{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":93,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":93,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"19825013d8a7","filters":{"venue":"Machine Vision and Applications"}},"results":[{"id":"W2084286148","doi":"10.1007/s00138-007-0097-8","title":"Retrieving articulated 3-D models using medial surfaces","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":256,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; McGill University","funders":"McGill University","keywords":"Adjacency matrix; Representation (politics); Computer science; Precision and recall; Eigenvalues and eigenvectors; Graph; Benchmark (surveying); Mathematics; Homogeneous space; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.01404466645843134,"gpt":0.2669295886267373,"spread":0.252884922168306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002070055,0.00009049331,0.0001077818,0.00009153227,0.0001346748,0.00003562077,0.0000533482,0.00004798557,0.0000188998],"category_scores_gemma":[0.000005176199,0.00007877548,0.00003303367,0.0003072213,0.00001984436,0.00007057826,0.00002007939,0.00009266062,0.000006365096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001518309,"about_ca_system_score_gemma":0.000003403205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002834148,"about_ca_topic_score_gemma":0.00001870237,"domain_scores_codex":[0.9994399,0.000005754965,0.0001851415,0.0001311504,0.00009873178,0.0001393001],"domain_scores_gemma":[0.9997,0.0000370224,0.00001869746,0.0001315752,0.00002653377,0.00008618544],"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":[0.000002762965,0.000020593,0.0003087006,0.00001651249,0.00002104507,8.64473e-7,0.00009961599,0.9424413,0.0277559,0.0006155627,0.00004473666,0.02867238],"study_design_scores_gemma":[0.00009355859,0.000003590436,0.0001672971,0.000009892583,0.00002398089,0.000002264154,0.00001850228,0.9973124,0.0009138543,0.0007840321,0.00057448,0.00009616307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3707556,0.0005475318,0.6276922,0.00003619969,0.00001592111,0.00006117489,0.000004747806,0.0001594016,0.0007271817],"genre_scores_gemma":[0.9961485,0.0001036432,0.003604315,0.0000254568,0.00005435115,0.00000351712,0.00001666302,0.00001553202,0.00002798441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6253929,"threshold_uncertainty_score":0.3212371,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078505501","doi":"10.1007/s00138-007-0110-2","title":"Model-based view planning","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":153,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Computer vision; Fidelity; Artificial intelligence; Object (grammar); Range (aeronautics); Laser scanning; Sampling (signal processing); Class (philosophy); High fidelity; Laser; Optics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.0253400679915649,"gpt":0.2927959396555226,"spread":0.2674558716639577,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002787188,0.00006784593,0.00006653654,0.00002993509,0.0002374259,0.00003314849,0.0000677315,0.00002839407,0.0002069643],"category_scores_gemma":[0.000004259459,0.00004688822,0.00001997345,0.0001334473,0.00002973521,0.0000445314,0.000004240739,0.0000717978,0.00007288656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001012996,"about_ca_system_score_gemma":0.000006690405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009783824,"about_ca_topic_score_gemma":0.0002557501,"domain_scores_codex":[0.9995349,0.00001241022,0.0001062454,0.0001409331,0.00008292719,0.0001225588],"domain_scores_gemma":[0.9997043,0.00006323155,0.00002385644,0.00009797268,0.00001308483,0.00009755174],"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":[0.00001389777,0.00002200518,0.1515554,0.000017809,0.000002656024,0.000001598467,0.00006740358,0.008169411,0.0001831728,0.0005089254,0.0003613638,0.8390964],"study_design_scores_gemma":[0.0002111884,0.00004407829,0.3748325,0.00001923661,0.000006583872,0.000004479899,0.00005287076,0.5421934,0.00005685901,0.0008596602,0.08155098,0.0001682361],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4066231,0.006522321,0.5101669,0.0008848641,0.00007318473,0.0005501811,0.0001521011,0.0003145169,0.07471281],"genre_scores_gemma":[0.9962381,0.00001976794,0.00279117,0.0004630556,0.00003002047,0.000001959738,0.000133218,0.000001845273,0.0003209134],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8389281,"threshold_uncertainty_score":0.2266114,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2028394888","doi":"10.1007/s00138-015-0679-9","title":"A comparative experimental study of image feature detectors and descriptors","year":2015,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":114,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"AUTO21 Network of Centres of Excellence; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Detector; Computer science; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Scaling; Rotation (mathematics); Matching (statistics); Feature matching; Scale-invariant feature transform; Feature extraction; Computer vision; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.03076380994994567,"gpt":0.3618723040289965,"spread":0.3311084940790509,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008373627,0.00008935838,0.0001385434,0.0000637035,0.00008682918,0.00005053671,0.0001607324,0.00002032364,0.000001285535],"category_scores_gemma":[0.000007106602,0.00006797904,0.00001256731,0.0002372242,0.0000591327,0.0002459241,0.0001990755,0.00007170209,0.000001353213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001026615,"about_ca_system_score_gemma":0.0000084026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002786098,"about_ca_topic_score_gemma":0.000004725135,"domain_scores_codex":[0.9994612,0.0000310219,0.0001072216,0.0002187104,0.0001125217,0.00006932193],"domain_scores_gemma":[0.9995457,0.00002609148,0.00005626597,0.0002219906,0.00005728614,0.00009268775],"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":[0.0002105415,0.009111773,0.01439906,0.00007336563,0.0001208958,0.0000146773,0.05427096,0.00001510054,0.4636849,0.04126502,0.01037818,0.4064555],"study_design_scores_gemma":[0.00536349,0.008958011,0.02391555,0.00005854891,0.00005168371,0.00006573534,0.01238783,0.03355479,0.8396503,0.01192604,0.06298459,0.001083478],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2951232,0.001388964,0.7020947,0.0001206647,0.00001491279,0.0006563522,0.000004796309,0.0001224874,0.0004738533],"genre_scores_gemma":[0.9641007,0.0000133598,0.03573276,0.00003400652,0.000009103738,0.00006373457,0.000001571724,0.000003552209,0.0000412277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6689774,"threshold_uncertainty_score":0.2772105,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2167666191","doi":"10.1007/s00138-012-0417-5","title":"A database for automatic classification of forest species","year":2012,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":97,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Benchmarking; Artificial intelligence; Database; Support vector machine; Confusion; Set (abstract data type); Feature (linguistics); Field (mathematics); Pattern recognition (psychology); Feature vector; Machine learning; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03735502072411806,"gpt":0.3433906846771851,"spread":0.3060356639530671,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001430283,0.00007000579,0.00009664862,0.00004405134,0.000120386,0.00001715216,0.0001014964,0.00003479487,0.0002419335],"category_scores_gemma":[0.00003427458,0.00005360454,0.000035169,0.00009832429,0.0000605572,0.00007592452,0.0000506936,0.00006105265,0.00001279724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008267692,"about_ca_system_score_gemma":0.00001120643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009036941,"about_ca_topic_score_gemma":0.000007123515,"domain_scores_codex":[0.9994581,0.000006264012,0.0001656846,0.0001190336,0.0001110887,0.000139826],"domain_scores_gemma":[0.9994178,0.0001282786,0.00006420212,0.0002603722,0.00003964901,0.0000897015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001434445,0.0006022836,0.02383183,0.0007199199,0.00002204186,4.818996e-8,0.0001261476,0.000001611554,0.7764671,0.09283652,0.003400642,0.1019775],"study_design_scores_gemma":[0.001701828,0.00007578079,0.1244461,0.0001490801,0.00009153737,0.00001084626,0.0007197789,0.05714469,0.1000834,0.002455825,0.712696,0.0004252449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8888645,0.001855561,0.06461962,0.00235143,0.00004390215,0.00114574,0.001203578,0.0002130641,0.03970262],"genre_scores_gemma":[0.9960858,0.00004947443,0.001850044,0.00001580556,0.0001310283,0.0002879045,0.0003394181,0.00001021971,0.001230285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7092953,"threshold_uncertainty_score":0.2649002,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1999376132","doi":"10.1007/s00138-006-0014-6","title":"An empirical evaluation of factors influencing camera calibration accuracy using three publicly available techniques","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Calibration; Camera resectioning; Artificial intelligence; Camera auto-calibration; Noise (video); Computer vision; Field (mathematics); Image (mathematics); Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.08117610329943122,"gpt":0.3780328171154858,"spread":0.2968567138160546,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006428532,0.000119948,0.0001288491,0.0001637343,0.0001643134,0.0002060887,0.0003163103,0.00006667297,0.00005215111],"category_scores_gemma":[0.00004124876,0.00009464299,0.00003091882,0.0003805419,0.0000522014,0.001076907,0.00008665423,0.00009577928,0.000002072274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004701332,"about_ca_system_score_gemma":0.00007027727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008086211,"about_ca_topic_score_gemma":0.0001075106,"domain_scores_codex":[0.9987695,0.00007408946,0.0003169575,0.0003151711,0.0003971674,0.0001271374],"domain_scores_gemma":[0.9990205,0.00005148835,0.0001421471,0.0004250845,0.0003006422,0.0000601052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008188399,0.0006497407,0.1514358,0.00003444337,0.00001280397,1.367164e-7,0.0002121036,0.0001483039,0.5763895,0.07288856,0.0007385859,0.1974818],"study_design_scores_gemma":[0.0001562805,0.0001777804,0.02392196,0.00003168539,0.00002688057,0.000001712567,0.00002363477,0.8207906,0.1367415,0.0168768,0.001043864,0.000207344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3693268,0.00007734435,0.6274529,0.0001983943,0.00001089394,0.0004944948,0.000004634885,0.0002394186,0.002195108],"genre_scores_gemma":[0.9690794,0.000005313979,0.03072293,0.00005583027,0.00003210253,0.00006904763,0.00002323426,0.000006694716,0.000005459154],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8206423,"threshold_uncertainty_score":0.3859429,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2062648832","doi":"10.1007/s00138-006-0066-7","title":"An image-based feature tracking algorithm for real-time measurement of clad height","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Thresholding; Artificial intelligence; Computer science; Computer vision; Feature (linguistics); Tracing; Tracking (education); Algorithm; Detector; Image (mathematics)","retraction":null,"screen_n_in":null,"score":{"opus":0.008724926310778093,"gpt":0.2657693550663378,"spread":0.2570444287555597,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002580423,0.0001054654,0.0001250527,0.00005468137,0.00009028805,0.00003142209,0.00006940615,0.00004609677,0.00004667018],"category_scores_gemma":[0.000004670417,0.00008726524,0.000027685,0.00006686988,0.00002385972,0.00005655029,0.000006757432,0.0000443331,0.000002932893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001417816,"about_ca_system_score_gemma":0.000006616145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008439994,"about_ca_topic_score_gemma":0.000005917773,"domain_scores_codex":[0.9994972,0.000006695066,0.0001445615,0.0001336082,0.0001038374,0.0001140839],"domain_scores_gemma":[0.9996356,0.00004479313,0.00003759987,0.0001341563,0.00008840803,0.0000594761],"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":[0.0000142793,0.00007816366,0.00001276911,0.0001863934,0.00001382569,2.941238e-7,0.00003177095,0.0002900552,0.459928,0.0001880873,0.0006950516,0.5385613],"study_design_scores_gemma":[0.000645629,0.0001288178,0.006491585,0.00005537445,0.00003706818,0.000002023672,0.0000207212,0.06731991,0.8581026,0.0006676302,0.06626129,0.0002673017],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01044664,0.0002185396,0.9875613,0.00008113706,0.00002710419,0.0004106018,0.0002435848,0.0001685017,0.0008426248],"genre_scores_gemma":[0.9277658,0.00006383831,0.07162218,0.00003036628,0.0001544695,0.00009588523,0.0002038198,0.00003444839,0.00002915233],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9173192,"threshold_uncertainty_score":0.3558573,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2148554518","doi":"10.1007/pl00013273","title":"Automatic mineral identification using genetic programming","year":2001,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Genetic programming; Thresholding; Artificial intelligence; Computer science; Mineral resource classification; Identification (biology); Mineral processing; Image processing; Suite; Mineral; Computer vision; Genetic algorithm; Computation; Image (mathematics); Pattern recognition (psychology); Geology; Machine learning; Algorithm; Geography; Materials science; Biology","retraction":null,"screen_n_in":null,"score":{"opus":0.01332112028148058,"gpt":0.2931154093691397,"spread":0.2797942890876591,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001298884,0.0001201592,0.000100034,0.0001138226,0.0004878629,0.0002005205,0.0003440554,0.00003974004,0.00002142561],"category_scores_gemma":[0.000006160628,0.0001092398,0.00003876689,0.0006694121,0.00005446919,0.0002580519,0.0001347199,0.00007889976,0.00004587676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002564364,"about_ca_system_score_gemma":0.00002355495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004414086,"about_ca_topic_score_gemma":0.000004756827,"domain_scores_codex":[0.9989748,0.00002654386,0.0002853074,0.0003762785,0.0001606954,0.0001763785],"domain_scores_gemma":[0.9991968,0.00003496984,0.0001038056,0.0004937524,0.00006208206,0.0001085638],"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":[5.294002e-7,0.0001962023,0.001285187,0.00001246263,0.000006779032,0.000001197391,0.00007938252,0.0002974193,0.004247281,0.05152727,0.0001414257,0.9422048],"study_design_scores_gemma":[0.0001518997,0.00001779465,0.0297735,0.000007844462,0.00001014114,0.00009500262,0.00001428747,0.9107859,0.00003560652,0.004854471,0.05410977,0.0001438492],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05565333,0.0003993083,0.9417441,0.001239654,0.00002824597,0.000482532,0.000003919366,0.000241041,0.0002078813],"genre_scores_gemma":[0.6465726,0.00008996385,0.352412,0.0001383299,0.000104388,0.0003619102,0.00002521729,0.00001194304,0.0002836774],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.942061,"threshold_uncertainty_score":0.4454668,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2085825493","doi":"10.1007/s00138-007-0099-6","title":"Detection and correction of specular reflections for automatic surgical tool segmentation in thoracoscopic images","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"","keywords":"Specular reflection; Artificial intelligence; Computer vision; Computer science; Specular highlight; Segmentation; Context (archaeology); Histogram; Image segmentation; Inpainting; Reflection (computer programming); Pattern recognition (psychology); Image (mathematics); Optics; Geology; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.01174063784864862,"gpt":0.3628423613537478,"spread":0.3511017235050992,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004027479,0.00007283219,0.0000991846,0.0002919187,0.0001339235,0.00005771887,0.0000747645,0.00004128563,0.000001765356],"category_scores_gemma":[0.00001040308,0.00006817238,0.00002473274,0.0005305213,0.00002908367,0.0001550599,0.00005041143,0.00005459749,2.645392e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001674525,"about_ca_system_score_gemma":0.000007920954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000367277,"about_ca_topic_score_gemma":0.00006304124,"domain_scores_codex":[0.9993387,0.00002568078,0.0002588263,0.0002110023,0.000083143,0.00008257972],"domain_scores_gemma":[0.9995522,0.0001231995,0.00008919107,0.0001429167,0.0000605458,0.00003192471],"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":[0.000007098162,0.0001324011,0.001124705,0.00004979881,0.000004915338,4.403439e-7,0.0001488333,0.00001756944,0.01501358,0.05858942,0.00002729819,0.924884],"study_design_scores_gemma":[0.0006513341,0.0002979882,0.03002751,0.00004501264,0.00001153479,0.00002949625,0.00004475391,0.8873962,0.06354783,0.01111788,0.00665244,0.0001780031],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05234312,0.0001062857,0.9466853,0.00007317513,0.00005323612,0.0005229242,0.000002507484,0.0001037279,0.0001097209],"genre_scores_gemma":[0.9664318,0.0001247831,0.03323143,0.00002752527,0.00002402822,0.0001196157,0.000009104503,0.000005371845,0.00002633715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9247059,"threshold_uncertainty_score":0.2779989,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4362606282","doi":"10.1007/s00138-023-01390-6","title":"Interpretable visual transmission lines inspections using pseudo-prototypical part network","year":2023,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric power transmission; Artificial intelligence; Computer science; Reliability (semiconductor); Identification (biology); Transmission (telecommunications); Similarity (geometry); Focus (optics); Position (finance); Computer vision; Power transmission; Pattern recognition (psychology); Image (mathematics); Power (physics); Engineering; Electrical engineering; Telecommunications; Physics; Optics","retraction":null,"screen_n_in":null,"score":{"opus":0.01926296997282099,"gpt":0.3304410745153472,"spread":0.3111781045425261,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001678861,0.0001854979,0.0001805003,0.0001331325,0.0008744918,0.0001328835,0.0003991346,0.00007511544,0.00002649305],"category_scores_gemma":[0.000009142932,0.0001601242,0.00006731928,0.001999925,0.00008220397,0.000300272,0.0002842743,0.0002235958,0.00008678086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002312962,"about_ca_system_score_gemma":0.00002670163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001106836,"about_ca_topic_score_gemma":0.00000560208,"domain_scores_codex":[0.9985501,0.00004664921,0.0003277333,0.0005637038,0.0001789011,0.00033289],"domain_scores_gemma":[0.9990339,0.0001474452,0.00008447149,0.0004824463,0.00005626618,0.0001954777],"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":[0.00002829301,0.0003658668,0.001265126,0.00003988585,0.00003053414,0.000003988446,0.000178554,0.05577466,0.01275276,0.2140832,0.007150644,0.7083265],"study_design_scores_gemma":[0.0001585976,0.00004058956,0.0007520598,0.00002604323,0.000008775082,0.00001644053,0.000008216116,0.8016804,0.0001524834,0.01393311,0.183058,0.0001652534],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004852239,0.0001945941,0.9904838,0.002144796,0.00008465972,0.000864831,0.000007459105,0.0008652778,0.0005023776],"genre_scores_gemma":[0.7956575,0.001175085,0.1971754,0.00107932,0.001151796,0.002252953,0.000133872,0.00007800598,0.001296085],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7933084,"threshold_uncertainty_score":0.6725972,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2007854896","doi":"10.1007/s00138-005-0009-8","title":"Estimation and monitoring of product aesthetics: application to manufacturing of “engineered stone” countertops","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Subspace topology; Computer science; Artificial intelligence; Projection (relational algebra); Product (mathematics); Computer vision; Obstacle; Quality (philosophy); Feature (linguistics); Pattern recognition (psychology); Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.005407939820326883,"gpt":0.2391212930975568,"spread":0.2337133532772299,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001291573,0.0001016746,0.0001551181,0.0001460639,0.00005461154,0.00001696298,0.00004404078,0.00004649953,0.000001804766],"category_scores_gemma":[0.000004690492,0.00009377321,0.00002032404,0.0001573795,0.00001773696,0.00005667473,0.0000183472,0.00005120525,0.000002867299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001678428,"about_ca_system_score_gemma":0.000003021101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001173068,"about_ca_topic_score_gemma":0.000004309882,"domain_scores_codex":[0.999349,0.000008858376,0.0003002828,0.0001508281,0.0001121896,0.00007883716],"domain_scores_gemma":[0.9996524,0.00002428016,0.00006066922,0.0001853046,0.00003777706,0.00003956448],"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":[0.00001713939,0.00005618901,0.001369134,0.0002671856,0.00001304893,1.247723e-7,0.00011986,0.08263873,0.2488188,0.001304129,0.00009350126,0.6653022],"study_design_scores_gemma":[0.0007269381,0.0001352681,0.0463274,0.000161839,0.00004338827,0.00002327684,0.00008995725,0.249581,0.6870008,0.0006485211,0.01491803,0.0003436568],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7631431,0.0003144068,0.2355592,0.00003500241,0.00004919582,0.0005640666,0.0000178185,0.00008649704,0.0002307084],"genre_scores_gemma":[0.9978105,0.00002617484,0.001927879,0.000001713151,0.00007777545,0.000103817,0.00001230334,0.00001550775,0.00002429396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6649585,"threshold_uncertainty_score":0.3823961,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2079761209","doi":"10.1007/s00138-007-0093-z","title":"Self-identifying patterns for plane-based camera calibration","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Fiducial marker; Artificial intelligence; Computer vision; Calibration; Computer science; Camera resectioning; Camera auto-calibration; Set (abstract data type); Grid; Software; Computer graphics (images); Pattern recognition (psychology); Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02629139995921539,"gpt":0.3182447791303142,"spread":0.2919533791710989,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002413992,0.00007954132,0.00007218029,0.00008422862,0.000158794,0.0001450512,0.0002142424,0.00003664518,0.000008934758],"category_scores_gemma":[0.00000813838,0.00006543622,0.00002805444,0.0001208902,0.00001283274,0.0001681388,0.00004324521,0.00005963505,0.000004164776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001457963,"about_ca_system_score_gemma":0.0000107123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001568471,"about_ca_topic_score_gemma":0.00002221932,"domain_scores_codex":[0.999363,0.00001011851,0.0001658698,0.0002173844,0.0001140051,0.0001296301],"domain_scores_gemma":[0.9995416,0.0000851494,0.00004584443,0.0002104024,0.00004678338,0.00007023369],"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":[0.00002935209,0.0005544768,0.01007686,0.0002083944,0.00002097447,0.000001205566,0.0002835759,0.000008279791,0.05111795,0.5335711,0.001367234,0.4027606],"study_design_scores_gemma":[0.001323954,0.0007235799,0.01959038,0.0001138364,0.00003742263,0.000007300825,0.00005599286,0.6893925,0.1601121,0.01540317,0.11251,0.0007297623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002594692,0.00003840437,0.9946309,0.0008766233,0.00002825828,0.0004577678,0.000008454796,0.0003088366,0.001056035],"genre_scores_gemma":[0.9228969,0.000009852322,0.07647062,0.0004012842,0.00004267697,0.0001172231,0.00002698892,0.00000528122,0.0000291827],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9203022,"threshold_uncertainty_score":0.2668411,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2015559606","doi":"10.1007/s00138-008-0141-3","title":"Precise ellipse estimation without contour point extraction","year":2008,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"","keywords":"Ellipse; Conic section; Operator (biology); Computer science; Boundary (topology); Enhanced Data Rates for GSM Evolution; Dual (grammatical number); Artificial intelligence; Representation (politics); Computer vision; Mathematics; Algorithm; Geometry; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.009226652504801853,"gpt":0.2902972506004869,"spread":0.281070598095685,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001348505,0.0001008859,0.0000975363,0.0001179945,0.0003821978,0.00007968745,0.0001682218,0.00004523058,0.00001201102],"category_scores_gemma":[0.00001533073,0.00008584547,0.00003396476,0.0002453784,0.00003992488,0.0004768681,0.00006411292,0.0001108682,0.00005227046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002159109,"about_ca_system_score_gemma":0.00001671819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005921334,"about_ca_topic_score_gemma":0.000007121529,"domain_scores_codex":[0.9992843,0.00002963107,0.0001797451,0.0002672476,0.0001357286,0.0001033006],"domain_scores_gemma":[0.999397,0.0000384677,0.00008141244,0.0003470423,0.00006351632,0.00007259619],"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":[0.000008668207,0.0001447067,0.0002178524,0.00001003331,0.000004722428,0.000002537249,0.0001849955,0.00004020768,0.009511194,0.004804537,0.002202001,0.9828686],"study_design_scores_gemma":[0.001115785,0.0003376375,0.01621755,0.00003983122,0.00002208685,0.0006530747,0.00003697974,0.5281867,0.2587166,0.02812305,0.165889,0.0006616843],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003895281,0.0001270522,0.9919913,0.0007004746,0.00003325363,0.0003648163,0.000002407747,0.0004742217,0.002411225],"genre_scores_gemma":[0.9178209,0.0002010712,0.08082133,0.0002096581,0.00004840671,0.0001893573,0.000007397548,0.000007945809,0.0006939057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9822069,"threshold_uncertainty_score":0.3500676,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2143039717","doi":"10.1007/s00138-012-0472-y","title":"Estimating 3D human shapes from measurements","year":2012,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; ENCODE; Artificial intelligence; Limit (mathematics); Active shape model; Statistical model; Data mining; Pattern recognition (psychology); Computer vision; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02583963144197704,"gpt":0.2920603540837711,"spread":0.266220722641794,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000976676,0.00008597878,0.00009211106,0.00004451962,0.0001734472,0.00003242122,0.00006061259,0.00002775512,0.0001060962],"category_scores_gemma":[0.000003951867,0.00007462321,0.00002621772,0.00009064132,0.00001029985,0.00006675746,0.00002164333,0.00006925007,0.00005811285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009890213,"about_ca_system_score_gemma":0.00000103704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005618707,"about_ca_topic_score_gemma":0.000008424002,"domain_scores_codex":[0.9995512,0.000007654704,0.0001300464,0.00009772544,0.00009575395,0.0001176381],"domain_scores_gemma":[0.9997575,0.0000151556,0.00001677963,0.000116248,0.00001267523,0.00008168149],"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":[0.000001810367,0.0002599912,0.03298131,0.00008309106,0.0001910357,1.981409e-7,0.0006870323,0.1508961,0.08097576,0.0008742779,0.001490671,0.7315587],"study_design_scores_gemma":[0.0001064951,0.000003290646,0.003603327,0.00001456057,0.00003948794,3.716292e-7,0.000008900561,0.991945,0.0002483812,0.0003527817,0.003561812,0.0001156245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1673069,0.001930502,0.8250961,0.00007412746,0.00003961027,0.000122439,0.00002990767,0.0003748667,0.005025592],"genre_scores_gemma":[0.9853966,0.00001159869,0.01423035,0.00003870386,0.0001688737,0.00003886626,0.00005623281,0.00001469697,0.00004414126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8410489,"threshold_uncertainty_score":0.3043046,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002002766","doi":"10.1007/s00138-013-0568-z","title":"Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Mixture model; Artificial intelligence; Computer science; Computer vision; Segmentation; Robustness (evolution); Gaussian; Shadow (psychology); Image segmentation; Gaussian network model; Pixel","retraction":null,"screen_n_in":null,"score":{"opus":0.02041648371758248,"gpt":0.312397982708344,"spread":0.2919814989907615,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002368041,0.00009692215,0.0001229374,0.0001762038,0.0002780029,0.0001446081,0.0001210254,0.00005405726,0.000009055057],"category_scores_gemma":[0.00003315942,0.00008116128,0.0000314022,0.000712497,0.00005401828,0.0003489223,0.0000772515,0.0001065426,0.000006598631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001403242,"about_ca_system_score_gemma":0.00001075822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004522207,"about_ca_topic_score_gemma":0.00003204213,"domain_scores_codex":[0.9992564,0.00006784609,0.0001973529,0.0002547697,0.0001068961,0.0001167197],"domain_scores_gemma":[0.9992628,0.0002134842,0.000111931,0.0002610694,0.00007457161,0.00007611627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000002395013,0.00009603114,0.004111274,0.00003027973,0.00001198866,2.059811e-7,0.00003709579,0.00005513895,0.0340701,0.01668599,0.0000265591,0.9448729],"study_design_scores_gemma":[0.0004930085,0.0001281319,0.5674115,0.0000302733,0.00002752616,0.00005212622,0.0000343174,0.3875822,0.01305455,0.02494247,0.005928574,0.0003152917],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06429787,0.0003672948,0.9343455,0.000353337,0.00004095327,0.000233128,0.00001205949,0.00005416025,0.0002957099],"genre_scores_gemma":[0.9677761,0.0001017479,0.03197443,0.00004080342,0.00003236921,0.00004088069,0.0000112306,0.00000510071,0.00001736497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9445577,"threshold_uncertainty_score":0.3309661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2064458142","doi":"10.1007/s00138-013-0525-x","title":"Multimedia event detection with multimodal feature fusion and temporal concept localization","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Interior Business Center; Georgia Institute of Technology; Seoul National University; U.S. Department of the Interior","keywords":"Computer science; Discriminative model; Event (particle physics); Artificial intelligence; Feature (linguistics); Feature learning; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.003225439544309228,"gpt":0.2290308128693001,"spread":0.2258053733249909,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008536394,0.000120246,0.0001159897,0.00009646142,0.0003249259,0.0001934968,0.0001122118,0.0000632403,0.00002031129],"category_scores_gemma":[0.000008310247,0.00008380139,0.00002163961,0.0003718193,0.00004987736,0.000360293,0.00009003204,0.00008677736,0.00001267231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000130265,"about_ca_system_score_gemma":0.00001005294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002947825,"about_ca_topic_score_gemma":0.0001213326,"domain_scores_codex":[0.9992074,0.00004004338,0.0001482642,0.0003368769,0.0001615523,0.0001058341],"domain_scores_gemma":[0.9994342,0.00003083194,0.00008684145,0.0002373142,0.0001025184,0.0001083387],"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":[0.00000417684,0.0001117244,0.007365521,0.00001049778,0.00001123346,3.289568e-7,0.0002690658,0.0006858521,0.00388698,0.001668945,0.0003482526,0.9856374],"study_design_scores_gemma":[0.0003796553,0.00007801547,0.03025722,0.000009840709,0.00001091471,0.000005942527,0.00002948481,0.9606746,0.0005507931,0.0004146746,0.007467431,0.0001214104],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009691383,0.0001913245,0.9883575,0.001096585,0.00001777591,0.0004653007,0.000003339408,0.00009811595,0.00007864356],"genre_scores_gemma":[0.9847146,0.00006124077,0.01458248,0.000214513,0.00003643036,0.0001163833,0.00007125517,0.000007340913,0.000195707],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.985516,"threshold_uncertainty_score":0.3417322,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2048359624","doi":"10.1007/s00138-013-0579-9","title":"Fully automatic expression-invariant face correspondence","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face recognition and analysis","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Artificial intelligence; Computer science; Invariant (physics); Face (sociological concept); Point (geometry); Computer vision; Pattern recognition (psychology); Expression (computer science); Set (abstract data type); Facial expression; Mathematics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.007117041141617277,"gpt":0.2528260352248051,"spread":0.2457089940831879,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001194875,0.0001131713,0.0001238366,0.0001191291,0.0002630032,0.0002812548,0.0003860445,0.00003877304,0.0006011662],"category_scores_gemma":[0.00002168634,0.00008388758,0.00004662469,0.000443489,0.00003877023,0.0003365854,0.0001935574,0.0000930721,0.001231914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008946649,"about_ca_system_score_gemma":0.00001900955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004020773,"about_ca_topic_score_gemma":0.000003280314,"domain_scores_codex":[0.9991131,0.00004818528,0.0001952087,0.0003204055,0.000177494,0.0001456024],"domain_scores_gemma":[0.9991708,0.0001026278,0.00007135242,0.0004328369,0.00006228644,0.0001601157],"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":[0.000001049425,0.0002063866,0.0003866373,0.00002676961,0.00001292255,0.000001514747,0.0002464891,0.00007016384,0.02880317,0.02490176,0.008987927,0.9363552],"study_design_scores_gemma":[0.0002458582,0.00003194561,0.005792635,0.00003360807,0.000009037721,0.00001620087,0.00007411467,0.9630902,0.001372958,0.006549125,0.02256669,0.0002176512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007996187,0.0001624294,0.985236,0.004278658,0.00002539323,0.0003442898,0.000005979841,0.0002437168,0.001707348],"genre_scores_gemma":[0.9546757,0.0000797206,0.0413817,0.00107873,0.0000278673,0.00039558,0.00001528181,0.000008486779,0.002336949],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.96302,"threshold_uncertainty_score":0.9995458,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1996547878","doi":"10.1007/s00138-006-0018-2","title":"Reconfigurable hardware implementation of a phase-correlation stereoalgorithm","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Subpixel rendering; Computer science; Field-programmable gate array; Computer hardware; Software; Filter (signal processing); Computation; Pixel; Artificial intelligence; Algorithm; Computer vision","retraction":null,"screen_n_in":null,"score":{"opus":0.008212257210457548,"gpt":0.3254690559927569,"spread":0.3172567987822994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001077953,0.00008188726,0.0000985549,0.0001072304,0.0001510159,0.0000592618,0.0001502649,0.00002020551,0.00005602561],"category_scores_gemma":[0.000002559342,0.00007215266,0.00002826147,0.0002869987,0.00002292116,0.0003537862,0.00004349152,0.00005780779,0.00001360612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001153677,"about_ca_system_score_gemma":0.00001423929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001373482,"about_ca_topic_score_gemma":0.00001509214,"domain_scores_codex":[0.9992731,0.0000203239,0.0002556151,0.0002296592,0.0001195926,0.0001017513],"domain_scores_gemma":[0.9994882,0.00003623634,0.0001267333,0.0002457112,0.00006436966,0.00003871584],"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":[0.000001494567,0.00007544877,0.0004474032,0.000006960525,0.000001809697,1.457613e-7,0.00004301001,0.00003490065,0.005656896,0.03576921,0.0004588051,0.9575039],"study_design_scores_gemma":[0.002986406,0.0002126213,0.01409277,0.00003575709,0.00001580724,0.00001856789,0.000193882,0.7265697,0.01597707,0.02704713,0.2125203,0.0003299399],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002256943,0.0002106799,0.9938956,0.0006775606,0.00002976304,0.0002756556,0.00002145798,0.00007903752,0.002553246],"genre_scores_gemma":[0.8895525,0.00005506236,0.1094012,0.0001755792,0.00005354059,0.00009319105,0.0001141563,0.00001067854,0.0005439863],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.957174,"threshold_uncertainty_score":0.29423,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2132864254","doi":"10.1007/s00138-005-0012-0","title":"Morphological segmentation and classification of underground pipe images","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Pipeline (software); Segmentation; Artificial intelligence; Image segmentation; Computer science; Visual inspection; Pipeline transport; Computer vision; Pattern recognition (psychology); Geology; Engineering; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.005870507811560283,"gpt":0.2463795253771771,"spread":0.2405090175656169,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004059494,0.00006514444,0.00006856241,0.00004216885,0.00006455297,0.00001984916,0.00002824125,0.0000330339,0.000007001214],"category_scores_gemma":[0.000001770928,0.00005300908,0.00001109387,0.00007750535,0.0000448551,0.00005586337,0.0000130719,0.00005077763,0.000001822521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001070186,"about_ca_system_score_gemma":0.000001684706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000236026,"about_ca_topic_score_gemma":0.000006087225,"domain_scores_codex":[0.9996611,0.000004947712,0.000124711,0.00009578467,0.00004933958,0.00006408214],"domain_scores_gemma":[0.9998369,0.00002134303,0.00002567685,0.00007828436,0.00001781626,0.00001995131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00000417935,0.00002899544,0.008752042,0.00005559779,0.000006481707,4.380423e-7,0.0000290953,0.0006390403,0.8568471,0.01829961,0.0008093633,0.1145281],"study_design_scores_gemma":[0.0006560739,0.00005847937,0.9048437,0.00002613085,0.00003479407,0.0000315875,0.0002673236,0.02524973,0.03884929,0.01667174,0.0130412,0.0002699649],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7089352,0.0006213869,0.2850378,0.0001616817,0.00004457623,0.00024775,0.00002186505,0.0001170164,0.004812752],"genre_scores_gemma":[0.9965563,0.0001347875,0.003101323,0.00001016816,0.00005678874,0.00003727049,0.00003853393,0.000006975412,0.00005781488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8960916,"threshold_uncertainty_score":0.2161647,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1988539193","doi":"10.1007/s00138-011-0353-9","title":"Three-dimensional human shape inference from silhouettes: reconstruction and validation","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Silhouette; Artificial intelligence; Computer vision; Computer science; Body shape; Prior probability; Inference; Mathematics; Principal component analysis; Image (mathematics); Pattern recognition (psychology); Shape analysis (program analysis); Geometric shape; Bayesian probability; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.02192251718407639,"gpt":0.2549253261418103,"spread":0.2330028089577339,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005500815,0.00009669997,0.00009924253,0.00007302532,0.000160894,0.00003058989,0.0000459907,0.0000478111,0.0002230192],"category_scores_gemma":[0.000004026743,0.00008645936,0.0000216002,0.0001008646,0.00003551678,0.00008462026,0.00002542043,0.00008935703,0.00001896349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006523588,"about_ca_system_score_gemma":0.00000259208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002169518,"about_ca_topic_score_gemma":0.0000793796,"domain_scores_codex":[0.9995208,0.00000703791,0.0001547426,0.0001796253,0.00006465449,0.00007309562],"domain_scores_gemma":[0.9997261,0.00002824024,0.00002607876,0.0001352472,0.00002463647,0.00005965266],"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":[0.000006241285,0.00008688075,0.02950237,0.00003374747,0.00008464076,5.835159e-7,0.000269916,0.003583767,0.0258563,0.003939918,0.0002165135,0.9364191],"study_design_scores_gemma":[0.000179058,0.00001664964,0.01926334,0.00002428637,0.00004574769,0.000002872561,0.00001484824,0.9668003,0.001014034,0.01217542,0.000302409,0.0001610576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8256534,0.0003575977,0.1726673,0.00005125559,0.0000212086,0.0001030806,0.00003307319,0.0001920229,0.000920986],"genre_scores_gemma":[0.9955105,0.00005847456,0.004217541,0.00002895443,0.00004259217,0.00003289834,0.00008712599,0.00001120969,0.00001076252],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9632165,"threshold_uncertainty_score":0.352571,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089913748","doi":"10.1007/s00138-006-0013-7","title":"The Agile Stereo Pair for active vision","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Computer vision; Agile software development; Stereopsis; Active vision; Software engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.005506638397844409,"gpt":0.297261996564828,"spread":0.2917553581669836,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001481206,0.0001000057,0.00008155575,0.0000438237,0.0008192569,0.0002043628,0.0003343099,0.00002248486,0.000004235389],"category_scores_gemma":[0.00001176035,0.00006280769,0.00004507826,0.0002106987,0.0000554464,0.0002491561,0.0001681037,0.00007673165,0.00001887571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001317163,"about_ca_system_score_gemma":0.00001054488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001489346,"about_ca_topic_score_gemma":0.00001129071,"domain_scores_codex":[0.9992442,0.00002165748,0.0001637603,0.0002930286,0.0001147054,0.000162722],"domain_scores_gemma":[0.9991747,0.0002430492,0.00007255171,0.0003938802,0.00006211179,0.00005368166],"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":[0.000004883448,0.00004617974,0.00003932557,0.000003092588,0.000001582677,8.799331e-8,0.0000192324,0.00001714242,0.001646143,0.1135536,0.004218268,0.8804505],"study_design_scores_gemma":[0.0002981298,0.00004740212,0.002821101,0.000007119133,0.000002491835,0.000003088343,0.00001940736,0.1867106,0.001005582,0.03530584,0.7736831,0.00009606126],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0004376464,0.0003599705,0.9893644,0.00692912,0.00004728701,0.0004838997,0.00001329781,0.0001274342,0.002236875],"genre_scores_gemma":[0.8339867,0.000182469,0.1576684,0.001687228,0.0002828452,0.0007973833,0.00006377073,0.00003394529,0.00529725],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8803544,"threshold_uncertainty_score":0.6301144,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2158497613","doi":"10.1007/s00138-006-0025-3","title":"Recent Developments in 3D Multi-modal Laser Imaging Applied to Cultural Heritage","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":41,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"Université Laval; Harvard University","keywords":"Computer science; Computer vision; Laser scanning; Perspective (graphical); Artificial intelligence; Laser; Scanner; Projection (relational algebra); Calibration; Modal; Compensation (psychology); Aperture (computer memory); Tracking (education); Lidar; Range (aeronautics); Optics; Algorithm; Acoustics; Mathematics; Physics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01230028583774663,"gpt":0.2566015589753084,"spread":0.2443012731375618,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001483,0.0001243156,0.0001116881,0.00005358001,0.0002110042,0.0000837969,0.0001051038,0.00002812469,0.0002248247],"category_scores_gemma":[0.000004764222,0.00009022376,0.00001504707,0.0003036065,0.00002714133,0.00008530843,0.00001999739,0.00009463599,0.0002148458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006887564,"about_ca_system_score_gemma":0.000008747441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001259185,"about_ca_topic_score_gemma":0.005671068,"domain_scores_codex":[0.9991879,0.00002547463,0.0001924607,0.0002747451,0.0001166926,0.0002027009],"domain_scores_gemma":[0.9997237,0.00002444821,0.00002874719,0.0001037518,0.00002183975,0.00009745933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001465971,0.00006000576,0.4095423,0.000006683609,0.000001893107,0.000001994559,0.000167855,0.0006899501,0.0006674,0.00007681387,0.0005235079,0.588247],"study_design_scores_gemma":[0.0003170537,0.000007414577,0.882414,0.000007442008,0.000001798906,0.000003163937,0.0001321846,0.0041174,0.00005908615,0.00004954886,0.1127366,0.000154325],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9721704,0.000950505,0.00175527,0.001047074,0.00007659692,0.0008472819,0.0001467287,0.0001485149,0.02285765],"genre_scores_gemma":[0.9919539,0.00007010264,0.006538831,0.0003792453,0.00003586563,0.00002545339,0.0003935745,0.000003765067,0.0005992739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5880927,"threshold_uncertainty_score":0.3679218,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2074179156","doi":"10.1007/s00138-002-0119-5","title":"Road feature detection and estimation","year":2003,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Air Canada","funders":"","keywords":"Homography; Artificial intelligence; A priori and a posteriori; Vanishing point; Computer vision; Computer science; Line (geometry); Feature (linguistics); Estimation; Line segment; Pattern recognition (psychology); Image (mathematics); Mathematics; Statistics; Engineering; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.003798827826803375,"gpt":0.240830266591569,"spread":0.2370314387647656,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001048395,0.00007939211,0.00005944068,0.00002511314,0.0003307385,0.00004318243,0.00003304573,0.00004545754,0.00007282953],"category_scores_gemma":[0.00001389665,0.00006662282,0.00001394477,0.0001900052,0.00007930885,0.000065564,0.00002669522,0.00008346322,0.00009528964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001703382,"about_ca_system_score_gemma":0.000001964157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009309896,"about_ca_topic_score_gemma":0.00005318268,"domain_scores_codex":[0.9995049,0.00002469287,0.00008050646,0.0002244181,0.00008206326,0.00008345315],"domain_scores_gemma":[0.9996942,0.00001972319,0.00003486518,0.0001700602,0.000004652753,0.00007654355],"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":[0.000001776695,0.0000359523,0.0007661915,0.000003482791,0.000001990974,8.597861e-8,0.00005622839,0.0001658689,0.01717822,0.002314832,0.0004094139,0.979066],"study_design_scores_gemma":[0.0004144119,0.00005687114,0.147378,0.000007680967,0.00002934077,0.00008491943,0.00006657606,0.1274434,0.003664008,0.008916699,0.7116529,0.0002851299],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3734498,0.0004666392,0.5368556,0.002975776,0.0000507427,0.001212894,0.00001386831,0.0002661225,0.08470858],"genre_scores_gemma":[0.9902987,0.00005111981,0.008670084,0.0001286959,0.00001107917,0.00002317198,0.000008781958,0.000007662458,0.0008006568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9787808,"threshold_uncertainty_score":0.27168,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2113876505","doi":"10.1007/s00138-015-0659-0","title":"Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation","year":2015,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Wood and Agarwood Research","field":"Chemistry","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Scale-invariant feature transform; Local binary patterns; Classifier (UML); Feature vector; Computer science; Feature selection; Support vector machine; Probabilistic logic; Random forest; Mathematics; Feature extraction; Histogram","retraction":null,"screen_n_in":null,"score":{"opus":0.03403744080307197,"gpt":0.3272146374337217,"spread":0.2931771966306497,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001160996,0.0001200893,0.0001060588,0.00007660502,0.0002334368,0.0001061435,0.00005884062,0.0001025362,0.000112332],"category_scores_gemma":[0.00005808919,0.00009654123,0.00002882553,0.0002029391,0.00005522489,0.0000798078,0.00003515663,0.000251394,0.00001446281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004150805,"about_ca_system_score_gemma":0.00002564166,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003008069,"about_ca_topic_score_gemma":0.0001188386,"domain_scores_codex":[0.9991925,0.00002884359,0.0001150117,0.0003191352,0.0002140735,0.0001303713],"domain_scores_gemma":[0.9994513,0.00008400987,0.00005127057,0.0001753706,0.00008056634,0.0001574158],"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":[0.0008857701,0.00186337,0.1191297,0.0005615142,0.00008251167,0.000008370409,0.0004007773,0.0005736093,0.3351249,0.003288552,0.02673682,0.5113441],"study_design_scores_gemma":[0.003380822,0.0003859168,0.1584682,0.0001901986,0.00008931951,0.00003282156,0.0005712364,0.6798971,0.02519728,0.007835536,0.1231955,0.0007561088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9188848,0.0003730053,0.01144606,0.01603355,0.00007942752,0.0009779128,0.0004919549,0.0004226271,0.0512907],"genre_scores_gemma":[0.9963248,0.00004334034,0.0005221655,0.00008678161,0.0001124197,0.0001184215,0.000620475,0.00001497058,0.002156585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6793235,"threshold_uncertainty_score":0.3936837,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2025741957","doi":"10.1007/s00138-006-0052-0","title":"Face recognition using localized features based on non-negative sparse coding","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Neural coding; Non-negative matrix factorization; Subtraction; Computer science; Facial recognition system; Coding (social sciences); Sparse approximation; Metric (unit); Face (sociological concept); Matrix decomposition; Mathematics; Arithmetic","retraction":null,"screen_n_in":null,"score":{"opus":0.01693025683881043,"gpt":0.2829088620211159,"spread":0.2659786051823055,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001230392,0.0001367973,0.0001199641,0.0001422774,0.0003583358,0.0001511836,0.0001584873,0.00006283493,0.00002710642],"category_scores_gemma":[0.00001265687,0.0001122236,0.00004275079,0.0003414804,0.00003743279,0.0001833631,0.00005927472,0.0001262251,0.00006249139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002116362,"about_ca_system_score_gemma":0.00001717254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001606082,"about_ca_topic_score_gemma":0.00001392926,"domain_scores_codex":[0.9991133,0.00004704351,0.0001682511,0.0003535874,0.0001734584,0.0001444186],"domain_scores_gemma":[0.999403,0.0001260174,0.00008988735,0.0002558814,0.00005791111,0.0000673296],"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":[0.0001127766,0.0008665583,0.0004008461,0.00008806094,0.00001675304,0.000007737797,0.0002224795,0.01880031,0.06605709,0.008690736,0.009947693,0.894789],"study_design_scores_gemma":[0.0008467778,0.00006352927,0.00180498,0.0001059557,0.0000105337,0.000005491808,0.00002781734,0.9669511,0.01804873,0.007644222,0.004267817,0.0002230532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01040218,0.00003120861,0.9849839,0.0008768829,0.00004021278,0.0004108274,0.00003243647,0.0001244276,0.003097887],"genre_scores_gemma":[0.9225944,0.00001647852,0.07623665,0.0007510236,0.00006722181,0.0001020679,0.000102647,0.000011088,0.0001183837],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9481508,"threshold_uncertainty_score":0.4576346,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012874325","doi":"10.1007/s00138-003-0131-4","title":"A CAD-based 3D data acquisition strategy for inspection","year":2003,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Athabasca University; University of Alberta; École de Technologie Supérieure","funders":"","keywords":"Computer science; Process (computing); Data acquisition; Orientation (vector space); Computer vision; Position (finance); Range (aeronautics); Artificial intelligence; CAD; Set (abstract data type); 3D reconstruction; Viewpoints; Laser scanning; Laser; Engineering drawing; Engineering; Mathematics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.03340950953192519,"gpt":0.3220530261154563,"spread":0.2886435165835312,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001351012,0.0000747942,0.00006934541,0.00006176361,0.0001131987,0.0000141191,0.00006495548,0.00004387392,0.00001417159],"category_scores_gemma":[0.000008439569,0.00006900204,0.0000123422,0.00009986946,0.00001879438,0.00008209111,0.000007621607,0.00005649323,0.000002810844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001490724,"about_ca_system_score_gemma":0.00000643989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002393341,"about_ca_topic_score_gemma":0.00001158638,"domain_scores_codex":[0.9996111,0.000008823632,0.00009809426,0.0001523239,0.0000437194,0.0000858889],"domain_scores_gemma":[0.9996544,0.00002320751,0.00001746452,0.0002478783,0.00002295596,0.0000340953],"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":[0.00006539268,0.0004418227,0.002122681,0.0003038831,0.00007210367,6.417435e-7,0.00003762522,0.01846112,0.2338668,0.1135067,0.01726101,0.6138602],"study_design_scores_gemma":[0.001042425,0.0001518349,0.001453681,0.00001560412,0.00005060663,0.00000407974,0.00001578017,0.2475531,0.01952099,0.007182898,0.7227308,0.0002782112],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001164872,0.0005086266,0.9942986,0.00007363541,0.00002418287,0.0004438462,0.0000758235,0.0004752132,0.00293525],"genre_scores_gemma":[0.9751381,0.00009228488,0.02396482,0.00008022333,0.00003573774,0.0003110797,0.0003304126,0.00001520196,0.00003215439],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9739732,"threshold_uncertainty_score":0.2813821,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2060776942","doi":"10.1007/s00138-013-0497-x","title":"3D segmentation of abdominal CT imagery with graphical models, conditional random fields and learning","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Conditional random field; CRFS; Graphical model; Artificial intelligence; Computer science; Segmentation; Cut; Inference; Markov random field; Discriminative model; Machine learning; Image segmentation; Pattern recognition (psychology); Structured prediction; Scale-space segmentation; Belief propagation; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.006238367072077861,"gpt":0.2636565299567762,"spread":0.2574181628846983,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001686485,0.00008119363,0.0001135932,0.00009351682,0.0001503916,0.0000781035,0.0001082436,0.00002184059,0.0000701629],"category_scores_gemma":[0.00001241034,0.00006017285,0.00001753831,0.0001653899,0.0001345092,0.0003970587,0.00006922059,0.0001445783,0.000003501429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004470669,"about_ca_system_score_gemma":0.00001280047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007135392,"about_ca_topic_score_gemma":0.000002178904,"domain_scores_codex":[0.9992763,0.00005927226,0.0001864448,0.0002093032,0.0001889886,0.00007963339],"domain_scores_gemma":[0.9994617,0.0001675568,0.00008691476,0.0001279565,0.00006981743,0.00008604332],"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":[0.00002587956,0.0001881292,0.001284748,0.00006618914,0.00002589485,0.000002230719,0.0002989723,0.0002531994,0.01760493,0.02494909,0.00112816,0.9541726],"study_design_scores_gemma":[0.002698,0.0003308998,0.007869466,0.0000496178,0.00002838019,0.00009284717,0.0001373307,0.9383739,0.01047472,0.03918813,0.0004703636,0.0002863015],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01175645,0.00008737743,0.9865216,0.0007938555,0.000004676091,0.0003814904,0.000003661194,0.00007323625,0.0003776762],"genre_scores_gemma":[0.7721574,0.00009548847,0.2270482,0.0003315291,0.00001203011,0.0002448496,0.00003965693,0.00000456598,0.00006632736],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9538863,"threshold_uncertainty_score":0.2453778,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046450404","doi":"10.1007/s00138-008-0128-0","title":"BearCam: automated wildlife monitoring at the arctic circle","year":2008,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Computer science; Computer vision; Arctic; Motion (physics); The arctic; Artificial intelligence; Camera trap; Extension (predicate logic); Computer graphics (images); Wildlife; Ecology","retraction":null,"screen_n_in":null,"score":{"opus":0.01020036571316164,"gpt":0.2550061647006515,"spread":0.2448057989874898,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001079507,0.00006496986,0.00005669288,0.00001307961,0.000919366,0.0000107176,0.0001071755,0.00003655356,0.0004601792],"category_scores_gemma":[0.00001368708,0.0000484256,0.00001967743,0.0001800742,0.0002048294,0.0000700122,0.0001429472,0.00008355572,0.0006817986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004252624,"about_ca_system_score_gemma":0.000004373759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001824789,"about_ca_topic_score_gemma":0.00005100534,"domain_scores_codex":[0.9994586,0.00003127701,0.0001094816,0.000182525,0.0001061864,0.0001119782],"domain_scores_gemma":[0.9996182,0.00007407036,0.00004055037,0.0002067453,0.000005290839,0.000055123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004345695,0.00004875283,0.9835739,0.000002040822,0.000003753051,7.137647e-7,0.00009632354,0.0001765991,0.0008999448,0.0000932109,0.006907825,0.008192628],"study_design_scores_gemma":[0.0001186725,0.00001486653,0.9518116,0.000002270236,0.000005338804,0.00001863625,0.00001191818,0.004330894,0.0000690992,0.0001728231,0.04338576,0.00005811816],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925492,0.00006751393,0.0004397773,0.004950233,0.00003728753,0.0002468972,0.000003546774,0.0001432486,0.001562319],"genre_scores_gemma":[0.9973148,0.00007995951,0.0002074264,0.0009395575,0.000037902,0.0001230638,0.00001126755,0.00000627268,0.00127981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03647794,"threshold_uncertainty_score":0.8763369,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1994064153","doi":"10.1007/s001380000043","title":"Machine vision system for curved surface inspection","year":2000,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; National Research Council Canada","funders":"","keywords":"Computer vision; Artificial intelligence; Structured light; Curvature; Computer science; Triangulation; Geodetic datum; Linearity; System of measurement; Mathematics; Engineering; Geology; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.006244543806893696,"gpt":0.2663803815078344,"spread":0.2601358377009407,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001447077,0.0001719611,0.0001589445,0.00005897276,0.0004233888,0.00009944098,0.0001352744,0.00007795628,0.00005534856],"category_scores_gemma":[0.000002489507,0.0001548568,0.0000527074,0.0002841277,0.00004000189,0.0001214739,0.00002086121,0.0001240891,0.00005382353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004309453,"about_ca_system_score_gemma":0.000006112176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003525622,"about_ca_topic_score_gemma":0.000008335772,"domain_scores_codex":[0.9992036,0.00001122531,0.0002533898,0.0002692862,0.00009224986,0.0001702604],"domain_scores_gemma":[0.9994913,0.00003904534,0.00003419651,0.0003047389,0.00004575423,0.00008497862],"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":[0.00003078612,0.0002064972,0.0001333675,0.0005081893,0.00002617747,3.688527e-7,0.0001064849,0.005506605,0.02850127,0.01687617,0.007680087,0.940424],"study_design_scores_gemma":[0.0003723467,0.00004493761,0.0003344989,0.00004421551,0.00002365495,0.00001315766,0.00001846957,0.6131567,0.003342784,0.000964452,0.3814583,0.0002264369],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0154478,0.001990323,0.9626124,0.0006786764,0.00004858499,0.001566187,0.0002277683,0.003717679,0.01371056],"genre_scores_gemma":[0.9777942,0.0004414647,0.02014967,0.00005042783,0.00008614222,0.0005700681,0.0001867793,0.00005097833,0.0006703181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9623464,"threshold_uncertainty_score":0.6314876,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2063785986","doi":"10.1007/s00138-010-0300-1","title":"People tracking using a network-based PTZ camera","year":2010,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer vision; Artificial intelligence; Computer science; Frame rate; Frame (networking); Tracking system; Tracking (education)","retraction":null,"screen_n_in":null,"score":{"opus":0.01653906349758039,"gpt":0.323863028058523,"spread":0.3073239645609426,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004920754,0.0001115622,0.0001361047,0.00006495133,0.000387757,0.0001977996,0.0003112876,0.00005262343,0.00001899559],"category_scores_gemma":[0.00002514578,0.00009510158,0.00004482143,0.0004976783,0.0000341271,0.00015307,0.00009134618,0.0002244354,0.00001077863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005504214,"about_ca_system_score_gemma":0.00003539825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009407887,"about_ca_topic_score_gemma":0.0002104683,"domain_scores_codex":[0.999141,0.00005228043,0.0001650868,0.0003211264,0.0001249612,0.0001955206],"domain_scores_gemma":[0.9990957,0.000197834,0.0000730032,0.0004858619,0.00005142988,0.00009619408],"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":[0.000003835195,0.0001228656,0.03786415,0.00001909334,0.000008400869,0.000001632933,0.0001287408,0.002731367,0.01654495,0.05388151,0.000184749,0.8885087],"study_design_scores_gemma":[0.0003729068,0.00002975988,0.07061064,0.00001427619,0.000008623067,0.00002411038,0.000005268592,0.8712094,0.0005914072,0.005972508,0.05090509,0.0002559562],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04907954,0.0001105021,0.9490141,0.0009135852,0.0001203869,0.0001826124,0.000003270371,0.0001561304,0.0004198324],"genre_scores_gemma":[0.7851987,0.000006953853,0.2142026,0.0003989134,0.0001336248,0.00002899776,0.000005264413,0.000008410335,0.00001650392],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8882527,"threshold_uncertainty_score":0.387813,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3023907044","doi":"10.1007/s00138-002-0083-0","title":"Range image segmentation using local approximation of scan lines with application to CAD model acquisition","year":2003,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Forest Service","keywords":"Artificial intelligence; CAD; Robustness (evolution); Computer vision; Computer science; Segmentation; Classification of discontinuities; Pixel; Range (aeronautics); Image segmentation; Noise (video); Geometric primitive; Range segmentation; Scale-space segmentation; Pattern recognition (psychology); Image (mathematics); Mathematics; Engineering; Engineering drawing","retraction":null,"screen_n_in":null,"score":{"opus":0.006973354389244341,"gpt":0.2515791495777225,"spread":0.2446057951884782,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001079104,0.0001353306,0.0001350157,0.0001433382,0.0001102613,0.00003122913,0.00004796361,0.00005329611,0.000007098961],"category_scores_gemma":[0.00000361819,0.0001216509,0.00002133562,0.0003801181,0.00003823902,0.0001273091,0.000008734726,0.00005375285,0.000004753073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005009299,"about_ca_system_score_gemma":0.00001314672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002830187,"about_ca_topic_score_gemma":0.00001755113,"domain_scores_codex":[0.9992756,0.00001994932,0.0002494872,0.0001933866,0.0001508939,0.0001107298],"domain_scores_gemma":[0.9995452,0.00001679132,0.0000604848,0.0001912,0.0001072997,0.0000790566],"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":[0.00001001782,0.00004319908,0.000190592,0.00006977788,0.000006415813,5.500512e-8,0.00008491921,0.8781226,0.0986952,0.006095692,0.00002248118,0.01665911],"study_design_scores_gemma":[0.0003423519,0.00003313003,0.000297785,0.00002093005,0.00002762061,0.000003436572,0.00006825129,0.9770827,0.02129207,0.0005208396,0.0001736079,0.0001372957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06281482,0.00007050009,0.9358405,0.00005271893,0.000009671457,0.0006876998,0.00002999523,0.00008595467,0.0004081572],"genre_scores_gemma":[0.9176259,0.00002965935,0.08196032,0.00004954451,0.00001872747,0.0001289127,0.0001476167,0.00002789088,0.00001141341],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8548111,"threshold_uncertainty_score":0.496078,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2007820761","doi":"10.1007/s00138-007-0089-8","title":"Fast pattern recognition using normalized grey-scale correlation in a pyramid image representation","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Toronto","funders":"","keywords":"Subpixel rendering; Artificial intelligence; Pyramid (geometry); Pattern recognition (psychology); Scale (ratio); Computer science; Representation (politics); Perspective (graphical); Computer vision; Distortion (music); Image (mathematics); Correlation; Gradient descent; Noise (video); Algorithm; Mathematics; Pixel; Artificial neural network","retraction":null,"screen_n_in":null,"score":{"opus":0.01871231427411469,"gpt":0.3435021365444196,"spread":0.324789822270305,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003348476,0.0001052495,0.0001125469,0.0002135789,0.0001464868,0.00009967747,0.0001478503,0.00005357615,0.00001128969],"category_scores_gemma":[0.0000221209,0.00009992502,0.00003063104,0.0006587185,0.00004119131,0.0007785046,0.0001070263,0.0001336983,0.00001597432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003840419,"about_ca_system_score_gemma":0.000008884168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001509813,"about_ca_topic_score_gemma":0.00005850542,"domain_scores_codex":[0.9990442,0.0000342743,0.0002991486,0.0003227367,0.0001431572,0.0001564359],"domain_scores_gemma":[0.9993882,0.00007430994,0.0001206758,0.0002689309,0.00008523384,0.00006272014],"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":[0.000009230545,0.00006963692,0.004113925,0.000007972395,0.000001213047,0.000002174472,0.0001354778,0.00001842578,0.0159513,0.0002155,0.00001643675,0.9794587],"study_design_scores_gemma":[0.001632755,0.0001576608,0.1039991,0.000117816,0.00001822277,0.00007643981,0.000160319,0.8017899,0.06537659,0.0224062,0.0036855,0.0005795123],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02709409,0.00006997884,0.9712723,0.0001768156,0.00002424206,0.0004474241,0.000007158689,0.0001634875,0.0007444623],"genre_scores_gemma":[0.7874557,0.0001211334,0.2119446,0.0002438204,0.00005094145,0.00005104387,0.00008223111,0.0000128686,0.00003759675],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9788792,"threshold_uncertainty_score":0.4074824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2169484847","doi":"10.1007/s00138-007-0108-9","title":"Augmented reality on cloth with realistic illumination","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Augmented reality; Computer science; Rendering (computer graphics); Computer vision; Computer graphics (images); Artificial intelligence; Virtual reality","retraction":null,"screen_n_in":null,"score":{"opus":0.01338394739458933,"gpt":0.3265785450382621,"spread":0.3131945976436728,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005142065,0.000102156,0.00009416859,0.0001760557,0.0002093954,0.0001084506,0.0002374974,0.00003347103,0.000004943874],"category_scores_gemma":[0.000007702701,0.00007501904,0.00002102618,0.0005588533,0.00004369641,0.00009489358,0.00008223701,0.00007595147,0.000004407154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001649827,"about_ca_system_score_gemma":0.00001022168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006311954,"about_ca_topic_score_gemma":0.00004223815,"domain_scores_codex":[0.9991154,0.00002981762,0.0002107538,0.0003212708,0.0002092325,0.0001135359],"domain_scores_gemma":[0.9991854,0.00008603897,0.00009803506,0.0004499388,0.00009495667,0.00008564573],"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":[0.000005023363,0.0000956273,0.0002764452,0.000005187083,0.000002666415,6.542055e-7,0.00004387454,0.000004619637,0.00006470387,0.8657888,0.0005895108,0.1331229],"study_design_scores_gemma":[0.001031899,0.001047479,0.1478554,0.00008039783,0.0000175005,0.00003189499,0.0000281969,0.4921327,0.003061799,0.05692596,0.2971706,0.0006161502],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001619656,0.00002182856,0.9936533,0.0008287012,0.0000147541,0.0002987028,0.000006692461,0.000263816,0.003292557],"genre_scores_gemma":[0.9871491,0.0000382842,0.01199348,0.0005878281,0.00003365809,0.00004167686,0.00004768329,0.000007588863,0.0001007049],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9855294,"threshold_uncertainty_score":0.3059188,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2910102176","doi":"10.1007/s00138-019-01004-0","title":"Hard negative mining for correlation filters in visual tracking","year":2019,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Eye tracking; Tracking (education); Frame (networking); Computer vision; Video tracking; Task (project management); Correlation; Pattern recognition (psychology); Noise (video); Object (grammar); Image (mathematics); Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01903159962725122,"gpt":0.3370084789500506,"spread":0.3179768793227994,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004058974,0.00008247789,0.0001197373,0.0001093675,0.00009760566,0.00009205126,0.0001515953,0.00003724006,0.000006878157],"category_scores_gemma":[0.00003692028,0.00007371887,0.00003178861,0.0002930096,0.00001457965,0.0002431758,0.00005175077,0.00007485871,0.00001416114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001373531,"about_ca_system_score_gemma":0.00001333925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001640364,"about_ca_topic_score_gemma":0.00001343488,"domain_scores_codex":[0.9992831,0.00004585979,0.0001697731,0.0002938008,0.000082534,0.000124924],"domain_scores_gemma":[0.9992455,0.0004322047,0.00006898335,0.0001814782,0.00003507625,0.00003673287],"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":[0.00001311505,0.00007229347,0.05387539,0.00002551578,0.000006003654,2.811124e-7,0.0006701509,0.0009684137,0.002932338,0.02230419,0.00008769071,0.9190446],"study_design_scores_gemma":[0.0008084932,0.0001035824,0.268517,0.00003587271,0.000002938356,0.000003130846,0.00006561168,0.7177649,0.0004787698,0.004853825,0.007199001,0.0001669173],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09865366,0.00006010546,0.8995928,0.000571654,0.00007355133,0.0004798797,0.00000448973,0.00005777293,0.0005061358],"genre_scores_gemma":[0.905338,0.00001091816,0.09413846,0.0002000557,0.00002672493,0.0001061324,0.00001306906,0.00000670081,0.0001599684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9188777,"threshold_uncertainty_score":0.3006169,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2276732348","doi":"10.1007/s00138-015-0730-x","title":"On a 3D analogue of the first Hu moment invariant and a family of shape ellipsoidness measures","year":2015,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Exeter; McGill University","keywords":"Ellipsoid; Compact space; Mathematics; Invariant (physics); Measure (data warehouse); Flattening; Moment (physics); Invariant measure; Planar; Mathematical analysis; Geometry; Computer science; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02568113583189967,"gpt":0.2691962163960713,"spread":0.2435150805641716,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002560204,0.00007049437,0.0001068736,0.00005829712,0.00008991853,0.00002724067,0.0002941778,0.00002841176,0.000001567848],"category_scores_gemma":[0.00002030411,0.00004273069,0.00002367208,0.0003378799,0.00008981178,0.00005307445,0.0001562445,0.00005768874,0.000001573589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009339751,"about_ca_system_score_gemma":0.00002982079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005162647,"about_ca_topic_score_gemma":0.000005928542,"domain_scores_codex":[0.9993582,0.00003008341,0.0001793988,0.0001735712,0.0001993637,0.00005941104],"domain_scores_gemma":[0.9993311,0.0000452972,0.0001093863,0.0003595224,0.00009990346,0.00005481827],"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":[0.00002636299,0.0005425361,0.00144972,0.00007115237,0.00002046569,3.018518e-7,0.0008313095,0.00002618773,0.01485451,0.525352,0.001526624,0.4552988],"study_design_scores_gemma":[0.002207766,0.001060268,0.1427911,0.0002951089,0.00006608524,0.00002434519,0.0002729698,0.4822143,0.06048481,0.07796596,0.2319398,0.0006775392],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01509471,0.0005036758,0.9784791,0.003939732,0.00002183626,0.0004905672,0.0000195895,0.00005566355,0.001395129],"genre_scores_gemma":[0.9965764,0.00009501195,0.002961505,0.0002525846,0.000006934751,0.00005371883,0.000002208004,0.00000325511,0.00004835054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9814817,"threshold_uncertainty_score":0.1742507,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1553140600","doi":"10.1007/s00138-007-0085-z","title":"Intelligent perception and control for space robotics","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Space Satellite Systems and Control","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Computer science; Artificial intelligence; Perception; Robotics; Robustness (evolution); Active vision; Computer vision; Robot; Human–computer interaction","retraction":null,"screen_n_in":null,"score":{"opus":0.005673510464214199,"gpt":0.2520451916215601,"spread":0.2463716811573459,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001703311,0.00008726504,0.0001038059,0.00005093841,0.00008501706,0.00003269704,0.00003049196,0.00004492349,0.000008225456],"category_scores_gemma":[0.000004053949,0.00007357408,0.00002475215,0.00005949974,0.00001883237,0.00002863111,0.000007128714,0.00005195727,0.000008468926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001347151,"about_ca_system_score_gemma":0.000001684335,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001371468,"about_ca_topic_score_gemma":0.00003348219,"domain_scores_codex":[0.9995838,0.000004210415,0.0001325607,0.0001158722,0.00004632901,0.0001172106],"domain_scores_gemma":[0.9997098,0.00006747116,0.00001706086,0.0001035393,0.00002420876,0.00007789134],"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":[0.00002926175,0.00004594457,0.001900965,0.00013534,0.00003829964,3.115477e-7,0.0002571875,0.003589277,0.04516654,0.03857701,0.0004724035,0.9097875],"study_design_scores_gemma":[0.001399029,0.0001107438,0.02027379,0.00002654041,0.00006026509,0.00001416273,0.0004620038,0.5376279,0.000487313,0.001481987,0.4377314,0.0003248121],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006068133,0.0013208,0.9906217,0.0004514368,0.00004205443,0.0006410999,0.00001693769,0.0001050263,0.0007328044],"genre_scores_gemma":[0.9965764,0.000274944,0.002668219,0.00006991139,0.0001127223,0.00007855038,0.00001160212,0.00001568768,0.0001919715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9905083,"threshold_uncertainty_score":0.3000264,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056695435","doi":"10.1007/s00138-011-0322-3","title":"Methods for geometrical video projector calibration","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Projector; Calibration; Camera resectioning; Computer vision; Computer science; Artificial intelligence; Camera auto-calibration; Projection (relational algebra); Planar; Usability; Computer graphics (images); Mathematics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.08107339554234062,"gpt":0.391150766992052,"spread":0.3100773714497114,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003347357,0.000076405,0.00008971518,0.0001274221,0.000110579,0.00007430527,0.0002660385,0.00004017918,0.00002593627],"category_scores_gemma":[0.00005798763,0.00005593761,0.0000337634,0.000348427,0.00002859316,0.0002194744,0.00009318731,0.00005760106,0.00000492871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008751192,"about_ca_system_score_gemma":0.0000110391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001479227,"about_ca_topic_score_gemma":0.000001277721,"domain_scores_codex":[0.9993959,0.00003441461,0.0001534827,0.0002414994,0.00006900384,0.0001056653],"domain_scores_gemma":[0.9994957,0.00009336406,0.00003903836,0.0002396569,0.00006352185,0.00006871509],"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":[0.000004891221,0.0001016127,0.0001488436,0.000008605768,0.000003483228,2.604123e-8,0.00007128515,5.394835e-8,0.005966314,0.3970982,0.0003983992,0.5961983],"study_design_scores_gemma":[0.0006520018,0.001376653,0.006481295,0.00002908878,0.00003160243,0.000006776206,0.00002387404,0.4918383,0.108937,0.1980767,0.1919914,0.0005552793],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001299876,0.00009519817,0.9937065,0.0004652407,0.00002414515,0.0005733631,0.000002731022,0.0002082825,0.004794541],"genre_scores_gemma":[0.2470932,0.00001777961,0.7520341,0.0001935468,0.00002658462,0.0005370974,0.000004747792,0.000004929135,0.00008796359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.595643,"threshold_uncertainty_score":0.228107,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1964539061","doi":"10.1007/s00138-007-0088-9","title":"Aggregation of classifiers based on image transformations in biometric face recognition","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Consejo Nacional de Ciencia y Tecnología; Yale University","keywords":"Pattern recognition (psychology); Artificial intelligence; Eigenface; Computer science; Facial recognition system; Isomap; Biometrics; Dimensionality reduction; Sobel operator; Machine learning; Feature vector; Linear discriminant analysis; Edge detection; Image (mathematics); Nonlinear dimensionality reduction; Image processing","retraction":null,"screen_n_in":null,"score":{"opus":0.01366579732617329,"gpt":0.283882184126869,"spread":0.2702163868006957,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004001228,0.00008921537,0.00009876821,0.0008872208,0.0001045826,0.00004042838,0.0001449916,0.00006048058,0.00001940051],"category_scores_gemma":[0.00002584323,0.00007766854,0.0000358964,0.001621451,0.00003639807,0.0003042798,0.00001876041,0.000102668,0.00003585238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002133499,"about_ca_system_score_gemma":0.00001505645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002158464,"about_ca_topic_score_gemma":0.00001617238,"domain_scores_codex":[0.9991591,0.00003126838,0.0002962655,0.0002096606,0.0001802044,0.000123532],"domain_scores_gemma":[0.9994016,0.0001664543,0.00009579176,0.0002060377,0.00006224211,0.00006785378],"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":[0.00001632086,0.0002547209,0.0002560185,0.00002822809,0.000001672782,4.354859e-7,0.0001328714,0.00007683723,0.01103149,0.00241005,0.0001493724,0.985642],"study_design_scores_gemma":[0.003044717,0.0004469824,0.05151498,0.0002953233,0.00001778314,0.000006894249,0.0003269344,0.8256988,0.09269918,0.01113297,0.0142734,0.0005420661],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02110543,0.0000431565,0.9724581,0.0009487584,0.00002637105,0.0003901345,0.00002905717,0.00005999593,0.004939023],"genre_scores_gemma":[0.9773017,0.0000609042,0.02221386,0.0002265034,0.0000100191,0.00005956413,0.0001014848,0.00000505941,0.00002097601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9850999,"threshold_uncertainty_score":0.3167231,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983752583","doi":"10.1007/s00138-011-0338-8","title":"Machine vision system for automated spectroscopy","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Emergent BioSolutions (Canada); Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Laser-induced breakdown spectroscopy; Artificial intelligence; Support vector machine; Computer science; Machine vision; Spectrum analyzer; Artificial neural network; Pattern recognition (psychology); Identification (biology); Matching (statistics); Element (criminal law); Computer vision; Machine learning; Laser; Optics; Mathematics; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.009621956232315465,"gpt":0.2600108868831991,"spread":0.2503889306508836,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001340208,0.0002090894,0.000207497,0.0001076329,0.0002437419,0.00004909442,0.0001504142,0.00009925182,0.00008937997],"category_scores_gemma":[0.000004515205,0.000174103,0.00006012707,0.0002151479,0.00002805625,0.0001007533,0.00003237102,0.0001409654,0.0001117982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004317886,"about_ca_system_score_gemma":0.000008111252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004139651,"about_ca_topic_score_gemma":0.00002869552,"domain_scores_codex":[0.9991164,0.00001606506,0.0002585909,0.0002692286,0.00009693867,0.0002427282],"domain_scores_gemma":[0.999422,0.00005364347,0.00004242511,0.0003144298,0.00002829615,0.0001392803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002510943,0.0007392233,0.001043323,0.001531256,0.0002188228,0.000009276511,0.0009243916,0.001675498,0.7870447,0.1341468,0.01615274,0.05626282],"study_design_scores_gemma":[0.0009356205,0.0002405463,0.002056803,0.00006312452,0.000058233,0.00002744139,0.00004874172,0.8073403,0.1288478,0.00085703,0.05912609,0.0003983206],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1812416,0.001840139,0.7079551,0.0004285804,0.0008226168,0.004431829,0.001216134,0.0117715,0.09029251],"genre_scores_gemma":[0.9855094,0.00009447523,0.01361153,0.0000394116,0.00008931131,0.000307249,0.0001504187,0.00004897627,0.0001492441],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8056648,"threshold_uncertainty_score":0.7099715,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2055480697","doi":"10.1007/s00138-011-0342-z","title":"Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Bishop's University","funders":"","keywords":"Clutter; Robustness (evolution); Computer vision; Computer science; Artificial intelligence; Particle filter; Tracking (education); Pedestrian; Frame (networking); Filter (signal processing); Radar; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.1004799064810504,"gpt":0.3476925107248356,"spread":0.2472126042437852,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000588735,0.0001240298,0.0001285221,0.00009023323,0.0003283317,0.0001333077,0.0002211956,0.000066195,0.00001025743],"category_scores_gemma":[0.00005893244,0.000105682,0.00002277968,0.0004757937,0.00005598586,0.0002140178,0.00006823312,0.0001275712,0.000004826284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001500222,"about_ca_system_score_gemma":0.00004348044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000133818,"about_ca_topic_score_gemma":0.00002108517,"domain_scores_codex":[0.9990593,0.0001107564,0.0001912789,0.0003362264,0.0001518013,0.0001506603],"domain_scores_gemma":[0.9992318,0.0001120569,0.0000963585,0.0003590651,0.0001108757,0.00008984777],"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":[0.0000239336,0.0003028569,0.04982949,0.00006850752,0.00002421446,0.000001805308,0.0008281011,0.0003475193,0.007914479,0.03355799,0.00001388689,0.9070872],"study_design_scores_gemma":[0.001604655,0.0002810925,0.4196191,0.0002389299,0.00006248031,0.00003229778,0.00007871162,0.5401995,0.008977347,0.02227269,0.005826849,0.0008063678],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04594595,0.0004166105,0.9524809,0.0003199682,0.0000424029,0.000271642,0.000002194822,0.0001044413,0.0004159118],"genre_scores_gemma":[0.7941658,0.0000136029,0.2054544,0.000280889,0.00003353279,0.0000369425,0.000002877874,0.000008022579,0.00000391664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9062809,"threshold_uncertainty_score":0.4309588,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1988025232","doi":"10.1007/s00138-012-0439-z","title":"Adaptive linear discriminant analysis for online feature extraction","year":2012,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Linear discriminant analysis; Principal component analysis; Computer science; Pattern recognition (psychology); Artificial intelligence; Optimal discriminant analysis; Adaptive filter; Computation; Feature (linguistics); Algorithm; Covariance matrix; Discriminant; Mathematics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02640750205380759,"gpt":0.3393430115869622,"spread":0.3129355095331546,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001184492,0.00009113885,0.0001131566,0.0001344264,0.0002294176,0.00004125889,0.0001297255,0.00005177063,0.00001206456],"category_scores_gemma":[0.000008798141,0.00006583553,0.00007822289,0.0004328718,0.00001628713,0.0003291433,0.00006518966,0.00008373293,0.00001549078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009066954,"about_ca_system_score_gemma":0.000005984804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001853745,"about_ca_topic_score_gemma":0.00001722704,"domain_scores_codex":[0.9994147,0.00001966021,0.000112398,0.0002153021,0.00009433764,0.0001436056],"domain_scores_gemma":[0.9994628,0.00007184388,0.00006700451,0.0002363594,0.00005399095,0.0001080335],"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":[0.00003199095,0.0009565392,0.002122879,0.00002559937,0.0001120639,2.218276e-7,0.0003459893,0.0002042628,0.01002816,0.03312476,0.004737302,0.9483102],"study_design_scores_gemma":[0.0004782425,0.0001148275,0.06427098,0.00001725612,0.0002419718,0.000007792052,0.0001522175,0.6022019,0.001906839,0.002040418,0.3282708,0.0002968293],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004628728,0.000329962,0.9923586,0.001993834,0.00004632999,0.00030682,0.00007896255,0.00007213674,0.0001846217],"genre_scores_gemma":[0.8116482,0.0001150309,0.1865127,0.0003452215,0.0001839282,0.0002389691,0.0003199643,0.000007403739,0.0006285037],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9480134,"threshold_uncertainty_score":0.2684695,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W784702625","doi":"10.1007/s00138-015-0697-7","title":"An adaptive ensemble-based system for face recognition in person re-identification","year":2015,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Facial recognition system; A priori and a posteriori; Ensemble learning; Computer vision; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.08291734158363036,"gpt":0.3516980138668422,"spread":0.2687806722832119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001021933,0.00009357145,0.0001187071,0.0001343974,0.0001250193,0.0001264688,0.0002134655,0.00005070886,6.214322e-7],"category_scores_gemma":[0.00002932296,0.00008541876,0.00002708996,0.000337188,0.00001843568,0.0002556768,0.00001687173,0.00006740592,0.00001438204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004740983,"about_ca_system_score_gemma":0.00003709832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007199452,"about_ca_topic_score_gemma":0.0000761032,"domain_scores_codex":[0.999055,0.0001414223,0.000186416,0.0003724184,0.0001247087,0.0001200801],"domain_scores_gemma":[0.9991869,0.0001298221,0.00009742822,0.0003616888,0.0001259902,0.0000981334],"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":[0.00005777313,0.00019547,0.000631852,0.00005090451,0.000004631412,9.799498e-7,0.000663125,0.001080745,0.003642394,0.01378524,0.0001515322,0.9797354],"study_design_scores_gemma":[0.001130366,0.0002525146,0.007494629,0.0000455672,0.000007736789,0.000005887502,0.0006767482,0.9797941,0.004304557,0.003833852,0.002238439,0.000215603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01003093,0.0001016615,0.9879008,0.0006977221,0.00005825629,0.0006220186,0.00002432098,0.0001475911,0.0004167202],"genre_scores_gemma":[0.9197878,0.00000285491,0.07960395,0.00007972905,0.00003677977,0.0003855113,0.00007635106,0.000007684742,0.00001931012],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9795197,"threshold_uncertainty_score":0.3483276,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2034891599","doi":"10.1007/s00138-013-0590-1","title":"Defect identification on specular machined surfaces","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria; McMaster University","funders":"","keywords":"Specular reflection; Reflection (computer programming); Optics; Specular highlight; Surface (topology); Machining; Materials science; Light-emitting diode; Computer science; Geometry; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.006455038286421097,"gpt":0.2284795732549099,"spread":0.2220245349684888,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000165926,0.0001439732,0.0001339244,0.0001258529,0.0001883676,0.0001444428,0.00007758173,0.00007478507,0.0001779308],"category_scores_gemma":[0.00001166157,0.0001164245,0.00006355246,0.000249131,0.00002010186,0.0001116419,0.00001764692,0.0001484466,0.0008640895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002476337,"about_ca_system_score_gemma":0.000003027513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009707662,"about_ca_topic_score_gemma":0.000006703801,"domain_scores_codex":[0.9992168,0.00003114469,0.0002554205,0.0002193262,0.0001473055,0.0001300224],"domain_scores_gemma":[0.9994973,0.00005230978,0.00004189513,0.0002842275,0.00003615018,0.00008812157],"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":[0.00001902838,0.000178162,0.001289009,0.00009961715,0.00009172611,0.000001115552,0.0001259472,0.01057741,0.2607175,0.01057146,0.03971317,0.6766159],"study_design_scores_gemma":[0.001240974,0.000223377,0.05030837,0.00006545691,0.00004561829,0.00002353186,0.0001050661,0.3306912,0.01614387,0.002834471,0.5975606,0.0007575115],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9040504,0.0009070841,0.07475587,0.0007727498,0.0004442463,0.002314641,0.00005005429,0.001107227,0.01559772],"genre_scores_gemma":[0.9989286,0.0000657293,0.0001118909,0.0001061677,0.00008896705,0.0002768104,0.00003267564,0.00002461144,0.0003645577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6758584,"threshold_uncertainty_score":0.9999139,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4242606816","doi":"10.1007/s001380050122","title":"Tracking a person with pre-recorded image database and a pan, tilt, and zoom camera","year":2000,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); University of Toronto; York University","funders":"","keywords":"Computer vision; Artificial intelligence; Zoom; Tracking (education); Computer science; Tilt (camera); Video tracking; Segmentation; Image (mathematics); Computer graphics (images); Object (grammar); Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01240692098803086,"gpt":0.2956511766217705,"spread":0.2832442556337397,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002626914,0.0001317138,0.0001440516,0.00005917058,0.0003205515,0.0002476169,0.0001459715,0.00002793266,0.00002559316],"category_scores_gemma":[0.000008555527,0.00009832985,0.00001468968,0.000232146,0.00009145482,0.0003277486,0.00005819668,0.000121225,0.00000576608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004468453,"about_ca_system_score_gemma":0.00001290606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001959936,"about_ca_topic_score_gemma":0.00008417995,"domain_scores_codex":[0.9991359,0.0000589088,0.000116242,0.0004345472,0.0001059581,0.0001484046],"domain_scores_gemma":[0.999355,0.0001113452,0.00004142509,0.0003602033,0.00002722865,0.0001048184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001396455,0.00005036247,0.003687769,0.00002122466,0.000007025139,0.000002860574,0.0003491973,0.000002748191,0.001242398,0.001129921,0.00009550612,0.993397],"study_design_scores_gemma":[0.003100724,0.0005782209,0.6027055,0.0001811936,0.00006408728,0.0004551141,0.0001627561,0.2220451,0.0007397647,0.002559919,0.1663715,0.00103617],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2703428,0.001345973,0.7233984,0.002252418,0.00001804714,0.0005322177,0.00004299991,0.0001903843,0.001876688],"genre_scores_gemma":[0.8764789,0.0006419016,0.1220335,0.0003660908,0.00004848571,0.00008395693,0.00001994848,0.00001326814,0.0003139817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9923608,"threshold_uncertainty_score":0.4009775,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406950133","doi":"10.1007/s00138-025-01663-2","title":"CorFormer: a hybrid transformer-CNN architecture for corrosion segmentation on metallic surfaces","year":2025,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Corrosion; Architecture; Segmentation; Transformer; Computer science; Materials science; Computer architecture; Artificial intelligence; Engineering; Metallurgy; Electrical engineering; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.007397664880133975,"gpt":0.2786244452948768,"spread":0.2712267804147429,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009746491,0.0001499757,0.000136709,0.000140101,0.0001770227,0.00003742502,0.00009289375,0.00003713827,0.00001500564],"category_scores_gemma":[0.00001081717,0.0001272608,0.00004532934,0.0001920916,0.00003684051,0.00005068995,0.00001057342,0.000118926,0.000006378073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003251732,"about_ca_system_score_gemma":0.000008087147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007909032,"about_ca_topic_score_gemma":0.000007150672,"domain_scores_codex":[0.9994202,0.00001064875,0.0001724678,0.0001966528,0.00007480202,0.0001252232],"domain_scores_gemma":[0.9996037,0.0001398058,0.00002589053,0.0001620088,0.00002811105,0.00004049945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006418957,0.0001301886,0.0005576356,0.0003994562,0.00003968028,2.749507e-7,0.0001004354,0.001325456,0.5440544,0.07544426,0.002769008,0.375115],"study_design_scores_gemma":[0.002286355,0.0006448469,0.006148227,0.000350569,0.0002083841,0.00001915806,0.0000677588,0.09647062,0.3054073,0.4930864,0.09439085,0.0009194424],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1040369,0.0002240572,0.8877905,0.0003613567,0.00004456715,0.001314772,0.0001081269,0.0006669516,0.005452767],"genre_scores_gemma":[0.8738031,0.0001302273,0.124846,0.0001411583,0.00001927413,0.0007828075,0.0001596847,0.00002657997,0.00009111409],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7697662,"threshold_uncertainty_score":0.5189545,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2093817806","doi":"10.1007/s00138-003-0129-y","title":"Low-cost interactive active range finder","year":2003,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Laser pointer; Computer vision; Computer science; Artificial intelligence; Laser; Computer graphics (images); Interactivity; Range (aeronautics); Triangulation; Rendering (computer graphics); Object (grammar); Optics; Engineering; Physics; Mathematics; Multimedia","retraction":null,"screen_n_in":null,"score":{"opus":0.007016394987284401,"gpt":0.2933182687609919,"spread":0.2863018737737075,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000274441,0.00009446629,0.00009297015,0.00003405673,0.0001450923,0.00002481061,0.00005855001,0.00002567299,0.0001685736],"category_scores_gemma":[0.00001305157,0.00007540985,0.00002892857,0.0001318325,0.00007035609,0.00007360961,0.00004427353,0.0001533778,0.00005899795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000112174,"about_ca_system_score_gemma":0.000005934126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000121713,"about_ca_topic_score_gemma":0.000001901096,"domain_scores_codex":[0.9995452,0.00001461172,0.00008800372,0.0001901497,0.00004856207,0.0001134907],"domain_scores_gemma":[0.9996139,0.00008998951,0.00004111067,0.0001810476,0.00002951718,0.00004439904],"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":[0.000009899796,0.000192331,0.001843529,0.000002308286,0.00001773797,2.178836e-7,0.00005410043,0.00002940248,0.001144231,0.3450872,0.0002297593,0.6513892],"study_design_scores_gemma":[0.00200974,0.00009329453,0.01694383,0.00005224276,0.00005788571,0.000005312921,0.001376882,0.004241598,0.0634698,0.3924908,0.5184773,0.0007813444],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05198119,0.00004844785,0.8915574,0.0009572176,0.00003778589,0.0007953777,0.00008223873,0.0001925667,0.05434779],"genre_scores_gemma":[0.9946505,0.000004486346,0.004868807,0.00006776377,0.00002273457,0.0001015722,0.00001928744,0.000009219947,0.0002556303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9426693,"threshold_uncertainty_score":0.3075124,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070415340","doi":"10.1007/s00138-010-0277-9","title":"Integrated imaging and vision techniques for industrial inspection: a special issue on machine vision and applications","year":2010,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Institute for Microstructural Sciences","funders":"","keywords":"Machine vision; Computer science; Context (archaeology); Aerospace; Automotive industry; Process (computing); Systems engineering; Artificial intelligence; Data science; Manufacturing engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.00927958701302217,"gpt":0.2835239932706333,"spread":0.2742444062576111,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004157433,0.0003277642,0.0003227136,0.0003769445,0.0006962183,0.0002743774,0.0001079667,0.0002906118,0.0000674543],"category_scores_gemma":[0.00003979256,0.0002703841,0.00006373899,0.0004574868,0.0001186467,0.0002101751,0.00007225345,0.0006449209,0.00001784194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004117333,"about_ca_system_score_gemma":0.00001600241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001058124,"about_ca_topic_score_gemma":0.00009589217,"domain_scores_codex":[0.998562,0.00003681398,0.0004649169,0.0005238244,0.0001853466,0.0002270756],"domain_scores_gemma":[0.9990836,0.0001648123,0.0001053465,0.0003529757,0.00009987299,0.0001933407],"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":[0.00007836027,0.00008501895,0.000226563,0.00003993313,0.0000133715,3.984772e-7,0.00004936958,0.00001528747,0.02254238,0.003066973,0.008266539,0.9656158],"study_design_scores_gemma":[0.001153979,0.0002698659,0.0006311925,0.00006562111,0.0000325679,0.00004406652,0.00007310334,0.03062203,0.003917867,0.0007595032,0.9621036,0.0003266171],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1839893,0.002669841,0.7114704,0.006690206,0.004877192,0.02802258,0.002093433,0.008309697,0.05187734],"genre_scores_gemma":[0.9871603,0.0003027914,0.002328737,0.0001435974,0.00791023,0.00153121,0.0002146158,0.00009896053,0.0003095576],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9652892,"threshold_uncertainty_score":0.9999748,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1987121049","doi":"10.1007/s00138-011-0361-9","title":"Human shape correspondence with automatically predicted landmarks","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Landmark; Artificial intelligence; Computer science; Point (geometry); Computer vision; Set (abstract data type); Motion (physics); Pattern recognition (psychology); Mathematics; Geometry","retraction":null,"screen_n_in":null,"score":{"opus":0.009370503035125327,"gpt":0.2308908065978667,"spread":0.2215203035627414,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006050568,0.00009481411,0.0001023645,0.00006957096,0.0001153099,0.00002430064,0.00009171059,0.0000359839,0.0003181486],"category_scores_gemma":[0.000002562303,0.00006874491,0.00002012807,0.0001754614,0.00003374638,0.00004109288,0.00001930093,0.00009513844,0.000025898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005481433,"about_ca_system_score_gemma":0.000003987329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001796114,"about_ca_topic_score_gemma":0.00001792187,"domain_scores_codex":[0.9995419,0.00000843517,0.0001292525,0.0001353775,0.00008565378,0.00009932703],"domain_scores_gemma":[0.9996819,0.00001352059,0.00001651619,0.0001821858,0.00002432706,0.00008153707],"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":[0.0001396219,0.00156279,0.05397395,0.0007375815,0.0008016623,0.00003714558,0.004266966,0.04158168,0.0296466,0.05023851,0.01021693,0.8067966],"study_design_scores_gemma":[0.0001813645,0.00004338435,0.01372926,0.00002218215,0.00003687411,0.00000512571,0.0000158814,0.9840705,0.00007338727,0.0003885375,0.001313719,0.0001198078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3553689,0.0002285798,0.6118729,0.00009199136,0.00001588615,0.0003062151,0.00004600077,0.001083464,0.03098611],"genre_scores_gemma":[0.9965366,0.00001908078,0.003041231,0.00003885083,0.00001874818,0.00006263454,0.00002878838,0.00001520978,0.0002388836],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9424888,"threshold_uncertainty_score":0.3483503,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014921455","doi":"10.1007/s00138-006-0016-4","title":"Deterioration of visual information in face classification using Eigenfaces and Fisherfaces","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Eigenface; Face (sociological concept); Facial recognition system; Artificial intelligence; Computer science; Pattern recognition (psychology); Sociology","retraction":null,"screen_n_in":null,"score":{"opus":0.01164272268448394,"gpt":0.2858001536139361,"spread":0.2741574309294522,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001212645,0.0000714385,0.00008409588,0.0001490581,0.00009913177,0.0001053697,0.00008083041,0.00004625469,0.000003820697],"category_scores_gemma":[0.000005391857,0.00006220517,0.00001071302,0.0002580833,0.00003192354,0.0008892096,0.00005702894,0.00004668378,0.000004046414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000101333,"about_ca_system_score_gemma":0.00001161961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001721741,"about_ca_topic_score_gemma":0.00003360898,"domain_scores_codex":[0.9994049,0.00002706153,0.000252479,0.0001411453,0.0001030538,0.00007134014],"domain_scores_gemma":[0.9996671,0.00002896761,0.0001258246,0.00011117,0.00004215665,0.00002479308],"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":[0.00001715845,0.0002318098,0.01849906,0.0001067696,0.000004121538,1.779779e-7,0.0006526999,0.001441373,0.294995,0.01343578,0.0001918654,0.6704242],"study_design_scores_gemma":[0.0003808525,0.00005194056,0.09449868,0.00003568946,0.000004437657,0.00000374722,0.00019647,0.8904855,0.007969631,0.002873936,0.003365841,0.0001333233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6098567,0.0001179977,0.3892461,0.0002736319,0.00001167879,0.0002119859,0.000005605242,0.0000277284,0.0002486304],"genre_scores_gemma":[0.9913915,0.00006081244,0.008421548,0.00003547956,0.000008710803,0.00003160917,0.00003684243,0.000002279469,0.00001120428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8890441,"threshold_uncertainty_score":0.2536653,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2144782373","doi":"10.1007/s00138-002-0100-3","title":"Evaluation of statistical and multiple-hypothesis tracking for video traffic surveillance","year":2003,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia; University of Calgary","funders":"","keywords":"Clutter; Computer science; Intersection (aeronautics); Artificial intelligence; Computer vision; Tracking (education); Boundary (topology); Video tracking; Ground truth; Data association; Pedestrian; Association (psychology); Object (grammar); Statistical model; Geography; Mathematics; Cartography; Radar; Probabilistic logic","retraction":null,"screen_n_in":null,"score":{"opus":0.03253956434981785,"gpt":0.3089952264560373,"spread":0.2764556621062195,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001228637,0.00008961611,0.0001351989,0.00005642226,0.0001800832,0.00006657746,0.0001251972,0.00003505579,0.00001277264],"category_scores_gemma":[0.0004022389,0.00007600295,0.00002196885,0.0001637037,0.00005377058,0.00009037327,0.00002965912,0.00005515843,0.00000154743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008154134,"about_ca_system_score_gemma":0.00002320281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005821994,"about_ca_topic_score_gemma":0.00001610218,"domain_scores_codex":[0.9989938,0.0001336586,0.0002199906,0.0003090081,0.0002281206,0.0001154923],"domain_scores_gemma":[0.9983547,0.001053585,0.00008279359,0.0002754353,0.0001660645,0.00006739925],"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":[0.000005335476,0.00009231048,0.0009246322,0.00002233335,0.000008494672,9.112896e-8,0.00006718009,0.001794694,0.0006195907,0.05504944,0.0003759565,0.9410399],"study_design_scores_gemma":[0.0009026157,0.00005683546,0.01401977,0.00001408786,0.00002605986,0.000009749395,0.00001999648,0.9561911,0.0002787418,0.00858507,0.01975003,0.0001460024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02727356,0.0006823181,0.9709837,0.0001699561,0.00004074336,0.0005103836,0.00009694353,0.00005370743,0.0001886289],"genre_scores_gemma":[0.9229877,0.00005945968,0.07679056,0.00005152146,0.00001694906,0.00006453251,0.0000165841,0.000006560898,0.000006154281],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9543964,"threshold_uncertainty_score":0.309931,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385374898","doi":"10.1007/s00138-023-01426-x","title":"Probabilistic multi-modal depth estimation based on camera–LiDAR sensor fusion","year":2023,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":9,"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":"Lidar; Computer science; Sensor fusion; Benchmark (surveying); Artificial intelligence; Computer vision; Modal; Monocular; Sensitivity (control systems); Process (computing); Remote sensing; Geography; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01695759763039903,"gpt":0.3238340311519646,"spread":0.3068764335215656,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001893335,0.0001494757,0.0001244967,0.0002195013,0.0004091378,0.0001275873,0.0002535589,0.00003640688,0.00001323839],"category_scores_gemma":[0.00006627864,0.00012097,0.00003998296,0.0007635062,0.00004511319,0.0001796244,0.0001414417,0.0001335997,0.0002599086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002434579,"about_ca_system_score_gemma":0.00002398518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001259048,"about_ca_topic_score_gemma":0.000004909174,"domain_scores_codex":[0.9988669,0.00004398199,0.0002042238,0.0004649943,0.0002272037,0.0001927017],"domain_scores_gemma":[0.9990931,0.0001552826,0.00007191742,0.0005003442,0.00005407434,0.0001252972],"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":[0.000007083569,0.0001749951,0.0002604774,0.00002580202,0.000001893451,0.000002574764,0.00008041952,0.01697317,0.002731694,0.01126505,0.0003629191,0.9681139],"study_design_scores_gemma":[0.0004450229,0.00005401807,0.009580981,0.00002640583,0.000003169264,0.000003058883,0.00001126644,0.9770716,0.0002125426,0.001523025,0.01092733,0.0001415974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002469369,0.00002277892,0.9926509,0.003110082,0.00005086681,0.0005041547,0.00001167046,0.0005425107,0.0006376665],"genre_scores_gemma":[0.7666627,0.00002578467,0.2314557,0.001120211,0.00003527695,0.0001924733,0.00008236719,0.00001981002,0.0004056348],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9679723,"threshold_uncertainty_score":0.4933015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}