","title":"Efficient shape matching using shape contexts","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":458,"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":"Shape analysis (program analysis); Heat kernel signature; Active shape model; Artificial intelligence; Shape context; Computer science; Matching (statistics); Computer vision; Pattern recognition (psychology); Vector quantization; Mathematics; Image (mathematics); Segmentation","authors":[{"name":"Giulio Mori","is_ca":true},{"name":"Serge Belongie","is_ca":false},{"name":"Jitendra Malik","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02776617728988811,"gpt":0.2924933018035573,"spread":0.2647271245136691,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005038269,0.0008323003,0.001410735,0.002644974,0.0009871569,0.001618635,0.001674308,0.001110166,0.005302539],"category_scores_gemma":[0.003447483,0.0004894135,0.0008567105,0.002862963,0.000858397,0.003127602,0.002697504,0.0009932773,0.002471938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004760782,"about_ca_system_score_gemma":0.0008550533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002038883,"about_ca_topic_score_gemma":0.003738307,"domain_scores_codex":[0.9988315,0.00014054,0.00007305892,0.0003118781,0.0005245314,0.0001185353],"domain_scores_gemma":[0.9986972,0.0003511892,0.0001283196,0.0004518381,0.0002853798,0.0000860865],"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":[0.0006889964,0.0001330444,0.002261887,0.0002182907,0.00005479828,0.0002636951,0.0002089544,0.02580831,0.1317686,0.02584721,0.005958009,0.8067882],"study_design_scores_gemma":[0.0001238176,0.0006007691,0.004080005,0.00006451065,0.0000811477,0.001420537,0.0004432741,0.7796981,0.1128847,0.07834683,0.02211554,0.0001406829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.100973,0.001187098,0.8862135,0.0002078574,0.0001289283,0.0002146268,0.0003767434,0.004482117,0.006216183],"genre_scores_gemma":[0.4128723,0.0005205251,0.5825346,0.0001855345,0.00007423592,0.000119017,0.0008408776,0.0004366155,0.002416273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005302539,"threshold_uncertainty_score":0.01773882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2121299550","doi":"10.1109/tpami.2007.70752","title":"Nonrigid Structure-from-Motion: Estimating Shape and Motion with Hierarchical Priors","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":454,"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":"Carnegie Mellon University","keywords":"Computer science; Artificial intelligence; Computer vision; Face (sociological concept); Prior probability; Motion (physics); Motion estimation; Subspace topology; Point distribution model; Active shape model; Probabilistic logic; Object (grammar); Structure from motion; Shape analysis (program analysis); Rigid transformation; Missing data; Transformation (genetics); Algorithm; Bayesian probability; Machine learning","authors":[{"name":"Lorenzo Torresani","is_ca":false},{"name":"Aaron Hertzmann","is_ca":true},{"name":"Christoph Bregler","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01685685939758975,"gpt":0.2681176636603932,"spread":0.2512608042628034,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001728315,0.001299908,0.0013096,0.001230577,0.0006210296,0.0009625353,0.002274354,0.00177258,0.001304264],"category_scores_gemma":[0.004781832,0.001529788,0.001563263,0.001672946,0.001303338,0.002306992,0.001774871,0.00199672,0.0008942892],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009128824,"about_ca_system_score_gemma":0.001705784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01029859,"about_ca_topic_score_gemma":0.01093019,"domain_scores_codex":[0.9989557,0.0002818462,0.00004312904,0.000277633,0.0003642035,0.00007747118],"domain_scores_gemma":[0.9987652,0.0005420927,0.0001583404,0.0003450009,0.0001371309,0.0000522066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001343535,0.00008441418,0.0008475941,0.0001529923,0.0001102532,0.00008394511,0.0001595105,0.63156,0.01507696,0.02518396,0.002658206,0.3239478],"study_design_scores_gemma":[0.000007688809,0.00001824394,0.0002810593,0.000008201096,0.000009307454,0.00004136956,0.000007245639,0.9844447,0.002186322,0.01153211,0.001445908,0.00001781389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001131382,0.0001310715,0.9983242,0.00004041953,0.000009314898,0.00001499822,0.00002291702,0.0002025225,0.000123087],"genre_scores_gemma":[0.08138517,0.0005933056,0.9154752,0.0001290259,0.00007869746,0.0001529645,0.0003824135,0.000247581,0.001555584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01029859,"threshold_uncertainty_score":0.02047729,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963809933","doi":"10.1109/tpami.2017.2706685","title":"3D Object Proposals Using Stereo Imagery for Accurate Object Class Detection","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":430,"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":"Office of Naval Research; Toyota Motor Corporation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Nvidia","keywords":"Artificial intelligence; Computer vision; Computer science; Object detection; Object (grammar); Object-class detection; Class (philosophy); Pattern recognition (psychology); Face detection; Facial recognition system","authors":[{"name":"Kaustav Kundu","is_ca":true},{"name":"Yukun Zhu","is_ca":true},{"name":"Huimin Ma","is_ca":false},{"name":"Sanja Fidler","is_ca":true},{"name":"Raquel Urtasun","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04354686040000782,"gpt":0.3256726781947614,"spread":0.2821258177947536,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006176914,0.00144442,0.0008449225,0.001656489,0.000290035,0.00108481,0.002017274,0.0009841589,0.002792451],"category_scores_gemma":[0.001689167,0.0009206552,0.000901366,0.001132892,0.000466851,0.001428959,0.001727024,0.0008868813,0.002317373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006512766,"about_ca_system_score_gemma":0.001079177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00533492,"about_ca_topic_score_gemma":0.009344698,"domain_scores_codex":[0.9994045,0.00004486875,0.00001451698,0.0001565065,0.0002932291,0.00008630165],"domain_scores_gemma":[0.9994571,0.0001073926,0.00007663979,0.0001548928,0.0001568546,0.00004713775],"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":[0.0004631749,0.0002011414,0.006293949,0.0002476425,0.0001367497,0.0002763292,0.0002010249,0.1217483,0.1198382,0.003903817,0.007581695,0.739108],"study_design_scores_gemma":[0.00002507219,0.00007482824,0.002380347,0.00002001525,0.00002260733,0.0002198685,0.00005915648,0.959639,0.03074586,0.003480371,0.00330919,0.00002385485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06679862,0.0003953963,0.9236078,0.000153444,0.00007945263,0.0001571089,0.0005450852,0.005848589,0.002414444],"genre_scores_gemma":[0.4773748,0.0003751301,0.5156912,0.0001981546,0.00006375773,0.000130944,0.002695078,0.0005082818,0.002962729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00533492,"threshold_uncertainty_score":0.01060772,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4285787895","doi":"10.1109/tpami.2022.3191696","title":"A Review of Generalized Zero-Shot Learning Methods","year":2022,"lang":"en","type":"review","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":396,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Categorization; Machine learning; Task (project management); Class (philosophy); Bridge (graph theory)","authors":[{"name":"Farhad Pourpanah","is_ca":true},{"name":"Moloud Abdar","is_ca":false},{"name":"Yuxuan Luo","is_ca":false},{"name":"Xinlei Zhou","is_ca":false},{"name":"Ran Wang","is_ca":false},{"name":"Chee Peng Lim","is_ca":false},{"name":"Xizhao Wang","is_ca":false},{"name":"Q. M. Jonathan Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1084461427647116,"gpt":0.3994721849126373,"spread":0.2910260421479256,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001667258,0.00152592,0.001981819,0.003071748,0.0004854642,0.001784451,0.002517045,0.00170245,0.003912671],"category_scores_gemma":[0.004116233,0.0006846824,0.001219318,0.004469574,0.0007888452,0.002755289,0.001077077,0.001826612,0.003103384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001551,"about_ca_system_score_gemma":0.001911425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004176221,"about_ca_topic_score_gemma":0.002814962,"domain_scores_codex":[0.999145,0.0001642857,0.0001017055,0.0002546291,0.00028896,0.0000453477],"domain_scores_gemma":[0.9983606,0.001005514,0.00008233839,0.00009165621,0.0004127841,0.00004719403],"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":[0.00005870618,0.0001022646,0.000587799,0.006885715,0.0001703033,0.0000904047,0.0001047545,0.007049601,0.0008600334,0.01839226,0.02990091,0.9357972],"study_design_scores_gemma":[0.00003773141,0.000319655,0.002837527,0.004915389,0.0003943478,0.001491207,0.0002126441,0.04485432,0.002528278,0.05675124,0.8854476,0.0002100473],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009532664,0.930454,0.06001833,0.0009010321,0.0006944747,0.0000594705,0.0002053266,0.0002243608,0.006489683],"genre_scores_gemma":[0.01431002,0.9346061,0.04310111,0.0009188788,0.001740239,0.0001560008,0.0008160847,0.0001046563,0.004246969],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004176221,"threshold_uncertainty_score":0.01308912,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2128089874","doi":"10.1109/tpami.2002.1114849","title":"Flux maximizing geometric flows","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":381,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; IBM (Canada)","funders":"","keywords":"Image segmentation; Surface (topology); Artificial intelligence; Segmentation; Computer science; Computer vision; Image (mathematics); Set (abstract data type); Field (mathematics); Level set (data structures); Algorithm; Mathematics; Geometry","authors":[{"name":"Alexander Vasilevskiy","is_ca":true},{"name":"Kaleem Siddiqi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03171995903845451,"gpt":0.2775371475731147,"spread":0.2458171885346601,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002121588,0.001675106,0.001609133,0.001941378,0.0007044073,0.001622569,0.0008778304,0.00238986,0.003338839],"category_scores_gemma":[0.005575524,0.0007454673,0.001184464,0.0009473988,0.001718612,0.002410419,0.001411401,0.0008401353,0.0007140712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001891595,"about_ca_system_score_gemma":0.0008935363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007297877,"about_ca_topic_score_gemma":0.0005236481,"domain_scores_codex":[0.9996246,0.0001565325,0.00002143633,0.00006684971,0.0000875393,0.00004299671],"domain_scores_gemma":[0.9989384,0.0006107214,0.0001305033,0.00005539029,0.0001954381,0.00006964009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000183261,0.00006115178,0.0005946065,0.0003690313,0.00004666,0.0001159711,0.0001872654,0.5076425,0.01413665,0.368609,0.003357901,0.1046959],"study_design_scores_gemma":[0.00003121506,0.00008811017,0.0002139896,0.00003318478,0.00001806499,0.0001038788,0.00001540352,0.9097753,0.003310858,0.08312738,0.003257726,0.00002499945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01599214,0.0008454071,0.976333,0.0004222174,0.00007163605,0.0001001891,0.00007655391,0.0002410012,0.005917876],"genre_scores_gemma":[0.4447431,0.002130844,0.5388593,0.00029115,0.0002758763,0.0005888889,0.0003205883,0.000454977,0.01233529],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003338839,"threshold_uncertainty_score":0.01372457,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2963047498","doi":"10.1109/tpami.2017.2695539","title":"Drawing and Recognizing Chinese Characters with Recurrent Neural Network","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Handwritten Text Recognition Techniques","field":"Computer Science","cited_by":356,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"National Natural Science Foundation of China","keywords":"Recurrent neural network; Computer science; Discriminative model; Artificial intelligence; Handwriting; Chinese characters; Generative grammar; Handwriting recognition; Convolutional neural network; Deep learning; Intelligent character recognition; Natural language processing; Generative model; Embedding; Task (project management); Pattern recognition (psychology); Machine learning; Speech recognition; Artificial neural network; Feature extraction; Character recognition; Image (mathematics)","authors":[{"name":"Xu-Yao Zhang","is_ca":false},{"name":"Fei Yin","is_ca":false},{"name":"Yan‐Ming Zhang","is_ca":false},{"name":"Cheng‐Lin Liu","is_ca":false},{"name":"Yoshua Bengio","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02285943870308256,"gpt":0.2794097470582382,"spread":0.2565503083551556,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002160073,0.0007824346,0.0005332553,0.0005364589,0.0002318289,0.0005067596,0.0008642405,0.0004344791,0.001935079],"category_scores_gemma":[0.0006813951,0.0003644063,0.0006909707,0.0007337788,0.0002505756,0.0008722973,0.0004312546,0.0006326842,0.0007423027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004772741,"about_ca_system_score_gemma":0.0004237298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009287333,"about_ca_topic_score_gemma":0.01154831,"domain_scores_codex":[0.9997327,0.00002974104,0.00001862143,0.0001019461,0.00008077094,0.00003620536],"domain_scores_gemma":[0.9998036,0.00004207437,0.0000329709,0.00004399792,0.00006023939,0.00001712799],"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":[0.000201204,0.0001164487,0.001409269,0.0001585373,0.00009786292,0.0004017012,0.0001069275,0.2018386,0.06180603,0.00392493,0.005812327,0.7241261],"study_design_scores_gemma":[0.000004753195,0.00003023193,0.0004449079,0.000004024628,0.00001450621,0.00005273613,0.000007400941,0.9885085,0.008852287,0.0009847475,0.001087176,0.000008656918],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08410576,0.000913714,0.9009354,0.0001863713,0.0001722441,0.00008846662,0.0003587073,0.008241815,0.004997583],"genre_scores_gemma":[0.6838607,0.0007750493,0.3033456,0.0001540921,0.00007728092,0.0001020534,0.001604431,0.0001713152,0.009909459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009287333,"threshold_uncertainty_score":0.01846653,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056380775","doi":"10.1109/tpami.2011.253","title":"Learning Sparse Representations for Human Action Recognition","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":349,"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; Pattern recognition (psychology); Cluster analysis; Sparse approximation; Vector quantization; K-SVD; Invariant (physics); Feature vector; Set (abstract data type); Representation (politics); Learning vector quantization; Mathematics","authors":[{"name":"Tanaya Guha","is_ca":true},{"name":"R.K. Ward","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1038711441174204,"gpt":0.3220549019212692,"spread":0.2181837578038487,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005818354,0.0004786945,0.0007963132,0.0008236353,0.0001777809,0.0004708609,0.0005931872,0.0006363182,0.001288897],"category_scores_gemma":[0.00303764,0.0002286686,0.0004314723,0.001093936,0.0004211536,0.0009287662,0.000501767,0.000842603,0.0005156929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003517215,"about_ca_system_score_gemma":0.0003865551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002895084,"about_ca_topic_score_gemma":0.002666933,"domain_scores_codex":[0.9995957,0.0001290416,0.0000206361,0.00009523046,0.0001185631,0.00004082007],"domain_scores_gemma":[0.9991953,0.0004375958,0.00008447304,0.000121326,0.0001392534,0.00002212751],"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":[0.0001775385,0.0001276327,0.0008636655,0.0001420552,0.00007723235,0.00007641698,0.00008837666,0.3384338,0.01283441,0.01877333,0.006519523,0.6218859],"study_design_scores_gemma":[0.000006995067,0.00002680977,0.0002910588,0.000005530873,0.000006181962,0.0000232882,0.00001229403,0.9888086,0.001383689,0.008694289,0.0007354204,0.000005951784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01252648,0.0004580898,0.9855447,0.0001467362,0.00003747603,0.00002160392,0.0001449032,0.0004815469,0.0006383768],"genre_scores_gemma":[0.5520255,0.001469532,0.4403907,0.0002396728,0.00027129,0.0001564126,0.002094225,0.00009461191,0.003258006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002895084,"threshold_uncertainty_score":0.005756438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2130843763","doi":"10.1109/tpami.2009.43","title":"Human Action Recognition by Semilatent Topic Models","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":325,"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":"Simon Fraser University; Harbin Institute of Technology; Microsoft Research","keywords":"Computer science; Artificial intelligence; Action recognition; Topic model; Class (philosophy); Probabilistic latent semantic analysis; Machine learning; Set (abstract data type); Training set; Frame (networking); Representation (politics); Hidden Markov model; Word (group theory); Action (physics); Latent variable; Natural language processing; Pattern recognition (psychology); Mathematics","authors":[{"name":"Yang Wang","is_ca":true},{"name":"Giulio Mori","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04873622529257237,"gpt":0.2883200060787379,"spread":0.2395837807861655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001999657,0.001290915,0.00146716,0.002337595,0.0003655911,0.00160235,0.002034992,0.001366208,0.00174358],"category_scores_gemma":[0.004900836,0.0007289925,0.002060705,0.002032477,0.0007962821,0.00347674,0.001242379,0.0016013,0.001485089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007109569,"about_ca_system_score_gemma":0.0006205761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005146128,"about_ca_topic_score_gemma":0.005966391,"domain_scores_codex":[0.9983048,0.0005906702,0.0001000478,0.0005282945,0.0003216938,0.0001544199],"domain_scores_gemma":[0.9973571,0.001479379,0.0003123839,0.0003666487,0.0003898674,0.00009473616],"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":[0.0009106298,0.000543752,0.006845161,0.0004021335,0.0005134245,0.0002724609,0.0007054951,0.2821654,0.01929343,0.01464505,0.0111207,0.6625823],"study_design_scores_gemma":[0.00001269597,0.00003558488,0.0007814016,0.000009747067,0.00002009715,0.00006315421,0.00002011195,0.991998,0.001256194,0.005114628,0.0006707403,0.00001760148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02185047,0.0009843168,0.9741458,0.0003030144,0.00007878758,0.00005688408,0.0003514111,0.001312745,0.0009165999],"genre_scores_gemma":[0.6264026,0.001766692,0.3619232,0.0003770799,0.000471698,0.0004988901,0.003205521,0.0003396149,0.005014696],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005146128,"threshold_uncertainty_score":0.01057535,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2047499569","doi":"10.1109/tpami.2011.228","title":"Discriminative Latent Models for Recognizing Contextual Group Activities","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":312,"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":"Canadian Institutes of Health Research","keywords":"Discriminative model; Computer science; Inference; Latent variable; Artificial intelligence; Feature (linguistics); Context (archaeology); Representation (politics); Action (physics); Focus (optics); Machine learning","authors":[{"name":"Tian Lan","is_ca":true},{"name":"Yang Wang","is_ca":true},{"name":"Weilong Yang","is_ca":false},{"name":"Stephen N. Robinovitch","is_ca":true},{"name":"Gajendrasinh Natvarsinh Mori","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08053272526626618,"gpt":0.2779632148624597,"spread":0.1974304895961936,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008338995,0.001081274,0.00110839,0.001153329,0.0003297586,0.000897147,0.001643057,0.0009578071,0.003062007],"category_scores_gemma":[0.002671505,0.0005305217,0.001242229,0.00150014,0.0006638612,0.001878887,0.001228402,0.001890071,0.001382674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008219492,"about_ca_system_score_gemma":0.0007049241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007343803,"about_ca_topic_score_gemma":0.01076956,"domain_scores_codex":[0.9991263,0.0002510119,0.00003371708,0.0003107655,0.0001329667,0.0001453254],"domain_scores_gemma":[0.9989619,0.0004297464,0.0001735416,0.0002641466,0.0001044808,0.00006617144],"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":[0.0007936684,0.0004611641,0.01077745,0.0003561038,0.0003630449,0.0003396197,0.0004717897,0.3660567,0.01494804,0.04903629,0.01176197,0.5446342],"study_design_scores_gemma":[0.0000203742,0.00005926453,0.001514542,0.00002023289,0.00003398935,0.00006227951,0.00004014335,0.9724357,0.001121729,0.02287298,0.001800899,0.00001775669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0194071,0.0007677837,0.976427,0.0001704457,0.00005073037,0.00004845968,0.0005989784,0.001284174,0.001245353],"genre_scores_gemma":[0.7634189,0.0009913401,0.2249525,0.000289192,0.0001990182,0.0002716036,0.004408062,0.0002691979,0.005200099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007343803,"threshold_uncertainty_score":0.01460212,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2165192967","doi":"10.1109/tpami.2005.158","title":"Example-based photometric stereo: shape reconstruction with general, varying BRDFs","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":308,"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":"Office of Naval Research; Microsoft; National Science Foundation","keywords":"Computer vision; Artificial intelligence; Photometric stereo; Computer science; Segmentation; Calibration; Sequence (biology); Simple (philosophy); Image segmentation; Computer graphics (images); Image (mathematics); Geometry; Mathematics","authors":[{"name":"Aaron Hertzmann","is_ca":true},{"name":"Steven M. Seitz","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03193799847270146,"gpt":0.2839978282215181,"spread":0.2520598297488167,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007869281,0.000453827,0.0006608574,0.0009303715,0.0002777385,0.0007525069,0.001505657,0.00101969,0.001574441],"category_scores_gemma":[0.002049364,0.000610679,0.0008532408,0.0008281955,0.0006102363,0.001239233,0.001263688,0.001001797,0.0008062277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003441382,"about_ca_system_score_gemma":0.0003714577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001199518,"about_ca_topic_score_gemma":0.001927099,"domain_scores_codex":[0.9992902,0.000138716,0.00001701964,0.00009402746,0.0004184995,0.0000415035],"domain_scores_gemma":[0.9993116,0.0001547235,0.00006490852,0.0002987738,0.0001409142,0.00002908547],"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":[0.0003914935,0.00009003337,0.001282588,0.000355332,0.0001282743,0.0002374763,0.0003465147,0.1753186,0.1397344,0.02941745,0.004575262,0.6481226],"study_design_scores_gemma":[0.0000275268,0.00004358807,0.0007848506,0.00001408331,0.0000174516,0.0005229783,0.00003724483,0.9512634,0.02943636,0.01377186,0.004041602,0.00003896686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008826083,0.00006977263,0.989621,0.00003797986,0.00001055291,0.00001689754,0.00004831884,0.0004903598,0.0008789812],"genre_scores_gemma":[0.0990749,0.0002046493,0.8995153,0.00006018671,0.00003170476,0.0000248719,0.0001944927,0.0001817687,0.0007121062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001574441,"threshold_uncertainty_score":0.005266964,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2154209944","doi":"10.1109/tpami.2011.145","title":"Texture Classification from Random Features","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":288,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Artificial intelligence; Feature extraction; Computer science; Random forest; Curse of dimensionality; Random projection; Contextual image classification; Texture (cosmology); Feature (linguistics); Image texture; Projection (relational algebra); Image (mathematics); Image processing; Algorithm","authors":[{"name":"Liu Li","is_ca":false},{"name":"Paul Fieguth","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03382540508508669,"gpt":0.267935829708922,"spread":0.2341104246238353,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004607025,0.000559017,0.001002561,0.002262937,0.0002237129,0.001057819,0.000566418,0.0006336877,0.001785931],"category_scores_gemma":[0.002978426,0.0002427477,0.0006340946,0.001253955,0.0004661059,0.001314562,0.0006390751,0.0007642235,0.00093424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003802523,"about_ca_system_score_gemma":0.0002789712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001174673,"about_ca_topic_score_gemma":0.001331541,"domain_scores_codex":[0.9995421,0.00006528753,0.00002209756,0.0001007976,0.0001856845,0.00008416601],"domain_scores_gemma":[0.9990441,0.0003212926,0.0001244639,0.000197806,0.0002690723,0.00004324056],"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":[0.0006905776,0.0001465826,0.003488243,0.0002250647,0.00007117216,0.0002148258,0.0000870362,0.06106942,0.07201747,0.006137943,0.008384399,0.8474674],"study_design_scores_gemma":[0.00004529358,0.0001367647,0.004791583,0.00002843082,0.00004858398,0.0003473703,0.0001016839,0.9557111,0.02484494,0.009292072,0.004615644,0.00003655986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1073911,0.0008468078,0.8847755,0.0005619578,0.0001789622,0.0001138493,0.0005421942,0.001775093,0.003814433],"genre_scores_gemma":[0.730214,0.0007582161,0.2630896,0.0002635602,0.0003606152,0.0001386169,0.001820759,0.000190003,0.003164579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002262937,"threshold_uncertainty_score":0.005974531,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134019950","doi":"10.1109/tpami.2009.102","title":"Shape and Spatially-Varying BRDFs from Photometric Stereo","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":271,"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":"Office of Naval Research; National Science Foundation","keywords":"Artificial intelligence; Computer science; Photometric stereo; Computer vision; Pixel; Specular reflection; Variety (cybernetics); Computer graphics (images); Image (mathematics); Pattern recognition (psychology); Optics; Physics","authors":[{"name":"Dan B Goldman","is_ca":false},{"name":"Brian Curless","is_ca":false},{"name":"Aaron Hertzmann","is_ca":true},{"name":"Steven M. Seitz","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02506737751831176,"gpt":0.29243413542075,"spread":0.2673667579024382,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005868244,0.0006502278,0.0005863231,0.001410187,0.0003074231,0.0009943112,0.0009886345,0.000639593,0.001569856],"category_scores_gemma":[0.002755145,0.0005526667,0.001055677,0.001179617,0.0005576911,0.00127599,0.001159014,0.001133936,0.0008400829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005309539,"about_ca_system_score_gemma":0.000536002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002453568,"about_ca_topic_score_gemma":0.003471294,"domain_scores_codex":[0.9992279,0.00009827271,0.00001773896,0.00009506632,0.0005115221,0.00004953258],"domain_scores_gemma":[0.9993626,0.0001232473,0.00006464878,0.0002465288,0.0001777332,0.00002510733],"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":[0.0001244581,0.00006564631,0.001450419,0.0002830081,0.00008593067,0.0001674589,0.0003748924,0.2003742,0.2051228,0.04303852,0.003995304,0.5449173],"study_design_scores_gemma":[0.00002774153,0.00004942879,0.003925643,0.00002433067,0.0000392808,0.0005748982,0.00007662101,0.8840963,0.06593112,0.03171647,0.01343182,0.0001062847],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01277328,0.0001177692,0.9831427,0.0000502902,0.00002244228,0.00001608229,0.0001042666,0.0005667714,0.003206441],"genre_scores_gemma":[0.3387374,0.000685715,0.6551507,0.0001077406,0.00009707914,0.00006441063,0.0008041365,0.001011369,0.003341532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002453568,"threshold_uncertainty_score":0.005251706,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2161604086","doi":"10.1109/tpami.2011.33","title":"Model-Based 3D Hand Pose Estimation from Monocular Video","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":268,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Canada Research Chairs; University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Monocular; Computer science; Visibility; Minification; Pose; Function (biology)","authors":[{"name":"Martin de La Gorce","is_ca":false},{"name":"D. J. Fleet","is_ca":true},{"name":"Nikolaos Paragios","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03730685530345185,"gpt":0.2602712437276418,"spread":0.22296438842419,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003011542,0.0008504579,0.00134884,0.000919955,0.0003398591,0.001031381,0.0009401543,0.0008466101,0.002153707],"category_scores_gemma":[0.0009857237,0.0008248035,0.0006511208,0.001082184,0.0003338439,0.0008584608,0.0009406807,0.0006884614,0.001412895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005325048,"about_ca_system_score_gemma":0.0008923069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007732761,"about_ca_topic_score_gemma":0.01400215,"domain_scores_codex":[0.9995524,0.00004693527,0.00001254262,0.0001216597,0.0002188086,0.00004765694],"domain_scores_gemma":[0.9997683,0.00005175137,0.00004389925,0.00005103585,0.00006727686,0.0000177656],"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":[0.0002027368,0.00009666511,0.001011319,0.0002527992,0.000120107,0.0002258098,0.0001172708,0.173544,0.1429977,0.004242503,0.005355156,0.6718339],"study_design_scores_gemma":[0.00001111285,0.00005535812,0.001388681,0.00002023664,0.00001789259,0.0002805947,0.00001748498,0.9766227,0.01710707,0.002122902,0.002329846,0.00002613581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008185619,0.0004667689,0.9887388,0.00004553008,0.00003739824,0.00002596609,0.0001095901,0.001188017,0.001202373],"genre_scores_gemma":[0.2889831,0.001186015,0.7039139,0.0001425315,0.00008397856,0.0001405218,0.0006442185,0.0003141031,0.004591657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007732761,"threshold_uncertainty_score":0.01537549,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2170570264","doi":"10.1109/tpami.2002.1046157","title":"Recognizing mathematical expressions using tree transformation","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Mathematics, Computing, and Information Processing","field":"Computer Science","cited_by":256,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Decimal; Expression (computer science); Algebraic expression; Symbol (formal); Notation; Tree (set theory); Regular expression; Operator (biology); Associative property; Arithmetic; Theoretical computer science; Algorithm; Programming language; Mathematics; Algebraic number; Combinatorics","authors":[{"name":"Richard Zanibbi","is_ca":true},{"name":"Dorothea Blostein","is_ca":true},{"name":"James R. Cordy","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05366700256195335,"gpt":0.2817114468208072,"spread":0.2280444442588539,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000455854,0.0007207113,0.0006829851,0.0009419875,0.0005121148,0.001740178,0.001054588,0.0004996689,0.008346002],"category_scores_gemma":[0.001682804,0.0003740605,0.0009749008,0.001439631,0.0006329516,0.003247334,0.001054545,0.0008547063,0.006954937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005368986,"about_ca_system_score_gemma":0.0008541698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001284125,"about_ca_topic_score_gemma":0.001955121,"domain_scores_codex":[0.9990602,0.0001348712,0.0001036945,0.000222371,0.0004116078,0.00006719702],"domain_scores_gemma":[0.9991587,0.0002335794,0.000101105,0.0002371249,0.0002341032,0.00003541762],"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":[0.0003045133,0.00009424057,0.001411653,0.0003826515,0.00007186265,0.0004917217,0.0007576694,0.01302375,0.1550253,0.06701728,0.02087222,0.7405471],"study_design_scores_gemma":[0.00008378236,0.0003411193,0.001348781,0.00008409961,0.0001321968,0.001557414,0.0002427685,0.44987,0.2399164,0.078022,0.2282511,0.0001503543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005196323,0.0001347757,0.9783784,0.00006128106,0.00003665491,0.0000463762,0.0001976974,0.01340302,0.002545539],"genre_scores_gemma":[0.08160189,0.0004363696,0.9085618,0.0001327561,0.00006382125,0.000164192,0.001915671,0.001860928,0.005262681],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008346002,"threshold_uncertainty_score":0.02792013,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158866619","doi":"10.1109/tpami.2006.149","title":"Recovering 3D human body configurations using shape contexts","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":249,"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; Simon Fraser University; Division of Mathematical Sciences; University of Toronto","keywords":"Computer vision; Artificial intelligence; Computer science; Human-body model; Kinematics; Context (archaeology); Process (computing); Matching (statistics); Human body; Joint (building); Articulated body pose estimation; Variety (cybernetics); Tracking (education); Shape context; Frame (networking); Image (mathematics); 3D pose estimation; Mathematics; Engineering","authors":[{"name":"Giulio Mori","is_ca":true},{"name":"Jitendra Malik","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02617981125504508,"gpt":0.2814156241246134,"spread":0.2552358128695683,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002671938,0.0011609,0.001017775,0.00237066,0.0004494189,0.0008791697,0.000828657,0.00116019,0.001713255],"category_scores_gemma":[0.001849661,0.0009005449,0.0009629339,0.001837471,0.0006267056,0.001129728,0.001178099,0.0009093501,0.001786328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003535989,"about_ca_system_score_gemma":0.0005772012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006132826,"about_ca_topic_score_gemma":0.01072092,"domain_scores_codex":[0.9996086,0.00005155961,0.00001265082,0.0001567491,0.0001218543,0.00004858303],"domain_scores_gemma":[0.9995763,0.00009025622,0.00006240744,0.0001675781,0.00006736683,0.00003615101],"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":[0.0006444586,0.0001600935,0.010292,0.0001690228,0.0001297943,0.0007216654,0.0003300064,0.1323147,0.1063407,0.003578728,0.003543313,0.7417756],"study_design_scores_gemma":[0.00005886262,0.0002538067,0.02000565,0.00007879183,0.00007850546,0.002013764,0.0004550924,0.9142054,0.04029941,0.01627951,0.006147698,0.0001234626],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1834232,0.001467047,0.8096724,0.0002118231,0.00009655609,0.0001252058,0.0007639913,0.002277706,0.00196221],"genre_scores_gemma":[0.6827801,0.00129268,0.3122411,0.0001235458,0.00008624598,0.00007865047,0.001555112,0.0002383899,0.001604127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006132826,"threshold_uncertainty_score":0.01219428,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4405907339","doi":"10.1109/tpami.2024.3524377","title":"Hyper-YOLO: When Visual Object Detection Meets Hypergraph Computation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":237,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Object detection; Hypergraph; Computer vision; Computation; Object (grammar); Cognitive neuroscience of visual object recognition; Visualization; Pattern recognition (psychology); Mathematics; Algorithm","authors":[{"name":"Yifan Feng","is_ca":false},{"name":"Jiangang Huang","is_ca":false},{"name":"Shaoyi Du","is_ca":false},{"name":"Shihui Ying","is_ca":false},{"name":"Jun‐Hai Yong","is_ca":false},{"name":"Yipeng Li","is_ca":false},{"name":"Guiguang Ding","is_ca":false},{"name":"Rongrong Ji","is_ca":true},{"name":"Yue Gao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01839729713418404,"gpt":0.286248052880697,"spread":0.267850755746513,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001178233,0.001404482,0.001236929,0.002292317,0.0008165988,0.002583856,0.002933974,0.0017229,0.003990557],"category_scores_gemma":[0.005514436,0.0007630661,0.0009033215,0.001445751,0.00131925,0.006176492,0.003200949,0.001747764,0.001824272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001483756,"about_ca_system_score_gemma":0.00179026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01219375,"about_ca_topic_score_gemma":0.01880462,"domain_scores_codex":[0.9992286,0.0001404477,0.0000306424,0.0003003742,0.0001831586,0.0001168252],"domain_scores_gemma":[0.9986005,0.0004997056,0.000119809,0.0004070574,0.0002763096,0.0000965908],"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":[0.0007838522,0.0004018756,0.008049675,0.0005013911,0.0003121082,0.0003980901,0.0004990344,0.2534674,0.02486361,0.05282407,0.03282822,0.6250707],"study_design_scores_gemma":[0.00002296651,0.00005738549,0.0006263161,0.00002465019,0.00002603037,0.00005805639,0.00007209933,0.9677143,0.00498175,0.02164842,0.004750635,0.00001743894],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02641531,0.0006917698,0.9579489,0.0004832165,0.0001145074,0.0002097813,0.0005277104,0.008101963,0.005506787],"genre_scores_gemma":[0.4792367,0.000699552,0.5030815,0.001164423,0.0002383541,0.0005178393,0.003308587,0.001198897,0.01055416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01219375,"threshold_uncertainty_score":0.0242455,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2153125595","doi":"10.1109/tpami.2012.269","title":"Learning with Hierarchical-Deep Models","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":235,"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; Computer science; Deep learning; Machine learning","authors":[{"name":"Ruslan Salakhutdinov","is_ca":true},{"name":"Joshua B. Tenenbaum","is_ca":false},{"name":"Antonio Torralba","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01505092565250138,"gpt":0.2282701358634082,"spread":0.2132192102109069,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00102257,0.0008450231,0.0008389485,0.0005950734,0.0003210611,0.001008593,0.002105688,0.001067626,0.004070126],"category_scores_gemma":[0.003755785,0.0006654997,0.001002784,0.0007335889,0.0008804183,0.002644031,0.002027323,0.002104741,0.001074047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00147443,"about_ca_system_score_gemma":0.001232692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008063307,"about_ca_topic_score_gemma":0.01790258,"domain_scores_codex":[0.9994047,0.0001873926,0.00002823847,0.0001601361,0.0001463385,0.00007330407],"domain_scores_gemma":[0.9991006,0.0004745898,0.00007264777,0.0001709205,0.0001197514,0.00006156183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001041138,0.00007338263,0.0009937143,0.0001296879,0.00008578841,0.00005910113,0.0001052366,0.7737068,0.001949738,0.109244,0.006794313,0.1067541],"study_design_scores_gemma":[0.000005466495,0.000008087027,0.0000402411,0.000004656917,0.000004078389,0.000005385446,0.000003424778,0.9613357,0.0002060029,0.03786581,0.0005184246,0.000002769537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01170513,0.0004119694,0.9834979,0.0004683942,0.00003820557,0.00002966878,0.0004812615,0.001107016,0.002260466],"genre_scores_gemma":[0.6049408,0.0007988352,0.3784149,0.000836724,0.0001259143,0.0002609286,0.002718346,0.0003442244,0.01155927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008063307,"threshold_uncertainty_score":0.01603276,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111596024","doi":"10.1109/tpami.2007.1138","title":"Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":232,"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":"Cluster analysis; Computer science; Categorical variable; Probabilistic logic; Voting; Entropy (arrow of time); Consensus clustering; Data mining; Algorithm; Mathematics; Correlation clustering; Artificial intelligence; CURE data clustering algorithm; Machine learning","authors":[{"name":"Hanan Ayad","is_ca":true},{"name":"Mohamed S. Kamel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03456688832831332,"gpt":0.3610294286362877,"spread":0.3264625403079743,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00409771,0.0009726681,0.001738732,0.001964199,0.001224103,0.001420137,0.002856963,0.001804594,0.00375009],"category_scores_gemma":[0.01111305,0.0004836399,0.001121823,0.002070851,0.001150807,0.002594985,0.001735975,0.001416606,0.001254139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00157404,"about_ca_system_score_gemma":0.001905193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004630226,"about_ca_topic_score_gemma":0.004506739,"domain_scores_codex":[0.9972192,0.0008593307,0.0001455347,0.0006256073,0.0009676598,0.0001826871],"domain_scores_gemma":[0.9969081,0.001157105,0.0002106704,0.0004854033,0.001110457,0.0001281966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000351115,0.00006643172,0.0008239607,0.0001737787,0.0001224978,0.0001181425,0.0003937367,0.5128079,0.00761568,0.1019542,0.005127124,0.3704454],"study_design_scores_gemma":[0.00002285496,0.00003007826,0.0001081892,0.000009946358,0.00001340854,0.00003085407,0.00002506918,0.9717997,0.001964138,0.02362669,0.00235442,0.00001472133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00269997,0.00007996673,0.9963361,0.00004373095,0.00002656988,0.00003662076,0.00002080869,0.0002149034,0.0005414262],"genre_scores_gemma":[0.1402124,0.0001581997,0.8545263,0.0001070104,0.00009081423,0.0003191469,0.0003295926,0.0002319009,0.004024473],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004630226,"threshold_uncertainty_score":0.02167106,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089515781","doi":"10.1109/tpami.2012.242","title":"Learning to Track and Identify Players from Broadcast Sports Videos","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":227,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"University of Cambridge","keywords":"Computer science; Artificial intelligence; Conditional random field; Identification (biology); Homography; Computer vision; Machine learning; Task (project management); Supervised learning; Exploit","authors":[{"name":"Wei-Lwun Lu","is_ca":false},{"name":"Jay Aljelo Saez Ting","is_ca":false},{"name":"James J. Little","is_ca":true},{"name":"Kevin Murphy","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01274498226121645,"gpt":0.252507597688138,"spread":0.2397626154269216,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006760113,0.0008683958,0.0005990812,0.0009069942,0.0004138395,0.0006627621,0.0009851068,0.0009079622,0.001128151],"category_scores_gemma":[0.002390518,0.0004177522,0.0004463081,0.0004632343,0.0003712734,0.001039539,0.0005934895,0.0009633693,0.001003548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004175955,"about_ca_system_score_gemma":0.0005638696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006962265,"about_ca_topic_score_gemma":0.01178072,"domain_scores_codex":[0.9995679,0.00007837478,0.00001907632,0.0002098955,0.00006297921,0.00006172316],"domain_scores_gemma":[0.9992329,0.0003277311,0.0001219364,0.0001004471,0.0001633475,0.00005367782],"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":[0.0005001706,0.0006030853,0.01454503,0.0001269672,0.0001343257,0.0001863641,0.0002252512,0.06912526,0.06194879,0.001216957,0.003977974,0.8474098],"study_design_scores_gemma":[0.00002252445,0.0001352549,0.005593543,0.000009668394,0.00003759764,0.00006299272,0.0001180652,0.9752152,0.01633438,0.001626447,0.0008313759,0.00001290078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3148153,0.0002817908,0.6774801,0.0002130699,0.00005391347,0.0002276408,0.0004155497,0.003246704,0.003266],"genre_scores_gemma":[0.7746479,0.0002316348,0.2162768,0.0001297988,0.00008167207,0.000150273,0.001817394,0.0001097127,0.006554945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006962265,"threshold_uncertainty_score":0.01384348,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2163431839","doi":"10.1109/tpami.2008.15","title":"IRGS: Image Segmentation Using Edge Penalties and Region Growing","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":227,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Tsinghua University; Canadian Space Agency; University of Calgary","keywords":"Markov random field; Artificial intelligence; Image segmentation; Context (archaeology); Segmentation; Pattern recognition (psychology); Computer vision; Computer science; Synthetic aperture radar; Enhanced Data Rates for GSM Evolution; Process (computing); Representation (politics)","authors":[{"name":"Qiyao Yu","is_ca":false},{"name":"David A. Clausi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03939144759579165,"gpt":0.3014967521572933,"spread":0.2621053045615016,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217551,0.0009643128,0.001204882,0.001710439,0.0003917828,0.0009522004,0.001830105,0.001307175,0.001110301],"category_scores_gemma":[0.002553702,0.0006031236,0.00111301,0.001510945,0.001218486,0.001997468,0.001330434,0.0009918961,0.0007117023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004795004,"about_ca_system_score_gemma":0.0008105959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001160763,"about_ca_topic_score_gemma":0.001379768,"domain_scores_codex":[0.9989569,0.0003304232,0.00004760389,0.0001963161,0.0004041887,0.00006447548],"domain_scores_gemma":[0.9991452,0.0003728102,0.0001193389,0.0001170457,0.0002059397,0.00003964724],"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":[0.000297296,0.00009200415,0.0009375827,0.0003287887,0.0001061921,0.0003190784,0.0004147263,0.4065702,0.08541899,0.05691092,0.002856735,0.4457474],"study_design_scores_gemma":[0.00001935026,0.00007423382,0.0003260562,0.00001624808,0.00001605519,0.0002216621,0.00003221062,0.9628118,0.01889944,0.01324304,0.00431062,0.00002935754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003694291,0.0001052655,0.9953443,0.00004242102,0.00001257486,0.00002566583,0.00001274608,0.0003419286,0.0004207577],"genre_scores_gemma":[0.06755278,0.0001554425,0.9307797,0.00006701078,0.00003374215,0.00007818755,0.0001065165,0.0002836624,0.0009430459],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001830105,"threshold_uncertainty_score":0.00643909,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2103894593","doi":"10.1109/tpami.2009.146","title":"Designing Highly Reliable Fiducial Markers","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":226,"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":"Fiducial marker; Computer science; Artificial intelligence; Computer vision; Robustness (evolution); Augmented reality; Pose; Pattern recognition (psychology)","authors":[{"name":"Mark A. Fiala","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01239260680279877,"gpt":0.2288069678692139,"spread":0.2164143610664152,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015392,0.0009880493,0.0006890266,0.001194019,0.000417447,0.001237418,0.001775674,0.001383049,0.001489392],"category_scores_gemma":[0.006218546,0.0009548222,0.00040424,0.0007668797,0.0007584828,0.001851541,0.001847206,0.0006408304,0.001428334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004799672,"about_ca_system_score_gemma":0.0005104819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003883296,"about_ca_topic_score_gemma":0.0004637926,"domain_scores_codex":[0.998248,0.000373685,0.0001327293,0.0002474294,0.0008230653,0.0001752071],"domain_scores_gemma":[0.9959608,0.000841361,0.0007907901,0.0009472824,0.001321109,0.000138666],"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":[0.0004514192,0.0000882434,0.002585733,0.0006824163,0.00007639168,0.0006260519,0.0004375339,0.1539328,0.4225265,0.0488759,0.004104119,0.365613],"study_design_scores_gemma":[0.0001292405,0.001165469,0.002176305,0.0001201925,0.00007418485,0.001705165,0.0001481073,0.5213264,0.3905353,0.01211072,0.0703191,0.0001898429],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01579191,0.0003441563,0.9815323,0.00007865965,0.00005029333,0.00006296776,0.00002331125,0.0006117371,0.001504639],"genre_scores_gemma":[0.2715446,0.0002940477,0.7253226,0.00006883719,0.00004079503,0.0001455022,0.00009647764,0.0001684949,0.002318717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001775674,"threshold_uncertainty_score":0.008140147,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2159786793","doi":"10.1109/tpami.2011.104","title":"Probabilistic Models for Inference about Identity","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":221,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Artificial intelligence; Pattern recognition (psychology); Identity (music); Computer science; Face (sociological concept); Generative model; Facial recognition system; Feature (linguistics); Probabilistic logic; Feature vector; Noise (video); Inference; Subspace topology; Bayesian probability; Machine learning; Image (mathematics); Generative grammar","authors":[{"name":"Peng Li","is_ca":false},{"name":"Yun Fu","is_ca":false},{"name":"U. Mohammed","is_ca":true},{"name":"James H. Elder","is_ca":false},{"name":"Simon J. D. Prince","is_ca":false}],"retraction":null,"screen_n_in":0,"score":{"opus":0.05955989066811044,"gpt":0.2959886083097542,"spread":0.2364287176416437,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007153785,0.001887913,0.002650407,0.003581929,0.001505516,0.004467178,0.006092599,0.003787283,0.009689616],"category_scores_gemma":[0.03087886,0.002108753,0.002798937,0.003272063,0.00354633,0.008695776,0.002610417,0.006029694,0.002328304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002690463,"about_ca_system_score_gemma":0.001474516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01179298,"about_ca_topic_score_gemma":0.01105095,"domain_scores_codex":[0.9955123,0.001834594,0.0002480609,0.001260282,0.0008695644,0.0002752322],"domain_scores_gemma":[0.9796835,0.01651765,0.0009968716,0.001656977,0.0008971678,0.0002479174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001380799,0.00008079327,0.003177494,0.000237408,0.0002664223,0.0002274017,0.000276438,0.3055159,0.0004109463,0.6260129,0.006450265,0.05720595],"study_design_scores_gemma":[0.00002730613,0.00001217163,0.0003841035,0.00003846667,0.00003298894,0.00007857918,0.00002286643,0.5004749,0.0001209457,0.4961874,0.002590003,0.00003034387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004610766,0.001361802,0.9877141,0.001372294,0.0001185297,0.00006048931,0.0007575488,0.000454419,0.003550047],"genre_scores_gemma":[0.5191759,0.006537586,0.4492739,0.001403789,0.001692262,0.001080042,0.005249209,0.0004635274,0.01512385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01179298,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2115139257","doi":"10.1109/tpami.2004.1265866","title":"An eigenspace projection clustering method for inexact graph matching","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":208,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Cluster analysis; Computer science; Eigenvalues and eigenvectors; Matching (statistics); Combinatorics; Graph homomorphism; Projection method; Graph; Theoretical computer science; Algorithm; Mathematics; Line graph; Artificial intelligence; Graph power; Dykstra's projection algorithm","authors":[{"name":"Terry Caelli","is_ca":true},{"name":"Serhiy Kosinov","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02158284435524615,"gpt":0.304401850513753,"spread":0.2828190061585069,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00163636,0.0008474576,0.001374229,0.002139958,0.001091897,0.001328387,0.002250588,0.001830319,0.003479816],"category_scores_gemma":[0.004480198,0.0005774199,0.001089464,0.002229648,0.0016743,0.002503087,0.002261158,0.001988924,0.001840768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008330874,"about_ca_system_score_gemma":0.001276695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002777929,"about_ca_topic_score_gemma":0.002487994,"domain_scores_codex":[0.9981734,0.0005755369,0.00006601034,0.0004025443,0.0007028523,0.00007958486],"domain_scores_gemma":[0.9984195,0.0004766466,0.0001203493,0.0005308336,0.0003807301,0.00007192229],"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":[0.0001541524,0.0001325898,0.000661201,0.0002011814,0.000145726,0.0002171717,0.000334558,0.3396819,0.02459366,0.2050346,0.005248612,0.4235947],"study_design_scores_gemma":[0.000008736372,0.00002451787,0.0001684781,0.000007791023,0.000009704216,0.0001031351,0.00002770692,0.9585459,0.003640506,0.03445698,0.00297682,0.00002972717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001713594,0.00006308052,0.9973259,0.00004577482,0.00001906023,0.00002325614,0.00001538724,0.0002579185,0.000535983],"genre_scores_gemma":[0.05852735,0.0001640499,0.9383032,0.00008528726,0.00004169679,0.0001309416,0.0001764288,0.0002307359,0.002340273],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003479816,"threshold_uncertainty_score":0.01164114,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123782934","doi":"10.1109/tpami.2003.1159942","title":"Transformation-invariant clustering using the EM algorithm","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":204,"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":"Cluster analysis; Artificial intelligence; Computer science; Pattern recognition (psychology); Invariant (physics); Transformation (genetics); Clutter; Computer vision; Algorithm; Mathematics","authors":[{"name":"Brendan J. Frey","is_ca":true},{"name":"Nebojša Jojić","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02908415944746115,"gpt":0.2861228390117631,"spread":0.2570386795643019,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002910073,0.002049099,0.002226466,0.002429856,0.001143846,0.001836158,0.003882787,0.002838898,0.004679095],"category_scores_gemma":[0.00780292,0.001504356,0.002608847,0.002977043,0.00128028,0.002487215,0.002534074,0.002917985,0.00426774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00150548,"about_ca_system_score_gemma":0.001879573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007332684,"about_ca_topic_score_gemma":0.007593642,"domain_scores_codex":[0.9981792,0.000647867,0.000117244,0.0005197275,0.0004099942,0.0001259626],"domain_scores_gemma":[0.9976981,0.001225241,0.0001649821,0.0003485006,0.0004992859,0.00006388623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001155759,0.00006609797,0.0006202026,0.000133735,0.0002725507,0.00009915206,0.0001517972,0.7281179,0.002174546,0.0306397,0.005957069,0.2316517],"study_design_scores_gemma":[0.00001506115,0.00001044482,0.0001009643,0.00000972239,0.00001180922,0.00002904519,0.00001353507,0.9776081,0.0007289503,0.01957204,0.001884162,0.00001609081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006881223,0.00006189572,0.9982309,0.00004289931,0.00001247474,0.0000282439,0.00003005553,0.0005229105,0.0003825662],"genre_scores_gemma":[0.03663392,0.0001921046,0.9596125,0.0001030875,0.00005429535,0.000292086,0.000557847,0.0004242318,0.00212995],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007332684,"threshold_uncertainty_score":0.01565307,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2174855376","doi":"10.1109/tpami.2002.1023808","title":"Detecting binocular half-occlusions: empirical comparisons of five approaches","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":202,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Matching (statistics); Binocular disparity; Binocular vision; Image (mathematics); Pattern recognition (psychology); Mathematics; Statistics","authors":[{"name":"Geoffrey Egnal","is_ca":false},{"name":"Richard P. Wildes","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07130721579069287,"gpt":0.3095824787666298,"spread":0.2382752629759369,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01061615,0.001355272,0.001274255,0.005290365,0.0009665097,0.001747985,0.001872541,0.002319273,0.001290918],"category_scores_gemma":[0.04211819,0.0005570204,0.001350925,0.002340452,0.001326116,0.001843901,0.002674798,0.0009406248,0.0005532085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001243867,"about_ca_system_score_gemma":0.0007364569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005793423,"about_ca_topic_score_gemma":0.009176345,"domain_scores_codex":[0.9912725,0.002620723,0.0008145578,0.001979258,0.002858749,0.000454195],"domain_scores_gemma":[0.9543959,0.0341267,0.003203518,0.003965125,0.00336603,0.0009425623],"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":[0.003219821,0.001323856,0.1177547,0.002869331,0.002205837,0.0003496396,0.00192227,0.08669823,0.01435382,0.003818051,0.006876924,0.7586075],"study_design_scores_gemma":[0.0005812537,0.003648607,0.2291104,0.0007910381,0.001907549,0.003599065,0.00278797,0.7076064,0.02867278,0.01042217,0.01042617,0.0004466008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8287203,0.01162736,0.1465379,0.0003416364,0.0001543248,0.0009723348,0.001946041,0.00177151,0.007928548],"genre_scores_gemma":[0.8949793,0.001553886,0.09924252,0.0000877975,0.0000524695,0.0001497848,0.002995946,0.0002547466,0.0006835401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01061615,"threshold_uncertainty_score":0.05614424,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137993841","doi":"10.1109/tpami.2005.106","title":"Multiregion level-set partitioning of synthetic aperture radar images","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":198,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Synthetic aperture radar; Speckle noise; Artificial intelligence; Multiplicative noise; Speckle pattern; Segmentation; Computer vision; Image segmentation; Radar imaging; Computer science; Scale-space segmentation; Regularization (linguistics); Inverse synthetic aperture radar; Multiplicative function; Segmentation-based object categorization; Pattern recognition (psychology); Algorithm; Mathematics; Radar; Transmission (telecommunications)","authors":[{"name":"Ismail Ben Ayed","is_ca":true},{"name":"Amar Mitiche","is_ca":true},{"name":"Ziad Belhadj","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03177248076099596,"gpt":0.2973464705193594,"spread":0.2655739897583634,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00108649,0.000541131,0.0006433535,0.001005384,0.0002620366,0.0009660476,0.0006566256,0.0007525564,0.0006034948],"category_scores_gemma":[0.002279649,0.0004055705,0.0007108617,0.0005439387,0.0006204622,0.0008189396,0.0005402224,0.0004753454,0.0002027282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008363868,"about_ca_system_score_gemma":0.0004056078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0013469,"about_ca_topic_score_gemma":0.001041722,"domain_scores_codex":[0.9995998,0.0001633694,0.00001757409,0.00006361578,0.000118065,0.00003771027],"domain_scores_gemma":[0.9992048,0.000507022,0.00007794492,0.00008054295,0.0001011926,0.00002845566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002455508,0.00007305441,0.001298612,0.0001206418,0.00005424378,0.0001090591,0.0003137577,0.8370365,0.04687054,0.01437651,0.0003507742,0.0991507],"study_design_scores_gemma":[0.000004907333,0.0000313355,0.0003526885,0.000006737006,0.000005773435,0.00002667293,0.00001951389,0.9885845,0.006892814,0.00362459,0.0004446786,0.000005770738],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08385666,0.0002001934,0.9147533,0.00008320887,0.000009827987,0.00008358243,0.00004082373,0.0001613535,0.0008111311],"genre_scores_gemma":[0.5219796,0.0002020047,0.4762469,0.00005316627,0.00001717907,0.0001235986,0.0002003221,0.0001338258,0.001043467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0013469,"threshold_uncertainty_score":0.006068468,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2154211011","doi":"10.1109/tpami.2008.48","title":"Tied Factor Analysis for Face Recognition across Large Pose Differences","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Computer science; Feature vector; Metric (unit); Identity (music); Feature extraction; Transformation (genetics); Face (sociological concept); Feature (linguistics); Pose; Noise (video); Image (mathematics)","authors":[{"name":"Simon J. D. Prince","is_ca":false},{"name":"Jonathan Warrell","is_ca":false},{"name":"James H. Elder","is_ca":true},{"name":"Fatima M. Felisberti","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05356022456716487,"gpt":0.2980524006241523,"spread":0.2444921760569874,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031453,0.0007867732,0.00100633,0.001112352,0.0007039027,0.0008430724,0.001149357,0.0009196418,0.002642512],"category_scores_gemma":[0.01109101,0.0004823671,0.001529886,0.001223152,0.001180005,0.001535041,0.001543428,0.001623825,0.001668893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000643593,"about_ca_system_score_gemma":0.0006589636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003049262,"about_ca_topic_score_gemma":0.003119957,"domain_scores_codex":[0.9979735,0.0007918887,0.00007604789,0.0005447316,0.000474911,0.0001389651],"domain_scores_gemma":[0.9971805,0.001567452,0.0001975125,0.0007293019,0.0002483974,0.00007681018],"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":[0.0004435329,0.0002049822,0.003954182,0.0001180811,0.0002588412,0.0001814548,0.0003055639,0.2769232,0.01548809,0.03933449,0.003765416,0.6590222],"study_design_scores_gemma":[0.00001592374,0.00005150898,0.001956647,0.000007290027,0.00001924649,0.00009190441,0.00002256267,0.9540039,0.002967899,0.0397179,0.001117403,0.00002771283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009767214,0.0001668299,0.9891295,0.00007158019,0.00002051542,0.00002597214,0.00006248066,0.0004511464,0.0003046634],"genre_scores_gemma":[0.4237808,0.0004255882,0.5706333,0.0001688262,0.0001352778,0.000265524,0.0009672371,0.0002548193,0.003368705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0031453,"threshold_uncertainty_score":0.01663417,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2154847188","doi":"10.1109/tpami.2004.1273960","title":"Separation of diffuse and specular components of surface reflection by use of polarization and statistical analysis of images","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Color Science and Applications","field":"Physics and Astronomy","cited_by":191,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Specular reflection; Diffuse reflection; Polarizer; Opacity; Optics; Reflection (computer programming); Specular highlight; Polarization (electrochemistry); Physics; Artificial intelligence; Computer science; Chemistry","authors":[{"name":"Shinji Umeyama","is_ca":false},{"name":"Guy Godin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02044408646295271,"gpt":0.3040058659244186,"spread":0.2835617794614659,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008741632,0.0006561705,0.0004945821,0.001076098,0.0003589203,0.0007948478,0.0004979745,0.0004464124,0.0005634771],"category_scores_gemma":[0.003094884,0.0003473243,0.0007825389,0.0009083651,0.0009189397,0.001015236,0.0006785367,0.0009065605,0.0002411043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003457824,"about_ca_system_score_gemma":0.0006051435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00134344,"about_ca_topic_score_gemma":0.001197279,"domain_scores_codex":[0.999567,0.0001198907,0.00002268913,0.00006249583,0.0001922804,0.00003570886],"domain_scores_gemma":[0.9986822,0.0007522615,0.0001164924,0.0001952334,0.0002247049,0.00002926227],"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":[0.0003665546,0.0001817615,0.004202922,0.0003657236,0.0002198975,0.0004587053,0.0004911985,0.3242544,0.1665215,0.1228064,0.00198972,0.3781412],"study_design_scores_gemma":[0.00001511632,0.00004238578,0.00215835,0.00001122113,0.00003490039,0.0002322185,0.00005808597,0.9213113,0.03722904,0.03735301,0.001505942,0.00004855579],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02093759,0.0000802247,0.978008,0.00004490235,0.0000111244,0.00002068087,0.00002618951,0.0001523317,0.0007189825],"genre_scores_gemma":[0.3167703,0.0004303082,0.6812149,0.00004073146,0.00005055999,0.00009362371,0.0002705454,0.000150021,0.0009789264],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00134344,"threshold_uncertainty_score":0.004623055,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2134963415","doi":"10.1109/tpami.2005.169","title":"A comparison of algorithms for inference and learning in probabilistic graphical models","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":175,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Inference; Graphical model; Machine learning; Algorithm; Belief propagation; Probabilistic logic; Expectation–maximization algorithm; Mathematics","authors":[{"name":"Brendan J. Frey","is_ca":true},{"name":"Nebojša Jojić","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05955866244286064,"gpt":0.3373903912953424,"spread":0.2778317288524818,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01284077,0.003161454,0.003638204,0.004379797,0.001410515,0.004635169,0.007515914,0.004767512,0.008051401],"category_scores_gemma":[0.04773533,0.001712073,0.0037886,0.006102436,0.002631875,0.01085309,0.004153138,0.00539148,0.003015288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003640004,"about_ca_system_score_gemma":0.004476764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008895933,"about_ca_topic_score_gemma":0.007987047,"domain_scores_codex":[0.9896862,0.005308855,0.0006310184,0.00124096,0.002767163,0.000365737],"domain_scores_gemma":[0.9570744,0.03574762,0.0006368303,0.004007227,0.002253752,0.0002801112],"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":[0.0002167286,0.000165576,0.0007715506,0.000631225,0.0002809326,0.00006576863,0.0002150272,0.2768552,0.0003080637,0.2883109,0.007511045,0.4246679],"study_design_scores_gemma":[0.00005294634,0.00003391537,0.0002441071,0.0001001903,0.00004933104,0.00009100848,0.0000473035,0.7452156,0.0003531017,0.2477346,0.00604238,0.00003550834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007424824,0.001882841,0.9946569,0.000273484,0.00006130182,0.00004994032,0.00008966636,0.0007080362,0.00153544],"genre_scores_gemma":[0.03531215,0.006064847,0.9546891,0.0003343562,0.0002542828,0.0004034845,0.0006833684,0.0006214736,0.001636879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01284077,"threshold_uncertainty_score":0.06790936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4287367114","doi":"10.1109/tpami.2022.3218591","title":"Deep Learning for Instance Retrieval: A Survey","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Image Retrieval and Classification 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 Waterloo","funders":"China Scholarship Council; Academy of Finland; National Natural Science Foundation of China","keywords":"Computer science; Deep learning; Image retrieval; Artificial intelligence; Feature extraction; Content-based image retrieval; Feature (linguistics); Process (computing); Machine learning; Information retrieval; Embedding; Image (mathematics)","authors":[{"name":"Wei Chen","is_ca":false},{"name":"Yu Liu","is_ca":false},{"name":"Weiping Wang","is_ca":false},{"name":"Erwin M. Bakker","is_ca":false},{"name":"Theodoros Georgiou","is_ca":false},{"name":"Paul Fieguth","is_ca":true},{"name":"Li Liu","is_ca":false},{"name":"Michael S. Lew","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03256208355514538,"gpt":0.2880512037855091,"spread":0.2554891202303637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001860848,0.001632261,0.002377674,0.002320335,0.0003643861,0.002406375,0.002633322,0.001640379,0.005509849],"category_scores_gemma":[0.005781345,0.0006538501,0.001147839,0.003842139,0.0005392488,0.004083376,0.001603505,0.002230127,0.002682624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001121765,"about_ca_system_score_gemma":0.001356428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005004635,"about_ca_topic_score_gemma":0.003648439,"domain_scores_codex":[0.9989434,0.0002717259,0.0001269077,0.0002586652,0.0003264285,0.00007291229],"domain_scores_gemma":[0.9983462,0.001048688,0.00006563004,0.000190222,0.0002852851,0.00006402333],"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":[0.0001112205,0.000189988,0.001214833,0.001712063,0.0001754105,0.00004481399,0.00006884009,0.02138134,0.0008955809,0.01535821,0.02121508,0.9376326],"study_design_scores_gemma":[0.00008811312,0.0006180462,0.002873169,0.001581694,0.0003397272,0.000650513,0.0002759956,0.6828272,0.005538648,0.09367156,0.2114123,0.0001230089],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01228383,0.4809479,0.4814491,0.004618121,0.0008184144,0.0002183276,0.001000833,0.001895186,0.01676829],"genre_scores_gemma":[0.183354,0.4729781,0.311112,0.003848821,0.002887578,0.0005259345,0.005251622,0.0007273487,0.01931461],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005509849,"threshold_uncertainty_score":0.01843232,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2100369947","doi":"10.1109/tpami.2002.1017621","title":"Region tracking via level set PDEs without motion computation","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":170,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Tracking (education); Artificial intelligence; Partial differential equation; Level set (data structures); Computer science; Computer vision; Prior probability; Computation; Parametric statistics; A priori and a posteriori; Bayesian probability; Motion (physics); Match moving; Motion estimation; Algorithm; Mathematics","authors":[{"name":"A.-R. Mansouri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07134117497782266,"gpt":0.3124153008137072,"spread":0.2410741258358846,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001460798,0.0006713808,0.00112802,0.001049808,0.0005523897,0.001709232,0.002114985,0.001970224,0.002317857],"category_scores_gemma":[0.004852858,0.001072638,0.001545057,0.000883255,0.001152591,0.002524249,0.002764209,0.001823226,0.001056956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313109,"about_ca_system_score_gemma":0.00139526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003834862,"about_ca_topic_score_gemma":0.002515293,"domain_scores_codex":[0.9993113,0.0001500782,0.00003709293,0.0001306543,0.0003183816,0.00005246475],"domain_scores_gemma":[0.9985595,0.0008055783,0.0001953414,0.0001812823,0.0001828249,0.0000754044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005049179,0.00003163866,0.000499903,0.0001020893,0.00004459469,0.0001061258,0.0001968811,0.8113204,0.01019705,0.09430074,0.00145526,0.08169481],"study_design_scores_gemma":[0.000004343278,0.000007653824,0.00003373543,0.00000511489,0.000003964456,0.00002056406,0.000003454195,0.9872785,0.001017579,0.01060178,0.001016716,0.000006621206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007722353,0.00002942176,0.9986934,0.00003731801,0.000008753707,0.00000983575,0.000009043499,0.000102967,0.0003370763],"genre_scores_gemma":[0.0826394,0.0002313057,0.913461,0.0001016845,0.00003790545,0.000163901,0.00009834101,0.0002153998,0.003051103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003834862,"threshold_uncertainty_score":0.009527266,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2096784803","doi":"10.1109/tpami.2007.1095","title":"High-Dimensional Unsupervised Selection and Estimation of a Finite Generalized Dirichlet Mixture Model Based on Minimum Message Length","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":169,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke; Concordia University","funders":"University of California, Irvine","keywords":"Minimum description length; Generalized Dirichlet distribution; Mixture model; Hierarchical Dirichlet process; Dirichlet distribution; Cluster analysis; Latent Dirichlet allocation; Mathematics; Computer science; Model selection; Algorithm; Determining the number of clusters in a data set; Automatic summarization; Pattern recognition (psychology); Artificial intelligence; Topic model; Dirichlet's principle; Correlation clustering; CURE data clustering algorithm","authors":[{"name":"Nizar Bouguila","is_ca":true},{"name":"Djemel Ziou","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01552536188303195,"gpt":0.2702911791012604,"spread":0.2547658172182285,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005170252,0.0008726295,0.002217145,0.002212464,0.001062252,0.002115807,0.002860949,0.001716326,0.001372297],"category_scores_gemma":[0.01873899,0.001079162,0.001738452,0.001760622,0.001670036,0.002502149,0.001820354,0.00206971,0.0007915364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001556094,"about_ca_system_score_gemma":0.001310929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00488041,"about_ca_topic_score_gemma":0.004446501,"domain_scores_codex":[0.9970113,0.001700058,0.0001260523,0.0005348664,0.0004457402,0.0001819295],"domain_scores_gemma":[0.990514,0.007330659,0.0006169091,0.0005309993,0.0008266189,0.0001806403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002338636,0.00008180459,0.002291716,0.0001670109,0.000152791,0.0001534645,0.0004166243,0.8416802,0.00301616,0.04038812,0.001755197,0.1096631],"study_design_scores_gemma":[0.00000581659,0.000008125781,0.0001487384,0.000007635752,0.000006242457,0.00001290596,0.00001014058,0.988147,0.000455887,0.01094402,0.0002401022,0.00001336323],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008314962,0.0001135603,0.9910486,0.0001249602,0.0000140209,0.00002359608,0.00003227088,0.0001304848,0.0001976047],"genre_scores_gemma":[0.3195092,0.000493832,0.6753384,0.0002055689,0.0001713262,0.0004384808,0.0008590191,0.0002565937,0.002727526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005170252,"threshold_uncertainty_score":0.02734327,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2105787212","doi":"10.1109/tpami.2013.216","title":"Mixtures of Shifted AsymmetricLaplace Distributions","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":168,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"Ontario Ministry of Research and Innovation","keywords":"Cluster analysis; Mixture model; Gaussian; Laplace distribution; Laplace transform; Computer science; Artificial intelligence; Pattern recognition (psychology); Inverse Gaussian distribution; Algorithm; Estimation theory; Mathematics; Distribution (mathematics); Physics","authors":[{"name":"Brian C. Franczak","is_ca":true},{"name":"Ryan P. Browne","is_ca":true},{"name":"Paul D. McNicholas","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01552783805678325,"gpt":0.2711309065625086,"spread":0.2556030685057253,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004279838,0.001102464,0.001507233,0.003619014,0.001146746,0.0030316,0.00276944,0.002420734,0.005951577],"category_scores_gemma":[0.01430601,0.000932791,0.002022024,0.002806452,0.002770365,0.005235048,0.003253994,0.002758008,0.0027578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001369648,"about_ca_system_score_gemma":0.0008987105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001516092,"about_ca_topic_score_gemma":0.001664414,"domain_scores_codex":[0.9969689,0.0009233965,0.0001495225,0.0007461705,0.0009381763,0.0002738248],"domain_scores_gemma":[0.9953979,0.001995876,0.0004702695,0.000928973,0.0009963493,0.0002106096],"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":[0.0003824371,0.0001312667,0.004695702,0.0001942053,0.0001428506,0.000612948,0.0007367536,0.1997999,0.01605746,0.588394,0.004733932,0.1841186],"study_design_scores_gemma":[0.00002080707,0.00006008437,0.0008098251,0.00003891645,0.00003218699,0.0004375246,0.00009545626,0.7925839,0.003936901,0.1954935,0.006410296,0.00008061937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007636855,0.0001816073,0.9900948,0.0001453189,0.00005111158,0.00003536471,0.00006979605,0.0002860713,0.001499087],"genre_scores_gemma":[0.5095952,0.001005813,0.4713525,0.000538761,0.0003633253,0.0003497953,0.0008704113,0.0004148227,0.01550932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005951577,"threshold_uncertainty_score":0.02263421,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2787460109","doi":"10.1109/tpami.2018.2799222","title":"A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":167,"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":"National Research Foundation Singapore","keywords":"Photometric stereo; Artificial intelligence; Ground truth; Benchmark (surveying); Computer science; Computer vision; Stereopsis; Reflectivity; Remote sensing; Image (mathematics); Geology; Optics","authors":[{"name":"Boxin Shi","is_ca":false},{"name":"Zhipeng Mo","is_ca":false},{"name":"Zhe Wu","is_ca":false},{"name":"Dinglong Duan","is_ca":false},{"name":"Sai-Kit Yeung","is_ca":false},{"name":"Ping Tan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03600593234483072,"gpt":0.3372482476394871,"spread":0.3012423152946564,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00311689,0.004235799,0.002219869,0.006200346,0.001659093,0.003020579,0.006005557,0.002851832,0.006702064],"category_scores_gemma":[0.00756138,0.0007498372,0.002825219,0.006342295,0.00113997,0.002402991,0.003369353,0.002389759,0.009864489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002676204,"about_ca_system_score_gemma":0.002291154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02433164,"about_ca_topic_score_gemma":0.03956724,"domain_scores_codex":[0.9950101,0.0006461715,0.0004451388,0.001194685,0.002286826,0.000417099],"domain_scores_gemma":[0.9956052,0.0005925289,0.0002938604,0.001483088,0.001710743,0.0003146162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008173364,0.001266314,0.005491059,0.003321107,0.0005893359,0.0003824482,0.0001449268,0.0303416,0.01489107,0.004382614,0.7002597,0.2381125],"study_design_scores_gemma":[0.000931514,0.0010287,0.03158195,0.001312755,0.0004119629,0.004162813,0.0008599754,0.2938866,0.05840272,0.01159053,0.5953915,0.0004391876],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.08796769,0.01208884,0.1212143,0.001788445,0.002546142,0.003766168,0.6618853,0.0644396,0.04430354],"genre_scores_gemma":[0.03793853,0.001171609,0.09729636,0.0004637637,0.0001632148,0.0009757768,0.8554286,0.001581668,0.004980452],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02433164,"threshold_uncertainty_score":0.04838002,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}