{"meta":{"query_hash":"d93c1929a359","filters":{"topic":"Currency Recognition and Detection"},"cohort_total":77,"direct_labels_cover":0,"predictions_cover":77,"exported":77,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/d93c1929a359","api":"https://metacan.xera.ac/api/v1/cohort?topic=Currency+Recognition+and+Detection"},"results":[{"id":"W1553839376","doi":"10.1017/cbo9780511635465","title":"Object Categorization","year":2009,"lang":"en","type":"book","venue":"Cambridge University Press eBooks","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Categorization; Multidisciplinary approach; Context (archaeology); Perspective (graphical); Object (grammar); Computer science; Discipline; Data science; Cognitive science; Human–computer interaction; Artificial intelligence; Psychology; Sociology; Geography; Social science","score_opus":0.020118788363432696,"score_gpt":0.199785116667801,"score_spread":0.1796663283043683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1553839376","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00000467355,0.00005068462,0.14804932,0.000008946507,0.0004523106,0.00021968398,0.000018023893,0.0004234399,0.8507729],"genre_scores_gemma":[0.00023752748,0.00005178662,0.0004049943,0.00009505186,0.00016368265,5.409059e-7,0.0000796995,0.000012404937,0.9989543],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987931,0.000068395704,0.00014409427,0.0005139901,0.00026393568,0.00021647572],"domain_scores_gemma":[0.998956,0.000034193847,0.0001813801,0.0005163102,0.00018764721,0.0001244351],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007522507,0.00024522337,0.00022794143,0.00027239625,0.00018154077,0.00012790044,0.00070056267,0.00028307535,0.0000016147645],"category_scores_gemma":[0.000008959415,0.0003049533,0.00015760791,0.00003965875,0.00004796993,0.00026283017,0.00020232299,0.00034093234,0.00005002173],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013197314,0.000016668146,2.746126e-7,0.000037031114,0.000028953762,0.000089511734,0.000045885874,0.000003740277,0.000022235285,0.6413056,0.3182409,0.040195946],"study_design_scores_gemma":[0.00035272792,0.00007332501,0.000021147578,0.000059303664,0.00004467099,0.000027817528,0.0000033382378,0.0012290025,0.00048943935,0.000092295704,0.99723005,0.00037685595],"about_ca_topic_score_codex":0.000028996068,"about_ca_topic_score_gemma":0.000001315648,"teacher_disagreement_score":0.6789892,"about_ca_system_score_codex":0.00031183756,"about_ca_system_score_gemma":0.00032339655,"threshold_uncertainty_score":0.9999403},"labels":[],"label_agreement":null},{"id":"W1789445635","doi":"10.1109/ictta.2004.1307859","title":"Reinforcement learning for parameter control of text detection in images from video sequences","year":2004,"lang":"en","type":"preprint","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Computer science; Artificial intelligence; Task (project management); Focus (optics); Machine learning; Image (mathematics); Search engine indexing; State (computer science); Contextual image classification; Pattern recognition (psychology); State space; Fuzzy logic; Computer vision; Mathematics; Algorithm","score_opus":0.026849094542039806,"score_gpt":0.2720564803280526,"score_spread":0.24520738578601275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1789445635","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016940925,0.00011579765,0.980563,0.00011223127,0.0008146225,0.0006478168,0.000006576005,0.00012186046,0.00067716825],"genre_scores_gemma":[0.98954666,0.000046590052,0.009923676,0.00007256108,0.000059174417,0.00022956831,0.000017661965,0.0000069033867,0.00009721587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853903,0.00007602363,0.0005016545,0.000486415,0.00021398657,0.00018286983],"domain_scores_gemma":[0.99891365,0.00029544136,0.00032854767,0.00027187195,0.0001490349,0.00004143135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029801825,0.00018255487,0.0003054182,0.0002603869,0.000050498486,0.00012564159,0.0003096473,0.00018015844,0.00005756839],"category_scores_gemma":[0.00019721991,0.00017069619,0.00016879699,0.00014626363,0.000034728513,0.00024619742,0.00015676749,0.00035198923,0.000013329028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000154184,0.00009952963,0.00044614944,0.00024774743,0.000102742415,0.0000029831322,0.00079084764,0.3654592,0.0127956215,0.00055919663,0.000026487804,0.6193153],"study_design_scores_gemma":[0.0018786505,0.0003308846,0.0012776935,0.0003220889,0.000028129829,0.0000028788224,0.000064663174,0.7828604,0.16177331,0.05081527,0.00026011618,0.00038589127],"about_ca_topic_score_codex":0.0017895057,"about_ca_topic_score_gemma":0.0002585279,"teacher_disagreement_score":0.9726057,"about_ca_system_score_codex":0.00013461709,"about_ca_system_score_gemma":0.000113365095,"threshold_uncertainty_score":0.69607884},"labels":[],"label_agreement":null},{"id":"W1899045043","doi":"10.13182/nt05-a3616","title":"Detection and Localization of Money Bills Concealed Behind Wooden Walls Using Compton Scattering","year":2005,"lang":"en","type":"article","venue":"Nuclear Technology","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Compton scattering; Nuclear physics; Scattering; Nuclear engineering; Physics; Optics; Computer science; Engineering","score_opus":0.01738724382567193,"score_gpt":0.2425457895348522,"score_spread":0.22515854570918026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899045043","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.759115,0.000067114954,0.2398831,0.00026474564,0.00012642743,0.00010186035,9.258092e-7,0.0002904251,0.00015040208],"genre_scores_gemma":[0.991706,0.000027321697,0.008147819,0.00007694694,0.000024515813,0.0000018921601,4.967516e-7,0.000008766562,0.0000062276795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993373,0.000021730693,0.00019555117,0.0002253066,0.00009009591,0.00013000239],"domain_scores_gemma":[0.9995802,0.000012102923,0.00011339592,0.00019557892,0.00007008753,0.000028604321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007946185,0.00008095366,0.00012857211,0.00032971284,0.00010276371,0.000030970685,0.00013231098,0.00014977832,0.000021323262],"category_scores_gemma":[0.000021206959,0.00008649767,0.000022037497,0.00032190426,0.000107443026,0.000264745,0.00011812253,0.00011294055,0.000021015461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012076376,0.000056901106,0.0007815259,0.000024465491,0.0000146512,0.0000016482535,0.00041467027,0.00053889834,0.35501403,0.0011767251,0.000021406095,0.64194304],"study_design_scores_gemma":[0.0005753259,0.00016897253,0.000772196,0.00005304995,0.0000123876125,0.00017405617,0.00008017179,0.8572601,0.13505726,0.0016653205,0.0039985306,0.00018262223],"about_ca_topic_score_codex":0.000023664948,"about_ca_topic_score_gemma":0.000025176489,"teacher_disagreement_score":0.8567212,"about_ca_system_score_codex":0.000043610235,"about_ca_system_score_gemma":0.000008090969,"threshold_uncertainty_score":0.35272723},"labels":[],"label_agreement":null},{"id":"W1899315810","doi":"10.1007/s10032-015-0246-y","title":"Machine-assisted authentication of paper currency: an experiment on Indian banknotes","year":2015,"lang":"en","type":"article","venue":"International Journal on Document Analysis and Recognition (IJDAR)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Currency; Authentication (law); Computer science; Banknote; Computer security; Artificial intelligence; Economics; Monetary economics","score_opus":0.0396712521175821,"score_gpt":0.3219999670659646,"score_spread":0.2823287149483825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1899315810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92004716,0.00037287225,0.06790106,0.003944889,0.003460987,0.0002834461,0.000066839726,0.000122021964,0.0038007202],"genre_scores_gemma":[0.9968782,0.0002501681,0.0020505528,0.00036446477,0.00019743833,0.000014607933,0.00013904889,0.0000073407127,0.00009818237],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99774593,0.00020711063,0.00059092743,0.00034607228,0.0009580979,0.00015185188],"domain_scores_gemma":[0.9982672,0.00006927031,0.0004810671,0.0002092862,0.0006996115,0.0002735941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006318645,0.00018132533,0.00023813822,0.0011804713,0.00011733185,0.0004429769,0.00038027417,0.00007078551,0.0004889267],"category_scores_gemma":[0.00008618997,0.00014841546,0.00022237754,0.0005166551,0.000040435614,0.00093266513,0.000057108122,0.00021724316,0.0000691577],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024990793,0.0009845007,0.001903921,0.000006274236,0.0012237619,0.000029922376,0.0021656286,0.00014362988,0.0011712979,0.0036708226,0.00027280377,0.98817754],"study_design_scores_gemma":[0.029101312,0.013594714,0.21108271,0.001900192,0.0038451212,0.0016463365,0.0043900097,0.14094408,0.17323633,0.3741472,0.04063135,0.005480635],"about_ca_topic_score_codex":0.000052072373,"about_ca_topic_score_gemma":0.00002670753,"teacher_disagreement_score":0.9826969,"about_ca_system_score_codex":0.00013703541,"about_ca_system_score_gemma":0.00006560797,"threshold_uncertainty_score":0.60522074},"labels":[],"label_agreement":null},{"id":"W1965238829","doi":"10.5539/cis.v2n4p129","title":"A NN Image Classification Method Driven by the Mixed Fitness Function","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Harbin Institute of Technology","keywords":"Fitness function; Computer science; Genetic algorithm; Transformation (genetics); Pattern recognition (psychology); Image (mathematics); Artificial intelligence; Value (mathematics); Function (biology); Representation (politics); Machine learning","score_opus":0.023098557327096456,"score_gpt":0.281630446941861,"score_spread":0.25853188961476453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965238829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050533717,0.00001620434,0.98898137,0.0016466319,0.0006926333,0.00015251692,0.000001940269,0.00013698897,0.0033183515],"genre_scores_gemma":[0.9425724,0.00003278977,0.053943284,0.0033179536,0.00007727357,0.000014468263,0.000013920998,0.0000016052892,0.000026313122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896747,0.00004878255,0.000248352,0.0002025133,0.00036366275,0.00016923853],"domain_scores_gemma":[0.9991668,0.00004990876,0.00013502047,0.0002793046,0.0002879145,0.000081048034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007436233,0.00009101827,0.00007365313,0.0001895298,0.00054143777,0.00097590534,0.000487731,0.00003177933,0.000006116941],"category_scores_gemma":[0.000032875203,0.00006530626,0.000028292832,0.0010196378,0.000121723664,0.009987981,0.00009145968,0.000102367565,0.000059612838],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032439596,0.000011700717,0.000014493402,0.0000028536758,0.0000010045195,3.7991352e-8,0.0005398715,0.000028944414,0.0014945257,0.017362202,0.0031680283,0.9773731],"study_design_scores_gemma":[0.00025200297,0.00012668205,0.04504961,0.000007979101,0.0000030976926,0.000033635206,0.000055177137,0.9324529,0.0016976891,0.0020533649,0.018140709,0.00012715596],"about_ca_topic_score_codex":0.0000026116484,"about_ca_topic_score_gemma":2.9961612e-7,"teacher_disagreement_score":0.9772459,"about_ca_system_score_codex":0.00003143059,"about_ca_system_score_gemma":0.000054289885,"threshold_uncertainty_score":0.94106776},"labels":[],"label_agreement":null},{"id":"W1996374080","doi":"10.1117/12.648407","title":"Visual and optical evaluation of bank notes in circulation","year":2006,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Brightness; Deflection (physics); Geology; Materials science; Computer science; Artificial intelligence; Mathematics; Optics; Physics","score_opus":0.019942462471743132,"score_gpt":0.2621869988627784,"score_spread":0.24224453639103527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996374080","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9959534,0.0000835956,0.0016736061,0.0007412417,0.0001666378,0.00039444808,0.00000607993,0.00004314538,0.0009378525],"genre_scores_gemma":[0.9467579,0.000018409562,0.05299025,0.00001704511,0.0001322792,0.00006098341,0.0000042977385,0.000012838717,0.000005987945],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980808,3.446958e-8,0.0005955853,0.00031109038,0.0008088177,0.00020369988],"domain_scores_gemma":[0.9978097,0.00015206939,0.0002768554,0.00003786172,0.0016736886,0.00004984162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010830056,0.00016894485,0.0002471018,0.00016392094,0.00004119344,0.00008670178,0.00037081112,0.00013159921,0.0000054622005],"category_scores_gemma":[0.0005561111,0.00015398118,0.00022183594,0.00041799946,0.00011955225,0.00069603854,0.00010601357,0.00016221164,5.984002e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004596005,0.00019084426,0.002480159,0.00022945934,0.00006532609,3.0976096e-8,0.00013931091,0.0005854802,0.43836373,0.549466,0.00009420116,0.008339485],"study_design_scores_gemma":[0.0016209764,0.00021635562,0.045399927,0.00023347429,0.000087651744,0.000015490406,0.0001736712,0.77595884,0.15829985,0.017665273,0.000067424575,0.0002610677],"about_ca_topic_score_codex":0.00002019072,"about_ca_topic_score_gemma":9.383772e-7,"teacher_disagreement_score":0.77537334,"about_ca_system_score_codex":0.000116302544,"about_ca_system_score_gemma":0.000037976246,"threshold_uncertainty_score":0.627917},"labels":[],"label_agreement":null},{"id":"W2110019748","doi":"10.1109/iwfhr.2002.1030926","title":"Recognition of courtesy amounts on bank checks based on a segmentation approach","year":2003,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Segmentation; Artificial intelligence; Pattern recognition (psychology); Computer science; Classifier (UML); Cursive; Image segmentation; Courtesy; Scale-space segmentation; Computer vision","score_opus":0.041842273272140795,"score_gpt":0.2584469668363723,"score_spread":0.2166046935642315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110019748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03401186,0.0000044275203,0.78239447,0.000054529657,0.00051994453,0.00030436614,0.0000064365718,0.000145297,0.18255864],"genre_scores_gemma":[0.97189367,0.0000033230092,0.027151115,0.00061817525,0.000019004954,0.000041570067,0.00002954911,0.000006132357,0.00023743717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989922,0.00010746207,0.00019568329,0.00028013554,0.0003000751,0.00012446327],"domain_scores_gemma":[0.9994595,0.000068302696,0.00010000312,0.00021948101,0.00010172181,0.000050979786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002540137,0.00010438636,0.00009939328,0.00019411158,0.00006074423,0.000044951343,0.00010622163,0.00005596485,0.00022589168],"category_scores_gemma":[0.000060488877,0.00009471834,0.000059208785,0.0003669377,0.000016654807,0.00019748264,0.00000677132,0.00009071825,0.00015918251],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021494589,0.0037365234,0.0010264042,0.00016393258,0.000043445107,0.0000038004848,0.00064564665,0.0017833324,0.011359844,0.021412093,0.0055489657,0.9540611],"study_design_scores_gemma":[0.0039358744,0.0018104658,0.0044554737,0.00015811124,0.000022852893,0.00001779794,0.00018213002,0.5117143,0.46799472,0.0077065676,0.0013690946,0.00063262734],"about_ca_topic_score_codex":0.000007790106,"about_ca_topic_score_gemma":0.0000019320091,"teacher_disagreement_score":0.95342845,"about_ca_system_score_codex":0.000045522185,"about_ca_system_score_gemma":0.000037546542,"threshold_uncertainty_score":0.38625017},"labels":[],"label_agreement":null},{"id":"W2133528180","doi":"10.1117/12.524865","title":"Perception and detection of counterfeit currency in Canada: note quality, training, and security features","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Counterfeit; Computer science; Cash; Currency; Quality (philosophy); Feature (linguistics); Computer security; Perception; Artificial intelligence; Test (biology); Business; Finance; Psychology; Economics","score_opus":0.015576020266045676,"score_gpt":0.247455271686612,"score_spread":0.2318792514205663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133528180","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9978339,0.0001083679,0.00045888723,0.0007662083,0.00022806227,0.0002479832,0.000021298094,0.000029825796,0.00030545998],"genre_scores_gemma":[0.9876785,0.00013720666,0.012023078,0.000038809547,0.0000741571,0.000030560415,0.0000016598058,0.00001026394,0.000005751077],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99856365,3.1267824e-8,0.00048543667,0.00030051704,0.00044812637,0.00020223578],"domain_scores_gemma":[0.9989051,0.00006781434,0.0002687315,0.000035543253,0.0006472492,0.000075511074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000460559,0.00017314218,0.00027259873,0.00010587561,0.00005094988,0.000066554254,0.0003431309,0.00010223379,0.0000016769707],"category_scores_gemma":[0.00024948438,0.00015791938,0.00013584821,0.00026984405,0.000109507106,0.0005777886,0.00010943431,0.00024928883,1.0699495e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085653824,0.00014261589,0.001473387,0.0009388271,0.000115289266,1.2935588e-7,0.0036195964,0.00008967971,0.7097969,0.26551118,0.0001111297,0.018115563],"study_design_scores_gemma":[0.010727465,0.0021669108,0.24105346,0.0027010026,0.00026654286,0.0003249724,0.018128393,0.17659917,0.48322192,0.061039425,0.0015606672,0.0022100655],"about_ca_topic_score_codex":0.008535212,"about_ca_topic_score_gemma":0.0012564849,"teacher_disagreement_score":0.23958007,"about_ca_system_score_codex":0.0002686283,"about_ca_system_score_gemma":0.00010594989,"threshold_uncertainty_score":0.998067},"labels":[],"label_agreement":null},{"id":"W2301179781","doi":"","title":"The Comparative Comparison of AHP, Raster calculators and weighed Overly Analytical Models, for Recognition and Preference of Cities Central Tissues Development (The Case Study: Mashhad Celebration Quarter's)","year":2012,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Preference; Raster graphics; Computer science; Arithmetic; Artificial intelligence; Mathematics; Statistics; Geography; Archaeology","score_opus":0.1794481589485428,"score_gpt":0.33258929207584903,"score_spread":0.15314113312730623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2301179781","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8204992,0.00014174983,0.17860763,0.000036703255,0.00010998653,0.00048184412,0.000004734809,0.000015941758,0.000102245336],"genre_scores_gemma":[0.99746317,0.000007660124,0.0024302516,0.000008956505,0.000019587364,0.000043188724,0.000005107107,0.0000025806921,0.00001950645],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990049,0.00012162089,0.00037378116,0.00016547002,0.0001615589,0.0001726783],"domain_scores_gemma":[0.9992381,0.00026120606,0.00015276531,0.00012277864,0.00016129736,0.00006389792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029812736,0.00010895101,0.00019311394,0.000053138352,0.00022444688,0.00008198339,0.000082545965,0.00003394279,0.000005349418],"category_scores_gemma":[0.000010412039,0.0000669766,0.000025533132,0.00011570298,0.00009651465,0.00048676407,0.000048048605,0.00006301724,9.699785e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006089593,0.002025546,0.07659542,0.00027387674,0.00050329533,0.0000026566636,0.3243575,0.00014635519,0.00034330698,0.0237415,0.0009001731,0.5705014],"study_design_scores_gemma":[0.0017275888,0.001231452,0.04104122,0.000056460274,0.00011408559,0.0000916235,0.059447654,0.87711316,0.013764596,0.0048350417,0.00021008513,0.00036704767],"about_ca_topic_score_codex":0.000077589815,"about_ca_topic_score_gemma":0.00041626542,"teacher_disagreement_score":0.8769668,"about_ca_system_score_codex":0.00001750272,"about_ca_system_score_gemma":0.000026256266,"threshold_uncertainty_score":0.27312267},"labels":[],"label_agreement":null},{"id":"W2338282586","doi":"10.1007/978-3-319-26450-9_11","title":"Visualization of Handwritten Signatures Based on Haptic Information","year":2015,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"","keywords":"Computer science; Haptic technology; Identification (biology); Visualization; Authentication (law); Biometrics; Dimension (graph theory); Human–computer interaction; Kinesthetic learning; Variety (cybernetics); Process (computing); Class (philosophy); Key (lock); Artificial intelligence; Data mining; Information retrieval; Computer security","score_opus":0.11728639509639473,"score_gpt":0.36650091211799973,"score_spread":0.24921451702160502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2338282586","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000036386402,0.0008881738,0.9201106,0.00013007323,0.0010102647,0.00029319213,0.000022987802,0.00008020146,0.07746086],"genre_scores_gemma":[0.95564145,0.0007453493,0.032936625,0.0016050377,0.0003123215,0.00010580515,0.0005611742,0.000056908473,0.008035331],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981315,0.00003881071,0.0006780614,0.0002845205,0.00073864474,0.00012843133],"domain_scores_gemma":[0.9975657,0.0005051409,0.00041508404,0.00020985806,0.0012600062,0.00004422261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036998864,0.000244758,0.00032580443,0.0007213389,0.00006562934,0.000044396103,0.00034023836,0.00015544875,0.00003758256],"category_scores_gemma":[0.0002799573,0.00024124056,0.00008898725,0.00022239148,0.00016053613,0.00039328722,0.00012018335,0.00023566798,0.00011808871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032275486,0.00003712043,0.0000045481197,0.0001399153,0.00003808437,0.0000033221986,0.00067808945,0.43425047,4.123456e-7,0.5043937,0.0017253206,0.05869673],"study_design_scores_gemma":[0.00014205705,0.0002459192,0.000016272399,0.0006016144,0.000009772471,0.0000029091834,0.000048921214,0.6486574,0.000043778495,0.34590432,0.0040844698,0.00024261641],"about_ca_topic_score_codex":0.0000040162545,"about_ca_topic_score_gemma":0.000009033858,"teacher_disagreement_score":0.9556378,"about_ca_system_score_codex":0.00019334587,"about_ca_system_score_gemma":0.00017566881,"threshold_uncertainty_score":0.98375046},"labels":[],"label_agreement":null},{"id":"W2506923177","doi":"10.1142/9789814656535_0008","title":"COMPUTER RECOGNITION AND EVALUATION OF COINS","year":2015,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science","score_opus":0.20056161803151548,"score_gpt":0.3014999633056728,"score_spread":0.10093834527415732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506923177","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00051921554,0.00036188925,0.014150927,0.000034723846,0.004063624,0.00057869125,0.00006238375,0.00013807829,0.98009044],"genre_scores_gemma":[0.020788023,0.0000025457564,0.0046459017,0.00006576411,0.00030534118,0.000029827092,0.00017830788,0.000031503576,0.9739528],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974982,0.00007717089,0.00041492074,0.0006694417,0.001177546,0.00016269869],"domain_scores_gemma":[0.99756706,0.000051143154,0.00035599575,0.00052191986,0.0013684366,0.00013542235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021804087,0.00021632541,0.00026955202,0.0007839458,0.00015765602,0.0002746242,0.0002780945,0.00011939414,0.0001936704],"category_scores_gemma":[0.000012342443,0.00022159808,0.000096302276,0.000094255236,0.00026790446,0.00017614543,0.0001873922,0.00021626714,0.00025670708],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000066265643,0.000017591625,0.0000010177245,0.000043059375,0.000026962867,0.0000018722611,0.00041125657,0.0000064529363,0.0001232378,0.023348507,0.02382804,0.9521854],"study_design_scores_gemma":[0.0010852615,0.00013527535,0.000024139124,0.0005917356,0.00018745894,0.000045275418,0.0000063362495,0.046046223,0.0009395401,0.45201612,0.49827603,0.0006466162],"about_ca_topic_score_codex":0.0000027358374,"about_ca_topic_score_gemma":0.00045648424,"teacher_disagreement_score":0.95153874,"about_ca_system_score_codex":0.00009536406,"about_ca_system_score_gemma":0.00028185992,"threshold_uncertainty_score":0.9036507},"labels":[],"label_agreement":null},{"id":"W2571973395","doi":"10.1109/m2vip.2016.7827286","title":"Analysis of methods for the recognition of Indian coins: A challenging application of machine vision to automated inspection","year":2016,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Artificial intelligence; Computer science; Sorting; Matching (statistics); Computer vision; Machine vision; Line (geometry); Pattern recognition (psychology); Conveyor belt; Engineering; Mathematics; Algorithm; Statistics","score_opus":0.03878847685662771,"score_gpt":0.37709205034895493,"score_spread":0.3383035734923272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2571973395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01594594,0.000021133472,0.98258924,0.0005233902,0.0000966361,0.00046821317,0.000018703151,0.00021005393,0.00012671048],"genre_scores_gemma":[0.9262797,0.00002243941,0.07355371,0.000038068512,0.000009062961,0.00008199092,0.000006394553,0.0000036268475,0.000004992512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991697,0.000075758675,0.00035553394,0.0002059706,0.00011166682,0.00008137391],"domain_scores_gemma":[0.99876434,0.00036770935,0.00021615777,0.0002759994,0.0003357719,0.000040017705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000730051,0.00006627466,0.00018424654,0.00074844115,0.00005205765,0.000008360557,0.00017585304,0.0000395832,0.0000110621495],"category_scores_gemma":[0.00016463139,0.000040747356,0.00011458895,0.0019434373,0.000016697404,0.00015220977,0.00003772843,0.00002208095,0.0000047843205],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027996424,0.000055539407,0.00006673663,0.0000200027,0.000086863205,7.523617e-9,0.00036564717,0.0002594822,0.0625539,0.00040648942,0.000025376052,0.93613195],"study_design_scores_gemma":[0.00027361116,0.00028055668,0.008646873,0.00004394003,0.00009865732,8.9600593e-7,0.000050325383,0.82060283,0.16919719,0.0005777026,0.00015312097,0.00007430439],"about_ca_topic_score_codex":0.00015064007,"about_ca_topic_score_gemma":0.00014229961,"teacher_disagreement_score":0.9360577,"about_ca_system_score_codex":0.000027988348,"about_ca_system_score_gemma":0.000012663494,"threshold_uncertainty_score":0.1661629},"labels":[],"label_agreement":null},{"id":"W2612764263","doi":"10.1109/icit.2017.7915503","title":"Multi-class SVM based gradient feature for banknote recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"","keywords":"Banknote; Computer science; Artificial intelligence; Support vector machine; Histogram; Pattern recognition (psychology); Class (philosophy); Feature (linguistics); Feature extraction; Computer vision; Image (mathematics)","score_opus":0.0809304161946277,"score_gpt":0.3051243811917754,"score_spread":0.22419396499714767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612764263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035401904,0.00001603372,0.98688054,0.0029413311,0.0017976499,0.00037920967,0.000022023265,0.00026219242,0.0041608163],"genre_scores_gemma":[0.7012049,0.000008433262,0.2939135,0.001286965,0.0002359396,0.00018062386,0.000051762916,0.000015329202,0.003102539],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919915,0.000022671162,0.000113053226,0.00034219527,0.00012637231,0.00019657923],"domain_scores_gemma":[0.9990378,0.000053124804,0.00011858229,0.00052265817,0.00018014667,0.000087696535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001860608,0.000110557005,0.00009864289,0.000084745945,0.00055184925,0.00040827927,0.0004157067,0.000081401544,0.000050943938],"category_scores_gemma":[0.00015691157,0.00009754648,0.00011470402,0.000060166814,0.000028915429,0.000523321,0.000056123572,0.00009380873,0.00015656525],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000384122,0.0002148435,0.00013402812,0.00004341991,0.000014547931,0.0000033732001,0.000081862025,0.00000857247,0.0027323405,0.00093899923,0.01470311,0.9810865],"study_design_scores_gemma":[0.0029930633,0.00023395613,0.008086341,0.00006085171,0.00001636666,0.0000135599075,0.0000115557505,0.88669693,0.041245777,0.0045604543,0.055661432,0.00041971754],"about_ca_topic_score_codex":0.000019189836,"about_ca_topic_score_gemma":0.00012435688,"teacher_disagreement_score":0.98066676,"about_ca_system_score_codex":0.00003126779,"about_ca_system_score_gemma":0.00003427419,"threshold_uncertainty_score":0.42444342},"labels":[],"label_agreement":null},{"id":"W2740236408","doi":"10.1007/978-3-319-65172-9_44","title":"Machine Vision for Coin Recognition with ANNs: Effect of Training and Testing Parameters","year":2017,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Artificial neural network; Cognitive neuroscience of visual object recognition; Set (abstract data type); Segmentation; Pattern recognition (psychology); Object (grammar); Machine learning; Training set; Engineering","score_opus":0.1031899970968408,"score_gpt":0.32765766531948254,"score_spread":0.22446766822264175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740236408","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020999536,0.0004775802,0.9134764,0.00034030073,0.0003660419,0.0015639073,0.00007740535,0.00013300245,0.081465416],"genre_scores_gemma":[0.39849937,0.001371982,0.59935075,0.0002579251,0.000033471384,0.00013037422,0.0001609022,0.000017461169,0.00017777969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989093,0.000032268617,0.00044087393,0.00023857938,0.00023609506,0.0001428724],"domain_scores_gemma":[0.99739856,0.0007119551,0.00054937124,0.0008813389,0.00039169795,0.00006708814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013226602,0.00017396527,0.00026816613,0.00065058575,0.00053457025,0.00044897306,0.00089408224,0.000081973674,7.657105e-7],"category_scores_gemma":[0.0001943246,0.00014946682,0.000030584248,0.0001518224,0.0006759468,0.004505577,0.0004991838,0.00021673046,0.0000027335657],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011011561,0.0000053609233,0.000040981715,0.000075035765,0.000003876482,8.600004e-8,0.00069639954,0.00001828853,0.000007071407,0.0051168427,0.00000846296,0.9940166],"study_design_scores_gemma":[0.0015008226,0.0015974479,0.001133596,0.0016363758,0.00002605676,0.000107040774,0.000018576853,0.97421175,0.00016512659,0.010956811,0.008200422,0.00044597068],"about_ca_topic_score_codex":0.000013773564,"about_ca_topic_score_gemma":0.00001332927,"teacher_disagreement_score":0.9935706,"about_ca_system_score_codex":0.00003663277,"about_ca_system_score_gemma":0.000109093606,"threshold_uncertainty_score":0.60950804},"labels":[],"label_agreement":null},{"id":"W2796390756","doi":"10.1007/978-3-319-89656-4_13","title":"Mobile App for Detection of Counterfeit Banknotes","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Counterfeit; Computer science; Banknote; Android (operating system); Mobile phone; Authentication (law); Mobile apps; Embedded system; Artificial intelligence; Computer security; Telecommunications; Operating system; World Wide Web","score_opus":0.019704809844711624,"score_gpt":0.25781256671194164,"score_spread":0.23810775686723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796390756","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00030747236,0.00031500735,0.9941106,0.000030719442,0.0032237452,0.0006517421,0.000016342874,0.00015967462,0.0011847139],"genre_scores_gemma":[0.76083636,0.00009427608,0.23590223,0.00048074225,0.0017828778,0.00013210642,0.000013205415,0.00005990186,0.0006982888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99744105,0.000018675088,0.0005144655,0.0010571014,0.0005807707,0.00038794105],"domain_scores_gemma":[0.99776745,0.00034750922,0.0003710023,0.0007955524,0.00062517007,0.0000933218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006500961,0.00034898077,0.00041153314,0.0008014872,0.00018814314,0.00021102959,0.0013325372,0.0002869932,0.000045177374],"category_scores_gemma":[0.000091843765,0.0003346472,0.00017801201,0.00048231235,0.00053578336,0.00046893206,0.00037451892,0.00031523756,0.00004485513],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016899254,0.000021178272,0.0000034733685,0.000073459836,0.000008471672,0.0000016021138,0.00047355198,0.00095656625,0.0026632885,0.0005915762,0.000017912253,0.995172],"study_design_scores_gemma":[0.00059488375,0.0016298397,0.000025171914,0.0004580954,0.000018701587,0.000068789115,4.0631159e-7,0.66398084,0.1347612,0.18470478,0.013007027,0.00075027224],"about_ca_topic_score_codex":0.0000094137795,"about_ca_topic_score_gemma":0.00005628505,"teacher_disagreement_score":0.9944217,"about_ca_system_score_codex":0.00016551938,"about_ca_system_score_gemma":0.00024043913,"threshold_uncertainty_score":0.99991053},"labels":[],"label_agreement":null},{"id":"W2895717878","doi":"10.1109/ciact.2018.8480089","title":"Image Processing and IoT Based Innovative Energy Conservation Technique","year":2018,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Energy consumption; Computer science; Energy conservation; Analytics; Cloud computing; Energy (signal processing); Lecture hall; Efficient energy use; Table (database); Multimedia; Database; Operating system; Engineering; Electrical engineering","score_opus":0.022915601170938518,"score_gpt":0.2703400114196249,"score_spread":0.24742441024868636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2895717878","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002593894,0.000005684859,0.9840268,0.00076001306,0.00006270983,0.00005901987,3.979912e-7,0.00020575918,0.012285747],"genre_scores_gemma":[0.76589525,7.203712e-7,0.23161179,0.0023111124,0.000036886733,0.00002979213,0.0000017890552,0.0000030299238,0.00010965403],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995687,0.000021994054,0.00009588113,0.00016626941,0.00007328225,0.000073870186],"domain_scores_gemma":[0.99942726,0.000018700202,0.000045822497,0.00008605019,0.00039964585,0.000022504593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011677495,0.00005354309,0.000045063734,0.00010829993,0.00010252154,0.000102895414,0.00007108037,0.000031117546,0.000034295877],"category_scores_gemma":[0.00002970729,0.00004734999,0.0000065945087,0.0006418412,0.0000678007,0.00027165422,0.000032581793,0.000034860735,0.000007246907],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013400355,0.00005543598,0.00033595963,0.00002259747,0.00000301143,0.0000016071995,0.00018014392,8.792379e-8,0.12276428,0.021125933,0.0032349213,0.8522626],"study_design_scores_gemma":[0.0003083342,0.00018637136,0.001572918,0.000040225314,0.0000013080511,0.000019850339,0.000017881663,0.37091404,0.6058366,0.009367281,0.011555056,0.00018014177],"about_ca_topic_score_codex":0.00003728222,"about_ca_topic_score_gemma":0.000026179367,"teacher_disagreement_score":0.8520825,"about_ca_system_score_codex":0.0000112804855,"about_ca_system_score_gemma":0.00005914294,"threshold_uncertainty_score":0.19308765},"labels":[],"label_agreement":null},{"id":"W2901141376","doi":"10.1142/s0219691319500061","title":"High correlation-based banknote gradient assessment of ensemble classifier","year":2018,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Scale-invariant feature transform; Principal component analysis; Histogram; Banknote; Computer science; Histogram of oriented gradients; Classifier (UML); Support vector machine; Feature extraction; Image (mathematics)","score_opus":0.0158244455020748,"score_gpt":0.2864434084277487,"score_spread":0.2706189629256739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901141376","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052818783,0.0000297958,0.9435733,0.00070010923,0.0015193655,0.00006720899,0.000004884782,0.000024436262,0.0012621367],"genre_scores_gemma":[0.95414245,0.000022313101,0.045292918,0.0003785716,0.00013580664,0.0000018166376,0.0000106639445,0.0000027709737,0.000012695934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842316,0.00004051175,0.0007496203,0.00008479312,0.0005966487,0.000105279345],"domain_scores_gemma":[0.9965674,0.000051436375,0.0010203494,0.00007722341,0.002212591,0.00007098171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043211094,0.00009532542,0.00013376967,0.00052354904,0.00012481996,0.00019172353,0.00024738192,0.000059985323,0.000029595174],"category_scores_gemma":[0.00013463027,0.0000845554,0.000061987004,0.00022092159,0.000083949395,0.0036536434,0.000044182587,0.00014066012,0.000006839967],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008623224,0.000116943345,0.0011633098,0.00004097312,0.00003801481,0.0000019152883,0.0010107568,0.0012171428,0.0014737402,0.01282754,0.0003591274,0.9816643],"study_design_scores_gemma":[0.0015680229,0.00023148625,0.031043665,0.00021132959,0.000010480623,0.00009037658,0.000074317366,0.9556992,0.0046111806,0.0007463385,0.005590625,0.0001229703],"about_ca_topic_score_codex":0.0000111457375,"about_ca_topic_score_gemma":0.0000026472069,"teacher_disagreement_score":0.98154134,"about_ca_system_score_codex":0.00011227885,"about_ca_system_score_gemma":0.00023960651,"threshold_uncertainty_score":0.34480694},"labels":[],"label_agreement":null},{"id":"W2904571180","doi":"10.1134/s1054661818040028","title":"An Enhanced Histogram of Oriented Gradient Descriptor for Numismatic Applications","year":2018,"lang":"en","type":"article","venue":"Pattern Recognition and Image Analysis","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Universiti Malaya","keywords":"Histogram; Sliding window protocol; Object (grammar); Window (computing); Histogram of oriented gradients; Computer science; Artificial intelligence; Pattern recognition (psychology); Image (mathematics); Cognitive neuroscience of visual object recognition; Computer vision","score_opus":0.027595251320353162,"score_gpt":0.28240650598068295,"score_spread":0.2548112546603298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904571180","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07572944,0.000020852007,0.92340136,0.000042423268,0.000092768794,0.00028977878,0.00006543972,0.000083500134,0.00027443958],"genre_scores_gemma":[0.9601841,0.00002540205,0.038992986,0.00018532515,0.00007794307,0.0002591123,0.0002410947,0.0000066446173,0.000027391423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990079,0.000054745862,0.00030923402,0.00035482837,0.00012708761,0.00014621155],"domain_scores_gemma":[0.9988374,0.000040148694,0.00018301027,0.00028515584,0.0005488787,0.00010538879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016722397,0.000108565655,0.00020129887,0.00040882823,0.00015319219,0.00008205478,0.00014006543,0.000038568032,0.00015796747],"category_scores_gemma":[0.000024052873,0.00010726491,0.00014976862,0.0009153186,0.00008502938,0.00039600857,0.00002143759,0.00004160005,0.00005000676],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011735891,0.00024743972,0.0002549731,0.00004794655,0.00015792546,1.4885211e-7,0.0007179719,3.603002e-7,0.017222542,0.000027878395,0.000077507946,0.9812336],"study_design_scores_gemma":[0.0034111822,0.0021697497,0.010062389,0.0001159584,0.0029178062,0.000020643536,0.0011785397,0.62258655,0.33475074,0.015857967,0.005549061,0.0013794368],"about_ca_topic_score_codex":0.000045604324,"about_ca_topic_score_gemma":0.00009506999,"teacher_disagreement_score":0.9798541,"about_ca_system_score_codex":0.000019855042,"about_ca_system_score_gemma":0.000012107202,"threshold_uncertainty_score":0.43741363},"labels":[],"label_agreement":null},{"id":"W2910682931","doi":"10.1109/m2vip.2018.8600819","title":"Small Parts Classification with Flexible Machine Vision and a Hybrid Classifier","year":2018,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Support vector machine; Classifier (UML); Artificial intelligence; Computer science; Artificial neural network; Pattern recognition (psychology); Cable gland; Machine vision; Machine learning; Flexibility (engineering); Feature extraction; Computer vision","score_opus":0.05368258528207882,"score_gpt":0.26802758731530246,"score_spread":0.21434500203322365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910682931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055900704,0.00003235485,0.90788776,0.000881201,0.00024429377,0.0001299726,0.0000012488274,0.00030831565,0.034614146],"genre_scores_gemma":[0.9837093,0.000014551458,0.014430545,0.0003453943,0.00007132074,0.000011744183,0.0000041457424,0.0000049354758,0.0014081077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929506,0.00002976927,0.00011304071,0.0003109511,0.000121081095,0.00013008228],"domain_scores_gemma":[0.99949557,0.000023519913,0.000047611513,0.0002434326,0.00010831934,0.000081565464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012749243,0.00008919662,0.00007249624,0.00008890626,0.00014466466,0.00016066583,0.00011625605,0.000025533185,0.00008858808],"category_scores_gemma":[0.0000096395215,0.00006202549,0.000015709245,0.00020701677,0.00007104254,0.00033091631,0.00005459226,0.000064031476,0.00014660576],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000068745816,0.00013434351,0.00237286,0.000015705753,0.000012788939,0.0000035105431,0.00020916938,7.8899416e-7,0.0042843134,0.021434536,0.0037296766,0.96773356],"study_design_scores_gemma":[0.001833406,0.00209187,0.066724084,0.00008391322,0.000020442427,0.0003761645,0.00005636035,0.77110547,0.053329885,0.011044543,0.09268749,0.0006464008],"about_ca_topic_score_codex":0.000018572402,"about_ca_topic_score_gemma":0.00007845781,"teacher_disagreement_score":0.96708715,"about_ca_system_score_codex":0.000013144268,"about_ca_system_score_gemma":0.000022417693,"threshold_uncertainty_score":0.25293258},"labels":[],"label_agreement":null},{"id":"W2951175117","doi":"10.1142/9789811203527_0009","title":"Automatic Detection of Counterfeit Coins by Visual Measurements","year":2019,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Counterfeit; Computer science; Artificial intelligence; Computer vision; History; Archaeology","score_opus":0.04031596951887542,"score_gpt":0.2550499877657687,"score_spread":0.21473401824689328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951175117","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010061099,0.00021040095,0.027877646,0.000016913134,0.009027396,0.00075311377,0.000054592434,0.00036412379,0.9606897],"genre_scores_gemma":[0.17792992,5.315336e-7,0.00015636683,0.000047569803,0.000051999523,0.000012245942,0.000021539965,0.000028658002,0.8217512],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9970195,0.000045554512,0.0006279755,0.00082321203,0.0011951699,0.000288537],"domain_scores_gemma":[0.99809784,0.00005265457,0.0005766798,0.0007639629,0.00039070417,0.000118157914],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00073358486,0.00033203806,0.0004065389,0.00083690806,0.00024730936,0.00038139152,0.0006313092,0.00015956587,0.0003006696],"category_scores_gemma":[0.000010671289,0.00034289403,0.00022915914,0.00014215325,0.00021493968,0.00019695531,0.00017415344,0.00032082127,0.000907301],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000452767,0.00024380811,0.000023453156,0.0008190169,0.00035512928,0.000008757442,0.0009688204,0.000009837444,0.16673227,0.022822278,0.06793104,0.7400403],"study_design_scores_gemma":[0.001177365,0.00035653822,0.000026286329,0.0012290825,0.00014382723,0.00003150839,0.000009823912,0.03826586,0.1289765,0.007962683,0.8205233,0.0012971887],"about_ca_topic_score_codex":0.000005164541,"about_ca_topic_score_gemma":0.00063851394,"teacher_disagreement_score":0.7525923,"about_ca_system_score_codex":0.00017212334,"about_ca_system_score_gemma":0.00022442499,"threshold_uncertainty_score":0.9999023},"labels":[],"label_agreement":null},{"id":"W2951337953","doi":"10.1142/9789811203527_0010","title":"An Ensemble of Character Features and Fine-Tuned Convolutional Neural Network for Spurious Coin Detection","year":2019,"lang":"en","type":"book-chapter","venue":"WORLD SCIENTIFIC eBooks","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Spurious relationship; Convolutional neural network; Character (mathematics); Computer science; Pattern recognition (psychology); Artificial intelligence; Machine learning; Mathematics","score_opus":0.024054889138330305,"score_gpt":0.23962853806820814,"score_spread":0.21557364892987782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951337953","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023490626,0.0028897002,0.18116604,0.0004577726,0.064153336,0.00776176,0.0008930169,0.0013242025,0.71786356],"genre_scores_gemma":[0.16226149,0.0000013247144,0.0020691322,0.00011302031,0.0005008954,0.000032545588,0.00012405383,0.00003232038,0.8348652],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99803096,0.00003730646,0.00040013975,0.0008247224,0.00039623538,0.00031065822],"domain_scores_gemma":[0.9984695,0.00012397712,0.00038014553,0.0005778716,0.00032275644,0.00012579464],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047839864,0.00028464384,0.0003573887,0.0004512229,0.0003736568,0.00035417362,0.00034950534,0.0001756807,0.00004218372],"category_scores_gemma":[0.000008195013,0.000285199,0.00018187087,0.00008244048,0.0002354295,0.00021203767,0.00010033408,0.0002889332,0.000030215151],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042867783,0.00010110913,0.00005538999,0.0004624702,0.00016089737,0.000011612197,0.0007503674,0.00013706836,0.044554517,0.3417138,0.04294513,0.568679],"study_design_scores_gemma":[0.002376142,0.0008739911,0.0016319879,0.0005862361,0.00016979757,0.0002473,0.000008453567,0.043075193,0.020724759,0.10215729,0.8264037,0.0017451864],"about_ca_topic_score_codex":0.0000048733787,"about_ca_topic_score_gemma":0.0021005818,"teacher_disagreement_score":0.78345853,"about_ca_system_score_codex":0.000043941236,"about_ca_system_score_gemma":0.00013080543,"threshold_uncertainty_score":0.99996},"labels":[],"label_agreement":null},{"id":"W2951582072","doi":"10.48550/arxiv.1401.0689","title":"Machine Assisted Authentication of Paper Currency: an Experiment on Indian Banknotes","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Currency; Authentication (law); Computer science; Computer security; Artificial intelligence; Economics; Monetary economics","score_opus":0.08397439697156463,"score_gpt":0.2220512815437349,"score_spread":0.13807688457217027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951582072","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.52574795,0.00006850742,0.46816376,0.00011244604,0.001554107,0.00038415476,0.00003374836,0.00031455758,0.003620749],"genre_scores_gemma":[0.99894005,0.000047589703,0.00066936965,0.000054241344,0.00005240177,0.000002289022,0.00006986599,0.000010364693,0.00015385226],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984651,0.0002107999,0.00023195481,0.0007855966,0.00012416393,0.00018235024],"domain_scores_gemma":[0.9983092,0.00006221132,0.0003239345,0.001007773,0.0001544631,0.00014242674],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020306975,0.00023333388,0.00024757552,0.00036192164,0.00010139121,0.00006556317,0.0007846173,0.00020138522,0.00013060129],"category_scores_gemma":[0.000032471988,0.00025665536,0.00015362247,0.00034505446,0.00006394488,0.00030669422,0.00036612537,0.00033120537,0.00008674817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041152711,0.004369511,0.005293528,0.000849592,0.00048394306,0.00012815623,0.007861256,0.045413386,0.0078959875,0.5521779,0.0004869794,0.37462822],"study_design_scores_gemma":[0.0013423521,0.00059525383,0.016736047,0.00038293263,0.00010597477,0.000008367053,0.00009843064,0.9098469,0.009971547,0.058399234,0.0015066181,0.0010063463],"about_ca_topic_score_codex":0.00010422935,"about_ca_topic_score_gemma":0.000030225827,"teacher_disagreement_score":0.8644335,"about_ca_system_score_codex":0.000108602624,"about_ca_system_score_gemma":0.000089464396,"threshold_uncertainty_score":0.99998856},"labels":[],"label_agreement":null},{"id":"W2953212565","doi":"10.24908/iqurcp.8997","title":"Reflectance Transformation Imaging for Roman Coin Identification: Archaeology and Education","year":2016,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Byzantine architecture; Presentation (obstetrics); Period (music); Visual arts; Archaeology; Cultural heritage; Identification (biology); Art; History; Computer science","score_opus":0.0853180373924522,"score_gpt":0.3824200360305896,"score_spread":0.2971019986381374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953212565","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0467146,0.0001150498,0.85752326,0.09155651,0.0005061488,0.0010099907,0.000005257406,0.00025657122,0.0023125766],"genre_scores_gemma":[0.99491566,0.0005644751,0.0030637812,0.000104512044,0.00015243547,0.0005437152,0.0000068438976,0.000013017254,0.0006355542],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9979022,0.0000787399,0.00039476962,0.0006406402,0.0004831078,0.0005005079],"domain_scores_gemma":[0.9973392,0.00023497302,0.00014004951,0.00019758269,0.0019114274,0.00017678841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014272964,0.00016157303,0.0001625974,0.00055634254,0.00036420563,0.0004350744,0.00049730396,0.00007861234,0.0000077921095],"category_scores_gemma":[0.00037844165,0.00013382021,0.000045162404,0.0005938115,0.0004742397,0.0023087764,0.00012294202,0.00019852024,0.000042197727],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000039180548,0.000047566038,0.00029068298,0.00010260023,0.000007935389,2.5935387e-7,0.00277744,3.9710347e-8,0.03695893,0.5511844,0.0018272197,0.40676373],"study_design_scores_gemma":[0.00080807216,0.0002120215,0.0014260138,0.0002705549,0.0000068982613,0.000072404495,0.00078718923,0.0075688045,0.0250145,0.9512908,0.012234522,0.00030825983],"about_ca_topic_score_codex":0.000024097353,"about_ca_topic_score_gemma":0.000014982737,"teacher_disagreement_score":0.94820106,"about_ca_system_score_codex":0.00015782852,"about_ca_system_score_gemma":0.0003839969,"threshold_uncertainty_score":0.545703},"labels":[],"label_agreement":null},{"id":"W2996310699","doi":"10.1109/snpd.2019.8935752","title":"Assets Predictive Maintenance Using Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Convolutional neural network; Computer science; Support vector machine; Artificial intelligence; Perceptron; Predictive maintenance; Transformation (genetics); Pattern recognition (psychology); Random forest; Multilayer perceptron; Classifier (UML); Machine learning; Representation (politics); Data mining; Artificial neural network; Engineering","score_opus":0.02025516740115408,"score_gpt":0.2445189863372457,"score_spread":0.22426381893609162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2996310699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06835659,0.000026732781,0.92375153,0.00008498387,0.0016242578,0.00011371067,0.0000015409314,0.00015790128,0.0058827684],"genre_scores_gemma":[0.9950233,0.0000021386015,0.004144108,0.00028119626,0.00007931841,0.0000033039232,0.0000026599669,0.0000030256049,0.0004609684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931425,0.000030927647,0.00011005086,0.00022834842,0.00014275758,0.00017368818],"domain_scores_gemma":[0.99961406,0.00003770473,0.000044535424,0.0001558253,0.00009716444,0.000050697363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009175957,0.000069403926,0.0000703391,0.000046656583,0.000058694546,0.000063267245,0.00016531473,0.000039590617,0.00016999],"category_scores_gemma":[0.000009927456,0.00006110578,0.000044354027,0.00020358589,0.000018466275,0.00046843904,0.000076247576,0.000106042084,0.00010603444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002530171,0.0006043603,0.12691192,0.00006685487,0.00018798448,0.00004184373,0.0005550227,0.33813587,0.0054707574,0.26042318,0.021252401,0.2460968],"study_design_scores_gemma":[0.0002290862,0.00004815033,0.010479701,0.00000803384,0.0000015481705,0.000045196048,0.0000078567,0.98782504,0.00007787418,0.00088036334,0.00031228957,0.00008486437],"about_ca_topic_score_codex":0.000012242982,"about_ca_topic_score_gemma":0.0000029370763,"teacher_disagreement_score":0.9266667,"about_ca_system_score_codex":0.00004380812,"about_ca_system_score_gemma":0.000026424092,"threshold_uncertainty_score":0.24918213},"labels":[],"label_agreement":null},{"id":"W3003627710","doi":"10.1109/iscc47284.2019.8969683","title":"Deep Learning for Recognizing the Anatomy of Tables on Datasheets","year":2019,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Table (database); Segmentation; Task (project management); Artificial intelligence; Machine learning; Deep learning; Market segmentation; Pattern recognition (psychology); Data mining; Engineering","score_opus":0.030188570447219926,"score_gpt":0.2860496347198737,"score_spread":0.25586106427265376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003627710","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046187084,0.00007455693,0.923623,0.00049483584,0.0008663574,0.00042131345,0.0000030296897,0.00013524125,0.028194554],"genre_scores_gemma":[0.9932833,0.000012512394,0.005860734,0.00018862718,0.000025975489,0.000012258894,0.000010018847,0.000003695891,0.00060288137],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995187,0.000029229068,0.000103711696,0.0001550538,0.000097510325,0.00009582969],"domain_scores_gemma":[0.99945885,0.00021541849,0.00005751217,0.00019088255,0.00006033034,0.000017018107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023291314,0.000047364523,0.00006401876,0.00005189399,0.00007118059,0.000045614357,0.00022265578,0.000020251005,0.00012170291],"category_scores_gemma":[0.000062550105,0.000031234602,0.000036639532,0.00016567516,0.000008464535,0.00020128788,0.000049835944,0.000067651694,0.00010269614],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014879424,0.00003503587,0.00069979636,0.000028351911,0.0000123207765,1.3988848e-7,0.00021480962,0.0004691962,0.001027636,0.015470991,0.0012088153,0.98081803],"study_design_scores_gemma":[0.00085399026,0.00045236066,0.0017807002,0.00006476962,0.000008515987,0.000008890161,0.00032493842,0.8300145,0.031018885,0.0053515537,0.12989524,0.00022561349],"about_ca_topic_score_codex":0.000012614609,"about_ca_topic_score_gemma":0.000015322066,"teacher_disagreement_score":0.9805924,"about_ca_system_score_codex":0.0000074264512,"about_ca_system_score_gemma":0.000010287853,"threshold_uncertainty_score":0.13325615},"labels":[],"label_agreement":null},{"id":"W3036192158","doi":"","title":"Abstract for Australian Banknotes: Assisting People with Vision Impairment | Bulletin – March Quarter 2015","year":2015,"lang":"en","type":"article","venue":"Philadelphia Museum of Art Bulletin","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Optometry; Medicine; Advertising; Gerontology; Geography; Business","score_opus":0.03570644211503835,"score_gpt":0.28873661064198813,"score_spread":0.25303016852694976,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036192158","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017994948,0.000121242076,0.070050016,0.924896,0.00086841744,0.0010631164,0.000043040443,0.0003616761,0.0007969655],"genre_scores_gemma":[0.980227,0.00001057967,0.018821897,0.0003869666,0.00024709132,0.00012499581,0.000041564588,0.000028313245,0.00011158929],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976579,0.00007117076,0.00057551917,0.00061173365,0.00059753057,0.00048613083],"domain_scores_gemma":[0.9981875,0.00020386548,0.0003250211,0.0005276767,0.00044401406,0.0003119504],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008536086,0.00028782422,0.0003518366,0.00022989867,0.00014653448,0.00015518731,0.00048084446,0.00011349404,0.00058637006],"category_scores_gemma":[0.00010301004,0.00025176548,0.0001500862,0.00030843724,0.00006541164,0.00019942723,0.000119310615,0.00022193883,0.00059063075],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025576973,0.0003363787,0.00010446837,0.00012161827,0.00003299416,0.000005778572,0.00063223875,0.00014798247,0.0004108899,0.00010313859,0.99713284,0.0007159042],"study_design_scores_gemma":[0.0024590038,0.001629844,0.0069232713,0.00023421831,0.000026432917,0.000061688756,0.00022785054,0.0020254496,0.0013122072,0.00019703397,0.98440737,0.0004956344],"about_ca_topic_score_codex":0.00007911907,"about_ca_topic_score_gemma":0.000030861716,"teacher_disagreement_score":0.9784275,"about_ca_system_score_codex":0.00008289312,"about_ca_system_score_gemma":0.00010676128,"threshold_uncertainty_score":0.99999344},"labels":[],"label_agreement":null},{"id":"W3036597989","doi":"","title":"Australian Banknotes: Assisting People with Vision Impairment | Bulletin – March Quarter 2015","year":2015,"lang":"en","type":"article","venue":"Philadelphia Museum of Art Bulletin","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Optometry; Medicine; History","score_opus":0.02742236683563255,"score_gpt":0.27187208297463683,"score_spread":0.2444497161390043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036597989","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018934397,0.00015589679,0.04673333,0.94788086,0.0008341845,0.0005865375,0.000016537375,0.0004309619,0.0014682404],"genre_scores_gemma":[0.98667026,0.000016079079,0.012296548,0.00052668777,0.00021506354,0.00005480487,0.000024950092,0.000026141217,0.00016948102],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9973427,0.00017085101,0.0005635889,0.00064169144,0.00078805047,0.0004930853],"domain_scores_gemma":[0.9981909,0.00012109762,0.00029389822,0.00064118434,0.00038759364,0.00036534722],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007775641,0.0003030584,0.00036263777,0.00027006655,0.00013896868,0.00016004882,0.00055219146,0.0001116396,0.0009821275],"category_scores_gemma":[0.000080920196,0.0002631202,0.00012233682,0.00049383845,0.00008389585,0.00021062537,0.00020767022,0.00029084636,0.0015447428],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015303047,0.00033441538,0.00026971352,0.00006248617,0.000031121595,0.000015063349,0.0009531816,0.00013413817,0.0002604789,0.00012304609,0.9971562,0.0005071175],"study_design_scores_gemma":[0.0020234683,0.001694887,0.0050986526,0.00023194685,0.000026844053,0.00014787262,0.00027918463,0.0023670376,0.0010766932,0.00014234408,0.98636734,0.0005437182],"about_ca_topic_score_codex":0.00012015482,"about_ca_topic_score_gemma":0.000033280245,"teacher_disagreement_score":0.9847768,"about_ca_system_score_codex":0.00009183571,"about_ca_system_score_gemma":0.000106206964,"threshold_uncertainty_score":0.9999821},"labels":[],"label_agreement":null},{"id":"W3047507127","doi":"10.1007/s11042-020-09447-8","title":"Statistical edge-based feature selection for counterfeit coin detection","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Feature vector; Pattern recognition (psychology); Artificial intelligence; Counterfeit; Feature (linguistics); Feature selection; Robustness (evolution); Edge detection; Enhanced Data Rates for GSM Evolution; Precision and recall; Pixel; Test set; Computer vision; Mathematics; Image processing; Image (mathematics)","score_opus":0.0363787805505537,"score_gpt":0.2740267961423607,"score_spread":0.23764801559180698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047507127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072898273,0.000036598074,0.99615264,0.0017862527,0.000091316,0.0007085045,0.00013997807,0.00022254161,0.00013319003],"genre_scores_gemma":[0.8710251,0.000017130054,0.12514348,0.0012956458,0.000526027,0.0017372029,0.00018823304,0.000015278914,0.000051897558],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928916,0.000019680605,0.00012977215,0.0003280129,0.000100855854,0.00013251843],"domain_scores_gemma":[0.9993876,0.0002045052,0.00005477247,0.00009825373,0.00011801793,0.00013684701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000661654,0.000096916876,0.00009727431,0.000039380615,0.00022186381,0.0001686421,0.00010484145,0.00007027725,0.000016918033],"category_scores_gemma":[0.00007210176,0.00009668942,0.00003438402,0.00027294908,0.000031844666,0.00019312945,0.00001926198,0.00011489567,0.00004230771],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002996576,0.00005921349,0.0001412925,0.000045769688,0.000007829771,1.244055e-7,0.00009065032,0.000055801353,0.011329911,0.001747785,0.0022826314,0.984209],"study_design_scores_gemma":[0.00068831706,0.00014672823,0.001811521,0.000004877377,0.000015070776,0.000004545853,0.000013573295,0.85306215,0.013892013,0.0005336565,0.12967719,0.00015032283],"about_ca_topic_score_codex":0.0000051110687,"about_ca_topic_score_gemma":0.000018004732,"teacher_disagreement_score":0.9840587,"about_ca_system_score_codex":0.000021811757,"about_ca_system_score_gemma":0.00003709855,"threshold_uncertainty_score":0.39428803},"labels":[],"label_agreement":null},{"id":"W3090921718","doi":"10.1117/12.2583450","title":"Hyperspectral VIS/SWIR wide-field imaging for ink analysis","year":2020,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Photon Etc (Canada)","funders":"","keywords":"Hyperspectral imaging; Counterfeit; Inkwell; Reflectivity; Identification (biology); Remote sensing; Fidelity; Computer science; Field (mathematics); Camouflage; Artificial intelligence; Geology; Optics; Archaeology; Geography; Physics; Telecommunications; Mathematics","score_opus":0.026519173485281947,"score_gpt":0.26687387806206236,"score_spread":0.24035470457678043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090921718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029553086,0.000028920482,0.97565144,0.012873745,0.00016089878,0.00008461295,0.000001201346,0.00025686505,0.007987003],"genre_scores_gemma":[0.95320386,0.000003934376,0.039433856,0.0071379878,0.00009370175,0.0000123435575,0.0000029044922,0.0000033855856,0.000108052685],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935776,0.0000126067425,0.0001225196,0.000267842,0.000099913406,0.00013938245],"domain_scores_gemma":[0.9995803,0.000099343844,0.000031441563,0.00013397275,0.00006239954,0.00009251835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000065062086,0.00006736589,0.00010466153,0.00010613485,0.000070540314,0.00012664097,0.00020505737,0.000018960058,0.00016532552],"category_scores_gemma":[0.000094449715,0.00006214319,0.00016349196,0.0007952233,0.000006468821,0.00029558034,0.000046841113,0.00005993257,0.000057828634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008681272,0.00016342096,0.016805502,0.00005761446,0.00054301805,0.000015496205,0.0030913746,0.00027230754,0.010107483,0.051779266,0.054147482,0.86293024],"study_design_scores_gemma":[0.00038327245,0.00011639713,0.0011263043,0.0000023844032,0.00008313784,0.0000032552268,0.000118483564,0.9671712,0.02040414,0.0026949886,0.0076895296,0.00020690505],"about_ca_topic_score_codex":0.000021871701,"about_ca_topic_score_gemma":0.000017346772,"teacher_disagreement_score":0.9668989,"about_ca_system_score_codex":0.0000114232835,"about_ca_system_score_gemma":0.00001757737,"threshold_uncertainty_score":0.25341257},"labels":[],"label_agreement":null},{"id":"W3091895434","doi":"10.1007/978-3-030-59830-3_58","title":"A Blob Detector Images-Based Method for Counterfeit Coin Detection by Fuzzy Association Rules Mining","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Counterfeit; Data mining; Fuzzy logic; Field (mathematics); Association rule learning; Artificial intelligence; Detector; Image (mathematics); Pattern recognition (psychology); Image processing; Computer vision; Mathematics","score_opus":0.020018459240340385,"score_gpt":0.2700852879106995,"score_spread":0.2500668286703591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091895434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00006919891,0.00021843563,0.9942288,0.0008829042,0.0025971443,0.0007440833,0.00010227105,0.00038026363,0.00077691756],"genre_scores_gemma":[0.09885784,0.000019000108,0.8958131,0.0037737018,0.0009745658,0.00013528997,0.00006074037,0.000084917534,0.00028082365],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99596196,0.00010019746,0.0006421826,0.0016673768,0.0010217556,0.0006065356],"domain_scores_gemma":[0.9965308,0.0014627092,0.0006786666,0.00059132697,0.00053577125,0.00020076791],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014116829,0.00055583304,0.00058301416,0.0006952541,0.00039991408,0.000835385,0.0013055714,0.0004642488,0.000017778399],"category_scores_gemma":[0.000544672,0.00057854806,0.00026957906,0.00065025035,0.00013167576,0.0005888258,0.00028474643,0.0006761418,0.00004782994],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000036742647,0.00002976472,0.000014088451,0.000079622834,0.000022399636,0.000007215577,0.00019783428,0.0025292109,0.0069557973,0.00016514225,0.00025046468,0.9897117],"study_design_scores_gemma":[0.0007906866,0.0004984046,0.00002189401,0.00018923289,0.00003203115,0.000021931939,2.9773145e-7,0.9100772,0.06452837,0.017623682,0.005488674,0.0007275885],"about_ca_topic_score_codex":0.000024988361,"about_ca_topic_score_gemma":0.00011119629,"teacher_disagreement_score":0.9889841,"about_ca_system_score_codex":0.0009062784,"about_ca_system_score_gemma":0.0003774659,"threshold_uncertainty_score":0.9996666},"labels":[],"label_agreement":null},{"id":"W3119900290","doi":"10.18280/ts.370623","title":"A Hybrid Classifier for Handwriting Recognition on Multi-domain Financial Bills Based on DCNN and SVM","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Support vector machine; Classifier (UML); Handwriting; Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Machine learning; Benchmark (surveying); Artificial neural network; Finance","score_opus":0.07036183805954983,"score_gpt":0.25473640476131487,"score_spread":0.18437456670176505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119900290","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0683447,0.000006972113,0.9287934,0.0014115073,0.000215972,0.00050563057,0.000082963146,0.00018161692,0.00045724676],"genre_scores_gemma":[0.97796744,0.0000018301613,0.017947135,0.003595227,0.00030375342,0.00012383643,0.000042579075,0.0000106660655,0.0000075447283],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988169,0.00006464553,0.00024063473,0.00043847362,0.0002231646,0.00021614856],"domain_scores_gemma":[0.99948376,0.00015365667,0.0000842798,0.000075946235,0.00006936351,0.00013300113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024555373,0.0001540199,0.00013941735,0.00010542454,0.00021439581,0.00012933678,0.00010837475,0.000042390075,0.000058474252],"category_scores_gemma":[0.00007340878,0.00014767457,0.00008649562,0.0001379806,0.000024800263,0.00016989921,0.00002168726,0.0001220808,0.000046179233],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052528956,0.00037265022,0.000057365276,0.000113705544,0.000012975927,0.000018727322,0.0003636023,0.00013437105,0.006771944,0.0012867101,0.0025815673,0.9877611],"study_design_scores_gemma":[0.005732796,0.0021196522,0.0009847106,0.0001801659,0.000016754986,0.000007579703,0.00005471967,0.95899844,0.02435054,0.0010046953,0.0061692283,0.0003806868],"about_ca_topic_score_codex":0.0000020243344,"about_ca_topic_score_gemma":0.0000023479713,"teacher_disagreement_score":0.9873804,"about_ca_system_score_codex":0.000041078318,"about_ca_system_score_gemma":0.00004336051,"threshold_uncertainty_score":0.60219944},"labels":[],"label_agreement":null},{"id":"W3120669780","doi":"10.18280/ts.370618","title":"An Image Classification Algorithm of Financial Instruments Based on Convolutional Neural Network","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Chongqing Municipal Education Commission","keywords":"Convolutional neural network; Boom; Computer science; Finance; Artificial intelligence; Preprocessor; Machine learning; Financial instrument; Algorithm; Pattern recognition (psychology); Business; Engineering","score_opus":0.034895588779371316,"score_gpt":0.25068226340749633,"score_spread":0.215786674628125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120669780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051777203,0.000003725065,0.94644725,0.0005545908,0.00034724467,0.000190719,0.000034197976,0.00014496717,0.00050008454],"genre_scores_gemma":[0.97418094,5.551578e-7,0.024477433,0.0009439115,0.00031401424,0.000018639497,0.00005863473,0.000004604115,0.0000012957425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988621,0.00008409066,0.00025891775,0.00028362387,0.00035086254,0.00016043306],"domain_scores_gemma":[0.9995296,0.000023742574,0.000120504075,0.00012038665,0.00009080044,0.000114943505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013969788,0.00010598818,0.00010706616,0.00005176928,0.000103846745,0.00004921716,0.0002421338,0.0000389607,0.00013992084],"category_scores_gemma":[0.0000087303115,0.00010860399,0.000062965664,0.00029247598,0.000039101702,0.00032430392,0.000020074776,0.00010017837,0.000029798248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020751698,0.0005667138,0.0014644368,0.000037045906,0.000012578099,0.0000060141515,0.00023515034,0.007064172,0.011539939,0.011515276,0.0024919584,0.9648592],"study_design_scores_gemma":[0.00082698074,0.00060894486,0.035911515,0.000011704149,0.000005421663,0.0000012968871,0.0000087739745,0.9602804,0.0017481319,0.00016766696,0.00032459712,0.00010455339],"about_ca_topic_score_codex":0.0000034705874,"about_ca_topic_score_gemma":7.020043e-7,"teacher_disagreement_score":0.96475464,"about_ca_system_score_codex":0.000038535876,"about_ca_system_score_gemma":0.00007583089,"threshold_uncertainty_score":0.44287422},"labels":[],"label_agreement":null},{"id":"W4220667474","doi":"10.18280/ria.360119","title":"A Novel Autoregressive Co-Variance Matrix and Gabor Filter Ensemble Convolutional Neural Network (ARCM-GF-E-CNN) Model for E-Commerce Product Classification","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Autoregressive model; Variance (accounting); Classifier (UML); Artificial neural network; Machine learning; Data mining; Mathematics; Statistics","score_opus":0.09496910152210582,"score_gpt":0.31549550693884454,"score_spread":0.22052640541673874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220667474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049738893,0.0005701698,0.99008715,0.002173274,0.00089870987,0.00070130226,0.00007571952,0.00015036282,0.00036942438],"genre_scores_gemma":[0.9633722,0.00003063469,0.031020226,0.000364339,0.00026749933,0.0005702429,0.000074748095,0.000020790349,0.004279362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805015,0.00008748083,0.00044783202,0.0007457082,0.000261376,0.00040745537],"domain_scores_gemma":[0.99872285,0.00022577016,0.00024361351,0.000491292,0.00021142523,0.00010505392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053766044,0.00019495645,0.00020491645,0.00011185821,0.0008545145,0.00015101528,0.00046825764,0.000048484788,0.00009801404],"category_scores_gemma":[0.00009996478,0.00021306291,0.00010315428,0.00043634296,0.00009313354,0.00039637682,0.00019749768,0.0002779959,0.000045216908],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018896819,0.0004456819,0.00020597612,0.00010954152,0.000031963566,0.0000037686154,0.0025562758,0.76480854,0.023240458,0.09615269,0.013288346,0.09896782],"study_design_scores_gemma":[0.00013603413,0.00011709458,0.00017931538,0.000020208478,0.000010949789,0.0001062199,0.00013881817,0.9842799,0.0022312072,0.005149726,0.0073730727,0.00025745705],"about_ca_topic_score_codex":0.000011367082,"about_ca_topic_score_gemma":0.000007033526,"teacher_disagreement_score":0.9590669,"about_ca_system_score_codex":0.00010228894,"about_ca_system_score_gemma":0.000103048325,"threshold_uncertainty_score":0.86884534},"labels":[],"label_agreement":null},{"id":"W4225555546","doi":"10.5430/wjel.v12n3p32","title":"An Overview of Hybrid, Digital and Virtual Library","year":2022,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Digitization; Computer science; Digital library; Jargon; World Wide Web; sort; Multimedia; Newspaper; Graphics; The Internet; Telecommunications; Information retrieval; Computer graphics (images); Advertising","score_opus":0.017134209443064003,"score_gpt":0.2534594988137594,"score_spread":0.23632528937069538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225555546","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9871614,0.0045679556,0.0018100904,0.00006688836,0.0014860018,0.000054567638,0.000050694907,0.000058856724,0.0047435565],"genre_scores_gemma":[0.9989142,0.000059944818,0.0005000474,0.00012584066,0.00020667721,8.556861e-7,0.0000032553057,0.000005271061,0.00018394216],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993216,0.00006134862,0.00022939623,0.000088463334,0.00022240945,0.00007677371],"domain_scores_gemma":[0.9994953,0.000045591634,0.00018856792,0.00012863759,0.0000639729,0.00007790569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016369614,0.000056707897,0.0001237287,0.00023492688,0.000051371375,0.00011326248,0.00028626816,0.0000052156697,0.00025191763],"category_scores_gemma":[0.00006183116,0.000052752108,0.00006432133,0.00030875474,0.000017716946,0.0014530529,0.00013886567,0.00018465809,7.899355e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012653737,0.0005381302,0.00208172,0.00004735251,0.000057769717,0.0003481657,0.0144673595,0.00014147128,0.0014381177,0.009830835,0.008726068,0.96219647],"study_design_scores_gemma":[0.008883244,0.01088607,0.010618857,0.00062145904,0.00014472281,0.0032070058,0.028753122,0.011610951,0.054025967,0.011833183,0.8573596,0.0020558436],"about_ca_topic_score_codex":0.0000010967867,"about_ca_topic_score_gemma":9.3453764e-7,"teacher_disagreement_score":0.96014065,"about_ca_system_score_codex":0.000009798236,"about_ca_system_score_gemma":0.000036723606,"threshold_uncertainty_score":0.27583215},"labels":[],"label_agreement":null},{"id":"W4256026626","doi":"10.1017/s1047759419000394","title":"Images on Greek, Hellenistic and Roman coins: a conference held at Athens in 2012 - P. P. IOSSIF, FR. DE CALLATAŸ, R. VEYMIERS (edd.), TYPOI. GREEK AND ROMAN COINS SEEN THROUGH THEIR IMAGES. NOBLE <i>ISSUES</i>, HUMBLE <i>USERS? PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ORGANIZED BY THE BELGIAN AND FRENCH SCHOOLS AT ATHENS, 26-28 SEPTEMBER 2012</i> (Série Histoire 3; Presses Universitaires de Liège2018). Pp. 526, charts, pls. 71 mostly in colour. ISBN 978-2-87562-157-3.","year":2019,"lang":"en","type":"article","venue":"Journal of Roman Archaeology","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Art; Classics; Ancient history; History","score_opus":0.011979802030823604,"score_gpt":0.2212000224869808,"score_spread":0.2092202204561572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256026626","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98361516,0.0038984718,0.00046481364,0.010115758,0.00032015776,0.00053482776,0.00008778224,0.000034199467,0.0009288412],"genre_scores_gemma":[0.994349,0.0026291162,0.00078909606,0.00041575803,0.000044035005,0.0000106150155,0.000007688891,0.000027722384,0.0017269395],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977442,0.00023421207,0.0005908069,0.00052846613,0.0003958586,0.0005064712],"domain_scores_gemma":[0.9981398,0.00031820935,0.00070399896,0.00028502176,0.00035844286,0.00019450719],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062630576,0.0003683362,0.00060052937,0.00018982601,0.000304763,0.00010896395,0.00086787064,0.0002045046,0.00020189716],"category_scores_gemma":[0.000105608065,0.0002709415,0.000088706816,0.00027895946,0.0009458401,0.00095258944,0.0009738209,0.0007150351,0.000010502313],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023488663,0.0011207379,0.38053346,0.00076001603,0.00077277084,0.00046916617,0.07782992,0.00008582291,0.23814714,0.012154163,0.2815647,0.0042132414],"study_design_scores_gemma":[0.030701496,0.00847368,0.57075346,0.004723606,0.0006179333,0.018848749,0.011511136,0.020394048,0.04467486,0.08507213,0.20045912,0.0037697912],"about_ca_topic_score_codex":0.00069655443,"about_ca_topic_score_gemma":0.0026479082,"teacher_disagreement_score":0.19347228,"about_ca_system_score_codex":0.00031870187,"about_ca_system_score_gemma":0.00026437987,"threshold_uncertainty_score":0.99997425},"labels":[],"label_agreement":null},{"id":"W4281779216","doi":"10.5281/zenodo.6606758","title":"Coinbase Customer Service (+1 888.926.1893) 🎈 Number 🎈","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Business; Service (business); Marketing","score_opus":0.04072263912779542,"score_gpt":0.2508141807703313,"score_spread":0.21009154164253588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281779216","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03744899,0.00009481373,0.13152137,0.006613992,0.001813621,0.001132381,0.00044729497,0.0062941597,0.81463337],"genre_scores_gemma":[0.9923317,0.000027729579,0.001041277,0.0020184936,0.00017305402,3.4869507e-7,0.000806485,0.0007730163,0.0028278858],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998052,0.00043166426,0.00021168911,0.00045024263,0.00053757057,0.00031684368],"domain_scores_gemma":[0.9987486,0.000021526563,0.00009480788,0.00050632574,0.00045961302,0.00016912752],"candidate_categories":["sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00059947337,0.000121399426,0.000104642284,0.00020367153,0.0033926805,0.00070155674,0.0013848742,0.000030128873,0.044758312],"category_scores_gemma":[0.000110617555,0.00014085199,0.00005493045,0.0015059798,0.00004224482,0.0004637829,0.0022629912,0.00035845596,0.03712956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006460313,0.00041586944,0.000007753452,0.000051578998,0.000036054982,0.000042754244,0.0024532157,0.00017681652,0.0021155037,0.01630309,0.5975215,0.38081124],"study_design_scores_gemma":[0.00044412195,0.00008701794,0.00011582082,0.000005276265,0.0000050521903,0.00043304436,0.00020983211,0.0062066675,0.00027674547,0.00045898499,0.9915744,0.00018302072],"about_ca_topic_score_codex":0.000026940359,"about_ca_topic_score_gemma":4.546593e-7,"teacher_disagreement_score":0.95488274,"about_ca_system_score_codex":0.00017152772,"about_ca_system_score_gemma":0.000007227591,"threshold_uncertainty_score":0.9979048},"labels":[],"label_agreement":null},{"id":"W4285273740","doi":"10.1063/5.0081778","title":"State of art on: Features extraction, recognition and detection of currency notes","year":2022,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Counterfeit; Currency; Computer science; Artificial intelligence; Deep learning; Feature extraction; Political science; Economics; Monetary economics","score_opus":0.041935499169725006,"score_gpt":0.2660731756123366,"score_spread":0.22413767644261162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285273740","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86672217,0.00013688386,0.12813298,0.00042934675,0.0007805991,0.0003861132,0.000045777746,0.00018155163,0.0031845751],"genre_scores_gemma":[0.9991533,0.00009165834,0.0005560101,0.000047468075,0.000020549935,0.000057527315,0.000006299514,0.0000053729736,0.00006182007],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895126,0.00002266075,0.0002706503,0.000308087,0.00031477213,0.00013259063],"domain_scores_gemma":[0.9990615,0.00007000533,0.0003056003,0.00008637724,0.00042674676,0.0000497462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025954354,0.000114805945,0.00015344202,0.0002677894,0.00015854208,0.000056002074,0.00017042109,0.0000303109,0.00006753096],"category_scores_gemma":[0.00011716877,0.00012163857,0.00004434552,0.0004634758,0.00005328437,0.00050895906,0.00009845833,0.00024626162,0.000010887899],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010067308,0.00019035296,0.0009970925,0.00010557915,0.000015842594,6.372335e-7,0.002130925,0.000002439937,0.1536162,0.00096044055,0.00024482375,0.841635],"study_design_scores_gemma":[0.001938679,0.0049333875,0.058901533,0.0003184259,0.00006535551,0.00038677352,0.0011754453,0.028288372,0.7452956,0.15257725,0.0051364033,0.0009828076],"about_ca_topic_score_codex":0.00002386928,"about_ca_topic_score_gemma":0.000007537832,"teacher_disagreement_score":0.84065217,"about_ca_system_score_codex":0.000029262996,"about_ca_system_score_gemma":0.000046136578,"threshold_uncertainty_score":0.4960277},"labels":[],"label_agreement":null},{"id":"W4289455316","doi":"10.12816/0047901","title":"Possibility of using polymer banknotes for Egyptian currency production","year":2018,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Environmental science","score_opus":0.41846258819908044,"score_gpt":0.5907550724327945,"score_spread":0.17229248423371402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289455316","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86066365,0.004241397,0.12935928,0.00015530453,0.0038851718,0.00069619116,0.00003187892,0.00006866481,0.0008984443],"genre_scores_gemma":[0.9953266,0.00033252215,0.003604632,0.000051706043,0.0005602497,0.000022302022,0.000004039741,0.000017461985,0.00008053442],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976392,0.0001844004,0.00082745677,0.000575016,0.00047417323,0.00029978686],"domain_scores_gemma":[0.99715275,0.0001427399,0.00091229787,0.0006123936,0.0010300388,0.00014978882],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012732422,0.00021786077,0.00045204224,0.00068877405,0.00032922294,0.00062151364,0.0018012807,0.00008618647,0.0013349764],"category_scores_gemma":[0.0005010991,0.00020277163,0.00020173818,0.0013233684,0.00019326036,0.0036799395,0.0004976284,0.00018106647,0.000008340108],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024807325,0.0005641304,0.11876778,0.00024039665,0.00011502639,0.0000013960228,0.00060820713,0.000024036406,0.39220044,0.00039573153,0.005023462,0.4818113],"study_design_scores_gemma":[0.00053727464,0.000054791042,0.16596681,0.00043771678,0.00006303233,0.000029853865,0.000035647776,0.00515463,0.80171895,0.024181232,0.0013678309,0.0004522233],"about_ca_topic_score_codex":0.00042012933,"about_ca_topic_score_gemma":0.000045599107,"teacher_disagreement_score":0.4813591,"about_ca_system_score_codex":0.000069590584,"about_ca_system_score_gemma":0.00019826303,"threshold_uncertainty_score":0.99957794},"labels":[],"label_agreement":null},{"id":"W4312178529","doi":"10.18280/ria.360507","title":"Target Detection in Video Images Using HOG-Based Cascade Classifier","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Mustansiriyah University","keywords":"Magnification; Artificial intelligence; AdaBoost; Computer vision; Classifier (UML); Computer science; Cascading classifiers; True positive rate; Pattern recognition (psychology); Cascade; Feature (linguistics); Engineering","score_opus":0.05846822758956228,"score_gpt":0.28543199133284514,"score_spread":0.22696376374328286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312178529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055074748,0.00015661014,0.9419426,0.00028751924,0.001146631,0.00022472136,0.0000065172835,0.0001488856,0.0010117639],"genre_scores_gemma":[0.99388355,0.00000633882,0.0055282884,0.0002069062,0.000057150944,0.0000740388,0.0000041428957,0.000012466856,0.00022712932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983098,0.00019707915,0.00040489912,0.0005054151,0.00025754515,0.000325272],"domain_scores_gemma":[0.9992128,0.00011094638,0.00012795188,0.00040983545,0.000065686996,0.00007280137],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055780826,0.00014472936,0.00015740148,0.00040813087,0.00040073317,0.00010796916,0.00042860623,0.000050790557,0.00045735433],"category_scores_gemma":[0.00007749655,0.00016932914,0.00010541012,0.0014156805,0.00004280377,0.00034625462,0.00016853459,0.00038651406,0.0001223053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000059228307,0.00047209242,0.0006125458,0.00005385838,0.0000094617035,0.00007972985,0.001245353,0.5619328,0.099206105,0.001011833,0.00026743556,0.33504957],"study_design_scores_gemma":[0.0000517337,0.00008102113,0.00007949479,0.000012747708,0.0000023812925,0.00004920296,0.00021475197,0.74162173,0.25270885,0.00178547,0.0032281845,0.0001644334],"about_ca_topic_score_codex":0.00014497375,"about_ca_topic_score_gemma":0.000049669736,"teacher_disagreement_score":0.9388088,"about_ca_system_score_codex":0.00024369845,"about_ca_system_score_gemma":0.00007152435,"threshold_uncertainty_score":0.6905042},"labels":[],"label_agreement":null},{"id":"W4313344236","doi":"10.1007/978-3-031-23028-8_30","title":"An Autoencoding Method for Detecting Counterfeit Coins","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Counterfeit; Computer science; Artificial intelligence; Image (mathematics); Similarity (geometry); Scale-invariant feature transform; Anomaly detection; Pattern recognition (psychology); Computer vision","score_opus":0.04054337409621988,"score_gpt":0.3143644285758608,"score_spread":0.2738210544796409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313344236","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000030528772,0.00011677475,0.99214184,0.00021584146,0.004216062,0.00058289955,0.000019427322,0.00038187936,0.0022947174],"genre_scores_gemma":[0.043277405,0.000020051766,0.9543289,0.0014028719,0.00067326875,0.00007815962,0.000014880361,0.000051714538,0.00015276216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99622214,0.00007522919,0.00053243793,0.0017119566,0.0008346841,0.0006235808],"domain_scores_gemma":[0.9973334,0.00081674935,0.0003546022,0.001043757,0.00027279032,0.00017870463],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019913593,0.00044792227,0.00045461228,0.0010481658,0.0008141035,0.0007182961,0.0023979095,0.00021152297,0.00011337889],"category_scores_gemma":[0.00014219701,0.0004680816,0.00018722436,0.000670882,0.00016988078,0.00105696,0.00069624814,0.00086470525,0.00001379357],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000090421845,0.00002510814,0.0000051940087,0.00003392909,0.0000073520087,0.000014373538,0.0006368745,0.030450603,0.00049863465,0.0074637374,0.000009272251,0.9608459],"study_design_scores_gemma":[0.0002803449,0.00046583035,0.000008569998,0.00010016822,0.000009851643,0.0001436653,5.6314786e-7,0.92040086,0.0020419378,0.06856453,0.0074317753,0.0005519313],"about_ca_topic_score_codex":0.000034512297,"about_ca_topic_score_gemma":0.00013890602,"teacher_disagreement_score":0.96029395,"about_ca_system_score_codex":0.0004992015,"about_ca_system_score_gemma":0.00048240775,"threshold_uncertainty_score":0.9997771},"labels":[],"label_agreement":null},{"id":"W4315606042","doi":"10.1109/globecom48099.2022.10001253","title":"Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Header; Table (database); Task (project management); Modal; Big data; Information extraction; Information retrieval; Artificial intelligence; Data mining; Natural language processing","score_opus":0.1309356717956981,"score_gpt":0.3231001213748782,"score_spread":0.1921644495791801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315606042","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032692982,0.00581551,0.97111106,0.00095672073,0.0017798203,0.00090152567,0.0045131906,0.0004404323,0.01121244],"genre_scores_gemma":[0.9545271,0.0008552892,0.04169151,0.00030447502,0.000032476826,0.00032718343,0.0019052916,0.000017460188,0.00033925625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99604744,0.00097097334,0.00080032775,0.00095095014,0.0007400003,0.0004902875],"domain_scores_gemma":[0.9922508,0.00015538122,0.00048251112,0.0066528423,0.00028419244,0.00017428324],"candidate_categories":["metaepi_narrow","sts","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.0012269263,0.00033642355,0.00046919918,0.00020355894,0.0018472379,0.00043760284,0.010357384,0.00008062944,0.00025218772],"category_scores_gemma":[0.00015728522,0.00039011598,0.00016491869,0.002098623,0.00021224817,0.0006706711,0.0084529575,0.00082688034,0.000036565412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003383396,0.010400862,0.013138822,0.00044222208,0.0010501631,0.000041757023,0.0050482415,0.005584355,0.007791681,0.12777503,0.036617044,0.7917715],"study_design_scores_gemma":[0.0009439481,0.000144759,0.0004518409,0.000039157723,0.000050134342,0.00009970469,0.0007623026,0.95991486,0.00012471928,0.0011664442,0.035840057,0.00046208827],"about_ca_topic_score_codex":0.0019219128,"about_ca_topic_score_gemma":0.0011520741,"teacher_disagreement_score":0.9543305,"about_ca_system_score_codex":0.00030202413,"about_ca_system_score_gemma":0.00054556,"threshold_uncertainty_score":0.9998551},"labels":[],"label_agreement":null},{"id":"W4320920518","doi":"10.9734/ajrcos/2023/v15i1313","title":"Auto Encoder Fixed-Target Training Features Extraction Approach for Binary Classification Problems","year":2023,"lang":"en","type":"article","venue":"Asian Journal of Research in Computer Science","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Autoencoder; Artificial intelligence; Computer science; Feature extraction; Binary classification; Classifier (UML); Pattern recognition (psychology); Domain knowledge; Binary number; Feature (linguistics); Machine learning; Encoder; Deep belief network; Data mining; Deep learning; Support vector machine; Mathematics","score_opus":0.23440281118136352,"score_gpt":0.4196693921892278,"score_spread":0.18526658100786425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320920518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005306784,0.000058846505,0.9896698,0.0027166642,0.0011764701,0.00035472156,0.0000013281219,0.00008067189,0.00063469756],"genre_scores_gemma":[0.7284446,0.000025908961,0.27110526,0.0000294724,0.00030587488,0.000034873436,0.0000024538383,0.0000068873387,0.000044683176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99685544,0.0002554089,0.00045837899,0.00048037962,0.0013313169,0.00061910565],"domain_scores_gemma":[0.99809927,0.00027291293,0.00022068505,0.00031145525,0.00087244605,0.0002232104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009372333,0.00011243155,0.00018048004,0.0024983021,0.0004681671,0.0005923123,0.0013880589,0.00007045927,0.0000035634143],"category_scores_gemma":[0.00023033294,0.000098305034,0.0000944398,0.0054015727,0.00028615474,0.0021901338,0.00018752662,0.00063869456,0.00001499399],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000326239,0.00018105349,0.00031825312,0.00008357902,0.00000899869,0.00003490063,0.005538777,0.009521086,0.007967352,0.0022099593,0.00270998,0.97139347],"study_design_scores_gemma":[0.00045537678,0.000534887,0.015933553,0.00013437927,0.0000012403957,0.00029355512,0.00049276184,0.96948534,0.00079813926,0.010854869,0.00088524277,0.00013066182],"about_ca_topic_score_codex":0.0000028332795,"about_ca_topic_score_gemma":0.0000017814647,"teacher_disagreement_score":0.97126275,"about_ca_system_score_codex":0.00018167854,"about_ca_system_score_gemma":0.0007225896,"threshold_uncertainty_score":0.5711681},"labels":[],"label_agreement":null},{"id":"W4377832616","doi":"10.18280/ts.400233","title":"Deep Learning-Based Intelligent Image Recognition and Its Applications in Financial Technology Services","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Image (mathematics); Computer vision; Business","score_opus":0.02385016223493508,"score_gpt":0.25182909465585746,"score_spread":0.22797893242092238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832616","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36989653,0.00009141398,0.62798446,0.00044696094,0.00010310567,0.00046262637,0.0000074990007,0.0007866794,0.000220715],"genre_scores_gemma":[0.99858844,0.00003467889,0.00093889807,0.000067016,0.000037469545,0.00025468995,0.0000644065,0.0000054191078,0.00000896739],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991869,0.00003685769,0.00019464485,0.00027985452,0.00012566528,0.00017612646],"domain_scores_gemma":[0.9996972,0.000051587023,0.000060336835,0.000075129945,0.000073542265,0.000042196465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018981597,0.00009424641,0.000087257635,0.00048061274,0.00012575652,0.000059671445,0.00015648144,0.000063536856,0.000052284577],"category_scores_gemma":[0.000013005741,0.000099315934,0.000026065403,0.001057217,0.000021255273,0.00019073697,0.00005577518,0.00014177852,0.00028877708],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000029227542,0.00023357337,0.0013917229,0.0002111525,0.0000087700555,0.000016798247,0.00087521574,0.0011744892,0.008458354,0.0026261865,0.000036208352,0.9849383],"study_design_scores_gemma":[0.0009316539,0.0002679561,0.009807801,0.00008925741,0.000011255972,0.000009666917,0.00045422566,0.94731534,0.03006176,0.007127918,0.00360527,0.00031790853],"about_ca_topic_score_codex":0.000007151658,"about_ca_topic_score_gemma":0.0000525636,"teacher_disagreement_score":0.9846204,"about_ca_system_score_codex":0.00003488774,"about_ca_system_score_gemma":0.000021313232,"threshold_uncertainty_score":0.40499863},"labels":[],"label_agreement":null},{"id":"W4378446257","doi":"10.2352/issn.2169-4451.2005.21.1.art00065_1","title":"Advances in Digital Imaging and their Impact on the Bank of Canada's Bank Note Image Distribution Policy","year":2005,"lang":"en","type":"article","venue":"Technical programs and proceedings/Technical program and proceedings","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Digital watermarking; Image (mathematics); Distribution (mathematics); Quality (philosophy); Digital image; Business; Computer science; Digital imaging; Artificial intelligence; Image processing; Mathematics","score_opus":0.008635553019184578,"score_gpt":0.2682925960239789,"score_spread":0.25965704300479436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378446257","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9305865,0.005065741,0.008966767,0.024731798,0.00015658433,0.006535432,0.00011964264,0.0027280261,0.021109477],"genre_scores_gemma":[0.9968139,0.0005514963,0.0021098133,0.00015377422,0.000107752705,0.00022623557,0.000009653749,0.000019620662,0.0000077739805],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9976508,0.000006916332,0.00057844917,0.0007288505,0.0003779283,0.0006570467],"domain_scores_gemma":[0.9989141,0.00012956373,0.0002480988,0.00012885362,0.0002847473,0.00029459852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006610428,0.00042862346,0.00043253065,0.00015996512,0.00027301742,0.00079696503,0.00042518327,0.00016636147,0.0000026385262],"category_scores_gemma":[0.0003896584,0.00026564754,0.00010614234,0.0011188912,0.00053731987,0.0015064874,0.00036567164,0.0006709964,6.2586844e-7],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007599066,0.00022241603,0.0029483426,0.00009091122,0.0000049285027,9.1059326e-7,0.000093456525,1.543529e-7,0.0009465811,0.015237183,0.00027473096,0.9801044],"study_design_scores_gemma":[0.009031732,0.015656598,0.17444165,0.004337212,0.00015188,0.0037882267,0.0014273762,0.10464691,0.01933751,0.37986642,0.2803894,0.0069250893],"about_ca_topic_score_codex":0.00081681384,"about_ca_topic_score_gemma":0.00067701424,"teacher_disagreement_score":0.9731793,"about_ca_system_score_codex":0.00015891244,"about_ca_system_score_gemma":0.00012100222,"threshold_uncertainty_score":0.99997956},"labels":[],"label_agreement":null},{"id":"W4381334545","doi":"10.2139/ssrn.4485825","title":"A Framework for Image-Based Counterfeit Coin Detection Using Pruned Fuzzy Associative Classifier","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Counterfeit; Associative property; Artificial intelligence; Pattern recognition (psychology); Computer science; Fuzzy logic; Image (mathematics); Classifier (UML); Computer vision; Mathematics; Geography","score_opus":0.06480989640754443,"score_gpt":0.3329041642220432,"score_spread":0.2680942678144988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381334545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014633929,0.00030403942,0.9786061,0.0007681431,0.004550002,0.00066151965,0.00003441286,0.00035936796,0.00008246763],"genre_scores_gemma":[0.9458013,0.0006891292,0.050127435,0.0003075931,0.0020949005,0.00025474068,0.00004034076,0.00013951861,0.0005450305],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.995644,0.0003025702,0.00062815484,0.00073469,0.00059094146,0.0020996465],"domain_scores_gemma":[0.9973031,0.0004052349,0.0010298456,0.0004146558,0.0007200841,0.0001270232],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0028149984,0.00040592268,0.00045478065,0.0005339638,0.00060311507,0.00075295725,0.0007587321,0.0006044782,0.00000827299],"category_scores_gemma":[0.00061115273,0.00042155493,0.0005483288,0.0005555695,0.000050260234,0.00039603814,0.0002325118,0.00552199,0.000051195424],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020382705,0.0016031916,0.0009500854,0.0010538413,0.005379218,0.00008148396,0.003774618,0.011451564,0.01291849,0.19427949,0.0012203912,0.7652494],"study_design_scores_gemma":[0.0008046321,0.0003527061,0.00014107871,0.00027041958,0.000097553355,0.00010390424,0.00022523722,0.21612777,0.0012790402,0.7799656,0.00018111106,0.00045093938],"about_ca_topic_score_codex":0.00007122464,"about_ca_topic_score_gemma":0.00073686114,"teacher_disagreement_score":0.93116736,"about_ca_system_score_codex":0.0042134896,"about_ca_system_score_gemma":0.0051091765,"threshold_uncertainty_score":0.99982363},"labels":[],"label_agreement":null},{"id":"W4384158177","doi":"10.1109/aiiot58121.2023.10174559","title":"AI-Based Currency Exchange and Identification Model","year":2023,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Liberian dollar; Currency; Exchange rate; Tourism; Identification (biology); Us dollar; Foreign exchange market; Destinations; Business; Computer science; Economics; Monetary economics; Geography; Finance","score_opus":0.05264975781068616,"score_gpt":0.3007036795187115,"score_spread":0.24805392170802537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384158177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007920276,0.000031397696,0.98761296,0.0018942958,0.000326654,0.00007901993,0.0000025007766,0.00055844494,0.0015744185],"genre_scores_gemma":[0.9950074,0.00003912948,0.0023778526,0.00056666014,0.000027912936,0.00003994302,0.000013810265,0.000003996736,0.001923292],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947727,0.000014873647,0.00009705379,0.00020049537,0.000113978414,0.000096307514],"domain_scores_gemma":[0.9997009,0.000017567087,0.00002364453,0.00016337365,0.000051315637,0.000043201086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014686042,0.00004878668,0.000040609193,0.00016164518,0.00007569003,0.00010957681,0.000109616296,0.000024287947,0.000027887918],"category_scores_gemma":[0.000014031764,0.00004634796,0.000019557274,0.00045650738,0.000009150596,0.0003202642,0.00004364573,0.000042166725,0.0003357986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000031318743,0.00005465208,0.00032670976,0.000058874328,0.0000034349875,0.0000016564577,0.00037550626,0.0005380136,0.0025756345,0.016909298,0.027704287,0.9514488],"study_design_scores_gemma":[0.00012721153,0.000010377788,0.0009857194,0.0000042908596,0.0000012558068,0.0000011138187,0.000004395166,0.9828552,0.002030884,0.013162755,0.0007502416,0.0000665517],"about_ca_topic_score_codex":0.0000075664993,"about_ca_topic_score_gemma":0.000014746956,"teacher_disagreement_score":0.98708713,"about_ca_system_score_codex":0.000010598106,"about_ca_system_score_gemma":0.000021834292,"threshold_uncertainty_score":0.43161234},"labels":[],"label_agreement":null},{"id":"W4385078307","doi":"10.18280/isi.280319","title":"Evaluation of Top Pretrained Models Using Transfer Learning on Banknote Dataset with Quality Parameter","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Banknote; Transfer of learning; Quality (philosophy); Computer science; Transfer (computing); Artificial intelligence; Machine learning","score_opus":0.11915356156544098,"score_gpt":0.3276175598565155,"score_spread":0.20846399829107448,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47588688,0.0000048787692,0.5227491,0.000011092424,0.00010976716,0.00024595574,0.00006279107,0.0001379833,0.00079150434],"genre_scores_gemma":[0.9974159,0.0000049857495,0.0019529408,0.000044981687,0.000014708899,0.000035018034,0.00052271143,0.0000051905863,0.0000035366438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980545,0.00029132018,0.00047034537,0.00015393058,0.0008488409,0.00018104463],"domain_scores_gemma":[0.99889356,0.00011459041,0.00016778063,0.00025976243,0.00051985437,0.000044433757],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023437145,0.00012288083,0.00015322793,0.0003558566,0.0001679431,0.00015319265,0.00016322585,0.00007046656,0.000020508549],"category_scores_gemma":[0.00030091143,0.00010901039,0.000043171658,0.0008388973,0.00004737268,0.0037099,0.000029193885,0.000127474,0.0000310289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009917868,0.000035909554,0.00016532966,0.00023484968,0.00005565451,5.4487924e-7,0.009821815,0.40990987,0.00061851455,0.003562016,0.00007703858,0.57541925],"study_design_scores_gemma":[0.00062180846,0.00017612029,0.001571447,0.000121811165,0.000028618415,0.000007770273,0.00025328036,0.98708695,0.0033936212,0.0065022535,0.00008226499,0.00015405033],"about_ca_topic_score_codex":0.000051928637,"about_ca_topic_score_gemma":0.0000095029945,"teacher_disagreement_score":0.5771771,"about_ca_system_score_codex":0.0001509152,"about_ca_system_score_gemma":0.00012601806,"threshold_uncertainty_score":0.44453147},"labels":[],"label_agreement":null},{"id":"W4386432560","doi":"10.1007/978-981-19-9822-5_106","title":"Deep Learning Models for Future Occupancy Prediction in Residential Buildings","year":2023,"lang":"en","type":"book-chapter","venue":"Environmental science and engineering","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Occupancy; Computer science; Deep learning; Artificial intelligence; Viewpoints; Sequence (biology); Multilayer perceptron; Machine learning; Perceptron; Recurrent neural network; Term (time); Artificial neural network; Construct (python library); Long short term memory; Pattern recognition (psychology); Engineering","score_opus":0.01299913745571064,"score_gpt":0.18990043620653954,"score_spread":0.1769012987508289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386432560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015484646,0.0013070604,0.9572161,0.0001906014,0.0056402814,0.0011506851,0.000037528705,0.0009008624,0.018072234],"genre_scores_gemma":[0.9225908,0.004360549,0.016171884,0.000091518385,0.0017746782,0.00020900986,0.00007976069,0.00015027686,0.05457151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876267,0.0000024053038,0.00017321404,0.00046830013,0.00035488297,0.00023853345],"domain_scores_gemma":[0.99972105,0.00002205329,0.00005073196,0.000116596726,0.000007566727,0.000081971564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003082952,0.00015831548,0.00012137391,0.00034026237,0.00016993313,0.00011769548,0.00021795774,0.000110124165,0.000011358554],"category_scores_gemma":[0.000012928973,0.00017510542,0.000041897983,0.00011240946,0.000057960868,0.0007710133,0.00016381485,0.00024607088,0.000020596884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032921325,0.000043883174,0.00019313955,0.0002431177,0.00003592626,0.0000357763,0.003164976,0.176246,0.015588,0.089662544,0.00031995194,0.7144338],"study_design_scores_gemma":[0.00022404738,0.000076076714,0.0014140793,0.00009746511,0.0000067947462,0.00001752443,0.0000415132,0.98517656,0.00021092169,0.0041265935,0.008333567,0.00027487494],"about_ca_topic_score_codex":0.0000031081481,"about_ca_topic_score_gemma":0.0000035247738,"teacher_disagreement_score":0.9410442,"about_ca_system_score_codex":0.00016663896,"about_ca_system_score_gemma":0.000015890384,"threshold_uncertainty_score":0.7140592},"labels":[],"label_agreement":null},{"id":"W4392327608","doi":"10.51644/9780889207837-fm","title":"Front Matter","year":2006,"lang":"en","type":"paratext","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"D2L (Canada)","funders":"Wilfrid Laurier University; Simon Fraser University; University of Calgary","keywords":"Front (military); Geology; Oceanography","score_opus":0.01759148640702269,"score_gpt":0.2489699783957621,"score_spread":0.23137849198873942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392327608","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000024848498,0.00012951027,0.41903389,0.00012081034,0.0037357346,0.000049920916,0.0000060391376,0.000054948716,0.5768666],"genre_scores_gemma":[0.00020739826,0.000018271634,0.004669398,0.0009538773,0.0003853755,0.00001855714,0.00007344146,0.000008217199,0.99366546],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991865,0.000023669705,0.00016276295,0.00032309667,0.00015168461,0.000152268],"domain_scores_gemma":[0.9995085,0.000013193473,0.000064030275,0.0003276265,0.00004851518,0.000038126414],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00003674457,0.00014043978,0.00013782538,0.0001258875,0.000045738056,0.00018856782,0.00034628005,0.00014588595,0.07822886],"category_scores_gemma":[0.0000010101136,0.000120642166,0.000088208115,0.000084637286,0.0000100376,0.00014779682,0.00009363479,0.00016533631,0.6621258],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[3.104378e-7,0.0000129918635,0.000002098288,0.000010022592,0.0000029539592,6.6248816e-7,0.000005023004,0.0000027893373,0.0000036883773,0.000049718663,0.98750883,0.012400906],"study_design_scores_gemma":[0.0000728146,0.000012434797,0.000090193644,0.000016809277,0.000002278223,0.000011273242,5.102617e-7,0.0008968559,0.0002931485,0.00033463506,0.99807584,0.00019323258],"about_ca_topic_score_codex":0.0001230024,"about_ca_topic_score_gemma":0.000021301988,"teacher_disagreement_score":0.583897,"about_ca_system_score_codex":0.00003047425,"about_ca_system_score_gemma":0.00003668443,"threshold_uncertainty_score":0.92261374},"labels":[],"label_agreement":null},{"id":"W4392467796","doi":"10.1016/j.eswa.2024.123577","title":"A framework for image-based counterfeit coin detection using pruned fuzzy associative classifier","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Associative property; Counterfeit; Artificial intelligence; Pattern recognition (psychology); Fuzzy logic; Classifier (UML); Data mining; Machine learning; Mathematics","score_opus":0.0442918992567024,"score_gpt":0.3288920458183135,"score_spread":0.2846001465616111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392467796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000528456,0.0005537205,0.99491316,0.00034835466,0.0007632241,0.001632277,0.000038391707,0.00063313165,0.00058926427],"genre_scores_gemma":[0.88570154,0.0000069551984,0.10631079,0.00017107364,0.0005773301,0.007001966,0.000021632992,0.000037363967,0.00017135237],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986578,0.00006588649,0.00027826667,0.0005127205,0.0002537277,0.00023155572],"domain_scores_gemma":[0.99877805,0.00033091032,0.00013669922,0.00036859862,0.00030032903,0.00008541114],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023340853,0.00017056247,0.00017492613,0.0001866785,0.00036334465,0.00057641405,0.0002145006,0.00013013117,0.0000058599276],"category_scores_gemma":[0.000033034685,0.00014797646,0.000087490385,0.000824745,0.000041958752,0.00039818897,0.000020703754,0.00016858088,0.00007282707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044107842,0.0015787075,0.00029247857,0.0023840796,0.0012058825,0.000030425173,0.014928457,0.0027859337,0.15066367,0.51628405,0.012157547,0.29724768],"study_design_scores_gemma":[0.00043314055,0.0001532822,0.000045116594,0.0003841158,0.000028912205,0.00004589851,0.0003812551,0.9269576,0.006908167,0.0042206785,0.060067844,0.00037399313],"about_ca_topic_score_codex":0.00007039627,"about_ca_topic_score_gemma":0.000023175753,"teacher_disagreement_score":0.9241717,"about_ca_system_score_codex":0.0003042266,"about_ca_system_score_gemma":0.00017691615,"threshold_uncertainty_score":0.6034305},"labels":[],"label_agreement":null},{"id":"W4392838374","doi":"10.23977/jaip.2024.070108","title":"The research on banknote authenticity discrimination analysis algorithm based on wavelet transform features","year":2024,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Banknote; Wavelet; Computer science; Artificial intelligence; Wavelet transform; Pattern recognition (psychology); Algorithm; Computer vision","score_opus":0.12594016363380794,"score_gpt":0.432988656651899,"score_spread":0.30704849301809106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392838374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005802372,0.00017130036,0.97117555,0.023401594,0.0017806725,0.00013470952,0.000004168199,0.000038677157,0.002713075],"genre_scores_gemma":[0.9888718,0.0002614732,0.009891127,0.00032907652,0.000449949,0.000008162813,0.0000018056962,0.000009978973,0.00017663767],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687266,0.00064793805,0.0005692423,0.00027610446,0.0013655394,0.00026852934],"domain_scores_gemma":[0.99371797,0.004737122,0.00021551174,0.0002918026,0.0009300088,0.000107596905],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.005873824,0.00012647176,0.00015582678,0.0009927428,0.000543025,0.0013767642,0.0005582002,0.00007671645,0.000047454505],"category_scores_gemma":[0.001818355,0.00008192464,0.0002877319,0.0026654184,0.00009764051,0.0011285119,0.000026707288,0.0011416724,0.0001234951],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017408558,0.00022273211,2.431854e-7,0.0000078556595,0.00010815603,0.00005094227,0.00102213,0.0015768181,0.00009210652,0.02419971,0.0004175386,0.9721277],"study_design_scores_gemma":[0.000029605766,0.0010170693,0.00008785605,0.000108960594,0.0002119858,0.00006068631,0.0012306786,0.91000843,0.03319925,0.039944235,0.013964683,0.00013654314],"about_ca_topic_score_codex":0.00003226656,"about_ca_topic_score_gemma":0.00006296281,"teacher_disagreement_score":0.98829156,"about_ca_system_score_codex":0.000168448,"about_ca_system_score_gemma":0.00019628757,"threshold_uncertainty_score":0.9996599},"labels":[],"label_agreement":null},{"id":"W4394525114","doi":"10.6084/m9.figshare.23819794","title":"Additional file 1 of Identification of ancient glass categories based on distance discriminant analysis","year":2023,"lang":"en","type":"dataset","venue":"Figshare","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University Health Network","funders":"","keywords":"Linear discriminant analysis; Identification (biology); Discriminant; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Biology; Botany","score_opus":0.038549221871318255,"score_gpt":0.267474047889653,"score_spread":0.22892482601833475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394525114","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.6674543e-8,0.000008314842,0.00030543178,0.000009196969,0.00014165933,0.00011529297,0.99934316,0.00004191561,0.000034964607],"genre_scores_gemma":[0.000025167861,0.0000011400629,0.00006953483,0.000026416037,0.000038084643,0.000757748,0.9988919,0.000005455812,0.00018454339],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984404,0.000048543927,0.00042397948,0.0004057836,0.0005496232,0.00013169234],"domain_scores_gemma":[0.9973604,0.0008746337,0.00070594536,0.0006644818,0.00034715649,0.000047347967],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000045177112,0.00015554391,0.00026896142,0.00052462175,0.00006616708,0.000056173307,0.00054035994,0.00010365274,0.8183229],"category_scores_gemma":[0.00200048,0.00014831855,0.00024288078,0.001710583,0.000015503858,0.00012712013,0.000098207485,0.0001285367,0.003384538],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000039089896,0.00007212873,4.5153524e-8,0.00029059828,0.000031868578,0.0000021988924,0.000005076479,0.00009344521,0.0000011314215,0.000002148463,0.99889755,0.0005998744],"study_design_scores_gemma":[0.00004478249,0.0000421017,0.0016111333,0.0010238809,0.000041302446,3.8376857e-7,0.0000053007843,0.017220374,0.00009885224,0.000021951902,0.9797426,0.00014736607],"about_ca_topic_score_codex":0.000021395477,"about_ca_topic_score_gemma":0.00017368386,"teacher_disagreement_score":0.81493837,"about_ca_system_score_codex":0.000050271447,"about_ca_system_score_gemma":0.00016944144,"threshold_uncertainty_score":0.99739146},"labels":[],"label_agreement":null},{"id":"W4401418492","doi":"10.2139/ssrn.4919717","title":"Detection of Counterfeit Coins Using Multimodal Gpt-4 and Vision Transformer","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Counterfeit; Transformer; Artificial intelligence; Computer vision; Computer science; Engineering; Electrical engineering; History; Voltage","score_opus":0.014290228576176515,"score_gpt":0.29151042888161244,"score_spread":0.2772202003054359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401418492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33603895,0.0042481786,0.65758014,0.000116654104,0.0015964281,0.00016725132,0.000006973029,0.00006692267,0.00017847531],"genre_scores_gemma":[0.99557966,0.0035807998,0.0005269965,0.000014379289,0.0002101353,0.000004383912,0.0000020341865,0.000019490031,0.00006209753],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978342,0.00008790681,0.0004553215,0.0004118833,0.00035206164,0.00085859356],"domain_scores_gemma":[0.99928033,0.000028520437,0.00024644472,0.00019831565,0.00016872973,0.000077674114],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0010494954,0.00023702727,0.0002755146,0.00042661716,0.00015377851,0.00024158019,0.00029087224,0.00023319473,0.000006664985],"category_scores_gemma":[0.000015465886,0.00021873083,0.00020521277,0.00023312721,0.00004976778,0.00026549582,0.00017237155,0.0033031118,0.000009268274],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011217934,0.00012930554,0.000033343014,0.00033222314,0.000331102,0.000007243895,0.0009658445,0.00086198474,0.053166796,0.007028024,0.000008364371,0.9370236],"study_design_scores_gemma":[0.00094681117,0.0008645824,0.00013008418,0.00066915294,0.00018581307,0.0028964102,0.00027783026,0.55132747,0.0149976965,0.42683908,0.00033361677,0.0005314356],"about_ca_topic_score_codex":0.00013179975,"about_ca_topic_score_gemma":0.00057940994,"teacher_disagreement_score":0.93649215,"about_ca_system_score_codex":0.00065603794,"about_ca_system_score_gemma":0.0014397447,"threshold_uncertainty_score":0.9989963},"labels":[],"label_agreement":null},{"id":"W4401516372","doi":"10.53555/sfs.v10i1.2957","title":"Analysis Of Caffeine And Artificial Sweeteners As Active Ingredients In Popular Energy Drinks Available In Indian Market For Forensic Prospects","year":2023,"lang":"en","type":"article","venue":"Journal of Survey in Fisheries Sciences","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial Sweetener; Caffeine; Biotechnology; Biochemical engineering; Forensic science; Business; Toxicology; Chemistry; Food science; Biology; Engineering","score_opus":0.11351552829361815,"score_gpt":0.28276635191786864,"score_spread":0.16925082362425048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401516372","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998764,0.00003656305,0.00027607195,0.00013648256,0.00041554193,0.00008225247,0.000009994224,0.0000064450333,0.00027264963],"genre_scores_gemma":[0.9993459,0.00009023624,0.0004490828,0.00002363134,0.000016005417,0.0000070869955,0.000003610051,0.000002628662,0.000061803046],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9984603,0.00018485387,0.00052907714,0.00023708417,0.00035143594,0.00023725774],"domain_scores_gemma":[0.99910194,0.00026799258,0.00033569982,0.00008388691,0.00015434991,0.00005615251],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029272188,0.00009076229,0.0003347724,0.002056335,0.00006452027,0.00010564105,0.00028893855,0.000056938825,0.000022209573],"category_scores_gemma":[0.00069390656,0.00007984416,0.00006227222,0.0059273983,0.00017087907,0.0008355834,0.000071963914,0.00010777777,5.5545013e-7],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001378393,0.00006414508,0.9753578,0.00001245213,0.000035203226,0.000023235945,0.0013970231,0.00041871145,0.00021371743,0.000071985036,0.0003430434,0.021924887],"study_design_scores_gemma":[0.0004292282,0.00047009948,0.9753002,0.00006957092,0.000013058518,0.000008234808,0.00060181617,0.01784668,0.0016424191,0.0034137545,0.000080185724,0.00012474608],"about_ca_topic_score_codex":0.0020610346,"about_ca_topic_score_gemma":0.049470317,"teacher_disagreement_score":0.04740928,"about_ca_system_score_codex":0.00006653351,"about_ca_system_score_gemma":0.00015098166,"threshold_uncertainty_score":0.96787435},"labels":[],"label_agreement":null},{"id":"W4401539334","doi":"10.1109/iaict62357.2024.10617670","title":"A Neuro-Symbolic Learning System for Analyzing Listing Images in the Short-Term Rental Industry","year":2024,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Athabasca University","funders":"","keywords":"Listing (finance); Computer science; Renting; Term (time); Artificial intelligence; Engineering; Finance; Business","score_opus":0.04103307992376811,"score_gpt":0.30259853736120673,"score_spread":0.26156545743743864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401539334","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49311706,0.0003042496,0.49168277,0.00062168547,0.0015593857,0.00040760793,0.0000031137968,0.0009631618,0.011340963],"genre_scores_gemma":[0.9990729,0.0000022984968,0.0005470136,0.00004373599,0.00014927545,0.00004030551,0.0000024826836,0.0000056849267,0.00013627288],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992125,0.00007778611,0.00017240581,0.00026062803,0.00011575254,0.00016095166],"domain_scores_gemma":[0.9995759,0.00023787764,0.0000192711,0.000118181815,0.000022714576,0.000026052607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004008666,0.000078695666,0.0000768134,0.00016308187,0.00013508112,0.00056941545,0.00023024858,0.00005731186,0.0000059833583],"category_scores_gemma":[0.00006363885,0.000056838973,0.00006299038,0.0005248805,0.0000103515995,0.0003107609,0.000059553622,0.00041031084,0.00001341242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012277602,0.000080171,0.026635779,0.00071972073,0.000032462245,0.00022518444,0.0053661605,0.00034645433,0.011309608,0.01694132,0.0018077075,0.93652314],"study_design_scores_gemma":[0.00032071484,0.00017008405,0.0156841,0.0007528174,0.000029304949,0.00085030386,0.0025924514,0.96997625,0.006168033,0.00040269172,0.0026212775,0.00043200073],"about_ca_topic_score_codex":0.000016687483,"about_ca_topic_score_gemma":0.000012469125,"teacher_disagreement_score":0.96962976,"about_ca_system_score_codex":0.000033243057,"about_ca_system_score_gemma":0.000022431026,"threshold_uncertainty_score":0.5490886},"labels":[],"label_agreement":null},{"id":"W4402169092","doi":"10.32920/26866531.v1","title":"A U-Net Convolutional Neural Network Deep Learning Model Application for Identification of Energy Loss of Infrared Thermographic Exterior Building Envelope Images","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Algonquin College","funders":"","keywords":"Convolutional neural network; Envelope (radar); Artificial intelligence; Identification (biology); Deep learning; Artificial neural network; Building envelope; Infrared; Computer science; Computer vision; Remote sensing; Physics; Optics; Geology; Biology; Telecommunications; Meteorology; Botany; Thermal","score_opus":0.01814901784450036,"score_gpt":0.26407157809680454,"score_spread":0.2459225602523042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402169092","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017878423,0.0007826732,0.9797537,0.000081365586,0.0006003594,0.00041053258,0.000035821424,0.0002003233,0.00025683132],"genre_scores_gemma":[0.95856375,0.00016216746,0.04026329,0.000023355906,0.00012658925,0.00045064028,0.00013082284,0.000019374642,0.00026002867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804896,0.00008008077,0.0007687243,0.0006216008,0.00028137426,0.00019928374],"domain_scores_gemma":[0.99834573,0.0001139446,0.00063833134,0.00039909876,0.0004551571,0.000047706573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045637254,0.00021424636,0.00029793024,0.00033832144,0.00009789064,0.000116442294,0.0005028503,0.00019462763,0.000008330734],"category_scores_gemma":[0.000038286114,0.00022047588,0.0002554454,0.0005194136,0.000084798216,0.00017588258,0.00057105144,0.00027339917,0.0000018519727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060034963,0.0000896043,0.00024589052,0.0007753302,0.00012541447,3.8872906e-7,0.00029788297,0.64073735,0.045525357,0.07423425,0.00023099403,0.23767751],"study_design_scores_gemma":[0.00012844303,0.000026239384,0.00066602504,0.00008394628,0.00003189111,0.000004035219,0.0000065635268,0.81328994,0.0085388,0.17698096,0.00007155291,0.00017162172],"about_ca_topic_score_codex":0.000026270971,"about_ca_topic_score_gemma":0.000009194204,"teacher_disagreement_score":0.94068533,"about_ca_system_score_codex":0.00003981687,"about_ca_system_score_gemma":0.00011959156,"threshold_uncertainty_score":0.89907455},"labels":[],"label_agreement":null},{"id":"W4402170757","doi":"10.32920/26866531","title":"A U-Net Convolutional Neural Network Deep Learning Model Application for Identification of Energy Loss of Infrared Thermographic Exterior Building Envelope Images","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; Algonquin College","funders":"","keywords":"Convolutional neural network; Envelope (radar); Building envelope; Identification (biology); Artificial intelligence; Artificial neural network; Deep learning; Infrared; Energy (signal processing); Computer science; Net (polyhedron); Remote sensing; Pattern recognition (psychology); Machine learning; Meteorology; Geology; Physics; Optics; Mathematics; Telecommunications; Geometry; Statistics; Biology","score_opus":0.01814901784450036,"score_gpt":0.26407157809680454,"score_spread":0.2459225602523042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402170757","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017878423,0.0007826732,0.9797537,0.000081365586,0.0006003594,0.00041053258,0.000035821424,0.0002003233,0.00025683132],"genre_scores_gemma":[0.95856375,0.00016216746,0.04026329,0.000023355906,0.00012658925,0.00045064028,0.00013082284,0.000019374642,0.00026002867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804896,0.00008008077,0.0007687243,0.0006216008,0.00028137426,0.00019928374],"domain_scores_gemma":[0.99834573,0.0001139446,0.00063833134,0.00039909876,0.0004551571,0.000047706573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045637254,0.00021424636,0.00029793024,0.00033832144,0.00009789064,0.000116442294,0.0005028503,0.00019462763,0.000008330734],"category_scores_gemma":[0.000038286114,0.00022047588,0.0002554454,0.0005194136,0.000084798216,0.00017588258,0.00057105144,0.00027339917,0.0000018519727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060034963,0.0000896043,0.00024589052,0.0007753302,0.00012541447,3.8872906e-7,0.00029788297,0.64073735,0.045525357,0.07423425,0.00023099403,0.23767751],"study_design_scores_gemma":[0.00012844303,0.000026239384,0.00066602504,0.00008394628,0.00003189111,0.000004035219,0.0000065635268,0.81328994,0.0085388,0.17698096,0.00007155291,0.00017162172],"about_ca_topic_score_codex":0.000026270971,"about_ca_topic_score_gemma":0.000009194204,"teacher_disagreement_score":0.94068533,"about_ca_system_score_codex":0.00003981687,"about_ca_system_score_gemma":0.00011959156,"threshold_uncertainty_score":0.89907455},"labels":[],"label_agreement":null},{"id":"W4402905736","doi":"10.1167/jov.24.10.1523","title":"When Machines Outshine Humans in Object Recognition, Benchmarking Dilemma","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Benchmarking; Dilemma; Computer science; Artificial intelligence; Object (grammar); Psychology; Business; Philosophy; Epistemology","score_opus":0.027523302541218153,"score_gpt":0.303763590257136,"score_spread":0.27624028771591785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402905736","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6365404,0.0024317217,0.3450256,0.003216669,0.009057199,0.00018451514,0.000006304753,0.0001723053,0.0033652806],"genre_scores_gemma":[0.99113315,0.00016465722,0.007992196,0.00010995673,0.0005345066,0.0000014813551,0.0000029543996,0.000007519574,0.000053594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887556,0.0000817391,0.00045373538,0.00016449929,0.0003016136,0.00012283385],"domain_scores_gemma":[0.9994452,0.00011150006,0.00013174057,0.00010519934,0.00014741786,0.00005896499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074230635,0.0000928671,0.00014497434,0.00056234974,0.000056157773,0.00031639077,0.00021959619,0.000048840848,0.00018970543],"category_scores_gemma":[0.00008141006,0.000072797324,0.0001194427,0.00040124703,0.0000115761595,0.0011555372,0.00005578883,0.00031421016,0.000055963912],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018075008,0.00007407084,0.00046735842,0.000037483205,0.000010885465,0.000117165735,0.0008858234,0.000027785874,0.0016993629,0.0002298195,0.004169244,0.9922629],"study_design_scores_gemma":[0.0035073117,0.0039188447,0.15626618,0.0068531926,0.000090246875,0.0029928575,0.00015242315,0.38216004,0.00349149,0.3452159,0.09412935,0.001222197],"about_ca_topic_score_codex":0.000018609167,"about_ca_topic_score_gemma":0.000048392296,"teacher_disagreement_score":0.9910407,"about_ca_system_score_codex":0.000063797066,"about_ca_system_score_gemma":0.00005244437,"threshold_uncertainty_score":0.30509636},"labels":[],"label_agreement":null},{"id":"W4402946737","doi":"10.1167/jov.24.10.1078","title":"Spatial scrambling in human vision: investigating efficiency for discriminating scrambled letters using convolutional neural networks and confusion matrices","year":2024,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Scrambling; Confusion; Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Psychology; Algorithm; Psychoanalysis","score_opus":0.04023638596095853,"score_gpt":0.3388836333550856,"score_spread":0.29864724739412707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402946737","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5181099,0.000607621,0.48004407,0.0003032811,0.0008185365,0.00008798156,5.596594e-7,0.000019976664,0.000008068331],"genre_scores_gemma":[0.9868359,0.00002115718,0.012652353,0.00010256022,0.00037461065,0.0000014072976,0.0000017951378,0.000008643839,0.0000015922931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985393,0.000090707974,0.00059853756,0.00024052299,0.00033116975,0.00019971323],"domain_scores_gemma":[0.9991374,0.00026704502,0.00029515062,0.00007081735,0.00013978891,0.00008979028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010516589,0.00012428415,0.00018813313,0.0004943467,0.00029733023,0.000526765,0.00016883586,0.00006108583,0.0000045623906],"category_scores_gemma":[0.0001227124,0.00010509162,0.00009315716,0.00043783724,0.00005299162,0.0010413078,0.000107617154,0.00030772688,3.214515e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007754532,0.00017456566,0.001774531,0.0003887808,0.000023896178,0.00008625035,0.0015684165,0.07632452,0.42144343,0.0022970154,0.00027937716,0.4955617],"study_design_scores_gemma":[0.0005911323,0.00032021155,0.0034905733,0.0011861606,0.000014681092,0.00016679287,0.000057676953,0.9927473,0.00038029236,0.0009002899,0.000023120807,0.00012178667],"about_ca_topic_score_codex":0.00003313727,"about_ca_topic_score_gemma":0.000009456632,"teacher_disagreement_score":0.9164228,"about_ca_system_score_codex":0.000078852434,"about_ca_system_score_gemma":0.00004786761,"threshold_uncertainty_score":0.50796074},"labels":[],"label_agreement":null},{"id":"W4403178171","doi":"10.62118/jmmc.v15i1.451","title":"The rise of internet derived information obstruction treatment (IDIOT) syn-drome in Pakistan.","year":2024,"lang":"en","type":"article","venue":"JMMC","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Idiot; The Internet; Business; Computer science; Art; World Wide Web; Literature","score_opus":0.014953315939230655,"score_gpt":0.27633063964312177,"score_spread":0.2613773237038911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403178171","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.817717,0.0008855364,0.17340188,0.000629465,0.0031441844,0.00036719913,0.000008579434,0.00026193899,0.0035842059],"genre_scores_gemma":[0.9993011,0.00012652467,0.0003632442,0.000009228937,0.000021497091,0.0000263814,0.0000033731421,0.0000018567897,0.00014680847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948066,0.00003353771,0.00021279286,0.00009005342,0.000102866186,0.00008010447],"domain_scores_gemma":[0.9997196,0.000055344037,0.00004447401,0.00013392283,0.00002731681,0.000019349272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011677272,0.000057562112,0.00005944872,0.0001116565,0.000030414696,0.00015493599,0.000106337364,0.000028230364,0.000013458474],"category_scores_gemma":[0.0000106503685,0.000039522856,0.000042527405,0.0002804057,0.000019740522,0.00056743075,0.000026511894,0.00005064864,0.00008319522],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000013260571,0.000021199214,0.00009667596,0.000029685287,0.000012708138,0.0000023188704,0.0025732438,0.00002120897,0.000645659,0.0062912344,0.00027853902,0.99001426],"study_design_scores_gemma":[0.0016777657,0.00092944305,0.035760593,0.00038281002,0.000027442547,0.00016986114,0.00086794357,0.5697141,0.08776228,0.016085627,0.2861327,0.00048938225],"about_ca_topic_score_codex":0.00010924266,"about_ca_topic_score_gemma":0.00009467739,"teacher_disagreement_score":0.9895249,"about_ca_system_score_codex":0.00012321537,"about_ca_system_score_gemma":0.000033203032,"threshold_uncertainty_score":0.16116953},"labels":[],"label_agreement":null},{"id":"W4409726305","doi":"10.1016/j.measurement.2025.117629","title":"Novel method to automatize flash point detection in small volumes of liquid by computer vision using thermal images","year":2025,"lang":"en","type":"article","venue":"Measurement","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Canada Foundation for Innovation","keywords":"Flash point; Flash (photography); Computer vision; Point (geometry); Thermal; Computer graphics (images); Artificial intelligence; Computer science; Optics; Physics; Mathematics; Geometry","score_opus":0.04265314188275637,"score_gpt":0.29614250070272197,"score_spread":0.2534893588199656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409726305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.092381336,0.000066938126,0.9062264,0.00019398319,0.0005602835,0.00026374406,0.00000203665,0.00008688217,0.00021839284],"genre_scores_gemma":[0.6887463,0.0000018228569,0.3109647,0.00021250908,0.000026694948,0.0000194602,7.324818e-7,0.0000058566216,0.000021951728],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986944,0.00015313248,0.00034855973,0.00032022697,0.0003105233,0.00017314964],"domain_scores_gemma":[0.9993605,0.000032498298,0.00009077213,0.0002233951,0.00024670453,0.000046101013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012404143,0.00012352338,0.00017631937,0.00030336887,0.00006307597,0.00006845572,0.00021839875,0.00005071911,0.000006933007],"category_scores_gemma":[0.00003535612,0.00012089492,0.00006524631,0.00055700715,0.00001130989,0.00017980485,0.0001401188,0.00009520748,0.000009002806],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027658803,0.00010403853,0.000020429774,0.000025794972,0.000008540017,2.6516486e-7,0.00011124437,0.0005403426,0.6561301,0.00001630511,0.00012264983,0.34289265],"study_design_scores_gemma":[0.00055864314,0.0002875076,0.0038340467,0.00024244844,0.000007100585,0.0000046624987,0.000010324707,0.2701997,0.7240278,0.000084346204,0.0006096151,0.00013378383],"about_ca_topic_score_codex":0.00017993795,"about_ca_topic_score_gemma":0.00012964272,"teacher_disagreement_score":0.5963649,"about_ca_system_score_codex":0.00022441702,"about_ca_system_score_gemma":0.000055030334,"threshold_uncertainty_score":0.49299517},"labels":[],"label_agreement":null},{"id":"W4412732308","doi":"10.1142/9789819807154_0002","title":"2D and 3D Detection of Counterfeit Coins","year":2025,"lang":"en","type":"book-chapter","venue":"Series in computer vision","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Counterfeit; Computer science; Computer security; History; Archaeology","score_opus":0.011672642875274034,"score_gpt":0.24300387474694948,"score_spread":0.23133123187167545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412732308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042630854,0.00061556126,0.92879593,0.00011092817,0.0034083507,0.00034783888,0.000020565327,0.00017559757,0.06609893],"genre_scores_gemma":[0.5824009,0.011107876,0.14781012,0.0017909969,0.0022768308,0.0000980125,0.00020393479,0.00024053646,0.25407076],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986858,0.000029656501,0.00042595319,0.0004908845,0.00023190764,0.0001358013],"domain_scores_gemma":[0.99915075,0.00008075657,0.00019814784,0.0003961418,0.00013261389,0.000041594085],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016467723,0.000239446,0.00033934167,0.00047060548,0.0000683259,0.00009658268,0.00027224942,0.00024704137,0.000023689494],"category_scores_gemma":[0.0000070559504,0.00024887142,0.00007511905,0.000107408836,0.00009162613,0.0004152151,0.00044452,0.00029035067,0.000014213791],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000050769144,0.00002427995,0.00000527629,0.00018924847,0.000017455051,0.000011695292,0.00015010482,0.000042840413,0.00013830906,0.012624635,0.0003968623,0.9863485],"study_design_scores_gemma":[0.0021243973,0.0037307008,0.0008350675,0.0051306733,0.000067366316,0.00037828437,0.000012525292,0.4438918,0.0045241234,0.08276637,0.454915,0.0016236926],"about_ca_topic_score_codex":0.00001376812,"about_ca_topic_score_gemma":0.00010669912,"teacher_disagreement_score":0.9847248,"about_ca_system_score_codex":0.000062243635,"about_ca_system_score_gemma":0.00005240968,"threshold_uncertainty_score":0.99999636},"labels":[],"label_agreement":null},{"id":"W4413059711","doi":"10.26634/jmt.12.1.22104","title":"AI-driven drug pill recognition system: A CNN-based android application for visually impaired and senior citizens","year":2025,"lang":"en","type":"article","venue":"i-manager s Journal on Mobile Applications and Technologies","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trinity College","funders":"","keywords":"Usability; Pill; Android (operating system); Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Autonomy; Multimedia; Machine learning; Human–computer interaction; Medicine; Operating system","score_opus":0.008330367265598049,"score_gpt":0.2615756713065535,"score_spread":0.25324530404095547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413059711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025138814,0.00056215865,0.9675663,0.0032428934,0.00011441845,0.0019042274,0.000029506997,0.001065844,0.00037582632],"genre_scores_gemma":[0.98154986,0.00037567853,0.013820936,0.00039006872,0.000034746772,0.0037341707,0.000016086191,0.00001164706,0.000066811765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878985,0.000036193258,0.000360154,0.0004642494,0.00013696993,0.00021260604],"domain_scores_gemma":[0.9988851,0.00018517449,0.000220811,0.0003891636,0.0002650213,0.000054755463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002815229,0.00018422308,0.00021654721,0.0006351095,0.0005946347,0.00033290675,0.0003164311,0.00013140756,0.0000013109868],"category_scores_gemma":[0.000033400458,0.00016654294,0.0000809223,0.0006711269,0.000110207795,0.0002527684,0.00009630799,0.00024971477,0.000010677677],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023854911,0.00011470809,0.00010858348,0.00015875687,0.000033929366,0.0000016725999,0.00003183255,0.000056643938,0.0012086435,0.019481078,0.0009988821,0.9777814],"study_design_scores_gemma":[0.007966545,0.0017673784,0.002926955,0.0012207858,0.00046168113,0.00050529797,0.0042144153,0.35832715,0.04984684,0.30291674,0.26781315,0.002033081],"about_ca_topic_score_codex":0.0000037067734,"about_ca_topic_score_gemma":0.0000038487647,"teacher_disagreement_score":0.97574836,"about_ca_system_score_codex":0.00007946853,"about_ca_system_score_gemma":0.0000507419,"threshold_uncertainty_score":0.6791424},"labels":[],"label_agreement":null},{"id":"W4413145518","doi":"10.1109/icoeca66273.2025.00189","title":"Waste Management Using Convolutional Neural Network and Object Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Object detection; Object (grammar); Artificial neural network; Pattern recognition (psychology)","score_opus":0.020985156040744708,"score_gpt":0.253682549342688,"score_spread":0.2326973933019433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413145518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054906998,0.00011530286,0.93269855,0.000092090304,0.0012300324,0.000106803,2.2028253e-7,0.00013290235,0.010717123],"genre_scores_gemma":[0.9898322,0.000013828465,0.009184929,0.00029646012,0.00006314151,0.000006516337,5.444822e-7,0.0000015628606,0.0006007768],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994732,0.00002913518,0.00010216259,0.00019262075,0.00008168125,0.00012117164],"domain_scores_gemma":[0.9998016,0.000019999812,0.000022419154,0.00009978474,0.000030686893,0.000025508076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000097476244,0.00005969541,0.000052266794,0.00009701252,0.00017722821,0.0000908728,0.00007320511,0.000023822071,0.000012450285],"category_scores_gemma":[0.0000032955013,0.000057712885,0.00002492085,0.00037552853,0.000017085938,0.00020366504,0.00011083443,0.000052244304,0.0000062618583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023855133,0.00003962674,0.0008315671,0.00005116432,0.0000525457,0.0000049865744,0.000040967305,0.0052459487,0.0010630257,0.062169265,0.0009806531,0.9294964],"study_design_scores_gemma":[0.00028771383,0.000023007644,0.004008687,0.00001980548,0.000009896245,0.000020237609,0.000026504507,0.985021,0.0010582899,0.008251806,0.0011855629,0.000087476445],"about_ca_topic_score_codex":0.000015698977,"about_ca_topic_score_gemma":0.000023801373,"teacher_disagreement_score":0.9797751,"about_ca_system_score_codex":0.000030366315,"about_ca_system_score_gemma":0.000009515028,"threshold_uncertainty_score":0.2353463},"labels":[],"label_agreement":null},{"id":"W4414210127","doi":"10.52064/vamz.58.1.2","title":"Alberto Fortis and the advancement of numismatics in the late 18th century","year":2025,"lang":"en","type":"article","venue":"Vjesnik Arheološkog muzeja u Zagrebu","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Numismatics; Quarter (Canadian coin); Period (music); Salient; Kingdom; Donkey","score_opus":0.007198955904988788,"score_gpt":0.23855688002485206,"score_spread":0.23135792411986328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414210127","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5443154,0.00603143,0.3786143,0.024950203,0.002973027,0.0025160608,0.000016230699,0.00020702308,0.040376313],"genre_scores_gemma":[0.99441195,0.0016935674,0.001356559,0.0013356314,0.00002788906,0.000038366423,0.0000034161064,0.0000053718804,0.001127241],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986108,0.00021784606,0.00043131443,0.000245692,0.00025854888,0.00023581096],"domain_scores_gemma":[0.9987475,0.0004722412,0.00016477799,0.0005115701,0.000071304195,0.000032556207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006607877,0.00015262788,0.00022790892,0.00015118426,0.00014994552,0.00008270113,0.00055221445,0.000057366855,0.000020352352],"category_scores_gemma":[0.00013075683,0.00009033087,0.0000726457,0.0007113082,0.00017833109,0.00022353795,0.00021460041,0.0001924673,0.000014049142],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016806272,0.00030934715,0.0021079818,0.00019366453,0.00007589306,0.000007472054,0.013370694,0.00013693744,0.00027386172,0.49795237,0.0010612082,0.4843425],"study_design_scores_gemma":[0.022638839,0.0009998735,0.07090189,0.001384865,0.0003071022,0.00016803404,0.0065281866,0.3479004,0.009568161,0.33171663,0.20612966,0.0017563715],"about_ca_topic_score_codex":0.00009772122,"about_ca_topic_score_gemma":0.00017583421,"teacher_disagreement_score":0.48258612,"about_ca_system_score_codex":0.0000295378,"about_ca_system_score_gemma":0.0000492407,"threshold_uncertainty_score":0.36835858},"labels":[],"label_agreement":null},{"id":"W6892539751","doi":"10.5281/zenodo.10694261","title":"Substrate Durability Assessment","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège Montmorency","funders":"","keywords":"Ageing; Metric (unit); Durability; Banknote; Substrate (aquarium); Statistical analysis; Lead (geology)","score_opus":0.04924884236181702,"score_gpt":0.2851112724720999,"score_spread":0.23586243011028285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892539751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026174333,0.00016483529,0.6478784,0.0024543677,0.0009907417,0.00043245647,0.00007704848,0.0053420477,0.31648576],"genre_scores_gemma":[0.9978281,0.000040728813,0.0011276543,0.00006860488,0.00010070765,4.1538776e-8,0.00018665663,0.00027006253,0.0003774148],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99870205,0.00020948845,0.0001749572,0.00041916125,0.00028076652,0.00021358085],"domain_scores_gemma":[0.9992097,0.000023301753,0.000029143703,0.00036430047,0.00025882458,0.00011473634],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00066332513,0.00008880012,0.00007179931,0.00017745483,0.0009920145,0.00213006,0.00071285217,0.00003600709,0.0050746244],"category_scores_gemma":[0.00010357719,0.000088697394,0.000052396514,0.00071850524,0.000057847283,0.00057862455,0.00052349875,0.0002422258,0.008417783],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007717915,0.00012122397,0.0000036369743,0.00010409414,0.000027319806,0.000026108282,0.0006308075,0.000022013,0.0036197333,0.0735162,0.0644948,0.85742635],"study_design_scores_gemma":[0.00014741276,0.0001322774,0.0018766538,0.000028342407,0.0000047341555,0.00015644355,0.000044690627,0.02147844,0.000783467,0.003899246,0.97130287,0.00014545125],"about_ca_topic_score_codex":0.0000045527013,"about_ca_topic_score_gemma":1.7856613e-7,"teacher_disagreement_score":0.9716538,"about_ca_system_score_codex":0.00013039472,"about_ca_system_score_gemma":0.0000065100157,"threshold_uncertainty_score":0.99890584},"labels":[],"label_agreement":null},{"id":"W6892616596","doi":"10.5281/zenodo.10694260","title":"Substrate Durability Assessment","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège Montmorency","funders":"","keywords":"Ageing; Metric (unit); Durability; Banknote; Substrate (aquarium); Statistical analysis; Lead (geology)","score_opus":0.04924884236181702,"score_gpt":0.2851112724720999,"score_spread":0.23586243011028285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6892616596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026174333,0.00016483529,0.6478784,0.0024543677,0.0009907417,0.00043245647,0.00007704848,0.0053420477,0.31648576],"genre_scores_gemma":[0.9978281,0.000040728813,0.0011276543,0.00006860488,0.00010070765,4.1538776e-8,0.00018665663,0.00027006253,0.0003774148],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99870205,0.00020948845,0.0001749572,0.00041916125,0.00028076652,0.00021358085],"domain_scores_gemma":[0.9992097,0.000023301753,0.000029143703,0.00036430047,0.00025882458,0.00011473634],"candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00066332513,0.00008880012,0.00007179931,0.00017745483,0.0009920145,0.00213006,0.00071285217,0.00003600709,0.0050746244],"category_scores_gemma":[0.00010357719,0.000088697394,0.000052396514,0.00071850524,0.000057847283,0.00057862455,0.00052349875,0.0002422258,0.008417783],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007717915,0.00012122397,0.0000036369743,0.00010409414,0.000027319806,0.000026108282,0.0006308075,0.000022013,0.0036197333,0.0735162,0.0644948,0.85742635],"study_design_scores_gemma":[0.00014741276,0.0001322774,0.0018766538,0.000028342407,0.0000047341555,0.00015644355,0.000044690627,0.02147844,0.000783467,0.003899246,0.97130287,0.00014545125],"about_ca_topic_score_codex":0.0000045527013,"about_ca_topic_score_gemma":1.7856613e-7,"teacher_disagreement_score":0.9716538,"about_ca_system_score_codex":0.00013039472,"about_ca_system_score_gemma":0.0000065100157,"threshold_uncertainty_score":0.99890584},"labels":[],"label_agreement":null},{"id":"W6908682898","doi":"10.30424/oejs2406200","title":"Jens Pothmann / Holger Schmidt (2022): Soziale Arbeit – die Organisationen und Institutionen (211 Seiten). Opladen und Toronto: Verlag Barbara Budrich","year":2024,"lang":"de","type":"review","venue":"Multilingual Matters (Channel View Publications)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Government (linguistics)","score_opus":0.050932034933455224,"score_gpt":0.3520907867889536,"score_spread":0.3011587518554984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6908682898","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008809093,0.93848175,0.029866502,0.007889561,0.013828355,0.0040556593,0.00092649576,0.0016308466,0.0032327308],"genre_scores_gemma":[0.001994398,0.9644723,0.003412565,0.0056305174,0.0045428034,0.0022558458,0.0052654664,0.0004590956,0.011966996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.98913956,0.00085681817,0.0030976753,0.0035357047,0.0017435529,0.0016267105],"domain_scores_gemma":[0.992499,0.0005109465,0.0014567864,0.0028956188,0.0016568794,0.0009807969],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001759346,0.001947598,0.0022774958,0.0014207404,0.0016277862,0.003664048,0.0028964737,0.0011752152,0.0026149298],"category_scores_gemma":[0.00078263285,0.001932442,0.0011775135,0.0033385546,0.0004882069,0.0032514024,0.0014105482,0.0017094485,0.025286812],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003783035,0.001515181,0.000019499255,0.014335274,0.0031137087,0.00009600773,0.004944577,0.00015351448,0.00007808062,0.004540351,0.08502849,0.8861375],"study_design_scores_gemma":[0.0007931459,0.00012460045,0.00008359515,0.004312859,0.0029265396,0.00012028811,0.00020884382,0.009344182,0.00004599897,0.0005002819,0.9795019,0.0020377433],"about_ca_topic_score_codex":0.0009465699,"about_ca_topic_score_gemma":0.0007417786,"teacher_disagreement_score":0.89447343,"about_ca_system_score_codex":0.0017712319,"about_ca_system_score_gemma":0.0023442819,"threshold_uncertainty_score":0.99967194},"labels":[],"label_agreement":null},{"id":"W6911541379","doi":"10.5281/zenodo.11300962","title":"IN Kentau/Skørping +2778915305 SSD Chemical Solutions activation powder Bajram Curri","year":2024,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Nucleofection; Fusible alloy; Gestational period; Hyporeflexia; Diafiltration; Articular cartilage damage; Pretext","score_opus":0.05002755372932357,"score_gpt":0.2702038885242334,"score_spread":0.2201763347949098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6911541379","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007692766,0.00031401607,0.06158076,0.0013515743,0.0009475515,0.00061429775,0.00012392201,0.00297699,0.932014],"genre_scores_gemma":[0.17101948,0.0018035652,0.008397054,0.002756859,0.0060918117,0.0000034557181,0.007845076,0.037515875,0.76456684],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99797285,0.00019227757,0.00029113912,0.00071552343,0.00041518957,0.00041299616],"domain_scores_gemma":[0.99904144,0.000015072854,0.00013011972,0.00050671975,0.00016948076,0.00013717814],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00038226208,0.00022438576,0.00018798771,0.00089334784,0.00042454508,0.0010036259,0.0010142222,0.00020257833,0.016378768],"category_scores_gemma":[0.00022346785,0.0002538317,0.00008501575,0.0011758007,0.0000862489,0.00041448386,0.0012724131,0.0006005375,0.027687544],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000058719247,0.000105707026,1.2133617e-7,0.00010690311,0.000024627689,0.000008657539,0.00033280757,0.0000028573415,0.001590647,0.0062569133,0.92823225,0.06333261],"study_design_scores_gemma":[0.00028484705,0.000045876997,0.0000079381425,0.00028632334,0.000008893582,0.000073466435,0.000048045345,0.0016386654,0.00042809255,0.0012268893,0.99567604,0.00027493987],"about_ca_topic_score_codex":0.00004693752,"about_ca_topic_score_gemma":0.0000014465602,"teacher_disagreement_score":0.17094256,"about_ca_system_score_codex":0.0002804755,"about_ca_system_score_gemma":0.000009282722,"threshold_uncertainty_score":0.9999914},"labels":[],"label_agreement":null},{"id":"W6920705511","doi":"10.6084/m9.figshare.26607718.v1","title":"Additional file 1 of The one-week prevalence of neck pain and low back pain in post-secondary students at two Canadian institutions","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Low back pain; Neck pain; Back pain; MEDLINE; Population","score_opus":0.03865120904554482,"score_gpt":0.25777852785696054,"score_spread":0.21912731881141573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6920705511","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00090155366,0.00011825028,0.0000057098055,0.000075343974,0.00005874896,0.00017209051,0.9968441,0.000016676055,0.0018075207],"genre_scores_gemma":[0.3039321,0.000010371548,0.0012635019,0.0016007083,0.00012526677,0.0010405419,0.68897825,0.000020967907,0.0030282931],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99927723,0.00009707542,0.0001528019,0.00018412733,0.00017633947,0.00011244391],"domain_scores_gemma":[0.9992078,0.00044559795,0.00004428171,0.00015296218,0.000081186656,0.00006819901],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000102407525,0.00006502127,0.00006663741,0.000118299606,0.00006652721,0.00004734348,0.00028887726,0.000035478275,0.8685827],"category_scores_gemma":[0.0010665285,0.00005832301,0.00004257515,0.00034906168,0.000016898834,0.00022136985,0.0001441205,0.00011538326,0.00043028584],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000011074331,0.000031016294,0.00003156078,0.00041813345,0.000005233332,0.0000030367469,0.00010927237,0.000004457021,0.000005835123,0.000013369002,0.9693939,0.029983059],"study_design_scores_gemma":[0.0002967653,0.00022011627,0.13193907,0.016256172,0.0000039200463,0.00002499069,0.000027187405,0.019784851,0.00018288374,0.00028452332,0.8307403,0.00023922039],"about_ca_topic_score_codex":0.00053311314,"about_ca_topic_score_gemma":0.019537585,"teacher_disagreement_score":0.86815244,"about_ca_system_score_codex":0.00007316813,"about_ca_system_score_gemma":0.00059262104,"threshold_uncertainty_score":0.9983533},"labels":[],"label_agreement":null},{"id":"W6977892184","doi":"10.7916/d8-n0g0-za22","title":"Page No. 019 - Plate XXXII. Banking Room, Bank of Montreal, Canada. McKim, Mead & White, and Andrew T. Taylor, Asso. The Ornamental Bronze Work was made by The Wm. H. Jackson Company, New York","year":2020,"lang":"en","type":"article","venue":"Columbia Academic Commons (Columbia University)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Bronze; Work (physics); George (robot); Assemblage (archaeology)","score_opus":0.02216027174652382,"score_gpt":0.19225363606790277,"score_spread":0.17009336432137895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6977892184","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9492058,0.0023045507,0.009503936,0.012039182,0.0017291325,0.002096708,0.00039494594,0.0004987906,0.022226915],"genre_scores_gemma":[0.98778516,0.00018810034,0.0002199353,0.0011622338,0.00014636146,0.000004667947,0.00003138538,0.000029022844,0.01043312],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99724597,0.00037758405,0.0004766021,0.0007116175,0.000607743,0.00058047625],"domain_scores_gemma":[0.99796426,0.00042924454,0.00043658988,0.0006049278,0.00012617192,0.00043877782],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028842062,0.00028164615,0.00047981512,0.00008220018,0.0009720574,0.0004295937,0.0019483098,0.0002665229,0.00029882524],"category_scores_gemma":[0.00009543498,0.00034433164,0.00014887749,0.0018576222,0.0002287358,0.00050151424,0.00087503786,0.0013349607,0.000021966203],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008982398,0.000061715975,0.06950476,0.000037867583,0.00014545332,0.000029210269,0.0016542976,0.000042518623,0.00046809958,0.00007696609,0.8831995,0.04468979],"study_design_scores_gemma":[0.005304506,0.00043994474,0.16768177,0.00041629697,0.0003952436,0.00009243792,0.0021784496,0.028060183,0.0002521899,0.0006704797,0.7929811,0.0015274312],"about_ca_topic_score_codex":0.4404242,"about_ca_topic_score_gemma":0.79607445,"teacher_disagreement_score":0.35565025,"about_ca_system_score_codex":0.00030026195,"about_ca_system_score_gemma":0.00041092967,"threshold_uncertainty_score":0.9999009},"labels":[],"label_agreement":null},{"id":"W7031696000","doi":"","title":"9639406902","year":2008,"lang":"hu","type":"other","venue":"University of Debrecen Electronic Archive (University of Debrecen)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Relation (database); Identification (biology); Work (physics); Context (archaeology)","score_opus":0.008632250309460472,"score_gpt":0.16620716134264554,"score_spread":0.15757491103318508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7031696000","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008926254,0.002880333,0.5328491,0.0012116994,0.0012618959,0.0016319562,0.00073075,0.00056059286,0.44994748],"genre_scores_gemma":[0.26559824,0.12957783,0.050910477,0.0003297196,0.00054031453,4.865123e-7,0.001182058,0.0005928693,0.551268],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9954162,0.0006005188,0.0003926389,0.0014360389,0.0008835659,0.0012710321],"domain_scores_gemma":[0.9962911,0.00029859474,0.0013524564,0.0011364217,0.00045625202,0.00046517435],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00028546658,0.0007572283,0.0013432492,0.0015202854,0.0008647911,0.000022527987,0.0028125271,0.00064671016,0.01091702],"category_scores_gemma":[0.000043951808,0.0011298473,0.0009719042,0.0013582163,0.0014841071,0.00070597953,0.0010059885,0.0012541928,0.0015400781],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027362178,0.003273413,0.0011611717,0.001183607,0.0041634417,0.0006445223,0.027234012,0.00017086393,0.0037811517,0.019320156,0.32681754,0.6095139],"study_design_scores_gemma":[0.005397031,0.0015738253,0.0024531125,0.00087229186,0.000658662,0.00026021173,0.0034459862,0.01774235,0.0002592989,0.0032147758,0.9623371,0.00178539],"about_ca_topic_score_codex":0.004234252,"about_ca_topic_score_gemma":0.011716431,"teacher_disagreement_score":0.6355195,"about_ca_system_score_codex":0.000514293,"about_ca_system_score_gemma":0.0018508909,"threshold_uncertainty_score":0.99923736},"labels":[],"label_agreement":null},{"id":"W7031698171","doi":"","title":"","year":2021,"lang":"en","type":"other","venue":"Directory of Open access Books (OAPEN Foundation)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"General partnership; Quarter (Canadian coin); European union","score_opus":0.13675090235840942,"score_gpt":0.42430768116724343,"score_spread":0.287556778808834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7031698171","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015564587,0.00044809745,0.075216055,0.00022139635,0.0039160224,0.0019408822,0.000005032688,0.0006210763,0.91747576],"genre_scores_gemma":[0.007897108,0.0007651497,0.018914035,0.0012310102,0.00065586466,0.0009856279,0.0012018969,0.00070885563,0.96764046],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9947272,0.0005582146,0.0012027029,0.0018899956,0.0010728701,0.00054899737],"domain_scores_gemma":[0.9945578,0.00025144874,0.0020619954,0.0022399824,0.00058620016,0.00030255085],"candidate_categories":["metaepi_narrow","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007588069,0.00077348197,0.0013448162,0.0013331258,0.0003480736,0.008377238,0.011755012,0.0002510442,0.032813158],"category_scores_gemma":[0.00025004483,0.00085518777,0.0003679194,0.0014197108,0.00022683806,0.006325561,0.00674734,0.0004817359,0.0003748643],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000047130918,0.00048931,0.0013727341,0.0004907612,0.00044258,0.000039678765,0.00015868772,0.0000064610845,0.000611564,0.043697283,0.044800423,0.9078434],"study_design_scores_gemma":[0.0013731855,0.00006360479,0.006295867,0.0012481735,0.000055595774,0.000030437524,0.000022598337,0.00043723156,0.004021319,0.0032468836,0.98200107,0.0012040534],"about_ca_topic_score_codex":0.0041586803,"about_ca_topic_score_gemma":0.0054446165,"teacher_disagreement_score":0.9372006,"about_ca_system_score_codex":0.00018417287,"about_ca_system_score_gemma":0.0010619701,"threshold_uncertainty_score":0.9993899},"labels":[],"label_agreement":null},{"id":"W7031759486","doi":"","title":"Belgium","year":2018,"lang":"en","type":"other","venue":"Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Human rights; International law; Intellectual property; Foreign policy; Subject (documents)","score_opus":0.010374611743260142,"score_gpt":0.19660752337540627,"score_spread":0.18623291163214611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7031759486","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018768468,0.0024257984,0.39616838,0.0013144944,0.0025785656,0.00084548775,0.00028410228,0.0035531593,0.5909532],"genre_scores_gemma":[0.019433098,0.006614504,0.04108581,0.0021714582,0.0033427596,0.000032152715,0.0007002727,0.00091411016,0.92570585],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99490917,0.00040699213,0.0005092346,0.0017393407,0.0008162025,0.0016190414],"domain_scores_gemma":[0.9961654,0.00013877456,0.0007051354,0.0016572239,0.0002740619,0.0010593813],"candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00039695684,0.001151359,0.0009020136,0.0020628802,0.0013032845,0.00051685405,0.0036921685,0.0016502264,0.005194884],"category_scores_gemma":[0.00006591608,0.0014385442,0.0007603066,0.0023130819,0.00063260325,0.0017646654,0.0015946277,0.0010395775,0.0035930746],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032845844,0.00037832194,0.00033249686,0.00024189492,0.00039726394,0.0011357103,0.0025707844,0.00014762781,0.00024922867,0.028859507,0.94014394,0.025214786],"study_design_scores_gemma":[0.0046530706,0.0002746063,0.00077082944,0.0007141302,0.00026907594,0.0010442683,0.0014965595,0.010232735,0.000504484,0.0013542604,0.976978,0.0017079498],"about_ca_topic_score_codex":0.0012579228,"about_ca_topic_score_gemma":0.0009147877,"teacher_disagreement_score":0.35508257,"about_ca_system_score_codex":0.0013499672,"about_ca_system_score_gemma":0.0017318082,"threshold_uncertainty_score":0.9999969},"labels":[],"label_agreement":null},{"id":"W7033174143","doi":"","title":"Perehdytyksen kehittäminen Suomen Terveystalo Oy:n ostoreskontrassa","year":2010,"lang":"fi","type":"other","venue":"Theseus (Ammattikorkeakoulujen)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Value (mathematics); Quarter (Canadian coin); Context (archaeology)","score_opus":0.02672811899605364,"score_gpt":0.25939907167232196,"score_spread":0.2326709526762683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033174143","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046965303,0.001471373,0.012037697,0.0010547033,0.009022136,0.0016766186,0.00037194736,0.0014531515,0.9682158],"genre_scores_gemma":[0.04071352,0.00052562146,0.0026555755,0.00045083996,0.0030131536,0.00023067552,0.0002152501,0.0006651229,0.9515302],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99133784,0.00084481086,0.0015559061,0.0025168038,0.0016679221,0.0020766943],"domain_scores_gemma":[0.9935896,0.00052207994,0.0013003151,0.003053852,0.0004971671,0.0010369812],"candidate_categories":["metaepi_narrow","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0016182766,0.001825046,0.0016596579,0.001297933,0.00067606306,0.0013734734,0.0037110238,0.0020625335,0.021585671],"category_scores_gemma":[0.00036075062,0.0017786018,0.0010127589,0.0013790992,0.00064114446,0.0009768584,0.0011222876,0.002893728,0.021866621],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010512822,0.0004750665,0.00019313543,0.00029907378,0.00031369788,0.00015236465,0.0012907232,0.0000038500893,0.0019778423,0.0014369356,0.0020760493,0.99167615],"study_design_scores_gemma":[0.0015778504,0.00042370788,0.0018350732,0.00067813153,0.00018568123,0.00040237236,0.00014450007,0.0024624588,0.0009838572,0.0021528378,0.9870088,0.0021446971],"about_ca_topic_score_codex":0.001457578,"about_ca_topic_score_gemma":0.003326147,"teacher_disagreement_score":0.98953146,"about_ca_system_score_codex":0.0003813146,"about_ca_system_score_gemma":0.00057009165,"threshold_uncertainty_score":0.9996632},"labels":[],"label_agreement":null},{"id":"W7033352410","doi":"","title":"Production de pommes de terre du cultivar Shepody resistantes aux virus PVX et PVY par transformation génétique","year":2000,"lang":"fr","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Potato virus Y; Cultivar; Virus; Transformation (genetics); Luteovirus","score_opus":0.0063373399441885294,"score_gpt":0.16950615399752797,"score_spread":0.16316881405333944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033352410","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021427112,0.0013726249,0.014120905,0.03119857,0.0013923242,0.00067100534,0.00065081584,0.00008552068,0.92908114],"genre_scores_gemma":[0.90935457,0.004475442,0.0037024717,0.002631827,0.00044804628,0.000052404965,0.000059876314,0.00009506561,0.0791803],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99715364,0.00033869638,0.0004652048,0.00047528185,0.0011209714,0.000446194],"domain_scores_gemma":[0.9989589,0.00018448709,0.0002798814,0.0002661243,0.0000020363616,0.00030861475],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00004233457,0.00037259576,0.0003309323,0.000082594684,0.00034094483,0.00012800489,0.00032139022,0.00011041345,0.00042177402],"category_scores_gemma":[0.00001565725,0.0003871651,0.00006981636,0.00017641079,0.00014125786,0.0011244077,0.00006114415,0.00033686764,4.9603496e-8],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024057466,0.0006251941,0.008764563,0.0032831444,0.00049776235,0.000340714,0.009287219,0.0009255506,0.11017188,0.26950234,0.04698453,0.54721135],"study_design_scores_gemma":[0.00072754186,0.00029997353,0.048195105,0.0018137146,0.00009946515,0.00024620967,0.0016328433,0.021268504,0.22928314,0.0069255694,0.6884208,0.0010871846],"about_ca_topic_score_codex":0.012010482,"about_ca_topic_score_gemma":0.13179873,"teacher_disagreement_score":0.8879275,"about_ca_system_score_codex":0.000040226063,"about_ca_system_score_gemma":0.0033691619,"threshold_uncertainty_score":0.999858},"labels":[],"label_agreement":null},{"id":"W7033674578","doi":"","title":"Representation of Women and Visible Minorities on Agencies, Boards, and Commissions in Ontario","year":2020,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Representation (politics); Government (linguistics); Context (archaeology); Agency (philosophy); Ethnic group","score_opus":0.03360813566047649,"score_gpt":0.2570178011076761,"score_spread":0.2234096654471996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7033674578","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96048796,0.00016514536,0.0000022349618,0.000024119277,0.00064514996,0.000375572,0.00007957738,0.00009727522,0.038122945],"genre_scores_gemma":[0.995354,0.00025919764,0.0008788227,0.00011782563,0.00001206048,0.000089038,0.00016461093,0.000027732107,0.0030967],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99761266,0.00021234939,0.0005837276,0.0007773088,0.00046908116,0.00034485696],"domain_scores_gemma":[0.99866486,0.00020693647,0.00032284745,0.0003781242,0.0001537197,0.0002735178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036382844,0.00033834213,0.00054747594,0.00048114688,0.0003609684,0.00011976003,0.00031478886,0.0002884823,0.000080649246],"category_scores_gemma":[0.00034744613,0.00037117096,0.00009082312,0.0006164931,0.00004343902,0.0007221902,0.0001388139,0.00083763315,0.000015637519],"study_design_candidate":"design_other","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085229933,0.00050526945,0.0018904222,0.0012242284,0.00019669293,0.000101528574,0.00320491,0.000019385694,0.024048893,0.14730291,0.00004566058,0.8206078],"study_design_scores_gemma":[0.009380853,0.0053718407,0.23260361,0.004391629,0.00025620853,0.00016909992,0.013356501,0.0023599972,0.25535148,0.321287,0.1501536,0.00531815],"about_ca_topic_score_codex":0.0072945575,"about_ca_topic_score_gemma":0.054224633,"teacher_disagreement_score":0.8152897,"about_ca_system_score_codex":0.0007187219,"about_ca_system_score_gemma":0.0001192928,"threshold_uncertainty_score":0.999874},"labels":[],"label_agreement":null}]}