{"meta":{"query_hash":"3fcb9d8ca475","filters":{"topic":"Face and Expression Recognition"},"cohort_total":895,"direct_labels_cover":0,"predictions_cover":895,"exported":895,"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/3fcb9d8ca475","api":"https://metacan.xera.ac/api/v1/cohort?topic=Face+and+Expression+Recognition"},"results":[{"id":"W12156430","doi":"10.1007/978-3-642-02611-9_53","title":"Face Recognition Based on Wavelet-Curvelet-Fractal Technique","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Windsor","funders":"","keywords":"Curvelet; Wavelet; Artificial intelligence; Pattern recognition (psychology); Computer science; Facial recognition system; Fractal; Classifier (UML); Face (sociological concept); Diagonal; Euclidean distance; Euclidean geometry; Computer vision; Wavelet transform; Mathematics; Geometry","score_opus":0.01882467052748733,"score_gpt":0.24301736739823226,"score_spread":0.22419269687074495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W12156430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07913265,0.0015493723,0.91269124,0.00014018621,0.00019868992,0.000038327624,0.000088234825,0.00098447,0.005176897],"genre_scores_gemma":[0.41957042,0.002396674,0.56727004,0.0001228051,0.00019650014,0.00005867009,0.00030695493,0.00009755235,0.009980486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987125,0.000013105701,0.0000055654145,0.000024967878,0.000069644855,0.000015471684],"domain_scores_gemma":[0.9998354,0.000057120127,0.000012473844,0.000022355276,0.000063809064,0.000008818403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001867813,0.00023512013,0.0005440574,0.00058991654,0.0001491381,0.00031859704,0.00034249842,0.00047767465,0.0017599259],"category_scores_gemma":[0.00040403582,0.00016626218,0.00040774237,0.00050088606,0.00017175962,0.00057332095,0.00018125505,0.00035577544,0.0009492614],"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":[0.00019341684,0.000052973824,0.0007877394,0.00012357703,0.000030651063,0.00016696422,0.000052001313,0.005624335,0.31311598,0.0034931917,0.002627955,0.6737312],"study_design_scores_gemma":[0.000039301536,0.00036821625,0.009266865,0.00004729232,0.00014375905,0.003379809,0.00008067444,0.6009046,0.3684815,0.0036057208,0.01360165,0.00008064313],"about_ca_topic_score_codex":0.00046232168,"about_ca_topic_score_gemma":0.0004496998,"teacher_disagreement_score":0.0017599259,"about_ca_system_score_codex":0.00011708346,"about_ca_system_score_gemma":0.00017089168,"threshold_uncertainty_score":0.0058875084},"labels":[],"label_agreement":null},{"id":"W1411846290","doi":"10.1007/978-3-319-23528-8_19","title":"Generalization in Unsupervised Learning","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"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":"Generalization; Unsupervised learning; Cluster analysis; Computer science; Artificial intelligence; Set (abstract data type); Extension (predicate logic); Dimensionality reduction; Reduction (mathematics); Generalization error; Supervised learning; Machine learning; Curse of dimensionality; Task (project management); Algorithm; Mathematics; Artificial neural network","score_opus":0.030384918548997504,"score_gpt":0.2566342340745744,"score_spread":0.22624931552557692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1411846290","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076575666,0.0031951305,0.9758186,0.0008967898,0.00016181404,0.000029586741,0.00017110603,0.00048393523,0.011585385],"genre_scores_gemma":[0.48141813,0.007532257,0.44248828,0.0010992243,0.0017284577,0.0004370565,0.0019071284,0.001152836,0.062236633],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990011,0.0003858125,0.00006313793,0.00029415239,0.00020716942,0.000048603815],"domain_scores_gemma":[0.996183,0.0024730694,0.00016779744,0.0008142755,0.00028780018,0.000074070485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001926266,0.0006966315,0.0014727988,0.0008506946,0.0006432143,0.0011392528,0.0014789158,0.0011411422,0.004162155],"category_scores_gemma":[0.0064772256,0.00066883076,0.0013565083,0.0014328226,0.0023545776,0.0031245959,0.0024409392,0.0029178662,0.0011023349],"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":[0.000045927478,0.000042904427,0.0009880515,0.00030953827,0.00017205247,0.0000780191,0.00021600847,0.15019429,0.0010963331,0.61381155,0.014938573,0.21810675],"study_design_scores_gemma":[0.0000053123786,0.00001468284,0.00037138656,0.000026596774,0.000017523238,0.000063633175,0.000017140344,0.28038213,0.0004586607,0.7130413,0.0055899867,0.000011614517],"about_ca_topic_score_codex":0.0025572001,"about_ca_topic_score_gemma":0.00235681,"teacher_disagreement_score":0.004162155,"about_ca_system_score_codex":0.0011706505,"about_ca_system_score_gemma":0.000570807,"threshold_uncertainty_score":0.013923824},"labels":[],"label_agreement":null},{"id":"W14536956","doi":"","title":"A Robust Wavelet Based Feature Extraction Method.","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Feature extraction; Pattern recognition (psychology); Artificial intelligence; Computer science; Wavelet; White noise; Additive white Gaussian noise; Robustness (evolution); Classifier (UML); Wavelet transform; Facial recognition system; Feature (linguistics); Hidden Markov model; Gaussian","score_opus":0.03246287009805182,"score_gpt":0.29209211744025454,"score_spread":0.2596292473422027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W14536956","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026097195,0.00047867198,0.9946937,0.00006604032,0.00013623506,0.000057938232,0.00013244421,0.00074066897,0.0010844984],"genre_scores_gemma":[0.06774989,0.0012024009,0.91825527,0.00019920942,0.00015043456,0.00024867407,0.0010137545,0.00039113755,0.010789283],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906033,0.00009952885,0.000056768917,0.0001812658,0.0005442571,0.000057783513],"domain_scores_gemma":[0.9995209,0.00011214434,0.00006944731,0.000108497356,0.00016635697,0.00002267614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068482175,0.0007002705,0.00082539866,0.0013270482,0.00030402694,0.0006373816,0.00070100196,0.0010454471,0.0041630245],"category_scores_gemma":[0.0018465301,0.00042942437,0.00093733513,0.001297654,0.00037874564,0.0011298971,0.0006930131,0.0010269891,0.005868],"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":[0.000152593,0.00007825983,0.00039401778,0.00037954745,0.00010258014,0.00016709414,0.000054462074,0.007182279,0.24366401,0.0052004424,0.007648297,0.7349764],"study_design_scores_gemma":[0.00010411854,0.00054600433,0.0060304087,0.00017050712,0.00025318484,0.0037667258,0.00009240064,0.47363168,0.3861146,0.008787238,0.120285384,0.00021773073],"about_ca_topic_score_codex":0.00048138903,"about_ca_topic_score_gemma":0.00044653242,"teacher_disagreement_score":0.0041630245,"about_ca_system_score_codex":0.00021251987,"about_ca_system_score_gemma":0.00039206367,"threshold_uncertainty_score":0.013926685},"labels":[],"label_agreement":null},{"id":"W1486953413","doi":"10.1007/978-3-642-10677-4_80","title":"A Mutual Information Based Face Recognition Method","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"","keywords":"Computer science; Facial recognition system; Face (sociological concept); Mutual information; Artificial intelligence; Pattern recognition (psychology); Computer vision","score_opus":0.021447901540436052,"score_gpt":0.26113659225987657,"score_spread":0.23968869071944052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486953413","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033757943,0.00033920657,0.9925297,0.000067241606,0.00007809266,0.000049309612,0.000065807275,0.0009765176,0.0025183929],"genre_scores_gemma":[0.08120939,0.0005235845,0.90047723,0.00019884741,0.00016383531,0.00018716791,0.0004839126,0.00028463887,0.016471429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988932,0.0001905927,0.000040763458,0.00022010852,0.0005816013,0.00007380091],"domain_scores_gemma":[0.9995105,0.00016641953,0.000028492916,0.00009892685,0.00017623547,0.000019371846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094667333,0.00075497275,0.0012086125,0.0013834356,0.00061462127,0.000774541,0.0017389364,0.0011510771,0.0062443297],"category_scores_gemma":[0.0012199478,0.0005394038,0.0011902977,0.0010111998,0.00044634216,0.0013353525,0.0013792542,0.0010876133,0.0040859194],"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":[0.0001615916,0.00011852164,0.0004083348,0.0001175567,0.000115759416,0.00007338201,0.000059552276,0.015352769,0.047407918,0.010856225,0.0070350356,0.91829324],"study_design_scores_gemma":[0.000027282857,0.00014370127,0.0015442935,0.000022859662,0.00010745354,0.0008273843,0.000036922575,0.91729695,0.059098918,0.0067548784,0.014065385,0.00007402188],"about_ca_topic_score_codex":0.0015624304,"about_ca_topic_score_gemma":0.002006013,"teacher_disagreement_score":0.0062443297,"about_ca_system_score_codex":0.0004204195,"about_ca_system_score_gemma":0.000595278,"threshold_uncertainty_score":0.020889342},"labels":[],"label_agreement":null},{"id":"W1490682138","doi":"10.1007/978-3-642-02611-9_51","title":"A Novel Technique for Human Face Recognition Using Nonlinear Curvelet Feature Subspace","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"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 Windsor","funders":"","keywords":"Curvelet; Artificial intelligence; Pattern recognition (psychology); Kernel principal component analysis; Computer science; Principal component analysis; Facial recognition system; Kernel (algebra); Wavelet; Wavelet transform; Feature extraction; Subspace topology; Face (sociological concept); Kernel method; Support vector machine; Mathematics","score_opus":0.04356246535080898,"score_gpt":0.2926561831340138,"score_spread":0.24909371778320483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1490682138","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059464457,0.00025902977,0.99151635,0.00007043936,0.00010632182,0.000041440842,0.000053960826,0.0008195204,0.0011865565],"genre_scores_gemma":[0.06617115,0.00078790664,0.9243151,0.00012607769,0.00013048573,0.00009924597,0.00032172506,0.00012803868,0.007920163],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99964917,0.0000335027,0.000012007868,0.000060592814,0.00021792007,0.000026777938],"domain_scores_gemma":[0.99966836,0.000056991106,0.000018327075,0.00008687537,0.00014920588,0.000020270867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034352049,0.0005421498,0.0006783766,0.00069720304,0.00033947633,0.00048041376,0.0008077191,0.0006710636,0.003065685],"category_scores_gemma":[0.0005351696,0.00026958247,0.00045731154,0.001086511,0.00032688858,0.0010417427,0.000720241,0.00096439145,0.0027517253],"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":[0.00011942125,0.000092635164,0.0002982781,0.000093180264,0.000031335097,0.00008187032,0.00005261402,0.0037826358,0.27548948,0.0031549963,0.004301386,0.7125022],"study_design_scores_gemma":[0.00003563579,0.0003172599,0.0026365544,0.00002531587,0.00006810312,0.0018863741,0.00007215976,0.60929537,0.347862,0.003649959,0.034073386,0.00007788903],"about_ca_topic_score_codex":0.0008491627,"about_ca_topic_score_gemma":0.0015316411,"teacher_disagreement_score":0.003065685,"about_ca_system_score_codex":0.0002049291,"about_ca_system_score_gemma":0.0004039557,"threshold_uncertainty_score":0.010255694},"labels":[],"label_agreement":null},{"id":"W1491079761","doi":"10.1109/iscas.2015.7168936","title":"Kernel-based mixture of experts models for linear regression","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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","funders":"","keywords":"Kernel (algebra); Computer science; Polynomial kernel; Polynomial regression; Kernel method; Principal component regression; Curse of dimensionality; Radial basis function kernel; Artificial intelligence; Kernel principal component analysis; Linear model; Linear regression; Maximization; Support vector machine; Mathematical optimization; Machine learning; Mathematics","score_opus":0.07021343969270653,"score_gpt":0.29999579217239675,"score_spread":0.2297823524796902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1491079761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001850907,0.0003119222,0.9968465,0.000100187746,0.000025598421,0.000016534881,0.000035268593,0.00012573767,0.00068726495],"genre_scores_gemma":[0.4857902,0.0022958172,0.4893661,0.00038307233,0.0004274793,0.0004972619,0.0008034554,0.0004089116,0.020027703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99709094,0.0014441692,0.000114985516,0.0005364437,0.00058839185,0.00022506437],"domain_scores_gemma":[0.9955571,0.0029152194,0.00032698453,0.00032437063,0.00075863366,0.00011772396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042197616,0.0015205223,0.0020766854,0.0011914717,0.0005460534,0.0016864906,0.003400157,0.002567962,0.003279677],"category_scores_gemma":[0.012056062,0.0011009832,0.0021761958,0.0013575242,0.0013313694,0.0028575,0.001631169,0.0034840435,0.0022429256],"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":[0.00013364317,0.00006660701,0.00080095755,0.00015457749,0.00016200353,0.0001088597,0.00018963394,0.8441304,0.0012323081,0.09820986,0.0030174062,0.051793776],"study_design_scores_gemma":[0.000003870191,0.000011157789,0.00007511535,0.000006603059,0.000009085989,0.000021626473,0.000005877914,0.9843717,0.00017508301,0.014453989,0.0008550489,0.000010871498],"about_ca_topic_score_codex":0.00542969,"about_ca_topic_score_gemma":0.004024238,"teacher_disagreement_score":0.00542969,"about_ca_system_score_codex":0.0013378118,"about_ca_system_score_gemma":0.00094395736,"threshold_uncertainty_score":0.022316456},"labels":[],"label_agreement":null},{"id":"W1493469155","doi":"10.1007/11788034_23","title":"Finding Faces in Gray Scale Images Using Locally Linear Embeddings","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"McGill University","funders":"","keywords":"Computer science; Grayscale; Artificial intelligence; Computer vision; Scale (ratio); Computer graphics (images); Pattern recognition (psychology); Image (mathematics); Cartography; Geography","score_opus":0.018329547117629675,"score_gpt":0.26402468462012246,"score_spread":0.24569513750249278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493469155","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08021849,0.0005633688,0.9136722,0.00020106354,0.000053739674,0.000114050425,0.00032781414,0.0023575532,0.002491669],"genre_scores_gemma":[0.37190947,0.0009149274,0.61903346,0.00011664844,0.00008126694,0.00013084679,0.0010599202,0.00047080437,0.0062826653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997137,0.000035929672,0.00001492056,0.000097120086,0.00009008459,0.000048269736],"domain_scores_gemma":[0.9996282,0.00013520065,0.000043434884,0.00009006133,0.0000727532,0.000030368681],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026567042,0.00094388763,0.0011996751,0.0014371406,0.0003001204,0.0011552442,0.00096213736,0.00078795117,0.005663664],"category_scores_gemma":[0.0012629421,0.0006165078,0.0008433038,0.0013427231,0.0005347339,0.0022219708,0.0014177002,0.0008803835,0.002521791],"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":[0.0003431678,0.00014273991,0.0020931344,0.0002703308,0.00007025914,0.00017726327,0.00017717556,0.030397369,0.093870185,0.005785164,0.00545083,0.86122245],"study_design_scores_gemma":[0.00005303881,0.0003499587,0.0042499085,0.000061471255,0.00009095578,0.00091565534,0.00061826425,0.9008116,0.053604748,0.034129832,0.005061207,0.000053379248],"about_ca_topic_score_codex":0.001547301,"about_ca_topic_score_gemma":0.0023885714,"teacher_disagreement_score":0.005663664,"about_ca_system_score_codex":0.00030361302,"about_ca_system_score_gemma":0.00030238443,"threshold_uncertainty_score":0.018946826},"labels":[],"label_agreement":null},{"id":"W1498387692","doi":"10.1007/978-3-540-89646-3_29","title":"Frontal Face Recognition from Video","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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 British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Facial recognition system; Pattern recognition (psychology); Feature extraction; Three-dimensional face recognition; Computer vision; Classifier (UML); Curse of dimensionality; Support vector machine; Face (sociological concept); Face detection","score_opus":0.02337926010392559,"score_gpt":0.229833696552865,"score_spread":0.2064544364489394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498387692","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04538837,0.0037520886,0.9084734,0.00025308295,0.0004962525,0.00017886245,0.0025181288,0.0062007643,0.032739017],"genre_scores_gemma":[0.323623,0.007742128,0.58054954,0.00044560176,0.00050556613,0.0001677682,0.01055701,0.0005859965,0.07582343],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998394,0.000008653799,0.0000052643345,0.00004807008,0.0000617682,0.000036840658],"domain_scores_gemma":[0.9999126,0.000014477242,0.0000065544937,0.000021911306,0.00003739879,0.000007031315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017238292,0.0007002592,0.0006356751,0.0008992318,0.00019461448,0.0007519618,0.00055694924,0.000603098,0.010906447],"category_scores_gemma":[0.00033692888,0.00024637373,0.00042922687,0.0006964537,0.00015149225,0.00062167656,0.00045282554,0.00035387333,0.009142577],"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":[0.00014853477,0.000053416,0.0003322197,0.000087431334,0.000022874745,0.00012158844,0.000014277837,0.0023202486,0.18455452,0.0011264686,0.011437994,0.7997804],"study_design_scores_gemma":[0.000025033969,0.0002850169,0.010738469,0.00009270916,0.00010812379,0.0027905249,0.00012338531,0.33801705,0.5965626,0.0048820158,0.04631616,0.000058826718],"about_ca_topic_score_codex":0.0021351106,"about_ca_topic_score_gemma":0.0032315229,"teacher_disagreement_score":0.010906447,"about_ca_system_score_codex":0.00023131912,"about_ca_system_score_gemma":0.00022027911,"threshold_uncertainty_score":0.03648573},"labels":[],"label_agreement":null},{"id":"W1498915505","doi":"10.1023/a:1011183429707","title":"Face Recognition Using the Discrete Cosine Transform","year":2001,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":429,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Discrete cosine transform; Normalization (sociology); Artificial intelligence; Robustness (evolution); Computer science; Facial recognition system; Pattern recognition (psychology); Feature extraction; Computer vision; Image (mathematics)","score_opus":0.02842829422288995,"score_gpt":0.3140763903320113,"score_spread":0.28564809610912134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1498915505","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056381907,0.0012590117,0.9341391,0.0002750582,0.00033917412,0.0000923365,0.00024273568,0.001531591,0.005739048],"genre_scores_gemma":[0.41353688,0.0015398562,0.5750093,0.00015971456,0.00021410768,0.000099174795,0.0008660874,0.00017172794,0.008403137],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993979,0.00008261533,0.000028527686,0.000100385856,0.0003422942,0.00004831315],"domain_scores_gemma":[0.99945587,0.00012790001,0.00003822329,0.000108417276,0.000234017,0.000035536843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055690017,0.00032313043,0.0006820871,0.0013855645,0.00027843707,0.0008858897,0.0005140697,0.0005352989,0.003191656],"category_scores_gemma":[0.0019496452,0.00024391539,0.0004871183,0.0012923521,0.00033550378,0.0010779183,0.0006224179,0.0006817036,0.0021105444],"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":[0.00030959028,0.0001143863,0.0015141928,0.00013610408,0.00005717125,0.000077261066,0.000047719604,0.007198969,0.14843118,0.008149547,0.0049439487,0.82902],"study_design_scores_gemma":[0.000090055015,0.0003802966,0.011812716,0.000051323987,0.000076073906,0.0011319525,0.00011520485,0.75419015,0.2060489,0.008787033,0.017231913,0.00008436738],"about_ca_topic_score_codex":0.0019240598,"about_ca_topic_score_gemma":0.0014496725,"teacher_disagreement_score":0.003191656,"about_ca_system_score_codex":0.00026400894,"about_ca_system_score_gemma":0.0006340016,"threshold_uncertainty_score":0.010677159},"labels":[],"label_agreement":null},{"id":"W1501863976","doi":"10.1007/11559573_128","title":"Face Recognition – Combine Generic and Specific Solutions","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Computer science; Facial recognition system; A priori and a posteriori; Face (sociological concept); Artificial intelligence; Set (abstract data type); Task (project management); Identification (biology); Scheme (mathematics); Sample (material); Pattern recognition (psychology); Machine learning; Image (mathematics)","score_opus":0.03877427809063533,"score_gpt":0.23507754084759344,"score_spread":0.1963032627569581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1501863976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062062074,0.008028071,0.9618792,0.00043045654,0.00023686005,0.00008419362,0.00010727353,0.0021834068,0.020844268],"genre_scores_gemma":[0.114559904,0.018468672,0.83325654,0.0010004948,0.0007622288,0.00014923979,0.0011849146,0.0008757446,0.029742239],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992162,0.00008268448,0.000059208814,0.00025785962,0.00029897923,0.00008511389],"domain_scores_gemma":[0.9995369,0.000054147953,0.00002005275,0.0002270354,0.00013417438,0.000027759412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010923186,0.0014756782,0.0012778316,0.0016484705,0.0003064902,0.0018387379,0.0019499785,0.0015887308,0.0042016674],"category_scores_gemma":[0.0010320044,0.0006619908,0.0011308905,0.0012260095,0.00085530756,0.0045372597,0.0024287016,0.001576531,0.005012286],"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":[0.000047400645,0.00006871996,0.0008007783,0.00036409684,0.00011052758,0.000073056355,0.000068080626,0.004618612,0.028626997,0.022768417,0.007871587,0.9345817],"study_design_scores_gemma":[0.000039791386,0.0005259845,0.006997848,0.00072873425,0.00061179406,0.0064583193,0.00036707937,0.27161932,0.18445194,0.2049218,0.32301837,0.00025898468],"about_ca_topic_score_codex":0.00028552095,"about_ca_topic_score_gemma":0.0006394939,"teacher_disagreement_score":0.0042016674,"about_ca_system_score_codex":0.00046484623,"about_ca_system_score_gemma":0.00037342362,"threshold_uncertainty_score":0.014056027},"labels":[],"label_agreement":null},{"id":"W1502653702","doi":"10.1007/978-3-642-13772-3_14","title":"A New SVM + NDA Model for Improved Classification and Recognition","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"","keywords":"Support vector machine; Computer science; Decision boundary; Artificial intelligence; Linear discriminant analysis; Pattern recognition (psychology); Extension (predicate logic); Boundary (topology); Machine learning; Data mining; Mathematics","score_opus":0.0378367155671324,"score_gpt":0.2588933518337797,"score_spread":0.22105663626664732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1502653702","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068812114,0.0008720025,0.98723674,0.00023780481,0.000523643,0.000049147326,0.00028402344,0.0023910191,0.0015244201],"genre_scores_gemma":[0.18346812,0.0008530549,0.7831333,0.000503402,0.00050812546,0.00033811308,0.002292447,0.0004723348,0.028431024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993405,0.000109556946,0.00005074166,0.00017375647,0.0002758946,0.00004954761],"domain_scores_gemma":[0.99913627,0.00014139236,0.000028869177,0.00014368622,0.0005029368,0.00004683482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008047834,0.000684292,0.0012904598,0.00059456663,0.0005253895,0.0011302808,0.0022008114,0.0012722325,0.004774817],"category_scores_gemma":[0.0013249007,0.00045090492,0.0010615891,0.0006461725,0.00023887263,0.0018004924,0.0010999071,0.0016548424,0.006344153],"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":[0.00024022671,0.00021400834,0.0010581297,0.00014705057,0.00011888536,0.000066548935,0.00003082918,0.051048473,0.036775436,0.0047848704,0.01497536,0.8905403],"study_design_scores_gemma":[0.0000062400977,0.00003810149,0.0003417541,0.00000705992,0.000023776702,0.000065428445,0.000004285645,0.98736006,0.005233939,0.0010488848,0.00585859,0.000011940761],"about_ca_topic_score_codex":0.004018438,"about_ca_topic_score_gemma":0.0063236672,"teacher_disagreement_score":0.004774817,"about_ca_system_score_codex":0.00056059053,"about_ca_system_score_gemma":0.0007692805,"threshold_uncertainty_score":0.01597333},"labels":[],"label_agreement":null},{"id":"W1503813329","doi":"10.1109/icsmc.2005.1571391","title":"A New Approach to Appearance-based Face Recognition","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Artificial intelligence; Facial recognition system; Computer science; Face (sociological concept); Pattern recognition (psychology); Three-dimensional face recognition; Computer vision; Principal component analysis; Feature (linguistics); Gabor filter; Facial expression; Face hallucination; Feature extraction; Matching (statistics); Face detection; Mathematics","score_opus":0.02662262134460998,"score_gpt":0.22800961002577644,"score_spread":0.20138698868116645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1503813329","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010252062,0.00046547045,0.9965552,0.000094764946,0.0001362359,0.000036067777,0.00003428567,0.0005339873,0.0011187294],"genre_scores_gemma":[0.048002567,0.0013785985,0.9429309,0.00029996183,0.00030325926,0.00015016517,0.00020584895,0.00009565593,0.0066330903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894947,0.00012620787,0.000049115613,0.00032410375,0.00050965825,0.000041487332],"domain_scores_gemma":[0.99956304,0.00008154911,0.00003462349,0.00014897363,0.0001485078,0.000023330234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068101566,0.00069295336,0.0012840764,0.001028109,0.00030161682,0.0010921457,0.0017289731,0.00091776275,0.0024200813],"category_scores_gemma":[0.0014127701,0.00036836884,0.00094701786,0.0011588747,0.00081202417,0.0015522483,0.0011024042,0.0014244195,0.0019776565],"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":[0.000065310946,0.000098384095,0.0004337076,0.00024537864,0.00010672163,0.00013217353,0.00007875104,0.013221925,0.08377299,0.030561987,0.0053778067,0.8659049],"study_design_scores_gemma":[0.000037154347,0.0004231473,0.002778354,0.00006797448,0.00014502785,0.0021309024,0.0000707079,0.81819797,0.061012182,0.043339074,0.07168363,0.00011393129],"about_ca_topic_score_codex":0.00079123554,"about_ca_topic_score_gemma":0.0009470748,"teacher_disagreement_score":0.0024200813,"about_ca_system_score_codex":0.0003288842,"about_ca_system_score_gemma":0.00038909126,"threshold_uncertainty_score":0.00809592},"labels":[],"label_agreement":null},{"id":"W1506904746","doi":"10.1007/978-3-540-27868-9_77","title":"Control of Sparseness for Feature Selection","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Curse of dimensionality; Feature selection; Pattern recognition (psychology); Linear discriminant analysis; Decision boundary; Mathematics; Linear programming; Artificial intelligence; Feature vector; Norm (philosophy); Function (biology); Feature (linguistics); Minification; Boundary (topology); Mathematical optimization; Computer science; Support vector machine","score_opus":0.014520397970377111,"score_gpt":0.23944741615161155,"score_spread":0.22492701818123445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506904746","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004784955,0.00013429557,0.99392164,0.00004892899,0.000021827893,0.000011564417,0.000021455286,0.0001481385,0.0009072843],"genre_scores_gemma":[0.48174745,0.0005597571,0.5093443,0.00016521018,0.00019736878,0.0001684775,0.00025389617,0.0002733455,0.007290201],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9996543,0.000080790254,0.000017007422,0.0000876454,0.00012066585,0.00003948326],"domain_scores_gemma":[0.9987513,0.0007510784,0.00009696008,0.0001518861,0.0002053996,0.00004341901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062617305,0.00053760293,0.00058588467,0.00037689254,0.00023963225,0.0005993405,0.00071119476,0.00045540405,0.0028463872],"category_scores_gemma":[0.0037252852,0.00026227834,0.0002653497,0.0006042997,0.0005800212,0.0008073397,0.0007915323,0.00086784083,0.00044176637],"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":[0.00035109324,0.00017082473,0.00044994178,0.00020758176,0.000056978326,0.000060004884,0.00009277417,0.26072884,0.11961603,0.06761315,0.0065290728,0.5441237],"study_design_scores_gemma":[0.000018901132,0.00005643794,0.00017925493,0.00000719013,0.0000075511393,0.000038071565,0.0000057347897,0.9761355,0.010685233,0.011427289,0.0014306627,0.000008122759],"about_ca_topic_score_codex":0.0011956465,"about_ca_topic_score_gemma":0.002034147,"teacher_disagreement_score":0.0028463872,"about_ca_system_score_codex":0.00032068847,"about_ca_system_score_gemma":0.00033489708,"threshold_uncertainty_score":0.00952214},"labels":[],"label_agreement":null},{"id":"W1515477238","doi":"","title":"Spectral Clustering and Kernel PCA are Learning Eigenfunctions","year":2004,"lang":"en","type":"preprint","venue":"Érudit documents and data repository (Érudit Consortium, University of Montreal)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Cluster analysis; Mathematics; Spectral clustering; Kernel principal component analysis; Eigenfunction; Kernel embedding of distributions; Smoothing; Embedding; Kernel (algebra); Artificial intelligence; Laplace operator; Variable kernel density estimation; Kernel method; Pattern recognition (psychology); Computer science; Mathematical analysis; Pure mathematics; Eigenvalues and eigenvectors; Support vector machine; Physics","score_opus":0.017089040657932097,"score_gpt":0.2226260457377032,"score_spread":0.2055370050797711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1515477238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02165075,0.0008563772,0.9714633,0.000408468,0.00008070405,0.000040143386,0.000096865806,0.0004295528,0.0049738195],"genre_scores_gemma":[0.53286725,0.0027575102,0.44658652,0.00040386457,0.0005208488,0.00028524487,0.00062982633,0.00056908914,0.01537987],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965873,0.0009343245,0.0001391162,0.0009489058,0.001137708,0.00025267873],"domain_scores_gemma":[0.9933695,0.0029328594,0.0006903153,0.0016256429,0.0011757709,0.00020592514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002215095,0.0010747751,0.0013541128,0.0024615547,0.00093518407,0.003382936,0.0013068694,0.0017866531,0.0039091306],"category_scores_gemma":[0.013356335,0.00080975087,0.0011490948,0.0027785748,0.0037021032,0.005493978,0.0024440945,0.0021997686,0.0013402764],"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":[0.00032346984,0.0001164628,0.0037818446,0.00044932816,0.00017091565,0.0001448745,0.0010987758,0.05949141,0.010883648,0.51779866,0.0036864975,0.4020541],"study_design_scores_gemma":[0.000030676823,0.00013073438,0.0064553116,0.00008819431,0.00006545344,0.00043211592,0.00043023858,0.44357014,0.009066548,0.52619565,0.01343377,0.00010111498],"about_ca_topic_score_codex":0.0023653912,"about_ca_topic_score_gemma":0.002348566,"teacher_disagreement_score":0.0039091306,"about_ca_system_score_codex":0.00091251335,"about_ca_system_score_gemma":0.0011840571,"threshold_uncertainty_score":0.013077378},"labels":[],"label_agreement":null},{"id":"W1516580029","doi":"10.1109/bigdatacongress.2015.16","title":"A GPU Based SVM Method with Accelerated Kernel Matrix Calculation","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; CUDA; Kernel (algebra); Graphics processing unit; Support vector machine; Parallel computing; Massively parallel; General-purpose computing on graphics processing units; Computation; Matrix multiplication; Coprocessor; Graphics; Computational science; Algorithm; Artificial intelligence; Computer graphics (images); Mathematics","score_opus":0.059197531003134254,"score_gpt":0.330445069901932,"score_spread":0.27124753889879777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1516580029","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012916825,0.00037847838,0.98109424,0.00014659288,0.0002778212,0.00006462158,0.00008416106,0.0020093273,0.0030278775],"genre_scores_gemma":[0.21136793,0.00049563684,0.7724931,0.00014765821,0.00016207727,0.00017808926,0.0005686212,0.00030739972,0.014279405],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996463,0.00004404036,0.000017632085,0.00006247589,0.00019818019,0.00003136629],"domain_scores_gemma":[0.9997181,0.000028292674,0.000017236673,0.000040348365,0.00017441677,0.000021474965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023429526,0.00046629526,0.00072498916,0.0007220997,0.00045254722,0.000628589,0.0010995436,0.0006694704,0.003926011],"category_scores_gemma":[0.00082515454,0.00027469735,0.0005881896,0.00091778906,0.00019788303,0.0008568395,0.0005509311,0.00075754087,0.0020669566],"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":[0.00028626015,0.00016713243,0.0018202111,0.0001597326,0.000077762794,0.00026306714,0.00009117239,0.060680654,0.059719894,0.009790228,0.015295583,0.8516483],"study_design_scores_gemma":[0.000030960993,0.00005450484,0.00070867053,0.000007889713,0.0000123410755,0.0002644632,0.000014275312,0.97451323,0.0109922355,0.0014264133,0.0119540095,0.000020980988],"about_ca_topic_score_codex":0.0036748163,"about_ca_topic_score_gemma":0.0031033382,"teacher_disagreement_score":0.003926011,"about_ca_system_score_codex":0.00034994885,"about_ca_system_score_gemma":0.0007248254,"threshold_uncertainty_score":0.013133824},"labels":[],"label_agreement":null},{"id":"W1518136722","doi":"10.1109/icsmc.2005.1571345","title":"Dynamic Facial Expression Recognition Using Fuzzy Hidden Markov Models","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Hidden Markov model; Computer science; Flexibility (engineering); Artificial intelligence; Fuzzy logic; Facial expression; Human–computer interaction; Machine learning; Context (archaeology); Feature (linguistics); Activity recognition","score_opus":0.026759197688031363,"score_gpt":0.24705825054123381,"score_spread":0.22029905285320245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1518136722","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05683219,0.00023635369,0.94111013,0.00016990022,0.000037800004,0.000023186058,0.000071814145,0.0005132025,0.0010054162],"genre_scores_gemma":[0.84156203,0.00023271528,0.15583032,0.00007210242,0.00002545732,0.0000425783,0.00018098322,0.00003703326,0.0020167306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997359,0.000073912495,0.000014409456,0.00007250454,0.00006820421,0.000035119636],"domain_scores_gemma":[0.99959105,0.00025293182,0.00004127526,0.000032153177,0.000068519235,0.000014127099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006464432,0.00034198977,0.00041100386,0.0003416349,0.00020768993,0.00044310986,0.0005088094,0.00044255122,0.0008395971],"category_scores_gemma":[0.0016872558,0.00027623665,0.00060419063,0.00023281509,0.0002408249,0.00057006924,0.00027277626,0.0006474226,0.0002959814],"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":[0.00042682447,0.00016007017,0.0043756748,0.00007484016,0.00013010188,0.00018843886,0.00020974933,0.511143,0.050048392,0.0083613545,0.0018697162,0.4230118],"study_design_scores_gemma":[0.000002401027,0.000009411209,0.00032033923,0.0000018598747,0.0000051032475,0.000011207572,0.0000039955353,0.99718994,0.0014690466,0.0008936671,0.00008856469,0.000004494238],"about_ca_topic_score_codex":0.00860647,"about_ca_topic_score_gemma":0.007224835,"teacher_disagreement_score":0.00860647,"about_ca_system_score_codex":0.000515733,"about_ca_system_score_gemma":0.00043985806,"threshold_uncertainty_score":0.017112732},"labels":[],"label_agreement":null},{"id":"W1519885944","doi":"10.1109/icassp.2001.941210","title":"Nose shape estimation and tracking for model-based coding","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"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":"Artificial intelligence; Computer science; Computer vision; Feature extraction; Coding (social sciences); Pattern recognition (psychology); Feature (linguistics); Template; Face (sociological concept); Nostril; Facial expression; Nose; Mathematics","score_opus":0.0611400067104228,"score_gpt":0.2724997248452936,"score_spread":0.2113597181348708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1519885944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006510125,0.000084951156,0.9917355,0.000036968675,0.0000306772,0.00002186717,0.000025796337,0.0007917635,0.0007624299],"genre_scores_gemma":[0.25140873,0.00031028397,0.7442046,0.00006834233,0.000026942402,0.00008105774,0.00026727165,0.00017581391,0.0034569334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999845,0.000026168069,0.000007054253,0.00003000842,0.00007933976,0.0000123757345],"domain_scores_gemma":[0.99972993,0.000077516364,0.000022204596,0.00008676439,0.00007252513,0.000011055613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027888344,0.00031673873,0.0003616859,0.00041119865,0.00018321801,0.00037527437,0.000546427,0.0005301314,0.001830461],"category_scores_gemma":[0.0010921858,0.00020578929,0.0003410173,0.00035961048,0.00019329683,0.0005611422,0.0003880069,0.0005037145,0.0010488194],"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":[0.00025751875,0.000059893693,0.0007604433,0.00005916346,0.00001915585,0.00012260485,0.00005941825,0.09706743,0.30907235,0.0083617065,0.0028179626,0.5813424],"study_design_scores_gemma":[0.000007231847,0.000030356867,0.00045695805,0.000006898236,0.000006901381,0.00012953665,0.000006046164,0.942946,0.05284144,0.0013125566,0.0022411349,0.000014913339],"about_ca_topic_score_codex":0.002407664,"about_ca_topic_score_gemma":0.002773254,"teacher_disagreement_score":0.002407664,"about_ca_system_score_codex":0.00036538288,"about_ca_system_score_gemma":0.0005354102,"threshold_uncertainty_score":0.006123483},"labels":[],"label_agreement":null},{"id":"W1526167907","doi":"10.1007/978-3-540-24586-5_64","title":"A New Approach That Selects a Single Hyperplane from the Optimal Pairwise Linear Classifier","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"","keywords":"Pairwise comparison; Hyperplane; Classifier (UML); Quadratic classifier; Pattern recognition (psychology); Artificial intelligence; Margin classifier; Linear classifier; Computer science; Machine learning; Quadratic equation; Mathematics; Combinatorics","score_opus":0.039264540055579354,"score_gpt":0.2319581402843241,"score_spread":0.19269360022874474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1526167907","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036444378,0.0002437316,0.9926636,0.00011776753,0.00018379677,0.000082709725,0.00006239978,0.0012726544,0.0017288319],"genre_scores_gemma":[0.041354578,0.00024375049,0.9450103,0.00028572616,0.000262733,0.0002325045,0.00064160774,0.00048975286,0.011479065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980124,0.00022513278,0.000090092406,0.00064700557,0.00090412726,0.000121307414],"domain_scores_gemma":[0.99880517,0.0001934248,0.00007081548,0.00021839677,0.0006381299,0.00007414727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014111209,0.0019934836,0.0026379481,0.001896725,0.0010475098,0.0018988703,0.003545159,0.0019936524,0.0066526458],"category_scores_gemma":[0.0022274759,0.00079017505,0.001632642,0.0021840285,0.00072049047,0.0023632352,0.0025584179,0.0020859307,0.0051926407],"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":[0.00014976665,0.00025247096,0.00061685464,0.000111828354,0.00016245469,0.00004614627,0.00005984433,0.014366209,0.01927863,0.0057851085,0.016171938,0.9429987],"study_design_scores_gemma":[0.00009144299,0.00028656432,0.001304706,0.00003299299,0.000178555,0.00045768748,0.000075645185,0.9398992,0.020569678,0.013178559,0.023841206,0.000083751715],"about_ca_topic_score_codex":0.0027452712,"about_ca_topic_score_gemma":0.005189075,"teacher_disagreement_score":0.0066526458,"about_ca_system_score_codex":0.00064476376,"about_ca_system_score_gemma":0.0012595184,"threshold_uncertainty_score":0.022255361},"labels":[],"label_agreement":null},{"id":"W1529882053","doi":"10.5772/4773","title":"Intelligent Global Vision for Teams of Mobile Robots","year":2007,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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 Manitoba","funders":"University of Auckland","keywords":"Mobile robot; Computer science; Human–computer interaction; Artificial intelligence; Robot; Computer vision; Robot vision","score_opus":0.029705732506970516,"score_gpt":0.31108589521303703,"score_spread":0.28138016270606653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1529882053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068221097,0.0034084616,0.9492959,0.0017345827,0.0005401092,0.000058031383,0.00007995792,0.0010051472,0.037055716],"genre_scores_gemma":[0.5215161,0.0049292487,0.41974467,0.0007945692,0.000815803,0.0004920912,0.00051878026,0.00038027184,0.05080852],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996221,0.00008855086,0.00001546137,0.00010058463,0.00012278791,0.000050445185],"domain_scores_gemma":[0.9996526,0.00010489984,0.000040682095,0.000056669145,0.00009046123,0.00005466705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064391724,0.0006135485,0.0005788992,0.000619466,0.0007691086,0.0015969854,0.0011998415,0.0014853043,0.0055666217],"category_scores_gemma":[0.0014594384,0.00033415272,0.000654594,0.00042023516,0.0015418151,0.0024233114,0.0021470205,0.0018150908,0.0016707806],"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":[0.00007049899,0.000032085216,0.00037943947,0.00019033602,0.000032735585,0.00017307498,0.00037712863,0.12503535,0.0042105545,0.72935957,0.019488549,0.12065066],"study_design_scores_gemma":[0.0000296337,0.00006913402,0.00034234606,0.000051653464,0.000015134044,0.00013135994,0.00011248044,0.4533704,0.0010647688,0.4958024,0.04898352,0.000027073942],"about_ca_topic_score_codex":0.0038189103,"about_ca_topic_score_gemma":0.0025094037,"teacher_disagreement_score":0.0055666217,"about_ca_system_score_codex":0.0013667621,"about_ca_system_score_gemma":0.0007660529,"threshold_uncertainty_score":0.01862222},"labels":[],"label_agreement":null},{"id":"W1536434785","doi":"10.1007/978-3-642-11534-9_14","title":"Detecting and Preventing the Electronic Transmission of Illicit Images and Its Network Performance","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Euclidean distance; Estimator; Similarity (geometry); Computer science; Overhead (engineering); The Internet; Artificial intelligence; Transmission (telecommunications); Filter (signal processing); Euclidean geometry; Machine learning; Pattern recognition (psychology); Algorithm; Mathematics; Image (mathematics); Computer vision; Statistics; Telecommunications","score_opus":0.012900310862547011,"score_gpt":0.2180072680450081,"score_spread":0.2051069571824611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1536434785","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9307262,0.0024015347,0.053659037,0.00046187232,0.00008954444,0.000041826268,0.00021904093,0.00071151357,0.011689427],"genre_scores_gemma":[0.9924958,0.0004263475,0.0048118476,0.000023168706,0.000028784276,0.0000072364037,0.00010125709,0.000020845464,0.0020846636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940264,0.00015100389,0.000024306286,0.0001103797,0.00020422097,0.00010747258],"domain_scores_gemma":[0.9920128,0.0060693747,0.0005915959,0.00042407465,0.00081082736,0.00009139066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009398297,0.0004188995,0.0004189007,0.0007361749,0.00035810703,0.0011524151,0.0006422624,0.0008159326,0.0021939073],"category_scores_gemma":[0.006688491,0.00019819026,0.00015354951,0.00044369086,0.00051085773,0.001332494,0.0005113729,0.0004902077,0.0005305358],"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":[0.0044569434,0.0009131633,0.050861705,0.0004721021,0.00025345274,0.0005337229,0.00037185213,0.3078738,0.13733083,0.0153913535,0.0061060535,0.47543502],"study_design_scores_gemma":[0.000018679964,0.0005994786,0.01275793,0.000028068915,0.00010613059,0.0005249496,0.00016742639,0.8716722,0.109077774,0.0036174327,0.001394606,0.000035355428],"about_ca_topic_score_codex":0.0025432054,"about_ca_topic_score_gemma":0.0026164874,"teacher_disagreement_score":0.0025432054,"about_ca_system_score_codex":0.0006131428,"about_ca_system_score_gemma":0.00030609625,"threshold_uncertainty_score":0.0073392987},"labels":[],"label_agreement":null},{"id":"W1537934","doi":"","title":"A comparative analysis of neural and statistical classifiers for dimensionality reduction-based face recognition systems.","year":2006,"lang":"en","type":"article","venue":"ASDC journal of dentistry for children","topic":"Face and Expression Recognition","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":"Dimensionality reduction; Artificial intelligence; Pattern recognition (psychology); Computer science; Face (sociological concept); Facial recognition system; Artificial neural network; Machine learning","score_opus":0.03187866656722372,"score_gpt":0.2992586492278472,"score_spread":0.26737998266062346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1537934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094378375,0.055249054,0.8113481,0.0011610319,0.00090077316,0.0006049836,0.00078549975,0.0019546738,0.033617597],"genre_scores_gemma":[0.4799812,0.020620052,0.48706543,0.00034659545,0.00039760754,0.00065479544,0.0019157383,0.00017143956,0.008847208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964246,0.00076152134,0.0002434617,0.0002780378,0.0022144886,0.000077889505],"domain_scores_gemma":[0.995434,0.0022646985,0.00017818973,0.00030831704,0.001765322,0.000049321032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051129675,0.00065207534,0.0007560577,0.0028271906,0.000443183,0.0010441327,0.0006064475,0.0005506534,0.002616679],"category_scores_gemma":[0.009728485,0.0002116598,0.00077965914,0.0015543884,0.0003600104,0.0017467511,0.00046362265,0.0005850228,0.000686577],"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":[0.0003652447,0.0001170099,0.004827474,0.0007292539,0.00032409336,0.00007347573,0.00012407695,0.028904377,0.0077763456,0.01616466,0.008092044,0.932502],"study_design_scores_gemma":[0.00006824662,0.002369861,0.03276748,0.00064190663,0.00062008103,0.0012758315,0.00046287075,0.84033704,0.03494768,0.022355814,0.063963294,0.00018992175],"about_ca_topic_score_codex":0.0017139904,"about_ca_topic_score_gemma":0.0021409467,"teacher_disagreement_score":0.0051129675,"about_ca_system_score_codex":0.0008719022,"about_ca_system_score_gemma":0.0006246986,"threshold_uncertainty_score":0.027040243},"labels":[],"label_agreement":null},{"id":"W1543603119","doi":"10.1007/s10462-009-9139-0","title":"A unified framework for improving the accuracy of all holistic face identification algorithms","year":2009,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"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 Northern British Columbia","funders":"","keywords":"Computer science; Identification (biology); Benchmark (surveying); Face (sociological concept); Process (computing); Machine learning; Algorithm; Artificial intelligence; Baseline (sea); Stability (learning theory); Data mining","score_opus":0.15819327592009239,"score_gpt":0.4015026794538817,"score_spread":0.2433094035337893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543603119","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017674465,0.001791195,0.9938765,0.00015536399,0.00011948625,0.000053435615,0.000037113095,0.0005857636,0.0016136237],"genre_scores_gemma":[0.040516026,0.0022809235,0.9537062,0.00021180938,0.0002464993,0.00015883653,0.00019321345,0.00015933598,0.0025272116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971884,0.0005442319,0.00016737037,0.00047392756,0.0014358434,0.00019017981],"domain_scores_gemma":[0.99752504,0.00040872468,0.00011862813,0.00062896876,0.001247003,0.00007152373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036116797,0.0021758594,0.0030872768,0.0021760778,0.0010567029,0.002747681,0.00338498,0.0017299001,0.0029105034],"category_scores_gemma":[0.0066013755,0.0006132798,0.0016539249,0.0017458161,0.001002416,0.0041946545,0.0033266481,0.0024670828,0.0023816798],"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":[0.00012258773,0.0001790504,0.0009780889,0.00051570183,0.00023482823,0.00008491558,0.00010178748,0.046734393,0.03315586,0.066485345,0.009425186,0.8419823],"study_design_scores_gemma":[0.000035643614,0.00039123956,0.0019699899,0.00016454679,0.00031840845,0.0005742776,0.00009953634,0.8719336,0.040202543,0.05752315,0.026659682,0.00012733792],"about_ca_topic_score_codex":0.003217322,"about_ca_topic_score_gemma":0.00513115,"teacher_disagreement_score":0.0036116797,"about_ca_system_score_codex":0.0010030007,"about_ca_system_score_gemma":0.0023138237,"threshold_uncertainty_score":0.019100666},"labels":[],"label_agreement":null},{"id":"W1543918438","doi":"10.1007/978-3-540-71457-6_51","title":"Classification of Facial Expressions Using K-Nearest Neighbor Classifier","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":51,"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; Canada Research Chairs","keywords":"Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Facial expression; Computer science; Computer vision; Feature vector; k-nearest neighbors algorithm; Feature (linguistics); Face (sociological concept); Support vector machine; Point (geometry); Mathematics","score_opus":0.07209120055610711,"score_gpt":0.30398888908995286,"score_spread":0.23189768853384574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543918438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2274804,0.0020183688,0.75301355,0.00018916983,0.00053189235,0.0004421006,0.0009837144,0.0033899213,0.011950869],"genre_scores_gemma":[0.6676352,0.0015548724,0.31317768,0.00010351664,0.00011398804,0.00028944848,0.0017612401,0.0001560387,0.015208033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993837,0.00006255366,0.000038824972,0.00014979392,0.0002794953,0.00008562164],"domain_scores_gemma":[0.99964523,0.00006650795,0.000018636889,0.000030815896,0.00022186556,0.000016996062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005195757,0.00045071132,0.0010982712,0.0009555352,0.0004123798,0.0005572261,0.0005674379,0.00049547385,0.002771854],"category_scores_gemma":[0.0007214614,0.00017140557,0.0006638416,0.0008513648,0.00021371245,0.00057654665,0.00029498382,0.00042445367,0.0024159923],"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":[0.00033605975,0.00016277932,0.0030171166,0.00012396027,0.000052714022,0.00009355228,0.00007800441,0.004290975,0.064396955,0.0005542506,0.0033836248,0.9235099],"study_design_scores_gemma":[0.000041277788,0.0004998532,0.03386795,0.00009740983,0.00018324167,0.0011531747,0.00044951306,0.8677157,0.08767615,0.0014725702,0.006746581,0.00009655698],"about_ca_topic_score_codex":0.0034000054,"about_ca_topic_score_gemma":0.0036804983,"teacher_disagreement_score":0.0034000054,"about_ca_system_score_codex":0.00028439186,"about_ca_system_score_gemma":0.00040572323,"threshold_uncertainty_score":0.009272754},"labels":[],"label_agreement":null},{"id":"W1550651188","doi":"10.1007/978-3-642-01513-7_36","title":"An Efficient Wavelet Based Feature Extraction Method for Face Recognition","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Windsor","funders":"","keywords":"Computer science; Facial recognition system; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Wavelet; Face (sociological concept); Computer vision","score_opus":0.025674943918926496,"score_gpt":0.3003327080841094,"score_spread":0.2746577641651829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550651188","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005245779,0.0004770509,0.99264187,0.000037129827,0.00008702511,0.00003548471,0.00008503496,0.0006299586,0.0007605591],"genre_scores_gemma":[0.037824098,0.0010560633,0.9513295,0.000073110044,0.00008969451,0.00012669683,0.0005137558,0.00017006605,0.008816968],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99979943,0.000017526743,0.000011546924,0.00002559819,0.0001287652,0.000017217078],"domain_scores_gemma":[0.9998078,0.000054769578,0.000013826341,0.000028487502,0.00008598676,0.000009182887],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024560283,0.0005426952,0.0006531501,0.0007042966,0.00024005506,0.00036466148,0.0006763654,0.000488331,0.0046128416],"category_scores_gemma":[0.0005009023,0.0003404753,0.00057792815,0.001003642,0.00017305254,0.00065448356,0.00042088062,0.00064740476,0.0027978667],"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":[0.00011872424,0.000064220505,0.00012752044,0.00013114244,0.000029500874,0.00006305241,0.000024134328,0.0042712875,0.20979863,0.001971023,0.004915751,0.77848506],"study_design_scores_gemma":[0.00007137844,0.00040508504,0.004824749,0.00006424933,0.00018502968,0.0015658583,0.000054818323,0.644526,0.30151746,0.0037139228,0.04298108,0.00009032715],"about_ca_topic_score_codex":0.0009557774,"about_ca_topic_score_gemma":0.001499952,"teacher_disagreement_score":0.0046128416,"about_ca_system_score_codex":0.00017604344,"about_ca_system_score_gemma":0.00033683862,"threshold_uncertainty_score":0.015431464},"labels":[],"label_agreement":null},{"id":"W1552211690","doi":"10.1007/11494683_31","title":"Data Partitioning Evaluation Measures for Classifier Ensembles","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Computer science; Classifier (UML); Disjoint sets; Artificial intelligence; Probabilistic logic; Machine learning; Training set; Quadratic classifier; Probabilistic classification; Data mining; Support vector machine; Naive Bayes classifier; Mathematics","score_opus":0.11925411094769287,"score_gpt":0.3259983224447972,"score_spread":0.20674421149710434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1552211690","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13583343,0.010841016,0.84193254,0.0007072679,0.0005532644,0.00051653123,0.0016515399,0.0018471717,0.006117372],"genre_scores_gemma":[0.6610476,0.0015344527,0.32532507,0.00020645454,0.00058822794,0.0006587978,0.0067137647,0.0006624798,0.0032630484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98328066,0.0065261605,0.0013920729,0.0014832777,0.0067415987,0.00057632034],"domain_scores_gemma":[0.94910395,0.03269107,0.001665112,0.004933258,0.010542255,0.0010643412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02192348,0.0018715293,0.003075877,0.0059566456,0.001097502,0.0031204948,0.0023328287,0.0019366643,0.0019119395],"category_scores_gemma":[0.054440174,0.00044856526,0.0015464622,0.003469965,0.00084869313,0.0037733784,0.0020704158,0.0021647473,0.0006900135],"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":[0.0017152622,0.00057608256,0.016912522,0.0006124699,0.001114491,0.00006401088,0.00023357756,0.17086756,0.005845193,0.013479929,0.0153747555,0.77320415],"study_design_scores_gemma":[0.00007632282,0.001042048,0.01169135,0.00017535701,0.00041743895,0.00028055717,0.00016237542,0.9516193,0.007894356,0.023374315,0.0031858003,0.000080732214],"about_ca_topic_score_codex":0.0016094773,"about_ca_topic_score_gemma":0.0017956867,"teacher_disagreement_score":0.02192348,"about_ca_system_score_codex":0.0021513577,"about_ca_system_score_gemma":0.0010660132,"threshold_uncertainty_score":0.11594385},"labels":[],"label_agreement":null},{"id":"W1556059795","doi":"","title":"A Bayesian Kernel logistic discriminant model: an improvement to the Kernel Fisher's discriminant","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Face and Expression Recognition","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 de Sherbrooke; University of Windsor","funders":"","keywords":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Kernel (algebra); Fisher kernel; Pattern recognition (psychology); Artificial intelligence; Discriminant; Kernel method; Mathematics; Optimal discriminant analysis; Bayesian probability; Covariance matrix; Covariance; Computer science; Statistics; Machine learning; Support vector machine; Combinatorics","score_opus":0.2148833050964134,"score_gpt":0.3546056745877623,"score_spread":0.13972236949134892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556059795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006419739,0.0008693933,0.99014246,0.00042994684,0.00011359661,0.00004402496,0.00016057042,0.00055544433,0.0012648451],"genre_scores_gemma":[0.34821784,0.0028134047,0.63255143,0.0007021106,0.0005714238,0.00031617386,0.0015042218,0.00053227466,0.0127911465],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805427,0.00063302834,0.00007849618,0.00029181535,0.000824827,0.000117523836],"domain_scores_gemma":[0.9984401,0.00045457488,0.00013672367,0.00025047595,0.0006328855,0.00008528421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028182033,0.00094804756,0.0015614771,0.0012803779,0.0004768137,0.0011797358,0.002030814,0.0010315051,0.00217165],"category_scores_gemma":[0.0057985005,0.00043172945,0.001018995,0.0017657486,0.0005691257,0.0026484155,0.0017987286,0.0019813492,0.0025293338],"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":[0.00043888894,0.00032284806,0.004199738,0.0003191802,0.00024440198,0.00014144604,0.0001286398,0.117760375,0.013656911,0.04148026,0.017801538,0.8035057],"study_design_scores_gemma":[0.000025690126,0.00007551281,0.0010202298,0.000020665033,0.000034273875,0.0001585656,0.00001726501,0.97745466,0.0017679255,0.010729286,0.008650197,0.000045638986],"about_ca_topic_score_codex":0.0046904143,"about_ca_topic_score_gemma":0.004077782,"teacher_disagreement_score":0.0046904143,"about_ca_system_score_codex":0.00067473715,"about_ca_system_score_gemma":0.0015400575,"threshold_uncertainty_score":0.014904201},"labels":[],"label_agreement":null},{"id":"W1567354125","doi":"10.3233/ida-140658","title":"Interactive document clustering with feature supervision through reweighting1","year":2014,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Cluster analysis; Computer science; Feature (linguistics); Set (abstract data type); Feature selection; Data mining; Ask price; Information retrieval; Artificial intelligence; Machine learning; Pattern recognition (psychology)","score_opus":0.02899920739437957,"score_gpt":0.29621411390202496,"score_spread":0.2672149065076454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1567354125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012307821,0.00024356743,0.9760122,0.00008056335,0.00006900359,0.00018228879,0.00033496477,0.009567067,0.0012026163],"genre_scores_gemma":[0.08902493,0.0001583989,0.9024267,0.00007934745,0.00010214234,0.00026830635,0.0015942483,0.0009634387,0.005382349],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815387,0.00034181224,0.00013014876,0.0006071109,0.00061530195,0.00015178308],"domain_scores_gemma":[0.997018,0.000916306,0.00020091969,0.0009869592,0.00075799035,0.00011986106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017744075,0.0013845362,0.0015836932,0.0027538838,0.0011127859,0.0015502637,0.0028382838,0.0013456778,0.0057400786],"category_scores_gemma":[0.0053842943,0.00059936964,0.0012370733,0.0034412518,0.0008018115,0.0018489328,0.0022269208,0.0013941919,0.004423703],"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":[0.00041942415,0.0002910613,0.0012943696,0.00020041739,0.00017931407,0.00008673733,0.00032105934,0.015166696,0.04957822,0.002499973,0.010406833,0.9195559],"study_design_scores_gemma":[0.00011430251,0.00025060878,0.003387925,0.000040651506,0.00014536291,0.00036432917,0.00017267493,0.86791825,0.09291619,0.010344907,0.024238214,0.00010659156],"about_ca_topic_score_codex":0.007188525,"about_ca_topic_score_gemma":0.017039942,"teacher_disagreement_score":0.007188525,"about_ca_system_score_codex":0.000688198,"about_ca_system_score_gemma":0.001052873,"threshold_uncertainty_score":0.01920247},"labels":[],"label_agreement":null},{"id":"W1567964559","doi":"10.1007/978-3-540-89689-0_90","title":"Local Metric Learning on Manifolds with Application to Query–Based Operations","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"McGill University","funders":"","keywords":"Metric (unit); Euclidean distance; Benchmark (surveying); Curse of dimensionality; Manifold (fluid mechanics); Context (archaeology); Computer science; Mathematics; Nonlinear dimensionality reduction; Metric space; Artificial intelligence; Euclidean geometry; Algorithm; Dimensionality reduction; Pattern recognition (psychology); Discrete mathematics; Geometry","score_opus":0.012450119396712218,"score_gpt":0.23524443593604058,"score_spread":0.22279431653932835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1567964559","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009831725,0.0002512626,0.9885882,0.00011223888,0.000020634961,0.000023747163,0.00004480135,0.00045593356,0.0006714524],"genre_scores_gemma":[0.3109259,0.00077899575,0.6827354,0.00008135906,0.00014979772,0.00014021328,0.00045381126,0.00046600855,0.0042685913],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99917275,0.00023537311,0.00006117262,0.00020434542,0.00026766286,0.00005861544],"domain_scores_gemma":[0.99816376,0.00068653113,0.00016245796,0.0004634151,0.0004237139,0.00010011036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010382249,0.0006850499,0.0014341616,0.0011839827,0.00052275794,0.0012485334,0.0017012035,0.00080301694,0.0031588254],"category_scores_gemma":[0.004949502,0.00043822225,0.00069404586,0.0024306828,0.0014202205,0.0032033192,0.002683048,0.0015254305,0.00080203125],"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":[0.00023737695,0.00014173146,0.00082991284,0.00022080391,0.00005520916,0.0000808221,0.00028169283,0.18372256,0.019043997,0.14785416,0.0073670303,0.6401647],"study_design_scores_gemma":[0.0000054148672,0.000077855395,0.00021907025,0.0000045004726,0.000007288947,0.000059543527,0.000029140001,0.94234794,0.0031607412,0.052290585,0.0017852125,0.000012692598],"about_ca_topic_score_codex":0.0026135587,"about_ca_topic_score_gemma":0.0019933917,"teacher_disagreement_score":0.0031588254,"about_ca_system_score_codex":0.0007039785,"about_ca_system_score_gemma":0.00050284143,"threshold_uncertainty_score":0.010567367},"labels":[],"label_agreement":null},{"id":"W1572041619","doi":"10.1007/11815921_91","title":"On Optimizing Kernel-Based Fisher Discriminant Analysis Using Prototype Reduction Schemes","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Carleton University","funders":"","keywords":"Kernel Fisher discriminant analysis; Dimensionality reduction; Linear discriminant analysis; Kernel (algebra); Computer science; Computation; Reduction (mathematics); Pattern recognition (psychology); Kernel method; Artificial intelligence; Fisher kernel; Data set; Discriminant; Algorithm; Mathematics; Support vector machine; Discrete mathematics","score_opus":0.02490450889626933,"score_gpt":0.2629764465489416,"score_spread":0.23807193765267226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572041619","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010527098,0.00013134748,0.98801315,0.0000458769,0.0000194919,0.000019519952,0.000020207883,0.0005980487,0.00062527735],"genre_scores_gemma":[0.13669948,0.00018531893,0.85887754,0.000060495535,0.000032870408,0.000081235725,0.00021978746,0.00032856714,0.00351467],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938023,0.00019592977,0.0000336489,0.00009756096,0.00023989273,0.000052733787],"domain_scores_gemma":[0.99883014,0.0006203213,0.000059015612,0.0002015413,0.00026262485,0.00002629543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010652443,0.00082366867,0.0012325993,0.0005783877,0.00033380665,0.0007445861,0.0011283256,0.00076708075,0.0033093167],"category_scores_gemma":[0.0041059614,0.0005010687,0.0006329302,0.0008653818,0.00043262015,0.0015549703,0.001147946,0.00095807126,0.0012881506],"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":[0.0002754964,0.0001285717,0.00042459165,0.000100063386,0.000050598264,0.00003180437,0.00006796511,0.19929555,0.024010317,0.014067553,0.003965325,0.75758207],"study_design_scores_gemma":[0.000008618095,0.00002502448,0.00014427768,0.0000031648342,0.000008274404,0.000018589148,0.0000070541178,0.9927408,0.0029065942,0.0036069064,0.00052293204,0.0000077164395],"about_ca_topic_score_codex":0.0033289304,"about_ca_topic_score_gemma":0.0036962437,"teacher_disagreement_score":0.0033289304,"about_ca_system_score_codex":0.00043462683,"about_ca_system_score_gemma":0.0007090529,"threshold_uncertainty_score":0.011070728},"labels":[],"label_agreement":null},{"id":"W1572450274","doi":"10.1109/ijcnn.2005.1555966","title":"Algorithms of fast SVM evaluation based on subspace projection","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"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":"MNIST database; Support vector machine; Kernel (algebra); Subspace topology; Computer science; Binary number; Pattern recognition (psychology); Projection (relational algebra); Block (permutation group theory); Algorithm; Class (philosophy); Set (abstract data type); Artificial intelligence; Mathematics; Deep learning; Combinatorics; Arithmetic","score_opus":0.05208124229365415,"score_gpt":0.28903741298827507,"score_spread":0.2369561706946209,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572450274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025627764,0.000108895925,0.995082,0.000039801584,0.00003510515,0.00008707703,0.000033551056,0.0013123155,0.0007383985],"genre_scores_gemma":[0.07645284,0.0001930869,0.91889864,0.000047322723,0.000057020563,0.0005082653,0.00037301626,0.00034511823,0.0031247747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767095,0.00070280104,0.00016856426,0.00027589558,0.0010032987,0.00017852409],"domain_scores_gemma":[0.9964954,0.0007200141,0.00016750146,0.0003501914,0.0021314493,0.00013539736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028178187,0.0012491542,0.0016601533,0.0018766578,0.0010014639,0.002054161,0.0019152755,0.0011840368,0.0067893886],"category_scores_gemma":[0.0065732915,0.00070029387,0.0007499651,0.0016891307,0.00063752866,0.0024258487,0.0025199435,0.0018409261,0.0039466345],"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":[0.00020025558,0.000082351515,0.0008004297,0.00007760417,0.000047313057,0.00003109039,0.00008061681,0.06637748,0.0076546427,0.016416838,0.005537981,0.9026934],"study_design_scores_gemma":[0.00001839133,0.00006776444,0.0003095583,0.000012027444,0.000008810365,0.000050633957,0.000023492381,0.98286426,0.005718408,0.008276798,0.002629828,0.000019964173],"about_ca_topic_score_codex":0.0029430406,"about_ca_topic_score_gemma":0.0025101344,"teacher_disagreement_score":0.0067893886,"about_ca_system_score_codex":0.0009704979,"about_ca_system_score_gemma":0.002044127,"threshold_uncertainty_score":0.022712767},"labels":[],"label_agreement":null},{"id":"W1578823017","doi":"10.1007/978-3-540-30125-7_31","title":"Significance Test for Feature Subset Selection on Image Recognition","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":20,"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 Waterloo","funders":"","keywords":"Computer science; Feature selection; Artificial intelligence; Pattern recognition (psychology); Selection (genetic algorithm); Feature (linguistics); Image (mathematics); Computer vision","score_opus":0.019712254924873725,"score_gpt":0.2487813713266538,"score_spread":0.22906911640178007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1578823017","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13688554,0.00079303485,0.8567711,0.00058764196,0.00036139096,0.00021767919,0.000588346,0.0022669306,0.0015283589],"genre_scores_gemma":[0.80826783,0.00017431944,0.1857222,0.00024504337,0.00043906106,0.0003345933,0.0022229545,0.00043253324,0.002161432],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9893618,0.005513285,0.0006626439,0.0014462767,0.0024086537,0.0006072805],"domain_scores_gemma":[0.939649,0.05192521,0.00087294955,0.0034979894,0.0033202227,0.0007346445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012347579,0.0010883253,0.0031781902,0.0030355195,0.0015079697,0.0016450572,0.0023611416,0.0017568233,0.0044655325],"category_scores_gemma":[0.043662637,0.0005190401,0.0020671515,0.0021539172,0.0016159681,0.0014621997,0.0014834484,0.002561782,0.0008836637],"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":[0.006130141,0.00061979046,0.040776808,0.0005256277,0.0016269705,0.0008618053,0.00019644007,0.032674704,0.028968595,0.008510898,0.010615051,0.8684931],"study_design_scores_gemma":[0.00071287877,0.004705417,0.048963178,0.00007084551,0.001039279,0.0013830676,0.00043705286,0.86162406,0.032229997,0.044236273,0.0044832677,0.00011475457],"about_ca_topic_score_codex":0.0008050604,"about_ca_topic_score_gemma":0.0006350157,"teacher_disagreement_score":0.012347579,"about_ca_system_score_codex":0.0004675711,"about_ca_system_score_gemma":0.0018084784,"threshold_uncertainty_score":0.065301},"labels":[],"label_agreement":null},{"id":"W1580342420","doi":"10.1007/978-3-642-15696-0_57","title":"Curvelet Entropy for Facial Expression Recognition","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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 Windsor","funders":"","keywords":"Curvelet; Artificial intelligence; Computer science; Pattern recognition (psychology); Facial expression; Entropy (arrow of time); Wavelet; Face (sociological concept); Computer vision; Facial recognition system; Orientation (vector space); Gabor wavelet; Wavelet transform; Mathematics; Discrete wavelet transform; Physics","score_opus":0.02358156096880048,"score_gpt":0.2545956949735735,"score_spread":0.231014134004773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1580342420","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0082398895,0.0023770363,0.9834237,0.00016329644,0.00020018741,0.000030059673,0.00023990161,0.0008828051,0.004443123],"genre_scores_gemma":[0.38683414,0.008697383,0.56142646,0.00020365314,0.0011375934,0.00021795141,0.0020238159,0.00088076503,0.038578223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999734,0.00004882854,0.000013446122,0.00003579404,0.00015140887,0.000016515922],"domain_scores_gemma":[0.9995763,0.00021489877,0.000019967265,0.00008202035,0.00009193828,0.000014789628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047411522,0.00048884284,0.000721382,0.000875675,0.00019864299,0.00064101303,0.0005037961,0.0004200988,0.005054415],"category_scores_gemma":[0.0013108824,0.00019961527,0.0002678521,0.0012254845,0.00041039052,0.0011236266,0.0005471363,0.0008642335,0.0018080642],"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":[0.00009436012,0.000053447664,0.00038431483,0.00014125969,0.000027114076,0.000074310396,0.00005070477,0.05991469,0.029213138,0.05273989,0.011852523,0.84545416],"study_design_scores_gemma":[0.0000066588423,0.000052194002,0.0014895918,0.000029942466,0.000015386597,0.00018871417,0.000016799326,0.92085177,0.023822289,0.04108948,0.01240526,0.00003191435],"about_ca_topic_score_codex":0.0005641615,"about_ca_topic_score_gemma":0.00048068652,"teacher_disagreement_score":0.005054415,"about_ca_system_score_codex":0.00040600318,"about_ca_system_score_gemma":0.00026913508,"threshold_uncertainty_score":0.016908705},"labels":[],"label_agreement":null},{"id":"W1583943159","doi":"10.1007/3-540-44732-6_9","title":"Invariant Face Detection in Color Images Using Orthogonal Fourier-Mellin Moments and Support Vector Machines","year":2001,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Université Laval","funders":"","keywords":"Chrominance; Artificial intelligence; Computer science; Pattern recognition (psychology); Support vector machine; Face detection; Segmentation; Computer vision; Invariant (physics); Feature extraction; Robustness (evolution); Facial recognition system; Mathematics; Luminance","score_opus":0.020419657892194156,"score_gpt":0.2537845137343033,"score_spread":0.23336485584210911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583943159","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029919056,0.0006650866,0.9659681,0.0000736954,0.00006953772,0.000028877044,0.00010152087,0.0012272282,0.0019468004],"genre_scores_gemma":[0.24296047,0.0009378975,0.75089115,0.00007251544,0.00009377932,0.00006831771,0.00032592376,0.00017634609,0.0044736825],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997706,0.000028977343,0.000009319911,0.000038952025,0.00011242929,0.0000397713],"domain_scores_gemma":[0.9997012,0.000097785225,0.000040582076,0.000045529854,0.00009966338,0.000015213288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029280028,0.0004516334,0.00072208565,0.0010675474,0.00020501667,0.00060147635,0.0006065441,0.0004125627,0.0020168014],"category_scores_gemma":[0.00089094037,0.00026724092,0.00054490933,0.0009572679,0.00030408925,0.00079958927,0.0003421484,0.00045072654,0.0010405524],"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":[0.00016104504,0.00007446743,0.000740411,0.000101600155,0.00003855612,0.000053624706,0.000029384584,0.012459138,0.09483203,0.0037261676,0.0029123651,0.8848712],"study_design_scores_gemma":[0.00002461394,0.0001267928,0.006261202,0.000025101608,0.000072552015,0.0004782988,0.000055437376,0.85694104,0.12352268,0.008136855,0.00430017,0.00005520503],"about_ca_topic_score_codex":0.0009844416,"about_ca_topic_score_gemma":0.0017203924,"teacher_disagreement_score":0.0020168014,"about_ca_system_score_codex":0.00025537502,"about_ca_system_score_gemma":0.0002824404,"threshold_uncertainty_score":0.006746888},"labels":[],"label_agreement":null},{"id":"W1586572846","doi":"10.1109/ijcnn.2005.1556439","title":"Embedding via clustering: using spectral information to guide dimensionality reduction","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Dimensionality reduction; Maxima and minima; Cluster analysis; Spectral clustering; Eigendecomposition of a matrix; Computer science; Iterative method; Embedding; Eigenvalues and eigenvectors; Curse of dimensionality; Isomap; Reduction (mathematics); Artificial intelligence; Nonlinear dimensionality reduction; Algorithm; Pattern recognition (psychology); Data mining; Mathematics","score_opus":0.040532765643194406,"score_gpt":0.2912275605935026,"score_spread":0.25069479495030816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1586572846","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032642507,0.000118880824,0.99539375,0.00008904424,0.000022332657,0.000027372462,0.000033435506,0.00036929327,0.0006816535],"genre_scores_gemma":[0.06583433,0.00030370057,0.9312513,0.000116604715,0.000055080378,0.0001581497,0.00025538082,0.00032831312,0.0016971899],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988263,0.00034685349,0.00006623816,0.00027193147,0.000396405,0.000092251306],"domain_scores_gemma":[0.99807525,0.00060527335,0.00019762434,0.00054115563,0.0005078915,0.00007279948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013319452,0.0013566712,0.0010504384,0.002340639,0.0011576705,0.0018366126,0.0015115343,0.0013775037,0.0024164577],"category_scores_gemma":[0.0061834273,0.0007616713,0.00090644264,0.0024615817,0.0013024862,0.0032750103,0.0025273785,0.0015863949,0.0020109334],"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":[0.00013277697,0.00018429178,0.0015986655,0.00027711442,0.00013444836,0.0001160111,0.0008791801,0.24496554,0.025290847,0.10180783,0.009375327,0.61523795],"study_design_scores_gemma":[0.000018146873,0.000051870153,0.0003578273,0.00003902429,0.000025068442,0.00009911434,0.00009323023,0.89848506,0.010121142,0.0797531,0.010896985,0.000059417947],"about_ca_topic_score_codex":0.002296762,"about_ca_topic_score_gemma":0.003118723,"teacher_disagreement_score":0.0024164577,"about_ca_system_score_codex":0.0006142541,"about_ca_system_score_gemma":0.0011167614,"threshold_uncertainty_score":0.00808388},"labels":[],"label_agreement":null},{"id":"W1588637626","doi":"10.1007/978-3-540-24581-0_67","title":"On Using Prototype Reduction Schemes and Classifier Fusion Strategies to Optimize Kernel-Based Nonlinear Subspace Methods","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Carleton University","funders":"","keywords":"Computer science; Subspace topology; Classifier (UML); Kernel method; Kernel (algebra); Artificial intelligence; Computation; Pattern recognition (psychology); Dimensionality reduction; Algorithm; Support vector machine; Mathematics","score_opus":0.040077387975911045,"score_gpt":0.32467688606166084,"score_spread":0.2845994980857498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1588637626","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004883485,0.00012949712,0.994189,0.000060850554,0.000022957567,0.000028623517,0.000012511762,0.00022760074,0.00044538776],"genre_scores_gemma":[0.12361992,0.00025959205,0.8727098,0.00010658779,0.00004831387,0.00015309668,0.00014028966,0.00023240541,0.0027300972],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920875,0.00029709795,0.000051681876,0.00011014904,0.00028645844,0.000045911518],"domain_scores_gemma":[0.9981135,0.0008683208,0.000095798685,0.0002869993,0.0005961448,0.000039310828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002303002,0.0010253027,0.0013842408,0.00066952495,0.0005460211,0.0009610142,0.0014676368,0.0015295743,0.002202125],"category_scores_gemma":[0.0071501005,0.00068132207,0.0007361075,0.001034341,0.0009147331,0.0026647104,0.0016019944,0.0014204957,0.0007911556],"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":[0.00021563852,0.00013304137,0.00042142617,0.00012623219,0.000086622196,0.000032570115,0.00012817691,0.42770115,0.013631063,0.029522652,0.0040636566,0.52393776],"study_design_scores_gemma":[0.000008018564,0.000027083981,0.00009745618,0.0000049406544,0.000010458039,0.000014010641,0.000007239237,0.99136305,0.0017426211,0.0062537,0.00046056905,0.000010780982],"about_ca_topic_score_codex":0.0033622268,"about_ca_topic_score_gemma":0.0037955535,"teacher_disagreement_score":0.0033622268,"about_ca_system_score_codex":0.00051858515,"about_ca_system_score_gemma":0.000785773,"threshold_uncertainty_score":0.012179613},"labels":[],"label_agreement":null},{"id":"W1591713534","doi":"10.1007/978-3-540-76414-4_22","title":"Cumulative Global Distance for Dimension Reduction in Handwritten Digits Database","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"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":"Mahalanobis distance; Bhattacharyya distance; Principal component analysis; Computer science; Pattern recognition (psychology); Dimension (graph theory); Eigenvalues and eigenvectors; Dimensionality reduction; Mathematics; Statistics; Artificial intelligence; Combinatorics","score_opus":0.033194525331946764,"score_gpt":0.29639471992671335,"score_spread":0.26320019459476657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1591713534","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16940437,0.0066723656,0.81210715,0.0004425992,0.00043637902,0.00013783573,0.002683361,0.0040274933,0.004088452],"genre_scores_gemma":[0.48147818,0.0023875877,0.493216,0.00012380847,0.00022961828,0.0002053639,0.010631542,0.00033503148,0.011392879],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99921656,0.000116051815,0.00006960516,0.00017065478,0.00033991295,0.00008715369],"domain_scores_gemma":[0.99932754,0.00015870338,0.000033696353,0.00017903895,0.00026729083,0.00003371882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005505573,0.00064343197,0.0011348077,0.0016427429,0.0004655074,0.000871967,0.0009791184,0.00044180197,0.0027940765],"category_scores_gemma":[0.0016151667,0.00019946117,0.00070140127,0.0017407608,0.0002794842,0.00086084486,0.00083646836,0.00065316685,0.0009771367],"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":[0.00056343444,0.00011527351,0.0016064154,0.00016084274,0.000070665024,0.00006601684,0.000051969837,0.021713678,0.01799826,0.0034606156,0.017252475,0.93694025],"study_design_scores_gemma":[0.00007490194,0.0004527369,0.011376184,0.000047872545,0.00013378066,0.00043380502,0.00019515473,0.89512974,0.060884867,0.0125154555,0.01867678,0.00007882588],"about_ca_topic_score_codex":0.0047771805,"about_ca_topic_score_gemma":0.005663444,"teacher_disagreement_score":0.0047771805,"about_ca_system_score_codex":0.00047693352,"about_ca_system_score_gemma":0.0010239873,"threshold_uncertainty_score":0.009498775},"labels":[],"label_agreement":null},{"id":"W1597421340","doi":"10.1007/978-3-642-15555-0_12","title":"A Discriminative Latent Model of Object Classes and Attributes","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":221,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Discriminative model; Computer science; Object (grammar); Artificial intelligence; Pattern recognition (psychology)","score_opus":0.03255496440562197,"score_gpt":0.25531685308170515,"score_spread":0.22276188867608318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597421340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061227377,0.00037959445,0.9908285,0.0001808056,0.00007296496,0.000027871605,0.0008862779,0.0009173763,0.0005838858],"genre_scores_gemma":[0.44587308,0.0018599277,0.5228948,0.0008002986,0.0004277878,0.00039754625,0.01185328,0.0006120442,0.015281281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989064,0.00023261861,0.000055382276,0.00042543386,0.000250419,0.0001297886],"domain_scores_gemma":[0.9984586,0.00051591074,0.00014570552,0.00058628176,0.00021517437,0.00007831282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014574946,0.00078546756,0.0015034033,0.0011355121,0.00038892124,0.0014228426,0.0028190531,0.0014174553,0.0032777165],"category_scores_gemma":[0.003305712,0.00091835065,0.001666419,0.0021657022,0.000949142,0.0025237903,0.001802612,0.0031530512,0.0032592255],"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":[0.00071546715,0.0005460212,0.0044881534,0.00040217311,0.00028727436,0.00022547875,0.0002897073,0.11502977,0.03549171,0.0907994,0.026244255,0.7254806],"study_design_scores_gemma":[0.000040317613,0.000071002694,0.0016782682,0.000043155185,0.00009030514,0.00024832814,0.0000327798,0.93702847,0.003603709,0.05272588,0.0044039786,0.000033806995],"about_ca_topic_score_codex":0.005412406,"about_ca_topic_score_gemma":0.009716258,"teacher_disagreement_score":0.005412406,"about_ca_system_score_codex":0.0008366904,"about_ca_system_score_gemma":0.0012819585,"threshold_uncertainty_score":0.010965049},"labels":[],"label_agreement":null},{"id":"W1600470703","doi":"10.1007/3-540-44886-1_65","title":"Accent Classification Using Support Vector Machine and Hidden Markov Model","year":2003,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"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 New Brunswick","funders":"","keywords":"Hidden Markov model; Computer science; Stress (linguistics); Support vector machine; Artificial intelligence; Markov model; Maximum-entropy Markov model; Speech recognition; Natural language processing; Machine learning; Pattern recognition (psychology); Markov chain; Variable-order Markov model","score_opus":0.03799489310180855,"score_gpt":0.268994810596097,"score_spread":0.23099991749428844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1600470703","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075555146,0.0011813093,0.9147455,0.00016263766,0.0002594297,0.00009538035,0.0005504512,0.0044787102,0.0029714007],"genre_scores_gemma":[0.6427634,0.0008431231,0.34432092,0.000097183736,0.00024729606,0.00010808203,0.0022561166,0.0002757185,0.009088232],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948406,0.00011921529,0.000042567885,0.00014006862,0.00012373693,0.00009031478],"domain_scores_gemma":[0.9989158,0.0005416346,0.00006860949,0.000107530715,0.0003104631,0.00005605498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012451219,0.0008193721,0.0013042178,0.0019220344,0.00047186765,0.0011530196,0.00081453775,0.0007289206,0.003817456],"category_scores_gemma":[0.0016218254,0.00033359986,0.0010533505,0.0011640071,0.00025344666,0.0013212457,0.0005943187,0.000986097,0.0033059823],"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":[0.0005692175,0.00018327119,0.0039548785,0.00008699493,0.000094875315,0.00010034745,0.0000472149,0.024688581,0.015478846,0.000988149,0.004672195,0.9491353],"study_design_scores_gemma":[0.000029754348,0.00012655038,0.006142893,0.000017264763,0.000083766514,0.00014884598,0.000073626565,0.97388804,0.014912048,0.0028834548,0.0016516644,0.000042102336],"about_ca_topic_score_codex":0.0020983007,"about_ca_topic_score_gemma":0.0023791613,"teacher_disagreement_score":0.003817456,"about_ca_system_score_codex":0.00026795143,"about_ca_system_score_gemma":0.00034538508,"threshold_uncertainty_score":0.012770712},"labels":[],"label_agreement":null},{"id":"W1601393362","doi":"10.1007/978-3-642-02611-9_46","title":"A Novel Bayesian Logistic Discriminant Model with Dirichlet Distributions: An Application to Face Recognition","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Computer science; Dirichlet distribution; Latent Dirichlet allocation; Support vector machine; Bayesian probability; Classifier (UML); Heteroscedasticity; Machine learning; Mathematics; Topic model","score_opus":0.03573526822977385,"score_gpt":0.2719943367381998,"score_spread":0.23625906850842593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601393362","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033394725,0.00039464913,0.9949019,0.00021523492,0.000070414055,0.000028251532,0.00009391742,0.00039723833,0.00055882585],"genre_scores_gemma":[0.12773691,0.0012642632,0.859745,0.00036499748,0.0004451015,0.00034623093,0.0008110498,0.00046625314,0.0088202],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979621,0.0010130059,0.000091535694,0.0003779391,0.00044000286,0.00011539492],"domain_scores_gemma":[0.99712855,0.0019277647,0.00012911666,0.00024692394,0.00046229368,0.000105384854],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004886852,0.0011323477,0.0023810295,0.001291198,0.0010407348,0.0019752344,0.0037351486,0.0019104914,0.0036920214],"category_scores_gemma":[0.01055512,0.0009800717,0.001505993,0.002666796,0.0010491348,0.0031698083,0.0027839164,0.003078304,0.002800409],"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":[0.0005321579,0.00025416576,0.0016696018,0.00032362985,0.00019767002,0.00016025906,0.00029498173,0.2531075,0.0034787552,0.08002942,0.019999916,0.6399519],"study_design_scores_gemma":[0.000017476938,0.000014117221,0.00013825693,0.000012088085,0.000018264322,0.000052273816,0.000019257697,0.97222126,0.0004925955,0.025294036,0.0016988928,0.00002147784],"about_ca_topic_score_codex":0.005117901,"about_ca_topic_score_gemma":0.005703833,"teacher_disagreement_score":0.005117901,"about_ca_system_score_codex":0.0011246407,"about_ca_system_score_gemma":0.0015338712,"threshold_uncertainty_score":0.025844455},"labels":[],"label_agreement":null},{"id":"W1605340286","doi":"10.1109/icb.2015.7139093","title":"Quaternion-based Local Binary Patterns for illumination invariant face recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 de Sherbrooke","funders":"","keywords":"Quaternion; Local binary patterns; Pixel; Artificial intelligence; Invariant (physics); Computer vision; Facial recognition system; Binary number; Pattern recognition (psychology); Computer science; Representation (politics); Face (sociological concept); Mathematics; Histogram; Image (mathematics); Geometry; Arithmetic","score_opus":0.06111385351649087,"score_gpt":0.27065667055513404,"score_spread":0.20954281703864316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1605340286","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013539679,0.0007996628,0.9818923,0.00021771746,0.00012753002,0.0000821095,0.0002045436,0.00087241223,0.0022640622],"genre_scores_gemma":[0.3578232,0.001810241,0.63353723,0.00024508472,0.00018634975,0.00024207421,0.0009345069,0.00016566971,0.0050556734],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999595,0.0001236959,0.000031842323,0.000051021787,0.00017298515,0.000025497553],"domain_scores_gemma":[0.999539,0.0001039015,0.000060040627,0.00012906971,0.00014557286,0.000022417598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056170573,0.00032835035,0.00040833806,0.00065217906,0.00015415643,0.00049217086,0.0005228671,0.00028927834,0.0041650888],"category_scores_gemma":[0.001611358,0.00013389424,0.00027111376,0.0011737641,0.00031652924,0.00078859646,0.00035166182,0.0004109884,0.0018354363],"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":[0.00027297222,0.00010489818,0.00054057164,0.0002269215,0.00004047067,0.00012634072,0.00009340743,0.046907667,0.098959625,0.037536655,0.0076147476,0.8075758],"study_design_scores_gemma":[0.00007008619,0.00040052086,0.0019307047,0.0000612197,0.000045011824,0.00039412375,0.00007138609,0.878034,0.07123202,0.024385214,0.02331324,0.00006244019],"about_ca_topic_score_codex":0.0008064389,"about_ca_topic_score_gemma":0.0007032579,"teacher_disagreement_score":0.0041650888,"about_ca_system_score_codex":0.00033658263,"about_ca_system_score_gemma":0.00033607346,"threshold_uncertainty_score":0.013933599},"labels":[],"label_agreement":null},{"id":"W1613559259","doi":"10.1007/978-3-642-04146-4_45","title":"On Improving the Efficiency of Eigenface Using a Novel Facial Feature Localization","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Eigenface; Computer science; Facial recognition system; Preprocessor; Robustness (evolution); Biometrics; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Face (sociological concept); Computer vision","score_opus":0.018204674089456873,"score_gpt":0.24678818329599048,"score_spread":0.2285835092065336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1613559259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015809959,0.00040217215,0.9807668,0.00008127011,0.00006706667,0.000027320437,0.000036334597,0.0016238374,0.0011852465],"genre_scores_gemma":[0.12537077,0.0005851785,0.86805934,0.00013833815,0.00006784271,0.000069898204,0.00024890542,0.0003494707,0.0051103183],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920696,0.00015317422,0.00003951817,0.0001550997,0.0003659271,0.000079307436],"domain_scores_gemma":[0.9990722,0.00035253362,0.00003534137,0.00026800195,0.00024593313,0.000025904701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071061216,0.0011404325,0.0011929595,0.00085033395,0.00042244187,0.0006928589,0.0011579368,0.0008165233,0.005140572],"category_scores_gemma":[0.0019276233,0.00040829624,0.0007540632,0.0010670429,0.00039955365,0.002018113,0.0011391779,0.00082947774,0.0026688816],"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":[0.0002476878,0.00012342808,0.00064072345,0.0000825542,0.000058262718,0.00006408515,0.000060061637,0.015320791,0.1486821,0.0048232363,0.0039084256,0.82598877],"study_design_scores_gemma":[0.00003247899,0.00017042055,0.0017795324,0.00001646255,0.00006742408,0.00053760596,0.00004681915,0.8803543,0.10898797,0.0038809387,0.0040894104,0.00003665021],"about_ca_topic_score_codex":0.0027230356,"about_ca_topic_score_gemma":0.003629946,"teacher_disagreement_score":0.005140572,"about_ca_system_score_codex":0.0002564525,"about_ca_system_score_gemma":0.0004688359,"threshold_uncertainty_score":0.017196953},"labels":[],"label_agreement":null},{"id":"W1629332708","doi":"10.48550/arxiv.1212.2490","title":"On the Convergence of Bound Optimization Algorithms","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"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":"Convergence (economics); Preprocessor; Computer science; Algorithm; Upper and lower bounds; Mathematical optimization; Optimization problem; Mathematics; Artificial intelligence","score_opus":0.06638514464853387,"score_gpt":0.1772630716709672,"score_spread":0.11087792702243332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1629332708","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010686539,0.0015771645,0.98095006,0.0010177164,0.00009028336,0.000059935843,0.000051442203,0.0004475021,0.0051193195],"genre_scores_gemma":[0.3797685,0.0038234715,0.6044551,0.001243281,0.0003827165,0.00088329084,0.00043992006,0.0015213116,0.0074824714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9912129,0.005077059,0.0003801925,0.0010201928,0.0018735426,0.0004360103],"domain_scores_gemma":[0.9108321,0.07608646,0.0021100608,0.0048179124,0.005567284,0.00058617606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016089516,0.0017741624,0.0018387432,0.001960855,0.0014759303,0.003065315,0.0019223373,0.0027336564,0.0044602505],"category_scores_gemma":[0.14180322,0.0011687858,0.0010158921,0.0018063053,0.0046889363,0.0057577235,0.003771108,0.0045324746,0.0018823238],"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":[0.00041853453,0.00011619713,0.0033646827,0.00054406223,0.0001464011,0.00013704131,0.0006049032,0.5225019,0.0029577995,0.34446928,0.0068230387,0.11791619],"study_design_scores_gemma":[0.000031809137,0.00006232859,0.0004329753,0.000113205926,0.000013932065,0.00006459106,0.000046901143,0.8916366,0.0015597685,0.10375759,0.0022588305,0.000021433625],"about_ca_topic_score_codex":0.0030747545,"about_ca_topic_score_gemma":0.0016267945,"teacher_disagreement_score":0.016089516,"about_ca_system_score_codex":0.001986648,"about_ca_system_score_gemma":0.0021175798,"threshold_uncertainty_score":0.08509052},"labels":[],"label_agreement":null},{"id":"W164388623","doi":"10.1007/978-3-540-74260-9_72","title":"Face Recognition by Curvelet Based Feature Extraction","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Canada Research Chairs","keywords":"Curvelet; Computer science; Artificial intelligence; Pattern recognition (psychology); Face (sociological concept); Feature extraction; Facial recognition system; Wavelet; Wavelet transform; Support vector machine; Feature (linguistics); Set (abstract data type); Feature vector; Image (mathematics); Computer vision","score_opus":0.028537403801803845,"score_gpt":0.27259675784773624,"score_spread":0.2440593540459324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W164388623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023419144,0.0007719462,0.96711046,0.000109045526,0.0001450007,0.00008709236,0.00020868276,0.0025883077,0.0055602533],"genre_scores_gemma":[0.2538921,0.0020579733,0.71521914,0.00024484572,0.0002082233,0.00019858271,0.0013655116,0.00036760347,0.026445987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997632,0.000016810309,0.000008962821,0.000040759605,0.00014218038,0.00002808083],"domain_scores_gemma":[0.9997538,0.000060454386,0.000014461807,0.000049625756,0.00010951417,0.000012157094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023427316,0.00049572135,0.0008767333,0.0009797463,0.00021601822,0.00055839104,0.00072583836,0.00079508184,0.004924716],"category_scores_gemma":[0.00054151326,0.0003103607,0.00047829727,0.001105474,0.00021129627,0.00079892325,0.0004117645,0.0006327879,0.005282484],"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":[0.0001226393,0.00007305591,0.00042212097,0.00008749261,0.000019055646,0.00008518843,0.000020074976,0.0055066925,0.22574274,0.0013061716,0.004138035,0.7624768],"study_design_scores_gemma":[0.00002791929,0.00025645396,0.006230465,0.00004203976,0.00006196883,0.0013828705,0.000033860208,0.5094901,0.4611121,0.003207548,0.018089488,0.00006526781],"about_ca_topic_score_codex":0.00063656387,"about_ca_topic_score_gemma":0.00059449265,"teacher_disagreement_score":0.004924716,"about_ca_system_score_codex":0.0002170629,"about_ca_system_score_gemma":0.00022887628,"threshold_uncertainty_score":0.016474783},"labels":[],"label_agreement":null},{"id":"W1645673120","doi":"","title":"The set covering machine with data-dependent half-spaces","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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; Université Laval","funders":"","keywords":"Generalization; Set (abstract data type); Generalization error; Support vector machine; Training set; Data set; Computer science; Upper and lower bounds; Artificial intelligence; Selection (genetic algorithm); Algorithm; Machine learning; Mathematics; Data mining; Artificial neural network","score_opus":0.030441269599329135,"score_gpt":0.2558528287149728,"score_spread":0.22541155911564364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1645673120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04975866,0.000646716,0.9473761,0.0003388265,0.000040612944,0.000041435258,0.00009107567,0.00029190665,0.0014146903],"genre_scores_gemma":[0.6660754,0.00081238657,0.32961905,0.0002643164,0.00015122729,0.00035215728,0.0006598297,0.00013665544,0.001929018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9978077,0.0011702584,0.0001243057,0.00025292183,0.00048974145,0.00015511339],"domain_scores_gemma":[0.99037457,0.0067702434,0.0004049573,0.0015645091,0.0006610942,0.00022466441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034478614,0.00095514156,0.0018715747,0.0010044919,0.0006169101,0.001543977,0.00181033,0.0016159528,0.002080922],"category_scores_gemma":[0.020591931,0.00051162986,0.0009433541,0.0013914508,0.0019106442,0.004030766,0.0030134993,0.001677946,0.0005509111],"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":[0.0004180402,0.00011320336,0.0021456683,0.000249461,0.00013250638,0.00022068997,0.0003545044,0.6086683,0.0046599414,0.13692424,0.0041429186,0.24197051],"study_design_scores_gemma":[0.000006718546,0.000048836784,0.00015191818,0.000011174466,0.000006113626,0.00004942325,0.000014894336,0.9546501,0.0011262017,0.043403663,0.00052031927,0.00001060976],"about_ca_topic_score_codex":0.00075645954,"about_ca_topic_score_gemma":0.00033297832,"teacher_disagreement_score":0.0034478614,"about_ca_system_score_codex":0.00065267907,"about_ca_system_score_gemma":0.00041271688,"threshold_uncertainty_score":0.018234253},"labels":[],"label_agreement":null},{"id":"W1718965518","doi":"","title":"Improving embeddings by flexible exploitation of side information","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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":"Pairwise comparison; Embedding; Metric (unit); Computer science; Dimensionality reduction; Nonlinear dimensionality reduction; Salient; Class (philosophy); Equivalence class (music); Equivalence (formal languages); Curse of dimensionality; Set (abstract data type); Manifold (fluid mechanics); Artificial intelligence; Theoretical computer science; Mathematics; Discrete mathematics","score_opus":0.008449264610382923,"score_gpt":0.2379996137289133,"score_spread":0.22955034911853037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1718965518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04350321,0.00036438348,0.9525659,0.00017267483,0.00004975753,0.0000438719,0.00013053024,0.0010943953,0.0020753036],"genre_scores_gemma":[0.43856683,0.00070493575,0.55412453,0.00025703857,0.00012338102,0.00017614203,0.0011643604,0.00057685777,0.004305955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989981,0.00032256352,0.000079803074,0.00022975975,0.00030652885,0.00006326032],"domain_scores_gemma":[0.99555284,0.0015078465,0.00048169773,0.0017133311,0.0005965645,0.00014761099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012887605,0.0017079845,0.0014378245,0.00087258,0.00038455447,0.0011608487,0.0012236888,0.001410953,0.0020967387],"category_scores_gemma":[0.007293785,0.0007091093,0.00065128435,0.0011646398,0.001010609,0.0051537715,0.0031207204,0.002152407,0.0020330215],"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":[0.00039048772,0.00038096824,0.002994479,0.0003114913,0.000096744065,0.00028108293,0.00038046864,0.2347543,0.088180676,0.038081132,0.006820656,0.6273275],"study_design_scores_gemma":[0.000026440042,0.00020417482,0.0004929379,0.00001994024,0.000026902888,0.00022069825,0.00004816263,0.9469078,0.018951762,0.029499244,0.0035685382,0.000033396358],"about_ca_topic_score_codex":0.00030471204,"about_ca_topic_score_gemma":0.0006216564,"teacher_disagreement_score":0.0020967387,"about_ca_system_score_codex":0.0003005006,"about_ca_system_score_gemma":0.00037683378,"threshold_uncertainty_score":0.0070142746},"labels":[],"label_agreement":null},{"id":"W171902450","doi":"10.1007/978-3-642-33783-3_58","title":"Disentangling Factors of Variation for Facial Expression Recognition","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":220,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Discriminative model; Artificial intelligence; Pattern recognition (psychology); Facial expression; Variation (astronomy); Facial recognition system; Identity (music); Face (sociological concept); Convolutional neural network; Feature (linguistics); Representation (politics); Expression (computer science); Computer vision","score_opus":0.03409717337005101,"score_gpt":0.25768890174365044,"score_spread":0.22359172837359942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W171902450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11672602,0.0033299725,0.8703459,0.00021394856,0.0001759673,0.00008228473,0.00067870243,0.0011912376,0.0072559323],"genre_scores_gemma":[0.7088198,0.002918822,0.2756698,0.0001288658,0.00025668676,0.000189042,0.002126088,0.0010127297,0.008878128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897385,0.0003418691,0.0000368102,0.00024272063,0.00027762767,0.00012725142],"domain_scores_gemma":[0.9976731,0.001603063,0.00009455105,0.0002957733,0.00027487532,0.000058705005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012886217,0.001156906,0.0008783838,0.0010450867,0.00037509625,0.0011723181,0.00056698703,0.0005984774,0.0032240208],"category_scores_gemma":[0.004767537,0.00040349038,0.0010755095,0.0014838086,0.00038892752,0.0014119457,0.0008325232,0.0010532928,0.0017155347],"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":[0.00062354753,0.00012419725,0.006718088,0.00019514127,0.00018171499,0.00012406147,0.00018027732,0.010710466,0.18834682,0.004155701,0.0018553552,0.78678447],"study_design_scores_gemma":[0.000057512287,0.00076869683,0.10552229,0.00015123235,0.0007841377,0.0015141584,0.0004226851,0.66181225,0.18440738,0.024583947,0.019705705,0.00026994685],"about_ca_topic_score_codex":0.0012957063,"about_ca_topic_score_gemma":0.003373304,"teacher_disagreement_score":0.0032240208,"about_ca_system_score_codex":0.00020821557,"about_ca_system_score_gemma":0.00042709976,"threshold_uncertainty_score":0.010785401},"labels":[],"label_agreement":null},{"id":"W1736428142","doi":"10.1002/9780470522356.ch2","title":"A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions for Biometric Signal Recognition","year":2009,"lang":"en","type":"book-chapter","venue":"Biometrics","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"University of Illinois at Urbana-Champaign","keywords":"Multilinear map; Linear discriminant analysis; Biometrics; Discriminant; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Pure mathematics","score_opus":0.1361821124535303,"score_gpt":0.28630619056693524,"score_spread":0.15012407811340495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1736428142","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016822658,0.030187683,0.9555723,0.00064257864,0.0003194808,0.000111889894,0.0002205916,0.0006277239,0.010635532],"genre_scores_gemma":[0.017391643,0.032156054,0.9381025,0.00028492723,0.00042117026,0.00025882572,0.00089601503,0.00031078586,0.010178184],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981646,0.00039357462,0.00024545207,0.00035542465,0.0007620237,0.00007898917],"domain_scores_gemma":[0.99788505,0.00080392254,0.00012020786,0.00030827377,0.0008276832,0.000054770593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031707606,0.0018493138,0.0015229196,0.0044404995,0.0008303318,0.0048855217,0.0026224523,0.0013654415,0.008160624],"category_scores_gemma":[0.0061413185,0.0009756948,0.0013440709,0.007361096,0.0012985374,0.004557074,0.002280058,0.0035379922,0.005760883],"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":[0.000045907793,0.000079298334,0.00053995114,0.0010296479,0.000050112994,0.00009246579,0.0002570141,0.0069627753,0.003531271,0.17773712,0.011960886,0.7977135],"study_design_scores_gemma":[0.000029042048,0.00022790159,0.0014395323,0.0011774683,0.000071307186,0.0015010955,0.00041869454,0.22733451,0.007443459,0.4811355,0.27904955,0.00017196649],"about_ca_topic_score_codex":0.00073642854,"about_ca_topic_score_gemma":0.0010024799,"teacher_disagreement_score":0.008160624,"about_ca_system_score_codex":0.0010847378,"about_ca_system_score_gemma":0.0008571228,"threshold_uncertainty_score":0.0273},"labels":[],"label_agreement":null},{"id":"W1743548136","doi":"","title":"Database construction & recognition for multi-view face","year":2004,"lang":"en","type":"article","venue":"IEEE International Conference on Automatic Face and Gesture Recognition","topic":"Face and Expression Recognition","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":"Facial recognition system; Computer science; Artificial intelligence; Rendering (computer graphics); Three-dimensional face recognition; Face (sociological concept); Computer vision; Face Recognition Grand Challenge; Face detection; Pattern recognition (psychology)","score_opus":0.10974905321066654,"score_gpt":0.33270669685496573,"score_spread":0.2229576436442992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1743548136","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040914964,0.0005952156,0.9484645,0.00020230663,0.00023614921,0.00044239208,0.0024819237,0.0041315663,0.0025309115],"genre_scores_gemma":[0.23306571,0.00049820414,0.746865,0.00016644421,0.000061295206,0.00076928973,0.014723155,0.00022317858,0.003627758],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981426,0.00016763121,0.00013941452,0.0005090191,0.00087398937,0.00016727412],"domain_scores_gemma":[0.9983553,0.00010887678,0.000063486856,0.00076562207,0.0006262331,0.00008049536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011770906,0.00048623595,0.0013209305,0.0019582058,0.0006738433,0.0015662436,0.0022051916,0.000736537,0.0052917954],"category_scores_gemma":[0.0026245597,0.00035327201,0.0010592832,0.001585759,0.0003386545,0.0022456432,0.0017350692,0.0009041107,0.0033952794],"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":[0.000561475,0.00036468895,0.003999729,0.00030262148,0.000089150446,0.00029008277,0.00019942461,0.009948273,0.114366494,0.010449994,0.017956443,0.8414716],"study_design_scores_gemma":[0.00015961012,0.0013602558,0.015792372,0.000059045076,0.00016604646,0.0038844622,0.0007841831,0.46051508,0.42555988,0.016788816,0.074697405,0.00023294271],"about_ca_topic_score_codex":0.003924225,"about_ca_topic_score_gemma":0.002526095,"teacher_disagreement_score":0.0052917954,"about_ca_system_score_codex":0.00071737904,"about_ca_system_score_gemma":0.000952106,"threshold_uncertainty_score":0.017702818},"labels":[],"label_agreement":null},{"id":"W1744969496","doi":"10.1007/11559573_126","title":"Rotation-Invariant Facial Feature Detection Using Gabor Wavelet and Entropy","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Gabor wavelet; Computer science; Wavelet; Computer vision; Facial recognition system; Brightness; Entropy (arrow of time); Invariant (physics); Face detection; Feature (linguistics); Wavelet transform; Mathematics; Discrete wavelet transform; Physics; Optics","score_opus":0.01470655609832851,"score_gpt":0.23727863539404956,"score_spread":0.22257207929572104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1744969496","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057308305,0.00073935464,0.93801856,0.00008482408,0.00008349275,0.000044691944,0.00016245215,0.00074704725,0.002811244],"genre_scores_gemma":[0.49281755,0.0013473629,0.49922213,0.00006394763,0.00012882125,0.00006701322,0.00049785327,0.00019838543,0.0056568757],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978906,0.000024718394,0.000012373367,0.000037783902,0.000114935916,0.000021173615],"domain_scores_gemma":[0.99977344,0.0000943727,0.00002622538,0.000030155732,0.00006350625,0.00001225335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038678988,0.00035857863,0.00068845943,0.0011224011,0.0001574621,0.0005823416,0.0003212417,0.00026585057,0.0020737168],"category_scores_gemma":[0.00079944794,0.00026596134,0.00052550994,0.00080544746,0.00027697373,0.0008571943,0.0004408661,0.00029694266,0.0009207574],"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":[0.0003255371,0.00006471665,0.0021721972,0.00013548638,0.00005561458,0.00010796847,0.000055668865,0.009279012,0.23164943,0.0053495676,0.0019138237,0.7488909],"study_design_scores_gemma":[0.000046339734,0.00036889155,0.024176715,0.000052727388,0.0001786182,0.0012948317,0.000090424124,0.71097416,0.24640916,0.009898908,0.0064169285,0.00009216178],"about_ca_topic_score_codex":0.0003459296,"about_ca_topic_score_gemma":0.0004186462,"teacher_disagreement_score":0.0020737168,"about_ca_system_score_codex":0.00017588421,"about_ca_system_score_gemma":0.00020267502,"threshold_uncertainty_score":0.006937206},"labels":[],"label_agreement":null},{"id":"W176175254","doi":"10.20982/tqmp.05.1.p001","title":"A Review of Multidimensional Scaling (MDS) and its Utility in Various Psychological Domains","year":2009,"lang":"en","type":"review","venue":"Tutorials in Quantitative Methods for Psychology","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":236,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Multidimensional scaling; Scaling; Psychology; Computer science; Cognitive psychology; Mathematics; Machine learning; Geometry","score_opus":0.3003607317124638,"score_gpt":0.5871351913844766,"score_spread":0.28677445967201276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W176175254","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005370299,0.96920174,0.022098837,0.0019525053,0.0006535896,0.00005819706,0.00014154792,0.00008297487,0.00527357],"genre_scores_gemma":[0.004620358,0.96735597,0.025735347,0.00043926245,0.0004947709,0.00011998236,0.00013496156,0.000033100918,0.0010663735],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99690694,0.0012297975,0.00039748242,0.00028866486,0.0011202907,0.00005678048],"domain_scores_gemma":[0.992502,0.0052085817,0.00041861332,0.00027152323,0.001489553,0.000109730725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066419654,0.0011876945,0.0017340983,0.007371912,0.0007186171,0.0020782335,0.001275729,0.0011247087,0.0033452064],"category_scores_gemma":[0.012897912,0.0005361366,0.00085579144,0.011009499,0.0020288418,0.0027925014,0.0011791835,0.0014881904,0.0020333563],"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":[0.00001765795,0.00001889141,0.00041194627,0.0069004865,0.000081475686,0.00006448817,0.00031330556,0.00049956434,0.00034949026,0.011375391,0.023115957,0.9568513],"study_design_scores_gemma":[0.000012315114,0.00008881693,0.005734113,0.011653741,0.00016752347,0.0012191894,0.0007315335,0.00078088866,0.0009632839,0.033400964,0.94512874,0.00011888623],"about_ca_topic_score_codex":0.00373725,"about_ca_topic_score_gemma":0.00553533,"teacher_disagreement_score":0.007371912,"about_ca_system_score_codex":0.001647031,"about_ca_system_score_gemma":0.002696097,"threshold_uncertainty_score":0.035126507},"labels":[],"label_agreement":null},{"id":"W1763412770","doi":"10.1080/10618600.2015.1043010","title":"Reinforced Angle-Based Multicategory Support Vector Machines","year":2015,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":34,"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":"National Science Foundation; National Institutes of Health; National Cancer Institute; National Institute of Biomedical Imaging and Bioengineering; National Center for Research Resources; National Institute of Mental Health","keywords":"Support vector machine; Coordinate descent; Quadratic programming; Consistency (knowledge bases); Binary classification; Computer science; Machine learning; Dual (grammatical number); Mathematical optimization; Artificial intelligence; Quadratic equation; Sequential quadratic programming; Class (philosophy); Mathematics; Algorithm","score_opus":0.023325030408022324,"score_gpt":0.26742443207681543,"score_spread":0.2440994016687931,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1763412770","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030703366,0.00050291145,0.9658821,0.0001519908,0.00008726174,0.000044080032,0.00011186084,0.0007377185,0.0017786216],"genre_scores_gemma":[0.71255517,0.00047001682,0.28149992,0.00024842026,0.00017157261,0.0001422608,0.0007085313,0.00016475197,0.0040392927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894494,0.00029164265,0.000087354645,0.0002583062,0.0003098705,0.00010782211],"domain_scores_gemma":[0.998372,0.00047981707,0.00021223961,0.00028047574,0.0005630213,0.00009247336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010950676,0.00075689825,0.0011617783,0.00075013953,0.00031746883,0.0010271246,0.0019454483,0.0008990343,0.002179151],"category_scores_gemma":[0.0049860347,0.00034611713,0.00067875814,0.0009767597,0.0005145557,0.001568386,0.0014180331,0.0014129545,0.0011057267],"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":[0.0002461206,0.000084855834,0.0033303527,0.00018110708,0.00009331818,0.00014414448,0.00013173171,0.3170699,0.011192129,0.033609033,0.006389044,0.6275283],"study_design_scores_gemma":[0.0000051141637,0.00003294251,0.00036357122,0.000007592481,0.0000058622654,0.000047711474,0.0000125132165,0.9910641,0.0011737118,0.0064212275,0.00085320603,0.000012405494],"about_ca_topic_score_codex":0.0013537613,"about_ca_topic_score_gemma":0.0012341007,"teacher_disagreement_score":0.002179151,"about_ca_system_score_codex":0.00035055153,"about_ca_system_score_gemma":0.00059341197,"threshold_uncertainty_score":0.007289946},"labels":[],"label_agreement":null},{"id":"W1779908107","doi":"10.1016/j.patrec.2015.08.020","title":"On affinity matrix normalization for graph cuts and spectral clustering","year":2015,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Face and Expression Recognition","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":"Natural Resources Canada","funders":"","keywords":"Normalization (sociology); Cluster analysis; Spectral clustering; Mathematics; Pattern recognition (psychology); Algorithm; Computer science; Artificial intelligence","score_opus":0.04107238526947851,"score_gpt":0.2670929863180538,"score_spread":0.22602060104857527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1779908107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001289698,0.00019421362,0.99674267,0.000102287566,0.00007982941,0.000048874186,0.00006477149,0.0005434221,0.00093434495],"genre_scores_gemma":[0.047849324,0.0005477321,0.9429652,0.00020960413,0.0002848646,0.0002606708,0.0009323054,0.0008862963,0.0060639144],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99495333,0.0014493648,0.00023633664,0.0011378662,0.0019569157,0.00026610307],"domain_scores_gemma":[0.9930401,0.0031522482,0.00030461964,0.0013635085,0.001899465,0.00024002513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038708707,0.0018919468,0.0021371138,0.0036791693,0.0017929835,0.0028533507,0.0043482883,0.002610156,0.008899679],"category_scores_gemma":[0.018170673,0.0012890275,0.0019484106,0.005326321,0.0024621694,0.0042187036,0.0033995851,0.004374949,0.004891485],"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":[0.0002903958,0.00022396713,0.0005989263,0.0002809911,0.00015686984,0.00010308851,0.0002654015,0.21671717,0.009906095,0.12180062,0.013488705,0.6361678],"study_design_scores_gemma":[0.000017876331,0.000028896107,0.00040945306,0.000025589768,0.000024655037,0.000076181655,0.00005487394,0.89552575,0.0028742417,0.094992094,0.00593639,0.000034087832],"about_ca_topic_score_codex":0.012712852,"about_ca_topic_score_gemma":0.016074538,"teacher_disagreement_score":0.012712852,"about_ca_system_score_codex":0.0018370679,"about_ca_system_score_gemma":0.0021629166,"threshold_uncertainty_score":0.0297724},"labels":[],"label_agreement":null},{"id":"W1783659610","doi":"10.3968/j.ans.1715787020080101.006","title":"A Novel Algorithm Model for Multi-class Classiﬁcation","year":2009,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Face and Expression Recognition","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":"Support vector machine; Machine learning; Class (philosophy); Algorithm; Artificial intelligence; Computer science; Key (lock); Code (set theory); Data mining","score_opus":0.02374133505450049,"score_gpt":0.3435717789876636,"score_spread":0.3198304439331631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1783659610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016854932,0.00004322125,0.9977099,0.00006927284,0.000013631138,0.000020049494,0.000008502242,0.00015196262,0.00029805631],"genre_scores_gemma":[0.15088686,0.00018676669,0.8445464,0.00016609092,0.000107295076,0.00033230684,0.00018114828,0.0001242722,0.0034688716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967816,0.0009185623,0.0001522976,0.00069885433,0.0011931288,0.000255487],"domain_scores_gemma":[0.99688405,0.0011367922,0.00028480458,0.00057910726,0.0009837005,0.00013149333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026199578,0.0007677729,0.0012897551,0.0008105893,0.00062528974,0.0016189512,0.0033464723,0.0017932302,0.0025768322],"category_scores_gemma":[0.0064496114,0.00043312937,0.0010461321,0.0010327017,0.0012522241,0.0038083019,0.0016924887,0.0027367414,0.0012139098],"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":[0.00022305203,0.00015276027,0.0014599882,0.00017866293,0.00009282422,0.00013914183,0.0002662264,0.4351838,0.005369611,0.15136902,0.0053505697,0.40021437],"study_design_scores_gemma":[0.000009837828,0.000026758697,0.0000530933,0.000004839779,0.0000045458237,0.00004853407,0.000008034303,0.9855583,0.0007201495,0.012278378,0.0012797529,0.00000778329],"about_ca_topic_score_codex":0.0016623068,"about_ca_topic_score_gemma":0.0012616479,"teacher_disagreement_score":0.0033464723,"about_ca_system_score_codex":0.0011747534,"about_ca_system_score_gemma":0.0014307186,"threshold_uncertainty_score":0.013855815},"labels":[],"label_agreement":null},{"id":"W1828854590","doi":"10.48550/arxiv.1306.3476","title":"Hyperparameter Optimization and Boosting for Classifying Facial Expressions: How good can a \"Null\" Model be?","year":2013,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"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":"Rowland Institute at Harvard; National Science Foundation","keywords":"Hyperparameter; Hyperparameter optimization; Computer science; Artificial intelligence; Machine learning; Normalization (sociology); Benchmark (surveying); Data set; Boosting (machine learning); Set (abstract data type); Test set; Pattern recognition (psychology); Support vector machine","score_opus":0.14199642384198208,"score_gpt":0.20196290730462307,"score_spread":0.059966483462640996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1828854590","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1620484,0.0024380363,0.82356673,0.0036622444,0.00036010626,0.00014031828,0.000391747,0.0024102172,0.004982172],"genre_scores_gemma":[0.85674757,0.00066496804,0.13707177,0.0009976404,0.0002568925,0.00018846673,0.0011781418,0.00050584145,0.002388788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9940923,0.0037256598,0.00021793251,0.00093651685,0.00071284134,0.00031475228],"domain_scores_gemma":[0.9938059,0.0029788392,0.00028514166,0.0017536411,0.0009062111,0.00027034953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013672936,0.0021321634,0.0020300297,0.0009737882,0.0008229849,0.0026771673,0.0021154678,0.0016790983,0.0013051244],"category_scores_gemma":[0.023867873,0.0007691914,0.0018225115,0.00069867435,0.0018018073,0.0041533476,0.0024183884,0.0037487368,0.0011985833],"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":[0.0016915313,0.0004276834,0.016836712,0.00026906573,0.00082498736,0.00016437027,0.00038589517,0.43873176,0.012650898,0.019550119,0.019871844,0.48859504],"study_design_scores_gemma":[0.000030754098,0.00012980944,0.0012178042,0.000033932545,0.00006759391,0.000062773775,0.00005016594,0.9698481,0.0039905477,0.02323598,0.001306023,0.000026490963],"about_ca_topic_score_codex":0.002045251,"about_ca_topic_score_gemma":0.001897116,"teacher_disagreement_score":0.013672936,"about_ca_system_score_codex":0.0010517115,"about_ca_system_score_gemma":0.0009771888,"threshold_uncertainty_score":0.07231027},"labels":[],"label_agreement":null},{"id":"W185001486","doi":"10.1007/978-3-540-72432-2_15","title":"Fuzzy C-Means, Gustafson-Kessel FCM, and Kernel-Based FCM: A Comparative Study","year":2007,"lang":"en","type":"book-chapter","venue":"Advances in soft computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":40,"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":"Kernel (algebra); Pattern recognition (psychology); Fuzzy logic; Cluster analysis; Mathematics; Artificial intelligence; Fuzzy clustering; Computer science; Data mining; Algorithm; Discrete mathematics","score_opus":0.03750152592511446,"score_gpt":0.32087268051427903,"score_spread":0.28337115458916456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W185001486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21108416,0.18063554,0.5378267,0.0022617911,0.00086052046,0.00021336123,0.00070014753,0.0012206638,0.06519703],"genre_scores_gemma":[0.76710266,0.040304948,0.17911503,0.00015994727,0.00036198762,0.000077648765,0.00046396075,0.00017209545,0.012241779],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985996,0.0002411133,0.00006272385,0.00015364743,0.0008546933,0.0000881857],"domain_scores_gemma":[0.9962309,0.0023167112,0.0001563171,0.00018565876,0.001058706,0.00005167174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022442024,0.0007406415,0.0012068592,0.0031879188,0.00086588203,0.002291651,0.0019186931,0.0015066349,0.0023624597],"category_scores_gemma":[0.008103586,0.00026209035,0.0006202683,0.007509483,0.001041317,0.0030020042,0.0005129309,0.00093134225,0.000428336],"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":[0.0006107349,0.000101194666,0.0025876055,0.0008522953,0.00011190903,0.00008719832,0.00038505622,0.05094812,0.0010104803,0.034438923,0.005378954,0.90348756],"study_design_scores_gemma":[0.00002923446,0.00034292313,0.01037483,0.00030813922,0.00024635505,0.0004454805,0.0011360061,0.91123766,0.00656795,0.04341625,0.025784943,0.000110164096],"about_ca_topic_score_codex":0.03140611,"about_ca_topic_score_gemma":0.01827343,"teacher_disagreement_score":0.03140611,"about_ca_system_score_codex":0.0025905678,"about_ca_system_score_gemma":0.0017128021,"threshold_uncertainty_score":0.062446594},"labels":[],"label_agreement":null},{"id":"W1865074779","doi":"10.1007/b101848","title":"Facial Analysis from Continuous Video with Applications to Human-Computer Interface","year":2004,"lang":"en","type":"book","venue":"Kluwer Academic Publishers eBooks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Toronto","keywords":"Computer science; Interface (matter); Computer graphics (images); User interface; Human–computer interaction; Multimedia; Human interface device; Computer vision; Video camera; Artificial intelligence; Operating system","score_opus":0.01583179412329712,"score_gpt":0.26182786983748185,"score_spread":0.24599607571418475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1865074779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03316944,0.040706947,0.76925844,0.0013970323,0.0023500803,0.00023485957,0.0028889338,0.0057149823,0.14427932],"genre_scores_gemma":[0.23888257,0.049482863,0.46809578,0.0008460551,0.0011533783,0.0002600498,0.0046944134,0.0012232732,0.23536168],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988997,0.000009181539,0.0000047011495,0.000018200317,0.00006934216,0.000008664815],"domain_scores_gemma":[0.99987376,0.00004606435,0.000008580659,0.000012918629,0.000052357038,0.0000063586717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019209173,0.0004402043,0.00031196585,0.0011710085,0.00018108162,0.0009534018,0.00044026537,0.0006285818,0.01417809],"category_scores_gemma":[0.0006053065,0.00014740042,0.00022363332,0.0013701845,0.00017799597,0.00056030124,0.0004196529,0.00036936527,0.0058787526],"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":[0.00011084588,0.00002612008,0.0004446467,0.00038406427,0.00002324378,0.00032413926,0.00010532869,0.0027409922,0.053477004,0.0049849288,0.036139008,0.90123963],"study_design_scores_gemma":[0.000051253613,0.0004345789,0.025065806,0.00080927234,0.00018037454,0.007903846,0.0008056012,0.20185468,0.16628833,0.023287198,0.57315075,0.00016825956],"about_ca_topic_score_codex":0.0013000217,"about_ca_topic_score_gemma":0.0019471628,"teacher_disagreement_score":0.01417809,"about_ca_system_score_codex":0.00020942352,"about_ca_system_score_gemma":0.00016212602,"threshold_uncertainty_score":0.047430456},"labels":[],"label_agreement":null},{"id":"W1877062207","doi":"","title":"Scaling up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":47,"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":"Stochastic gradient descent; Gradient descent; Covariance matrix; Algorithm; Scaling; Fisher information; Gaussian; Mathematics; Matrix (chemical analysis); Applied mathematics; Computer science; Inverse; Mathematical optimization; Gradient method; Artificial intelligence; Statistics; Artificial neural network","score_opus":0.068452524059194,"score_gpt":0.2633277899671421,"score_spread":0.19487526590794813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1877062207","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068889423,0.000072443545,0.99120635,0.0000830612,0.000032025942,0.000030015317,0.00003247708,0.0007495511,0.0009050409],"genre_scores_gemma":[0.19208848,0.00017520408,0.8045977,0.00017080926,0.00006426634,0.000120852485,0.00020603294,0.00033823363,0.0022384287],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951816,0.00017587667,0.00002576063,0.00010017108,0.00013326442,0.000046852627],"domain_scores_gemma":[0.99850345,0.0007101246,0.000106046,0.00036638314,0.00026130176,0.00005274081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012256439,0.0011983098,0.0008738205,0.0006405856,0.00047675965,0.00065004075,0.0010385539,0.0009999131,0.0036349106],"category_scores_gemma":[0.007761017,0.00055111543,0.00065328827,0.00058639894,0.0010175476,0.0017119421,0.0011919711,0.0014733123,0.0017019234],"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":[0.00013085682,0.00010053589,0.0012593088,0.00016743118,0.00008075051,0.00013742807,0.00015546555,0.65883285,0.020991229,0.059622716,0.008774895,0.24974649],"study_design_scores_gemma":[0.000005920111,0.000020311267,0.00009693559,0.000004609712,0.000003717615,0.000029332332,0.0000051339744,0.9891794,0.0013362926,0.008333681,0.00097650744,0.000008080759],"about_ca_topic_score_codex":0.005505772,"about_ca_topic_score_gemma":0.009356094,"teacher_disagreement_score":0.005505772,"about_ca_system_score_codex":0.000707639,"about_ca_system_score_gemma":0.0012132105,"threshold_uncertainty_score":0.012159944},"labels":[],"label_agreement":null},{"id":"W1898273237","doi":"10.1109/crv.2005.42","title":"Face Recognition with Weighted Locally Linear Embedding","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Facial recognition system; Dimensionality reduction; Principal component analysis; Pattern recognition (psychology); Face (sociological concept); Nonlinear dimensionality reduction; Embedding; Artificial intelligence; Computer science; Graph; Graph embedding; Manifold (fluid mechanics); Mathematics; Theoretical computer science","score_opus":0.017158911723381756,"score_gpt":0.2494809013736369,"score_spread":0.23232198965025513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1898273237","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012356686,0.00016786727,0.98600096,0.00005773289,0.000018763372,0.000019466343,0.000028897419,0.0009031719,0.00044643803],"genre_scores_gemma":[0.34594637,0.00038706462,0.6494746,0.00017011308,0.00008733669,0.00013207785,0.0003060164,0.00016853077,0.0033279217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913776,0.00028180398,0.000042683605,0.00019154072,0.00028623926,0.000059990085],"domain_scores_gemma":[0.99923563,0.0002739949,0.00008605778,0.0002285029,0.00015449534,0.000021385122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067380443,0.000644934,0.0007670091,0.00091990636,0.0002155407,0.0006145648,0.0010062141,0.00071122934,0.0018930518],"category_scores_gemma":[0.0021649462,0.00029465524,0.0007079607,0.0009150041,0.00043756687,0.0019450225,0.0009047273,0.00069892406,0.0011889985],"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":[0.00020141828,0.00012969342,0.001015359,0.00010714039,0.00014049519,0.00015192747,0.00012396672,0.12205615,0.081756756,0.0080901235,0.0026810693,0.78354585],"study_design_scores_gemma":[0.000009119181,0.00011442812,0.00058861275,0.0000069940997,0.000024220977,0.00018098313,0.000021032956,0.96519566,0.02313498,0.009197934,0.001499092,0.00002692723],"about_ca_topic_score_codex":0.0013593659,"about_ca_topic_score_gemma":0.0018424577,"teacher_disagreement_score":0.0018930518,"about_ca_system_score_codex":0.0002917275,"about_ca_system_score_gemma":0.00022805657,"threshold_uncertainty_score":0.0063328743},"labels":[],"label_agreement":null},{"id":"W1921128344","doi":"10.1002/sta4.74","title":"Spanifold: spanning tree flattening onto lower dimension","year":2015,"lang":"en","type":"article","venue":"Stat","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Isomap; Intrinsic dimension; Mathematics; Data point; Nonlinear dimensionality reduction; Pairwise comparison; Minimum spanning tree; Flattening; Tree (set theory); Dimension (graph theory); Hessian matrix; Manifold (fluid mechanics); Curse of dimensionality; Energy minimization; Embedding; Algorithm; Combinatorics; Computer science; Artificial intelligence; Statistics","score_opus":0.03618122214182163,"score_gpt":0.2608451679219877,"score_spread":0.22466394578016607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1921128344","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029580023,0.00027660787,0.9660565,0.00013282793,0.00006400502,0.000061878214,0.00018910533,0.0014668596,0.0021721711],"genre_scores_gemma":[0.30899882,0.00089282676,0.679994,0.00021251905,0.00010887048,0.00020639326,0.0018412236,0.00091819005,0.0068271165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994443,0.00010404305,0.000038121754,0.0001296464,0.00023026542,0.000053609052],"domain_scores_gemma":[0.9991873,0.00019862833,0.0000674533,0.000339188,0.00013923048,0.000068181085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000970909,0.0008037036,0.0009072017,0.0014634479,0.00056852173,0.0012711636,0.00088349654,0.0006134807,0.0051926626],"category_scores_gemma":[0.003604639,0.0003569426,0.00073996,0.0013544597,0.0009833673,0.0024061804,0.002593212,0.0013641996,0.0019727892],"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":[0.00037503915,0.00013535406,0.0023164987,0.00026872725,0.00008033647,0.00053827884,0.00076242926,0.084587716,0.03790721,0.1970601,0.012279869,0.6636884],"study_design_scores_gemma":[0.000045269488,0.00026240153,0.0014451408,0.000060418315,0.000030102536,0.0007903324,0.0003102757,0.6351194,0.026693907,0.30747995,0.027709901,0.000052898497],"about_ca_topic_score_codex":0.001019615,"about_ca_topic_score_gemma":0.00072783226,"teacher_disagreement_score":0.0051926626,"about_ca_system_score_codex":0.00033281394,"about_ca_system_score_gemma":0.00051127083,"threshold_uncertainty_score":0.017371178},"labels":[],"label_agreement":null},{"id":"W1939415340","doi":"10.1109/ijcnn.1999.833532","title":"Multiple classifier hierarchical architecture for handwritten Arabic character recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Classifier (UML); Artificial intelligence; Character recognition; Voting; Arabic; Architecture; Pattern recognition (psychology); Feature extraction; Machine learning","score_opus":0.030137455544238443,"score_gpt":0.24128717646078707,"score_spread":0.21114972091654863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1939415340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0516693,0.0024552816,0.93236405,0.00030002816,0.00018292126,0.00021260943,0.0001650798,0.007371597,0.0052791312],"genre_scores_gemma":[0.5313337,0.00064524444,0.4545171,0.00021038273,0.00012030606,0.00018568529,0.0005178221,0.000109204986,0.01236062],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994098,0.00010445294,0.000040398012,0.00012615552,0.00023672555,0.00008249781],"domain_scores_gemma":[0.9993999,0.0001101427,0.000049748665,0.000092020906,0.00031197647,0.000036124955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010457833,0.00041572415,0.0005540866,0.00080232625,0.00052699546,0.0006759965,0.0014064595,0.00069706555,0.0036791416],"category_scores_gemma":[0.001130038,0.00033467685,0.0004947836,0.00074227864,0.00029950953,0.0011419056,0.0004869528,0.00071377796,0.0015440639],"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":[0.0002870623,0.00016668327,0.001698526,0.00022560719,0.00021016895,0.0002703228,0.00012365392,0.11985378,0.053102348,0.0065364027,0.006314676,0.81121075],"study_design_scores_gemma":[0.000026596836,0.00019732179,0.0019907793,0.000028289,0.000091043396,0.00014010502,0.000035459252,0.96182203,0.022880327,0.0063055623,0.0064405547,0.00004203251],"about_ca_topic_score_codex":0.010612748,"about_ca_topic_score_gemma":0.017967843,"teacher_disagreement_score":0.010612748,"about_ca_system_score_codex":0.0010694304,"about_ca_system_score_gemma":0.0010328296,"threshold_uncertainty_score":0.021101952},"labels":[],"label_agreement":null},{"id":"W1943373613","doi":"10.1109/avss.2015.7301749","title":"Ensembles of exemplar-SVMs for video face recognition from a single sample per person","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure; Polytechnique Montréal; Université du Québec à Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Support vector machine; Classifier (UML); Computer vision; Three-dimensional face recognition; Feature extraction; Face detection; Linear discriminant analysis; Face (sociological concept)","score_opus":0.12853959075542087,"score_gpt":0.27127733710836016,"score_spread":0.1427377463529393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1943373613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14861102,0.0010625493,0.84744895,0.00014926105,0.000106255175,0.0000846226,0.00016416979,0.0011731387,0.0012001465],"genre_scores_gemma":[0.83103883,0.00043082747,0.16551122,0.00009823625,0.00010081533,0.0000823132,0.0008723982,0.000049590562,0.0018157008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993339,0.00014847009,0.000048096575,0.00018148239,0.00021812035,0.00006979794],"domain_scores_gemma":[0.9990056,0.00029473612,0.00009519295,0.0001955068,0.00035267876,0.00005634141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001329883,0.00061755575,0.0010140173,0.00059127144,0.00029120094,0.00045512148,0.00089124264,0.0005862022,0.00093705213],"category_scores_gemma":[0.00280886,0.00023263869,0.0005904495,0.00051767443,0.00020358028,0.0009957886,0.00068701163,0.00096712325,0.00064479635],"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":[0.00037951482,0.00030789463,0.005200809,0.00007584796,0.00018643844,0.00007549479,0.00008959655,0.12000498,0.026246347,0.0013845102,0.003074957,0.8429736],"study_design_scores_gemma":[0.000004542388,0.000096952295,0.0017596768,0.000004863614,0.000020445437,0.00006371677,0.00002292634,0.99134177,0.0054036393,0.0006579082,0.00061528385,0.000008273491],"about_ca_topic_score_codex":0.0017754516,"about_ca_topic_score_gemma":0.0020373836,"teacher_disagreement_score":0.0017754516,"about_ca_system_score_codex":0.0003526813,"about_ca_system_score_gemma":0.00031521716,"threshold_uncertainty_score":0.0070331693},"labels":[],"label_agreement":null},{"id":"W1957557007","doi":"10.5220/0004842602160221","title":"Expression, Pose, and Illumination Invariant Face Recognition using Lower Order Pseudo Zernike Moments","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Zernike polynomials; Artificial intelligence; Invariant (physics); Normalization (sociology); Pattern recognition (psychology); Facial expression; Computer vision; Facial recognition system; Computer science; Wavelet transform; Face (sociological concept); Wavelet; Gabor wavelet; Feature extraction; Mathematics; Discrete wavelet transform; Optics; Physics","score_opus":0.023281582073184818,"score_gpt":0.2442131241655371,"score_spread":0.22093154209235227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1957557007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09204987,0.00083172624,0.9024143,0.00016173355,0.0001861872,0.00008160396,0.00025916475,0.0011277321,0.0028876797],"genre_scores_gemma":[0.60745907,0.0015100703,0.3837642,0.00012600509,0.0001575367,0.00011739535,0.00095378276,0.00018371368,0.0057282555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963903,0.000033339937,0.000014665159,0.00006094153,0.00021491123,0.00003721432],"domain_scores_gemma":[0.9997737,0.000046098547,0.00004046506,0.00004194535,0.0000847523,0.000012983435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023520083,0.00038975244,0.000628776,0.00086076045,0.0001743223,0.00040588126,0.00051922665,0.00027550073,0.0011407156],"category_scores_gemma":[0.00078209874,0.00015615647,0.0005668076,0.0006867409,0.00024641838,0.00079620513,0.00037814642,0.00042570176,0.000675071],"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":[0.00016597545,0.00008272653,0.00172466,0.00015249179,0.00004867167,0.00018379887,0.00005399242,0.008026512,0.32283294,0.003006808,0.0022843892,0.661437],"study_design_scores_gemma":[0.000036753576,0.00058725465,0.029436829,0.00003592886,0.00015104329,0.0029969888,0.00018473169,0.52831364,0.41901368,0.0057191937,0.013362783,0.00016114242],"about_ca_topic_score_codex":0.000769737,"about_ca_topic_score_gemma":0.000982261,"teacher_disagreement_score":0.0011407156,"about_ca_system_score_codex":0.0002307319,"about_ca_system_score_gemma":0.00027734737,"threshold_uncertainty_score":0.0038160682},"labels":[],"label_agreement":null},{"id":"W1958778584","doi":"10.1109/icip.1999.821717","title":"Detection and tracking of faces and facial features","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":41,"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":"Army Research Laboratory","keywords":"Artificial intelligence; Computer science; Computer vision; Face detection; Feature (linguistics); Tracking (education); Facial expression; Facial motion capture; Face hallucination; Face (sociological concept); Detector; Feature extraction; Pattern recognition (psychology); Set (abstract data type); Facial recognition system; Tracking system; Kalman filter","score_opus":0.012997268406518301,"score_gpt":0.22983298147388248,"score_spread":0.21683571306736418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1958778584","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030588983,0.0006803528,0.960294,0.00013478073,0.00014385504,0.00016303835,0.0004920752,0.0033555976,0.004147336],"genre_scores_gemma":[0.18586071,0.0008000845,0.8002201,0.00023262436,0.00013037623,0.00040041035,0.0012061195,0.00021513538,0.010934461],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948573,0.000039877083,0.000016699525,0.0001774406,0.00023537187,0.00004489431],"domain_scores_gemma":[0.9994172,0.00014445354,0.00007808939,0.0001076435,0.00020245733,0.00005032703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056469464,0.00040726512,0.0004931375,0.0008122671,0.00029593258,0.00057013147,0.0011664624,0.0008249945,0.0034741675],"category_scores_gemma":[0.0011867632,0.00031651778,0.00025239022,0.0003958383,0.00026195345,0.00075504865,0.0005624252,0.00059740513,0.002195792],"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":[0.0002921741,0.00017793482,0.00353024,0.00015865715,0.000032202093,0.00012155551,0.00009151756,0.0034140006,0.3521139,0.001804263,0.005773454,0.63249016],"study_design_scores_gemma":[0.0001478904,0.0010792213,0.04413465,0.000075649725,0.000113129354,0.002899161,0.00011141019,0.28614417,0.5881963,0.004751285,0.0721703,0.00017696366],"about_ca_topic_score_codex":0.0021338144,"about_ca_topic_score_gemma":0.0026261879,"teacher_disagreement_score":0.0034741675,"about_ca_system_score_codex":0.0002925263,"about_ca_system_score_gemma":0.0004888922,"threshold_uncertainty_score":0.01162225},"labels":[],"label_agreement":null},{"id":"W1962472582","doi":"10.1007/s00426-015-0702-9","title":"On the three-quarter view advantage of familiar object recognition","year":2015,"lang":"en","type":"article","venue":"Psychological Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"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); Object (grammar); Computer science; Artificial intelligence; History","score_opus":0.35063670978378086,"score_gpt":0.45928792403610974,"score_spread":0.10865121425232888,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1962472582","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.92104715,0.0032391788,0.009505008,0.0019483274,0.000398132,0.000036548812,0.0006184743,0.00012667537,0.063080594],"genre_scores_gemma":[0.9896151,0.0009997569,0.0020120016,0.00084504177,0.00024919852,0.00002164553,0.0003464061,0.00014249189,0.0057684844],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996,0.00006976701,0.000024634608,0.000121650526,0.00014394594,0.00003997947],"domain_scores_gemma":[0.9915759,0.0060171997,0.00045743864,0.0012346469,0.00039675963,0.0003179955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012777938,0.00030114446,0.0004854001,0.00060691824,0.00023334357,0.001034149,0.0007939364,0.0007104527,0.022092357],"category_scores_gemma":[0.006698982,0.00021973519,0.00030947663,0.00043105218,0.00078352087,0.0025633324,0.00079581444,0.0008128204,0.0020390975],"study_design_candidate":"observational","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":[0.008171557,0.0004896119,0.014551969,0.00037993514,0.00014913619,0.0008335357,0.00057732896,0.00079423963,0.71931696,0.05175529,0.006406281,0.19657421],"study_design_scores_gemma":[0.00086502585,0.0017687952,0.66412526,0.00017501622,0.0005079775,0.007499099,0.000809791,0.014270635,0.13008375,0.15776251,0.021897204,0.00023484214],"about_ca_topic_score_codex":0.0011805553,"about_ca_topic_score_gemma":0.00080133486,"teacher_disagreement_score":0.022092357,"about_ca_system_score_codex":0.00020631032,"about_ca_system_score_gemma":0.00022579254,"threshold_uncertainty_score":0.07390636},"labels":[],"label_agreement":null},{"id":"W1963044959","doi":"10.1002/cem.2636","title":"Constrained kernelized partial least squares","year":2014,"lang":"en","type":"article","venue":"Journal of Chemometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"National Research Council Canada","keywords":"Partial least squares regression; Nonlinear system; Kernel (algebra); Latent variable; Noise (video); Mathematical optimization; Computer science; Variable (mathematics); Mathematics; Kernel method; Algorithm; Artificial intelligence; Machine learning; Support vector machine","score_opus":0.015193314255049988,"score_gpt":0.24293819630197694,"score_spread":0.22774488204692694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963044959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023669,0.00023850372,0.99625236,0.000058824135,0.000024822242,0.00002559739,0.00009031617,0.00041920046,0.00052355207],"genre_scores_gemma":[0.17788343,0.0008375372,0.8104711,0.00022870926,0.000110378365,0.00025873925,0.0011472548,0.0005362386,0.00852669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980756,0.00061575696,0.00008393247,0.00047871357,0.0006416087,0.000104361854],"domain_scores_gemma":[0.9978193,0.0009285675,0.0002480311,0.00041629694,0.0005244988,0.00006342572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013438769,0.0013841452,0.0016513349,0.0007727362,0.00047672214,0.001255686,0.0019127303,0.0015011029,0.0040827286],"category_scores_gemma":[0.0064138626,0.00064633175,0.0011442346,0.0014521511,0.0009796591,0.0018213323,0.0019351018,0.0016864545,0.0023292324],"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":[0.0002256164,0.00012530276,0.0011240178,0.0006207005,0.00034200313,0.00017534831,0.00017626156,0.4205434,0.02038983,0.03412775,0.009521656,0.5126282],"study_design_scores_gemma":[0.000009398712,0.000021741675,0.0004128362,0.000014367273,0.0000134669235,0.00006581987,0.000013984781,0.9826401,0.0035330534,0.009674701,0.003575554,0.000024873427],"about_ca_topic_score_codex":0.0040457593,"about_ca_topic_score_gemma":0.0043576895,"teacher_disagreement_score":0.0040827286,"about_ca_system_score_codex":0.00053556025,"about_ca_system_score_gemma":0.0018110342,"threshold_uncertainty_score":0.013658106},"labels":[],"label_agreement":null},{"id":"W1964539061","doi":"10.1007/s00138-007-0088-9","title":"Aggregation of classifiers based on image transformations in biometric face recognition","year":2007,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Consejo Nacional de Ciencia y Tecnología; Yale University","keywords":"Pattern recognition (psychology); Artificial intelligence; Eigenface; Computer science; Facial recognition system; Isomap; Biometrics; Dimensionality reduction; Sobel operator; Machine learning; Feature vector; Linear discriminant analysis; Edge detection; Image (mathematics); Nonlinear dimensionality reduction; Image processing","score_opus":0.01366579732617329,"score_gpt":0.283882184126869,"score_spread":0.2702163868006957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964539061","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13274279,0.002110486,0.8597039,0.00032781216,0.00026703198,0.0000973493,0.00023599448,0.0016512232,0.0028633885],"genre_scores_gemma":[0.7536506,0.0008774255,0.23990157,0.00012715481,0.0003047061,0.00012732469,0.0007513852,0.0001755873,0.00408419],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986407,0.00033871338,0.00011508628,0.00021012456,0.00055707875,0.00013830599],"domain_scores_gemma":[0.99688333,0.0008736492,0.0002059775,0.00069970696,0.0012197056,0.00011760838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021375043,0.00046101134,0.0017188068,0.0013298444,0.0005076818,0.0012505976,0.0008070359,0.0006713402,0.0012873686],"category_scores_gemma":[0.0048033954,0.0004038633,0.0006910546,0.001506678,0.0003761438,0.0015362414,0.00091566175,0.0007152931,0.0007201418],"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":[0.00054357504,0.00029978112,0.0070166355,0.00011569391,0.00021613398,0.00010606095,0.00015839188,0.09950347,0.03255993,0.011328213,0.006804976,0.84134704],"study_design_scores_gemma":[0.000017569078,0.0001823653,0.008357446,0.000017350536,0.00013316759,0.000107298576,0.000057550285,0.95334107,0.017523944,0.016551659,0.0036871352,0.000023416647],"about_ca_topic_score_codex":0.0026595716,"about_ca_topic_score_gemma":0.0039695464,"teacher_disagreement_score":0.0026595716,"about_ca_system_score_codex":0.00069169415,"about_ca_system_score_gemma":0.000703619,"threshold_uncertainty_score":0.011304319},"labels":[],"label_agreement":null},{"id":"W1964833136","doi":"10.1109/icdm.2012.85","title":"Adapting Component Analysis","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":16,"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":"Dimensionality reduction; Computer science; Curse of dimensionality; Kernel (algebra); Test data; Kernel method; Representation (politics); Embedding; Artificial intelligence; Feature (linguistics); Feature vector; Independence (probability theory); Reproducing kernel Hilbert space; Machine learning; Algorithm; Data mining; Hilbert space; Mathematics; Support vector machine; Statistics","score_opus":0.025770844725381447,"score_gpt":0.25206919528156396,"score_spread":0.22629835055618253,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964833136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029965588,0.0005413044,0.9906641,0.00012238692,0.00024944198,0.00016656099,0.00026253247,0.0016898987,0.0033071944],"genre_scores_gemma":[0.14339039,0.0019543164,0.83256924,0.0002734681,0.00041756846,0.00082932756,0.0033040543,0.0013339914,0.01592762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99797887,0.0004593325,0.000115801115,0.00059349235,0.00066750596,0.00018508955],"domain_scores_gemma":[0.9981627,0.00044050065,0.000077918674,0.00039253553,0.0008565947,0.0000697488],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014676745,0.0024939422,0.00172034,0.0028554287,0.0010157272,0.0021049138,0.0018478964,0.0013179573,0.012594279],"category_scores_gemma":[0.006671655,0.0005458286,0.0021431912,0.0035129387,0.000737889,0.0017723831,0.0019340024,0.002028025,0.008669353],"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":[0.00023194136,0.00012190995,0.001352054,0.00033773956,0.0002699112,0.0001282881,0.00013016861,0.05875993,0.012725426,0.029533686,0.018622292,0.8777866],"study_design_scores_gemma":[0.000034718418,0.00008997502,0.0031282685,0.000072001094,0.00013504933,0.0003197683,0.000106633306,0.88826454,0.014770251,0.035304893,0.057671104,0.00010286747],"about_ca_topic_score_codex":0.004553554,"about_ca_topic_score_gemma":0.003539443,"teacher_disagreement_score":0.012594279,"about_ca_system_score_codex":0.00072666653,"about_ca_system_score_gemma":0.0015734116,"threshold_uncertainty_score":0.04213208},"labels":[],"label_agreement":null},{"id":"W1965037736","doi":"10.1142/s0218126604001799","title":"A TIED-MIXTURE 2D HMM FACIAL IMAGE RETRIEVAL SYSTEM","year":2004,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Face and Expression Recognition","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":"University of Ottawa","funders":"","keywords":"Hidden Markov model; Tying; Computer science; Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Face (sociological concept); Mixture model; Speech recognition; Image (mathematics); Computer vision","score_opus":0.010345789827415365,"score_gpt":0.2165185683121104,"score_spread":0.20617277848469504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965037736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037075624,0.00047438624,0.95401335,0.00021150956,0.00018237036,0.000089407484,0.00021212023,0.004936399,0.0028048283],"genre_scores_gemma":[0.5719933,0.0003734142,0.4101257,0.000439025,0.00012952417,0.0001820082,0.00075731595,0.00012482391,0.015874807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996513,0.00004212292,0.000020947851,0.00013415264,0.00011890673,0.000032513584],"domain_scores_gemma":[0.99976856,0.00003866622,0.00001577007,0.000080468286,0.00007500548,0.00002165762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042276958,0.00027061047,0.0007837714,0.00033719832,0.00037166933,0.00057371776,0.000978274,0.0008503562,0.00423221],"category_scores_gemma":[0.0008836754,0.0003852127,0.0004536207,0.00034898386,0.00020587172,0.000987061,0.0008453634,0.00056169496,0.003077164],"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":[0.00084236107,0.00025137138,0.0020288252,0.00021219558,0.00012874696,0.0003682664,0.0002251863,0.057476353,0.23366433,0.0060871756,0.007428481,0.6912867],"study_design_scores_gemma":[0.000057574955,0.00017438455,0.0018919733,0.0000115072735,0.000071578674,0.00051069376,0.000024863795,0.9601398,0.030618984,0.0016706818,0.0047689094,0.000058967227],"about_ca_topic_score_codex":0.0030808086,"about_ca_topic_score_gemma":0.0038456495,"teacher_disagreement_score":0.00423221,"about_ca_system_score_codex":0.000351936,"about_ca_system_score_gemma":0.0004919241,"threshold_uncertainty_score":0.01415813},"labels":[],"label_agreement":null},{"id":"W1965463297","doi":"10.1007/s11042-012-1352-1","title":"Robust semi-automatic head pose labeling for real-world face video sequences","year":2013,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Face and Expression Recognition","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":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Pose; Face (sociological concept); Frame (networking); Ground truth; Interpolation (computer graphics); Pattern recognition (psychology); Image (mathematics)","score_opus":0.056926279693582334,"score_gpt":0.2934819565276538,"score_spread":0.23655567683407147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965463297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025781153,0.0003753901,0.9682484,0.00006928712,0.00010165403,0.00010524197,0.0006096566,0.0035568844,0.0011522833],"genre_scores_gemma":[0.32404453,0.00069270696,0.66224104,0.00018733747,0.0001625167,0.00030247107,0.004486719,0.0006391251,0.007243577],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992649,0.00014032549,0.000029102734,0.00025451157,0.00019656123,0.00011460713],"domain_scores_gemma":[0.99938285,0.0001294146,0.00009108669,0.00013737241,0.00021747113,0.000041750434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005050294,0.0012180685,0.0011012743,0.0015677932,0.0005522881,0.0007386685,0.0011096864,0.0008163269,0.002899304],"category_scores_gemma":[0.0014327263,0.0005613067,0.0007402671,0.0009798664,0.000435084,0.0006240722,0.0009136526,0.0010149586,0.0032825524],"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":[0.0006629918,0.00020819271,0.0012812354,0.00018637818,0.00008153729,0.0001078563,0.00010640108,0.02615956,0.16758415,0.0010784196,0.00632713,0.79621613],"study_design_scores_gemma":[0.000029468454,0.00021554847,0.007456643,0.000037422997,0.000051315634,0.00048142672,0.00012298187,0.8578965,0.12617372,0.0031717808,0.0043113185,0.000051834988],"about_ca_topic_score_codex":0.006229201,"about_ca_topic_score_gemma":0.016690869,"teacher_disagreement_score":0.006229201,"about_ca_system_score_codex":0.00042992263,"about_ca_system_score_gemma":0.0014321043,"threshold_uncertainty_score":0.012385845},"labels":[],"label_agreement":null},{"id":"W1965772718","doi":"10.1109/icsmc.2010.5642391","title":"Locally adaptive texture features for multispectral face recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":45,"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":"Pattern recognition (psychology); Artificial intelligence; Computer science; Facial recognition system; Subspace topology; Kernel (algebra); Multispectral image; Computer vision; Mathematics","score_opus":0.016957961036071736,"score_gpt":0.24981217521952065,"score_spread":0.23285421418344893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965772718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03258835,0.0018078258,0.9606994,0.00021328764,0.00013103854,0.000064035434,0.00027433335,0.0011440645,0.0030777324],"genre_scores_gemma":[0.43801138,0.0016567254,0.55282205,0.00017045207,0.0002182905,0.00017441469,0.0010922715,0.0002278783,0.0056266007],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997323,0.000044365806,0.000012210711,0.000037225884,0.00015003834,0.00002385585],"domain_scores_gemma":[0.9997248,0.000079218255,0.000032074906,0.000058251608,0.00009081751,0.000014844651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002498023,0.00028059032,0.00046434955,0.0009187546,0.00017260267,0.00046339424,0.00046076727,0.00036546405,0.0029679183],"category_scores_gemma":[0.00083100307,0.00012474516,0.0004685366,0.0009080964,0.00022798308,0.0008041637,0.00041069332,0.00047296894,0.0010395809],"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":[0.00012890117,0.00006159594,0.00089097,0.0001762388,0.000041402138,0.00012400148,0.000034680976,0.018637743,0.12809946,0.0054760017,0.0036674577,0.84266156],"study_design_scores_gemma":[0.00004003286,0.00027068902,0.009348124,0.000053278407,0.00009047927,0.0013314332,0.00008877842,0.8296237,0.11536319,0.014340545,0.029355362,0.000094408104],"about_ca_topic_score_codex":0.0009724795,"about_ca_topic_score_gemma":0.0012962199,"teacher_disagreement_score":0.0029679183,"about_ca_system_score_codex":0.00028785405,"about_ca_system_score_gemma":0.00021918674,"threshold_uncertainty_score":0.009928703},"labels":[],"label_agreement":null},{"id":"W1967659411","doi":"10.1109/btas.2010.5634501","title":"An expression transformation for improving the recognition of expression-variant faces from one sample image per person","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Transformation (genetics); Computer science; Expression (computer science); Feature extraction; Computer vision; Sample (material); Linear discriminant analysis; Image (mathematics); Feature (linguistics); Discriminant; Facial expression; Facial recognition system; Facial expression recognition","score_opus":0.028882561784355144,"score_gpt":0.2507271722370293,"score_spread":0.22184461045267415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1967659411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.094199345,0.00023215893,0.9023557,0.00009269913,0.00004533332,0.00007553799,0.000076058444,0.0011843091,0.0017389013],"genre_scores_gemma":[0.38778338,0.00056240964,0.60428196,0.00009084781,0.000045350083,0.00012613692,0.00047884713,0.00019740438,0.006433682],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973565,0.000051310493,0.000011076974,0.00006306702,0.000111532056,0.00002737238],"domain_scores_gemma":[0.9998615,0.000029855766,0.000011626695,0.00003153755,0.00005394135,0.000011409483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038357213,0.0004049846,0.00038510698,0.0003549884,0.0001530731,0.00021530817,0.00034005844,0.00017823561,0.0013964635],"category_scores_gemma":[0.0007787447,0.00015375727,0.00031615165,0.00034973404,0.0002471148,0.0005240511,0.00037470006,0.00042043312,0.0007593427],"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":[0.0002174513,0.00008710499,0.0005464031,0.00007835829,0.000021361924,0.00010905459,0.00010475792,0.0037071453,0.5739725,0.0022600063,0.0014138133,0.4174821],"study_design_scores_gemma":[0.00005445362,0.0006880003,0.0071252966,0.000016931754,0.000117154676,0.002079478,0.00010475639,0.2905708,0.68068016,0.0014167262,0.017081238,0.00006507246],"about_ca_topic_score_codex":0.0006729615,"about_ca_topic_score_gemma":0.00084206404,"teacher_disagreement_score":0.0013964635,"about_ca_system_score_codex":0.00012697086,"about_ca_system_score_gemma":0.0001911351,"threshold_uncertainty_score":0.004671693},"labels":[],"label_agreement":null},{"id":"W1968138685","doi":"10.1109/ccece.2012.6335026","title":"A novel algorithm for illumination invariant DCT-based face recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IntelliView Technologies (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discrete cosine transform; Facial recognition system; Artificial intelligence; Logarithm; Computer vision; Face (sociological concept); Computer science; Invariant (physics); Pattern recognition (psychology); Inverse; Image (mathematics); Mathematics; Geometry","score_opus":0.04498265230352096,"score_gpt":0.2642828253450553,"score_spread":0.21930017304153435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968138685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029759642,0.00032993095,0.9922834,0.000073361174,0.00018978221,0.00014351658,0.00012452739,0.0015725248,0.0023068897],"genre_scores_gemma":[0.046270728,0.00041600008,0.9444393,0.00014869311,0.000101717575,0.00024037556,0.00071790366,0.00013241584,0.0075329253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994684,0.000042603933,0.000031542175,0.00011346842,0.00030282207,0.000041075164],"domain_scores_gemma":[0.9997576,0.00003809881,0.00002135615,0.00004063452,0.00012735333,0.000014979443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003858651,0.00069621275,0.0006947632,0.0012022589,0.00048755863,0.0006449377,0.0010849735,0.00059479254,0.0053380937],"category_scores_gemma":[0.00082161365,0.00030875576,0.00050918147,0.0010570408,0.00032848466,0.0007104476,0.0006039393,0.00088763575,0.003805302],"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":[0.00016734352,0.00009282607,0.00031739057,0.00014365192,0.000033744327,0.00014490332,0.000036809746,0.008281511,0.13484183,0.0063819154,0.009894256,0.83966386],"study_design_scores_gemma":[0.00010403995,0.0002998693,0.0022493212,0.00004460378,0.000053790376,0.0017200689,0.000049474827,0.7398027,0.19425897,0.004168229,0.05715803,0.00009081885],"about_ca_topic_score_codex":0.0021084854,"about_ca_topic_score_gemma":0.0033377826,"teacher_disagreement_score":0.0053380937,"about_ca_system_score_codex":0.00039931928,"about_ca_system_score_gemma":0.0006925836,"threshold_uncertainty_score":0.01785773},"labels":[],"label_agreement":null},{"id":"W1968985103","doi":"10.1142/s0218001411008762","title":"CLASSIFYING FACIAL EXPRESSIONS USING LEVEL SET METHOD BASED LIP CONTOUR DETECTION AND MULTI-CLASS SUPPORT VECTOR MACHINES","year":2011,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Pattern recognition (psychology); Support vector machine; Facial expression; Computer science; Computer vision; Classifier (UML); Feature vector; Feature (linguistics); Face (sociological concept); Facial muscles","score_opus":0.3578585071330138,"score_gpt":0.3821586223753324,"score_spread":0.024300115242318565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1968985103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040317263,0.00016350308,0.9569468,0.00007116458,0.000037791688,0.00012418095,0.00007431203,0.0013874071,0.00087765465],"genre_scores_gemma":[0.35555306,0.00022758156,0.64172935,0.00006924489,0.000032272605,0.00025271205,0.00028807,0.0001072812,0.0017403441],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918216,0.0001381916,0.00005729016,0.00013445028,0.00042643983,0.00006142381],"domain_scores_gemma":[0.99930334,0.00025870904,0.000090096306,0.00007023697,0.0002454702,0.000032163054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008253488,0.0005678142,0.0007933159,0.001458859,0.0002448747,0.0007824237,0.0009428419,0.00064522267,0.001633708],"category_scores_gemma":[0.0020968753,0.0003079723,0.0007107472,0.0005343357,0.00035646767,0.00080980064,0.00042912687,0.0006165633,0.0008222869],"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":[0.0003180204,0.00020881154,0.0025229277,0.00015752035,0.000075225726,0.00011000237,0.00015395154,0.029961724,0.11050531,0.0021518378,0.0014609406,0.85237384],"study_design_scores_gemma":[0.00002142332,0.00017792202,0.0038636283,0.000024459574,0.000031132662,0.00019786623,0.00004997106,0.9498954,0.04249113,0.0017105609,0.0014908276,0.000045577683],"about_ca_topic_score_codex":0.0010059716,"about_ca_topic_score_gemma":0.0009609213,"teacher_disagreement_score":0.001633708,"about_ca_system_score_codex":0.00038616042,"about_ca_system_score_gemma":0.0003534937,"threshold_uncertainty_score":0.005465269},"labels":[],"label_agreement":null},{"id":"W1973406954","doi":"10.1109/fg.2013.6553769","title":"Using color texture sparsity for facial expression recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Artificial intelligence; Local binary patterns; Pattern recognition (psychology); Computer science; Face (sociological concept); Feature (linguistics); Facial recognition system; Sparse approximation; Computer vision; Texture (cosmology); Pixel; Feature extraction; Representation (politics); Image (mathematics); Histogram","score_opus":0.08150713348545141,"score_gpt":0.2811164700004425,"score_spread":0.19960933651499108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1973406954","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09022648,0.0004362756,0.90465736,0.00026639862,0.00007460358,0.000055238284,0.00025288615,0.0007309036,0.0032999278],"genre_scores_gemma":[0.70057225,0.00095713855,0.294678,0.00019064713,0.00018398585,0.000071331815,0.0008000716,0.000102008205,0.0024445655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996275,0.000090980386,0.00001550575,0.00005853612,0.00017023459,0.000037236045],"domain_scores_gemma":[0.9993568,0.00025939787,0.00008465599,0.00011179889,0.00016015032,0.000027134658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005411217,0.00039285194,0.0004974661,0.0008480768,0.00018454019,0.00044127018,0.00039033603,0.00025869685,0.0011951192],"category_scores_gemma":[0.00211222,0.00014334418,0.00038259214,0.0006256346,0.00031535933,0.0009036086,0.000505385,0.00043986074,0.00046372323],"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":[0.00038997515,0.00016093379,0.0036262376,0.0001469356,0.000081819504,0.00017861175,0.00009011379,0.05400464,0.15182683,0.005459677,0.003836762,0.7801974],"study_design_scores_gemma":[0.000019245355,0.00015182767,0.0046698535,0.000019725114,0.000050201852,0.0005377753,0.00005290956,0.92718846,0.059453342,0.0043390533,0.0034738937,0.00004374075],"about_ca_topic_score_codex":0.0012685595,"about_ca_topic_score_gemma":0.0016119565,"teacher_disagreement_score":0.0012685595,"about_ca_system_score_codex":0.00020378923,"about_ca_system_score_gemma":0.00025690036,"threshold_uncertainty_score":0.0039981008},"labels":[],"label_agreement":null},{"id":"W1975996955","doi":"10.1016/j.patrec.2012.05.003","title":"Recognizing occluded faces by exploiting psychophysically inspired similarity maps","year":2012,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia","keywords":"Artificial intelligence; Similarity (geometry); Computer science; Computer vision; Pattern recognition (psychology); Image (mathematics)","score_opus":0.03346249291110354,"score_gpt":0.24691427529295762,"score_spread":0.21345178238185408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975996955","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5162449,0.00029985385,0.47684446,0.00016033422,0.00007097249,0.000075405,0.00014192367,0.000609026,0.005553106],"genre_scores_gemma":[0.9538944,0.00014951236,0.04473831,0.00005483215,0.000018951563,0.00002249185,0.00011374593,0.000039317303,0.00096841936],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986064,0.00002102696,0.0000051679085,0.00003523147,0.000049933344,0.000028026634],"domain_scores_gemma":[0.99974996,0.0000945053,0.000037357062,0.00004613008,0.00004574875,0.000026364212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023794318,0.0003086934,0.00043201065,0.00048184762,0.00016687698,0.00077719416,0.00038737862,0.0003987384,0.0017391018],"category_scores_gemma":[0.001196473,0.00021814158,0.0003214672,0.0003404983,0.00032562422,0.0011741638,0.0008531685,0.00036300079,0.000282761],"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":[0.00054880657,0.00022905035,0.0041254726,0.000162065,0.00010369499,0.00022892217,0.00020779532,0.030134458,0.63676435,0.0066687055,0.001153938,0.31967276],"study_design_scores_gemma":[0.000034461376,0.0002432527,0.015364123,0.000018281327,0.000062173975,0.00049472344,0.0001853828,0.8606702,0.101584986,0.019920656,0.0013870945,0.00003472],"about_ca_topic_score_codex":0.00085559324,"about_ca_topic_score_gemma":0.0010345076,"teacher_disagreement_score":0.0017391018,"about_ca_system_score_codex":0.00020940935,"about_ca_system_score_gemma":0.00026329854,"threshold_uncertainty_score":0.00581789},"labels":[],"label_agreement":null},{"id":"W1976113053","doi":"10.1109/ijcnn.2012.6252658","title":"Incremental update of biometric models in face-based video surveillance","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Facial recognition system; Machine learning; Probabilistic logic; Pattern recognition (psychology); Biometrics; Ensemble learning; Matching (statistics); Data mining","score_opus":0.03078477780472801,"score_gpt":0.25485032934260304,"score_spread":0.22406555153787502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976113053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2580418,0.0006626727,0.7377665,0.00015921232,0.00006045421,0.00008250119,0.000109604756,0.0018942927,0.0012229843],"genre_scores_gemma":[0.9197819,0.00015334135,0.07909828,0.000051906893,0.00003080183,0.00003627554,0.00011942679,0.000023333123,0.0007047206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929273,0.00015506007,0.000036851077,0.00020039947,0.00024036269,0.000074531716],"domain_scores_gemma":[0.9989672,0.00035645548,0.00013427349,0.00022157197,0.00026310893,0.000057440462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014067468,0.00047853944,0.0009277504,0.0006509728,0.00036648157,0.0006253073,0.0013408682,0.0006270458,0.0003506946],"category_scores_gemma":[0.0034724607,0.00038435802,0.00050928077,0.00038509024,0.000272199,0.001309862,0.0008659698,0.0008356968,0.00025183402],"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":[0.00066800095,0.0003156799,0.0128075145,0.000055353503,0.00013919559,0.0002592446,0.0002673598,0.25605786,0.027811907,0.0013204861,0.001452951,0.6988444],"study_design_scores_gemma":[0.0000042662773,0.0000754949,0.0018897713,0.0000037448335,0.00002089577,0.00010112011,0.00001862337,0.99090075,0.0060543837,0.0006240437,0.00029456758,0.000012308897],"about_ca_topic_score_codex":0.0045794058,"about_ca_topic_score_gemma":0.005229251,"teacher_disagreement_score":0.0045794058,"about_ca_system_score_codex":0.0005868708,"about_ca_system_score_gemma":0.00033167808,"threshold_uncertainty_score":0.009105504},"labels":[],"label_agreement":null},{"id":"W1977039066","doi":"10.1016/j.neucom.2010.02.005","title":"What kind of color spaces is suitable for color face recognition?","year":2010,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Illinois at Urbana-Champaign; Nanjing University of Science and Technology; Concordia University","keywords":"Color space; Artificial intelligence; Facial recognition system; Face (sociological concept); Computer science; Pattern recognition (psychology); Computer vision; Linear discriminant analysis; Space (punctuation); Color normalization; Discriminant; Point (geometry); Mathematics; Color image; Image processing; Image (mathematics)","score_opus":0.02626865090148823,"score_gpt":0.2749671445933631,"score_spread":0.24869849369187486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977039066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1265466,0.006106977,0.8317636,0.0072581815,0.0014151364,0.00012816774,0.00066436926,0.001636034,0.024480933],"genre_scores_gemma":[0.7525169,0.005448225,0.2317237,0.0013832484,0.0010176387,0.00016608812,0.0006437633,0.0007255552,0.006374868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993326,0.00020894046,0.000036829144,0.00015404627,0.00013871644,0.00012895005],"domain_scores_gemma":[0.99768996,0.0005562056,0.00012581084,0.0005149578,0.00093471626,0.00017836463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011247938,0.00057794794,0.0008274083,0.00096699974,0.0007185463,0.002244577,0.00068837317,0.0012827623,0.0041253865],"category_scores_gemma":[0.0052253627,0.00034317124,0.0010498676,0.0014692099,0.0015732163,0.0051389136,0.00060484314,0.0009135433,0.0016912726],"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":[0.0010516397,0.00027603668,0.012696084,0.0013558783,0.00027789394,0.0003848446,0.0004526807,0.009300114,0.13728406,0.1503963,0.029566668,0.65695786],"study_design_scores_gemma":[0.0001969454,0.0007262722,0.024592025,0.0005741118,0.00042460358,0.0064350907,0.0039881165,0.29351938,0.15076,0.43748835,0.08083398,0.0004612265],"about_ca_topic_score_codex":0.0010120826,"about_ca_topic_score_gemma":0.0012106766,"teacher_disagreement_score":0.0041253865,"about_ca_system_score_codex":0.00030207584,"about_ca_system_score_gemma":0.00040278287,"threshold_uncertainty_score":0.0138008},"labels":[],"label_agreement":null},{"id":"W1977045321","doi":"10.1109/mmsp.2010.5662073","title":"Human emotion recognition using real 3D visual features from Gabor library","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"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","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Feature extraction; Kernel (algebra); Support vector machine; Facial expression; Canonical correlation; Feature (linguistics); Gabor filter; Orientation (vector space); Face (sociological concept); Computer vision; Clutter; Expression (computer science); Facial recognition system; Correlation; Mathematics","score_opus":0.021126543283288276,"score_gpt":0.27612870622494495,"score_spread":0.2550021629416567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977045321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097358786,0.00048415238,0.8959388,0.00013831515,0.00010685496,0.00009101874,0.00036506547,0.0024843826,0.0030326184],"genre_scores_gemma":[0.7000687,0.00075053086,0.2940992,0.00015436397,0.00010314088,0.00016366428,0.0011458328,0.00021282304,0.003301728],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995813,0.00008807755,0.00001779252,0.000089971094,0.00016835734,0.000054539112],"domain_scores_gemma":[0.999742,0.000052014868,0.000037457296,0.000044416694,0.000104964056,0.000019175739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004031411,0.0005565648,0.00063223724,0.00096020504,0.00013521964,0.00068436505,0.00033087336,0.00035431134,0.0017576431],"category_scores_gemma":[0.0010591152,0.00017404377,0.0007860262,0.0006904385,0.00023789893,0.0006609112,0.00042928028,0.00031249545,0.0013590441],"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":[0.00055195915,0.0001225559,0.003540687,0.00015651314,0.00011844634,0.00016955964,0.00011771876,0.013450182,0.20456262,0.0021530113,0.0059020175,0.7691548],"study_design_scores_gemma":[0.000063017884,0.00041009474,0.039110456,0.000046924797,0.00013766997,0.0010699761,0.00016348994,0.80369467,0.14321281,0.0050925654,0.0068527404,0.00014568468],"about_ca_topic_score_codex":0.00068221544,"about_ca_topic_score_gemma":0.00071880856,"teacher_disagreement_score":0.0017576431,"about_ca_system_score_codex":0.00023078336,"about_ca_system_score_gemma":0.00019726287,"threshold_uncertainty_score":0.0058799386},"labels":[],"label_agreement":null},{"id":"W1978163866","doi":"10.1109/icip.2008.4712361","title":"Single image per person face recognition with images synthesized by non-linear approximation","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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 British Columbia","funders":"","keywords":"Face (sociological concept); Facial recognition system; Image (mathematics); Computer science; Domain (mathematical analysis); Enhanced Data Rates for GSM Evolution; Artificial intelligence; Minification; Computer vision; Pattern recognition (psychology); Mathematics; World Wide Web","score_opus":0.026756647877880145,"score_gpt":0.22174720948408583,"score_spread":0.19499056160620568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1978163866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.066389345,0.00035572628,0.9297703,0.00008388373,0.000095126175,0.000059199177,0.000116751056,0.0011930739,0.001936599],"genre_scores_gemma":[0.36002326,0.00035067086,0.6339123,0.00009703097,0.000069740185,0.000076930475,0.0006243236,0.00012093102,0.0047247335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940526,0.00010704621,0.000022771965,0.00018297395,0.00022842536,0.000053627],"domain_scores_gemma":[0.999681,0.00009806126,0.000028123437,0.00010598331,0.000073655974,0.000013219384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005483407,0.0004918777,0.0009941766,0.00050715025,0.00014784171,0.00046794186,0.0006588515,0.00062797643,0.0021560304],"category_scores_gemma":[0.0014274938,0.00027295473,0.0007916586,0.00043065788,0.00023312162,0.0007020751,0.0004228558,0.0006013692,0.0012010611],"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":[0.0005344465,0.00013285973,0.0010073273,0.00012355234,0.0001448272,0.00009865324,0.000078694415,0.06696147,0.13518679,0.001778615,0.0013044671,0.7926483],"study_design_scores_gemma":[0.000018470135,0.00015954132,0.0021280292,0.000009323965,0.00005305028,0.0003031902,0.00004001525,0.92096156,0.073103435,0.0012393936,0.001961549,0.000022384014],"about_ca_topic_score_codex":0.001358055,"about_ca_topic_score_gemma":0.0020087997,"teacher_disagreement_score":0.0021560304,"about_ca_system_score_codex":0.000251012,"about_ca_system_score_gemma":0.0002641736,"threshold_uncertainty_score":0.0072125793},"labels":[],"label_agreement":null},{"id":"W1982751470","doi":"10.1016/j.neucom.2010.10.004","title":"Bimode model for face recognition and face representation","year":2010,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University; University of Illinois at Urbana-Champaign; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Face (sociological concept); Facial recognition system; Pattern recognition (psychology); Representation (politics); Three-dimensional face recognition; Computer vision; Face detection; Political science; Sociology","score_opus":0.06468946397661221,"score_gpt":0.29468530790210096,"score_spread":0.22999584392548875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982751470","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0057851057,0.00043198594,0.98934233,0.00014317776,0.00011490061,0.000017573624,0.00017917677,0.00025411524,0.0037317357],"genre_scores_gemma":[0.4840918,0.0022453745,0.45716822,0.0004851246,0.0002050562,0.00037640115,0.0013663272,0.00027146965,0.05379028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981433,0.00004436056,0.00000786054,0.000042055126,0.000066486726,0.000024873247],"domain_scores_gemma":[0.99986875,0.00003121724,0.000006366704,0.000025450452,0.000054971457,0.000013320105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027415677,0.00046135203,0.0007285341,0.00043056282,0.00039473738,0.0006065467,0.0011581403,0.00070882717,0.005081213],"category_scores_gemma":[0.00058247155,0.00019372045,0.0005822728,0.00061951677,0.00032186863,0.0010595284,0.00071791856,0.0012336898,0.0023277104],"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":[0.00024222353,0.00014824659,0.0007058296,0.00018868624,0.00010521098,0.00015880878,0.00010595579,0.35137838,0.023408944,0.24109292,0.009737017,0.37272784],"study_design_scores_gemma":[0.0000040909035,0.000024261217,0.00017343085,0.0000076020197,0.000010061267,0.000057715613,0.000015207321,0.9642835,0.0021356624,0.028891249,0.004386436,0.00001082417],"about_ca_topic_score_codex":0.0051403255,"about_ca_topic_score_gemma":0.0067346124,"teacher_disagreement_score":0.0051403255,"about_ca_system_score_codex":0.00045211994,"about_ca_system_score_gemma":0.00072186196,"threshold_uncertainty_score":0.01699835},"labels":[],"label_agreement":null},{"id":"W1983150145","doi":"10.1109/icdm.2014.63","title":"Towards Scalable and Accurate Online Feature Selection for Big Data","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Feature selection; Computer science; Scalability; Big data; Benchmark (surveying); Curse of dimensionality; Feature (linguistics); Data mining; Dimensionality reduction; Selection (genetic algorithm); Artificial intelligence; Machine learning; Feature extraction; Pattern recognition (psychology); Database","score_opus":0.06871331023400729,"score_gpt":0.2985533757496955,"score_spread":0.2298400655156882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983150145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011737207,0.0006891169,0.98283815,0.0003456094,0.000069270754,0.000109882785,0.0002584644,0.0035734435,0.00037882006],"genre_scores_gemma":[0.22153513,0.0005221482,0.7728868,0.0004071516,0.0003057469,0.00046884004,0.002069095,0.00035549223,0.001449589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997242,0.0008093161,0.00015529936,0.00044779584,0.001164924,0.00018077531],"domain_scores_gemma":[0.99234045,0.004189803,0.0006043304,0.001475125,0.0011721536,0.00021806985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030472353,0.0021837915,0.0023448083,0.0025779253,0.00078536395,0.0018154839,0.0024225414,0.0011681024,0.0016495428],"category_scores_gemma":[0.01260637,0.0009431527,0.0013083415,0.0031186235,0.00074644963,0.003493312,0.001981304,0.0022772416,0.0019997852],"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":[0.00061326264,0.00043937843,0.0051333653,0.0002716735,0.00029794703,0.0004088507,0.00020655563,0.18872648,0.025970785,0.004917059,0.019223282,0.7537914],"study_design_scores_gemma":[0.000053987773,0.00007822759,0.00083619537,0.00001086741,0.000020198802,0.00011696823,0.000059177593,0.9831367,0.0040277373,0.009304244,0.0023363747,0.000019258452],"about_ca_topic_score_codex":0.0029289813,"about_ca_topic_score_gemma":0.003890415,"teacher_disagreement_score":0.0030472353,"about_ca_system_score_codex":0.00059937674,"about_ca_system_score_gemma":0.0013244944,"threshold_uncertainty_score":0.016115546},"labels":[],"label_agreement":null},{"id":"W1983589939","doi":"10.1109/cvpr.2011.5995651","title":"Generalized group sparse classifiers with application in fMRI brain decoding","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":18,"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 British Columbia","funders":"","keywords":"Decoding methods; Computer science; Group (periodic table); Artificial intelligence; Pattern recognition (psychology); Speech recognition; Algorithm","score_opus":0.03998894544994691,"score_gpt":0.24205678202976696,"score_spread":0.20206783657982005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983589939","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012808763,0.0001909647,0.985892,0.0002545401,0.00002314633,0.000028315533,0.000050340885,0.00021128976,0.00054070994],"genre_scores_gemma":[0.30951038,0.00038540098,0.6867003,0.00021759733,0.00018740934,0.00021234233,0.0003413967,0.00015271576,0.0022924447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923587,0.00039258692,0.000030173856,0.00010457588,0.00017567094,0.000061124236],"domain_scores_gemma":[0.99782866,0.0013228399,0.00017639775,0.0002527863,0.00035020828,0.000069147354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022554037,0.0007030117,0.001060941,0.00074423215,0.00050165225,0.00073318323,0.0008373301,0.0014097039,0.0013766579],"category_scores_gemma":[0.008176518,0.00039989702,0.0006569251,0.0012135446,0.00081611145,0.001123906,0.0011391671,0.001515365,0.00044394174],"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":[0.00010602345,0.00008206471,0.00092586083,0.00009364419,0.00007921645,0.000117431264,0.00018844406,0.70791733,0.0067995484,0.03120698,0.0031134563,0.24937002],"study_design_scores_gemma":[0.0000055801784,0.000011659987,0.00007958487,0.0000029486573,0.0000034262484,0.000013309939,0.000006142471,0.98666096,0.00064199534,0.012213597,0.0003565058,0.00000437481],"about_ca_topic_score_codex":0.0025314256,"about_ca_topic_score_gemma":0.0031495001,"teacher_disagreement_score":0.0025314256,"about_ca_system_score_codex":0.00044355594,"about_ca_system_score_gemma":0.0008181312,"threshold_uncertainty_score":0.011927903},"labels":[],"label_agreement":null},{"id":"W1984745425","doi":"10.1016/j.cor.2012.02.007","title":"Extensions to the repetitive branch and bound algorithm for globally optimal clusterwise regression","year":2012,"lang":"en","type":"article","venue":"Computers & Operations Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Branch and bound; Computer science; Algorithm; Branch and cut; Integer programming; Operations research; Mathematical optimization; Mathematics","score_opus":0.06511072471575952,"score_gpt":0.38277607204500447,"score_spread":0.31766534732924495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984745425","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016582472,0.00012009884,0.9965191,0.00008372888,0.00004412581,0.00002692499,0.00003106116,0.000557965,0.0009588822],"genre_scores_gemma":[0.054907907,0.0001576698,0.9390031,0.00014196204,0.000140819,0.00025289977,0.0003268521,0.0006834578,0.004385236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729985,0.0011533609,0.00012791714,0.00039193933,0.00079871406,0.00022823345],"domain_scores_gemma":[0.9948874,0.002384664,0.00021738624,0.0012900182,0.001045816,0.00017465543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003911909,0.0015684814,0.0023350215,0.0014240226,0.0008609277,0.0013968599,0.004095502,0.001997783,0.011122097],"category_scores_gemma":[0.0131038455,0.0011359146,0.0015708646,0.0022862104,0.0011315068,0.0023346315,0.0032149407,0.0043223603,0.0041131],"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":[0.00038884275,0.00030622652,0.0007046968,0.00015971922,0.00021051615,0.00008953073,0.00012620632,0.5500743,0.0041980785,0.052123554,0.012428373,0.37918997],"study_design_scores_gemma":[0.000027830441,0.00002609135,0.000074135845,0.0000067311466,0.000011592373,0.000016625068,0.0000045191982,0.98624134,0.00052556524,0.011702671,0.0013548491,0.000008029496],"about_ca_topic_score_codex":0.007161514,"about_ca_topic_score_gemma":0.012598792,"teacher_disagreement_score":0.011122097,"about_ca_system_score_codex":0.0009816039,"about_ca_system_score_gemma":0.0030866899,"threshold_uncertainty_score":0.037207127},"labels":[],"label_agreement":null},{"id":"W1985244783","doi":"10.1109/ccece.2014.6901065","title":"One-shot facial feature extraction based on Gauss-Laguerre filter","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Feature extraction; Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Filter (signal processing); Feature (linguistics); Face (sociological concept); Computer vision","score_opus":0.04191894222432213,"score_gpt":0.28078308638709315,"score_spread":0.23886414416277102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985244783","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029052781,0.00022400952,0.96788555,0.000054299817,0.000043302036,0.000049584753,0.000052414558,0.0010283508,0.001609749],"genre_scores_gemma":[0.3259021,0.00061571726,0.6657032,0.00016947975,0.00005872775,0.000095260206,0.00037319577,0.00015036913,0.006931914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964476,0.00004254705,0.000013991565,0.00008989001,0.0001701142,0.00003871855],"domain_scores_gemma":[0.9997342,0.0000892702,0.000022389735,0.000043435142,0.000095631665,0.000015027685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042123566,0.00050254504,0.0005940645,0.000705569,0.00022066064,0.00047811493,0.00047735078,0.0005018433,0.0017785414],"category_scores_gemma":[0.0009143854,0.00021080163,0.00045770867,0.00045021082,0.00030051722,0.00065305637,0.00032131767,0.00039264772,0.0011826155],"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":[0.0003426973,0.000102353355,0.0012391314,0.00012955198,0.00006273034,0.00018933638,0.00013090545,0.011098159,0.26960856,0.0036211207,0.002283993,0.7111915],"study_design_scores_gemma":[0.00003545283,0.00043575198,0.009782775,0.00003463834,0.000093221206,0.001496666,0.0001356746,0.6423036,0.33138704,0.0033963905,0.010797341,0.00010147323],"about_ca_topic_score_codex":0.0017146464,"about_ca_topic_score_gemma":0.0031014509,"teacher_disagreement_score":0.0017785414,"about_ca_system_score_codex":0.00027661244,"about_ca_system_score_gemma":0.00044184428,"threshold_uncertainty_score":0.0059497952},"labels":[],"label_agreement":null},{"id":"W1985755891","doi":"10.1007/s13735-015-0081-4","title":"Distributed cross-media multiple binary subspace learning","year":2015,"lang":"en","type":"article","venue":"International Journal of Multimedia Information Retrieval","topic":"Face and Expression Recognition","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":"Simon Fraser University","funders":"","keywords":"Computer science; Subspace topology; Linear subspace; Scalability; Binary number; Dimensionality reduction; Artificial intelligence; Convergence (economics); Representation (politics); Multiplication (music); Data mining; Theoretical computer science; Machine learning; Mathematics; Database","score_opus":0.023321091063520007,"score_gpt":0.28417446601803936,"score_spread":0.2608533749545194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985755891","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025033798,0.00040574867,0.96976185,0.00023008401,0.00014179283,0.000057602636,0.00017273203,0.0014543443,0.002741988],"genre_scores_gemma":[0.5603432,0.0005304448,0.41863742,0.00039576401,0.00029047145,0.00018948734,0.0015438457,0.00026011883,0.017809264],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875724,0.00029404275,0.000059752707,0.00030161464,0.0004168719,0.00017052541],"domain_scores_gemma":[0.9984145,0.00034136177,0.00008765199,0.0005424804,0.0005123582,0.00010160674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011329531,0.00092619413,0.0013027751,0.0008542003,0.00078374083,0.0013291548,0.0016526238,0.0010323928,0.006912432],"category_scores_gemma":[0.0028082859,0.00046977762,0.00068634545,0.0017114778,0.0006846687,0.0026378012,0.0030632217,0.0011152831,0.0027285872],"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":[0.0008903429,0.000551256,0.0014963414,0.000101321464,0.0001617475,0.000070195034,0.000079473924,0.11155473,0.02695311,0.007477559,0.007890962,0.84277296],"study_design_scores_gemma":[0.000033284352,0.000112132715,0.00050031365,0.000005485001,0.000023447094,0.00007403551,0.000042275988,0.9814037,0.010519418,0.005339736,0.001929041,0.00001704994],"about_ca_topic_score_codex":0.0027270815,"about_ca_topic_score_gemma":0.004648921,"teacher_disagreement_score":0.006912432,"about_ca_system_score_codex":0.0004196085,"about_ca_system_score_gemma":0.0012358273,"threshold_uncertainty_score":0.023124397},"labels":[],"label_agreement":null},{"id":"W1985818460","doi":"10.1145/1378889.1378949","title":"slab","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"York University","funders":"","keywords":"Annotation; Cluster analysis; Computer science; Interface (matter); Face (sociological concept); Slab; Task (project management); Set (abstract data type); Computer graphics (images); User interface; Facial recognition system; Computer vision; Adobe photoshop; Artificial intelligence; Feature extraction; Software; Engineering; Operating system","score_opus":0.024877438134389446,"score_gpt":0.21076370205142092,"score_spread":0.18588626391703147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985818460","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013492649,0.0004986694,0.91517305,0.0002059907,0.00019295272,0.00023931233,0.0009899362,0.05690254,0.012304942],"genre_scores_gemma":[0.08805083,0.0004736088,0.8784929,0.00020681084,0.00006586091,0.00023301583,0.0031269423,0.002436991,0.02691307],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990363,0.0001181142,0.000045938985,0.00020093589,0.00051377615,0.00008490539],"domain_scores_gemma":[0.9987679,0.00020195029,0.00006681803,0.0004942696,0.0004019418,0.000067117304],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00064473023,0.0009785541,0.0008472904,0.0013881557,0.0008450522,0.0014892423,0.0022633637,0.0006727303,0.0274905],"category_scores_gemma":[0.0017506817,0.00061783654,0.0005370739,0.0012970782,0.00050851336,0.0020041799,0.00198175,0.0009125107,0.013685996],"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":[0.0007320666,0.000080827354,0.00065364194,0.0002980995,0.000044333516,0.000117879106,0.00034524983,0.0026453699,0.080335185,0.0075573283,0.04128348,0.86590654],"study_design_scores_gemma":[0.00020975429,0.00073961593,0.006864005,0.00018425418,0.00013809797,0.002557183,0.0008380551,0.21471529,0.2798613,0.016908914,0.47670665,0.00027694827],"about_ca_topic_score_codex":0.0047248206,"about_ca_topic_score_gemma":0.0076731797,"teacher_disagreement_score":0.9725095,"about_ca_system_score_codex":0.00055054395,"about_ca_system_score_gemma":0.0012064041,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W1988025232","doi":"10.1007/s00138-012-0439-z","title":"Adaptive linear discriminant analysis for online feature extraction","year":2012,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Linear discriminant analysis; Principal component analysis; Computer science; Pattern recognition (psychology); Artificial intelligence; Optimal discriminant analysis; Adaptive filter; Computation; Feature (linguistics); Algorithm; Covariance matrix; Discriminant; Mathematics; Machine learning","score_opus":0.026407502053807593,"score_gpt":0.33934301158696223,"score_spread":0.31293550953315463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988025232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043116664,0.00036247,0.99307513,0.000057976504,0.00006822779,0.000028079274,0.00010852571,0.0012667413,0.0007211457],"genre_scores_gemma":[0.13467243,0.000565863,0.852187,0.00012157636,0.000107233776,0.0002816123,0.0008868105,0.0002727414,0.010904771],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947685,0.00012277698,0.000035027693,0.00012942382,0.00018485189,0.0000511],"domain_scores_gemma":[0.9993518,0.0002690384,0.00004119523,0.00014002579,0.00017910598,0.000018875811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005161813,0.0007342198,0.00093267555,0.0007509821,0.0004230318,0.0005225953,0.0009930965,0.0005695639,0.0061524347],"category_scores_gemma":[0.0016583801,0.00035415028,0.00063316646,0.0010498295,0.00024524273,0.0008145874,0.00083870837,0.0011076048,0.004805274],"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":[0.00016828417,0.00011422601,0.00028464585,0.00008059692,0.000035033463,0.00005202001,0.00002488698,0.0139709655,0.034068093,0.003317383,0.007827411,0.94005656],"study_design_scores_gemma":[0.000023087392,0.00007945641,0.001455951,0.000016100163,0.00003433869,0.00013136427,0.000024015199,0.9572601,0.026423195,0.0052689933,0.009254755,0.000028704899],"about_ca_topic_score_codex":0.0017764034,"about_ca_topic_score_gemma":0.0028240182,"teacher_disagreement_score":0.0061524347,"about_ca_system_score_codex":0.00026827582,"about_ca_system_score_gemma":0.00062168605,"threshold_uncertainty_score":0.020581901},"labels":[],"label_agreement":null},{"id":"W1988626078","doi":"10.1167/7.9.944","title":"A dynamic facial expression database","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Expression (computer science); Facial expression; Computer science; Database; Artificial intelligence; Programming language","score_opus":0.010054858482727773,"score_gpt":0.2917919569237017,"score_spread":0.28173709844097394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988626078","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25970867,0.003788215,0.110502586,0.00068963424,0.0005631117,0.0023288447,0.566969,0.021354567,0.034095336],"genre_scores_gemma":[0.16770966,0.0017136337,0.05168233,0.0002816044,0.00007703704,0.0014529142,0.7542277,0.00068450463,0.022170626],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99962986,0.000032839405,0.000038068876,0.00013153198,0.00013177557,0.000035924837],"domain_scores_gemma":[0.9996481,0.000043360713,0.000022970838,0.00012590036,0.000109504166,0.00005018144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037479328,0.0012266284,0.0011377648,0.0019916412,0.0006831637,0.0007551879,0.001301804,0.0009076206,0.020033306],"category_scores_gemma":[0.00096081005,0.00050016446,0.00071585434,0.0016109117,0.00023319255,0.0009490724,0.00084566197,0.00078905275,0.01332708],"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":[0.0023715897,0.0016817114,0.008044018,0.0008342909,0.00029593465,0.0014697749,0.00021975159,0.0070578265,0.10328474,0.0022968717,0.25856012,0.6138834],"study_design_scores_gemma":[0.0010275468,0.0018307272,0.15555224,0.00038344422,0.00072919455,0.012092679,0.0010808917,0.13451642,0.14109497,0.0048851967,0.5463622,0.00044445664],"about_ca_topic_score_codex":0.008288337,"about_ca_topic_score_gemma":0.009442143,"teacher_disagreement_score":0.020033306,"about_ca_system_score_codex":0.00051341054,"about_ca_system_score_gemma":0.00076068076,"threshold_uncertainty_score":0.06701809},"labels":[],"label_agreement":null},{"id":"W1991385629","doi":"10.1109/tip.2011.2168413","title":"Color Local Texture Features for Color Face Recognition","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":154,"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":"Artificial intelligence; Local binary patterns; Computer vision; Pattern recognition (psychology); Computer science; Color histogram; Color space; Local color; Texture (cosmology); Discriminative model; Image texture; Color normalization; Texture filtering; Facial recognition system; Feature (linguistics); Face (sociological concept); Color image; Histogram; Image segmentation; Image processing; Segmentation; Image (mathematics)","score_opus":0.035307601198057245,"score_gpt":0.2611929479513447,"score_spread":0.22588534675328742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991385629","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04965693,0.0030242114,0.9391279,0.00022984057,0.00024298819,0.00011066579,0.000556655,0.0016976062,0.0053532734],"genre_scores_gemma":[0.6040372,0.0031192394,0.3852487,0.00036960194,0.00033052135,0.0002382324,0.0016751797,0.0002927669,0.0046885256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962497,0.000047669397,0.00001694852,0.00006463967,0.0002034547,0.00004235387],"domain_scores_gemma":[0.9996513,0.00007388841,0.000044104465,0.00006206939,0.00014792297,0.000020717962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037852474,0.00053194654,0.0006482494,0.0016609526,0.00020997298,0.00062407966,0.00063002366,0.00041072245,0.0025329515],"category_scores_gemma":[0.0011433271,0.00015940209,0.0006105402,0.0013733029,0.00028102,0.00085599197,0.00052112044,0.00065792195,0.0011026006],"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":[0.000285987,0.00009871304,0.0020897284,0.00028597855,0.00006364962,0.00015777157,0.000043432017,0.018910408,0.10074906,0.004878499,0.0060626017,0.86637414],"study_design_scores_gemma":[0.000043575274,0.00028100793,0.012917826,0.000099020785,0.000213187,0.0013776425,0.00012906372,0.8365102,0.11561017,0.00823224,0.024471413,0.00011457662],"about_ca_topic_score_codex":0.001548239,"about_ca_topic_score_gemma":0.0015049163,"teacher_disagreement_score":0.0025329515,"about_ca_system_score_codex":0.00032241776,"about_ca_system_score_gemma":0.0003243301,"threshold_uncertainty_score":0.008473575},"labels":[],"label_agreement":null},{"id":"W1991737658","doi":"10.1109/icmla.2012.178","title":"Cross-Domain Facial Expression Recognition Using Supervised Kernel Mean Matching","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":36,"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":"Discriminative model; Matching (statistics); Pattern recognition (psychology); Kernel (algebra); Computer science; Artificial intelligence; Support vector machine; Facial expression recognition; Class (philosophy); Facial expression; Domain (mathematical analysis); Expression (computer science); Machine learning; Facial recognition system; Mathematics; Statistics","score_opus":0.053064819454021736,"score_gpt":0.2969890372087989,"score_spread":0.24392421775477713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991737658","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11536149,0.00014530242,0.88221043,0.000056618577,0.000024275525,0.00003936048,0.000060747443,0.0012410295,0.00086066226],"genre_scores_gemma":[0.8249242,0.00009415539,0.17298532,0.000055371685,0.000018688113,0.00006205736,0.00036267194,0.00011860997,0.0013788901],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990682,0.00026031927,0.000042835833,0.00033298234,0.0002025809,0.0000930591],"domain_scores_gemma":[0.99920267,0.00016117276,0.00011315776,0.00024154784,0.00024795395,0.000033448792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009777537,0.00047521113,0.000823923,0.00084261346,0.0002703812,0.00049354736,0.00079445896,0.00048130853,0.000741123],"category_scores_gemma":[0.0021507046,0.00021193607,0.00066717865,0.00069804676,0.00036034707,0.00087028736,0.0009455862,0.0005655284,0.000578324],"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":[0.0005509513,0.00037459185,0.005761437,0.00007492226,0.0001948699,0.00010888968,0.00017263494,0.06980801,0.109623656,0.0025831019,0.0025514923,0.8081954],"study_design_scores_gemma":[0.0000116616,0.00005931174,0.0039018563,0.0000032369264,0.000021379286,0.00013815404,0.000050212275,0.97082996,0.022128835,0.0022414692,0.0005974208,0.00001641854],"about_ca_topic_score_codex":0.0013551404,"about_ca_topic_score_gemma":0.0014179614,"teacher_disagreement_score":0.0013551404,"about_ca_system_score_codex":0.0003282477,"about_ca_system_score_gemma":0.0004020461,"threshold_uncertainty_score":0.0051709414},"labels":[],"label_agreement":null},{"id":"W1994670097","doi":"10.1109/cibim.2014.7015449","title":"Adaptive multi-stream score fusion for illumination invariant face recognition","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Complex wavelet transform; Computer science; Color constancy; Normalization (sociology); Wavelet transform; Computer vision; Weighting; Biometrics; Wavelet; Face (sociological concept); Robustness (evolution); Invariant (physics); Mathematics; Discrete wavelet transform; Image (mathematics)","score_opus":0.06051294866077118,"score_gpt":0.2627607680725254,"score_spread":0.20224781941175424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994670097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054626197,0.000605167,0.9428097,0.000087843815,0.00009636068,0.00008931382,0.00006580453,0.0005497065,0.0010698665],"genre_scores_gemma":[0.6486811,0.0005530667,0.34700167,0.00010537509,0.0001044291,0.00012147745,0.00037137762,0.000053961718,0.0030075437],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993444,0.00010197885,0.000035509842,0.00010825951,0.00036386636,0.00004605687],"domain_scores_gemma":[0.9996008,0.000076228505,0.00004114071,0.00005062368,0.0002066988,0.000024540645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009434282,0.00052636315,0.0007196523,0.00094381924,0.0002756187,0.0005085883,0.0007265824,0.00038224272,0.0015950713],"category_scores_gemma":[0.001424783,0.0001604087,0.00063896,0.0007447766,0.0002515458,0.00075463037,0.0007726696,0.0005701763,0.0005233415],"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":[0.0005060244,0.00021295452,0.0019113787,0.00008883516,0.000104340215,0.000091812995,0.000070138005,0.0466959,0.07977716,0.0030825,0.0017944056,0.86566454],"study_design_scores_gemma":[0.000017762746,0.0002421734,0.003083585,0.000009834933,0.000056857207,0.00012163071,0.000028076414,0.955299,0.03798313,0.0014979315,0.0016287556,0.00003122663],"about_ca_topic_score_codex":0.0012675805,"about_ca_topic_score_gemma":0.0016575569,"teacher_disagreement_score":0.0015950713,"about_ca_system_score_codex":0.00042821275,"about_ca_system_score_gemma":0.00041362096,"threshold_uncertainty_score":0.005336046},"labels":[],"label_agreement":null},{"id":"W1996633793","doi":"10.1016/j.patrec.2011.02.002","title":"Guided Locally Linear Embedding","year":2011,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"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 Waterloo","funders":"","keywords":"Nonlinear dimensionality reduction; Dimensionality reduction; Embedding; Pattern recognition (psychology); Manifold (fluid mechanics); Artificial intelligence; Representation (politics); Curse of dimensionality; Computer science; Variation (astronomy); Visualization; Mathematics; Algorithm","score_opus":0.07036640600245594,"score_gpt":0.26740652041020185,"score_spread":0.1970401144077459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996633793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017342038,0.0006853041,0.96962297,0.00046407507,0.00020994306,0.00007390978,0.00041700093,0.0037224975,0.0074622184],"genre_scores_gemma":[0.4563254,0.00072518754,0.4730863,0.0007051751,0.00023063319,0.00032639055,0.0031183101,0.0012466955,0.064235955],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995561,0.0001170799,0.0000178544,0.00014709892,0.00010841109,0.000053427313],"domain_scores_gemma":[0.9995141,0.00014042541,0.000032149135,0.00018488873,0.00009437423,0.000034078494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034986594,0.0010644139,0.00092738366,0.00052993966,0.0003808901,0.00086121116,0.0011031296,0.0012943933,0.010976481],"category_scores_gemma":[0.001483649,0.00044289159,0.0006754549,0.0005242172,0.0005981754,0.0014251507,0.0017531494,0.0013858349,0.005858939],"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":[0.00055669976,0.00020001229,0.0005550834,0.00025048718,0.00011432475,0.00020721389,0.00012757181,0.18815984,0.0325169,0.053507738,0.042713426,0.6810907],"study_design_scores_gemma":[0.000025823438,0.00011237073,0.00015792968,0.000017467597,0.000019527644,0.00011798092,0.000029384471,0.96201175,0.008376422,0.020791523,0.00832033,0.00001951026],"about_ca_topic_score_codex":0.0023662825,"about_ca_topic_score_gemma":0.005005563,"teacher_disagreement_score":0.010976481,"about_ca_system_score_codex":0.00047168665,"about_ca_system_score_gemma":0.0007387383,"threshold_uncertainty_score":0.036720037},"labels":[],"label_agreement":null},{"id":"W1996911291","doi":"10.1016/j.neunet.2005.06.034","title":"Improving dimensionality reduction with spectral gradient descent","year":2005,"lang":"en","type":"article","venue":"Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Canada Research Chairs; Government of Canada; Canadian Institute for Advanced Research","keywords":"Gradient descent; Dimensionality reduction; Eigenvalues and eigenvectors; Maxima and minima; Curse of dimensionality; Matrix (chemical analysis); Reduction (mathematics); Computer science; Eigendecomposition of a matrix; Mathematics; Mathematical optimization; Gradient method; Iterative method; Algorithm; Artificial neural network; Artificial intelligence","score_opus":0.011260668451991774,"score_gpt":0.21201359202157577,"score_spread":0.200752923569584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996911291","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020870803,0.00043554683,0.9755955,0.00023823243,0.00012874432,0.000037051712,0.00008168617,0.0014765079,0.0011359722],"genre_scores_gemma":[0.28316322,0.00047515603,0.7087818,0.0003194181,0.00015079042,0.0001995322,0.00081407645,0.0004540591,0.0056419857],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991285,0.00032219486,0.000049624385,0.00015076088,0.00027960542,0.00006923638],"domain_scores_gemma":[0.9989673,0.00034957723,0.00005654751,0.0003000197,0.00029011682,0.00003645015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010022168,0.0012626264,0.0015736731,0.0009211084,0.00066771713,0.0008485836,0.0010925865,0.0011541486,0.002554635],"category_scores_gemma":[0.004295046,0.0006276216,0.0012515367,0.0009659193,0.0007135852,0.0019996357,0.0014411466,0.001609968,0.001708723],"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":[0.00029327918,0.00037419258,0.0012784646,0.00015339197,0.00022039877,0.000105213025,0.00016041496,0.29839835,0.018758528,0.017868184,0.020162426,0.6422271],"study_design_scores_gemma":[0.000009553897,0.000018482917,0.0001493149,0.0000034945754,0.000010182784,0.000022061933,0.000009145442,0.9921101,0.0017675698,0.0053041056,0.00059011526,0.0000058996643],"about_ca_topic_score_codex":0.004068402,"about_ca_topic_score_gemma":0.004869337,"teacher_disagreement_score":0.004068402,"about_ca_system_score_codex":0.00046754233,"about_ca_system_score_gemma":0.0009946267,"threshold_uncertainty_score":0.008546054},"labels":[],"label_agreement":null},{"id":"W1999859367","doi":"10.1117/12.2017943","title":"A multistep approach for infrared face recognition in texture space","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Face and Expression Recognition","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":"Facial recognition system; Artificial intelligence; Computer science; Pattern recognition (psychology); Face (sociological concept); Three-dimensional face recognition; Dimensionality reduction; Computer vision; Local binary patterns; Curse of dimensionality; Feature extraction; Face detection; Image (mathematics); Histogram","score_opus":0.014618190195601828,"score_gpt":0.22314985715970403,"score_spread":0.20853166696410222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999859367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012220921,0.00018075306,0.9857657,0.000042343825,0.00004647323,0.00008946134,0.000040193765,0.0005528786,0.0010612208],"genre_scores_gemma":[0.12208244,0.00025607378,0.8715673,0.000104277715,0.000034732642,0.00015147436,0.00019161562,0.000070004004,0.0055420552],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948114,0.000043511365,0.000020438381,0.00009863418,0.00030651304,0.000049653074],"domain_scores_gemma":[0.99975055,0.00005396459,0.000020282301,0.00005949344,0.000099116834,0.000016507913],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031875537,0.0003732177,0.00048207576,0.0008012097,0.00032245956,0.00057279546,0.00083053205,0.0005330261,0.0030728679],"category_scores_gemma":[0.0006770852,0.00022983296,0.0007931681,0.00061332574,0.00028168553,0.0005489515,0.00081637816,0.0007306464,0.0015450985],"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":[0.00017503837,0.00015243956,0.00096459093,0.000093505274,0.000063110456,0.00013799929,0.000083466846,0.009419583,0.3100234,0.004018353,0.0013635786,0.6735049],"study_design_scores_gemma":[0.000037034068,0.0007605973,0.008261988,0.00002859261,0.00011406164,0.0020438603,0.00011523086,0.7464341,0.22196016,0.0039034532,0.016235227,0.00010569409],"about_ca_topic_score_codex":0.0014611316,"about_ca_topic_score_gemma":0.0023356015,"teacher_disagreement_score":0.0030728679,"about_ca_system_score_codex":0.00023953084,"about_ca_system_score_gemma":0.00049735897,"threshold_uncertainty_score":0.010279715},"labels":[],"label_agreement":null},{"id":"W2001284201","doi":"10.1109/hpcs.2010.5547096","title":"An efficient method for face recognition under illumination variations","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Facial recognition system; Computer vision; Invariant (physics); Face (sociological concept); Reflectivity; Pattern recognition (psychology); Classifier (UML); Mathematics; Optics","score_opus":0.027954491031442374,"score_gpt":0.3190056248160777,"score_spread":0.2910511337846353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001284201","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025546416,0.00034629877,0.99424136,0.000041060663,0.000070423834,0.000079580335,0.00007992534,0.001520039,0.0010666676],"genre_scores_gemma":[0.042713527,0.0005440801,0.94903934,0.00008705526,0.000088015026,0.0002555063,0.00043962337,0.00018624002,0.006646651],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992047,0.00009175584,0.000032378317,0.00016536508,0.00045429304,0.000051540897],"domain_scores_gemma":[0.99971277,0.000047146936,0.000029053921,0.000074267955,0.00012382981,0.000012847873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004410254,0.00063678535,0.0010274985,0.001381193,0.00047542425,0.00055311044,0.0011653579,0.0007363931,0.005216606],"category_scores_gemma":[0.0006872503,0.00039129893,0.00067927846,0.0010848313,0.00035236537,0.0008837083,0.0006741911,0.00092638086,0.003280575],"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":[0.00007762791,0.00004745118,0.0002970882,0.00016028281,0.00004837715,0.0001017665,0.00004494246,0.004862169,0.13375254,0.0042834836,0.0069652237,0.84935904],"study_design_scores_gemma":[0.00007788835,0.00033716427,0.006341431,0.00007233427,0.00013347517,0.004604285,0.0000808234,0.56637764,0.32250625,0.008513556,0.09072601,0.00022923158],"about_ca_topic_score_codex":0.0011515723,"about_ca_topic_score_gemma":0.0014582492,"teacher_disagreement_score":0.005216606,"about_ca_system_score_codex":0.0003136894,"about_ca_system_score_gemma":0.00049233885,"threshold_uncertainty_score":0.017451286},"labels":[],"label_agreement":null},{"id":"W2002553917","doi":"10.1016/j.apm.2010.11.032","title":"Variable neighborhood search for harmonic means clustering","year":2010,"lang":"en","type":"article","venue":"Applied Mathematical Modelling","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis","funders":"","keywords":"Cluster analysis; Variable (mathematics); Variable neighborhood search; Computer science; Mathematics; Data mining; Artificial intelligence; Metaheuristic; Mathematical analysis","score_opus":0.03815690039728945,"score_gpt":0.25746047304917086,"score_spread":0.2193035726518814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002553917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012371996,0.000350578,0.9857653,0.000085868676,0.000041050735,0.000030175068,0.000041329793,0.0001751634,0.0011385678],"genre_scores_gemma":[0.35476232,0.0003819268,0.6369201,0.000086538625,0.00009373043,0.00023601543,0.0004619268,0.00026499134,0.0067924946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930906,0.00028893937,0.000027546172,0.0001255266,0.00019924773,0.000049657498],"domain_scores_gemma":[0.998892,0.00064833433,0.00005713866,0.0000996918,0.00026223622,0.00004057151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013994583,0.00039517623,0.0012628314,0.0013218159,0.0008317659,0.0009441655,0.0015918488,0.0010886888,0.0027751247],"category_scores_gemma":[0.0049933875,0.00052059017,0.00058356207,0.0012940642,0.00079106214,0.0010095653,0.0011460426,0.00091330573,0.0006328914],"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":[0.0003521183,0.00009983852,0.0010434913,0.00019177087,0.00012801135,0.000044104145,0.00020751261,0.61601555,0.0042593484,0.05822317,0.006959425,0.31247556],"study_design_scores_gemma":[0.000009004947,0.000014740959,0.000120135584,0.0000044791427,0.0000053579547,0.000009241925,0.000014941558,0.98930055,0.00040504258,0.00937971,0.00073179643,0.0000050053773],"about_ca_topic_score_codex":0.007975445,"about_ca_topic_score_gemma":0.0063295695,"teacher_disagreement_score":0.007975445,"about_ca_system_score_codex":0.0008605004,"about_ca_system_score_gemma":0.001081589,"threshold_uncertainty_score":0.015858054},"labels":[],"label_agreement":null},{"id":"W2003791493","doi":"10.1155/2011/745487","title":"Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications","year":2011,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada","keywords":"Local binary patterns; Artificial intelligence; Discriminative model; Computer science; Pattern recognition (psychology); Histogram; Biometrics; Feature extraction; Pixel; Face (sociological concept); Computer vision; Face detection; Facial recognition system; Feature (linguistics); Overhead (engineering); Image (mathematics)","score_opus":0.023656596273064018,"score_gpt":0.2859235098835882,"score_spread":0.26226691361052423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003791493","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45125738,0.0009718292,0.5420716,0.00026097614,0.000113953756,0.00010916247,0.00031175028,0.0019743352,0.002929078],"genre_scores_gemma":[0.86209905,0.00024262656,0.13623677,0.0000448701,0.000043982596,0.00004596589,0.00026413126,0.000043385284,0.0009792165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977463,0.0000469261,0.00001278745,0.000038582875,0.000103955725,0.000023158258],"domain_scores_gemma":[0.99933535,0.00020997254,0.00008244472,0.00006556563,0.00026031912,0.000046324367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034693198,0.00027757278,0.00037193214,0.0011850236,0.00017756235,0.00033331595,0.00034814494,0.00029409255,0.0017737217],"category_scores_gemma":[0.0014035739,0.00015156463,0.00021926114,0.0006704933,0.00013457764,0.00038939036,0.00023575517,0.00028072117,0.0006331701],"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":[0.0010258976,0.00035772717,0.008396653,0.00014353293,0.00005474269,0.00024291192,0.000054408443,0.016349794,0.21581641,0.0007965926,0.004334805,0.75242656],"study_design_scores_gemma":[0.000032536656,0.00028745268,0.015701316,0.000016775644,0.000047319838,0.0004303406,0.000046817004,0.8789651,0.10214682,0.00056335086,0.0017403126,0.000021887054],"about_ca_topic_score_codex":0.0010892536,"about_ca_topic_score_gemma":0.0016909554,"teacher_disagreement_score":0.0017737217,"about_ca_system_score_codex":0.00021017187,"about_ca_system_score_gemma":0.00024104012,"threshold_uncertainty_score":0.0059336424},"labels":[],"label_agreement":null},{"id":"W2004447583","doi":"10.1109/ccece.2013.6567723","title":"Automatic face recognition from video sequences using a template based cross correlation method","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Discriminant; Face (sociological concept); Three-dimensional face recognition; 3D single-object recognition; Computer vision; Cognitive neuroscience of visual object recognition; Feature (linguistics); Linear discriminant analysis; Feature extraction; Feature selection; Face detection","score_opus":0.05343347275698308,"score_gpt":0.3187981268172489,"score_spread":0.2653646540602658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004447583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044435166,0.0005732755,0.9519196,0.000061828905,0.000101469,0.0001254929,0.00011338527,0.0009874824,0.0016822442],"genre_scores_gemma":[0.32766536,0.0009208931,0.6672774,0.00012276677,0.00012977283,0.00023448076,0.00060230837,0.00009344869,0.0029535345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993217,0.00014057859,0.000041625037,0.0001751173,0.0002769251,0.00004412433],"domain_scores_gemma":[0.99946314,0.0001395235,0.00005900921,0.00008158009,0.00023444356,0.00002226979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070139125,0.00043229692,0.00054766063,0.0014160642,0.00025221318,0.0003941912,0.00044544405,0.00046247724,0.0016302228],"category_scores_gemma":[0.0014986513,0.00020624777,0.00046082525,0.00096593215,0.00025273024,0.0005442428,0.00025506283,0.00045558356,0.0009727894],"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":[0.00029627763,0.00017144551,0.0018756913,0.00013929041,0.000081190294,0.00018562165,0.000078608995,0.011880494,0.21875322,0.002375642,0.0018450015,0.7623175],"study_design_scores_gemma":[0.000041139032,0.00068876887,0.015252672,0.000042228272,0.00011013798,0.0020781888,0.000070073416,0.7305911,0.24185552,0.0014922715,0.007679183,0.00009870727],"about_ca_topic_score_codex":0.0018809019,"about_ca_topic_score_gemma":0.001794885,"teacher_disagreement_score":0.0018809019,"about_ca_system_score_codex":0.00023136678,"about_ca_system_score_gemma":0.0004886667,"threshold_uncertainty_score":0.005453646},"labels":[],"label_agreement":null},{"id":"W2005625450","doi":"10.1109/ccece.2013.6567720","title":"Non-linear sparse and group sparse classifier","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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 British Columbia","funders":"","keywords":"Classifier (UML); Sparse approximation; Pattern recognition (psychology); Artificial intelligence; Computer science; Linear classifier; Class (philosophy); Sample complexity; Optimization problem; Generalization; Machine learning; Mathematics; Algorithm","score_opus":0.02672891178389819,"score_gpt":0.23713890555809253,"score_spread":0.21040999377419434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2005625450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069175535,0.0002695985,0.9907607,0.0002753866,0.00004927781,0.000027963384,0.000050584276,0.00015613048,0.0014928299],"genre_scores_gemma":[0.44065627,0.001258331,0.5449588,0.00054470694,0.0005431748,0.0002500915,0.000736472,0.00013292367,0.010919332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834454,0.00053290266,0.00006139989,0.00026878083,0.00065440714,0.00013791177],"domain_scores_gemma":[0.99741334,0.001251722,0.0002526175,0.00042204402,0.0005874116,0.00007284937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019134798,0.00054392335,0.0012447656,0.0007551134,0.00041443956,0.0010022976,0.0012364179,0.0013377278,0.0028923438],"category_scores_gemma":[0.005951473,0.00028832702,0.0005608217,0.0013174603,0.00093261915,0.0019901632,0.0010834215,0.0016525703,0.0014038373],"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":[0.00027060643,0.00018120505,0.0015787496,0.0002428705,0.000086210035,0.00017767464,0.00019597741,0.32542956,0.010321972,0.08402567,0.010008249,0.5674812],"study_design_scores_gemma":[0.000005708355,0.000036335605,0.00026179926,0.0000064249466,0.000007911914,0.00006195725,0.000018019724,0.98400843,0.0014164828,0.012187205,0.0019830375,0.0000066604216],"about_ca_topic_score_codex":0.0017929578,"about_ca_topic_score_gemma":0.002456399,"teacher_disagreement_score":0.0028923438,"about_ca_system_score_codex":0.0005719123,"about_ca_system_score_gemma":0.00072250614,"threshold_uncertainty_score":0.010119557},"labels":[],"label_agreement":null},{"id":"W2007371667","doi":"10.5539/cis.v2n4p169","title":"Two-Dimensional Heteroscedastic Discriminant Analysis for Facial Gender Classification","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Face and Expression Recognition","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":"Peking University; Zhejiang University; National Science Foundation","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Computer science; Discriminant; Classifier (UML); Covariance matrix; Projection (relational algebra); Gradient descent; Constraint (computer-aided design); Heteroscedasticity; Facial recognition system; Mathematics; Algorithm; Machine learning; Artificial neural network","score_opus":0.044818479484571495,"score_gpt":0.29553797585740443,"score_spread":0.2507194963728329,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007371667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045179557,0.0014787525,0.94925034,0.0002650266,0.00021952961,0.00008968312,0.00040681325,0.0006508065,0.002459415],"genre_scores_gemma":[0.5183773,0.0013313987,0.4734105,0.0001207084,0.00020156428,0.00027504732,0.0009183313,0.00010910692,0.005256098],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992418,0.00028406654,0.000036123307,0.00012485054,0.0002611578,0.000052065752],"domain_scores_gemma":[0.9991873,0.00031676487,0.00006156282,0.00010524492,0.0002852811,0.000043887423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012409089,0.00062583474,0.00066117896,0.0011464494,0.00050895195,0.00047515632,0.0004503115,0.0003460716,0.002073428],"category_scores_gemma":[0.0019548277,0.00016552472,0.00060653046,0.0010057666,0.00031934635,0.0006267002,0.000423829,0.0005633836,0.0011990161],"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":[0.0003625056,0.00014403054,0.006299878,0.00023647638,0.00011040087,0.00017559492,0.00015111425,0.016142258,0.045034204,0.008355877,0.0069340575,0.9160537],"study_design_scores_gemma":[0.000059013237,0.00032600368,0.022416247,0.000058713806,0.0000939924,0.0007565618,0.00023057224,0.9128373,0.029563576,0.012182543,0.021335198,0.00014032715],"about_ca_topic_score_codex":0.0012373833,"about_ca_topic_score_gemma":0.0016411844,"teacher_disagreement_score":0.002073428,"about_ca_system_score_codex":0.0003189277,"about_ca_system_score_gemma":0.0006665803,"threshold_uncertainty_score":0.0069363117},"labels":[],"label_agreement":null},{"id":"W2007800125","doi":"10.1007/s10115-014-0801-8","title":"Greedy column subset selection for large-scale data sets","year":2014,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":57,"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 Waterloo","funders":"","keywords":"Computer science; Column (typography); Selection (genetic algorithm); Greedy algorithm; Algorithm; Representation (politics); Matrix (chemical analysis); Big data; Data mining; Artificial intelligence; Frame (networking)","score_opus":0.02411245776584549,"score_gpt":0.2674305953687463,"score_spread":0.2433181376029008,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007800125","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0808576,0.0021509107,0.90261775,0.0013024415,0.0002833099,0.0005031752,0.004837399,0.005789918,0.0016575218],"genre_scores_gemma":[0.3958808,0.0010549991,0.5665901,0.0008516598,0.0005967169,0.0010840751,0.028078448,0.0007914052,0.0050718975],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984737,0.00052997866,0.00014495624,0.0003221947,0.00033842897,0.00019069118],"domain_scores_gemma":[0.9951853,0.0031156563,0.00018120202,0.00079551624,0.000499669,0.00022266559],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002045721,0.001984775,0.003167829,0.0026762646,0.0014283281,0.0019950543,0.0025383146,0.0014076756,0.004025536],"category_scores_gemma":[0.007130517,0.000928579,0.001990488,0.0038001572,0.0008914231,0.0017953662,0.0015932142,0.0016728888,0.0020937347],"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":[0.0014396462,0.00088574336,0.007488468,0.0007926346,0.00062313234,0.00054044224,0.00025984508,0.1773816,0.019912066,0.0040727686,0.05562621,0.73097754],"study_design_scores_gemma":[0.00016201472,0.00022839873,0.0019358188,0.000039039885,0.00015809211,0.00025021782,0.00024000739,0.9735112,0.006154989,0.013845702,0.0034401133,0.000034278488],"about_ca_topic_score_codex":0.0047838097,"about_ca_topic_score_gemma":0.010496132,"teacher_disagreement_score":0.0047838097,"about_ca_system_score_codex":0.0005566605,"about_ca_system_score_gemma":0.0024025445,"threshold_uncertainty_score":0.013466775},"labels":[],"label_agreement":null},{"id":"W2008646744","doi":"10.5539/cis.v4n2p115","title":"Automatic Facial Expression Recognition System Based on Geometric and Appearance Features","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"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":"Sadness; Computer science; Disgust; Surprise; Facial expression; Artificial intelligence; Pattern recognition (psychology); Anger; Expression (computer science); Feature (linguistics); Feature extraction; Emotion classification; Speech recognition; Psychology","score_opus":0.02012680399924647,"score_gpt":0.215422235605221,"score_spread":0.19529543160597454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008646744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057030834,0.0005505005,0.9309972,0.00017242033,0.00018646888,0.0002242352,0.00032809496,0.0056926804,0.0048176134],"genre_scores_gemma":[0.36551973,0.0007214969,0.61678493,0.00026624923,0.00014764958,0.0005370376,0.0016942475,0.00031612418,0.014012544],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995403,0.00006366091,0.000023610157,0.00013797113,0.00019712177,0.000037333706],"domain_scores_gemma":[0.9997695,0.000028234688,0.000020934669,0.000027331598,0.00013905,0.000014911265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048054682,0.00047372174,0.00081327785,0.00062331196,0.00023458787,0.00030940288,0.00070527144,0.00037696873,0.0031908248],"category_scores_gemma":[0.00059729523,0.00020388563,0.0004118944,0.0003369994,0.00015648246,0.0005586388,0.00033081943,0.0003468873,0.0020095024],"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":[0.0003019449,0.00013150978,0.0014868898,0.00013166852,0.00004366429,0.00010369878,0.00007017795,0.0026960634,0.43911168,0.001253524,0.0051304796,0.54953873],"study_design_scores_gemma":[0.00015867516,0.0011115042,0.032655686,0.00007492346,0.0002768475,0.0024851286,0.00016630456,0.5408422,0.3809846,0.002120351,0.038931467,0.00019230232],"about_ca_topic_score_codex":0.0008014873,"about_ca_topic_score_gemma":0.0006989611,"teacher_disagreement_score":0.0031908248,"about_ca_system_score_codex":0.00019990059,"about_ca_system_score_gemma":0.00024148662,"threshold_uncertainty_score":0.010674357},"labels":[],"label_agreement":null},{"id":"W2008672983","doi":"10.1109/fg.2013.6553721","title":"Illumination invariant human face recognition: frequency or resonance?","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Yale University","keywords":"Artificial intelligence; Invariant (physics); Principal component analysis; Facial recognition system; Pattern recognition (psychology); Computer vision; Computer science; Face (sociological concept); Resonance (particle physics); Energy (signal processing); Mathematics; Physics; Statistics","score_opus":0.0391659670693123,"score_gpt":0.2571495290184603,"score_spread":0.21798356194914797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008672983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035587817,0.005163506,0.9470859,0.0017235538,0.00036034465,0.000043291908,0.00007013453,0.00097449025,0.008991018],"genre_scores_gemma":[0.6981792,0.007633864,0.28048882,0.0011344675,0.0010720431,0.00008464257,0.00025964875,0.00023071913,0.01091661],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996458,0.00007190625,0.000014635233,0.000099780445,0.00012377136,0.000044051427],"domain_scores_gemma":[0.9995548,0.00015094061,0.000055379343,0.00012843852,0.00009275029,0.000017674678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000615427,0.0003857713,0.00064381416,0.00045334885,0.00017921516,0.0006830931,0.000654452,0.00072020054,0.0023068113],"category_scores_gemma":[0.0014880076,0.00018325855,0.0004421582,0.00042480484,0.00096833374,0.0011987231,0.00033725717,0.00072196446,0.001683332],"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":[0.00027763422,0.00008278755,0.0019788607,0.00035863306,0.00007548431,0.000346183,0.00019891052,0.011078273,0.12898819,0.042170342,0.004338008,0.8101067],"study_design_scores_gemma":[0.000038169524,0.00070198916,0.018377936,0.000255786,0.00023422534,0.0060199955,0.00048109994,0.53401446,0.22685929,0.15955253,0.053199366,0.00026528366],"about_ca_topic_score_codex":0.00029489302,"about_ca_topic_score_gemma":0.00023097532,"teacher_disagreement_score":0.0023068113,"about_ca_system_score_codex":0.00022024676,"about_ca_system_score_gemma":0.00010711244,"threshold_uncertainty_score":0.007717073},"labels":[],"label_agreement":null},{"id":"W2008757140","doi":"10.1109/nafips.2011.5751956","title":"Spectral classification using fuzzy feature sampling","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Manitoba","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Fuzzy logic; Computer science; Feature (linguistics); Fuzzy set; Data mining; Machine learning","score_opus":0.1543991636049483,"score_gpt":0.29677095233874873,"score_spread":0.14237178873380044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008757140","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088300474,0.00012038982,0.9095601,0.00008523161,0.000025616564,0.00012173694,0.00008076611,0.0005761164,0.0011296185],"genre_scores_gemma":[0.58445865,0.00006234465,0.4141862,0.0000536069,0.000046056244,0.00011343691,0.00023729134,0.000029506202,0.00081279915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987925,0.00027232425,0.00007926048,0.00021857239,0.00056225515,0.00007496235],"domain_scores_gemma":[0.99806124,0.000857259,0.000114264905,0.0002739975,0.0006355904,0.00005768396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021108615,0.00040369006,0.0007176265,0.0019033552,0.00045936654,0.000902573,0.00066066434,0.0005914941,0.00089928234],"category_scores_gemma":[0.005555283,0.00017910908,0.0005845844,0.0010466729,0.0004582668,0.00088553317,0.00060400204,0.0005224093,0.00032616733],"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":[0.00046496422,0.00025629616,0.004865985,0.00009134486,0.000088582616,0.0001117308,0.00019559031,0.14566883,0.058301512,0.0068181586,0.0014486624,0.78168833],"study_design_scores_gemma":[0.00001483457,0.00009546025,0.002069307,0.000006651637,0.000015203579,0.00007545379,0.000033665085,0.9776264,0.014280942,0.0050841733,0.0006799892,0.000017927625],"about_ca_topic_score_codex":0.0025019846,"about_ca_topic_score_gemma":0.002563676,"teacher_disagreement_score":0.0025019846,"about_ca_system_score_codex":0.000599457,"about_ca_system_score_gemma":0.00048289643,"threshold_uncertainty_score":0.011163473},"labels":[],"label_agreement":null},{"id":"W2009328162","doi":"10.1109/smc.2014.6973888","title":"Multi-resolution fusion of DTCWT and DCT for shift invariant face recognition","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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 Calgary","funders":"","keywords":"Artificial intelligence; Discrete cosine transform; Pattern recognition (psychology); Complex wavelet transform; Facial recognition system; Computer science; Robustness (evolution); Discriminative model; Invariant (physics); Fusion; Subspace topology; Feature extraction; Linear discriminant analysis; Wavelet transform; Mathematics; Computer vision; Discrete wavelet transform; Wavelet; Image (mathematics)","score_opus":0.0376355870591742,"score_gpt":0.25480178899598355,"score_spread":0.21716620193680936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2009328162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020622212,0.001397868,0.9751883,0.0001207831,0.00018775027,0.00006514231,0.00008587967,0.00060871185,0.0017232763],"genre_scores_gemma":[0.30077052,0.0022135146,0.69311416,0.00020352956,0.00025411128,0.000101271944,0.00055557844,0.00010293024,0.0026843918],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99944276,0.00006661838,0.00003582336,0.00010319799,0.00030885538,0.000042752454],"domain_scores_gemma":[0.9995921,0.0001053631,0.0000472921,0.00007137523,0.00016286975,0.000020971198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006108583,0.00060380896,0.0006720687,0.0012521328,0.00020670885,0.0004610555,0.00058159034,0.00057193,0.0014425829],"category_scores_gemma":[0.0013850373,0.00023421577,0.0005751268,0.0011885563,0.00024087679,0.0010325143,0.00048978033,0.0005659355,0.0009207329],"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":[0.00018219442,0.000099960336,0.0008622171,0.00029957015,0.000052252613,0.00025913748,0.00006277362,0.010825895,0.38623777,0.0030620522,0.0023774158,0.59567875],"study_design_scores_gemma":[0.00004381639,0.00074641925,0.0059088706,0.00007428179,0.00015663936,0.0032371501,0.000110937275,0.61520904,0.34856546,0.0036919191,0.022123609,0.00013177835],"about_ca_topic_score_codex":0.00072927255,"about_ca_topic_score_gemma":0.0008298663,"teacher_disagreement_score":0.0014425829,"about_ca_system_score_codex":0.00023091324,"about_ca_system_score_gemma":0.0002859178,"threshold_uncertainty_score":0.00482589},"labels":[],"label_agreement":null},{"id":"W2011269401","doi":"10.1631/jzus.c1200156","title":"Adaptive online prediction method based on LS-SVR and its application in an electronic system","year":2012,"lang":"en","type":"article","venue":"Journal of Zhejiang University SCIENCE C","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Alberta","keywords":"Computer science; Support vector machine; Generalization; Process (computing); Data mining; Linear prediction; Machine learning; Predictive modelling; Artificial intelligence; Algorithm; Mathematics","score_opus":0.017287766365993152,"score_gpt":0.255198029860347,"score_spread":0.23791026349435385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011269401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058989096,0.00026193954,0.9381093,0.00020399735,0.00006377016,0.00003264468,0.000026776832,0.0008406217,0.0014719067],"genre_scores_gemma":[0.8985408,0.00026058618,0.098184615,0.000056007244,0.000040966435,0.00006726511,0.00006439168,0.000047134767,0.0027383198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996137,0.00010962139,0.000027887696,0.00008994175,0.00012821124,0.00003060908],"domain_scores_gemma":[0.9994802,0.000208359,0.000043371212,0.0000422032,0.00020800816,0.000017837558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008227674,0.00040875791,0.00064360804,0.00038985704,0.00030145585,0.00042511147,0.0006052182,0.0006496004,0.0012581779],"category_scores_gemma":[0.0015641564,0.00026250898,0.00044598343,0.00038094114,0.000238846,0.0007044208,0.00034835242,0.0005850022,0.00020807466],"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":[0.0001918977,0.00010818196,0.0030001395,0.000106284475,0.000067887326,0.00023828865,0.0001346499,0.776852,0.010832216,0.0032748214,0.0017270827,0.2034666],"study_design_scores_gemma":[0.000002018865,0.0000124015105,0.00012783706,9.314749e-7,0.0000025628967,0.000008490471,0.0000027935362,0.99919647,0.00043360618,0.000122043835,0.00008777017,0.0000031229276],"about_ca_topic_score_codex":0.0072327023,"about_ca_topic_score_gemma":0.0029021255,"teacher_disagreement_score":0.0072327023,"about_ca_system_score_codex":0.00027039743,"about_ca_system_score_gemma":0.00044520292,"threshold_uncertainty_score":0.01438117},"labels":[],"label_agreement":null},{"id":"W2011688148","doi":"10.1109/socialcom-passat.2012.83","title":"Dimensionality Reduction for Emotional Speech Recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":30,"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":"Dimensionality reduction; Principal component analysis; Pattern recognition (psychology); Computer science; Reduction (mathematics); Artificial intelligence; Curse of dimensionality; Projection (relational algebra); Filter (signal processing); Speech recognition; Feature extraction; Feature selection; Mathematics; Algorithm; Computer vision","score_opus":0.05657887062820809,"score_gpt":0.28468245357458966,"score_spread":0.22810358294638156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011688148","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024293043,0.002231595,0.9680472,0.0007023315,0.0001530996,0.00014320266,0.00035777764,0.0014286704,0.0026430858],"genre_scores_gemma":[0.2075894,0.0020291968,0.7855399,0.000200507,0.00017908862,0.00043381337,0.0011171082,0.000183066,0.0027279565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987979,0.0005123116,0.00009175645,0.00017479499,0.00036385993,0.000059356596],"domain_scores_gemma":[0.9989951,0.00041385356,0.00007281669,0.00024229838,0.0002480552,0.000027937447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013015058,0.0005901145,0.0007408056,0.0009309142,0.0005697433,0.00097521616,0.0004001424,0.00045616785,0.0023760318],"category_scores_gemma":[0.0039056246,0.00026177868,0.00088000065,0.0010774898,0.0004772796,0.00092334073,0.00083941955,0.0009927091,0.0016200313],"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":[0.00018545167,0.00013455404,0.001217859,0.0002528785,0.0001171899,0.00010998877,0.0002844353,0.03444879,0.051050074,0.023610793,0.012889421,0.8756986],"study_design_scores_gemma":[0.000029880572,0.00016608862,0.0051590074,0.00007075906,0.000062801606,0.000395314,0.00017119014,0.88650674,0.0402215,0.04689766,0.020229153,0.00009003141],"about_ca_topic_score_codex":0.0012420659,"about_ca_topic_score_gemma":0.0014116721,"teacher_disagreement_score":0.0023760318,"about_ca_system_score_codex":0.00046781852,"about_ca_system_score_gemma":0.0006110359,"threshold_uncertainty_score":0.007948577},"labels":[],"label_agreement":null},{"id":"W2012013481","doi":"10.1109/cimsa.2011.6059931","title":"A Gaussian radial basis function based feature selection algorithm","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Support vector machine; Pattern recognition (psychology); Feature vector; Feature selection; Kernel (algebra); Gaussian function; Radial basis function; Gaussian; Feature (linguistics); Artificial intelligence; Function (biology); Trigonometric functions; Similarity (geometry); Computer science; Radial basis function kernel; Basis (linear algebra); Cosine similarity; Mathematics; Selection (genetic algorithm); Algorithm; Kernel method; Artificial neural network; Discrete mathematics","score_opus":0.017655314480746996,"score_gpt":0.20727231266602233,"score_spread":0.18961699818527533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012013481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004606289,0.00044999595,0.99238855,0.00009479411,0.00006523907,0.00009864636,0.00006283393,0.0014181117,0.00081558764],"genre_scores_gemma":[0.12771815,0.0005335614,0.8636725,0.0003024528,0.00011534023,0.0004929768,0.00072517956,0.00026180575,0.00617813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976922,0.00046865197,0.000125099,0.00042681626,0.0011257761,0.00016137098],"domain_scores_gemma":[0.9990864,0.00016766862,0.000046143356,0.000074804404,0.0005893294,0.000035781806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017994993,0.0011135845,0.002207243,0.002203778,0.0006941341,0.0009182535,0.0020201912,0.0014014603,0.0023319342],"category_scores_gemma":[0.0026918678,0.00057079346,0.0013692023,0.0023964653,0.00045331512,0.00083384785,0.0010052028,0.0010333852,0.0026935427],"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":[0.0001970789,0.00013734832,0.0009048303,0.000104042236,0.00013209338,0.00012921152,0.000057809953,0.056120362,0.01635251,0.0035006476,0.008792205,0.9135719],"study_design_scores_gemma":[0.000056922214,0.000111564936,0.0011194709,0.00001677748,0.000049817234,0.00027369996,0.000016902732,0.9770026,0.009898576,0.0020252997,0.009382547,0.00004591642],"about_ca_topic_score_codex":0.0051828832,"about_ca_topic_score_gemma":0.0034293986,"teacher_disagreement_score":0.0051828832,"about_ca_system_score_codex":0.0006385792,"about_ca_system_score_gemma":0.0012836105,"threshold_uncertainty_score":0.010305464},"labels":[],"label_agreement":null},{"id":"W2012799976","doi":"10.1109/crv.2014.37","title":"Scale-Space Decomposition and Nearest Linear Combination Based Approach for Face Recognition","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Northern British Columbia","funders":"Yale University","keywords":"Facial recognition system; Pattern recognition (psychology); Artificial intelligence; Subspace topology; Face (sociological concept); Computer science; Scale (ratio); Linear subspace; Scale space; k-nearest neighbors algorithm; Decomposition; Invariant (physics); Computer vision; Wavelet transform; Wavelet; Mathematics; Image (mathematics); Image processing","score_opus":0.02080144341621807,"score_gpt":0.25768792446480826,"score_spread":0.2368864810485902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012799976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076205335,0.0012257268,0.9872851,0.0001007989,0.00011277567,0.000070711925,0.000092800445,0.0013352443,0.002156414],"genre_scores_gemma":[0.24747902,0.0020789297,0.7405231,0.00022434376,0.00021268513,0.000293711,0.00086695456,0.00015868958,0.0081625655],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998555,0.00022419154,0.00007368234,0.00030768567,0.0007626442,0.0000767266],"domain_scores_gemma":[0.9997079,0.00005607809,0.0000251082,0.000060432416,0.00013454873,0.00001583935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068305404,0.0006613021,0.0010814824,0.0015986429,0.00040498035,0.00058776536,0.0009258867,0.000787233,0.0033495591],"category_scores_gemma":[0.0010298167,0.00029582565,0.0011482106,0.0020359086,0.00039867364,0.00087109086,0.0006195006,0.0009111926,0.0024052842],"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":[0.0001249959,0.0001407674,0.0005738464,0.00011402496,0.00011985574,0.000093130344,0.00006276935,0.033490036,0.027979154,0.0050501185,0.0062614293,0.92598987],"study_design_scores_gemma":[0.000013957444,0.0001373468,0.002252772,0.000014876777,0.00005651967,0.00035909977,0.000044217668,0.96968096,0.015126576,0.0058135064,0.006449255,0.000050909955],"about_ca_topic_score_codex":0.0027873842,"about_ca_topic_score_gemma":0.002462043,"teacher_disagreement_score":0.0033495591,"about_ca_system_score_codex":0.00038783182,"about_ca_system_score_gemma":0.0003809195,"threshold_uncertainty_score":0.011205435},"labels":[],"label_agreement":null},{"id":"W2013066341","doi":"10.1016/j.patrec.2012.10.030","title":"Non-parametric Fisher’s discriminant analysis with kernels for data classification","year":2012,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; University of Windsor","funders":"","keywords":"Fisher kernel; Kernel Fisher discriminant analysis; Linear discriminant analysis; Kernel (algebra); Pattern recognition (psychology); Nonparametric statistics; Artificial intelligence; Kernel method; Mathematics; Parametric statistics; Kernel principal component analysis; Machine learning; Computer science; Discriminant; Support vector machine; Statistics","score_opus":0.11715066943969728,"score_gpt":0.30161356389869065,"score_spread":0.1844628944589934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013066341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012447952,0.0004544025,0.9858385,0.00011015707,0.000045026023,0.000029977326,0.00010774915,0.00063370465,0.00033262177],"genre_scores_gemma":[0.3022727,0.0007099197,0.6923724,0.00005955953,0.00007003226,0.00019466899,0.0007267547,0.000230894,0.0033630703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982886,0.0005990913,0.00018292436,0.00029530618,0.00050606206,0.00012804763],"domain_scores_gemma":[0.9965905,0.0015945224,0.00020032623,0.0007202139,0.0008143829,0.0000800957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023372264,0.0006512505,0.0010543303,0.0009836119,0.00052491296,0.00096445705,0.0010706971,0.00066161325,0.001929258],"category_scores_gemma":[0.008348772,0.00035899886,0.0011781731,0.0014346848,0.00052783877,0.0017466996,0.001384233,0.0015878886,0.0012300542],"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":[0.00044725058,0.00019726423,0.0014125118,0.0002547373,0.000106896776,0.000071736446,0.000113715294,0.04201478,0.019007381,0.014279769,0.0049205474,0.9171734],"study_design_scores_gemma":[0.00001227514,0.00006901669,0.0022388413,0.000021326856,0.000033168868,0.00012682169,0.00003830339,0.9736313,0.010043344,0.01056513,0.0031857288,0.000034745233],"about_ca_topic_score_codex":0.0023877318,"about_ca_topic_score_gemma":0.0023048038,"teacher_disagreement_score":0.0023877318,"about_ca_system_score_codex":0.0004904477,"about_ca_system_score_gemma":0.0012813458,"threshold_uncertainty_score":0.012360632},"labels":[],"label_agreement":null},{"id":"W2014921455","doi":"10.1007/s00138-006-0016-4","title":"Deterioration of visual information in face classification using Eigenfaces and Fisherfaces","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Eigenface; Face (sociological concept); Facial recognition system; Artificial intelligence; Computer science; Pattern recognition (psychology); Sociology","score_opus":0.011642722684483943,"score_gpt":0.2858001536139361,"score_spread":0.27415743092945216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014921455","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19645567,0.002281072,0.7967293,0.00050057453,0.00022708545,0.000052380186,0.00019395363,0.0007755282,0.0027843954],"genre_scores_gemma":[0.7092486,0.001717359,0.2836865,0.00014628864,0.00012920113,0.000056156863,0.0003642857,0.00020588828,0.0044456436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885404,0.00029774857,0.00008627953,0.00014122171,0.0005420814,0.00007850134],"domain_scores_gemma":[0.99539244,0.0022056077,0.0002711468,0.00072446896,0.001295668,0.00011066355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021695462,0.0005825399,0.0006435362,0.0011017097,0.0005525684,0.0009150294,0.0004095987,0.000732541,0.0017941736],"category_scores_gemma":[0.011048205,0.00033704517,0.00063830934,0.0009562388,0.0006543607,0.0021018041,0.0007767421,0.0011624935,0.0007578762],"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":[0.0006732692,0.00020937242,0.0076464317,0.00022172037,0.000094631636,0.00011340688,0.00022640369,0.039446797,0.11609053,0.008349282,0.0029030484,0.82402503],"study_design_scores_gemma":[0.000021549715,0.00039673183,0.0296735,0.00007086685,0.00010798981,0.0010557896,0.00014502875,0.8184138,0.13481162,0.012454755,0.0027701696,0.00007819507],"about_ca_topic_score_codex":0.002367036,"about_ca_topic_score_gemma":0.002459481,"teacher_disagreement_score":0.002367036,"about_ca_system_score_codex":0.00045811347,"about_ca_system_score_gemma":0.0006398717,"threshold_uncertainty_score":0.0114738345},"labels":[],"label_agreement":null},{"id":"W2015307889","doi":"10.1142/s0218001409007260","title":"FACIAL BIOMETRICS USING NONTENSOR PRODUCT WAVELET AND 2D DISCRIMINANT TECHNIQUES","year":2009,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":43,"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 Calgary","funders":"National Natural Science Foundation of China; University of Hong Kong; Hong Kong Baptist University","keywords":"Wavelet; Pattern recognition (psychology); Artificial intelligence; Biometrics; Linear discriminant analysis; Computer science; Feature (linguistics); Gabor wavelet; Facial expression; Support vector machine; Feature extraction; Dimension (graph theory); Wavelet transform; Feature vector; Mathematics; Discrete wavelet transform","score_opus":0.12837303328809516,"score_gpt":0.33963453233077817,"score_spread":0.21126149904268302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015307889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023418969,0.0011495847,0.97275203,0.00018931417,0.00017717265,0.00006746228,0.00011449428,0.0003253487,0.00180562],"genre_scores_gemma":[0.2102284,0.0022368052,0.78069955,0.0001406137,0.0001599104,0.00014743175,0.00035328272,0.00006398103,0.0059700585],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99915946,0.00019490113,0.000054141394,0.00014064478,0.0004152113,0.000035598136],"domain_scores_gemma":[0.99955183,0.00010499635,0.000079100384,0.000098570905,0.00014573839,0.000019761748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007405173,0.00052765466,0.0009108407,0.0014940994,0.00024601156,0.00070303783,0.00053143495,0.0005867788,0.001749294],"category_scores_gemma":[0.0015240809,0.00026250546,0.00065291394,0.0014364825,0.00034899547,0.0014836618,0.00077295647,0.0005581122,0.0010892719],"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":[0.00023195469,0.00009156273,0.0019081631,0.00032801414,0.000056953366,0.00014907752,0.0001126069,0.007961839,0.11482151,0.012899412,0.0024801283,0.8589589],"study_design_scores_gemma":[0.00007876147,0.00093682384,0.017753053,0.00012417677,0.00015117651,0.0039496995,0.00015699271,0.84112173,0.08254464,0.013831357,0.03914635,0.00020519613],"about_ca_topic_score_codex":0.00043356454,"about_ca_topic_score_gemma":0.00044251975,"teacher_disagreement_score":0.001749294,"about_ca_system_score_codex":0.0002978849,"about_ca_system_score_gemma":0.00033723545,"threshold_uncertainty_score":0.0058520436},"labels":[],"label_agreement":null},{"id":"W2015392120","doi":"10.1007/s00034-011-9337-2","title":"A Discriminant Model for the Pattern Recognition of Linearly Independent Samples","year":2011,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Face and Expression Recognition","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":"Linear discriminant analysis; Optimal discriminant analysis; Discriminant; Kernel Fisher discriminant analysis; Pattern recognition (psychology); Subspace topology; Artificial intelligence; Multiple discriminant analysis; Dimension (graph theory); Hyperplane; Mathematics; Sample (material); Computer science; Facial recognition system","score_opus":0.13225884771003998,"score_gpt":0.26037731473885023,"score_spread":0.12811846702881025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015392120","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007559232,0.00021714601,0.9912663,0.0001179873,0.000050191058,0.000015708927,0.00011540646,0.00027083472,0.00038714026],"genre_scores_gemma":[0.49670786,0.0010520043,0.48068136,0.00031186553,0.00029937693,0.00034835588,0.0014865316,0.0002696644,0.018843107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954104,0.000104442544,0.000022870281,0.00012486172,0.00015556849,0.000051133993],"domain_scores_gemma":[0.9993753,0.00025329817,0.000049873794,0.000119893215,0.00017443561,0.00002723474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009367406,0.0007013859,0.0010180634,0.00069037173,0.000322062,0.0008085612,0.001369422,0.0009859505,0.0023696013],"category_scores_gemma":[0.0024953187,0.00036339046,0.0008238036,0.00085909176,0.00048679914,0.0010933471,0.0006814425,0.0015159213,0.0020517851],"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":[0.0007314962,0.00024277506,0.0019204154,0.0002524726,0.00016056234,0.00015298699,0.00011036582,0.31478313,0.052858114,0.076997004,0.010299038,0.5414917],"study_design_scores_gemma":[0.000009790871,0.000030255145,0.00021769438,0.0000045766524,0.000011484478,0.000039855673,0.000003821854,0.9908388,0.0015341039,0.0062967814,0.001003763,0.000009045721],"about_ca_topic_score_codex":0.0019062953,"about_ca_topic_score_gemma":0.0018039339,"teacher_disagreement_score":0.0023696013,"about_ca_system_score_codex":0.00046216874,"about_ca_system_score_gemma":0.00058307865,"threshold_uncertainty_score":0.00792712},"labels":[],"label_agreement":null},{"id":"W2015404074","doi":"10.5244/c.25.3","title":"Semi Supervised Learning for Wild Faces and Video","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision","score_opus":0.03937219460397411,"score_gpt":0.23729035044310648,"score_spread":0.19791815583913236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015404074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037334442,0.00053289416,0.95624447,0.00021590211,0.000111475085,0.00016191494,0.00064973376,0.0036678482,0.0010812405],"genre_scores_gemma":[0.5361298,0.00046357577,0.44229156,0.00041018592,0.0002378824,0.00056120224,0.007879428,0.0007003825,0.011326048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982237,0.00047002628,0.00009465494,0.00067707256,0.0003220978,0.00021241176],"domain_scores_gemma":[0.9958533,0.0018024145,0.0002936845,0.0010755039,0.0008066191,0.00016838108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023248505,0.0016389794,0.0018310855,0.0010580684,0.0008758469,0.0012537912,0.0029889229,0.0021995164,0.0030306447],"category_scores_gemma":[0.005739001,0.0010204357,0.0016277513,0.0008203631,0.0013007171,0.0026249639,0.002163663,0.0028162193,0.002469519],"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":[0.0006606426,0.0006476962,0.0027456246,0.00028712305,0.00025481288,0.00015926542,0.00013039615,0.16126496,0.022982933,0.004061584,0.015722038,0.79108286],"study_design_scores_gemma":[0.000015358573,0.00007419944,0.00049579213,0.000010127676,0.000015233427,0.0000627061,0.000030647665,0.989268,0.004261519,0.005010734,0.0007456139,0.000009987087],"about_ca_topic_score_codex":0.005907615,"about_ca_topic_score_gemma":0.010835675,"teacher_disagreement_score":0.005907615,"about_ca_system_score_codex":0.0009014811,"about_ca_system_score_gemma":0.0015534097,"threshold_uncertainty_score":0.012295127},"labels":[],"label_agreement":null},{"id":"W2015549092","doi":"10.1080/18756891.2011.9727894","title":"Edge Eigenface Weighted Hausdorff Distance for Face Recognition","year":2011,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Face and Expression Recognition","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":"Ministry of Transportation of Ontario","funders":"Beijing Institute of Technology; National Natural Science Foundation of China; Yale University","keywords":"Discriminative model; Eigenface; Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Hausdorff distance; Face (sociological concept); Weighting; Computer science; Enhanced Data Rates for GSM Evolution; Hausdorff space; Mathematics; Combinatorics; Medicine","score_opus":0.07079183525587678,"score_gpt":0.3061753582838025,"score_spread":0.2353835230279257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015549092","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04229211,0.0019765473,0.95326847,0.00012507338,0.00010158022,0.000032019867,0.00014626827,0.00043593987,0.0016220242],"genre_scores_gemma":[0.5160089,0.001635976,0.4786386,0.00008064799,0.00013507389,0.00011056723,0.00058116805,0.00007493263,0.002734104],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993647,0.00015973378,0.00004774287,0.00012312467,0.00027014175,0.000034566834],"domain_scores_gemma":[0.9994916,0.00017256197,0.000050640792,0.00010505301,0.00015559314,0.000024573512],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005531589,0.00032134677,0.00052722683,0.0012008386,0.0002391087,0.00062232127,0.0005393015,0.0004132885,0.0011058073],"category_scores_gemma":[0.0018730672,0.00010901213,0.00038305728,0.0010940821,0.0003267864,0.0011296108,0.00046894187,0.0005032583,0.0004683303],"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":[0.00020657788,0.00007911032,0.0022791077,0.00018681865,0.0001086756,0.00016165787,0.00011459783,0.046084907,0.04603506,0.032667704,0.0051106703,0.86696506],"study_design_scores_gemma":[0.000010579351,0.00015744504,0.006573634,0.000025436233,0.000041595074,0.00055067777,0.00007711582,0.9217594,0.03603674,0.024124352,0.010561619,0.00008133861],"about_ca_topic_score_codex":0.00089422404,"about_ca_topic_score_gemma":0.0006482744,"teacher_disagreement_score":0.0012008386,"about_ca_system_score_codex":0.0004530717,"about_ca_system_score_gemma":0.00033453555,"threshold_uncertainty_score":0.0036993027},"labels":[],"label_agreement":null},{"id":"W2016122613","doi":"10.1117/12.849764","title":"Infrared face recognition using texture descriptors","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Face and Expression Recognition","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":true,"ca_institutions":"Université Laval","funders":"","keywords":"Facial recognition system; Artificial intelligence; Computer science; Multispectral image; Pattern recognition (psychology); Computer vision; Face (sociological concept); Three-dimensional face recognition; Local binary patterns; Night vision; Dimensionality reduction; Face detection; Histogram; Image (mathematics)","score_opus":0.017499194843269464,"score_gpt":0.23225476536155787,"score_spread":0.21475557051828842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016122613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16286515,0.0018406133,0.81755406,0.00027408465,0.000298106,0.00019135726,0.0011368807,0.0027268112,0.01311298],"genre_scores_gemma":[0.7166432,0.0016572728,0.26807764,0.00018956752,0.00018739761,0.00013414523,0.001987766,0.0001401347,0.010982656],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999648,0.000035310317,0.0000132290115,0.00006618582,0.00019201575,0.000045335495],"domain_scores_gemma":[0.99978405,0.00004395537,0.000032733275,0.00004259569,0.00008483729,0.000011778395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002846726,0.0003498426,0.0006221665,0.001520541,0.00016677869,0.0007125485,0.0004360847,0.00040608252,0.00289116],"category_scores_gemma":[0.0007656245,0.00013551975,0.0005360449,0.0011534062,0.00023090067,0.00070815167,0.00048262384,0.00035372394,0.0017758497],"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":[0.00030837578,0.00014599267,0.00257141,0.00016831528,0.000058582624,0.0001536132,0.0000387889,0.011421174,0.18514182,0.002624013,0.004580193,0.7927878],"study_design_scores_gemma":[0.0000736386,0.0005408467,0.033270083,0.000069591486,0.00018245625,0.0020017992,0.0001860584,0.69689727,0.23821698,0.007420544,0.021037228,0.00010357191],"about_ca_topic_score_codex":0.0014392636,"about_ca_topic_score_gemma":0.0012462254,"teacher_disagreement_score":0.00289116,"about_ca_system_score_codex":0.00023755146,"about_ca_system_score_gemma":0.0001816781,"threshold_uncertainty_score":0.009671867},"labels":[],"label_agreement":null},{"id":"W2016484971","doi":"10.1109/icif.2007.4408144","title":"An empirical study on diversity measures and margin theory for ensembles of classifiers","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; École de Technologie Supérieure","funders":"","keywords":"Margin (machine learning); Diversity (politics); Machine learning; Computer science; Artificial intelligence; Empirical research; Voting; Majority rule; Selection (genetic algorithm); Mathematics; Statistics; Sociology","score_opus":0.07262546475396592,"score_gpt":0.3351700001451854,"score_spread":0.26254453539121947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016484971","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.62736684,0.007823775,0.33715346,0.0027066509,0.00013969671,0.00013989054,0.00026114678,0.00009587269,0.02431265],"genre_scores_gemma":[0.97782236,0.00048537337,0.020805074,0.00009641269,0.00016378079,0.000067683075,0.00015756911,0.000024566263,0.0003771802],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9717882,0.016728178,0.0011894889,0.0019560512,0.0077629993,0.0005751052],"domain_scores_gemma":[0.55979145,0.40479562,0.010961942,0.010701386,0.012273286,0.0014763933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038850553,0.0007553412,0.0011872125,0.0040076924,0.0016340277,0.002810943,0.001374612,0.001817855,0.0026939935],"category_scores_gemma":[0.25035918,0.00035453198,0.00072700344,0.003599322,0.0043049417,0.008876303,0.0029027904,0.0026198206,0.0002329743],"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":[0.00076216273,0.0006069659,0.14632,0.0008741226,0.0009862065,0.00037948092,0.0038160542,0.08555484,0.0036985478,0.44459766,0.003935421,0.30846867],"study_design_scores_gemma":[0.000092868555,0.0014394022,0.105531536,0.00055837823,0.00024895996,0.0014297623,0.0026241713,0.35104674,0.0069059883,0.52176243,0.0081556635,0.00020409342],"about_ca_topic_score_codex":0.00046856806,"about_ca_topic_score_gemma":0.0003893445,"teacher_disagreement_score":0.038850553,"about_ca_system_score_codex":0.001546527,"about_ca_system_score_gemma":0.00060443854,"threshold_uncertainty_score":0.20546389},"labels":[],"label_agreement":null},{"id":"W2019113107","doi":"10.1109/isspa.2012.6310540","title":"Sift-flow registration for facial expression analysis using Gabor wavelets","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Université de Montréal","funders":"","keywords":"Sadness; Facial expression; Gabor wavelet; Expression (computer science); Computer science; Artificial intelligence; Happiness; Set (abstract data type); Affective computing; Computer vision; Scale-invariant feature transform; Disgust; Field (mathematics); Wavelet; Pattern recognition (psychology); Anger; Psychology; Wavelet transform; Discrete wavelet transform; Feature extraction; Mathematics; Social psychology","score_opus":0.046802918566158584,"score_gpt":0.2992246040656174,"score_spread":0.2524216854994588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019113107","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013915627,0.00017658739,0.9825225,0.000088936686,0.00006373032,0.000076237535,0.00017305318,0.0018852368,0.0010980973],"genre_scores_gemma":[0.23470117,0.000592075,0.75752664,0.00006681973,0.00006539361,0.00019798883,0.0010947175,0.00044703358,0.0053082146],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996965,0.0000475115,0.000020614243,0.00007105613,0.000116408024,0.000047876336],"domain_scores_gemma":[0.99982566,0.000031056672,0.000018161072,0.000048238096,0.000064205946,0.0000127424255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063046545,0.00048189194,0.0005969196,0.0015041198,0.00035717967,0.00061931665,0.0005434922,0.000533775,0.0059996643],"category_scores_gemma":[0.0009711235,0.00035022452,0.0007567901,0.0013330976,0.0003100599,0.00072922144,0.0005762137,0.0006261533,0.0033973083],"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":[0.00037022698,0.00013769927,0.0009655687,0.0001247157,0.000054686705,0.000085243526,0.00008190491,0.016405916,0.13570273,0.0066896165,0.008572828,0.8308089],"study_design_scores_gemma":[0.000065549,0.00023203749,0.0070900614,0.00003724291,0.000063741034,0.00041777024,0.000097912896,0.845185,0.121007554,0.0071151517,0.018630756,0.0000571305],"about_ca_topic_score_codex":0.0026275958,"about_ca_topic_score_gemma":0.0028421772,"teacher_disagreement_score":0.0059996643,"about_ca_system_score_codex":0.0003555557,"about_ca_system_score_gemma":0.0008786283,"threshold_uncertainty_score":0.02007085},"labels":[],"label_agreement":null},{"id":"W2019979708","doi":"10.1109/icip.2011.6115801","title":"Face recognition through regional weight estimation","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Northern British Columbia","funders":"","keywords":"Voting; Facial recognition system; Computer science; Face (sociological concept); Embedding; Artificial intelligence; Field (mathematics); Pattern recognition (psychology); Machine learning; Weighted voting; Mathematics; Political science","score_opus":0.09403673836172757,"score_gpt":0.25844954042005175,"score_spread":0.16441280205832418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019979708","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018093973,0.00026536695,0.9791397,0.000035588924,0.000040092164,0.000032363107,0.000043806034,0.0010308605,0.0013183643],"genre_scores_gemma":[0.42304727,0.0006719389,0.56710184,0.000097962475,0.00010444752,0.00012915763,0.00040068122,0.0003017835,0.008144876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945706,0.000087144916,0.00002407437,0.00019269738,0.0001774553,0.000061672596],"domain_scores_gemma":[0.9994591,0.0000905731,0.00006616448,0.0001480763,0.00021188025,0.000024336561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006461876,0.0007584445,0.00097571063,0.0010864907,0.00033609653,0.00077245943,0.0010034776,0.00062924804,0.002901557],"category_scores_gemma":[0.0020549195,0.0003844606,0.0006370591,0.0008425452,0.00030552712,0.0014881011,0.0010943594,0.00075220823,0.0024323575],"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":[0.00020772273,0.00006394135,0.0014032957,0.00006542379,0.00008874617,0.00006459026,0.00006119519,0.040645882,0.1289102,0.0047092075,0.0026563643,0.82112354],"study_design_scores_gemma":[0.000018529629,0.00013563427,0.0032754703,0.000016403103,0.00012060732,0.0003670951,0.000049396327,0.88832295,0.09234725,0.009509068,0.0057826713,0.00005497708],"about_ca_topic_score_codex":0.0013420935,"about_ca_topic_score_gemma":0.0017812932,"teacher_disagreement_score":0.002901557,"about_ca_system_score_codex":0.00026654368,"about_ca_system_score_gemma":0.00029388373,"threshold_uncertainty_score":0.009706676},"labels":[],"label_agreement":null},{"id":"W2020353520","doi":"10.1109/icdm.2012.134","title":"Low Dimensional Localized Clustering (LDLC)","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Cluster analysis; Linear subspace; Subspace topology; Dimensionality reduction; Computer science; Clustering high-dimensional data; Visualization; Curse of dimensionality; Intuition; Data point; Artificial intelligence; Data space; Mathematics; Pattern recognition (psychology); Algorithm; Topology (electrical circuits); Combinatorics; Pure mathematics","score_opus":0.01824752891808431,"score_gpt":0.24423646898220713,"score_spread":0.22598894006412282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020353520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003938626,0.00042209032,0.99393797,0.00014332154,0.000041502382,0.00007148401,0.00012445627,0.0006372729,0.00068326545],"genre_scores_gemma":[0.14863685,0.0008952105,0.84517324,0.00037451735,0.00013402411,0.0003942008,0.0014138451,0.00042409787,0.0025540278],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976794,0.00065084716,0.00014247194,0.0006012356,0.0007667713,0.00015933946],"domain_scores_gemma":[0.9968226,0.0008626035,0.0003173731,0.0008449106,0.0010530014,0.000099487734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023498782,0.0012971454,0.0020397885,0.003064215,0.0019299027,0.0019415239,0.0023971596,0.0021213596,0.0019682213],"category_scores_gemma":[0.00819021,0.0005745153,0.0015502047,0.0036229296,0.002016525,0.0017428428,0.0023617619,0.0018222341,0.0019130534],"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":[0.00019546553,0.00013206639,0.0033450515,0.00070676865,0.00041250646,0.00022747803,0.0007436805,0.2664568,0.0125714075,0.060909048,0.03074462,0.62355506],"study_design_scores_gemma":[0.000023702127,0.00007080502,0.0012130929,0.000088086344,0.000052454987,0.00027385287,0.00017203102,0.93074125,0.0046002446,0.052929625,0.009737228,0.00009768773],"about_ca_topic_score_codex":0.009115708,"about_ca_topic_score_gemma":0.008762995,"teacher_disagreement_score":0.009115708,"about_ca_system_score_codex":0.0013072753,"about_ca_system_score_gemma":0.0025883066,"threshold_uncertainty_score":0.018125296},"labels":[],"label_agreement":null},{"id":"W2022432366","doi":"10.1109/icassp.2010.5494933","title":"Weakly trained dual features extraction based detector for frontal face detection","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Toronto","funders":"","keywords":"Detector; Artificial intelligence; Computer science; Face (sociological concept); Haar-like features; Robustness (evolution); Face detection; Pattern recognition (psychology); Computer vision; Feature extraction; Object-class detection; Facial recognition system; Cluster analysis","score_opus":0.010362174753883145,"score_gpt":0.25099240434854175,"score_spread":0.2406302295946586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2022432366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055812582,0.00032980935,0.94034404,0.00008845186,0.0000825591,0.00007754216,0.00014601393,0.0014761969,0.0016426865],"genre_scores_gemma":[0.3591703,0.00025045732,0.63207585,0.0001582511,0.000077619516,0.00013945754,0.0006628561,0.00012943514,0.0073357457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992168,0.0001173634,0.00003880109,0.0002043316,0.0003157316,0.0001068771],"domain_scores_gemma":[0.9990741,0.00027850657,0.00007087955,0.00018334558,0.00033535788,0.00005772356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097189145,0.00067582866,0.001148384,0.000982292,0.00039075455,0.00067347195,0.0011211782,0.0008179862,0.0032594178],"category_scores_gemma":[0.0018800569,0.00044972272,0.0005206433,0.0004717966,0.00033319616,0.0010147783,0.0008977588,0.00078623183,0.0026575918],"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":[0.0007578551,0.00031527507,0.0046749935,0.00011434128,0.00011805121,0.00018105615,0.000078862715,0.012629267,0.3298171,0.002552277,0.0031846135,0.6455763],"study_design_scores_gemma":[0.000035749974,0.0003538644,0.0065922732,0.0000153477,0.00009397403,0.0007951985,0.00004683229,0.7108743,0.27283782,0.0017661819,0.006536993,0.000051450806],"about_ca_topic_score_codex":0.00081405434,"about_ca_topic_score_gemma":0.0012362265,"teacher_disagreement_score":0.0032594178,"about_ca_system_score_codex":0.00048432965,"about_ca_system_score_gemma":0.0006601507,"threshold_uncertainty_score":0.010903835},"labels":[],"label_agreement":null},{"id":"W2023528898","doi":"10.1109/ccece.2006.277536","title":"FPGA Implementation of a Face Detector using Neural Networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":18,"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 Saskatchewan","funders":"","keywords":"Field-programmable gate array; Computer science; Detector; Artificial neural network; Computer hardware; Reduction (mathematics); Frame rate; Floating point; Fixed-point arithmetic; Arithmetic; Algorithm; Artificial intelligence; Mathematics","score_opus":0.02107098620046875,"score_gpt":0.2837390342934296,"score_spread":0.26266804809296085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023528898","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28257427,0.000938491,0.6784398,0.00029932763,0.00066754094,0.0003247049,0.00043995332,0.009920238,0.02639569],"genre_scores_gemma":[0.7660155,0.00033966728,0.22226264,0.00016007714,0.000040462954,0.00013918623,0.00035956505,0.000076857505,0.010605886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982834,0.000028145505,0.000012551775,0.0000300338,0.000071810544,0.000029151373],"domain_scores_gemma":[0.9997733,0.00006549423,0.00001537426,0.000027343853,0.00010807382,0.000010399738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020964189,0.00032643578,0.00025184336,0.00036322995,0.00017462973,0.00038547887,0.000613595,0.00026344394,0.004324405],"category_scores_gemma":[0.0004974055,0.00017376636,0.00016409594,0.00024577993,0.00010155649,0.0003565693,0.00013472953,0.00024992594,0.0007680785],"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":[0.0009833776,0.00022137426,0.0041865613,0.00067624147,0.00020771788,0.0008264467,0.00015904637,0.07443194,0.19517733,0.007770067,0.009782102,0.70557773],"study_design_scores_gemma":[0.0001482936,0.0010850702,0.0066182166,0.0001111575,0.00014939626,0.00094619306,0.00007242274,0.6500155,0.30938998,0.0010800115,0.03031086,0.00007299229],"about_ca_topic_score_codex":0.0032605815,"about_ca_topic_score_gemma":0.0033172448,"teacher_disagreement_score":0.004324405,"about_ca_system_score_codex":0.00044027166,"about_ca_system_score_gemma":0.00037542303,"threshold_uncertainty_score":0.014466584},"labels":[],"label_agreement":null},{"id":"W2023744375","doi":"10.1109/icpr.2008.4761675","title":"Adaptive asymmetrical SVM and genetic algorithms based iris recognition","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"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":"Support vector machine; Feature selection; Computer science; Pattern recognition (psychology); Feature (linguistics); Artificial intelligence; Genetic algorithm; Selection (genetic algorithm); Machine learning; Algorithm; Data mining","score_opus":0.10897863998054433,"score_gpt":0.2893857647867768,"score_spread":0.18040712480623247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023744375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021678563,0.00028845915,0.9750668,0.00015974499,0.000060341164,0.00004027439,0.000020019539,0.00036030516,0.002325552],"genre_scores_gemma":[0.49680611,0.0002700292,0.49856356,0.00022921598,0.00008978276,0.00014817534,0.00009660454,0.00005654881,0.003740043],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991936,0.00023324872,0.000047453195,0.00014096421,0.00031578567,0.00006905374],"domain_scores_gemma":[0.999316,0.00023388854,0.00011794519,0.00011464437,0.00018714544,0.000030385236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00094283826,0.00046552095,0.000623552,0.0011243867,0.00025289485,0.00068923505,0.00096857554,0.0008289536,0.0011762779],"category_scores_gemma":[0.0024120617,0.00021707015,0.00046309247,0.0009678385,0.0005696716,0.000818031,0.0005914781,0.000751956,0.00041632104],"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":[0.00015895777,0.00014605605,0.0026341574,0.00008421293,0.000080184676,0.00014191489,0.000072794144,0.36938432,0.024255438,0.025847152,0.0013751795,0.57581955],"study_design_scores_gemma":[0.000010653022,0.000041330997,0.0005824807,0.0000051882453,0.000009880861,0.000052952408,0.0000062274557,0.9912723,0.003137991,0.0038935768,0.0009785697,0.000008912995],"about_ca_topic_score_codex":0.001474982,"about_ca_topic_score_gemma":0.0014488285,"teacher_disagreement_score":0.001474982,"about_ca_system_score_codex":0.0004580421,"about_ca_system_score_gemma":0.00041583157,"threshold_uncertainty_score":0.004986286},"labels":[],"label_agreement":null},{"id":"W2024496231","doi":"10.1109/cibim.2014.7015444","title":"Efficient adaptive face recognition systems based on capture conditions","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Mila - Quebec Artificial Intelligence Institute; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Computer science; Face (sociological concept); A priori and a posteriori; Facial recognition system; Set (abstract data type); Artificial intelligence; Pruning; Exploit; Login; Computer vision; Template; Machine learning; Pattern recognition (psychology)","score_opus":0.02073846115159262,"score_gpt":0.229884429934408,"score_spread":0.20914596878281538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024496231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13026689,0.0009837527,0.85880625,0.00014458621,0.000105718806,0.0003240887,0.00026157647,0.0059615457,0.0031456228],"genre_scores_gemma":[0.73015934,0.0007267763,0.26165396,0.00025933402,0.000109881374,0.00032705354,0.00095140625,0.00033238818,0.0054798904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987079,0.0001237188,0.000058150308,0.0005088423,0.00048673662,0.000114698254],"domain_scores_gemma":[0.9986609,0.00040936613,0.00019117362,0.0003736352,0.00032003314,0.000044839042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009585402,0.0009005891,0.0011983231,0.0008575771,0.00045970496,0.0009893356,0.0015105229,0.0006523423,0.0019995063],"category_scores_gemma":[0.0038794721,0.00072925695,0.0005799626,0.0004911555,0.0005101001,0.0017716384,0.001633936,0.0009674725,0.001758182],"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":[0.00076313334,0.00022681896,0.00557794,0.000107918786,0.0001150715,0.00015224825,0.00020735343,0.05420442,0.16455053,0.0016698666,0.0031477124,0.7692769],"study_design_scores_gemma":[0.00004043975,0.00032729565,0.014954672,0.000026637408,0.000115089686,0.0007168151,0.00010731886,0.84644645,0.12999871,0.0030714415,0.0040944275,0.00010059432],"about_ca_topic_score_codex":0.002068035,"about_ca_topic_score_gemma":0.0029799417,"teacher_disagreement_score":0.002068035,"about_ca_system_score_codex":0.0006079945,"about_ca_system_score_gemma":0.0004211144,"threshold_uncertainty_score":0.006689012},"labels":[],"label_agreement":null},{"id":"W2025568499","doi":"10.1007/s10115-012-0538-1","title":"Efficient greedy feature selection for unsupervised learning","year":2012,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":95,"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 Waterloo","funders":"","keywords":"Feature selection; Dimensionality reduction; Computer science; Artificial intelligence; Greedy algorithm; Machine learning; Pattern recognition (psychology); Curse of dimensionality; Feature (linguistics); Feature learning; Unsupervised learning; Selection (genetic algorithm); Dimension (graph theory); Data mining; Algorithm; Mathematics","score_opus":0.013086839058054958,"score_gpt":0.237677893728842,"score_spread":0.22459105467078702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025568499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008496701,0.00020465472,0.9889966,0.00012168762,0.000030693158,0.000059262165,0.00014756649,0.0015231131,0.00041965878],"genre_scores_gemma":[0.30557588,0.00027470718,0.68501997,0.0003004976,0.00013604044,0.0006072868,0.0023228233,0.00049567095,0.005267175],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985158,0.0004636955,0.00010968347,0.00032503688,0.00036920863,0.00021664763],"domain_scores_gemma":[0.9977502,0.0013555934,0.00011056121,0.00033380033,0.0003651651,0.00008458199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015042552,0.0013780108,0.0025662028,0.0012827188,0.00096095103,0.0011310531,0.0028299855,0.0015080508,0.0037415845],"category_scores_gemma":[0.0049296725,0.000917985,0.0014215107,0.0018801402,0.001006509,0.0016120118,0.0018854521,0.0016341887,0.0018443915],"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":[0.00059214764,0.00030477735,0.0012952622,0.00016427822,0.00017772368,0.00016003654,0.00008150204,0.17676528,0.013483602,0.009667578,0.014639473,0.7826682],"study_design_scores_gemma":[0.000053623477,0.000055407407,0.0004023163,0.0000061302603,0.000025125702,0.000059661328,0.000018742681,0.98372525,0.002868221,0.011907968,0.0008634177,0.000014208739],"about_ca_topic_score_codex":0.007707199,"about_ca_topic_score_gemma":0.011249158,"teacher_disagreement_score":0.007707199,"about_ca_system_score_codex":0.0009909511,"about_ca_system_score_gemma":0.0024960414,"threshold_uncertainty_score":0.015324712},"labels":[],"label_agreement":null},{"id":"W2025741957","doi":"10.1007/s00138-006-0052-0","title":"Face recognition using localized features based on non-negative sparse coding","year":2006,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Neural coding; Non-negative matrix factorization; Subtraction; Computer science; Facial recognition system; Coding (social sciences); Sparse approximation; Metric (unit); Face (sociological concept); Matrix decomposition; Mathematics; Arithmetic","score_opus":0.01693025683881043,"score_gpt":0.2829088620211159,"score_spread":0.2659786051823055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025741957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048987456,0.00022752045,0.9487776,0.00014288984,0.00006132571,0.0000357273,0.0000783269,0.0003053682,0.001383712],"genre_scores_gemma":[0.5812919,0.00045369938,0.41368905,0.00021442908,0.00014074582,0.00012096378,0.00046395656,0.000055401695,0.003569788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998235,0.00003575279,0.0000059905296,0.000027550182,0.00009003229,0.000017217542],"domain_scores_gemma":[0.99952936,0.00020236577,0.00005021555,0.0000680038,0.00012800764,0.00002218412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024925845,0.0003009464,0.0005201463,0.00054087676,0.00020605985,0.00033116053,0.00039961096,0.00039376473,0.0010475168],"category_scores_gemma":[0.0011410639,0.0001941613,0.0003458719,0.0005035743,0.000323558,0.0006416634,0.00038980597,0.00043385083,0.0004318937],"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":[0.00043342717,0.00020386899,0.0017832852,0.00011927143,0.00006214048,0.00015153781,0.00008341988,0.061435554,0.24376154,0.010860603,0.0039669056,0.6771384],"study_design_scores_gemma":[0.00001796084,0.00010523244,0.002361541,0.0000097107,0.000027778846,0.00016356473,0.00002364211,0.9621879,0.029763946,0.0043592127,0.00096363726,0.000015924872],"about_ca_topic_score_codex":0.0011142625,"about_ca_topic_score_gemma":0.0023201462,"teacher_disagreement_score":0.0011142625,"about_ca_system_score_codex":0.00017863663,"about_ca_system_score_gemma":0.00027871065,"threshold_uncertainty_score":0.0035043359},"labels":[],"label_agreement":null},{"id":"W2026748890","doi":"10.1109/est.2013.11","title":"An Efficient Facial Expression Recognition System in Infrared Images","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Technology Futures","keywords":"Artificial intelligence; Computer vision; Pattern recognition (psychology); Feature extraction; Computer science; Wavelet; Filter (signal processing); Feature (linguistics); Infrared; Physics; Optics","score_opus":0.013203277130309419,"score_gpt":0.22912307701314963,"score_spread":0.2159197998828402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026748890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10849457,0.00031869992,0.87281376,0.0001978167,0.0001400625,0.00020792887,0.0005770869,0.008478859,0.008771205],"genre_scores_gemma":[0.38690788,0.00027955178,0.5945668,0.00020194707,0.00006241181,0.00023407511,0.0012692456,0.00017114649,0.016307006],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997187,0.000026118067,0.000011189745,0.00007460218,0.00013491356,0.000034391687],"domain_scores_gemma":[0.9999082,0.000008716492,0.000009212949,0.000016280512,0.000049229893,0.000008353583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039236856,0.00041861567,0.00059776276,0.00044809407,0.00020954777,0.00038235882,0.00071207,0.00036307191,0.0036068696],"category_scores_gemma":[0.0003280972,0.00015838638,0.0002559859,0.00030136362,0.000119351134,0.0005738031,0.00036884448,0.0003143612,0.0024524166],"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":[0.00055391714,0.00016025746,0.0011409505,0.00010562724,0.000037405724,0.00015330773,0.0000614491,0.004912233,0.523578,0.0013408951,0.007271926,0.46068412],"study_design_scores_gemma":[0.00007295852,0.00049076555,0.01080706,0.000037275404,0.00010082429,0.0007268777,0.00007965246,0.4867322,0.48016918,0.0010582212,0.019656172,0.00006877652],"about_ca_topic_score_codex":0.001100209,"about_ca_topic_score_gemma":0.0012182634,"teacher_disagreement_score":0.0036068696,"about_ca_system_score_codex":0.0002552952,"about_ca_system_score_gemma":0.00024410803,"threshold_uncertainty_score":0.012066126},"labels":[],"label_agreement":null},{"id":"W2028166157","doi":"10.5539/mas.v3n11p64","title":"Modular PCA Face Recognition Based on Weighted Average","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"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":"Modular design; Pattern recognition (psychology); Computer science; Principal component analysis; Artificial intelligence; Classifier (UML); Facial recognition system; Block (permutation group theory); Matrix (chemical analysis); Training set; Mathematics; Combinatorics","score_opus":0.0172442601981617,"score_gpt":0.22858708865297803,"score_spread":0.21134282845481633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028166157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008191865,0.00014739331,0.99005616,0.000028777504,0.000044898006,0.00002893657,0.000032654712,0.0005654494,0.0009038489],"genre_scores_gemma":[0.261794,0.0007371016,0.73190457,0.00008844278,0.00021863698,0.0001455388,0.00041399425,0.00026901838,0.004428679],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836606,0.00026661708,0.000052241154,0.000357105,0.0008378913,0.00012007178],"domain_scores_gemma":[0.9991549,0.00020007243,0.0000659521,0.00014416658,0.00039977947,0.000035110108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009497047,0.0010744183,0.0011222883,0.0017740713,0.00040708482,0.0008545597,0.0011111102,0.0004114896,0.0024599535],"category_scores_gemma":[0.0023743077,0.00036366895,0.0011793262,0.0019241314,0.0004936916,0.0017927089,0.0009749115,0.00077057845,0.0011680423],"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":[0.00016662572,0.000062587394,0.0009155181,0.00011074796,0.00014273607,0.000057789173,0.0000840361,0.044020656,0.04950899,0.010368463,0.0030170262,0.8915449],"study_design_scores_gemma":[0.00001709006,0.00013315777,0.0026665584,0.000011267404,0.000082048406,0.00029287854,0.000026830932,0.9545745,0.029958436,0.006404872,0.0057678935,0.00006446118],"about_ca_topic_score_codex":0.0020659184,"about_ca_topic_score_gemma":0.0018089474,"teacher_disagreement_score":0.0024599535,"about_ca_system_score_codex":0.00036681085,"about_ca_system_score_gemma":0.00053965964,"threshold_uncertainty_score":0.008229315},"labels":[],"label_agreement":null},{"id":"W2029175610","doi":"10.1109/est.2013.10","title":"Modified Multiscale Vesselness Filter for Facial Feature Detection","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Artificial intelligence; Filter (signal processing); Computer science; Computer vision; Biometrics; Face (sociological concept); Feature (linguistics); Noise (video); Pattern recognition (psychology); Feature extraction; Image (mathematics)","score_opus":0.016985599529061986,"score_gpt":0.23330843721378294,"score_spread":0.21632283768472096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2029175610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0128918635,0.00030064976,0.98560053,0.00005538923,0.000051520037,0.000025782962,0.000045078046,0.0003809394,0.0006481951],"genre_scores_gemma":[0.13642046,0.0006708028,0.85891765,0.00008769321,0.00011188154,0.000087981876,0.00019125793,0.00013251373,0.0033798364],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963045,0.00004788463,0.00001790844,0.00009537374,0.0001748166,0.00003357567],"domain_scores_gemma":[0.9996063,0.00012258906,0.000035703288,0.00006629803,0.00014613672,0.00002297759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005685743,0.00046649328,0.0005870563,0.0009858814,0.0002518167,0.0004433357,0.00053305255,0.00075241656,0.00200455],"category_scores_gemma":[0.0011814057,0.00023357572,0.00079468085,0.0007038976,0.00030052467,0.0008781604,0.00035201266,0.00060556154,0.0006539399],"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":[0.00024350188,0.00009229219,0.0014386898,0.00019498981,0.0001120075,0.00021325766,0.000109310124,0.020602198,0.35461462,0.009904369,0.0032866616,0.60918814],"study_design_scores_gemma":[0.000033228036,0.0003144268,0.0062971544,0.000027113882,0.0001450259,0.0010555,0.00003211438,0.8124549,0.15431888,0.003764171,0.021471964,0.00008556724],"about_ca_topic_score_codex":0.0019221287,"about_ca_topic_score_gemma":0.002099737,"teacher_disagreement_score":0.00200455,"about_ca_system_score_codex":0.00042636108,"about_ca_system_score_gemma":0.00040257483,"threshold_uncertainty_score":0.00670594},"labels":[],"label_agreement":null},{"id":"W2030723399","doi":"10.1016/j.comgeo.2008.06.004","title":"A linear-space algorithm for distance preserving graph embedding","year":2008,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Carleton University","funders":"National Research Council Canada; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Mitacs; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Mathematics; Scaling; Embedding; Combinatorics; Euclidean distance; Algorithm; Diagonal; Multidimensional scaling; Discrete mathematics; Computer science; Statistics; Geometry","score_opus":0.026321080000358724,"score_gpt":0.28478862768333224,"score_spread":0.25846754768297353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030723399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037408818,0.00015896161,0.9917671,0.00015027265,0.000072549665,0.00007703401,0.000160154,0.0025619627,0.0013110553],"genre_scores_gemma":[0.052335974,0.00018332056,0.9399146,0.00012056477,0.000061029255,0.00019881409,0.0009841697,0.00045864008,0.0057428507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848443,0.00027099508,0.00009619597,0.00045579797,0.0005658692,0.00012680708],"domain_scores_gemma":[0.9985991,0.00042685642,0.00007085038,0.000481495,0.000330586,0.000091116686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077836146,0.0014961881,0.0013753906,0.002078543,0.0009933629,0.0021453276,0.003095334,0.0014769279,0.013247309],"category_scores_gemma":[0.0036376195,0.0007229153,0.0012072915,0.0027499343,0.0012148612,0.0036119972,0.0041758283,0.003172054,0.007079656],"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":[0.00026682965,0.0002358067,0.00032873562,0.00018296902,0.00007277929,0.00007065563,0.00013824234,0.03886687,0.010106601,0.040266965,0.016013017,0.89345056],"study_design_scores_gemma":[0.00014564609,0.0002779597,0.0004943221,0.000035591936,0.00005879608,0.0003195546,0.00018483715,0.8485026,0.018082699,0.11046219,0.02136383,0.00007198263],"about_ca_topic_score_codex":0.0049124914,"about_ca_topic_score_gemma":0.006486004,"teacher_disagreement_score":0.013247309,"about_ca_system_score_codex":0.0010618014,"about_ca_system_score_gemma":0.0013875319,"threshold_uncertainty_score":0.04431665},"labels":[],"label_agreement":null},{"id":"W2030849416","doi":"10.1007/s11042-014-1954-x","title":"Conditional Gabor phase–based disparity estimation applied to facial tracking for person–specific facial action recognition: a preliminary study","year":2014,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Université de Montréal; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Computer vision; Facial expression; Face hallucination; Pattern recognition (psychology); Face (sociological concept); Pose; Pyramid (geometry); Gabor wavelet; Gabor filter; Feature extraction; Facial recognition system; Face detection; Mathematics","score_opus":0.09729836338207747,"score_gpt":0.3325094532577945,"score_spread":0.23521108987571704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030849416","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2869946,0.001024737,0.7077325,0.000116814386,0.00007679454,0.00010527416,0.00020784713,0.0005379855,0.0032033953],"genre_scores_gemma":[0.8319522,0.0008603292,0.16493921,0.000043859192,0.000039145234,0.000028419834,0.00034078085,0.00005966983,0.0017363372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970347,0.00006546087,0.000013242116,0.00005907825,0.000118049444,0.000040647592],"domain_scores_gemma":[0.9990847,0.00040139808,0.000044459517,0.00012575791,0.00031735914,0.000026260677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006907788,0.0003205815,0.00041160613,0.0005227859,0.00019860889,0.000456075,0.00045510023,0.00031936765,0.0021168583],"category_scores_gemma":[0.0018269619,0.00023462028,0.0003270223,0.00070593785,0.00022195559,0.0006926071,0.00040081906,0.00037469488,0.00044310442],"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":[0.0009605537,0.0004093306,0.013731361,0.00019371287,0.0001568829,0.00012034549,0.0001283894,0.030199084,0.33422503,0.0035435709,0.001370908,0.61496085],"study_design_scores_gemma":[0.00002786977,0.00040923568,0.0303262,0.00002384585,0.00013393359,0.0003874739,0.00006658098,0.83702797,0.1279843,0.00087647134,0.002695946,0.000040246377],"about_ca_topic_score_codex":0.0065318034,"about_ca_topic_score_gemma":0.0052745696,"teacher_disagreement_score":0.0065318034,"about_ca_system_score_codex":0.00025111472,"about_ca_system_score_gemma":0.00067731395,"threshold_uncertainty_score":0.012987554},"labels":[],"label_agreement":null},{"id":"W2031265081","doi":"10.1007/s11042-013-1548-z","title":"Face detection and facial expression recognition using simultaneous clustering and feature selection via an expectation propagation statistical learning framework","year":2013,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"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; Yale University","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Feature selection; Cluster analysis; Feature (linguistics); Facial expression; Local binary patterns; Inference; Dirichlet process; Face (sociological concept); Facial recognition system; Machine learning","score_opus":0.0201757162951749,"score_gpt":0.2651599190249089,"score_spread":0.24498420272973398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031265081","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006314416,0.000038908685,0.9932452,0.000044461434,0.0000062734616,0.000013902125,0.000011564882,0.00020021856,0.00012504421],"genre_scores_gemma":[0.2885222,0.0001539949,0.7077079,0.000129902,0.00006703235,0.00015739653,0.00024385688,0.00016272023,0.0028550583],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989741,0.0002755358,0.00004496073,0.00023731614,0.00037153854,0.00009650453],"domain_scores_gemma":[0.99891996,0.00066708337,0.00008833251,0.00007468873,0.00021309515,0.000036768746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017677785,0.0008698484,0.0013780757,0.00073419075,0.0005179257,0.000823159,0.0017882524,0.0010521961,0.0009375156],"category_scores_gemma":[0.0024931296,0.00076375646,0.0013808814,0.0008634857,0.0007458865,0.001313844,0.0011017717,0.001207854,0.00057347753],"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":[0.0004527938,0.00037785035,0.0018405492,0.000105148734,0.000291238,0.0001463763,0.00013162721,0.41576123,0.06548167,0.011274615,0.001708585,0.5024283],"study_design_scores_gemma":[0.0000053934455,0.000022888818,0.00031806828,0.0000012725563,0.000011508551,0.000021737389,0.0000038949916,0.9945439,0.0035159322,0.0014288517,0.0001195601,0.000007050572],"about_ca_topic_score_codex":0.0052106283,"about_ca_topic_score_gemma":0.006838361,"teacher_disagreement_score":0.0052106283,"about_ca_system_score_codex":0.0005846028,"about_ca_system_score_gemma":0.0010349946,"threshold_uncertainty_score":0.010360599},"labels":[],"label_agreement":null},{"id":"W2032339756","doi":"10.1016/j.patcog.2014.11.010","title":"An evaluation of classifier-specific filter measure performance for feature selection","year":2014,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":98,"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 Waterloo","funders":"","keywords":"Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Feature selection; Maximization; Computer science; Mutual information; Machine learning; Random subspace method; Support vector machine; Data mining; Mathematics","score_opus":0.07349366252233368,"score_gpt":0.280831378672566,"score_spread":0.20733771615023233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032339756","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6622799,0.0037449868,0.32356292,0.00020885307,0.0003001301,0.00047502315,0.001133093,0.0044253813,0.003869729],"genre_scores_gemma":[0.8388147,0.00054964627,0.1546382,0.000090356625,0.00008162621,0.00019708957,0.0029353364,0.0002921076,0.0024009987],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965984,0.00084385095,0.00040838297,0.00068536,0.0012190815,0.0002449833],"domain_scores_gemma":[0.98524076,0.008356567,0.0006090973,0.001410227,0.004070182,0.000313153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009181995,0.001292272,0.0014641881,0.0016786515,0.00062360615,0.0014355212,0.0010726961,0.0016532793,0.001791252],"category_scores_gemma":[0.020294964,0.000264017,0.0009103353,0.0013204971,0.0003636634,0.0014068139,0.0008015434,0.0006394354,0.0007038869],"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":[0.00791318,0.0013200033,0.03629889,0.0007221996,0.001883075,0.00018987627,0.00017557554,0.09135521,0.076925494,0.0013448915,0.005019027,0.77685267],"study_design_scores_gemma":[0.00030406954,0.004619147,0.04822067,0.00004555516,0.0007635383,0.0007556445,0.00013511637,0.8639701,0.078702174,0.0006907525,0.0016771695,0.00011604253],"about_ca_topic_score_codex":0.003980158,"about_ca_topic_score_gemma":0.0043542357,"teacher_disagreement_score":0.009181995,"about_ca_system_score_codex":0.00070519786,"about_ca_system_score_gemma":0.0007958387,"threshold_uncertainty_score":0.048559606},"labels":[],"label_agreement":null},{"id":"W2032722848","doi":"10.5430/air.v3n3p1","title":"A hierarchical target recognition method based on image processing","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","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":"Zhengzhou University of Light Industry; China Postdoctoral Science Foundation","keywords":"Wavelet packet decomposition; Computer science; Artificial intelligence; Pattern recognition (psychology); Wavelet; Feature extraction; Feature (linguistics); Fuzzy logic; Transformation (genetics); Wavelet transform; Image processing; Process (computing); Stationary wavelet transform; Signal processing; Matching (statistics); Computer vision; Image (mathematics); Mathematics; Digital signal processing","score_opus":0.1686362379318915,"score_gpt":0.4320528284783743,"score_spread":0.2634165905464828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2032722848","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037144762,0.00022553395,0.9929929,0.000052509098,0.00006415818,0.000052065796,0.000029043336,0.00095382176,0.001915657],"genre_scores_gemma":[0.14628364,0.0005714036,0.8449854,0.00020078706,0.00010260757,0.00016541104,0.00023946875,0.00014631063,0.0073048933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991798,0.000060206512,0.000039375795,0.00021875223,0.0004439585,0.00005786581],"domain_scores_gemma":[0.99968576,0.000057837726,0.000030286985,0.000056780842,0.00015341914,0.000015948921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044436238,0.00062309636,0.00056542223,0.0013458476,0.00041683074,0.000685312,0.0010034092,0.00067149155,0.003040735],"category_scores_gemma":[0.000915694,0.00031367238,0.000795482,0.00096436375,0.00043194517,0.0014324016,0.0007208823,0.00075361313,0.0015184771],"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":[0.000094724746,0.00006799883,0.0006863009,0.0002359297,0.000047835074,0.00009928271,0.00015261178,0.016748067,0.116729446,0.009161914,0.0040020207,0.8519739],"study_design_scores_gemma":[0.000051527626,0.00029238386,0.0033494832,0.0000485023,0.0001196247,0.0008981398,0.000111073496,0.82003486,0.13867392,0.008576926,0.027716389,0.00012721092],"about_ca_topic_score_codex":0.0029567825,"about_ca_topic_score_gemma":0.0022590603,"teacher_disagreement_score":0.003040735,"about_ca_system_score_codex":0.00045792686,"about_ca_system_score_gemma":0.00065095705,"threshold_uncertainty_score":0.010172248},"labels":[],"label_agreement":null},{"id":"W2034910714","doi":"10.1117/12.481377","title":"Segmentation of multiple sclerosis lesions using support vector machines","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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 Calgary","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Multiple sclerosis; Vector (molecular biology); Computer vision; Medicine; Biology","score_opus":0.029065665411817536,"score_gpt":0.24633255947359142,"score_spread":0.21726689406177388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034910714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17205825,0.0016989193,0.8220184,0.00018022119,0.0000953807,0.00010873068,0.0001717647,0.0026339386,0.001034384],"genre_scores_gemma":[0.56694883,0.00057664793,0.43008527,0.00006133417,0.000061600884,0.00007951611,0.00054667436,0.00014306868,0.0014969902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920744,0.00021429891,0.000080515216,0.00014985092,0.00027117395,0.00007665039],"domain_scores_gemma":[0.9985857,0.000730276,0.00014315841,0.0000955443,0.00040286197,0.000042452062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013023645,0.00078057795,0.00078620814,0.0019464403,0.0003206362,0.000818805,0.0005177623,0.00094178383,0.0010643034],"category_scores_gemma":[0.0028684894,0.0002544721,0.0005754538,0.0008029481,0.00028139484,0.00082413846,0.0002988099,0.00037878304,0.0007981601],"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":[0.0005557546,0.00014858459,0.0039768303,0.0002438092,0.00013438755,0.00021310568,0.00016576661,0.09198964,0.1121403,0.0008680126,0.0017294947,0.78783435],"study_design_scores_gemma":[0.000023445666,0.00019643945,0.005106019,0.000036042315,0.0000400695,0.00026681315,0.000078963814,0.9302101,0.059495803,0.0015704054,0.002940942,0.00003491379],"about_ca_topic_score_codex":0.0013960187,"about_ca_topic_score_gemma":0.0014900714,"teacher_disagreement_score":0.0019464403,"about_ca_system_score_codex":0.00033569045,"about_ca_system_score_gemma":0.00030585704,"threshold_uncertainty_score":0.0068876147},"labels":[],"label_agreement":null},{"id":"W2035669857","doi":"10.1117/12.436980","title":"&lt;title&gt;Facial expression recognition using constructive neural networks&lt;/title&gt;","year":2001,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Instituto de Telecomunicações","keywords":"Constructive; Pruning; Computer science; Artificial neural network; Artificial intelligence; Face (sociological concept); Discrete cosine transform; Feature (linguistics); Constructive proof; Process (computing); Pattern recognition (psychology); Speech recognition; Facial expression; Expression (computer science); Facial recognition system; Feature extraction; Image (mathematics); Computer vision; Mathematics","score_opus":0.01741915977759414,"score_gpt":0.23300341379558953,"score_spread":0.21558425401799539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035669857","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021257266,0.03384872,0.26723564,0.012061918,0.05895327,0.0011650127,0.0039202212,0.0178389,0.583719],"genre_scores_gemma":[0.094860226,0.02027101,0.06293309,0.0029318912,0.004931637,0.00052418385,0.007902051,0.0024083592,0.8032376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996495,0.00003463893,0.000020179099,0.00009622596,0.00015982076,0.0000396359],"domain_scores_gemma":[0.9994599,0.000106016574,0.0000369175,0.00006634664,0.0002857884,0.00004496076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035376629,0.0010131868,0.0010782378,0.00064372673,0.0003905534,0.001678483,0.0015044535,0.0009799639,0.12971316],"category_scores_gemma":[0.0009948764,0.0002199144,0.00054712116,0.0009438737,0.0004690248,0.0018099921,0.00070509274,0.0013305594,0.080756456],"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":[0.0003156062,0.0001349129,0.00027159517,0.0010002455,0.000045325294,0.0002900136,0.000031454267,0.00483035,0.027948268,0.009177664,0.38248006,0.5734745],"study_design_scores_gemma":[0.00006423161,0.0003781187,0.0015864574,0.00033829722,0.00007400039,0.00040710092,0.00004998805,0.108256295,0.033694573,0.007878179,0.84718174,0.000090987916],"about_ca_topic_score_codex":0.0027935635,"about_ca_topic_score_gemma":0.0028320071,"teacher_disagreement_score":0.12971316,"about_ca_system_score_codex":0.000717594,"about_ca_system_score_gemma":0.0004904725,"threshold_uncertainty_score":0.43393373},"labels":[],"label_agreement":null},{"id":"W2036024554","doi":"10.1109/icmew.2014.6890712","title":"A semi-supervised temporal clustering method for facial emotion analysis","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Computer science; Cluster analysis; Categorization; Artificial intelligence; Kernel (algebra); Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.02566355636187622,"score_gpt":0.2933704078626007,"score_spread":0.2677068515007245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036024554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034041302,0.0000857803,0.9950824,0.000041658284,0.00003624761,0.000054977194,0.000075004005,0.00058405416,0.0006358248],"genre_scores_gemma":[0.09786752,0.00015637573,0.8971413,0.00008541905,0.000087403685,0.0003052884,0.00086247193,0.00042383623,0.0030705496],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874085,0.0003027188,0.00007365994,0.00041166792,0.0003911343,0.00007996984],"domain_scores_gemma":[0.99864167,0.0002920027,0.00010661307,0.00023038109,0.0006691084,0.000060143335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010866164,0.00085807196,0.0007975677,0.0015563923,0.00084575143,0.0008012983,0.001661962,0.0008744281,0.0035558867],"category_scores_gemma":[0.0026119445,0.0003719768,0.001374974,0.0012927337,0.0006165043,0.0011008689,0.0008803168,0.0012983938,0.0026985905],"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":[0.0003787188,0.00023144744,0.0013378924,0.00024560175,0.00024360916,0.00009293879,0.00028865997,0.08573648,0.06706799,0.013554335,0.011144612,0.81967777],"study_design_scores_gemma":[0.000013549142,0.00005950251,0.0010618705,0.000016818864,0.00003504664,0.00015582767,0.00007664773,0.97534025,0.012817291,0.00584924,0.0045310706,0.00004280111],"about_ca_topic_score_codex":0.0035434933,"about_ca_topic_score_gemma":0.0050259475,"teacher_disagreement_score":0.0035558867,"about_ca_system_score_codex":0.00060058385,"about_ca_system_score_gemma":0.0010905878,"threshold_uncertainty_score":0.011895657},"labels":[],"label_agreement":null},{"id":"W2037865861","doi":"10.1145/2254129.2254157","title":"An efficient ensemble classification method based on novel classifier selection technique","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Dalhousie University","funders":"University of California, Irvine","keywords":"Classifier (UML); Computer science; Artificial intelligence; Machine learning; Binary classification; Random subspace method; Divide and conquer algorithms; Cascading classifiers; Pattern recognition (psychology); Ensemble learning; Statistical classification; Multiclass classification; Computation; Linear classifier; Data mining; Support vector machine; Algorithm","score_opus":0.04731881566908378,"score_gpt":0.3234176239550948,"score_spread":0.276098808286011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037865861","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00728547,0.00045174218,0.9900575,0.00009296897,0.00014850903,0.000068497306,0.000057739562,0.00078456977,0.001053044],"genre_scores_gemma":[0.2131122,0.00070821884,0.78005904,0.00021079254,0.00045341544,0.00034718268,0.0006033979,0.0001675892,0.0043381555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807954,0.00038926216,0.0000966022,0.0003715304,0.0009243119,0.0001386895],"domain_scores_gemma":[0.99852306,0.00041927793,0.00011197834,0.0001972482,0.00068992894,0.000058463585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001634706,0.0010612695,0.002299945,0.0026699812,0.0009102039,0.00077313033,0.0014840722,0.0011560401,0.0019286696],"category_scores_gemma":[0.0026178453,0.00041044867,0.0012998737,0.002510287,0.00030212855,0.0016069586,0.00092460884,0.0012256205,0.001172216],"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":[0.00010587273,0.00013468703,0.0017235482,0.00008527802,0.00019410047,0.00015965282,0.00009286852,0.042725693,0.017893337,0.0044415235,0.008595384,0.92384815],"study_design_scores_gemma":[0.000027366059,0.000117571566,0.0013218056,0.000016179054,0.00009992643,0.00041376823,0.000028536231,0.97845787,0.008126461,0.004279131,0.0070701484,0.000041178322],"about_ca_topic_score_codex":0.001775454,"about_ca_topic_score_gemma":0.0024279696,"teacher_disagreement_score":0.0026699812,"about_ca_system_score_codex":0.00036138913,"about_ca_system_score_gemma":0.00071562687,"threshold_uncertainty_score":0.0086452365},"labels":[],"label_agreement":null},{"id":"W2038615544","doi":"10.1109/sitis.2012.67","title":"Multinomial Bayesian Kernel Logistic Discriminant Based Method for Skin Detection","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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 Sherbrooke","funders":"","keywords":"Kernel (algebra); Artificial intelligence; Pattern recognition (psychology); Linear discriminant analysis; Bayesian probability; Computer science; Pixel; Multinomial logistic regression; Distortion (music); Logistic regression; Discriminant; Machine learning; Mathematics; Bandwidth (computing)","score_opus":0.04523327531554583,"score_gpt":0.32104741666749076,"score_spread":0.27581414135194493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038615544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010729338,0.00033841818,0.98718345,0.000099900375,0.00004090487,0.000039624472,0.00008050956,0.0007398884,0.00074789],"genre_scores_gemma":[0.33085862,0.00048111548,0.65774065,0.00015623773,0.00011794724,0.00020099332,0.00055137207,0.0002531762,0.0096399095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865854,0.00055073656,0.00005730111,0.00021920432,0.00042488723,0.000089393485],"domain_scores_gemma":[0.9990716,0.00032022037,0.00008801156,0.000112569265,0.00036141806,0.000046113993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001691288,0.0006413097,0.0010728135,0.0013661679,0.00047692237,0.00059356954,0.0014343495,0.00077221065,0.0034547793],"category_scores_gemma":[0.0029479715,0.0003368683,0.0007131589,0.0009206632,0.0003572154,0.0008767583,0.00087843987,0.0011650376,0.0025902414],"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":[0.00048288426,0.00025328013,0.004158967,0.00020713329,0.00013688089,0.0001676564,0.000109494584,0.063901044,0.025106587,0.009578308,0.006315534,0.8895823],"study_design_scores_gemma":[0.000016473055,0.000037205187,0.0013219513,0.000011611307,0.000020351445,0.00016678947,0.000016344364,0.9888475,0.005122754,0.00221168,0.0021987078,0.000028566825],"about_ca_topic_score_codex":0.0028250243,"about_ca_topic_score_gemma":0.0034246093,"teacher_disagreement_score":0.0034547793,"about_ca_system_score_codex":0.00051386293,"about_ca_system_score_gemma":0.00078745204,"threshold_uncertainty_score":0.0115574},"labels":[],"label_agreement":null},{"id":"W2041733204","doi":"10.1142/s0218001403002265","title":"AN EFFICIENT HUMAN FACE RECOGNITION SYSTEM USING PSEUDO ZERNIKE MOMENT INVARIANT AND RADIAL BASIS FUNCTION NEURAL NETWORK","year":2003,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zernike polynomials; Artificial intelligence; Pattern recognition (psychology); Computer science; Facial recognition system; Radial basis function; Feature extraction; Invariant (physics); Artificial neural network; Face (sociological concept); Classifier (UML); Radial basis function network; Computer vision; Mathematics; Physics","score_opus":0.08656028909214335,"score_gpt":0.2993554513160117,"score_spread":0.21279516222386835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041733204","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027003946,0.00041293819,0.96815133,0.00010448681,0.00008327452,0.0001050862,0.000069899164,0.0022988087,0.0017701522],"genre_scores_gemma":[0.23883688,0.00036992604,0.7531685,0.00016160672,0.00006372987,0.00020978463,0.0002779243,0.00006476336,0.0068468708],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996308,0.00004156388,0.000013533174,0.000086821754,0.00019889396,0.000028304261],"domain_scores_gemma":[0.99982435,0.000031540952,0.000016584638,0.000025406762,0.00009043009,0.000011657995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054887147,0.00037755189,0.0005887047,0.00042816292,0.00025594543,0.00035304358,0.0007707132,0.00063164363,0.0019321414],"category_scores_gemma":[0.00056093314,0.00022159274,0.00032541502,0.00027057124,0.00016941802,0.0008891934,0.00040623333,0.0004095442,0.0011846042],"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":[0.00035254878,0.0001533424,0.0009469706,0.00017422132,0.00006631844,0.00011561322,0.000056464534,0.011470826,0.23016953,0.0026779221,0.0035928458,0.7502234],"study_design_scores_gemma":[0.00009370901,0.0006095032,0.0065977713,0.000031642223,0.00010565579,0.0011061524,0.000042497355,0.80037165,0.17275895,0.0020149,0.016176095,0.00009141104],"about_ca_topic_score_codex":0.0014611294,"about_ca_topic_score_gemma":0.0021779,"teacher_disagreement_score":0.0019321414,"about_ca_system_score_codex":0.00029388093,"about_ca_system_score_gemma":0.00036833461,"threshold_uncertainty_score":0.006463647},"labels":[],"label_agreement":null},{"id":"W2042230250","doi":"10.1016/j.neunet.2015.03.013","title":"Incremental learning for <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si17.gif\" display=\"inline\" overflow=\"scroll\"><mml:mi>ν</mml:mi></mml:math>-Support Vector Regression","year":2015,"lang":"en","type":"article","venue":"Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":426,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St Joseph's Health Care; Victoria Hospital; Western University","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Scroll; Support vector machine; Computer science; Algorithm; Artificial intelligence; Mathematics; Philosophy; Theology","score_opus":0.025152273719453357,"score_gpt":0.2577014007664726,"score_spread":0.23254912704701922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042230250","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049794954,0.00043129668,0.92844087,0.0010618274,0.0006310182,0.0002070471,0.0067422898,0.032679953,0.024826093],"genre_scores_gemma":[0.11217181,0.00047451022,0.7952944,0.00045073527,0.00027639372,0.0007222164,0.023554524,0.004923531,0.062131837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999456,0.000083401166,0.000035205765,0.00011670044,0.00024886313,0.00005989239],"domain_scores_gemma":[0.99874336,0.0004910719,0.000040869443,0.00028533605,0.00037702848,0.00006238482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009000547,0.00072026503,0.00039035815,0.00082929357,0.000370941,0.0011846675,0.0022522232,0.00094087876,0.112108104],"category_scores_gemma":[0.008373505,0.00045150428,0.00060916826,0.0009131007,0.00024284796,0.002019306,0.001152201,0.0016902124,0.028881604],"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":[0.00027608668,0.00021665642,0.00077375205,0.00035393005,0.000050992738,0.00011611756,0.00007779439,0.029003814,0.0041157845,0.053325236,0.27040693,0.641283],"study_design_scores_gemma":[0.00012945259,0.00008522212,0.001095708,0.00008279626,0.000038624836,0.00015303715,0.000052392035,0.7543591,0.019179963,0.07281954,0.15196179,0.00004228823],"about_ca_topic_score_codex":0.008494178,"about_ca_topic_score_gemma":0.019082358,"teacher_disagreement_score":0.112108104,"about_ca_system_score_codex":0.00094195467,"about_ca_system_score_gemma":0.0016008903,"threshold_uncertainty_score":0.37503898},"labels":[],"label_agreement":null},{"id":"W2043746809","doi":"10.1016/j.imavis.2006.02.010","title":"Facial pose from 3D data","year":2006,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Albert-Ludwigs-Universität Freiburg","keywords":"Artificial intelligence; Invariant (physics); Facial expression; Computer vision; Computer science; Pattern recognition (psychology); Pose; Face (sociological concept); Active appearance model; Wavelet; Identity (music); Facial recognition system; Mathematics; Image (mathematics)","score_opus":0.02073771474467124,"score_gpt":0.3077299016746751,"score_spread":0.2869921869300039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043746809","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07833901,0.00091317296,0.8881712,0.00042679234,0.00070165197,0.00035893737,0.014093231,0.008149755,0.008846253],"genre_scores_gemma":[0.63647026,0.0025682633,0.31682447,0.00034079215,0.00030930265,0.0005435082,0.025802368,0.0009239393,0.01621703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995215,0.00004297629,0.000016601689,0.00014945025,0.00021494356,0.000054522276],"domain_scores_gemma":[0.999694,0.000034527937,0.000030402412,0.00011539559,0.00010240628,0.000023376902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032073923,0.0011620478,0.00089353503,0.0016909955,0.00029503842,0.0012056086,0.000557266,0.00074143877,0.009897185],"category_scores_gemma":[0.0013333382,0.000567647,0.0010569652,0.0015861968,0.0004233495,0.00081225584,0.001417192,0.0009274751,0.011508502],"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":[0.0005420579,0.00021403105,0.01089212,0.000310609,0.00017219214,0.00047293297,0.00019568652,0.030120311,0.15864272,0.0027438153,0.020241655,0.7754518],"study_design_scores_gemma":[0.0000676874,0.00060202443,0.07159411,0.00020383374,0.00019600683,0.0030940468,0.00069731,0.7131048,0.1499202,0.01573024,0.044623163,0.00016674736],"about_ca_topic_score_codex":0.0030235443,"about_ca_topic_score_gemma":0.0050605386,"teacher_disagreement_score":0.009897185,"about_ca_system_score_codex":0.00033374934,"about_ca_system_score_gemma":0.000590651,"threshold_uncertainty_score":0.033109367},"labels":[],"label_agreement":null},{"id":"W2044026937","doi":"10.1109/icip.2006.313053","title":"Palmprint Classification using Dual-Tree Complex Wavelets","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":34,"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":"Complex wavelet transform; Pattern recognition (psychology); Wavelet; Artificial intelligence; Discrete wavelet transform; Computer science; Wavelet transform; Stationary wavelet transform; Support vector machine; Invariant (physics); Mathematics; Wavelet packet decomposition","score_opus":0.07628197738163145,"score_gpt":0.27864240868764045,"score_spread":0.202360431306009,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044026937","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023198197,0.00031058726,0.97491586,0.00012564151,0.00009344563,0.000030819774,0.00006502072,0.0004408992,0.0008196146],"genre_scores_gemma":[0.34495178,0.0008006363,0.6504143,0.0001384823,0.00015202023,0.00006750807,0.00032206174,0.00011749097,0.003035765],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993843,0.00008584932,0.00003963351,0.00010858727,0.00031808435,0.00006351963],"domain_scores_gemma":[0.9990759,0.00022140259,0.00010889563,0.00015501666,0.0003754147,0.000063425585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006424433,0.0004682138,0.0009342494,0.001987443,0.00028824786,0.0011269281,0.0006602464,0.0006987579,0.0014246703],"category_scores_gemma":[0.0021293478,0.00025992387,0.0006617882,0.0014229321,0.00040251343,0.0015254057,0.00059107505,0.00086966564,0.0011063167],"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":[0.0003399847,0.00013436958,0.0024851512,0.00014840004,0.00007856282,0.00020182575,0.00007478985,0.022851145,0.08146827,0.0076082256,0.003598712,0.88101065],"study_design_scores_gemma":[0.000034447563,0.0001225692,0.0036575503,0.000018440633,0.00006290589,0.00070597976,0.000046512618,0.94691765,0.035311118,0.0056288675,0.0074332873,0.000060733702],"about_ca_topic_score_codex":0.0005965778,"about_ca_topic_score_gemma":0.00046197607,"teacher_disagreement_score":0.001987443,"about_ca_system_score_codex":0.00028575514,"about_ca_system_score_gemma":0.00026186905,"threshold_uncertainty_score":0.0047659874},"labels":[],"label_agreement":null},{"id":"W2044378538","doi":"10.1109/icassp.2013.6638087","title":"Face detection in mobile phones using Co-occurrence of adjacent Local Binary Patterns","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Local binary patterns; Histogram; Feature extraction; Discriminative model; Pattern recognition (psychology); Computer science; Artificial intelligence; Face detection; Face (sociological concept); Binary number; Feature (linguistics); Computation; Detector; Facial recognition system; Computer vision; Mathematics; Algorithm; Image (mathematics); Telecommunications","score_opus":0.022189035197740653,"score_gpt":0.2677356250410242,"score_spread":0.24554658984328356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044378538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2437855,0.0014107836,0.74847,0.00014015834,0.00012242631,0.00013636064,0.00018244007,0.0010836244,0.0046686134],"genre_scores_gemma":[0.7492584,0.00078502763,0.24705514,0.000110726854,0.000074187075,0.00008231708,0.00021794566,0.000068003916,0.0023482137],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995759,0.00005260436,0.000018130477,0.000084285326,0.00023147154,0.00003753295],"domain_scores_gemma":[0.9993886,0.00025520223,0.000087757246,0.00006016077,0.00017565524,0.000032625165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028586984,0.00032758148,0.00050654565,0.0015112278,0.00018061895,0.00040053204,0.00052128435,0.00039577452,0.0014555758],"category_scores_gemma":[0.0013125363,0.0002035892,0.0003442442,0.0008320688,0.00021773783,0.00074746646,0.00044824637,0.00029928487,0.000846135],"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":[0.00030342684,0.00013343315,0.007805618,0.00032426315,0.00009829178,0.0004257926,0.000104385974,0.0044034584,0.26635483,0.0008702768,0.0011079228,0.7180683],"study_design_scores_gemma":[0.000039703893,0.0007989752,0.071900494,0.00007198056,0.00022461226,0.0050902693,0.0002745168,0.51610255,0.39585635,0.0023113338,0.0072259842,0.00010325667],"about_ca_topic_score_codex":0.000551252,"about_ca_topic_score_gemma":0.0011853002,"teacher_disagreement_score":0.0015112278,"about_ca_system_score_codex":0.00012752756,"about_ca_system_score_gemma":0.00014547518,"threshold_uncertainty_score":0.0048694015},"labels":[],"label_agreement":null},{"id":"W2045797804","doi":"10.1504/ijbm.2014.067141","title":"Face recognition using multiple content-based image features for biometric security applications","year":2014,"lang":"en","type":"article","venue":"International Journal of Biometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biometrics; Content-based image retrieval; Facial recognition system; Artificial intelligence; Feature (linguistics); Computation; Face (sociological concept); Pattern recognition (psychology); Field (mathematics); Image retrieval; Computer vision; Feature extraction; Image (mathematics)","score_opus":0.06625305567337537,"score_gpt":0.3154309521477541,"score_spread":0.2491778964743787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045797804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23398167,0.00249989,0.7576567,0.00026272194,0.00016173192,0.00014169366,0.00023515076,0.001263444,0.0037970343],"genre_scores_gemma":[0.7030805,0.0009154199,0.2930403,0.000103657636,0.00009050333,0.00007625346,0.00024172952,0.000046552923,0.0024051263],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996735,0.00004706867,0.000014628062,0.000058651076,0.00017301014,0.000033142947],"domain_scores_gemma":[0.9997359,0.000073146686,0.000028143684,0.000043359476,0.000107902124,0.0000115518305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034999606,0.0003167976,0.00059787504,0.0013341939,0.0002461528,0.00046597797,0.00047567132,0.0005419504,0.0019582822],"category_scores_gemma":[0.00071500655,0.00015986452,0.00051181647,0.00077395147,0.0002061937,0.0007521406,0.00035091917,0.00029881444,0.0010102985],"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":[0.00034106788,0.00014360377,0.0020460433,0.00017612414,0.000066010725,0.00015416194,0.000053955668,0.004305113,0.3666354,0.0010235843,0.0011747282,0.62388015],"study_design_scores_gemma":[0.000048125734,0.000736329,0.026921097,0.00007554061,0.00032985862,0.002950596,0.00014941658,0.5355422,0.42211992,0.002757999,0.008243235,0.00012569976],"about_ca_topic_score_codex":0.000796558,"about_ca_topic_score_gemma":0.0012561854,"teacher_disagreement_score":0.0019582822,"about_ca_system_score_codex":0.00027153155,"about_ca_system_score_gemma":0.00024365669,"threshold_uncertainty_score":0.006551087},"labels":[],"label_agreement":null},{"id":"W2046459100","doi":"10.1016/j.eswa.2012.01.148","title":"Integrated classifier hyperplane placement and feature selection","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","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":"Carleton University","funders":"","keywords":"Hyperplane; Feature selection; Computer science; Process (computing); Classifier (UML); Linear programming; Mathematics; Mathematical optimization; Algorithm; Pattern recognition (psychology); Artificial intelligence; Combinatorics","score_opus":0.015432903261025154,"score_gpt":0.24420810583319041,"score_spread":0.22877520257216527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046459100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007390769,0.00014705428,0.98877037,0.000058286834,0.000105734536,0.00008429007,0.00009264131,0.002383959,0.0009669493],"genre_scores_gemma":[0.19991624,0.0001622468,0.7875242,0.00008471329,0.00013526177,0.00026907658,0.0010538095,0.00049616233,0.010358213],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813855,0.00025138896,0.0001130627,0.00046116972,0.00080325635,0.00023259835],"domain_scores_gemma":[0.9984388,0.00019871286,0.000077569166,0.00028687608,0.0009358715,0.00006221307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001531388,0.0013453997,0.002225977,0.0015966773,0.00088383676,0.0023539392,0.0021664598,0.0014528104,0.010407396],"category_scores_gemma":[0.0035090416,0.0009067363,0.0012375831,0.0017001291,0.00035989715,0.0015490693,0.0015241033,0.001453541,0.006063755],"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":[0.00046268996,0.00025183885,0.0011857668,0.0000859523,0.00013474487,0.000062430445,0.00005241007,0.034563493,0.02426228,0.0024434272,0.008266075,0.9282288],"study_design_scores_gemma":[0.000071955816,0.000253385,0.003200749,0.00002089069,0.000100318925,0.00019152038,0.00006155147,0.93540007,0.0488617,0.0039975457,0.0077987695,0.0000414199],"about_ca_topic_score_codex":0.0038868317,"about_ca_topic_score_gemma":0.0053173,"teacher_disagreement_score":0.010407396,"about_ca_system_score_codex":0.00075232086,"about_ca_system_score_gemma":0.0016780755,"threshold_uncertainty_score":0.034816206},"labels":[],"label_agreement":null},{"id":"W2048741410","doi":"10.1109/tsmcb.2012.2237394","title":"Feature-Selected Tree-Based Classification","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":59,"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":"Multiclass classification; Artificial intelligence; Support vector machine; Classifier (UML); Pattern recognition (psychology); Feature selection; Computer science; Linear classifier; Structured support vector machine; Binary classification; Machine learning; Data mining","score_opus":0.018919123538247106,"score_gpt":0.2293816702992891,"score_spread":0.210462546761042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048741410","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026870647,0.0007940874,0.9656555,0.00015293209,0.00010068191,0.00033563515,0.0004489312,0.0025370782,0.0031044006],"genre_scores_gemma":[0.35449952,0.00086731033,0.6368498,0.000178659,0.00017451837,0.000547416,0.0024460917,0.0002722723,0.004164404],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904114,0.00020995358,0.00007515848,0.0002035553,0.00038265137,0.000087529515],"domain_scores_gemma":[0.99822456,0.0007925402,0.00015142342,0.00018925057,0.00059482874,0.00004737156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015111681,0.0008183206,0.0011169986,0.002493151,0.0005010627,0.000736451,0.0011216331,0.0007791021,0.0041309344],"category_scores_gemma":[0.003705066,0.00023374904,0.0009367986,0.0021978677,0.0003247159,0.001311315,0.00053952745,0.000663475,0.0017052385],"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":[0.00013860836,0.00014618588,0.0030181522,0.00019809039,0.00011874089,0.00008230527,0.00008214539,0.047664553,0.011455335,0.004226241,0.008974824,0.92389476],"study_design_scores_gemma":[0.000048013484,0.00021187917,0.003465821,0.00005896963,0.000096472206,0.00023024283,0.000047901936,0.96736354,0.0109760575,0.0081089195,0.009355861,0.00003642533],"about_ca_topic_score_codex":0.0030485103,"about_ca_topic_score_gemma":0.004025989,"teacher_disagreement_score":0.0041309344,"about_ca_system_score_codex":0.00058546686,"about_ca_system_score_gemma":0.00074582535,"threshold_uncertainty_score":0.013819337},"labels":[],"label_agreement":null},{"id":"W2049017883","doi":"10.1007/s00357-014-9161-z","title":"Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?","year":2014,"lang":"en","type":"article","venue":"Journal of Classification","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3773,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Hierarchical clustering; Cluster analysis; Software; Computer science; Data mining; Algorithm; Single-linkage clustering; Mathematics; Artificial intelligence; Canopy clustering algorithm; Correlation clustering","score_opus":0.039587181575319444,"score_gpt":0.3420034486997508,"score_spread":0.30241626712443137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049017883","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009740884,0.0022046058,0.98115516,0.002530707,0.00039726697,0.00021499954,0.00037493368,0.0007867717,0.002594568],"genre_scores_gemma":[0.09204724,0.0027267025,0.897986,0.00057262974,0.0002672454,0.0004406262,0.0008617684,0.0008033323,0.0042944616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99127126,0.0039683487,0.0008561944,0.0008811488,0.0026285516,0.00039447792],"domain_scores_gemma":[0.98924595,0.0031174836,0.00080555945,0.0011334708,0.005323748,0.00037383303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013757154,0.0013043148,0.003279242,0.003026616,0.0021692407,0.004241122,0.0044386764,0.0031526363,0.003743544],"category_scores_gemma":[0.038770143,0.0011804871,0.0016772154,0.006054485,0.0020147248,0.004531956,0.0019527661,0.0019248815,0.00361695],"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":[0.00037334423,0.000115227165,0.006070847,0.0010447317,0.00086746993,0.000117888405,0.0007458263,0.02841209,0.0025944083,0.0901348,0.06349298,0.8060304],"study_design_scores_gemma":[0.00019532732,0.0002504356,0.0109982705,0.0006314501,0.00045549337,0.0007943392,0.0013566493,0.66690046,0.0079536075,0.2422206,0.067694806,0.0005485734],"about_ca_topic_score_codex":0.013496442,"about_ca_topic_score_gemma":0.017022945,"teacher_disagreement_score":0.013757154,"about_ca_system_score_codex":0.0024424258,"about_ca_system_score_gemma":0.0045859097,"threshold_uncertainty_score":0.072755635},"labels":[],"label_agreement":null},{"id":"W2050089427","doi":"10.1142/s0218001403002423","title":"A FAST SVM TRAINING ALGORITHM","year":2003,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"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; Chinese Academy of Sciences; Royal Society of Canada","keywords":"MNIST database; Support vector machine; Computer science; Kernel (algebra); Scalability; Artificial intelligence; Algorithm; Machine learning; Generalization; Radial basis function kernel; Test set; Key (lock); Pattern recognition (psychology); Principal component analysis; Kernel method; Deep learning; Mathematics; Database","score_opus":0.10729732080374058,"score_gpt":0.3113037660715855,"score_spread":0.20400644526784492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050089427","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022752227,0.00013346992,0.99469393,0.00008627473,0.00007953611,0.000058984035,0.00009148983,0.001786645,0.0007943712],"genre_scores_gemma":[0.06636321,0.00023004152,0.9259079,0.00015929164,0.00014004341,0.0003539343,0.0010600961,0.00026588654,0.00551956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917245,0.0001424399,0.00006984425,0.0002001546,0.0003071378,0.00010805956],"domain_scores_gemma":[0.9989698,0.00023408036,0.000059117152,0.00014330608,0.00054596766,0.000047693567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010004725,0.0008914053,0.0012700114,0.0011062382,0.00065809407,0.0011520952,0.0015240606,0.0014578972,0.008137107],"category_scores_gemma":[0.0028289373,0.0006164132,0.0008664461,0.0010880848,0.00030793267,0.0013286188,0.0013363061,0.0017622085,0.0069170617],"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":[0.0001422485,0.00007489038,0.0005846845,0.00009753975,0.000052725536,0.000076056094,0.00003848062,0.08017546,0.010527404,0.007965705,0.012949218,0.8873156],"study_design_scores_gemma":[0.00003378155,0.000045666784,0.00022845222,0.000011876638,0.000010980712,0.000085854095,0.000011980306,0.98275775,0.0040299417,0.004896485,0.007875666,0.00001159394],"about_ca_topic_score_codex":0.002208059,"about_ca_topic_score_gemma":0.0019194137,"teacher_disagreement_score":0.008137107,"about_ca_system_score_codex":0.00048815485,"about_ca_system_score_gemma":0.0014949534,"threshold_uncertainty_score":0.027221322},"labels":[],"label_agreement":null},{"id":"W2050605581","doi":"10.1198/1061860031220","title":"Feature Extraction for Nonparametric Discriminant Analysis","year":2003,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Optimal discriminant analysis; Kernel Fisher discriminant analysis; Multiple discriminant analysis; Mathematics; Artificial intelligence; Discriminant; Dimensionality reduction; Nonparametric statistics; Curse of dimensionality; Projection pursuit; Parametric statistics; Gaussian; Statistics; Computer science; Facial recognition system","score_opus":0.014343301322935882,"score_gpt":0.28878006122655664,"score_spread":0.27443675990362076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050605581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016635077,0.00015098235,0.996968,0.00006569619,0.000024587534,0.000025402573,0.00009655776,0.00038636217,0.0006190225],"genre_scores_gemma":[0.09899823,0.00048105736,0.89646226,0.00010846281,0.00013219821,0.00037214573,0.00087330706,0.00021543907,0.0023568354],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902856,0.0003236139,0.000054594173,0.00018990635,0.0003348281,0.00006863963],"domain_scores_gemma":[0.99864286,0.0005537918,0.0001126165,0.00031120874,0.00034158054,0.000038027592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001420704,0.0009695939,0.0012013041,0.0015188725,0.0005578208,0.0009304238,0.0010330181,0.0008041449,0.0037404718],"category_scores_gemma":[0.0067499494,0.00036541614,0.00094465323,0.0020846436,0.000627974,0.0011433641,0.0013584037,0.0014113554,0.0033811776],"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":[0.00014562489,0.00008977252,0.00078536343,0.00021087061,0.000059029084,0.00015263775,0.000083152954,0.043874834,0.02900254,0.044480316,0.010497599,0.8706183],"study_design_scores_gemma":[0.000032048898,0.000076454824,0.0019040353,0.00004821835,0.000033969296,0.00031573928,0.00005136303,0.8857945,0.012299943,0.07930845,0.020078747,0.000056540946],"about_ca_topic_score_codex":0.0009872067,"about_ca_topic_score_gemma":0.0009114237,"teacher_disagreement_score":0.0037404718,"about_ca_system_score_codex":0.0004033731,"about_ca_system_score_gemma":0.0006583253,"threshold_uncertainty_score":0.012513101},"labels":[],"label_agreement":null},{"id":"W2051132546","doi":"10.1142/s0218126611007955","title":"A METHOD FOR FACE RECOGNITION USING IMAGE REGISTRATION","year":2011,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Face and Expression Recognition","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":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Feature (linguistics); Computer vision; Computer science; Zernike polynomials; Pattern recognition (psychology); Face (sociological concept); Transformation (genetics); Image registration; Facial recognition system; Outlier; Wavelet; Point (geometry); Image (mathematics); Mathematics","score_opus":0.09318712364927637,"score_gpt":0.29393169210980735,"score_spread":0.200744568460531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051132546","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010614459,0.0005538835,0.9940724,0.00009334187,0.000263915,0.00011431503,0.000062023806,0.0015880472,0.0021907538],"genre_scores_gemma":[0.022021478,0.00096897414,0.9670564,0.00017406164,0.00027634518,0.00032990816,0.0003770788,0.00023605968,0.008559603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983038,0.00025472915,0.000080593214,0.00040030555,0.00088984956,0.00007078958],"domain_scores_gemma":[0.9994604,0.00011579764,0.000047456677,0.00017586967,0.00017736573,0.00002313284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009663251,0.0010503873,0.0013282513,0.0023319775,0.000993822,0.0010199497,0.002026441,0.0016755164,0.006933042],"category_scores_gemma":[0.0014271812,0.00056056835,0.0014258479,0.0018958695,0.00094239996,0.0015603943,0.0012633698,0.0017957798,0.008179387],"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":[0.00013237324,0.00009596062,0.00038980306,0.00036271548,0.00010048131,0.00022568184,0.000142928,0.0057859905,0.07624065,0.01782174,0.015154554,0.883547],"study_design_scores_gemma":[0.00014279889,0.0006180849,0.003917213,0.00022323034,0.00023084716,0.008811578,0.00020297307,0.370787,0.21591093,0.028882947,0.3698949,0.00037745893],"about_ca_topic_score_codex":0.00081078004,"about_ca_topic_score_gemma":0.0007303941,"teacher_disagreement_score":0.006933042,"about_ca_system_score_codex":0.00040903,"about_ca_system_score_gemma":0.0005587019,"threshold_uncertainty_score":0.0231933},"labels":[],"label_agreement":null},{"id":"W2051526232","doi":"10.1109/tmm.2014.2321113","title":"Prototype-Based Modeling for &lt;newline/&gt;Facial Expression Analysis","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"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","funders":"","keywords":"Facial expression; Computer science; Expression (computer science); Set (abstract data type); Artificial intelligence; Computer vision; Representation (politics); Face (sociological concept); Active appearance model; Pattern recognition (psychology); Class (philosophy); Scale-invariant feature transform; Image (mathematics)","score_opus":0.023489392110629188,"score_gpt":0.26342804113451024,"score_spread":0.23993864902388104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051526232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013776172,0.00041324535,0.9779214,0.00012343706,0.00011741592,0.00015853319,0.0006478009,0.0049894676,0.0018525007],"genre_scores_gemma":[0.3103154,0.0009334574,0.66796225,0.0002450332,0.00009599373,0.000386975,0.006623817,0.0013076124,0.012129481],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919873,0.00016118429,0.000039679853,0.00025116853,0.00028301324,0.00006629395],"domain_scores_gemma":[0.9995171,0.00007849444,0.000035681143,0.00018208745,0.00016922619,0.000017568578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000815879,0.0009977692,0.000990155,0.000859337,0.0003476325,0.0011232456,0.0018741677,0.00076064165,0.006318704],"category_scores_gemma":[0.0016748888,0.00038926714,0.0014307214,0.0006476667,0.00032792802,0.0014567857,0.0007821852,0.0009726102,0.0039916127],"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":[0.00036710227,0.00022965894,0.0016216238,0.00022797193,0.00016072934,0.00015459607,0.00010614406,0.09550557,0.05474891,0.0057939515,0.018112417,0.8229712],"study_design_scores_gemma":[0.000011829084,0.000094099596,0.000942241,0.00001483643,0.000020269326,0.00017619411,0.000038497234,0.96981806,0.018022431,0.0021391655,0.00870113,0.00002122235],"about_ca_topic_score_codex":0.005507446,"about_ca_topic_score_gemma":0.0068282313,"teacher_disagreement_score":0.006318704,"about_ca_system_score_codex":0.00065371336,"about_ca_system_score_gemma":0.00050353014,"threshold_uncertainty_score":0.021138191},"labels":[],"label_agreement":null},{"id":"W2052079879","doi":"10.1016/j.neucom.2007.09.021","title":"An approach for directly extracting features from matrix data and its application in face recognition","year":2008,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University; Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Decorrelation; Pattern recognition (psychology); Principal component analysis; Basis (linear algebra); Computer science; Property (philosophy); Feature (linguistics); Artificial intelligence; Face (sociological concept); Scheme (mathematics); Matrix (chemical analysis); Facial recognition system; Mathematics; Algorithm","score_opus":0.06368421441283932,"score_gpt":0.3081520163588911,"score_spread":0.24446780194605178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052079879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021713965,0.00014611735,0.9968219,0.000045635996,0.000044101893,0.000025056497,0.00003345258,0.00032088894,0.00039135045],"genre_scores_gemma":[0.031185186,0.00037831074,0.96512735,0.00007054169,0.00007335378,0.000085950116,0.00011539014,0.00007658397,0.0028873796],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995915,0.000045171186,0.00002361587,0.000074737174,0.00023884053,0.000026196085],"domain_scores_gemma":[0.99945134,0.00018228813,0.00003579153,0.00011971176,0.0001822704,0.00002861821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039072023,0.0006268854,0.0006375743,0.0010405742,0.00051496347,0.00081838225,0.0009882126,0.0008600877,0.0026527038],"category_scores_gemma":[0.0015157835,0.0004513244,0.0008006037,0.0013824406,0.00051586766,0.0011065287,0.000982299,0.0010697285,0.0016987288],"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":[0.000095611096,0.00010301453,0.0004409361,0.00016478465,0.00006695476,0.00017517763,0.00012722405,0.011197729,0.12343876,0.016380945,0.003586673,0.8442223],"study_design_scores_gemma":[0.000050083072,0.0003536026,0.0027874142,0.00004289719,0.00012298301,0.001956808,0.0001348763,0.8107438,0.1252906,0.029150074,0.029233405,0.00013343582],"about_ca_topic_score_codex":0.0025641958,"about_ca_topic_score_gemma":0.0030391712,"teacher_disagreement_score":0.0026527038,"about_ca_system_score_codex":0.00022523823,"about_ca_system_score_gemma":0.00049492676,"threshold_uncertainty_score":0.008874178},"labels":[],"label_agreement":null},{"id":"W2054036188","doi":"10.1109/iccvw.2011.6130520","title":"Object representation based on gabor wave vector binning: An application to human head pose detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Discriminative model; Pattern recognition (psychology); Computer science; Computer vision; Kernel (algebra); Histogram; Support vector machine; Object detection; Histogram of oriented gradients; Gabor wavelet; Feature (linguistics); Feature extraction; Representation (politics); Set (abstract data type); Object (grammar); Mathematics; Wavelet transform; Wavelet; Image (mathematics)","score_opus":0.06625827070365636,"score_gpt":0.30984019563941856,"score_spread":0.2435819249357622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054036188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046179697,0.0005478654,0.95036846,0.00013894626,0.000046598147,0.000057864767,0.00012444459,0.0015032882,0.0010328372],"genre_scores_gemma":[0.4949961,0.0011191535,0.5007659,0.00009280607,0.000072378774,0.00006252789,0.00032324056,0.00013137187,0.0024365054],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998343,0.000037473044,0.000008424647,0.000039061946,0.000056493966,0.00002417744],"domain_scores_gemma":[0.9997085,0.00010854525,0.000034763325,0.000063436404,0.00006321126,0.000021565698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005377111,0.00035617535,0.0006198913,0.0010628109,0.0001512277,0.00059594156,0.0004049174,0.00045189698,0.0014279236],"category_scores_gemma":[0.0010101598,0.00016004234,0.00026429328,0.0015724704,0.00031570258,0.0006115329,0.0005021646,0.00040457305,0.00070175197],"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":[0.00021148504,0.000091467715,0.0016003307,0.00010312257,0.0000380918,0.00007989704,0.00007393364,0.019897642,0.0854299,0.003718895,0.0015142077,0.88724107],"study_design_scores_gemma":[0.000035918063,0.00026169664,0.013439091,0.000033581324,0.00005668711,0.0007228023,0.0001375434,0.87169826,0.09407378,0.012713953,0.0067653083,0.000061425744],"about_ca_topic_score_codex":0.0013968406,"about_ca_topic_score_gemma":0.001258884,"teacher_disagreement_score":0.0014279236,"about_ca_system_score_codex":0.0002167056,"about_ca_system_score_gemma":0.00024908548,"threshold_uncertainty_score":0.0047768354},"labels":[],"label_agreement":null},{"id":"W2054302069","doi":"10.1109/cw.2011.44","title":"Face Detection Using Skin Color Recursive Clustering and Recognition Using Multilinear PCA","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Principal component analysis; Computer vision; Feature (linguistics); Face (sociological concept); Feature extraction; Biometrics; Cluster analysis; Multilinear map; Feature vector; Face detection; Mathematics","score_opus":0.09853609248491259,"score_gpt":0.27278290515226394,"score_spread":0.17424681266735137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054302069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010889793,0.000120792116,0.98776555,0.00003140485,0.00001910365,0.0000292863,0.000023972912,0.0006518291,0.00046816317],"genre_scores_gemma":[0.17861314,0.00029420148,0.8189324,0.000046012075,0.000050695748,0.00008856829,0.00015952498,0.00013765912,0.001677769],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898595,0.00019280725,0.000031406493,0.00030031303,0.00040787118,0.00008164673],"domain_scores_gemma":[0.99959666,0.000100014186,0.000062306426,0.00008181008,0.00013889172,0.00002029959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004690865,0.00070805283,0.00079111115,0.0013995076,0.00036762338,0.00061039627,0.0008461387,0.0004956822,0.0014403592],"category_scores_gemma":[0.001491567,0.00035877852,0.00083244545,0.0009407865,0.00045650278,0.00083773636,0.00066074217,0.0005547677,0.0012489258],"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":[0.00013891503,0.000100381716,0.0015438141,0.00014533522,0.000120425204,0.00013434685,0.00016635803,0.059485633,0.13241908,0.004863496,0.0016080921,0.7992741],"study_design_scores_gemma":[0.000011636234,0.00012034248,0.0046365866,0.000011324472,0.000034327877,0.0004054102,0.00005382557,0.92508316,0.062003057,0.004123264,0.0034614457,0.000055596174],"about_ca_topic_score_codex":0.0019840233,"about_ca_topic_score_gemma":0.0015912176,"teacher_disagreement_score":0.0019840233,"about_ca_system_score_codex":0.00032820262,"about_ca_system_score_gemma":0.00042893318,"threshold_uncertainty_score":0.0048184395},"labels":[],"label_agreement":null},{"id":"W2054704569","doi":"10.1016/j.neucom.2014.11.012","title":"Nonparametric discriminant multi-manifold learning for dimensionality reduction","year":2014,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Science Foundation of Hubei Province; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Dimensionality reduction; Nonlinear dimensionality reduction; Discriminant; Linear discriminant analysis; Artificial intelligence; Pattern recognition (psychology); Curse of dimensionality; Nonparametric statistics; Manifold alignment; Multiple discriminant analysis; Computer science; Reduction (mathematics); Manifold (fluid mechanics); Machine learning; Mathematics; Statistics","score_opus":0.031053069350008313,"score_gpt":0.27485896602667026,"score_spread":0.24380589667666194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054704569","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006923165,0.00062315015,0.9911021,0.00020117348,0.000063122156,0.000027899594,0.0001490338,0.00040253444,0.00050783076],"genre_scores_gemma":[0.2985777,0.0010602099,0.6927664,0.00014618288,0.00018862236,0.00030579724,0.0014770579,0.00027051935,0.005207502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990056,0.00047348512,0.000050138573,0.00018911816,0.00022361886,0.000058063444],"domain_scores_gemma":[0.9984754,0.0005474233,0.000087938075,0.0004835944,0.0003523383,0.00005337189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011610979,0.0007967337,0.0013963358,0.00096287974,0.0008554905,0.0008469034,0.0011777551,0.00083201914,0.0023535597],"category_scores_gemma":[0.0054759416,0.00035464505,0.0011414792,0.0013964376,0.00077736826,0.0013195863,0.0016407742,0.0022211792,0.0014910557],"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":[0.00027384044,0.00022293603,0.0015063307,0.00031584437,0.00018086497,0.00010057181,0.00020265808,0.14236222,0.0127291,0.059990987,0.018519295,0.76359534],"study_design_scores_gemma":[0.0000070777137,0.00004256501,0.000572655,0.0000137298775,0.000015461108,0.000042890053,0.00003538486,0.96756536,0.0017509768,0.02689701,0.0030378809,0.000019073394],"about_ca_topic_score_codex":0.0026164611,"about_ca_topic_score_gemma":0.002915901,"teacher_disagreement_score":0.0026164611,"about_ca_system_score_codex":0.00044772177,"about_ca_system_score_gemma":0.0009935697,"threshold_uncertainty_score":0.007873416},"labels":[],"label_agreement":null},{"id":"W2056345567","doi":"10.1109/mlsp.2013.6661950","title":"Classification based on local feature selection via linear programming","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Feature (linguistics); Discriminative model; Linear programming; Feature selection; Computer science; Feature vector; Artificial intelligence; Pattern recognition (psychology); Set (abstract data type); Point (geometry); Realization (probability); Feature extraction; Algorithm; Mathematics","score_opus":0.015100745354956882,"score_gpt":0.24126820508739724,"score_spread":0.22616745973244035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056345567","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039472603,0.00008161834,0.9951703,0.00005026404,0.00000802402,0.000024855726,0.000014427418,0.00035601962,0.00034724432],"genre_scores_gemma":[0.31620428,0.00028165607,0.6783292,0.00020923199,0.00018800492,0.00048675228,0.00040067054,0.0002882695,0.0036118997],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985056,0.00046766506,0.000056015826,0.00032978988,0.0004962517,0.00014465097],"domain_scores_gemma":[0.9987618,0.00069915433,0.00016518054,0.00009836299,0.00022890962,0.000046587087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013793594,0.00092068635,0.002093414,0.0015416581,0.00057167327,0.0011217084,0.0015323566,0.0007840138,0.002124018],"category_scores_gemma":[0.002812867,0.0004983546,0.0009765276,0.0017240528,0.00081376586,0.0014215837,0.0010554524,0.0011755336,0.0011152262],"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":[0.00016334638,0.00016559198,0.0010236097,0.00011576688,0.000093592396,0.00010274185,0.000082659855,0.35377133,0.014158178,0.008135766,0.0036536166,0.6185338],"study_design_scores_gemma":[0.000008093731,0.000037603193,0.00015840463,0.0000040864056,0.000008521342,0.000029810759,0.0000099083745,0.994849,0.0016018911,0.002877387,0.0004072082,0.000007994913],"about_ca_topic_score_codex":0.002063686,"about_ca_topic_score_gemma":0.0017443824,"teacher_disagreement_score":0.002124018,"about_ca_system_score_codex":0.00072663807,"about_ca_system_score_gemma":0.00068266323,"threshold_uncertainty_score":0.0072948337},"labels":[],"label_agreement":null},{"id":"W2058138187","doi":"10.1109/conielecomp.2012.6189911","title":"Evaluation of machine learning techniques for face detection and recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Artificial intelligence; Facial recognition system; Preprocessor; Face (sociological concept); Three-dimensional face recognition; Face detection; Biometrics; Object-class detection; Computer vision; Field (mathematics); Pattern recognition (psychology); Identification (biology); Mathematics","score_opus":0.06189716171133764,"score_gpt":0.309734164209718,"score_spread":0.24783700249838037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2058138187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3917552,0.03497851,0.5456964,0.0010842457,0.0015563724,0.0009841375,0.0020380206,0.004765825,0.017141424],"genre_scores_gemma":[0.7910972,0.005326191,0.19448242,0.00014895927,0.00032981025,0.00047175965,0.003148284,0.00022109412,0.0047743623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.988577,0.0042511956,0.0007600322,0.0010447572,0.005026329,0.000340659],"domain_scores_gemma":[0.97602534,0.016429724,0.00072804495,0.0012376173,0.0053154184,0.00026395867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009961805,0.001387699,0.0011454074,0.0036959315,0.0005214255,0.0008995715,0.001144963,0.0013341366,0.0018357682],"category_scores_gemma":[0.023128241,0.00021086859,0.0009355378,0.0018278683,0.00030657233,0.0018572339,0.00072007766,0.0007563939,0.0008432789],"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":[0.0013723774,0.00067407556,0.012931908,0.00076845125,0.00070185825,0.00008929825,0.00008811425,0.0940588,0.0064077806,0.0020467942,0.004365967,0.8764946],"study_design_scores_gemma":[0.00010105294,0.002083618,0.021686042,0.00011932307,0.00023188077,0.00031758152,0.00013217675,0.9533647,0.014940762,0.0017313529,0.005222958,0.000068646004],"about_ca_topic_score_codex":0.0032830767,"about_ca_topic_score_gemma":0.00205679,"teacher_disagreement_score":0.009961805,"about_ca_system_score_codex":0.001103528,"about_ca_system_score_gemma":0.0007306649,"threshold_uncertainty_score":0.05268365},"labels":[],"label_agreement":null},{"id":"W2059175078","doi":"10.1109/icdar.2007.4377037","title":"Hybrid Mathematical Symbol Recognition Using Support Vector Machines","year":2007,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Document Analysis and Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":50,"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":"Support vector machine; Computer science; Artificial intelligence; Probabilistic logic; Word error rate; Machine learning; Pattern recognition (psychology); Symbol (formal); Set (abstract data type); Task (project management); Class (philosophy); Isolation (microbiology); Structured support vector machine; Engineering","score_opus":0.03877055866983099,"score_gpt":0.2917924507306187,"score_spread":0.25302189206078773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059175078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060751155,0.0003993523,0.9278516,0.00016513377,0.00013963917,0.00009469839,0.00024454072,0.008838127,0.0015157019],"genre_scores_gemma":[0.44116077,0.000247387,0.5539506,0.00018156099,0.00013042262,0.00016929285,0.00081091525,0.00020265381,0.0031463644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99784124,0.00042335285,0.00019287359,0.00036527158,0.0010044485,0.0001728784],"domain_scores_gemma":[0.9960444,0.0016792577,0.0004328183,0.00062003796,0.001091076,0.00013241971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011934612,0.00088935095,0.0014705746,0.0019983612,0.0003677725,0.0017262183,0.0016874068,0.0009220646,0.003272847],"category_scores_gemma":[0.005303321,0.00037072168,0.00077829545,0.0015947432,0.0003977286,0.0026258617,0.0011884685,0.0010348186,0.0030352063],"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":[0.0004985133,0.00021481805,0.0019208957,0.00013532535,0.00015764116,0.00020750727,0.00007532888,0.022411231,0.042240463,0.0015490534,0.0034048508,0.9271844],"study_design_scores_gemma":[0.00004591305,0.00034814223,0.0017058809,0.000024537405,0.000055264583,0.00044259484,0.00006862484,0.91937447,0.068098314,0.005517251,0.0042474926,0.00007146151],"about_ca_topic_score_codex":0.0007235903,"about_ca_topic_score_gemma":0.0007066049,"teacher_disagreement_score":0.003272847,"about_ca_system_score_codex":0.00030871047,"about_ca_system_score_gemma":0.00038169397,"threshold_uncertainty_score":0.010948837},"labels":[],"label_agreement":null},{"id":"W2060252091","doi":"10.1109/icpr.2014.414","title":"Gender Recognition Using Complexity-Aware Local Features","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Computer science; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Classifier (UML); Computation; Support vector machine; Facial recognition system; Gabor wavelet; Wavelet; Machine learning; Wavelet transform; Algorithm; Discrete wavelet transform","score_opus":0.09678112641096204,"score_gpt":0.2853579969754592,"score_spread":0.18857687056449718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060252091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15161526,0.0014093341,0.83760256,0.000279245,0.0003635618,0.00017625354,0.00097388966,0.0017624035,0.0058173994],"genre_scores_gemma":[0.82598895,0.00095166516,0.16198438,0.00021203046,0.00030890977,0.00016711198,0.0020470712,0.00015029404,0.008189671],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99955124,0.000056187495,0.000023016062,0.00010925344,0.00018757532,0.0000727621],"domain_scores_gemma":[0.99958104,0.00006453189,0.00006351711,0.00006579613,0.0001843161,0.00004075537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045282836,0.0007212455,0.00082770013,0.0015378973,0.00026553392,0.00057483575,0.0006409119,0.00041758557,0.0030210814],"category_scores_gemma":[0.0012051223,0.00018135687,0.00067906326,0.000961228,0.00024857823,0.0009461047,0.0007126412,0.00038312722,0.001964843],"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":[0.0005288374,0.00022564716,0.013890049,0.00015157484,0.00010168086,0.00035144642,0.000073995354,0.010125123,0.112946615,0.0022267867,0.0076301317,0.8517481],"study_design_scores_gemma":[0.00007422116,0.0010415843,0.058025822,0.00008199291,0.00027369405,0.0033461915,0.00037201255,0.7659532,0.14303242,0.007989338,0.019637775,0.00017183986],"about_ca_topic_score_codex":0.0010608232,"about_ca_topic_score_gemma":0.001979713,"teacher_disagreement_score":0.0030210814,"about_ca_system_score_codex":0.00023262207,"about_ca_system_score_gemma":0.00037246683,"threshold_uncertainty_score":0.010106564},"labels":[],"label_agreement":null},{"id":"W2060690281","doi":"10.5244/c.20.91","title":"Tied factor analysis for face recognition across large pose changes","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Computer science; Feature vector; Identity (music); Metric (unit); Face (sociological concept); Feature extraction; Transformation (genetics); Noise (video); Feature (linguistics); Representation (politics); Computer vision; Image (mathematics)","score_opus":0.03624816021093658,"score_gpt":0.29094689886943415,"score_spread":0.25469873865849757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060690281","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031731836,0.0001801119,0.9666208,0.00010180011,0.000030948955,0.000032950084,0.00010568207,0.0006706852,0.00052521564],"genre_scores_gemma":[0.7128982,0.00023265208,0.28278446,0.00011669231,0.00009910586,0.000135627,0.00066953554,0.0001982368,0.0028655762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998616,0.000486515,0.000046438174,0.00043257675,0.00029150993,0.00012693125],"domain_scores_gemma":[0.9977387,0.0012934686,0.00015731038,0.0004972225,0.00024214809,0.00007106971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020950653,0.00063420343,0.0008636008,0.000993367,0.00059927447,0.0007780403,0.00083576416,0.00078053924,0.0032903543],"category_scores_gemma":[0.007583834,0.0003860231,0.0011716116,0.0009530168,0.00093351305,0.0011889793,0.0010980264,0.0012902054,0.0015500379],"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":[0.0006542623,0.00023974305,0.006573463,0.00009623147,0.00026242432,0.00018427672,0.00023340034,0.3287715,0.024387913,0.013557069,0.0038498116,0.62118983],"study_design_scores_gemma":[0.0000075242933,0.000035392117,0.0021570018,0.0000049664245,0.000012468384,0.0000551725,0.00001314467,0.9860169,0.0030527536,0.008120799,0.0005099411,0.000013800163],"about_ca_topic_score_codex":0.0035579046,"about_ca_topic_score_gemma":0.0030251516,"teacher_disagreement_score":0.0035579046,"about_ca_system_score_codex":0.000661612,"about_ca_system_score_gemma":0.00046649572,"threshold_uncertainty_score":0.011079848},"labels":[],"label_agreement":null},{"id":"W2060702420","doi":"10.1049/iet-ipr.2013.0792","title":"Local gradient‐based illumination invariant face recognition using local phase quantisation and multi‐resolution local binary pattern fusion","year":2014,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":50,"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 Windsor","funders":"","keywords":"Local binary patterns; Fusion; Invariant (physics); Artificial intelligence; Binary number; Pattern recognition (psychology); Facial recognition system; Computer science; Face (sociological concept); Mathematics; Computer vision; Histogram; Image (mathematics)","score_opus":0.039192534245218694,"score_gpt":0.29025386910358614,"score_spread":0.25106133485836746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060702420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020376012,0.00037474162,0.9769617,0.00008938211,0.00004526685,0.000073422605,0.000053939148,0.0009945752,0.0010310315],"genre_scores_gemma":[0.3580525,0.00039362072,0.63757634,0.00014435439,0.000049982784,0.00014686667,0.00030451495,0.000091826216,0.0032399273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994487,0.00006666199,0.0000236161,0.0001073199,0.00030817432,0.00004542937],"domain_scores_gemma":[0.9997359,0.000053261447,0.000050048104,0.000048895545,0.00009590317,0.00001598444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069593743,0.0004418003,0.0009331466,0.0009557528,0.00023516163,0.0005943403,0.0010294076,0.000519868,0.001354499],"category_scores_gemma":[0.0009871634,0.00030230632,0.0006378171,0.00073077035,0.00042757008,0.0012501421,0.0006750221,0.00059067947,0.0007143251],"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":[0.00028579487,0.00017929124,0.00084698864,0.00013822589,0.000079823876,0.00011355413,0.00008620876,0.04715481,0.1834222,0.005314327,0.0024308276,0.7599479],"study_design_scores_gemma":[0.00003437757,0.00021028428,0.0029552765,0.000014959381,0.0000531357,0.00036144682,0.000029039675,0.8981282,0.09311575,0.0025886162,0.0024527407,0.000056265573],"about_ca_topic_score_codex":0.0016273088,"about_ca_topic_score_gemma":0.0015424642,"teacher_disagreement_score":0.0016273088,"about_ca_system_score_codex":0.00044201934,"about_ca_system_score_gemma":0.00046790103,"threshold_uncertainty_score":0.0045312047},"labels":[],"label_agreement":null},{"id":"W2062450215","doi":"10.1109/tsmcb.2004.827609","title":"Face Recognition Using Fuzzy Integral and Wavelet Decomposition Method","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":91,"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":"Face (sociological concept); Wavelet; Artificial intelligence; Fuzzy logic; Consistency (knowledge bases); Pattern recognition (psychology); Diagonal; Choquet integral; Facial recognition system; Computer science; Decomposition; Mathematics; Wavelet transform; Geometry","score_opus":0.03513357894305298,"score_gpt":0.28622347201667353,"score_spread":0.25108989307362056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062450215","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009747482,0.00020792813,0.9892693,0.00003907711,0.00002007263,0.0000139751855,0.0000076105703,0.00012945294,0.0005650229],"genre_scores_gemma":[0.23546322,0.0004870023,0.7624708,0.00005125639,0.00005719209,0.000054066903,0.0000611898,0.000029686036,0.001325728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993699,0.00009343175,0.000035767185,0.000098648976,0.00035990574,0.00004239636],"domain_scores_gemma":[0.9996805,0.0001284428,0.000026282427,0.00003447654,0.000115404386,0.000014935312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010104505,0.00029983322,0.00092725287,0.0013031755,0.00028004672,0.00054104894,0.00050506333,0.0004750313,0.00087091373],"category_scores_gemma":[0.0017853183,0.00021756081,0.000729145,0.0007833902,0.00041438625,0.0010913678,0.0004887417,0.00066642155,0.00034458714],"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":[0.00015991286,0.00008395179,0.0012331733,0.00018640481,0.00008134962,0.00014181715,0.00022890298,0.07880174,0.071588546,0.03078527,0.0010606238,0.81564826],"study_design_scores_gemma":[0.00000840488,0.000049466766,0.00093825685,0.000015399228,0.000028203029,0.00016496124,0.000023742628,0.97575206,0.012749733,0.008543835,0.0017027407,0.000023194356],"about_ca_topic_score_codex":0.0011641785,"about_ca_topic_score_gemma":0.0007063746,"teacher_disagreement_score":0.0013031755,"about_ca_system_score_codex":0.00034992112,"about_ca_system_score_gemma":0.00035476446,"threshold_uncertainty_score":0.005343795},"labels":[],"label_agreement":null},{"id":"W2063381257","doi":"10.1109/hpcsim.2014.6903749","title":"Effectiveness of various classification techniques on human face recognition","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Computer science; Linear discriminant analysis; Principal component analysis; Dimensionality reduction; Classifier (UML); k-nearest neighbors algorithm; Local binary patterns; Extreme learning machine; Feature vector; Support vector machine; Feature extraction; Face (sociological concept); Discriminant; Artificial neural network; Histogram; Image (mathematics)","score_opus":0.04175712009232833,"score_gpt":0.2992481379274356,"score_spread":0.25749101783510725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063381257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27453932,0.01584926,0.68811435,0.00076067273,0.0004752874,0.00018593388,0.00031992717,0.0034881013,0.016267153],"genre_scores_gemma":[0.76559263,0.0069341995,0.22357297,0.00020534561,0.00025308164,0.000109825356,0.00036196446,0.000121755635,0.0028480892],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9981046,0.00046426637,0.00008220791,0.00022303718,0.001036483,0.00008938489],"domain_scores_gemma":[0.99802655,0.0012430515,0.000120353616,0.00017511264,0.0004158015,0.000018994228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017073505,0.0005568667,0.00063840573,0.0015512214,0.00030838777,0.0006155237,0.0004972464,0.00074789254,0.0015455354],"category_scores_gemma":[0.0050226175,0.00015609767,0.00046546105,0.0010332026,0.0003275393,0.0012003365,0.0003480867,0.00032606625,0.00090583053],"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":[0.00025442275,0.000104617524,0.0030110474,0.00025264273,0.0000647749,0.000041042094,0.000042801217,0.0133085,0.024684053,0.0010093462,0.0012731922,0.95595354],"study_design_scores_gemma":[0.00004262667,0.0011259657,0.026814518,0.00020566679,0.00029420905,0.0013886356,0.00022396016,0.79045194,0.16518474,0.0044921185,0.009641096,0.00013447512],"about_ca_topic_score_codex":0.0010846118,"about_ca_topic_score_gemma":0.000836893,"teacher_disagreement_score":0.0017073505,"about_ca_system_score_codex":0.00024650438,"about_ca_system_score_gemma":0.0002022372,"threshold_uncertainty_score":0.009029388},"labels":[],"label_agreement":null},{"id":"W2063517468","doi":"10.1109/icdm.2013.155","title":"Distributed Column Subset Selection on MapReduce","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":44,"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":"Computer science; Benchmark (surveying); Column (typography); Selection (genetic algorithm); Big data; Set (abstract data type); Representation (politics); Preprocessor; Data set; Random projection; External Data Representation; Matrix (chemical analysis); Data mining; Algorithm; Theoretical computer science; Artificial intelligence","score_opus":0.010500078812081428,"score_gpt":0.2134554219615589,"score_spread":0.20295534314947747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063517468","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029345889,0.00035076385,0.9604458,0.0002831737,0.00008482016,0.00026369133,0.0006123878,0.00657995,0.0020334516],"genre_scores_gemma":[0.3430701,0.00029174387,0.64760643,0.00027117977,0.00010326113,0.0005701009,0.0034076113,0.00043690312,0.004242631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993393,0.00015757626,0.000031145828,0.00015797633,0.00022987308,0.00008412983],"domain_scores_gemma":[0.9992495,0.00020888084,0.000036132475,0.00026026377,0.0001808558,0.00006437281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007104456,0.0009875209,0.0011680723,0.00068102963,0.0008251498,0.000959059,0.0016379553,0.00041927802,0.0023189555],"category_scores_gemma":[0.0018592434,0.00040120058,0.00080213323,0.0011983411,0.00036538442,0.0010160073,0.0011863438,0.00069487345,0.0011411487],"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":[0.0007493183,0.00039913267,0.0024750486,0.0002838998,0.00018074155,0.00041108835,0.00024084063,0.34775442,0.025442015,0.007924133,0.03497676,0.5791626],"study_design_scores_gemma":[0.00007239141,0.00008886871,0.00053396844,0.000005953361,0.000021764075,0.00015319958,0.00015294107,0.9697017,0.01012926,0.0135350395,0.0055866283,0.000018366485],"about_ca_topic_score_codex":0.0058396426,"about_ca_topic_score_gemma":0.008835742,"teacher_disagreement_score":0.0058396426,"about_ca_system_score_codex":0.00047902568,"about_ca_system_score_gemma":0.0015519409,"threshold_uncertainty_score":0.011611283},"labels":[],"label_agreement":null},{"id":"W2063836598","doi":"10.1109/icassp.2010.5494892","title":"Facial expression recognition using curvelet based local binary patterns","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Local binary patterns; Curvelet; Artificial intelligence; Computer science; Facial expression recognition; Expression (computer science); Pattern recognition (psychology); Binary number; Facial expression; Facial recognition system; Computer vision; Speech recognition; Image (mathematics); Mathematics; Wavelet; Histogram; Wavelet transform","score_opus":0.033033896152827764,"score_gpt":0.26417034655815697,"score_spread":0.2311364504053292,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063836598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083250485,0.000813825,0.9091833,0.00024020925,0.00011673519,0.000081509526,0.0001680509,0.001023115,0.0051226397],"genre_scores_gemma":[0.563345,0.0016801148,0.4267223,0.00017931806,0.00018934325,0.000086705346,0.00069321523,0.00015610788,0.0069479845],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999529,0.000060951214,0.000020242736,0.00007276999,0.00028822702,0.000028863178],"domain_scores_gemma":[0.9996427,0.00008762968,0.00004067102,0.00004995999,0.00015955833,0.000019453133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039296094,0.0002980201,0.0004460062,0.0012234166,0.0001204417,0.0005259547,0.00043085066,0.0003384113,0.0013239275],"category_scores_gemma":[0.0013297461,0.00014001166,0.00032729618,0.0009966671,0.00025505043,0.00086191756,0.00029004883,0.0003586212,0.00096557604],"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":[0.00019813514,0.00007522062,0.0012595886,0.00013546491,0.000038560138,0.00015503501,0.000081932325,0.011477368,0.16399102,0.0025040885,0.0020047156,0.8180789],"study_design_scores_gemma":[0.00004596462,0.0003650449,0.012890796,0.000048982027,0.00009884285,0.0018701743,0.000130145,0.75555116,0.20685123,0.0066074873,0.015454965,0.00008521762],"about_ca_topic_score_codex":0.0005958794,"about_ca_topic_score_gemma":0.00044820856,"teacher_disagreement_score":0.0013239275,"about_ca_system_score_codex":0.00019283348,"about_ca_system_score_gemma":0.00015355053,"threshold_uncertainty_score":0.0044290423},"labels":[],"label_agreement":null},{"id":"W2066063175","doi":"10.1007/s00500-013-1064-0","title":"A study in facial regions saliency: a fuzzy measure approach","year":2013,"lang":"en","type":"article","venue":"Soft Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Measure (data warehouse); Fuzzy logic; Property (philosophy); Artificial intelligence; Relevance (law); Face (sociological concept); Computer science; Fuzzy measure theory; Pattern recognition (psychology); Identification (biology); Realization (probability); Facial recognition system; Process (computing); Monotonic function; Fuzzy set; Point (geometry); Mathematics; Contrast (vision); Fuzzy classification; Data mining; Statistics; Linguistics","score_opus":0.03632649678093972,"score_gpt":0.2593300288095609,"score_spread":0.2230035320286212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066063175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3094297,0.0028040758,0.68189013,0.0006688532,0.000057699865,0.00009988162,0.00006837915,0.00009956891,0.0048817233],"genre_scores_gemma":[0.8730814,0.00057266554,0.12540898,0.00005284981,0.00006887704,0.000043669614,0.000044669494,0.000016870224,0.00070989155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991234,0.00031915278,0.000037906673,0.00017978575,0.00029599824,0.000043796816],"domain_scores_gemma":[0.9925706,0.00578574,0.0004200834,0.00042427867,0.00063856354,0.00016070755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026678944,0.0003953128,0.00050206407,0.0018205679,0.0004569268,0.0009135881,0.0006798257,0.00075657666,0.0010708394],"category_scores_gemma":[0.012662535,0.00020860703,0.0006824697,0.0011702876,0.0014267851,0.0018388474,0.0005901609,0.0006141177,0.0001291361],"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":[0.0007529499,0.00038726913,0.014231889,0.000748323,0.00029382503,0.00037934975,0.0017486571,0.13589905,0.12390925,0.15130919,0.001146465,0.5691938],"study_design_scores_gemma":[0.000028208784,0.0008055904,0.023606563,0.000059127044,0.00008336525,0.0006371638,0.00033752288,0.86636406,0.029897466,0.07575819,0.0023475296,0.00007528765],"about_ca_topic_score_codex":0.0019594904,"about_ca_topic_score_gemma":0.0010104843,"teacher_disagreement_score":0.0026678944,"about_ca_system_score_codex":0.001007413,"about_ca_system_score_gemma":0.00032420983,"threshold_uncertainty_score":0.0141093135},"labels":[],"label_agreement":null},{"id":"W2066245523","doi":"10.1016/j.patcog.2007.05.020","title":"Data-driven decomposition for multi-class classification","year":2007,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":66,"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":"Class (philosophy); Decomposition; Computer science; Artificial intelligence; Pattern recognition (psychology); Data mining; Chemistry","score_opus":0.18011371543695762,"score_gpt":0.372141871072316,"score_spread":0.19202815563535838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066245523","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020911132,0.00015007661,0.9965552,0.00006569524,0.000049905575,0.000037828428,0.0000953192,0.00074254355,0.00021238055],"genre_scores_gemma":[0.08706131,0.00020801474,0.90779907,0.0001658968,0.000089521585,0.00043593175,0.0012721721,0.0003300303,0.0026380275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986493,0.00037989326,0.000117057156,0.0003119667,0.00039243037,0.00014941258],"domain_scores_gemma":[0.99765354,0.0010143635,0.00010282351,0.00040973278,0.00071321614,0.00010631871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002632142,0.001264864,0.0019154049,0.0013901363,0.0007100628,0.0011832763,0.00200547,0.0013344289,0.005265103],"category_scores_gemma":[0.0043220217,0.0008141331,0.001976234,0.0014403606,0.00071653345,0.0012757435,0.0018785176,0.0024619692,0.0028728927],"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":[0.00034894256,0.00025759585,0.0005917598,0.00022870096,0.0001489479,0.00009477859,0.00013356187,0.1313898,0.014680282,0.013869987,0.010471112,0.8277845],"study_design_scores_gemma":[0.000013175653,0.00002659462,0.00012445578,0.000011277432,0.000014974861,0.000026826936,0.000016893884,0.98243207,0.0028242762,0.012959496,0.0015408617,0.000009047233],"about_ca_topic_score_codex":0.003948991,"about_ca_topic_score_gemma":0.0039103134,"teacher_disagreement_score":0.005265103,"about_ca_system_score_codex":0.0008245846,"about_ca_system_score_gemma":0.001407949,"threshold_uncertainty_score":0.01761359},"labels":[],"label_agreement":null},{"id":"W2069760243","doi":"10.1016/j.patcog.2010.01.018","title":"Multi-class pairwise linear dimensionality reduction using heteroscedastic schemes","year":2010,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; University of Windsor","funders":"","keywords":"Dimensionality reduction; Pairwise comparison; Heteroscedasticity; Class (philosophy); Mathematics; Reduction (mathematics); Curse of dimensionality; Pattern recognition (psychology); Computer science; Artificial intelligence; Statistics","score_opus":0.06490400647685426,"score_gpt":0.29709852704730905,"score_spread":0.23219452057045478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2069760243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063590123,0.00020932763,0.9924803,0.000109725086,0.000044798788,0.000046367346,0.00013960939,0.00021744345,0.0003934853],"genre_scores_gemma":[0.23232372,0.00065122795,0.75734395,0.00019334913,0.00015119664,0.00044908788,0.0021145674,0.0001788919,0.0065939976],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99715686,0.0011980925,0.00019117666,0.0006015202,0.0006686627,0.00018376268],"domain_scores_gemma":[0.9973941,0.00086296716,0.0001486403,0.0009141544,0.0006031104,0.00007707505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026920622,0.0011814219,0.0017050698,0.0006895405,0.0008459158,0.0013516647,0.001986731,0.00087113737,0.0031754181],"category_scores_gemma":[0.0064070043,0.0005836689,0.0023068113,0.0014117742,0.0008104155,0.0021967022,0.0021238362,0.0020517646,0.0018745734],"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":[0.0006149886,0.0003577102,0.001305637,0.00029303017,0.0003942077,0.00009886284,0.0002815105,0.13238521,0.016053824,0.043249004,0.008876519,0.79608953],"study_design_scores_gemma":[0.000025738278,0.00014285282,0.0011503336,0.000018000148,0.000057414443,0.000090647045,0.00005620298,0.96891105,0.0084467605,0.018243833,0.0028049217,0.000052296342],"about_ca_topic_score_codex":0.0029269434,"about_ca_topic_score_gemma":0.0039468706,"teacher_disagreement_score":0.0031754181,"about_ca_system_score_codex":0.0006441293,"about_ca_system_score_gemma":0.0017160478,"threshold_uncertainty_score":0.014237165},"labels":[],"label_agreement":null},{"id":"W2070110734","doi":"10.1016/j.patcog.2004.12.003","title":"Two-dimensional FLD for face recognition","year":2005,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":201,"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":"Facial recognition system; Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Face (sociological concept); Computer science; Feature extraction; Principal component analysis; Discriminant; Scheme (mathematics); Feature (linguistics); Mathematics","score_opus":0.043093560855476054,"score_gpt":0.27458564233342875,"score_spread":0.2314920814779527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070110734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024885589,0.0005401121,0.9849043,0.0002399706,0.00028813284,0.00005382966,0.0008469543,0.004294633,0.0063435044],"genre_scores_gemma":[0.1036301,0.0013512406,0.85685766,0.00090990716,0.0002544105,0.00051163614,0.005938656,0.0006190382,0.029927365],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997851,0.000030060755,0.000011454586,0.000038981205,0.00010059058,0.000033861194],"domain_scores_gemma":[0.9997726,0.00005629048,0.00001074426,0.000079884674,0.000067101246,0.000013445987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022072798,0.00074856565,0.0006402767,0.0007354561,0.00040459816,0.0008764417,0.001112503,0.00084831397,0.030314427],"category_scores_gemma":[0.00070889783,0.00019202344,0.0005118428,0.0008163938,0.00028275425,0.00074777205,0.00071123266,0.0007625987,0.008941938],"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":[0.00010421896,0.00008055045,0.00023480687,0.00015437917,0.000028051034,0.00009588455,0.000037165482,0.0146309845,0.04618539,0.012957736,0.051056974,0.8744338],"study_design_scores_gemma":[0.000033107717,0.00009262347,0.0011806092,0.000071075214,0.000026748263,0.00038787196,0.00006111007,0.81255114,0.06762276,0.018021345,0.099891976,0.000059719794],"about_ca_topic_score_codex":0.0026887588,"about_ca_topic_score_gemma":0.0038088756,"teacher_disagreement_score":0.030314427,"about_ca_system_score_codex":0.00034842154,"about_ca_system_score_gemma":0.00045180327,"threshold_uncertainty_score":0.10141182},"labels":[],"label_agreement":null},{"id":"W2070229819","doi":"10.1016/s1566-2535(01)00030-6","title":"Rank and response combination from confusion matrix data","year":2001,"lang":"en","type":"article","venue":"Information Fusion","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Confusion; Computer science; Confusion matrix; Classifier (UML); Artificial intelligence; Rank (graph theory); Ranking (information retrieval); Machine learning; Data mining; Pattern recognition (psychology); Mathematics","score_opus":0.022118530542757243,"score_gpt":0.26887796069310965,"score_spread":0.2467594301503524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070229819","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04819294,0.0009188538,0.9347152,0.00078673265,0.00040675438,0.00027064473,0.0021374896,0.0074669737,0.005104474],"genre_scores_gemma":[0.46823812,0.00073281256,0.50674576,0.00039912018,0.0005196848,0.00041851256,0.007677518,0.0008265712,0.014441934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9947758,0.00178373,0.00030057042,0.00062555366,0.0019247599,0.00058949436],"domain_scores_gemma":[0.9934947,0.002426026,0.00029512134,0.0010623776,0.0025090433,0.00021282269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003858366,0.0022943476,0.0017865434,0.003935185,0.000851723,0.0023372676,0.0011761743,0.0015907601,0.008310872],"category_scores_gemma":[0.014475397,0.00047701216,0.0014063905,0.00231633,0.0006137687,0.0025361485,0.001723681,0.001402696,0.009990383],"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":[0.0012735259,0.00028336697,0.0023768237,0.00029496851,0.00021724384,0.0001477332,0.00013046539,0.015797481,0.038078737,0.0035398062,0.013829024,0.92403084],"study_design_scores_gemma":[0.00009744192,0.0005685494,0.008802624,0.000099200464,0.0004631825,0.00090700056,0.00035502022,0.8107387,0.14425452,0.020419393,0.013030048,0.00026436607],"about_ca_topic_score_codex":0.0021158303,"about_ca_topic_score_gemma":0.0034367612,"teacher_disagreement_score":0.008310872,"about_ca_system_score_codex":0.00053852325,"about_ca_system_score_gemma":0.0011607729,"threshold_uncertainty_score":0.027802706},"labels":[],"label_agreement":null},{"id":"W2070639104","doi":"10.1007/s13042-011-0045-9","title":"Combining partially global and local characteristics for improved classification","year":2011,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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 Windsor","funders":"","keywords":"Support vector machine; Decision boundary; Computer science; Linear discriminant analysis; Artificial intelligence; Pattern recognition (psychology); Margin (machine learning); Computational intelligence; Boundary (topology); Machine learning; Nonparametric statistics; Data mining; Mathematics; Statistics","score_opus":0.021863985835660963,"score_gpt":0.2722486850180052,"score_spread":0.25038469918234424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070639104","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11312518,0.0014374597,0.8781651,0.00035499653,0.00031094128,0.00007725523,0.00037749563,0.002474697,0.00367682],"genre_scores_gemma":[0.62094235,0.00092795823,0.36759162,0.00031096212,0.00040738177,0.00011055946,0.0014110575,0.000603707,0.007694387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992163,0.00012827471,0.00004198302,0.00019284287,0.00029942696,0.00012115562],"domain_scores_gemma":[0.9986986,0.000339867,0.00009090356,0.0003075333,0.00048619809,0.00007677299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000979159,0.0010017729,0.0015912206,0.0016053261,0.000515773,0.0013892394,0.0008169607,0.0009926783,0.0026192926],"category_scores_gemma":[0.0016796507,0.00034410425,0.0009782303,0.0021954577,0.00045707577,0.0018743494,0.0011476213,0.0010434551,0.0019945786],"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":[0.0005418269,0.00020981386,0.0052255252,0.00018596032,0.00019080755,0.00014482025,0.00009140652,0.025362302,0.12379702,0.0020597961,0.0074701086,0.8347206],"study_design_scores_gemma":[0.000045510344,0.00027639887,0.011307001,0.00003898401,0.0003674322,0.0003821419,0.00013021094,0.91462,0.060425248,0.003956395,0.008382896,0.000067749774],"about_ca_topic_score_codex":0.001669809,"about_ca_topic_score_gemma":0.0036005876,"teacher_disagreement_score":0.0026192926,"about_ca_system_score_codex":0.0002776487,"about_ca_system_score_gemma":0.0006869147,"threshold_uncertainty_score":0.008762419},"labels":[],"label_agreement":null},{"id":"W2071361275","doi":"10.1109/icmla.2014.104","title":"An Accurate, Fast Embedded Feature Selection for SVMs","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":24,"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 Guelph","funders":"","keywords":"Benchmark (surveying); Feature selection; Computer science; Support vector machine; Field (mathematics); Feature (linguistics); Artificial intelligence; Selection (genetic algorithm); Set (abstract data type); Data mining; Filter (signal processing); Machine learning; Big data; Pattern recognition (psychology); Data set; Feature extraction; Mathematics","score_opus":0.012305665560175483,"score_gpt":0.2725444109758124,"score_spread":0.2602387454156369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071361275","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01593691,0.00028041485,0.98219967,0.000048959093,0.00005155767,0.000049464168,0.000082867,0.0011085494,0.00024160366],"genre_scores_gemma":[0.28376615,0.00037304347,0.7122248,0.000063232554,0.00008408993,0.00016183288,0.0006857401,0.0001701332,0.0024709902],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993542,0.00014233515,0.000047764228,0.0001130321,0.00029003908,0.00005255153],"domain_scores_gemma":[0.9990803,0.0003530085,0.00007974717,0.00014355313,0.00031281146,0.000030540305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008436728,0.0008481229,0.0010933351,0.00076909474,0.00024371686,0.0005577519,0.0006872955,0.00062491087,0.001607312],"category_scores_gemma":[0.0024714402,0.00042770483,0.00052318815,0.0008351392,0.00019339859,0.0009535241,0.00059517723,0.0009127533,0.0011275321],"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":[0.00021928764,0.000104306004,0.0012245459,0.00010171791,0.00007350864,0.00010311096,0.000040224364,0.062450428,0.06802657,0.001721535,0.0030131557,0.86292154],"study_design_scores_gemma":[0.000021340782,0.00013415297,0.0015905981,0.000013142874,0.000016362625,0.00013352359,0.000014990993,0.97450507,0.018829674,0.001749245,0.0029740173,0.000017883225],"about_ca_topic_score_codex":0.0007612344,"about_ca_topic_score_gemma":0.0009155671,"teacher_disagreement_score":0.001607312,"about_ca_system_score_codex":0.00019096496,"about_ca_system_score_gemma":0.00041756866,"threshold_uncertainty_score":0.005376935},"labels":[],"label_agreement":null},{"id":"W2072066020","doi":"10.1109/icip.2010.5653543","title":"Frontal face detection for surveillance purposes using dual Local Binary Patterns features","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Toronto","funders":"","keywords":"Local binary patterns; Discriminative model; Artificial intelligence; Histogram; Feature extraction; Detector; Computer science; Computer vision; Pattern recognition (psychology); Feature (linguistics); Face (sociological concept); Face detection; Pixel; Binary number; Object-class detection; Facial recognition system; Image (mathematics); Mathematics; Telecommunications","score_opus":0.014384992991102937,"score_gpt":0.25322921716109703,"score_spread":0.2388442241699941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072066020","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23137541,0.0011026524,0.7600637,0.00026877117,0.00017497982,0.00010135346,0.0002711327,0.0017664719,0.004875576],"genre_scores_gemma":[0.6758052,0.0006826864,0.319204,0.000164733,0.00009901751,0.000063818545,0.00035846123,0.00007086072,0.003551214],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998016,0.000028395105,0.0000069973034,0.000044101842,0.000096125936,0.000022692806],"domain_scores_gemma":[0.9996698,0.00008753717,0.000036784662,0.000037681664,0.00014251989,0.000025670259],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000345179,0.00036902598,0.00041643932,0.0009902635,0.00019279581,0.00034667805,0.00036738094,0.00041869047,0.0016345886],"category_scores_gemma":[0.00086924486,0.00019308296,0.00029862195,0.00031077696,0.00015358759,0.0005568959,0.00030019105,0.00034256603,0.0010016155],"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":[0.00037853708,0.00021555128,0.005123126,0.00012119881,0.000049181228,0.00020625461,0.000059713777,0.0044485386,0.33491448,0.0008089794,0.0028543207,0.6508202],"study_design_scores_gemma":[0.000059510272,0.00066593447,0.031595245,0.00004226161,0.00012953256,0.0029859806,0.000120950026,0.5775861,0.37703127,0.0015770954,0.008133578,0.00007254245],"about_ca_topic_score_codex":0.0006344951,"about_ca_topic_score_gemma":0.0010515507,"teacher_disagreement_score":0.0016345886,"about_ca_system_score_codex":0.00018518319,"about_ca_system_score_gemma":0.00020501003,"threshold_uncertainty_score":0.0054682493},"labels":[],"label_agreement":null},{"id":"W2072626792","doi":"10.1016/j.ipl.2010.06.006","title":"Block-wise 2D kernel PCA/LDA for face recognition","year":2010,"lang":"en","type":"article","venue":"Information Processing Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Ottawa","funders":"","keywords":"Kernel principal component analysis; Subspace topology; Pattern recognition (psychology); Linear discriminant analysis; Kernel Fisher discriminant analysis; Kernel (algebra); Block (permutation group theory); Kernel method; Principal component analysis; Artificial intelligence; Facial recognition system; Computer science; Mathematics; Linear subspace; Algorithm; Support vector machine; Combinatorics","score_opus":0.01519062657803476,"score_gpt":0.23624132395059055,"score_spread":0.22105069737255578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2072626792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013890663,0.0006926519,0.98219657,0.0001115914,0.00009328898,0.000041341977,0.00028419495,0.0013803525,0.001309321],"genre_scores_gemma":[0.25668898,0.0011714516,0.727864,0.00009700146,0.00009662173,0.0002235192,0.0012986248,0.000335324,0.012224451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996872,0.00007304261,0.000015633843,0.00004965251,0.00013228541,0.000042207957],"domain_scores_gemma":[0.9997509,0.000049245107,0.000016515603,0.00006689738,0.00010437047,0.000011966527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027011117,0.00040469496,0.0005826581,0.00061436475,0.00035032004,0.00042907512,0.00046044766,0.00034709755,0.0039356914],"category_scores_gemma":[0.0007809503,0.00022505448,0.00054570794,0.00086058123,0.00020532656,0.0005183939,0.00047281836,0.00049598346,0.0031341831],"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":[0.00031841075,0.000104823026,0.0006937897,0.00016615119,0.000058153706,0.00006103728,0.000070067465,0.02510343,0.10806192,0.007119182,0.009729149,0.84851384],"study_design_scores_gemma":[0.000014193271,0.00006510897,0.0028430028,0.000012863142,0.000033185286,0.00018611203,0.000029282222,0.9403902,0.04421068,0.0036519608,0.008531611,0.00003180878],"about_ca_topic_score_codex":0.003105462,"about_ca_topic_score_gemma":0.0047442876,"teacher_disagreement_score":0.0039356914,"about_ca_system_score_codex":0.0002478484,"about_ca_system_score_gemma":0.00058966415,"threshold_uncertainty_score":0.013166189},"labels":[],"label_agreement":null},{"id":"W2074353100","doi":"10.1155/2010/378652","title":"Knowledge‐Based Green′s Kernel for Support Vector Regression","year":2010,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Kernel (algebra); Polynomial kernel; Radial basis function kernel; Kernel method; Mathematics; Kernel embedding of distributions; Regularization (linguistics); Regularization perspectives on support vector machines; Artificial intelligence; Benchmark (surveying); Computer science; Pattern recognition (psychology); Machine learning; Inverse problem; Mathematical analysis; Pure mathematics","score_opus":0.021498149984607844,"score_gpt":0.26249896589083543,"score_spread":0.2410008159062276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074353100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002551228,0.000120862176,0.9967429,0.000055079807,0.000013275145,0.000009418247,0.000016271191,0.00015131391,0.00033970972],"genre_scores_gemma":[0.37278396,0.00092296477,0.6201539,0.00025621275,0.000135799,0.00017156356,0.00044592173,0.00024289753,0.004886776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984345,0.0004796531,0.00010023821,0.0002674886,0.00061014993,0.00010808226],"domain_scores_gemma":[0.99689376,0.0017170244,0.000240558,0.000412563,0.0006541673,0.00008186829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020273111,0.00093867665,0.0015182783,0.0011819173,0.00036477693,0.0012538771,0.0017154539,0.0019496833,0.001729221],"category_scores_gemma":[0.008257469,0.00042981506,0.0012858816,0.0012727057,0.0011930872,0.0026988124,0.0011414221,0.0018087727,0.0011962177],"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":[0.0002142241,0.000114259106,0.0008313578,0.00026401467,0.0001465501,0.00020222794,0.00012281968,0.6176835,0.012487777,0.09308361,0.0030305206,0.27181914],"study_design_scores_gemma":[0.000004311815,0.000023496952,0.00009618658,0.000008705323,0.000007513222,0.00003675638,0.0000044915982,0.98833,0.00209586,0.008453245,0.000928825,0.00001061151],"about_ca_topic_score_codex":0.0017185499,"about_ca_topic_score_gemma":0.00093165727,"teacher_disagreement_score":0.0020273111,"about_ca_system_score_codex":0.0009953324,"about_ca_system_score_gemma":0.0010525825,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2078164070","doi":"10.1109/icip.2011.6115808","title":"Human face classification based on localized blur descriptors","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Computer vision; Face (sociological concept); Classifier (UML); Feature extraction; Facial recognition system; Similarity (geometry); Principal component analysis; Feature (linguistics); Image (mathematics)","score_opus":0.09682840374749728,"score_gpt":0.2679985649196429,"score_spread":0.17117016117214562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078164070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18759333,0.0015623803,0.80594033,0.00016147614,0.000118276446,0.00020176682,0.0001947306,0.0011260699,0.0031016548],"genre_scores_gemma":[0.8314872,0.0006725331,0.16405658,0.000096029406,0.00011807945,0.00009545982,0.00043458043,0.000044815424,0.0029947653],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999405,0.000088824076,0.000020008521,0.000104227554,0.0003147745,0.000067205474],"domain_scores_gemma":[0.999548,0.00009942409,0.00005381224,0.000072402545,0.00019808307,0.000028187545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064062735,0.00039041735,0.00082073413,0.0022656452,0.00028811765,0.0006923556,0.0005125597,0.0004351282,0.0014612268],"category_scores_gemma":[0.0013971692,0.00013874483,0.0005037582,0.001213051,0.0003427661,0.00093112123,0.00049787463,0.0003407794,0.0008631468],"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":[0.0005467647,0.00018610815,0.004393839,0.00011356476,0.00007290602,0.00009665961,0.000072403134,0.018430753,0.061711274,0.0033148665,0.0024611922,0.90859973],"study_design_scores_gemma":[0.00002865445,0.00046185424,0.01664057,0.000022458771,0.00006322176,0.0007441843,0.00010724622,0.9278946,0.046647064,0.004355406,0.002978518,0.000056214307],"about_ca_topic_score_codex":0.0016712237,"about_ca_topic_score_gemma":0.0013792168,"teacher_disagreement_score":0.0022656452,"about_ca_system_score_codex":0.00041544822,"about_ca_system_score_gemma":0.00034498062,"threshold_uncertainty_score":0.0048883557},"labels":[],"label_agreement":null},{"id":"W2078277829","doi":"10.1109/t-affc.2012.30","title":"Projection into Expression Subspaces for Face Recognition from Single Sample per Person","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":56,"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":"Linear subspace; Subspace topology; Expression (computer science); Artificial intelligence; Projection (relational algebra); Pattern recognition (psychology); Linear discriminant analysis; Facial recognition system; Face (sociological concept); Facial expression; Computer science; Sample (material); Set (abstract data type); Biometrics; Discriminant; Image (mathematics); Computer vision; Mathematics; Algorithm","score_opus":0.05310404105780693,"score_gpt":0.2796202461226605,"score_spread":0.2265162050648536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078277829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020500476,0.000136765,0.9777344,0.00005104022,0.000017006327,0.000045309785,0.000102813545,0.0004900488,0.0009222169],"genre_scores_gemma":[0.30292785,0.0006043921,0.69166386,0.000089870344,0.00004533991,0.00033256455,0.0011451204,0.00018153252,0.0030094357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931085,0.00023930895,0.000022865757,0.00013262522,0.00024155465,0.00005279554],"domain_scores_gemma":[0.9996654,0.000085897875,0.000025403086,0.00011082552,0.00008981243,0.000022626446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009693547,0.00055608916,0.000597948,0.0004624898,0.00023274851,0.00034481505,0.00041096626,0.0002671897,0.002593312],"category_scores_gemma":[0.0019820633,0.00023603474,0.00069278624,0.0006403435,0.00039897283,0.00056171,0.0008339884,0.0007720948,0.0013225343],"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":[0.000367051,0.00016208932,0.0013718421,0.00011924665,0.000084687425,0.00014848003,0.00023543452,0.07811408,0.11079948,0.012541722,0.0034587397,0.79259706],"study_design_scores_gemma":[0.00002385041,0.00019968199,0.0039205197,0.0000196693,0.000033637647,0.00034811112,0.00010510617,0.92562896,0.049779296,0.014577636,0.0053187963,0.00004469087],"about_ca_topic_score_codex":0.0009926836,"about_ca_topic_score_gemma":0.0009998236,"teacher_disagreement_score":0.002593312,"about_ca_system_score_codex":0.00021969412,"about_ca_system_score_gemma":0.0004408842,"threshold_uncertainty_score":0.008675456},"labels":[],"label_agreement":null},{"id":"W2078374251","doi":"10.1007/s11045-009-0099-y","title":"Perfect histogram matching PCA for face recognition","year":2010,"lang":"en","type":"article","venue":"Multidimensional Systems and Signal Processing","topic":"Face and Expression Recognition","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":"University of Victoria","funders":"","keywords":"Histogram; Histogram matching; Artificial intelligence; Pattern recognition (psychology); Adaptive histogram equalization; Computer science; Facial recognition system; Principal component analysis; Face (sociological concept); Computer vision; Balanced histogram thresholding; Matching (statistics); Mathematics; Histogram equalization; Image (mathematics); Statistics","score_opus":0.02429434075875826,"score_gpt":0.2553203266123745,"score_spread":0.23102598585361628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078374251","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010939652,0.00084111316,0.9842285,0.00014386718,0.00013997378,0.000027367494,0.00016845606,0.0009642946,0.0025467672],"genre_scores_gemma":[0.37838683,0.0016590955,0.60300356,0.00018601323,0.00029998936,0.00011987101,0.00074983615,0.0003277938,0.015266977],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931574,0.00014660283,0.000026327716,0.00015718682,0.0002696485,0.000084502404],"domain_scores_gemma":[0.99962986,0.000067490764,0.000023601466,0.00015336143,0.000108123975,0.000017549877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047957597,0.00036017245,0.00063972134,0.0007138988,0.00047022768,0.00079879805,0.00073181646,0.0004851371,0.0063726553],"category_scores_gemma":[0.0015798955,0.0003561455,0.00059807365,0.0012193107,0.0006213736,0.0011500863,0.00091533334,0.000659454,0.002173977],"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":[0.00027656418,0.000071647395,0.00061643316,0.0001467869,0.000063232284,0.000053172833,0.000040680436,0.027428761,0.03417172,0.037371594,0.010052627,0.88970685],"study_design_scores_gemma":[0.0000371721,0.00015328762,0.004761629,0.00002555252,0.00006995914,0.0004259679,0.000045410325,0.867536,0.060247257,0.049660906,0.016980179,0.000056759927],"about_ca_topic_score_codex":0.002207355,"about_ca_topic_score_gemma":0.0021214746,"teacher_disagreement_score":0.0063726553,"about_ca_system_score_codex":0.00030993248,"about_ca_system_score_gemma":0.0008430242,"threshold_uncertainty_score":0.021318674},"labels":[],"label_agreement":null},{"id":"W2078383409","doi":"10.1016/j.patcog.2011.02.015","title":"Help-Training for semi-supervised support vector machines","year":2011,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Artificial intelligence; Machine learning; Computer science; Least squares support vector machine; Co-training; Relevance vector machine; Discriminative model; Classifier (UML); Pattern recognition (psychology); Semi-supervised learning; Generative grammar; Labeled data; Training (meteorology); Supervised learning; Structured support vector machine; Training set; Margin classifier; Artificial neural network","score_opus":0.11150941637932084,"score_gpt":0.27283192322925187,"score_spread":0.16132250684993105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078383409","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01765386,0.0003278947,0.96722645,0.0002560111,0.00012497444,0.000152531,0.00038695915,0.0120831365,0.0017882412],"genre_scores_gemma":[0.3245128,0.00011100797,0.6636978,0.000309051,0.00011678867,0.00055693864,0.0025384002,0.0010236608,0.007133613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846256,0.000694115,0.00009833576,0.0002687111,0.00032342284,0.00015296016],"domain_scores_gemma":[0.9944976,0.0030995673,0.00015135638,0.00091494067,0.0011565591,0.00017990152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023602096,0.001127138,0.0009250869,0.00059496745,0.000550914,0.00074638065,0.00229836,0.0020446312,0.010370349],"category_scores_gemma":[0.011913084,0.0007580742,0.0007899638,0.00043460753,0.00065854925,0.0015813156,0.0020561258,0.0022936654,0.004214325],"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":[0.0009528448,0.000316372,0.0020443476,0.0003277204,0.0001505371,0.00018999209,0.00018382975,0.16142388,0.006933172,0.0077294344,0.026804183,0.79294366],"study_design_scores_gemma":[0.00004372536,0.00008855842,0.00027175818,0.000016777547,0.000009950536,0.000052821983,0.000019382796,0.98696053,0.004350545,0.005768125,0.0024089543,0.00000892178],"about_ca_topic_score_codex":0.0021503998,"about_ca_topic_score_gemma":0.0035426784,"teacher_disagreement_score":0.010370349,"about_ca_system_score_codex":0.00043284887,"about_ca_system_score_gemma":0.0009291724,"threshold_uncertainty_score":0.034692228},"labels":[],"label_agreement":null},{"id":"W2078679182","doi":"10.1016/j.cviu.2006.06.009","title":"Face detection in gray scale images using locally linear embeddings","year":2006,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Support vector machine; Computer science; Face (sociological concept); Computer vision; Face detection; Facial expression; Facial recognition system; Dimensionality reduction; Embedding; Rotation (mathematics); Mathematics","score_opus":0.02115941015040386,"score_gpt":0.2685617894855427,"score_spread":0.24740237933513887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078679182","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0700536,0.00027131382,0.927069,0.00016128203,0.00004109412,0.00004371623,0.00010960753,0.0011449088,0.0011053985],"genre_scores_gemma":[0.565978,0.0005247297,0.4272842,0.00014074438,0.00007042889,0.00010118622,0.00043647652,0.00023787678,0.0052264263],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997155,0.0000645525,0.000014072071,0.00007871491,0.00008392131,0.000043217733],"domain_scores_gemma":[0.9995771,0.00014996156,0.000055659137,0.000095935044,0.000092161514,0.000029204979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034877946,0.0005619001,0.0006607434,0.00089640025,0.0002539481,0.00088563486,0.00056990085,0.00055713323,0.002794948],"category_scores_gemma":[0.0013035354,0.0003558594,0.0005921182,0.0008187824,0.00041635934,0.0015065911,0.00084863324,0.0006502833,0.0014138119],"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":[0.0005028538,0.0002272468,0.0022672457,0.00016660904,0.000100861704,0.00012518812,0.00013108883,0.045858737,0.178856,0.0066511715,0.0032864804,0.76182646],"study_design_scores_gemma":[0.00001951977,0.00016673621,0.002710406,0.000019951363,0.00003875567,0.00020952555,0.00009046861,0.9409869,0.047482505,0.006892263,0.0013613671,0.000021505213],"about_ca_topic_score_codex":0.0014702583,"about_ca_topic_score_gemma":0.0020796936,"teacher_disagreement_score":0.002794948,"about_ca_system_score_codex":0.0003162077,"about_ca_system_score_gemma":0.00032979256,"threshold_uncertainty_score":0.009350061},"labels":[],"label_agreement":null},{"id":"W2079467126","doi":"10.1109/crv.2010.20","title":"Multispectral Face Recognition in Texture Space","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Université Laval","funders":"","keywords":"Multispectral image; Face (sociological concept); Artificial intelligence; Computer science; Computer vision; Texture (cosmology); Facial recognition system; Image texture; Space (punctuation); Pattern recognition (psychology); Image processing; Image (mathematics); Linguistics","score_opus":0.010951580762499892,"score_gpt":0.2349667149283285,"score_spread":0.2240151341658286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079467126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09721932,0.00097458705,0.892629,0.00015870045,0.00017238258,0.000067994326,0.00023366377,0.0012056928,0.007338603],"genre_scores_gemma":[0.5253303,0.0014006742,0.4622675,0.00016641738,0.00013783546,0.00011879077,0.0006057875,0.0001459387,0.009826764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973625,0.000035496643,0.00001055907,0.000048112266,0.00014106015,0.000028425029],"domain_scores_gemma":[0.99979156,0.000049367256,0.000022258548,0.000047495498,0.00007737734,0.000011955572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022504805,0.00029246885,0.0004932849,0.00077071757,0.00018389554,0.00060151884,0.00037052998,0.00034552172,0.0029276907],"category_scores_gemma":[0.00064580125,0.00012563152,0.00045058827,0.00056273135,0.00024114933,0.0007806404,0.000511595,0.00030207928,0.0014289634],"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":[0.00022588397,0.00011037984,0.0009567718,0.00020359621,0.000040640523,0.00018248716,0.0000809138,0.015461901,0.3210703,0.007616158,0.0019961614,0.65205467],"study_design_scores_gemma":[0.000033358185,0.00039416604,0.012294952,0.000063718755,0.00010395985,0.0028421616,0.00019087656,0.65835345,0.29011276,0.013315544,0.022206414,0.00008857878],"about_ca_topic_score_codex":0.00063424,"about_ca_topic_score_gemma":0.0006227725,"teacher_disagreement_score":0.0029276907,"about_ca_system_score_codex":0.00019207982,"about_ca_system_score_gemma":0.00014462127,"threshold_uncertainty_score":0.009794116},"labels":[],"label_agreement":null},{"id":"W2080712811","doi":"10.1109/hpcsim.2013.6641476","title":"Sparse Support Vector Machine for pattern recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"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":"Support vector machine; Pattern recognition (psychology); Artificial intelligence; Computer science; Generalization; Outlier; Sparse approximation; Structured support vector machine; Representation (politics); Machine learning; Relevance vector machine; Ranking SVM; Mathematics","score_opus":0.030715499138171922,"score_gpt":0.24187432588590102,"score_spread":0.2111588267477291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080712811","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002325295,0.01714157,0.9665414,0.0015038016,0.0007316966,0.00013120698,0.00072999106,0.0024380423,0.008456908],"genre_scores_gemma":[0.14345853,0.029394414,0.79485047,0.0011072527,0.0026125757,0.0007340925,0.0045381603,0.00035398867,0.022950457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986443,0.00039115318,0.00010647292,0.00027556412,0.00051860855,0.00006401503],"domain_scores_gemma":[0.9984718,0.000678671,0.0001666078,0.0002687633,0.00037003908,0.00004408134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001008762,0.001009687,0.0012040323,0.0016691358,0.000283655,0.0015485162,0.0011237534,0.0013297081,0.009420275],"category_scores_gemma":[0.004220601,0.0002525674,0.0006201666,0.0030898843,0.00067059684,0.0014197547,0.0010076036,0.0019729177,0.007949733],"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":[0.0001018215,0.0000674646,0.0007167868,0.0008101174,0.00012738156,0.00018574676,0.00009167313,0.027779952,0.007684937,0.0735617,0.042274546,0.84659785],"study_design_scores_gemma":[0.000041082792,0.00016351107,0.0018239629,0.00041808563,0.00007156928,0.00062768394,0.000094777,0.5597596,0.008358088,0.19344838,0.23508635,0.0001068894],"about_ca_topic_score_codex":0.0010031755,"about_ca_topic_score_gemma":0.0006506937,"teacher_disagreement_score":0.009420275,"about_ca_system_score_codex":0.0005408105,"about_ca_system_score_gemma":0.0006984224,"threshold_uncertainty_score":0.03151399},"labels":[],"label_agreement":null},{"id":"W2081162720","doi":"10.7490/f1000research.1237.1","title":"Perceptual Learning of Inverted Faces across Different Spatial Frequency Bands","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"McMaster University","funders":"","keywords":"Open peer review; Plant biology; Perception; Neuroscience; Perceptual learning; Biology; Cognitive psychology; Psychology; Audiology; Physiology; Medicine","score_opus":0.039313265298242264,"score_gpt":0.2500853120894112,"score_spread":0.21077204679116895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081162720","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96139646,0.00016356334,0.029310789,0.00017043715,0.0001238816,0.000031301362,0.00008734641,0.000114683266,0.008601508],"genre_scores_gemma":[0.9911151,0.00014844452,0.005817406,0.00008464109,0.000016240212,0.000010660967,0.00015609014,0.00003839828,0.0026130471],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998425,0.000024264398,0.000006511992,0.00006197419,0.000036897523,0.00002774011],"domain_scores_gemma":[0.9992459,0.00037354257,0.00007443687,0.00010928945,0.000111607726,0.000085238265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003803237,0.0003511255,0.0003110043,0.00013921145,0.00012079804,0.00086158834,0.00035784565,0.00033981667,0.003940781],"category_scores_gemma":[0.0035580294,0.00028482138,0.00029128257,0.000097335505,0.00037745776,0.0011682971,0.0006047918,0.0009494667,0.0003939316],"study_design_candidate":"observational","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":[0.0029262726,0.0003508794,0.015566927,0.00014177673,0.00012675945,0.0001455604,0.00034535272,0.008771926,0.6855733,0.0035564795,0.0017625808,0.28073215],"study_design_scores_gemma":[0.0004768174,0.0046915184,0.27453792,0.00012618316,0.00045940524,0.0015228282,0.0014206468,0.32800975,0.34464273,0.036249522,0.007677154,0.00018552695],"about_ca_topic_score_codex":0.0011389387,"about_ca_topic_score_gemma":0.0012633728,"teacher_disagreement_score":0.003940781,"about_ca_system_score_codex":0.00023567224,"about_ca_system_score_gemma":0.0002686539,"threshold_uncertainty_score":0.013183236},"labels":[],"label_agreement":null},{"id":"W2081670769","doi":"10.1016/j.patrec.2004.09.055","title":"Robust centroids using fuzzy clustering with feature partitions","year":2004,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Centroid; Pattern recognition (psychology); Robustness (evolution); Artificial intelligence; Partition (number theory); Feature (linguistics); Cluster analysis; Mathematics; Metric (unit); Fuzzy logic; Fuzzy clustering; Computer science; Data mining; Combinatorics; Engineering","score_opus":0.04176947083660912,"score_gpt":0.22777635141779282,"score_spread":0.1860068805811837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081670769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055218404,0.00014660618,0.993323,0.00004106153,0.00003951512,0.00003754455,0.000036302492,0.0004107643,0.00044327276],"genre_scores_gemma":[0.13289566,0.00011161111,0.8648468,0.00003448939,0.000050411814,0.00012724006,0.00028287436,0.0002585787,0.0013922766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99657804,0.00072059693,0.0002235717,0.0009976652,0.0012331393,0.0002469006],"domain_scores_gemma":[0.99660975,0.0011585249,0.00029777366,0.0006546895,0.001172838,0.000106462016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032115532,0.0014281063,0.0024993168,0.0028057336,0.0016648393,0.002204387,0.0033235743,0.00221478,0.0024764107],"category_scores_gemma":[0.009874642,0.0012288269,0.002028613,0.0025228974,0.0013876377,0.0025052405,0.0025290379,0.0015284074,0.0015813622],"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":[0.0011584952,0.00010326633,0.00067557546,0.00022632838,0.0002800647,0.00008756739,0.00035093026,0.3230034,0.023352994,0.023767406,0.004319386,0.6226745],"study_design_scores_gemma":[0.00004579043,0.00008616105,0.00039209708,0.000020197434,0.0000473614,0.00007278101,0.00005552783,0.97353053,0.008161623,0.015806902,0.001729145,0.000051917108],"about_ca_topic_score_codex":0.0062726308,"about_ca_topic_score_gemma":0.0047725737,"teacher_disagreement_score":0.0062726308,"about_ca_system_score_codex":0.0014950623,"about_ca_system_score_gemma":0.0014560659,"threshold_uncertainty_score":0.016984522},"labels":[],"label_agreement":null},{"id":"W2081863860","doi":"10.1016/j.patcog.2004.12.013","title":"A new method of feature fusion and its application in image recognition","year":2005,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":524,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Pattern recognition (psychology); Canonical correlation; Artificial intelligence; Feature vector; Feature (linguistics); Projection (relational algebra); Facial recognition system; Computer science; Linear discriminant analysis; Face (sociological concept); Feature extraction; Mathematics; Range (aeronautics); Algorithm","score_opus":0.02170454728160103,"score_gpt":0.28732639701825513,"score_spread":0.2656218497366541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081863860","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019423317,0.00034728012,0.99653745,0.000046865334,0.00010122642,0.000019995015,0.000034150547,0.00034246285,0.0006282484],"genre_scores_gemma":[0.073924705,0.00081643043,0.92101324,0.000109813016,0.00025678636,0.0001520437,0.00022630411,0.00017476689,0.003325903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987877,0.00017921634,0.00006953635,0.00030175847,0.00058817497,0.000073597526],"domain_scores_gemma":[0.9992828,0.00018181973,0.000042249343,0.00016567983,0.00029014368,0.000037216494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011523156,0.0007983643,0.0015591505,0.0018816367,0.0007952656,0.0012544076,0.0013726093,0.0013259901,0.002955998],"category_scores_gemma":[0.0021038735,0.0005180548,0.0014059125,0.0027280126,0.0009367633,0.0025938756,0.0018474242,0.0013401174,0.0015205164],"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":[0.00018432802,0.00008228703,0.0005386168,0.00022677358,0.00016669999,0.00015747701,0.00016986081,0.01154205,0.07653397,0.03406159,0.005105704,0.87123066],"study_design_scores_gemma":[0.000054487035,0.00036453112,0.0036967378,0.000057898025,0.00029947504,0.0022386946,0.00010470748,0.8062223,0.09712572,0.048519954,0.041088372,0.00022708607],"about_ca_topic_score_codex":0.00081433175,"about_ca_topic_score_gemma":0.00062444556,"teacher_disagreement_score":0.002955998,"about_ca_system_score_codex":0.0003530234,"about_ca_system_score_gemma":0.0005039887,"threshold_uncertainty_score":0.009888828},"labels":[],"label_agreement":null},{"id":"W2082572780","doi":"10.1145/2783258.2783309","title":"Dimensionality Reduction Via Graph Structure Learning","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":107,"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 Victoria","funders":"National Institutes of Health; National Science Foundation","keywords":"Dimensionality reduction; Discriminative model; Cluster analysis; Computer science; Graph; Artificial intelligence; Feature vector; Curse of dimensionality; Visualization; Feature learning; Data structure; Pattern recognition (psychology); Clustering high-dimensional data; Theoretical computer science; Data mining","score_opus":0.022555932658552203,"score_gpt":0.24520974878217827,"score_spread":0.22265381612362606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082572780","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039057008,0.00024925746,0.9945633,0.00024249022,0.000034901324,0.000035350775,0.00013141587,0.00027816146,0.00055954873],"genre_scores_gemma":[0.14975905,0.0009429985,0.84405833,0.00031445528,0.0002227314,0.0003822731,0.0014969931,0.00019009138,0.0026330198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816656,0.0006626423,0.00008607358,0.00047884302,0.0005095218,0.00009640501],"domain_scores_gemma":[0.99770135,0.000818972,0.00026424738,0.0007948911,0.00031931704,0.00010116861],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012009317,0.0012504136,0.0015496839,0.0024625969,0.00093855144,0.0016854665,0.0020377599,0.001432274,0.001383875],"category_scores_gemma":[0.0057301624,0.00057602755,0.0018883763,0.0027811246,0.0015438866,0.002935086,0.0026919371,0.0029507591,0.0008244193],"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":[0.00009001249,0.00021068931,0.0028098926,0.00045773623,0.00030390255,0.00028448657,0.00041740082,0.36206874,0.00849771,0.1738436,0.01854535,0.43247038],"study_design_scores_gemma":[0.000013920132,0.000053417025,0.0004917083,0.000019617524,0.000025134907,0.00013156893,0.000042485455,0.85057235,0.0015641686,0.1415675,0.0054881754,0.000029961033],"about_ca_topic_score_codex":0.0019223711,"about_ca_topic_score_gemma":0.0026201946,"teacher_disagreement_score":0.0024625969,"about_ca_system_score_codex":0.0010378695,"about_ca_system_score_gemma":0.0011318211,"threshold_uncertainty_score":0.007530272},"labels":[],"label_agreement":null},{"id":"W2082692487","doi":"10.1109/ccece.2013.6567728","title":"Fast facial expression recognition based on local binary patterns","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"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":"Local binary patterns; Computer science; Facial expression; Artificial intelligence; Pattern recognition (psychology); Expression (computer science); Facial recognition system; Computer facial animation; Three-dimensional face recognition; Computer vision; Representation (politics); Face (sociological concept); Animation; Facial expression recognition; Binary number; Computer animation; Histogram; Face detection; Image (mathematics); Computer graphics (images); Mathematics","score_opus":0.018210869925277054,"score_gpt":0.22391164889243256,"score_spread":0.2057007789671555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2082692487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11962991,0.00051664596,0.8737855,0.00016784799,0.000077520504,0.000107981155,0.0001632642,0.002209248,0.0033421796],"genre_scores_gemma":[0.649212,0.000672056,0.3463342,0.00008100557,0.000046376375,0.00014451524,0.00037962152,0.00015515242,0.002975014],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995969,0.00008028309,0.000012548032,0.000046544697,0.00023260566,0.000031155698],"domain_scores_gemma":[0.9996075,0.00013443286,0.00003831859,0.000042182295,0.0001615818,0.000015958749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053294457,0.000316968,0.0004898368,0.00065618363,0.00013254228,0.00037574203,0.0003504152,0.00023837664,0.0019626727],"category_scores_gemma":[0.0017325991,0.00015467485,0.00017719298,0.0004423726,0.00018277256,0.00071979396,0.00027985882,0.00034786342,0.0008406482],"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":[0.0003985114,0.000074354764,0.001613045,0.00012766705,0.000032652057,0.00010586464,0.00009107609,0.017778948,0.2858267,0.0021704608,0.00245876,0.689322],"study_design_scores_gemma":[0.000051968043,0.0003878193,0.009946247,0.000030495363,0.000048705337,0.00059521,0.000099334065,0.836051,0.1462573,0.0024971412,0.0039876606,0.00004707466],"about_ca_topic_score_codex":0.0009775208,"about_ca_topic_score_gemma":0.0008945726,"teacher_disagreement_score":0.0019626727,"about_ca_system_score_codex":0.00017077733,"about_ca_system_score_gemma":0.00021559987,"threshold_uncertainty_score":0.0065657496},"labels":[],"label_agreement":null},{"id":"W2083652474","doi":"10.1109/icip.2010.5654251","title":"Fiducial point tracking for facial expression using multiple particle filters with kernel correlation analysis","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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","funders":"","keywords":"Fiducial marker; Particle filter; Kernel (algebra); Artificial intelligence; Tracking (education); Computer science; Computer vision; Correlation; Point (geometry); Pattern recognition (psychology); Kalman filter; Mathematics; Geometry; Combinatorics; Psychology","score_opus":0.027731607647279107,"score_gpt":0.2650038805470806,"score_spread":0.23727227289980146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083652474","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005232575,0.000043348176,0.99432725,0.000024621837,0.000009935322,0.000009915028,0.0000046312057,0.0001724662,0.000175308],"genre_scores_gemma":[0.2768076,0.00015518101,0.7215302,0.000032970947,0.000025211402,0.000087719345,0.00005492263,0.000077509336,0.0012286974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952626,0.00012189623,0.000023171415,0.00010604333,0.00018280023,0.000039901224],"domain_scores_gemma":[0.99913836,0.00040704795,0.00010335859,0.00012478107,0.00020201458,0.000024444593],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009734032,0.00050170004,0.0006179764,0.00083174615,0.00037412965,0.0006483492,0.0007634763,0.0006936035,0.0009142277],"category_scores_gemma":[0.0033553329,0.00045495838,0.00073828426,0.0008153077,0.00043147846,0.0009511998,0.0007053098,0.00079541036,0.0003467296],"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":[0.00021000906,0.00009499601,0.0031027268,0.000097675394,0.000089244066,0.00010812795,0.00017065802,0.35727388,0.03579621,0.020691713,0.0015604137,0.5808043],"study_design_scores_gemma":[0.0000054435563,0.000015249369,0.0004517832,0.0000026076755,0.0000071234394,0.000029696175,0.000005317832,0.9931757,0.004552602,0.0011709596,0.00057487114,0.000008580912],"about_ca_topic_score_codex":0.0055187657,"about_ca_topic_score_gemma":0.003930957,"teacher_disagreement_score":0.0055187657,"about_ca_system_score_codex":0.0006935939,"about_ca_system_score_gemma":0.0010071411,"threshold_uncertainty_score":0.010973275},"labels":[],"label_agreement":null},{"id":"W2084136177","doi":"10.1016/j.patcog.2008.10.023","title":"Model selection for the LS-SVM. Application to handwriting recognition","year":2008,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Pattern recognition (psychology); Artificial intelligence; Computer science; Margin classifier; Classifier (UML); Least squares support vector machine; Structural risk minimization; Maximization; Structured support vector machine; Margin (machine learning); Machine learning; Sequential minimal optimization; Mathematics; Mathematical optimization","score_opus":0.07240955933489673,"score_gpt":0.2721233515483665,"score_spread":0.19971379221346974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084136177","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008920153,0.000632545,0.98530966,0.00018443799,0.00011338809,0.00005948989,0.0002557839,0.0034317898,0.0010927725],"genre_scores_gemma":[0.41060734,0.0008112755,0.56407535,0.00019510556,0.00022536579,0.0003925151,0.0020619866,0.0008561495,0.020774892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969435,0.00010219266,0.000021715161,0.000060569902,0.00009383397,0.000027443208],"domain_scores_gemma":[0.9993505,0.0002763181,0.000045311328,0.000056655907,0.00024011447,0.000031054824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077643024,0.0006935803,0.00082733977,0.00049615535,0.0003783947,0.00063091685,0.00085885316,0.00065128313,0.005914259],"category_scores_gemma":[0.0027611388,0.00037773242,0.00060249964,0.0006697209,0.00013836408,0.00061577786,0.00061687984,0.0010715312,0.003541085],"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":[0.00018198878,0.00010051564,0.0010146309,0.00015407451,0.00008473812,0.000070584596,0.000032848282,0.1275176,0.01139075,0.0023210552,0.013740706,0.8433906],"study_design_scores_gemma":[0.000007927805,0.000021322357,0.0003886489,0.0000056681574,0.000012259846,0.00003631887,0.0000070018637,0.9928712,0.0029633557,0.0018391964,0.0018413453,0.000005870066],"about_ca_topic_score_codex":0.004053204,"about_ca_topic_score_gemma":0.0044038245,"teacher_disagreement_score":0.005914259,"about_ca_system_score_codex":0.00030761224,"about_ca_system_score_gemma":0.00070160185,"threshold_uncertainty_score":0.019785225},"labels":[],"label_agreement":null},{"id":"W2084269393","doi":"10.1109/cvpr.2014.342","title":"Dual Linear Regression Based Classification for Face Cluster Recognition","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":58,"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 Northern British Columbia","funders":"","keywords":"Linear subspace; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Computer science; Intersection (aeronautics); Subspace topology; Similarity (geometry); Cluster (spacecraft); Relation (database); Computer vision; Mathematics; Image (mathematics); Data mining; Geography; Geometry","score_opus":0.05570834887365179,"score_gpt":0.28737468162494423,"score_spread":0.23166633275129245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084269393","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01047454,0.0001955342,0.9878726,0.00013029412,0.00003228769,0.000028229077,0.00005126119,0.0005823647,0.0006329264],"genre_scores_gemma":[0.38257933,0.0003570112,0.6093099,0.00022954051,0.00013682629,0.00017632678,0.0008404543,0.00019503715,0.0061756345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981298,0.00051947683,0.000085399544,0.00054950843,0.0005144258,0.0002014069],"domain_scores_gemma":[0.9982109,0.00064496463,0.00018542467,0.00035809603,0.00052185514,0.00007888563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019672052,0.0007118488,0.0015386473,0.0013829133,0.00067202776,0.0012213994,0.0023031258,0.0013399693,0.0023006531],"category_scores_gemma":[0.005426233,0.0004258578,0.00095061125,0.0016862402,0.0008424628,0.0013124177,0.0016437506,0.0023968974,0.0019371214],"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":[0.00046569205,0.00034193328,0.0025096803,0.00013733325,0.00014116865,0.00012043216,0.00016840805,0.31276485,0.017931972,0.016486049,0.0068633854,0.64206916],"study_design_scores_gemma":[0.0000033866982,0.000018758974,0.0001537072,0.0000022135296,0.000004859852,0.000022917262,0.000012410199,0.99514943,0.0020461641,0.002115124,0.00046441704,0.0000066003668],"about_ca_topic_score_codex":0.0043538474,"about_ca_topic_score_gemma":0.0028135427,"teacher_disagreement_score":0.0043538474,"about_ca_system_score_codex":0.0010409253,"about_ca_system_score_gemma":0.00090944325,"threshold_uncertainty_score":0.010403693},"labels":[],"label_agreement":null},{"id":"W2086144086","doi":"10.1016/j.patcog.2011.07.024","title":"Heteroscedastic linear feature extraction based on sufficiency conditions","year":2011,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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 Toronto","funders":"","keywords":"Heteroscedasticity; Pattern recognition (psychology); Feature (linguistics); Feature extraction; Artificial intelligence; Computer science; Statistics; Mathematics; Econometrics","score_opus":0.05558796103483072,"score_gpt":0.2769879225709773,"score_spread":0.2213999615361466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086144086","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062512783,0.00024694522,0.9922776,0.000115078095,0.000025068684,0.000024485704,0.00014980698,0.00014952551,0.0007601916],"genre_scores_gemma":[0.5694998,0.0018775146,0.4158907,0.00040570562,0.00042252216,0.0004368915,0.003567133,0.00038103524,0.0075187655],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985354,0.0005182227,0.00015522208,0.00031082853,0.0003391468,0.00014120709],"domain_scores_gemma":[0.9918665,0.0061868066,0.00032654626,0.00052970636,0.0010099174,0.00008050684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038322888,0.0010658228,0.0013016044,0.0010510244,0.00040772487,0.0016528355,0.00084642135,0.0010004401,0.0053334576],"category_scores_gemma":[0.012016178,0.00075413863,0.0010074903,0.0009292194,0.00095497956,0.0027255826,0.0012097849,0.0012164963,0.002275233],"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":[0.0023313677,0.00031021866,0.0065272395,0.0015737588,0.00052862096,0.001998084,0.0003137611,0.1772449,0.11528302,0.26586294,0.014146931,0.41387925],"study_design_scores_gemma":[0.00007450325,0.00018874567,0.0023814372,0.00006934354,0.00009222095,0.00068150635,0.00005239909,0.8936198,0.028979616,0.07117306,0.0026346599,0.000052718577],"about_ca_topic_score_codex":0.0006718484,"about_ca_topic_score_gemma":0.00075265893,"teacher_disagreement_score":0.0053334576,"about_ca_system_score_codex":0.0002979914,"about_ca_system_score_gemma":0.001080957,"threshold_uncertainty_score":0.020267367},"labels":[],"label_agreement":null},{"id":"W2086156411","doi":"10.1016/j.sigpro.2009.03.007","title":"Curvelet based face recognition via dimension reduction","year":2009,"lang":"en","type":"article","venue":"Signal Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":117,"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 Windsor; University of British Columbia","funders":"","keywords":"Curvelet; Contourlet; Wavelet; Artificial intelligence; Pattern recognition (psychology); Wavelet transform; Computer science; Face (sociological concept); Dimensionality reduction; Facial recognition system; Feature extraction; Dimension (graph theory); Feature (linguistics); Curse of dimensionality; Discrete wavelet transform; Computer vision; Mathematics","score_opus":0.023353600170004662,"score_gpt":0.25184882448830165,"score_spread":0.228495224318297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086156411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054272957,0.00035210044,0.9403931,0.00024232442,0.000097411306,0.000061018523,0.00018050679,0.0014985283,0.0029021124],"genre_scores_gemma":[0.37988386,0.0008141333,0.60738146,0.00018715014,0.00017417365,0.00014959052,0.00084699393,0.00026199454,0.010300639],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996226,0.000054646694,0.000014258256,0.000055930654,0.00021596519,0.000036636342],"domain_scores_gemma":[0.9995009,0.00015700363,0.000027310054,0.00013006045,0.00016052694,0.000024246141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003050722,0.00047490498,0.0007488678,0.0009466814,0.00030800872,0.0007400298,0.0004522001,0.0004951894,0.0031961529],"category_scores_gemma":[0.0009146821,0.00026340174,0.0004291694,0.00084748096,0.00033069644,0.0008010639,0.0006379748,0.0008055603,0.0018791462],"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":[0.00021804757,0.000116021736,0.00091170485,0.000059827424,0.000037798593,0.00008277825,0.000054532593,0.021797752,0.17972088,0.006349523,0.0041128458,0.7865383],"study_design_scores_gemma":[0.000024044588,0.00013187635,0.0037954522,0.000013214557,0.000036743848,0.0005720011,0.000046562956,0.79419196,0.18833777,0.0054768906,0.0073320316,0.000041551342],"about_ca_topic_score_codex":0.00087688986,"about_ca_topic_score_gemma":0.00094371586,"teacher_disagreement_score":0.0031961529,"about_ca_system_score_codex":0.00029837605,"about_ca_system_score_gemma":0.0003597814,"threshold_uncertainty_score":0.010692179},"labels":[],"label_agreement":null},{"id":"W2089840238","doi":"10.4236/jsip.2013.43b015","title":"Combined Dictionary Learning in Facial Expression Recognition","year":2013,"lang":"en","type":"article","venue":"Journal of Signal and Information Processing","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; K-SVD; Pattern recognition (psychology); Facial recognition system; Facial expression recognition; Scheme (mathematics); Dictionary learning; Speech recognition; Computation; Expression (computer science); Discriminant; Face (sociological concept); Facial expression; Image (mathematics); Mathematics; Algorithm; Linguistics","score_opus":0.009571536193556763,"score_gpt":0.203321013007574,"score_spread":0.19374947681401722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089840238","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015364016,0.0011018565,0.98181194,0.00010676245,0.00006053204,0.00002523072,0.00003755872,0.00024757785,0.0012445404],"genre_scores_gemma":[0.43231595,0.0022059241,0.5572966,0.00017511753,0.0001822597,0.00010939226,0.00033146198,0.000095549316,0.007287806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992132,0.00021551798,0.000048630598,0.00015667205,0.00030727073,0.000058604284],"domain_scores_gemma":[0.99940884,0.00020864337,0.000041216175,0.00011682599,0.00019674953,0.000027781969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006594166,0.00037634163,0.000893848,0.00064317725,0.00019995854,0.0005849896,0.0006300834,0.0005778444,0.0017063755],"category_scores_gemma":[0.0014238037,0.0002691569,0.0005187209,0.0009812345,0.0004582109,0.001008113,0.0009109795,0.0007520442,0.0009210409],"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":[0.0003284961,0.00014264628,0.0009457788,0.00022980101,0.00013506819,0.00010983542,0.00009623438,0.1006965,0.05454878,0.016338304,0.0027927614,0.8236358],"study_design_scores_gemma":[0.000019203939,0.00015295777,0.00047550292,0.000013535492,0.000022981738,0.00017262525,0.000021299782,0.974841,0.015872516,0.0051296093,0.0032617503,0.000017112128],"about_ca_topic_score_codex":0.0011408151,"about_ca_topic_score_gemma":0.0012816851,"teacher_disagreement_score":0.0017063755,"about_ca_system_score_codex":0.00020988949,"about_ca_system_score_gemma":0.00027909756,"threshold_uncertainty_score":0.005708337},"labels":[],"label_agreement":null},{"id":"W2090036526","doi":"","title":"Distance Metric Learning Versus Fisher Discriminant Analysis.","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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 Waterloo","funders":"","keywords":"Linear discriminant analysis; Optimal discriminant analysis; Metric (unit); Artificial intelligence; Statistics; Kernel Fisher discriminant analysis; Mathematics; Discriminant; Computer science; Multiple discriminant analysis; Fisher kernel; Pattern recognition (psychology); Engineering","score_opus":0.19794439780867815,"score_gpt":0.35058680429940026,"score_spread":0.1526424064907221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090036526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023679743,0.02257721,0.9376214,0.0020299908,0.0012385439,0.000100843725,0.00037840265,0.0011467808,0.011227166],"genre_scores_gemma":[0.41801238,0.008798031,0.552855,0.00049356854,0.0014442913,0.0002091422,0.001466898,0.00046689375,0.016253784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99689436,0.0014433172,0.0002452033,0.0004062728,0.0008753093,0.00013550947],"domain_scores_gemma":[0.99536335,0.0024399522,0.0002645656,0.00065169996,0.0010783303,0.00020197502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0040695597,0.00088870083,0.0011456667,0.0028043545,0.0006991719,0.0018185637,0.0013208104,0.0013808359,0.0044769407],"category_scores_gemma":[0.015632933,0.0003205448,0.0004752496,0.0027368497,0.0014049032,0.0033630594,0.0014623159,0.0017604321,0.002619026],"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":[0.000557738,0.00012865906,0.0036474352,0.0003181114,0.00020529641,0.000073229945,0.00011108591,0.024612058,0.0029959807,0.07872932,0.025069058,0.86355215],"study_design_scores_gemma":[0.00007483137,0.00031581413,0.004714336,0.00014850266,0.000122009544,0.0005231019,0.00031625477,0.7262895,0.008093177,0.22588485,0.03342874,0.000088888104],"about_ca_topic_score_codex":0.002292786,"about_ca_topic_score_gemma":0.0023620303,"teacher_disagreement_score":0.0044769407,"about_ca_system_score_codex":0.00062462623,"about_ca_system_score_gemma":0.00078051025,"threshold_uncertainty_score":0.021522164},"labels":[],"label_agreement":null},{"id":"W2091110007","doi":"10.1109/icsmc.2010.5642284","title":"A new framework for face recognition in and beyond the visible spectrum","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université Laval","funders":"","keywords":"Normalization (sociology); Facial recognition system; Computer science; Multispectral image; Artificial intelligence; Computer vision; Face (sociological concept); Pattern recognition (psychology); Three-dimensional face recognition; Face detection; Feature extraction","score_opus":0.016041406784315506,"score_gpt":0.2608233437737681,"score_spread":0.24478193698945258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091110007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001892452,0.0005581757,0.9943217,0.00010184177,0.00009838138,0.00008199464,0.0000891485,0.00039518182,0.0024610737],"genre_scores_gemma":[0.04213925,0.0007910432,0.95100945,0.00014316684,0.00013274272,0.00028864533,0.00033466142,0.00010648767,0.0050545624],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985499,0.00022206569,0.00006836997,0.00035479513,0.00072738616,0.000077534896],"domain_scores_gemma":[0.99943036,0.00010785804,0.000051568764,0.00013760434,0.00023040698,0.000042105577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015235825,0.0009096014,0.00092244864,0.0014726544,0.00073174806,0.001747988,0.001694087,0.001049735,0.0031890932],"category_scores_gemma":[0.0014490369,0.0003332778,0.0014766257,0.00072977727,0.0011396061,0.001918032,0.0018423419,0.0012972159,0.0017314218],"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":[0.00020152165,0.00031839308,0.0014359122,0.00045747877,0.00020719989,0.00024164155,0.0003571963,0.07182828,0.0842259,0.23093553,0.0127409035,0.59705],"study_design_scores_gemma":[0.00004714016,0.000591461,0.004745105,0.00016172806,0.00013714455,0.0015250045,0.00021831196,0.70394564,0.04229276,0.11881849,0.1273155,0.0002018295],"about_ca_topic_score_codex":0.0040038307,"about_ca_topic_score_gemma":0.0041895625,"teacher_disagreement_score":0.0040038307,"about_ca_system_score_codex":0.00092580944,"about_ca_system_score_gemma":0.0012126738,"threshold_uncertainty_score":0.010668576},"labels":[],"label_agreement":null},{"id":"W2091462060","doi":"10.1109/icassp.2013.6638325","title":"Metric based Gaussian kernel learning for classification","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of British Columbia","funders":"","keywords":"Mahalanobis distance; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature vector; Metric (unit); k-nearest neighbors algorithm; Support vector machine; Gaussian; Machine learning; Metric space; Parametric statistics; Mathematics; Statistics","score_opus":0.03344707675680329,"score_gpt":0.2615691564160104,"score_spread":0.2281220796592071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091462060","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025117304,0.0010615763,0.9946957,0.00013211677,0.00006657925,0.000031859698,0.0000683847,0.00082491507,0.0006071288],"genre_scores_gemma":[0.2265798,0.0022110355,0.7638888,0.00027093815,0.00026207612,0.00030745924,0.0012752245,0.0003585107,0.0048461226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967991,0.00091500423,0.00022027345,0.0007436506,0.0011393233,0.00018259953],"domain_scores_gemma":[0.99753916,0.0006543412,0.0002332995,0.00059356896,0.000883214,0.00009645033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002037625,0.0012437997,0.0018222296,0.0018905902,0.0006226497,0.0014896089,0.0021613324,0.0015213266,0.0027444726],"category_scores_gemma":[0.009372845,0.00043127738,0.0010342386,0.0034138593,0.0009650812,0.002734651,0.001638988,0.002462918,0.0031656101],"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":[0.00014655062,0.00013522692,0.0013101987,0.0002970272,0.00013289523,0.000066978195,0.00011057285,0.14555426,0.0064777657,0.050582632,0.012565864,0.7826201],"study_design_scores_gemma":[0.000006562559,0.000037266826,0.00036176527,0.000016005763,0.000009798152,0.00006701047,0.000018897874,0.9646574,0.0026669353,0.026509361,0.0056213257,0.000027569764],"about_ca_topic_score_codex":0.0038547632,"about_ca_topic_score_gemma":0.002781701,"teacher_disagreement_score":0.0038547632,"about_ca_system_score_codex":0.0014801845,"about_ca_system_score_gemma":0.001277394,"threshold_uncertainty_score":0.010776162},"labels":[],"label_agreement":null},{"id":"W2092620989","doi":"10.1109/icip.2000.899284","title":"Face detection by facets: combined bottom-up and top-down search using compound templates","year":2000,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of British Columbia","funders":"","keywords":"Template; Computer science; Face (sociological concept); Artificial intelligence; Facial recognition system; Feature (linguistics); Pattern recognition (psychology); Feature extraction; Computer vision; Image (mathematics); Space (punctuation); Domain (mathematical analysis); Mathematics","score_opus":0.021501254971990746,"score_gpt":0.26086965303726506,"score_spread":0.23936839806527432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092620989","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022052886,0.0004569627,0.97249955,0.00006460825,0.000027239521,0.00009808748,0.0000753733,0.0026953463,0.0020299638],"genre_scores_gemma":[0.17668968,0.00043850543,0.81895685,0.00012056852,0.000044925222,0.000073847696,0.00035601246,0.000312911,0.0030066425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999134,0.000107340864,0.000046027064,0.00016775657,0.00042628203,0.00011871063],"domain_scores_gemma":[0.99881077,0.00048092398,0.000061305436,0.00038967867,0.00018421622,0.00007311407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077392015,0.0011653717,0.0022541874,0.0016839844,0.00048719512,0.0017685764,0.0024002255,0.0009951072,0.0039034714],"category_scores_gemma":[0.00218189,0.00065376004,0.0014199723,0.0015178547,0.0007994465,0.0026363628,0.0020145732,0.0008730486,0.0021097017],"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":[0.000418455,0.0001362179,0.0019891446,0.00014872283,0.00019491742,0.00020647096,0.00010525209,0.021401051,0.11735251,0.004156363,0.0030877527,0.8508032],"study_design_scores_gemma":[0.00007190259,0.0004629027,0.0032928325,0.000029046925,0.00018699572,0.0013468064,0.00012503218,0.88381183,0.09359594,0.01296392,0.0040269173,0.00008592935],"about_ca_topic_score_codex":0.004889413,"about_ca_topic_score_gemma":0.0068928003,"teacher_disagreement_score":0.004889413,"about_ca_system_score_codex":0.00042033533,"about_ca_system_score_gemma":0.0009556941,"threshold_uncertainty_score":0.013058424},"labels":[],"label_agreement":null},{"id":"W2094086078","doi":"10.1016/j.jmva.2005.09.014","title":"Minimum distance classification rules for high dimensional data","year":2006,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Dimension (graph theory); Inverse; Covariance matrix; Minimum distance; Covariance; Sample mean and sample covariance; Multivariate statistics; Combinatorics; Statistics; High dimensional; Distance matrix; Artificial intelligence; Geometry","score_opus":0.03869063265686028,"score_gpt":0.2969973400814654,"score_spread":0.2583067074246051,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094086078","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018533915,0.0005453552,0.9785452,0.00022203749,0.00008295574,0.000113321534,0.00035217055,0.000731095,0.00087394484],"genre_scores_gemma":[0.16333188,0.00041596225,0.8315669,0.00016794905,0.00008967933,0.0003507761,0.0023775396,0.00016187973,0.0015374225],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927468,0.0011362493,0.0015952775,0.0010686165,0.0032166415,0.00023641055],"domain_scores_gemma":[0.9766193,0.015898269,0.00089807966,0.0026241892,0.0036872812,0.00027287967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004883556,0.00070602936,0.0020049985,0.0028483502,0.0011721066,0.0033072599,0.0031670837,0.0019664809,0.0017607572],"category_scores_gemma":[0.03191276,0.000708526,0.0015204882,0.0019025039,0.0012671787,0.0034268117,0.0021594216,0.0029529512,0.0012571154],"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":[0.0003644028,0.00030059664,0.008376854,0.0005073485,0.00021362523,0.00047885228,0.00040093105,0.098761275,0.006241699,0.029973,0.008921287,0.8454601],"study_design_scores_gemma":[0.00006495507,0.000107897446,0.0018662388,0.00018462607,0.00010241902,0.00060370634,0.00012059293,0.8754268,0.0076711383,0.10716617,0.0066376487,0.000047799953],"about_ca_topic_score_codex":0.00171445,"about_ca_topic_score_gemma":0.0027953694,"teacher_disagreement_score":0.004883556,"about_ca_system_score_codex":0.0007193338,"about_ca_system_score_gemma":0.0012497683,"threshold_uncertainty_score":0.02582705},"labels":[],"label_agreement":null},{"id":"W2094504096","doi":"10.1109/iccat.2013.6522006","title":"Histograms of fuzzy oriented gradients for face recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; University of Cambridge","keywords":"Histogram; Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Face (sociological concept); Feature (linguistics); Computer science; Feature vector; Dimension (graph theory); Fuzzy logic; Histogram of oriented gradients; Feature extraction; Computer vision; Mathematics; Image (mathematics)","score_opus":0.023964913584002795,"score_gpt":0.23758296895287606,"score_spread":0.21361805536887327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094504096","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017783834,0.005503991,0.97112095,0.00030511268,0.00018346321,0.000069735135,0.0002571113,0.0011485429,0.0036272425],"genre_scores_gemma":[0.4150633,0.0039799768,0.5740937,0.00019753157,0.00022146,0.00012931798,0.00069267565,0.00013275005,0.0054892967],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998099,0.000047604975,0.000010938446,0.000028090608,0.00008695192,0.000016447211],"domain_scores_gemma":[0.99981123,0.000056292443,0.000016047275,0.0000338833,0.00006933564,0.000013116192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003448241,0.0002245007,0.000350113,0.0007724676,0.00015732177,0.00050271704,0.00035353852,0.00029406114,0.0020853311],"category_scores_gemma":[0.00094990106,0.00012850203,0.00020787655,0.00081937556,0.00028573634,0.00056066667,0.00026747465,0.00041557458,0.00073712366],"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":[0.00015403869,0.000056167457,0.0006434704,0.00016434859,0.000031956406,0.000059259404,0.000044484263,0.039178647,0.03811638,0.02429997,0.006682501,0.89056873],"study_design_scores_gemma":[0.000041242987,0.00015782741,0.004441257,0.0000717475,0.00003563233,0.00029077448,0.00005761891,0.8776541,0.03697888,0.0476151,0.032575633,0.000080233855],"about_ca_topic_score_codex":0.002310916,"about_ca_topic_score_gemma":0.0023312622,"teacher_disagreement_score":0.002310916,"about_ca_system_score_codex":0.00037294065,"about_ca_system_score_gemma":0.00035440765,"threshold_uncertainty_score":0.0069761276},"labels":[],"label_agreement":null},{"id":"W2095730740","doi":"10.1109/icarcv.2010.5707834","title":"A weighted voting scheme for recognition of faces with illumination variation","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Facial recognition system; Voting; Pattern recognition (psychology); Computer vision; Face (sociological concept); Classifier (UML); Variation (astronomy); Support vector machine","score_opus":0.014998584502095829,"score_gpt":0.23448827187070953,"score_spread":0.2194896873686137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095730740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018706406,0.00026165732,0.9793921,0.00003159272,0.0000942119,0.000075819524,0.000045454828,0.00042519503,0.0009675558],"genre_scores_gemma":[0.35893288,0.00031481983,0.63471854,0.00006586584,0.00011140611,0.00017091338,0.00035439432,0.00011792983,0.0052131824],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998985,0.00024866377,0.00006342441,0.00020521425,0.00038425124,0.00011328847],"domain_scores_gemma":[0.99949956,0.000104696795,0.000042768424,0.00011606509,0.00020901581,0.000027944836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010849945,0.0005240876,0.0010716507,0.0008646272,0.00042946593,0.00047367386,0.0013102082,0.00046517004,0.0019220838],"category_scores_gemma":[0.0014587569,0.0003180241,0.0006236551,0.0008104012,0.00041453083,0.0008751453,0.00073982956,0.0006115332,0.0008029776],"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":[0.00043636473,0.00008720871,0.0009464092,0.00012597074,0.00009380592,0.00007007558,0.00009071904,0.024547953,0.14751239,0.007914694,0.0024775816,0.8156968],"study_design_scores_gemma":[0.000054182772,0.00039272357,0.0025158925,0.000018959474,0.00009820453,0.00038942607,0.000045391756,0.8815383,0.09892301,0.006532008,0.0094155865,0.000076286975],"about_ca_topic_score_codex":0.0011543207,"about_ca_topic_score_gemma":0.0019815878,"teacher_disagreement_score":0.0019220838,"about_ca_system_score_codex":0.00029360421,"about_ca_system_score_gemma":0.00039601722,"threshold_uncertainty_score":0.0064299703},"labels":[],"label_agreement":null},{"id":"W2096856753","doi":"10.1109/icig.2004.26","title":"A Study of Aggregated 2D Gabor Features on Appearance-Based Face Recognition","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Artificial intelligence; Facial recognition system; Pattern recognition (psychology); Computer science; Face (sociological concept); Computer vision; Feature (linguistics); Three-dimensional face recognition; Gabor filter; Feature extraction; Facial expression; Feature vector; Face detection","score_opus":0.0278458310141105,"score_gpt":0.2638377317119111,"score_spread":0.2359919006978006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2096856753","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16983047,0.003186943,0.8229467,0.0003057686,0.000088210334,0.000036543915,0.000041742704,0.0003026301,0.0032610325],"genre_scores_gemma":[0.89064485,0.0016449734,0.10562604,0.00009350375,0.00017496795,0.00002605997,0.00006579401,0.000056377263,0.0016674962],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928504,0.00020742325,0.000025025114,0.000108695844,0.00032795864,0.00004592291],"domain_scores_gemma":[0.9972275,0.0018484668,0.0001552703,0.0003262397,0.00038374975,0.000058821544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092967314,0.00031714333,0.00079870335,0.0005090915,0.00016650047,0.0005787789,0.00042699274,0.0004708145,0.0007485157],"category_scores_gemma":[0.0045391554,0.00026021595,0.0003731928,0.000895256,0.0006271504,0.0011756177,0.00038445182,0.00038171123,0.00020574554],"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":[0.0005713601,0.00014287571,0.004193466,0.0003604351,0.00015280572,0.0011960162,0.00041659517,0.18711577,0.15598997,0.03822336,0.002101731,0.6095357],"study_design_scores_gemma":[0.000012645232,0.00024948522,0.005894246,0.000021576421,0.00004543529,0.0007196885,0.000039756553,0.9617809,0.02179157,0.0075019547,0.0019126665,0.000029997174],"about_ca_topic_score_codex":0.0008178124,"about_ca_topic_score_gemma":0.0005367688,"teacher_disagreement_score":0.00092967314,"about_ca_system_score_codex":0.0002833931,"about_ca_system_score_gemma":0.0001429493,"threshold_uncertainty_score":0.0049166083},"labels":[],"label_agreement":null},{"id":"W2097364796","doi":"10.1109/ijcnn.2007.4371390","title":"Distance-based Disagreement Classifiers Combination","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure","funders":"","keywords":"Euclidean distance; Computer science; Artificial intelligence; Confusion; Pattern recognition (psychology); Distance measures; Machine learning; Measure (data warehouse); Euclidean geometry; Data mining; Mathematics","score_opus":0.07915753826965116,"score_gpt":0.3199235630052325,"score_spread":0.24076602473558134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097364796","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02363884,0.00038368136,0.9724328,0.000085322026,0.00007604193,0.00013099556,0.00009453631,0.00042332654,0.0027343568],"genre_scores_gemma":[0.602332,0.00019065711,0.3930205,0.00009398081,0.00014230916,0.0003414075,0.0006134432,0.0002540695,0.003011622],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9842367,0.0038236345,0.001188224,0.0018229183,0.008264811,0.0006636834],"domain_scores_gemma":[0.98168737,0.0067984764,0.0014467436,0.0025891038,0.0070349234,0.0004433565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010107453,0.0019400987,0.002733733,0.005600516,0.001225352,0.0035395424,0.0031685126,0.001572713,0.002691617],"category_scores_gemma":[0.0310664,0.00060513715,0.0013772381,0.003424323,0.0011439038,0.0035234033,0.0031537334,0.0019480536,0.0013734729],"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":[0.0007986044,0.00025303516,0.014028915,0.00037462692,0.00074381486,0.00024026455,0.0006979392,0.22234172,0.023735864,0.027749198,0.003747191,0.7052888],"study_design_scores_gemma":[0.000028910643,0.00032242882,0.0030556584,0.000034348413,0.0000907373,0.00027774912,0.0001596589,0.9569159,0.018470176,0.016954515,0.0036073574,0.00008259819],"about_ca_topic_score_codex":0.0007353976,"about_ca_topic_score_gemma":0.0007274373,"teacher_disagreement_score":0.010107453,"about_ca_system_score_codex":0.0011895881,"about_ca_system_score_gemma":0.0010933585,"threshold_uncertainty_score":0.053453982},"labels":[],"label_agreement":null},{"id":"W2097842685","doi":"10.1142/s0219878907001204","title":"TWO-STAGE METRIC LEARNING PROCEDURE FOR FACIAL SIGNATURE AUTHENTICATION","year":2007,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McMaster University","keywords":"Computer science; Margin (machine learning); Metric (unit); Generalization; Artificial intelligence; Decision boundary; Pattern recognition (psychology); Authentication (law); Signature (topology); Boundary (topology); Biometrics; Class (philosophy); Machine learning; Support vector machine; Mathematics","score_opus":0.008628718007751134,"score_gpt":0.2875467239574417,"score_spread":0.2789180059496906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097842685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039715865,0.000030806958,0.9949922,0.000027347149,0.000020009185,0.0000518786,0.000016783093,0.0006110724,0.00027833108],"genre_scores_gemma":[0.17434849,0.000056779787,0.82259715,0.00007364772,0.000035254467,0.0002319437,0.00018389741,0.00009081401,0.0023821243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987987,0.00027593048,0.00008330706,0.00026646972,0.0004962303,0.00007922833],"domain_scores_gemma":[0.9989937,0.00018429337,0.00006669802,0.00033777597,0.0003714094,0.00004611154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011221456,0.00074956537,0.0006888276,0.00058487453,0.0004240448,0.00039344802,0.0014408688,0.00090906164,0.0033566614],"category_scores_gemma":[0.0025378112,0.00029943787,0.0005926009,0.00043286622,0.00045630307,0.0010200493,0.0012230327,0.0014230196,0.0015653758],"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":[0.0002444369,0.0001544047,0.00072359847,0.000108986766,0.000056718243,0.00010756448,0.00007376031,0.042000216,0.09726885,0.010736224,0.002591081,0.84593415],"study_design_scores_gemma":[0.000022702357,0.00026200234,0.0012929174,0.000005413615,0.00001771623,0.00035072363,0.0000107296655,0.90561503,0.081749395,0.005562209,0.005053408,0.000057749185],"about_ca_topic_score_codex":0.0010659442,"about_ca_topic_score_gemma":0.0015227093,"teacher_disagreement_score":0.0033566614,"about_ca_system_score_codex":0.00042185467,"about_ca_system_score_gemma":0.0009202198,"threshold_uncertainty_score":0.011229157},"labels":[],"label_agreement":null},{"id":"W2097844420","doi":"10.1109/iccit.2008.132","title":"Pseudo-Zernike Moment Invariants for Recognition of Faces Using Different Classifiers in FERET Database","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"","keywords":"Zernike polynomials; Artificial intelligence; Pattern recognition (psychology); Computer science; Invariant (physics); Facial recognition system; Feature extraction; Support vector machine; Moment (physics); Biometrics; Computer vision; Mathematics","score_opus":0.13956333358665363,"score_gpt":0.29169642879489854,"score_spread":0.15213309520824492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097844420","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63046443,0.004723581,0.33803323,0.00078867667,0.0007789807,0.0004928538,0.007441832,0.0061150594,0.011161375],"genre_scores_gemma":[0.8010205,0.0020121285,0.17186174,0.00012345062,0.00017848625,0.00041550424,0.017765412,0.00018194689,0.006440923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990693,0.00017112453,0.00009700381,0.000118463635,0.00044726813,0.00009699539],"domain_scores_gemma":[0.9993555,0.00022189604,0.00006154713,0.00010905832,0.00022425596,0.000027697843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012378207,0.0005997525,0.0006508268,0.0023641505,0.00031963695,0.0004131607,0.0006082351,0.00043583722,0.0030359006],"category_scores_gemma":[0.0029895299,0.0001003566,0.0005376542,0.0013460713,0.00021088809,0.00086170615,0.00032599788,0.00031943375,0.0012322142],"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":[0.001449351,0.00052625616,0.006698996,0.00032212437,0.00014372467,0.00041195226,0.00008999473,0.024553427,0.089569695,0.0026856386,0.029357351,0.84419143],"study_design_scores_gemma":[0.00013980624,0.0016165794,0.085784145,0.00012962126,0.00024260867,0.0030705312,0.0003251799,0.65328646,0.22294678,0.0033842782,0.02886399,0.00021000837],"about_ca_topic_score_codex":0.0026764926,"about_ca_topic_score_gemma":0.0025028787,"teacher_disagreement_score":0.0030359006,"about_ca_system_score_codex":0.0003695546,"about_ca_system_score_gemma":0.00039834806,"threshold_uncertainty_score":0.010156095},"labels":[],"label_agreement":null},{"id":"W2097937070","doi":"10.1109/fg.2013.6553755","title":"Ensemble of Randomized Linear Discriminant Analysis for face recognition with single sample per person","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Magnum Semiconductor (Canada); York University","funders":"","keywords":"Linear discriminant analysis; Artificial intelligence; Computer science; Facial recognition system; Pattern recognition (psychology); Kernel (algebra); Curse of dimensionality; Sample (material); Machine learning; Face (sociological concept); Kernel Fisher discriminant analysis; Constraint (computer-aided design); Linear subspace; Discriminant; Mathematics","score_opus":0.04461369146072735,"score_gpt":0.24810834302479534,"score_spread":0.20349465156406799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097937070","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03425405,0.0007494224,0.9624855,0.000091425885,0.00011055088,0.00004316932,0.000090984206,0.0012660942,0.0009088557],"genre_scores_gemma":[0.4619506,0.0005505509,0.53278166,0.00012374613,0.00015279879,0.00018867143,0.00080989354,0.00016204837,0.003279997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859256,0.00055088714,0.000057370973,0.0002794597,0.00042966663,0.00009013213],"domain_scores_gemma":[0.9988605,0.0003657413,0.000066816894,0.0003328591,0.00031928884,0.00005492519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020275984,0.00070393254,0.001503376,0.00066754763,0.00040564223,0.00046647506,0.0010017557,0.00051947543,0.0013223359],"category_scores_gemma":[0.00339251,0.00028173442,0.0008656424,0.0006734944,0.00032894942,0.0008791068,0.0008989135,0.0010548562,0.0012535271],"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":[0.0004830299,0.00031759302,0.0031531285,0.00010339183,0.00024351284,0.00008352557,0.000083834006,0.15565823,0.01990435,0.0056983125,0.0058320453,0.808439],"study_design_scores_gemma":[0.000008848291,0.00006553953,0.00071477075,0.00000437894,0.00002015502,0.00005208406,0.000010925473,0.9924785,0.0041945255,0.0013062577,0.001128282,0.000015764355],"about_ca_topic_score_codex":0.0018503141,"about_ca_topic_score_gemma":0.0027983724,"teacher_disagreement_score":0.0020275984,"about_ca_system_score_codex":0.00032312673,"about_ca_system_score_gemma":0.0006089459,"threshold_uncertainty_score":0.010723114},"labels":[],"label_agreement":null},{"id":"W2098216970","doi":"10.1109/ijcnn.2006.246986","title":"Combining Diversity and Classification Accuracy for Ensemble Selection in Random Subspaces","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":32,"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 du Québec à Montréal","funders":"","keywords":"Random subspace method; Classifier (UML); Linear subspace; Artificial intelligence; Ensemble learning; Computer science; Pattern recognition (psychology); Correlation; Machine learning; Random forest; Statistical classification; Data mining; Mathematics","score_opus":0.0688004532114124,"score_gpt":0.27433051043084883,"score_spread":0.20553005721943643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098216970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07626221,0.00053607766,0.92109424,0.0001974453,0.00003527621,0.000060756534,0.00006651141,0.00038404184,0.0013633773],"genre_scores_gemma":[0.83152896,0.00026975726,0.16704786,0.00007334409,0.00015153558,0.00015689789,0.00021537181,0.00007970406,0.00047660872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99360067,0.0030403247,0.0003848269,0.00060887117,0.00203355,0.00033168346],"domain_scores_gemma":[0.97564846,0.01610964,0.0015024837,0.0025511377,0.0037488195,0.00043946545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010936905,0.0010353255,0.0019316017,0.0031537167,0.0007814439,0.0017546954,0.0008820339,0.001406126,0.00064266956],"category_scores_gemma":[0.028885035,0.0003873138,0.0010582367,0.0019632042,0.0011625971,0.0031318704,0.0017824696,0.0011676803,0.00034206753],"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":[0.00036693402,0.00015266976,0.015075547,0.00012223532,0.00049036223,0.00015316048,0.0002514601,0.6612339,0.010643465,0.019229678,0.0013211226,0.29095948],"study_design_scores_gemma":[0.000015334148,0.00015702704,0.0026892156,0.000019538185,0.00005858346,0.00011226778,0.000027390613,0.9792302,0.0042410754,0.012877569,0.00053476356,0.00003710165],"about_ca_topic_score_codex":0.0008132124,"about_ca_topic_score_gemma":0.00082164264,"teacher_disagreement_score":0.010936905,"about_ca_system_score_codex":0.0010599435,"about_ca_system_score_gemma":0.0006990053,"threshold_uncertainty_score":0.057840526},"labels":[],"label_agreement":null},{"id":"W2098282960","doi":"10.1109/cisda.2009.5356524","title":"Local feature analysis for robust face recognition","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Face (sociological concept); Computer science; Feature (linguistics); Feature extraction; Set (abstract data type); Pruning; Wavelet; Wavelet transform; Computer vision; Mathematics","score_opus":0.030754109667151773,"score_gpt":0.2529451366329437,"score_spread":0.22219102696579193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098282960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003789518,0.00079853355,0.9928369,0.00008801827,0.000043430366,0.00003392737,0.00010895446,0.0012478409,0.0010528768],"genre_scores_gemma":[0.20327415,0.0015411729,0.79000485,0.00022838276,0.00022814068,0.0002557185,0.000968271,0.0003270167,0.0031721583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991079,0.00015651784,0.000047473837,0.00019324463,0.00043564424,0.00005910379],"domain_scores_gemma":[0.999311,0.0002536563,0.00009186016,0.00017306091,0.00015309305,0.000017401491],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000852245,0.0005904062,0.0011207242,0.001335191,0.00030980163,0.0006436393,0.0010494401,0.00062334375,0.0041136215],"category_scores_gemma":[0.0024241,0.00025742457,0.0007754992,0.0014278112,0.00049889594,0.0010946889,0.00073720305,0.0007873796,0.002784613],"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":[0.00018863544,0.00006373643,0.00071870186,0.00030729163,0.000101229605,0.00017933924,0.000073029376,0.028458001,0.09339712,0.012482546,0.007931843,0.85609853],"study_design_scores_gemma":[0.00004356886,0.0002706621,0.0037781112,0.00007409595,0.00013003376,0.0010568128,0.00008881175,0.8322575,0.103501014,0.022394747,0.03630678,0.000097966666],"about_ca_topic_score_codex":0.0011032736,"about_ca_topic_score_gemma":0.00095949956,"teacher_disagreement_score":0.0041136215,"about_ca_system_score_codex":0.0004448209,"about_ca_system_score_gemma":0.00043273935,"threshold_uncertainty_score":0.013761401},"labels":[],"label_agreement":null},{"id":"W2099082374","doi":"10.1109/ccece.2003.1226343","title":"A modified PCA algorithm for face recognition","year":2004,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":18,"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":"Yale University; Minnesota Pollution Control Agency","keywords":"Principal component analysis; Eigenvalues and eigenvectors; Facial recognition system; Pattern recognition (psychology); Linear discriminant analysis; Face (sociological concept); Artificial intelligence; Computer science; Feature (linguistics); Computation; Feature extraction; Feature vector; Algorithm; Mathematics","score_opus":0.03997030804081896,"score_gpt":0.26264708768256134,"score_spread":0.22267677964174237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099082374","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012704991,0.0005885439,0.9945485,0.00011233633,0.00016825189,0.00010835017,0.00012274979,0.0015491722,0.0015316413],"genre_scores_gemma":[0.018772215,0.0008135502,0.97217655,0.00014447032,0.00017277464,0.00031858808,0.00058773946,0.0001918537,0.006822314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867404,0.00018906801,0.00005604233,0.00034154952,0.0006713247,0.00006797853],"domain_scores_gemma":[0.9994972,0.000090472866,0.00002471707,0.00009858854,0.0002678195,0.000021266458],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007076447,0.0012417353,0.0010449128,0.0016291963,0.00077997235,0.0010711291,0.0014303427,0.0013178887,0.0075032203],"category_scores_gemma":[0.0018940053,0.0004504068,0.0010606808,0.0024604076,0.0005470784,0.001559408,0.0009142639,0.0016422425,0.0074709575],"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":[0.00008950242,0.00005534114,0.00027857584,0.0001364878,0.000058641894,0.00009217417,0.000047310965,0.014087492,0.022627762,0.011468406,0.017095739,0.9339626],"study_design_scores_gemma":[0.00006557381,0.00017249548,0.002152673,0.00005066359,0.00006925876,0.0015351286,0.00005063021,0.8078536,0.038955733,0.01916244,0.12977393,0.0001579382],"about_ca_topic_score_codex":0.0024206443,"about_ca_topic_score_gemma":0.0019313499,"teacher_disagreement_score":0.0075032203,"about_ca_system_score_codex":0.0004049846,"about_ca_system_score_gemma":0.00085393945,"threshold_uncertainty_score":0.025100768},"labels":[],"label_agreement":null},{"id":"W2100485099","doi":"10.1007/s10898-010-9571-3","title":"Evaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering","year":2010,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematics; Convex hull; Combinatorics; Centroid; Cluster analysis; Least-squares function approximation; Triangle inequality; Set (abstract data type); Explained sum of squares; Data point; Algorithm; Regular polygon; Statistics; Geometry; Computer science","score_opus":0.025646263397429522,"score_gpt":0.3237365574687904,"score_spread":0.29809029407136084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100485099","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028944956,0.00048757726,0.9646894,0.0003152722,0.00011989847,0.00011759478,0.00006199594,0.0018611966,0.0034021826],"genre_scores_gemma":[0.20331356,0.00016846658,0.792113,0.00017300213,0.00006288914,0.00025159758,0.0003264468,0.0004856937,0.0031054365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844426,0.0006024272,0.0000865721,0.0001981075,0.00051718333,0.00015150425],"domain_scores_gemma":[0.9944811,0.003788176,0.0001655106,0.00022190703,0.0011713748,0.00017193875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028576301,0.0015356423,0.001970224,0.0011634378,0.0008486543,0.0015978226,0.002185021,0.002584829,0.007975282],"category_scores_gemma":[0.011829892,0.0006815682,0.00091836817,0.0011782241,0.00085281,0.001822614,0.0014330224,0.0016660928,0.0019149429],"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":[0.0005508565,0.00015457429,0.00064425427,0.00013619683,0.00008709102,0.000036153335,0.000056633446,0.8071999,0.0030026438,0.004026029,0.0029963935,0.18110923],"study_design_scores_gemma":[0.000017772285,0.000027091359,0.00005734404,0.0000030645544,0.000005746807,0.0000057097955,0.0000075623675,0.99885225,0.00040770575,0.000493233,0.00011937627,0.0000031135623],"about_ca_topic_score_codex":0.01158436,"about_ca_topic_score_gemma":0.014235817,"teacher_disagreement_score":0.01158436,"about_ca_system_score_codex":0.0018093737,"about_ca_system_score_gemma":0.0037492125,"threshold_uncertainty_score":0.026679993},"labels":[],"label_agreement":null},{"id":"W2100716391","doi":"10.1109/icip.2002.1038010","title":"A kernel machine based approach for multi-view face recognition","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Face and Expression Recognition","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 Toronto","funders":"","keywords":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Facial recognition system; Kernel principal component analysis; Pattern recognition (psychology); Artificial intelligence; Kernel (algebra); Computer science; Face (sociological concept); Kernel method; Principal component analysis; Discriminant; Feature extraction; Feature (linguistics); Nonlinear system; Representation (politics); Support vector machine; Mathematics","score_opus":0.10625492763990559,"score_gpt":0.32823382041495525,"score_spread":0.22197889277504967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100716391","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031653445,0.00023812064,0.99564207,0.000054377986,0.000039564864,0.000020620728,0.000028431645,0.0004954747,0.00031587703],"genre_scores_gemma":[0.20763522,0.0006160974,0.7866556,0.00009386326,0.00008820379,0.00014932924,0.00033546795,0.000114543414,0.004311721],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993886,0.00013166261,0.00003760968,0.00013356522,0.00025159435,0.000056926274],"domain_scores_gemma":[0.99952793,0.00012114718,0.000044275504,0.00010960122,0.00017448755,0.000022543132],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054508593,0.0005618701,0.0010650692,0.0009504515,0.0003904382,0.00074503757,0.0009992613,0.0009486019,0.0019319096],"category_scores_gemma":[0.0015268495,0.0002750527,0.0009019523,0.0009300981,0.00035986304,0.0012461779,0.00085023465,0.0012159077,0.0014742089],"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":[0.00016422919,0.00014074489,0.0006553993,0.00014602918,0.00011177243,0.0001470073,0.000074653435,0.07559175,0.03102792,0.014765772,0.003977801,0.8731969],"study_design_scores_gemma":[0.000007864548,0.00006555612,0.0006918186,0.0000072963057,0.00001782766,0.00024482558,0.000016840173,0.98059076,0.008983374,0.0051156273,0.0042252033,0.000032937765],"about_ca_topic_score_codex":0.0013924178,"about_ca_topic_score_gemma":0.0012851451,"teacher_disagreement_score":0.0019319096,"about_ca_system_score_codex":0.00041063162,"about_ca_system_score_gemma":0.00040564977,"threshold_uncertainty_score":0.006462872},"labels":[],"label_agreement":null},{"id":"W2101156082","doi":"10.1016/j.patcog.2007.10.024","title":"Kernel quadratic discriminant analysis for small sample size problem","year":2007,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Quadratic classifier; Mathematics; Kernel (algebra); Kernel Fisher discriminant analysis; Linear discriminant analysis; Artificial intelligence; Pattern recognition (psychology); Discriminant; Kernel method; Quadratic equation; Gaussian; Variable kernel density estimation; Algorithm; Computer science; Support vector machine","score_opus":0.04851646627967997,"score_gpt":0.2736887037882475,"score_spread":0.22517223750856752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101156082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010031899,0.00037693116,0.98869306,0.00024372611,0.000042052154,0.000021568598,0.00004709362,0.00021625085,0.0003273706],"genre_scores_gemma":[0.46350595,0.0013045139,0.52504605,0.00025118355,0.00043595783,0.00041448371,0.0008858427,0.00037551264,0.0077804583],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9981188,0.00076914113,0.0001124115,0.00039739994,0.00046933437,0.00013293882],"domain_scores_gemma":[0.9839857,0.012332548,0.00062009046,0.0014059722,0.0013970012,0.00025869143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039946944,0.0008875094,0.002514704,0.00078785396,0.0005924338,0.0009920213,0.0016523093,0.0012258261,0.0028162904],"category_scores_gemma":[0.019562747,0.0006907063,0.0006917132,0.0012047609,0.0011688181,0.0024441236,0.0016709252,0.0023575379,0.00076750776],"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":[0.0011518403,0.00036645768,0.0027807085,0.00085877464,0.00028913756,0.0003710179,0.00023296355,0.3965098,0.01710552,0.11340289,0.017001623,0.44992936],"study_design_scores_gemma":[0.000028166414,0.000030266663,0.00034046132,0.0000043237083,0.000015106632,0.0000271964,0.000010188764,0.9805643,0.00071417645,0.017609887,0.00064669567,0.000009285711],"about_ca_topic_score_codex":0.002253159,"about_ca_topic_score_gemma":0.0013752923,"teacher_disagreement_score":0.0039946944,"about_ca_system_score_codex":0.00063893106,"about_ca_system_score_gemma":0.0011659303,"threshold_uncertainty_score":0.02112621},"labels":[],"label_agreement":null},{"id":"W2102377326","doi":"10.1155/s1110865703305128","title":"An Efficient Feature Extraction Method with Pseudo-Zernike Moment in RBF Neural Network-Based Human Face Recognition System","year":2003,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zernike polynomials; Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Feature extraction; Artificial neural network; Radial basis function; Face (sociological concept); Computer vision; Moment (physics)","score_opus":0.020168874623293784,"score_gpt":0.31761110340111665,"score_spread":0.29744222877782284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102377326","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011227932,0.0002742682,0.9868838,0.000058448448,0.000051391882,0.000045716293,0.000031868585,0.0007597021,0.0006669172],"genre_scores_gemma":[0.2258223,0.00040986715,0.7699364,0.00007938101,0.00007265429,0.00015883964,0.00018483502,0.00006777927,0.0032679206],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948573,0.00007892944,0.000026733784,0.0000943047,0.00028284438,0.00003146623],"domain_scores_gemma":[0.9997234,0.000056573528,0.00003149533,0.000035443416,0.00014207211,0.00001101642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061398634,0.00044893462,0.0007121429,0.00056177104,0.00023484477,0.00037440687,0.0007454349,0.00061286904,0.0013907983],"category_scores_gemma":[0.000952537,0.00022715531,0.00044967816,0.0004389309,0.00019433856,0.00075802754,0.00034892713,0.00047556523,0.0008758331],"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":[0.00030984843,0.00010574888,0.0007088836,0.00022765622,0.000064299915,0.00012355801,0.00006464466,0.02347985,0.19382511,0.0034748153,0.0025058382,0.7751097],"study_design_scores_gemma":[0.000056323333,0.00035245388,0.0033332258,0.000020481984,0.00007490262,0.0007367308,0.000021583026,0.8444966,0.14052138,0.0013049758,0.009014472,0.000066814595],"about_ca_topic_score_codex":0.0012151698,"about_ca_topic_score_gemma":0.001158541,"teacher_disagreement_score":0.0013907983,"about_ca_system_score_codex":0.00028388336,"about_ca_system_score_gemma":0.00029098167,"threshold_uncertainty_score":0.0046526194},"labels":[],"label_agreement":null},{"id":"W2102415018","doi":"10.1109/isspa.2007.4555395","title":"Incremental Hessian Locally Linear Embedding algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"","keywords":"Embedding; Hessian matrix; Dimension (graph theory); Manifold (fluid mechanics); Representation (politics); Projection (relational algebra); Nonlinear dimensionality reduction; Algorithm; Mathematics; Computer science; Basis (linear algebra); Dimensionality reduction; Artificial intelligence; Applied mathematics; Combinatorics; Geometry","score_opus":0.014124961372234058,"score_gpt":0.28213074144036365,"score_spread":0.2680057800681296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102415018","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008144863,0.00021082714,0.9880518,0.00008315159,0.000054259646,0.000057085526,0.00007906041,0.0020818214,0.0012372183],"genre_scores_gemma":[0.21524923,0.00024223451,0.7738156,0.00021639859,0.00009081465,0.00018962538,0.00081569195,0.00043411064,0.008946321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929225,0.00011428414,0.00003270098,0.00017689822,0.0003112683,0.00007251073],"domain_scores_gemma":[0.9993957,0.00014144891,0.000046412646,0.00016341786,0.0002100864,0.000042917847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052203244,0.00075327675,0.0011371543,0.00088927516,0.00040044574,0.00083170214,0.0019948152,0.00082185597,0.004799249],"category_scores_gemma":[0.0017243203,0.0004204261,0.0006387612,0.00073129666,0.00045531677,0.0016772074,0.0012507448,0.0011377741,0.0020290106],"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":[0.00021393574,0.00016311329,0.00097607926,0.00013552106,0.000106391475,0.00019286167,0.00011015883,0.11274279,0.024897842,0.014450162,0.013595063,0.832416],"study_design_scores_gemma":[0.000028146225,0.00010749147,0.0004028632,0.0000071603763,0.000022180806,0.00018675432,0.000022375716,0.98041576,0.008503401,0.0052368003,0.0050389394,0.00002797613],"about_ca_topic_score_codex":0.0030074397,"about_ca_topic_score_gemma":0.004490663,"teacher_disagreement_score":0.004799249,"about_ca_system_score_codex":0.00048806155,"about_ca_system_score_gemma":0.000796292,"threshold_uncertainty_score":0.016055107},"labels":[],"label_agreement":null},{"id":"W2102416599","doi":"10.1109/icpr.2002.1044743","title":"Experiments on eigenfaces robustness","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":44,"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 Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Eigenface; Artificial intelligence; Computer science; Robustness (evolution); Computer vision; Upsampling; Preprocessor; Image warping; Facial recognition system; Pattern recognition (psychology); Morphing; Zoom; Image processing; Face (sociological concept); Image (mathematics)","score_opus":0.03242333808098214,"score_gpt":0.2738155450164997,"score_spread":0.24139220693551755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102416599","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6783001,0.0037331786,0.2959283,0.00080746855,0.0006913864,0.0009785118,0.0023341903,0.004040399,0.013186529],"genre_scores_gemma":[0.8007423,0.0007373705,0.19116619,0.00023513492,0.00010226042,0.0004701127,0.0024772596,0.0006712798,0.0033981064],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99251735,0.0028609748,0.0009436815,0.0011470204,0.0020689256,0.00046203373],"domain_scores_gemma":[0.9635978,0.024476951,0.0012888619,0.006316337,0.0037985784,0.00052142935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005867794,0.0016569337,0.001235402,0.0018383372,0.0011238321,0.0011438231,0.0012735509,0.0016670006,0.005021045],"category_scores_gemma":[0.039437264,0.0006966564,0.0011432023,0.0011624907,0.0014007104,0.0017882668,0.0022855862,0.0013190202,0.0013462694],"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":[0.0093024615,0.0024639152,0.007621481,0.0024097313,0.0009896711,0.00071943924,0.0013998009,0.23845178,0.24094182,0.00866952,0.012353842,0.47467652],"study_design_scores_gemma":[0.0004352182,0.004290815,0.013693664,0.00022723165,0.00033115907,0.001854502,0.0006841428,0.66347075,0.29107624,0.01174328,0.011927641,0.00026525743],"about_ca_topic_score_codex":0.0012109166,"about_ca_topic_score_gemma":0.00079087896,"teacher_disagreement_score":0.005867794,"about_ca_system_score_codex":0.00051936577,"about_ca_system_score_gemma":0.00032670135,"threshold_uncertainty_score":0.031032205},"labels":[],"label_agreement":null},{"id":"W2104240779","doi":"10.1016/j.ins.2014.07.005","title":"Adaptive ensembles for face recognition in changing video surveillance environments","year":2014,"lang":"en","type":"article","venue":"Information Sciences","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Computer science; Classifier (UML); Artificial intelligence; Facial recognition system; Computer vision; Machine learning; Pattern recognition (psychology); Face detection","score_opus":0.0329683729434989,"score_gpt":0.2512069153217004,"score_spread":0.2182385423782015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104240779","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12471051,0.0003795358,0.87312067,0.00014342373,0.000087980734,0.00002971606,0.00009735478,0.0005245723,0.00090621965],"genre_scores_gemma":[0.81625915,0.0002691938,0.17973179,0.00012321745,0.00011991607,0.00007999429,0.00036286845,0.00010392627,0.002950025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994259,0.0001785239,0.000026583155,0.00014960635,0.00013606469,0.000083405575],"domain_scores_gemma":[0.99902153,0.00045580007,0.00007079421,0.00015328563,0.0002460126,0.000052555686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008962811,0.00048526246,0.0008067517,0.00043446125,0.000393699,0.0004897684,0.0009516535,0.00074379327,0.00095925404],"category_scores_gemma":[0.002710874,0.00032061787,0.00061677577,0.00039301976,0.0002772062,0.0008065508,0.00091712124,0.0011392881,0.0004316599],"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":[0.00038994907,0.00027555178,0.0039278995,0.000055292978,0.00019208631,0.00009023357,0.00015247094,0.47099233,0.04466584,0.0028235775,0.002747647,0.4736872],"study_design_scores_gemma":[0.0000021480248,0.000024929992,0.00064260355,0.0000020239806,0.000008481517,0.000017475306,0.00001025885,0.9960328,0.0023629402,0.000735499,0.00015639873,0.000004430027],"about_ca_topic_score_codex":0.003806226,"about_ca_topic_score_gemma":0.00602216,"teacher_disagreement_score":0.003806226,"about_ca_system_score_codex":0.00036932775,"about_ca_system_score_gemma":0.00034824503,"threshold_uncertainty_score":0.0075681806},"labels":[],"label_agreement":null},{"id":"W2104752854","doi":"","title":"Metric Learning by Collapsing Classes","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":665,"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":"Mahalanobis distance; Metric (unit); Metric space; Mathematics; Intrinsic metric; Artificial intelligence; Feature vector; Computer science; Convex metric space; Mathematical optimization; Pattern recognition (psychology); Algorithm; Discrete mathematics","score_opus":0.011895986466812848,"score_gpt":0.24569061429363617,"score_spread":0.23379462782682334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104752854","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040032654,0.00012119734,0.99423546,0.00009979419,0.000036391048,0.00006776992,0.00006047694,0.0004768662,0.00089880964],"genre_scores_gemma":[0.10764773,0.00027947355,0.8863634,0.0002572466,0.0001291071,0.00041337818,0.0008502666,0.00044271548,0.0036166015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946845,0.0011345367,0.0003891423,0.0016031727,0.0018960453,0.0002925894],"domain_scores_gemma":[0.99554104,0.0012694992,0.00037959244,0.0015748523,0.0009670167,0.00026802684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00337655,0.0015418652,0.0022196188,0.0032129576,0.0016810537,0.0025397881,0.004082011,0.0019261541,0.004280413],"category_scores_gemma":[0.014148323,0.0009780243,0.0019043474,0.0035056998,0.0025077618,0.0050699683,0.008377518,0.0038137292,0.00270983],"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":[0.00015560146,0.00014043055,0.0015811101,0.00016217747,0.000115284565,0.00010097263,0.00037683453,0.09729218,0.0060961116,0.106327504,0.008998886,0.77865285],"study_design_scores_gemma":[0.000032681804,0.00016210567,0.00052287045,0.000041100127,0.000031099542,0.00019332337,0.000100829035,0.7272092,0.005917555,0.25042996,0.01530305,0.00005618225],"about_ca_topic_score_codex":0.0030003493,"about_ca_topic_score_gemma":0.0028238813,"teacher_disagreement_score":0.004280413,"about_ca_system_score_codex":0.0018041021,"about_ca_system_score_gemma":0.0013318463,"threshold_uncertainty_score":0.017857075},"labels":[],"label_agreement":null},{"id":"W2105511331","doi":"10.1109/ccece.2007.333","title":"A Study on Significance of Color in Face Recognition using Several Eigenface Algorithms","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"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":"Eigenface; Facial recognition system; Computer science; Artificial intelligence; Face (sociological concept); Three-dimensional face recognition; Biometrics; Salient; Face Recognition Grand Challenge; Grayscale; Face detection; Pattern recognition (psychology); Computer vision; Image (mathematics)","score_opus":0.07815214584825307,"score_gpt":0.32694656991754845,"score_spread":0.24879442406929536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105511331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2487365,0.0152802775,0.7225722,0.0005778011,0.0004801088,0.00012681026,0.00008086228,0.0005361868,0.011609221],"genre_scores_gemma":[0.7436819,0.0057176393,0.24556836,0.00012982675,0.00024133004,0.00006670505,0.00015460655,0.00012332905,0.004316259],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871135,0.00042464514,0.000083693936,0.0001960707,0.0005178651,0.00006639122],"domain_scores_gemma":[0.9954125,0.0028329978,0.00016719934,0.00029231692,0.0012298989,0.00006500677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021145712,0.00045834787,0.0005249025,0.0014612561,0.00041991982,0.0008550953,0.0003342719,0.00047400856,0.0014286634],"category_scores_gemma":[0.0066483617,0.00016510322,0.00061683456,0.0013970436,0.0006057234,0.0014291805,0.0003171633,0.0006013483,0.00044227665],"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":[0.00039113182,0.00014875806,0.010528294,0.0003067933,0.00013085616,0.00012193052,0.0002990683,0.016521187,0.04919483,0.0076562697,0.0014318828,0.91326904],"study_design_scores_gemma":[0.00003977075,0.0018566877,0.07110676,0.00015170645,0.0004168607,0.002584339,0.0006507243,0.77212787,0.12416643,0.009289146,0.017398935,0.00021076521],"about_ca_topic_score_codex":0.0010527014,"about_ca_topic_score_gemma":0.0009557943,"teacher_disagreement_score":0.0021145712,"about_ca_system_score_codex":0.000244166,"about_ca_system_score_gemma":0.00023781616,"threshold_uncertainty_score":0.011183023},"labels":[],"label_agreement":null},{"id":"W2105536903","doi":"10.1109/iai.2000.839567","title":"Hybrid hidden Markov model for face recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Hidden Markov model; Computer science; Facial recognition system; Artificial intelligence; Face (sociological concept); Pattern recognition (psychology); Speech recognition; Domain (mathematical analysis); Mathematics","score_opus":0.0517822324055477,"score_gpt":0.24576761827202545,"score_spread":0.19398538586647776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105536903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004611514,0.0012266005,0.9900974,0.000148178,0.00012075716,0.000028932036,0.00021986176,0.001675513,0.0018711906],"genre_scores_gemma":[0.3972342,0.0026092737,0.5852484,0.00036556815,0.00023251888,0.00032313223,0.0016243337,0.00024089414,0.012121721],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995677,0.00014127167,0.000018513589,0.0000886176,0.00014211964,0.000041756484],"domain_scores_gemma":[0.9995466,0.00027202585,0.000024110233,0.00007231419,0.00007329839,0.000011736592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055168645,0.00047659336,0.0005883612,0.00048070805,0.00021032341,0.0005087313,0.00092711067,0.0008366586,0.0044599627],"category_scores_gemma":[0.0017183037,0.00026042992,0.000669212,0.00049607805,0.00021376114,0.0009151629,0.0005074264,0.00085720816,0.0021864364],"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":[0.00029698436,0.00012632842,0.0018009888,0.00030058715,0.00021329035,0.00036640302,0.00011985849,0.3267538,0.0147707015,0.052456852,0.010791097,0.5920031],"study_design_scores_gemma":[0.000008730755,0.000027882734,0.0003350346,0.000012706131,0.0000185447,0.00008706415,0.0000073651004,0.98353195,0.0019004267,0.010143538,0.0039107366,0.000015968217],"about_ca_topic_score_codex":0.0053049535,"about_ca_topic_score_gemma":0.005424838,"teacher_disagreement_score":0.0053049535,"about_ca_system_score_codex":0.0005141765,"about_ca_system_score_gemma":0.00047819832,"threshold_uncertainty_score":0.0149201155},"labels":[],"label_agreement":null},{"id":"W2106034961","doi":"10.1109/icpr.2000.906025","title":"A feature space for face image processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Linear discriminant analysis; Feature vector; Pattern recognition (psychology); Face (sociological concept); Artificial intelligence; Feature (linguistics); Computer science; Facial recognition system; Feature extraction; Feature detection (computer vision); Computer vision; Image (mathematics); Principal component analysis; Space (punctuation); Image processing","score_opus":0.021475125347286884,"score_gpt":0.2503331228737911,"score_spread":0.22885799752650424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106034961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012607559,0.00018934022,0.9975781,0.000085307016,0.0000286368,0.000027672797,0.00006749615,0.00017221845,0.0005904763],"genre_scores_gemma":[0.06021746,0.00039189425,0.93661326,0.00011726918,0.00017983699,0.00031099844,0.0004535642,0.00011872099,0.0015970013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987042,0.0003196325,0.0001353155,0.0002252929,0.00054977037,0.00006576343],"domain_scores_gemma":[0.99822265,0.00059737277,0.00011955159,0.0002975189,0.00068372505,0.000079141326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014722968,0.0006376869,0.00093090616,0.0016875779,0.00072144484,0.0016118729,0.0011316258,0.0010228242,0.003128735],"category_scores_gemma":[0.004370826,0.00027884913,0.0009626215,0.0017670466,0.0015653638,0.0023366828,0.0012010656,0.0014914604,0.0019988252],"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":[0.0001495658,0.00008692678,0.0006158506,0.00038318033,0.000062152336,0.00019556176,0.00019349983,0.050604507,0.028600583,0.43662682,0.008381421,0.47409984],"study_design_scores_gemma":[0.00003304174,0.00028116375,0.0011091635,0.00012246244,0.000036288213,0.00055395166,0.00009542889,0.5279472,0.017983604,0.39655963,0.05518635,0.000091723276],"about_ca_topic_score_codex":0.0010702009,"about_ca_topic_score_gemma":0.0006473668,"teacher_disagreement_score":0.003128735,"about_ca_system_score_codex":0.0005979275,"about_ca_system_score_gemma":0.0007533306,"threshold_uncertainty_score":0.010466695},"labels":[],"label_agreement":null},{"id":"W2106362766","doi":"10.1109/icpr.2008.4761777","title":"Fast and regularized local metric for query-based operations","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Face and Expression Recognition","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":"Benchmark (surveying); Metric (unit); Computer science; Context (archaeology); Manifold (fluid mechanics); Point (geometry); Metric space; Volume (thermodynamics); Query optimization; Ellipsoid; Algorithm; Data mining; Mathematics; Discrete mathematics; Geometry","score_opus":0.08765547898974181,"score_gpt":0.2944695077224567,"score_spread":0.20681402873271493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106362766","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041708867,0.000090332935,0.994549,0.000048885875,0.000021854214,0.00004594107,0.00006279603,0.00073750207,0.00027279745],"genre_scores_gemma":[0.17839174,0.00020062404,0.8178377,0.00009440267,0.00008769531,0.00028275637,0.0009335118,0.00043433183,0.0017372916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99658966,0.0007271716,0.00022270672,0.0007666609,0.001508683,0.00018506666],"domain_scores_gemma":[0.9959282,0.00095418305,0.00031159652,0.0013663928,0.0012386431,0.00020090824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025370086,0.0013169409,0.0024881256,0.0017613481,0.0006724656,0.0017662159,0.0033437824,0.0014061849,0.0029306517],"category_scores_gemma":[0.011588718,0.0004985689,0.0010661671,0.0021408265,0.0013194167,0.004574366,0.0033354848,0.0026934235,0.0019260985],"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":[0.00038765345,0.00027562273,0.0010605945,0.00020865143,0.00007819967,0.00011610786,0.00024017737,0.31758866,0.029978009,0.051140327,0.011365403,0.58756053],"study_design_scores_gemma":[0.0000136445315,0.00009538741,0.0001520806,0.000005400906,0.000005182294,0.00007489057,0.000024291949,0.9811435,0.0057748533,0.010875657,0.0018116194,0.000023512925],"about_ca_topic_score_codex":0.0037888507,"about_ca_topic_score_gemma":0.0039520063,"teacher_disagreement_score":0.0037888507,"about_ca_system_score_codex":0.0012658288,"about_ca_system_score_gemma":0.001776986,"threshold_uncertainty_score":0.013417184},"labels":[],"label_agreement":null},{"id":"W2106494881","doi":"10.1109/cvpr.2003.1211370","title":"Face alignment using statistical models and wavelet features","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":85,"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":"Artificial intelligence; Pattern recognition (psychology); Computer science; Maxima and minima; Face (sociological concept); Gabor wavelet; Computer vision; Feature (linguistics); Wavelet; Facial recognition system; Position (finance); Feature extraction; Statistical model; Wavelet transform; Mathematics; Discrete wavelet transform","score_opus":0.036188971733017855,"score_gpt":0.27357196024641883,"score_spread":0.23738298851340098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106494881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067439307,0.00009191266,0.99215394,0.000044744746,0.00001369238,0.000010057803,0.000023721886,0.00036065225,0.0005572797],"genre_scores_gemma":[0.42213073,0.00058284384,0.57289,0.00009436857,0.0000877313,0.00015887631,0.00035451757,0.00030565576,0.0033953302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952936,0.000110448666,0.000019660998,0.00009504504,0.00021092256,0.00003454577],"domain_scores_gemma":[0.99925905,0.0003124288,0.00013580928,0.00015137377,0.000115585,0.000025766825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075097027,0.0005615796,0.00062391197,0.0012962872,0.00031652773,0.0010570759,0.0008014682,0.0007993318,0.0012166632],"category_scores_gemma":[0.0029905518,0.00062563166,0.0009699816,0.0011869938,0.0005037486,0.001731364,0.0008232445,0.0008185913,0.00089830306],"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":[0.00010146283,0.000061445724,0.0012775131,0.00004675632,0.000088282926,0.00009993811,0.00006677011,0.66129065,0.015069554,0.025525317,0.001379301,0.29499295],"study_design_scores_gemma":[0.000003194411,0.000012973479,0.00018043742,0.0000025287372,0.0000043711375,0.000025174175,0.0000065107342,0.99220496,0.0015473503,0.0055057392,0.00050029176,0.0000064353117],"about_ca_topic_score_codex":0.0025905948,"about_ca_topic_score_gemma":0.0027116449,"teacher_disagreement_score":0.0025905948,"about_ca_system_score_codex":0.0004845806,"about_ca_system_score_gemma":0.00061272486,"threshold_uncertainty_score":0.0051510334},"labels":[],"label_agreement":null},{"id":"W2107845724","doi":"10.1167/9.2.22","title":"Comparing a novel model based on the transferable belief model with humans during the recognition of partially occluded facial expressions","year":2009,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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","funders":"Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Artificial intelligence; Facial expression; Pattern recognition (psychology); Psychology","score_opus":0.045039505790737885,"score_gpt":0.2700783330057017,"score_spread":0.22503882721496382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107845724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32077947,0.00035239017,0.67434007,0.00074112584,0.00014064292,0.000103168044,0.000114344286,0.00055904884,0.0028697215],"genre_scores_gemma":[0.9460195,0.000121913086,0.052303884,0.00009077291,0.000020836773,0.000077868426,0.00007647359,0.000027985887,0.0012607493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996451,0.00015531504,0.000011308145,0.00009303526,0.000056652458,0.000038656653],"domain_scores_gemma":[0.9985983,0.0009983405,0.000083490595,0.00014426459,0.00011345833,0.000062196246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012394547,0.00039836628,0.0005464676,0.00020381644,0.00020929777,0.0008165396,0.0007859277,0.00083394407,0.0016479266],"category_scores_gemma":[0.0041289185,0.00024102244,0.00060574943,0.00015026449,0.00054439774,0.0010582776,0.00050593354,0.00091798056,0.00021574218],"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":[0.00096447475,0.0002140394,0.004063653,0.0000968179,0.00017340417,0.00011250808,0.00036478843,0.90108603,0.012001652,0.009508328,0.0007509916,0.0706634],"study_design_scores_gemma":[0.000010622487,0.00006211629,0.0002646982,0.0000022924396,0.0000069633616,0.000011970117,0.000009473612,0.99750096,0.0006900408,0.0013285698,0.000105993604,0.000006253321],"about_ca_topic_score_codex":0.009328128,"about_ca_topic_score_gemma":0.0050515938,"teacher_disagreement_score":0.009328128,"about_ca_system_score_codex":0.0008335801,"about_ca_system_score_gemma":0.0006791963,"threshold_uncertainty_score":0.018547654},"labels":[],"label_agreement":null},{"id":"W2108165921","doi":"10.1109/icdar.2007.4378744","title":"K-Nearest Oracle for Dynamic Ensemble Selection","year":2007,"lang":"en","type":"article","venue":"Proceedings of the International Conference on Document Analysis and Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Oracle; Computer science; Classifier (UML); Artificial intelligence; Machine learning; Selection (genetic algorithm); Majority rule; Pattern recognition (psychology); Random subspace method; k-nearest neighbors algorithm; Voting; Data mining; Ensemble learning","score_opus":0.02360263459374964,"score_gpt":0.2877560323159383,"score_spread":0.2641533977221887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108165921","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009521327,0.00066021085,0.9864189,0.00012012542,0.00009300969,0.000061243925,0.00010026445,0.0009039589,0.002120897],"genre_scores_gemma":[0.5128855,0.0006664046,0.47699618,0.0002719128,0.00031309482,0.00025128477,0.0010938983,0.00022118606,0.007300536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971008,0.00090719014,0.00017635986,0.0006432779,0.0009613979,0.00021097429],"domain_scores_gemma":[0.9970161,0.0011211938,0.0001679336,0.00085772545,0.0006998995,0.00013715652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030069307,0.0008033086,0.0019374774,0.0012225639,0.00073088286,0.0011814389,0.0027147685,0.0013074869,0.0044345334],"category_scores_gemma":[0.008783641,0.00030885293,0.0006953461,0.0013765326,0.00066758384,0.0021593156,0.0015939882,0.0014164358,0.0017678477],"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":[0.00040993627,0.00017020943,0.0028215447,0.00013003076,0.00014199926,0.00016534814,0.000089508125,0.18330875,0.0045368928,0.03737205,0.00857222,0.76228154],"study_design_scores_gemma":[0.000014849613,0.00008443317,0.00072017557,0.00001174756,0.000022970442,0.00014824406,0.00001879508,0.979444,0.0026957013,0.012310757,0.0045051775,0.000023197234],"about_ca_topic_score_codex":0.0026826053,"about_ca_topic_score_gemma":0.0027033414,"teacher_disagreement_score":0.0044345334,"about_ca_system_score_codex":0.00058020535,"about_ca_system_score_gemma":0.00080908684,"threshold_uncertainty_score":0.0159024},"labels":[],"label_agreement":null},{"id":"W2109025268","doi":"10.1162/0899766041732396","title":"Learning Eigenfunctions Links Spectral Embedding and Kernel PCA","year":2004,"lang":"en","type":"article","venue":"Neural Computation","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":345,"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","funders":"","keywords":"Isomap; Mathematics; Embedding; Nonlinear dimensionality reduction; Spectral clustering; Principal component analysis; Pattern recognition (psychology); Cluster analysis; Artificial intelligence; Laplace operator; Kernel (algebra); Multidimensional scaling; Kernel principal component analysis; Eigenfunction; Dimensionality reduction; Kernel method; Eigenvalues and eigenvectors; Computer science; Mathematical analysis; Combinatorics; Statistics; Support vector machine","score_opus":0.019323092039218896,"score_gpt":0.2700799698759921,"score_spread":0.2507568778367732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109025268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006873533,0.00028423266,0.99038374,0.00029943552,0.000041088148,0.0000144484075,0.000033541564,0.0001950656,0.0018749797],"genre_scores_gemma":[0.5430668,0.0019400581,0.4457396,0.00039968692,0.0005045013,0.00021690478,0.00042919797,0.00030916446,0.0073942095],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902475,0.0003869747,0.000039569106,0.00020293676,0.0002861957,0.00005967485],"domain_scores_gemma":[0.9963018,0.0020463448,0.0003216659,0.0006158238,0.00061906595,0.0000954302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00164417,0.0009717085,0.0006932221,0.0011944785,0.00043283237,0.0015457748,0.0007115971,0.001082986,0.0024966353],"category_scores_gemma":[0.01202899,0.0003808861,0.000413918,0.0015354143,0.0018850699,0.0036095595,0.002299456,0.0018750383,0.0010470528],"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":[0.00009906752,0.00009612847,0.0012711504,0.00014844349,0.000067559915,0.000117344505,0.00031551326,0.1659499,0.004526578,0.55745053,0.0044216737,0.265536],"study_design_scores_gemma":[0.0000059222302,0.000029834488,0.0004518597,0.00002168155,0.000008802362,0.00008511847,0.000044719905,0.5270309,0.0014808421,0.46721545,0.0036011366,0.000023705077],"about_ca_topic_score_codex":0.00087347755,"about_ca_topic_score_gemma":0.0007019175,"teacher_disagreement_score":0.0024966353,"about_ca_system_score_codex":0.00050367584,"about_ca_system_score_gemma":0.000578092,"threshold_uncertainty_score":0.008695304},"labels":[],"label_agreement":null},{"id":"W2109486640","doi":"10.1109/tip.2010.2097270","title":"Normalization of Face Illumination Based on Large-and Small-Scale Features","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":147,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Concordia University; Royal Society of Canada","keywords":"Normalization (sociology); Artificial intelligence; Facial recognition system; Computer vision; Computer science; Pattern recognition (psychology); Three-dimensional face recognition; Face (sociological concept); Scale (ratio); Image quality; Image (mathematics); Face detection","score_opus":0.007831977955881019,"score_gpt":0.23721869455327246,"score_spread":0.22938671659739143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109486640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.088514954,0.0009158017,0.9018775,0.00011120003,0.00025002,0.00012889483,0.00020381146,0.0013903845,0.006607309],"genre_scores_gemma":[0.52444434,0.0020069971,0.4649752,0.00015058176,0.00019738954,0.0002186946,0.00093185954,0.0004651548,0.0066098147],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973637,0.000023829934,0.000013375824,0.00008445674,0.00011350577,0.000028583623],"domain_scores_gemma":[0.9997507,0.00004351646,0.00003073922,0.000054444776,0.00010767459,0.000012945526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023408068,0.00051796716,0.0005594424,0.0007269966,0.00026317683,0.00050493865,0.00040027418,0.00025030153,0.0022526402],"category_scores_gemma":[0.0009694111,0.00019616974,0.00073308224,0.00061550795,0.0003719192,0.00066916837,0.0004270558,0.00058733975,0.0008364716],"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":[0.0002179885,0.00008751301,0.002234917,0.00025935395,0.00006425772,0.00016295536,0.00013853498,0.018770136,0.31425902,0.0054724333,0.003562847,0.6547701],"study_design_scores_gemma":[0.000045894983,0.00031305852,0.040455583,0.00006792536,0.00023203532,0.0016954914,0.0002327017,0.508018,0.41474324,0.007661882,0.026391063,0.00014320663],"about_ca_topic_score_codex":0.001040485,"about_ca_topic_score_gemma":0.0014206173,"teacher_disagreement_score":0.0022526402,"about_ca_system_score_codex":0.0002780299,"about_ca_system_score_gemma":0.00038610335,"threshold_uncertainty_score":0.0075358152},"labels":[],"label_agreement":null},{"id":"W2109701649","doi":"10.1109/icsmc.2004.1401295","title":"An information-theoretic measure to evaluate data partitions in multiple classifiers","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Boosting (machine learning); Computer science; Classifier (UML); Data mining; Machine learning; Uncorrelated; Artificial intelligence; Random subspace method; Benchmark (surveying); Training set; Measure (data warehouse); Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.07174330820983159,"score_gpt":0.3092844785362228,"score_spread":0.23754117032639122,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109701649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040949408,0.0026318424,0.94904643,0.00047685942,0.0002534449,0.00031085755,0.0005441065,0.0005387183,0.0052483864],"genre_scores_gemma":[0.5459804,0.0012057773,0.4477603,0.00039570752,0.0006009115,0.00084234326,0.0014712185,0.0002524485,0.0014908722],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9870602,0.0038687235,0.001217507,0.0009963178,0.006455503,0.00040176985],"domain_scores_gemma":[0.9664669,0.022051647,0.0027756414,0.0031306192,0.0049636704,0.0006115278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017839529,0.0021367823,0.0028095748,0.01294097,0.0015767713,0.0042800168,0.0018911579,0.0025485647,0.0017665782],"category_scores_gemma":[0.04848643,0.00044877033,0.0014877652,0.005467768,0.002499029,0.0074526574,0.0032037226,0.002334676,0.00059485144],"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":[0.0010809737,0.0005724953,0.030750196,0.0010046959,0.0017973111,0.00038462598,0.0006796232,0.2578574,0.0174092,0.09263929,0.009047262,0.586777],"study_design_scores_gemma":[0.00011819325,0.0018723839,0.018845696,0.00038157983,0.0006397632,0.0011985989,0.00052499364,0.81651616,0.021252807,0.1275373,0.0108115105,0.00030093497],"about_ca_topic_score_codex":0.0005711183,"about_ca_topic_score_gemma":0.00065068225,"teacher_disagreement_score":0.017839529,"about_ca_system_score_codex":0.0022115728,"about_ca_system_score_gemma":0.0010788295,"threshold_uncertainty_score":0.09434563},"labels":[],"label_agreement":null},{"id":"W2110658950","doi":"10.1109/tnn.2011.2105888","title":"Semisupervised Learning Using Bayesian Interpretation: Application to LS-SVM","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Artificial intelligence; Support vector machine; Machine learning; Computer science; Bayesian probability; Inference; Bayesian inference; Kernel (algebra); Pattern recognition (psychology); Least squares support vector machine; Mathematics","score_opus":0.024875864552527778,"score_gpt":0.24588813345989805,"score_spread":0.22101226890737027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110658950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013195442,0.000076046956,0.99811256,0.000098302415,0.0000058419128,0.000009153094,0.000008203114,0.00007925594,0.00029116104],"genre_scores_gemma":[0.23132433,0.0004863146,0.7662005,0.00016604611,0.00011640787,0.00015922422,0.0001066184,0.00010898152,0.0013315836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812514,0.0011487422,0.00008580496,0.00017557309,0.00041932115,0.000045421417],"domain_scores_gemma":[0.9952767,0.0032156105,0.00039048324,0.00029377325,0.0007300919,0.00009333596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028743506,0.00063965644,0.00094156124,0.0009025593,0.00044620794,0.001045873,0.0011646954,0.0012590933,0.001123558],"category_scores_gemma":[0.011083067,0.00042989946,0.00057737343,0.00085265643,0.0012343598,0.0018096769,0.0014724481,0.0017625424,0.0004184078],"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":[0.00012682496,0.00009463415,0.0014131268,0.00020228222,0.000119807104,0.00020381159,0.00036636772,0.4603576,0.006113838,0.19619517,0.0035205227,0.331286],"study_design_scores_gemma":[0.0000049225077,0.000009668532,0.000082296785,0.000007079115,0.000004550114,0.00003365531,0.000009220623,0.9539844,0.0006080823,0.044444636,0.00080300204,0.000008486056],"about_ca_topic_score_codex":0.0013295783,"about_ca_topic_score_gemma":0.0016710254,"teacher_disagreement_score":0.0028743506,"about_ca_system_score_codex":0.00072011095,"about_ca_system_score_gemma":0.00082017743,"threshold_uncertainty_score":0.015201151},"labels":[],"label_agreement":null},{"id":"W2111118777","doi":"","title":"Applying fusion in thermal face recognition","year":2012,"lang":"en","type":"article","venue":"International Conference on Biometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Calgary","funders":"","keywords":"Facial recognition system; Computer science; Biometrics; Liveness; Artificial intelligence; Modal; Pattern recognition (psychology); Face (sociological concept); Computer vision; Machine learning; Theoretical computer science","score_opus":0.12351577270298053,"score_gpt":0.3224827659427461,"score_spread":0.19896699323976558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111118777","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05401108,0.0010066425,0.9421659,0.0001410327,0.00008934362,0.00004659577,0.00005049537,0.00059809984,0.0018908514],"genre_scores_gemma":[0.7728093,0.0007947115,0.22376047,0.000120328856,0.00011980352,0.00007500674,0.00015961293,0.000062667845,0.0020980423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837166,0.00038714905,0.00009024353,0.00040871496,0.00058336183,0.00015892216],"domain_scores_gemma":[0.9993197,0.00022031025,0.000070214206,0.00013777633,0.00022757542,0.00002455926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002050803,0.0005954772,0.00097399415,0.0011313979,0.00044462446,0.0008544384,0.00073432265,0.0009053145,0.0015396094],"category_scores_gemma":[0.002655141,0.0003347496,0.0011268691,0.0010253687,0.00056553783,0.0016007235,0.0017473218,0.0006027561,0.0008680873],"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":[0.0006301364,0.00015611062,0.0033990995,0.00025697774,0.00018848754,0.00025232573,0.0002771503,0.08091284,0.1390753,0.0074511277,0.0013533105,0.7660472],"study_design_scores_gemma":[0.000016163844,0.00035202218,0.0067614624,0.00004165926,0.00013542069,0.00077402574,0.000117986674,0.8825872,0.09656598,0.008489449,0.004069516,0.000089274545],"about_ca_topic_score_codex":0.0007086959,"about_ca_topic_score_gemma":0.0004785253,"teacher_disagreement_score":0.002050803,"about_ca_system_score_codex":0.0003192797,"about_ca_system_score_gemma":0.0002784748,"threshold_uncertainty_score":0.01084578},"labels":[],"label_agreement":null},{"id":"W2111272908","doi":"10.48550/arxiv.1206.4650","title":"Analysis of Kernel Mean Matching under Covariate Shift","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":49,"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":"Covariate; Estimator; Kernel (algebra); Statistics; Mathematics; Matching (statistics); Kernel density estimation; Variable kernel density estimation; Computer science; Econometrics; Kernel method; Artificial intelligence; Support vector machine","score_opus":0.07397430108549308,"score_gpt":0.1993821756426765,"score_spread":0.1254078745571834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111272908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034758534,0.00055732817,0.96265143,0.00052153465,0.000029869248,0.000053321834,0.000083530176,0.00027712577,0.001067304],"genre_scores_gemma":[0.87137324,0.0009496152,0.12220717,0.00039835038,0.000273401,0.00025863078,0.0004905418,0.0002659334,0.0037830619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99395424,0.0030288962,0.00023468862,0.0012019667,0.0011839827,0.0003962473],"domain_scores_gemma":[0.9280641,0.055619128,0.0052434215,0.0057506687,0.0042792493,0.0010435752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020578323,0.00091365434,0.0019756076,0.0015354984,0.000884525,0.0018002172,0.0033091702,0.0027516873,0.0030300035],"category_scores_gemma":[0.12214651,0.0008557991,0.0009994445,0.001601983,0.0037575262,0.005016219,0.004264924,0.0028682959,0.0005947613],"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":[0.00073542644,0.0001635682,0.011389591,0.0004794808,0.00029740567,0.00032440136,0.0006460989,0.48597398,0.005424904,0.40791166,0.0030692199,0.083584286],"study_design_scores_gemma":[0.000023353621,0.000082947045,0.0011120292,0.000032421376,0.000029041996,0.000079594465,0.000032927037,0.9089004,0.0013920648,0.087592945,0.0006971565,0.000025100886],"about_ca_topic_score_codex":0.0021093786,"about_ca_topic_score_gemma":0.0009973458,"teacher_disagreement_score":0.020578323,"about_ca_system_score_codex":0.0020707215,"about_ca_system_score_gemma":0.0018600232,"threshold_uncertainty_score":0.108829856},"labels":[],"label_agreement":null},{"id":"W2112223816","doi":"10.1109/icassp.2008.4517778","title":"Pseudo-Fisherface method for single image per person face recognition","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"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 British Columbia","funders":"","keywords":"Contourlet; Eigenface; Computer science; Facial recognition system; Artificial intelligence; Pattern recognition (psychology); Face (sociological concept); Wavelet transform; Curvelet; Computer vision; Image (mathematics); Scheme (mathematics); Wavelet; Speech recognition; Mathematics","score_opus":0.0859982450965017,"score_gpt":0.3015648812799612,"score_spread":0.21556663618345953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112223816","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036344083,0.0003015136,0.9938059,0.000078812875,0.00007905813,0.00003156208,0.00014397728,0.0008809347,0.0010438954],"genre_scores_gemma":[0.08570063,0.0005300081,0.9043588,0.00012666133,0.00010848598,0.00022294339,0.00093765644,0.00016564806,0.007849142],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992574,0.00014000709,0.000027010052,0.00016119574,0.00036286653,0.000051461833],"domain_scores_gemma":[0.99957114,0.00009529572,0.00002728386,0.00013625549,0.00015371613,0.000016249094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086858287,0.0005142098,0.000795235,0.0010886074,0.00035716945,0.00047213177,0.0010767693,0.00085282954,0.008216366],"category_scores_gemma":[0.0015614749,0.00023931144,0.0006017138,0.0008427459,0.0003400782,0.0012079363,0.000680544,0.00083007547,0.0048545003],"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":[0.00012652153,0.00006646323,0.0004412924,0.00008181777,0.00004574992,0.00008064847,0.000041992178,0.019368153,0.03367655,0.009810572,0.01015769,0.92610264],"study_design_scores_gemma":[0.000012737236,0.000105524574,0.0020773476,0.000024458886,0.000020003794,0.0008764027,0.00003193422,0.9251188,0.038630955,0.013508344,0.019536829,0.00005674071],"about_ca_topic_score_codex":0.0014042518,"about_ca_topic_score_gemma":0.001969581,"teacher_disagreement_score":0.008216366,"about_ca_system_score_codex":0.00026951873,"about_ca_system_score_gemma":0.00073057576,"threshold_uncertainty_score":0.027486444},"labels":[],"label_agreement":null},{"id":"W2112338564","doi":"10.1109/tnn.2009.2031143","title":"Semisupervised Least Squares Support Vector Machine","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Computer science; Heuristics; Generalization; Margin (machine learning); Artificial intelligence; Least squares support vector machine; Machine learning; Classifier (UML); Structured support vector machine; Maximization; Pattern recognition (psychology); Algorithm; Mathematical optimization; Mathematics","score_opus":0.013967953898349527,"score_gpt":0.2336830820277411,"score_spread":0.21971512812939156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112338564","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00900374,0.00013016335,0.9893339,0.00008983956,0.000019823428,0.000032167518,0.000058451296,0.00071672635,0.000615139],"genre_scores_gemma":[0.35211957,0.00020901808,0.64290416,0.0001833471,0.00011153436,0.00023808298,0.00086043566,0.00017697668,0.003196859],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816066,0.00069535756,0.0001221715,0.00043640868,0.00049532746,0.00009004566],"domain_scores_gemma":[0.99426645,0.0021726715,0.00077976385,0.001089202,0.0015813571,0.00011058054],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014340442,0.0009133266,0.0016142112,0.00066384726,0.00034326236,0.001066418,0.002037406,0.0014027768,0.0017001558],"category_scores_gemma":[0.008062693,0.00046074687,0.000689064,0.0009055445,0.00067440193,0.0015880112,0.0010574528,0.0012876831,0.0017060196],"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":[0.0003783708,0.00032918397,0.0022547664,0.000298999,0.00017600569,0.00018589337,0.00017727415,0.30363753,0.018498125,0.0163604,0.008141498,0.649562],"study_design_scores_gemma":[0.000011819259,0.000051844745,0.0002252263,0.000007776766,0.00000720081,0.000081510356,0.000017306429,0.9887581,0.0043996084,0.005453071,0.00097490527,0.000011678476],"about_ca_topic_score_codex":0.0004602837,"about_ca_topic_score_gemma":0.00073826424,"teacher_disagreement_score":0.002037406,"about_ca_system_score_codex":0.00029646797,"about_ca_system_score_gemma":0.00068157696,"threshold_uncertainty_score":0.0075840354},"labels":[],"label_agreement":null},{"id":"W2112651331","doi":"10.1109/cvpr.2005.116","title":"Creating Invariance to \"Nuisance Parameters\" in Face Recognition","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"York University","funders":"","keywords":"Artificial intelligence; Feature vector; Facial recognition system; Pattern recognition (psychology); Face (sociological concept); Invariant (physics); Feature (linguistics); Computer vision; Computer science; Manifold (fluid mechanics); Metric (unit); Position (finance); Pose; Mathematics; Engineering","score_opus":0.03643778383574562,"score_gpt":0.2651174559778315,"score_spread":0.22867967214208587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112651331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054906048,0.00026035213,0.9424864,0.00017158884,0.00004812624,0.000054266213,0.000066244174,0.00062631967,0.0013805663],"genre_scores_gemma":[0.5658755,0.00050752785,0.430491,0.00017356357,0.00014737988,0.00016334484,0.00026895167,0.00029263794,0.0020800305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99861324,0.00046898887,0.00006181671,0.0003485552,0.00039469515,0.00011263528],"domain_scores_gemma":[0.9956929,0.0018176647,0.00038329925,0.0017104154,0.00029600234,0.00009977649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016058183,0.00057837507,0.00068964803,0.0006896365,0.00038780263,0.00067936396,0.00087134825,0.0008975891,0.0016281836],"category_scores_gemma":[0.010380244,0.00038259418,0.00069085974,0.0006517253,0.0019103669,0.0020728952,0.0013950267,0.0012729804,0.0010201266],"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":[0.00033955235,0.00019827287,0.0039528916,0.00014985574,0.000090528854,0.00023251341,0.0004039443,0.07168823,0.1569953,0.0266728,0.0020030732,0.7372731],"study_design_scores_gemma":[0.000052353804,0.00056603935,0.012610705,0.000039735376,0.00009137485,0.0017884221,0.00014104233,0.7254715,0.18249641,0.06545989,0.011110667,0.00017178924],"about_ca_topic_score_codex":0.0006898738,"about_ca_topic_score_gemma":0.0006369438,"teacher_disagreement_score":0.0016281836,"about_ca_system_score_codex":0.00039385725,"about_ca_system_score_gemma":0.00030208964,"threshold_uncertainty_score":0.008492529},"labels":[],"label_agreement":null},{"id":"W2112686571","doi":"10.1109/fgr.2006.71","title":"Learning to Identify Facial Expression During Detection Using Markov Decision Process","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer science; AdaBoost; Classifier (UML); Face detection; Random subspace method; Pattern recognition (psychology); Decision tree; Cascading classifiers; Machine learning; Hidden Markov model; Expression (computer science); Facial expression; Facial recognition system","score_opus":0.013202819380232425,"score_gpt":0.28982376354525535,"score_spread":0.2766209441650229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112686571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07681285,0.00011879483,0.920992,0.0003097378,0.00002221759,0.00009499445,0.00006496917,0.00065874733,0.0009257374],"genre_scores_gemma":[0.8547558,0.00012042852,0.14348182,0.00015218454,0.000026237325,0.0001537947,0.00016046796,0.000042919437,0.0011063856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886936,0.0004266166,0.000056477136,0.00030887045,0.00018976729,0.00014887167],"domain_scores_gemma":[0.99647516,0.0028104067,0.00024173559,0.00011361167,0.0002652051,0.00009386297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027819222,0.0006704735,0.0010723155,0.00075890793,0.00045080498,0.0008540005,0.0010301778,0.000949318,0.0012641666],"category_scores_gemma":[0.0055604614,0.0005832073,0.0007555856,0.00050037116,0.00076386385,0.0013266059,0.00068549294,0.0016456916,0.00042120452],"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":[0.0005244945,0.00034454415,0.010899384,0.0000800987,0.00011881718,0.00015036207,0.00025473046,0.7843188,0.007243527,0.009844862,0.0013678441,0.18485256],"study_design_scores_gemma":[0.000006693693,0.000018790983,0.00024703637,0.000002553618,0.000005405448,0.000010563186,0.000004697177,0.9959913,0.0006991648,0.002951103,0.000057382953,0.000005274735],"about_ca_topic_score_codex":0.008685399,"about_ca_topic_score_gemma":0.007905246,"teacher_disagreement_score":0.008685399,"about_ca_system_score_codex":0.0011894464,"about_ca_system_score_gemma":0.0011571721,"threshold_uncertainty_score":0.01726973},"labels":[],"label_agreement":null},{"id":"W2112724657","doi":"10.1109/tsmcb.2009.2014245","title":"Color Face Recognition for Degraded Face Images","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":111,"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; University of Toronto","funders":"National Institute of Standards and Technology","keywords":"Face (sociological concept); Computer vision; Artificial intelligence; Facial recognition system; Computer science; Pattern recognition (psychology); Sociology","score_opus":0.03242392006122321,"score_gpt":0.2534927829636623,"score_spread":0.22106886290243907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112724657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27573457,0.0010436714,0.71333504,0.00029612708,0.0001624087,0.00013644501,0.0003879498,0.0024227228,0.006481023],"genre_scores_gemma":[0.721555,0.00066539884,0.27352238,0.00019820998,0.000064782296,0.00007847222,0.0004891519,0.00010273806,0.003323853],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948287,0.00011004275,0.00001693553,0.00011797017,0.00021439703,0.000057876077],"domain_scores_gemma":[0.9994981,0.00014492185,0.000054797958,0.00009922543,0.00018253118,0.000020415177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007056537,0.00044744895,0.0005657497,0.0008900054,0.0002510898,0.00052999426,0.00042443225,0.00042493158,0.0032242492],"category_scores_gemma":[0.0024603747,0.0001236958,0.0004734791,0.00047395483,0.00031353036,0.0006721206,0.00046416634,0.00043323918,0.0013855605],"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":[0.0005831693,0.00011647407,0.0034674532,0.00018285697,0.00006516988,0.00024221122,0.00015506662,0.018374605,0.2352996,0.0034439866,0.0035871784,0.7344822],"study_design_scores_gemma":[0.000025248593,0.00044591332,0.034290392,0.00004957645,0.000106305815,0.0021207125,0.00021822442,0.7104915,0.23731567,0.006516817,0.008328894,0.000090839945],"about_ca_topic_score_codex":0.0015022219,"about_ca_topic_score_gemma":0.0017804132,"teacher_disagreement_score":0.0032242492,"about_ca_system_score_codex":0.00036930927,"about_ca_system_score_gemma":0.0003178677,"threshold_uncertainty_score":0.010786176},"labels":[],"label_agreement":null},{"id":"W2112810953","doi":"10.1016/j.patcog.2011.01.009","title":"From classifiers to discriminators: A nearest neighbor rule induced discriminant analysis","year":2011,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Discriminant; Kernel Fisher discriminant analysis; Classifier (UML); Optimal discriminant analysis; k-nearest neighbors algorithm; Discriminator; Computer science; Multiple discriminant analysis; Feature extraction; Quadratic classifier; Mathematics; Facial recognition system","score_opus":0.08822907585514603,"score_gpt":0.26691865338752147,"score_spread":0.17868957753237544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112810953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032066077,0.0008062917,0.9636802,0.00031242354,0.00016983387,0.00010372711,0.00015033997,0.00069390977,0.0020171255],"genre_scores_gemma":[0.47312027,0.000695857,0.51762605,0.00024954043,0.00017703658,0.00018726986,0.0008152891,0.00031840036,0.006810242],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978642,0.0007414664,0.00011241927,0.00049445545,0.0006703793,0.0001170649],"domain_scores_gemma":[0.99811894,0.00076745526,0.00007582461,0.0003765119,0.00058164337,0.00007965563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027008243,0.000590778,0.0014722239,0.0009864968,0.00061707624,0.0016020539,0.0015007235,0.0008671824,0.002224172],"category_scores_gemma":[0.006480448,0.00045388346,0.0008535994,0.0008947555,0.00085403305,0.001794511,0.001485786,0.002700774,0.0013638202],"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":[0.00036798653,0.00024878563,0.002020319,0.00014665157,0.000114529554,0.000061469975,0.00017158654,0.031192914,0.012784045,0.020461993,0.005959344,0.92647034],"study_design_scores_gemma":[0.00004898825,0.00020037314,0.0015710881,0.000041517425,0.00006778041,0.00013149304,0.00009056866,0.94321597,0.007847657,0.042060792,0.0046771453,0.00004662057],"about_ca_topic_score_codex":0.0015203021,"about_ca_topic_score_gemma":0.0016553978,"teacher_disagreement_score":0.0027008243,"about_ca_system_score_codex":0.00040109866,"about_ca_system_score_gemma":0.0009235506,"threshold_uncertainty_score":0.014283478},"labels":[],"label_agreement":null},{"id":"W2113074748","doi":"10.1109/ism.2008.27","title":"Local Normalization with Optimal Adaptive Correlation for Automatic and Robust Face Detection on Video Sequences","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Artificial intelligence; Normalization (sociology); Computer science; Pattern recognition (psychology); Histogram; Computer vision; Face detection; AdaBoost; Feature extraction; False positive paradox; Facial recognition system; Boosting (machine learning); Classifier (UML)","score_opus":0.02413594187751251,"score_gpt":0.21654420814300884,"score_spread":0.19240826626549634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113074748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02326509,0.00033112653,0.9749356,0.000040542174,0.00004044748,0.000043393604,0.00002497662,0.00074458006,0.0005742217],"genre_scores_gemma":[0.2936582,0.00038478468,0.7041596,0.00007774335,0.00008396126,0.00012868026,0.00016263081,0.00013108992,0.0012132997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999186,0.00019090693,0.00003542399,0.00016732096,0.00035123725,0.000069200556],"domain_scores_gemma":[0.99937505,0.00020919296,0.00010190401,0.000091579925,0.00019074176,0.000031602154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009272493,0.00047697558,0.0006541681,0.0013126776,0.00030049513,0.00043479272,0.0007244587,0.00040541744,0.0009671302],"category_scores_gemma":[0.0020803062,0.00029286562,0.00047070996,0.0011053871,0.0004299171,0.00073539413,0.00046063442,0.000535264,0.0005990749],"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":[0.00027638697,0.00013409361,0.0016979474,0.00013568334,0.00006781065,0.00012970797,0.00008820583,0.031626556,0.16908775,0.0035840783,0.0021889058,0.79098284],"study_design_scores_gemma":[0.000021853215,0.00018599819,0.004680219,0.00001916616,0.0000396237,0.0003928366,0.000035756904,0.89604944,0.09363633,0.001806355,0.003085416,0.00004700381],"about_ca_topic_score_codex":0.0021189242,"about_ca_topic_score_gemma":0.0024960202,"teacher_disagreement_score":0.0021189242,"about_ca_system_score_codex":0.0004092937,"about_ca_system_score_gemma":0.0006543202,"threshold_uncertainty_score":0.004903853},"labels":[],"label_agreement":null},{"id":"W2113119678","doi":"10.1109/ispa.2005.195405","title":"Robust recognition of 3-D faces based on analytic forms and spectral analysis","year":2005,"lang":"en","type":"article","venue":"International symposium on image and signal processing and analysis/ISPA ...","topic":"Face and Expression Recognition","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":"National Research Council Canada","funders":"","keywords":"Facial recognition system; Artificial intelligence; Computer science; Pattern recognition (psychology); Robustness (evolution); Face (sociological concept); Computer vision; Three-dimensional face recognition; Noise (video); Facial expression; Coding (social sciences); Speech recognition; Face detection; Mathematics; Image (mathematics)","score_opus":0.014720935449220075,"score_gpt":0.2458537139181448,"score_spread":0.23113277846892474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113119678","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034622412,0.0002516175,0.9612561,0.00010156817,0.000049009323,0.000071623355,0.00009803262,0.0009053856,0.0026442455],"genre_scores_gemma":[0.35758886,0.0007300433,0.6380095,0.00013165623,0.00009925847,0.000138268,0.00035456658,0.00013638113,0.0028115362],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996014,0.00005995728,0.000014666482,0.000056964036,0.00024317417,0.000023768685],"domain_scores_gemma":[0.9995555,0.00015196753,0.000055273857,0.00007882566,0.00014390261,0.000014442168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003486546,0.0003459235,0.00043764737,0.0010203905,0.00019412058,0.0005408012,0.00045498204,0.00037560167,0.0019068378],"category_scores_gemma":[0.0015918214,0.00017641345,0.00041472967,0.00047150344,0.00048697347,0.00079281366,0.000570751,0.0003537725,0.0011559762],"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":[0.00020141134,0.000075075965,0.0009964848,0.00013922885,0.000043072756,0.00018367908,0.00020311924,0.019921511,0.3223192,0.009301849,0.0027468025,0.64386857],"study_design_scores_gemma":[0.000028808892,0.00029504724,0.009630425,0.000056346584,0.000050186285,0.0026068601,0.00024281895,0.77220386,0.17967841,0.020950422,0.014132297,0.00012452928],"about_ca_topic_score_codex":0.00053784,"about_ca_topic_score_gemma":0.0005149792,"teacher_disagreement_score":0.0019068378,"about_ca_system_score_codex":0.00018232818,"about_ca_system_score_gemma":0.00020049787,"threshold_uncertainty_score":0.0063790083},"labels":[],"label_agreement":null},{"id":"W2113481973","doi":"10.48550/arxiv.1212.2494","title":"Learning Generative Models of Similarity Matrices","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Toronto","funders":"","keywords":"Spectral clustering; Cluster analysis; Inference; Similarity (geometry); Pattern recognition (psychology); Mathematics; Data point; Artificial intelligence; Generative model; Computer science; Eigenvalues and eigenvectors; Algorithm; Generative grammar","score_opus":0.08650278462184266,"score_gpt":0.18721101122149156,"score_spread":0.1007082265996489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113481973","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063049607,0.000103525315,0.99257493,0.00014725426,0.000013884213,0.000024688954,0.00008089038,0.0001904142,0.00055942684],"genre_scores_gemma":[0.49157012,0.0006671245,0.49815688,0.0005526883,0.00022208468,0.0004898063,0.0014439314,0.0003741851,0.0065232217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963117,0.0014026941,0.0001481438,0.0010978247,0.0007862483,0.00025339398],"domain_scores_gemma":[0.9891455,0.0069276155,0.00088712136,0.0019017706,0.00082167936,0.00031633032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045680366,0.0010753475,0.0016819462,0.0030466658,0.0012187887,0.002921892,0.004805438,0.0025963983,0.003735509],"category_scores_gemma":[0.02128483,0.0016086447,0.0024611682,0.0026032014,0.0029020235,0.0049953167,0.0038284878,0.0038133922,0.0014701232],"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":[0.000089014895,0.000116841824,0.004364021,0.00014072045,0.00018199961,0.00023444719,0.00070802594,0.47253466,0.0029480932,0.4399843,0.0030310457,0.07566686],"study_design_scores_gemma":[0.000006593779,0.000011887471,0.00025398313,0.000012251505,0.000010815866,0.00007612173,0.000022522989,0.8544856,0.00038132913,0.1440936,0.00062816305,0.000017083677],"about_ca_topic_score_codex":0.004586702,"about_ca_topic_score_gemma":0.006934184,"teacher_disagreement_score":0.004805438,"about_ca_system_score_codex":0.0019076895,"about_ca_system_score_gemma":0.0009992283,"threshold_uncertainty_score":0.024158418},"labels":[],"label_agreement":null},{"id":"W2113588087","doi":"","title":"Classification of upper and lower face action units and facial expressions using hybrid tracking system and probabilistic neural networks","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Artificial intelligence; Pattern recognition (psychology); Computer science; Probabilistic logic; Face (sociological concept); Feature (linguistics); Facial recognition system; Facial expression; Computer vision; Artificial neural network; Feature extraction; Facial motion capture; Tracking (education); Face detection; Feature vector; Probabilistic neural network; Time delay neural network","score_opus":0.04955739329113534,"score_gpt":0.2624740382769427,"score_spread":0.21291664498580737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113588087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28967804,0.00017140912,0.70673984,0.00008678062,0.000040624454,0.000096090655,0.000086874104,0.0010730923,0.0020273337],"genre_scores_gemma":[0.8434899,0.00011023991,0.15392165,0.000048190297,0.000018628443,0.00010320883,0.000144062,0.00003039637,0.0021338153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999549,0.000079507045,0.000026773518,0.00014159166,0.00016240223,0.000040724455],"domain_scores_gemma":[0.99948704,0.00017494829,0.00006991411,0.000048725313,0.0001928473,0.00002645317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089745433,0.0003580345,0.0003829092,0.0004909655,0.00018651845,0.00046782364,0.00040588205,0.00044283734,0.00075905985],"category_scores_gemma":[0.0017800801,0.00025687684,0.0003722214,0.00027885658,0.00024513213,0.0006069914,0.00025709762,0.00033973617,0.00030813235],"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":[0.0006676154,0.00024689606,0.017509537,0.00008528522,0.00010958705,0.00011781713,0.00017313736,0.063207954,0.12646376,0.0014705198,0.0012351109,0.78871286],"study_design_scores_gemma":[0.000015864263,0.00012414884,0.013415661,0.0000075377707,0.000034423996,0.000097783086,0.00002505123,0.96858245,0.016786,0.0005723668,0.00032031615,0.000018449506],"about_ca_topic_score_codex":0.00310294,"about_ca_topic_score_gemma":0.0026577383,"teacher_disagreement_score":0.00310294,"about_ca_system_score_codex":0.00045953825,"about_ca_system_score_gemma":0.00029731746,"threshold_uncertainty_score":0.0061697364},"labels":[],"label_agreement":null},{"id":"W2113739150","doi":"","title":"Horizontal features based illumination normalization method for face recognition","year":2011,"lang":"en","type":"article","venue":"International Symposium on Image and Signal Processing and Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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","funders":"","keywords":"Artificial intelligence; Normalization (sociology); Decimation; Facial recognition system; Computer science; Computer vision; Face (sociological concept); Pattern recognition (psychology); Similarity (geometry); Gamma correction; Similarity measure; Transformation (genetics); Filter (signal processing); Image (mathematics)","score_opus":0.01778154857302504,"score_gpt":0.27077920564032576,"score_spread":0.2529976570673007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113739150","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025532993,0.0011502065,0.9678371,0.000079625,0.00014318223,0.00006289792,0.00014261273,0.0012586953,0.0037927488],"genre_scores_gemma":[0.26796216,0.0024190468,0.71908325,0.00014754571,0.00020096225,0.00020926961,0.00070134253,0.00021766989,0.0090586785],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996953,0.000032800297,0.00001397896,0.00008043585,0.00014808404,0.000029386218],"domain_scores_gemma":[0.99981433,0.000035742065,0.000022665801,0.00004156723,0.00007669804,0.000009034372],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023433234,0.00042883895,0.00045966115,0.00076458923,0.0003581994,0.00035305353,0.00046784148,0.00027912363,0.0033233184],"category_scores_gemma":[0.00046167517,0.00020520148,0.0005175839,0.0006999229,0.00026785518,0.0005910379,0.00038198647,0.0004914269,0.0014488664],"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":[0.00010059525,0.0000549756,0.0010461434,0.00014167614,0.000054598673,0.0000847585,0.00006583974,0.00442301,0.12801608,0.0030237462,0.003265193,0.8597234],"study_design_scores_gemma":[0.000047312806,0.00038452132,0.01818709,0.00009342197,0.0002451086,0.0027808873,0.0001869681,0.3693023,0.5434865,0.006233197,0.058893863,0.00015895537],"about_ca_topic_score_codex":0.0010885975,"about_ca_topic_score_gemma":0.0016925049,"teacher_disagreement_score":0.0033233184,"about_ca_system_score_codex":0.00026316557,"about_ca_system_score_gemma":0.000393158,"threshold_uncertainty_score":0.011117578},"labels":[],"label_agreement":null},{"id":"W2114160070","doi":"10.1109/icassp.2003.1199123","title":"Regularized D-LDA for face recognition","year":2004,"lang":"en","type":"article","venue":"2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03).","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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":"Computer science; Facial recognition system; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Speech recognition","score_opus":0.05705852244945824,"score_gpt":0.29362243903740803,"score_spread":0.23656391658794979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114160070","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026366045,0.0020283242,0.9924229,0.00023321278,0.00009799799,0.000034664547,0.00019885959,0.0010668216,0.0012807233],"genre_scores_gemma":[0.120262384,0.0035049738,0.8641096,0.0003184555,0.00032232338,0.00037794255,0.0015733991,0.00030074432,0.009230234],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935323,0.00020680847,0.000027705126,0.00016526149,0.00020864031,0.000038289152],"domain_scores_gemma":[0.999483,0.00017725101,0.000050609124,0.00013866139,0.00013263314,0.000017854218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063381426,0.0006070686,0.0007837269,0.0009767881,0.00033995876,0.00067152164,0.0007290043,0.0006929602,0.0029128306],"category_scores_gemma":[0.001966158,0.00031753996,0.0008307778,0.0013494034,0.000482064,0.0007893825,0.0008934405,0.0012437257,0.003871522],"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":[0.00015465266,0.000075149546,0.00064460555,0.00026037663,0.00009939912,0.00011509899,0.00008246034,0.08488022,0.0219756,0.03375268,0.031830855,0.826129],"study_design_scores_gemma":[0.000012624926,0.000034996672,0.00088224496,0.000027542574,0.000015939733,0.00016028232,0.000019547007,0.9420159,0.0051480946,0.031885818,0.019759187,0.000037838225],"about_ca_topic_score_codex":0.0023621833,"about_ca_topic_score_gemma":0.0023203106,"teacher_disagreement_score":0.0029128306,"about_ca_system_score_codex":0.0005203622,"about_ca_system_score_gemma":0.00048292844,"threshold_uncertainty_score":0.009744346},"labels":[],"label_agreement":null},{"id":"W2114486479","doi":"10.1109/ijcnn.2010.5596927","title":"Semi-supervised learning for weighted LS-SVM","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Support vector machine; Margin (machine learning); Artificial intelligence; Computer science; Weighting; Least squares support vector machine; Generalization; Machine learning; Pattern recognition (psychology); Supervised learning; Maximization; Semi-supervised learning; Ranking SVM; Structured support vector machine; Sample (material); Mathematics; Artificial neural network; Mathematical optimization","score_opus":0.01299906122134769,"score_gpt":0.24221735777983716,"score_spread":0.22921829655848946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114486479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027970632,0.00007370587,0.9964307,0.00006736607,0.000014174853,0.000024864312,0.000015096999,0.00028740842,0.0002895855],"genre_scores_gemma":[0.2581739,0.00022774734,0.7380504,0.0001811659,0.00014670745,0.00046971953,0.00035339152,0.00021396556,0.0021829284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699914,0.001612665,0.00018927909,0.0004299925,0.00067497423,0.00009392674],"domain_scores_gemma":[0.9923672,0.0044058803,0.0005842263,0.00080461457,0.001681488,0.00015653211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034589008,0.0006644907,0.0012181601,0.0008729118,0.00045666142,0.0010684314,0.0018042602,0.0014316106,0.0022988073],"category_scores_gemma":[0.013502157,0.0004671603,0.0007591719,0.00072395283,0.0011204147,0.0017561702,0.0016628538,0.001831423,0.0010074577],"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":[0.00022450599,0.00017370738,0.001097989,0.00028407635,0.00014723132,0.00012533338,0.00018502079,0.47300124,0.009087249,0.05139608,0.0047019785,0.4595756],"study_design_scores_gemma":[0.0000035996072,0.00001737708,0.00006654231,0.0000052756263,0.0000026048986,0.000020366419,0.0000047532085,0.9904834,0.0007833421,0.008198023,0.00040978324,0.0000049876653],"about_ca_topic_score_codex":0.00087877014,"about_ca_topic_score_gemma":0.0009142464,"teacher_disagreement_score":0.0034589008,"about_ca_system_score_codex":0.0006656646,"about_ca_system_score_gemma":0.0008722641,"threshold_uncertainty_score":0.018292665},"labels":[],"label_agreement":null},{"id":"W2115128541","doi":"10.1109/ccece.2012.6335036","title":"Two-dimensional face recognition algorithms in the frequency domain","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Concordia University","funders":"","keywords":"Principal component analysis; Computer science; Robustness (evolution); Feature extraction; Algorithm; Artificial intelligence; Facial recognition system; Pattern recognition (psychology); Frequency domain; Computational complexity theory; Fourier transform; Computer vision; Mathematics","score_opus":0.031517370190563984,"score_gpt":0.26816452166601545,"score_spread":0.23664715147545146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115128541","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003440704,0.00031315224,0.99327165,0.0000832962,0.00007099085,0.00009490444,0.000084628184,0.0011065706,0.0015341456],"genre_scores_gemma":[0.052352704,0.00057581876,0.9416728,0.00011754945,0.000057919897,0.00040018375,0.0003998591,0.00008421784,0.0043388363],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993119,0.00008931865,0.00004557137,0.00018942276,0.00030636764,0.000057266843],"domain_scores_gemma":[0.9994591,0.0001530712,0.00005009498,0.00010021962,0.00022386508,0.000013681106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008617579,0.00080861663,0.0010107418,0.0012283372,0.000579501,0.000983872,0.0012685818,0.001134061,0.005530951],"category_scores_gemma":[0.0019590615,0.00035438064,0.00090876454,0.0011345645,0.0004283742,0.0011304191,0.00068241055,0.0011151716,0.0034549532],"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":[0.000056881974,0.00006664017,0.00044476843,0.00009606824,0.00004851649,0.000035480392,0.00005474466,0.037850153,0.013282822,0.007244212,0.0046849446,0.93613476],"study_design_scores_gemma":[0.000024549874,0.00006482481,0.0016305941,0.000026947286,0.000023592189,0.00034408717,0.000042275336,0.95823514,0.021032182,0.0078032706,0.010722024,0.00005050709],"about_ca_topic_score_codex":0.0034299626,"about_ca_topic_score_gemma":0.0034073342,"teacher_disagreement_score":0.005530951,"about_ca_system_score_codex":0.0004957854,"about_ca_system_score_gemma":0.0007508951,"threshold_uncertainty_score":0.018502891},"labels":[],"label_agreement":null},{"id":"W2116019577","doi":"10.1109/tnn.2002.806647","title":"Face recognition using LDA-based algorithms","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":799,"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":"Linear discriminant analysis; Eigenface; Facial recognition system; Pattern recognition (psychology); Computer science; Artificial intelligence; Face (sociological concept); Feature (linguistics); Feature extraction; Statistical classification; Representation (politics); Principal component analysis; Machine learning","score_opus":0.04399648047983817,"score_gpt":0.2591615522076355,"score_spread":0.2151650717277973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116019577","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0058984947,0.00094006764,0.98905784,0.00013302322,0.000080481164,0.00005843136,0.00010466145,0.0014538581,0.002273064],"genre_scores_gemma":[0.12113319,0.0016793704,0.8702969,0.00017412285,0.00020398106,0.00030059373,0.0005565624,0.00011479616,0.0055404906],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992367,0.00018201396,0.000043692227,0.00015202255,0.0003331859,0.000052326326],"domain_scores_gemma":[0.9995915,0.00012024669,0.000045382112,0.00008775841,0.00014182851,0.000013309719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007955332,0.00064675225,0.0009894501,0.0016935919,0.00054825685,0.00095737417,0.0006195496,0.0006283086,0.0026178728],"category_scores_gemma":[0.0016124332,0.0002988566,0.0008091461,0.0014682135,0.0003208634,0.00101683,0.00080931734,0.0007442452,0.0034090898],"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":[0.00007434886,0.000069072135,0.0009523781,0.00014660705,0.000073689764,0.00004838928,0.0000616242,0.022251366,0.024995102,0.007965295,0.0070533343,0.9363089],"study_design_scores_gemma":[0.00003478119,0.000081168706,0.0030360166,0.000043053362,0.00005010679,0.0004611773,0.000066501234,0.9363773,0.023477407,0.015453317,0.020843253,0.00007588583],"about_ca_topic_score_codex":0.0014655063,"about_ca_topic_score_gemma":0.0016815629,"teacher_disagreement_score":0.0026178728,"about_ca_system_score_codex":0.00035893006,"about_ca_system_score_gemma":0.00035185931,"threshold_uncertainty_score":0.008757651},"labels":[],"label_agreement":null},{"id":"W2116495282","doi":"10.1145/1390156.1390165","title":"Nonnegative matrix factorization via rank-one downdate","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":33,"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":"Non-negative matrix factorization; Singular value decomposition; Matrix decomposition; Rank (graph theory); Separable space; Matrix (chemical analysis); Function (biology); Mathematics; Factorization; Nonnegative matrix; Sparse matrix; Computer science; Algorithm; Low-rank approximation; Combinatorics; Pattern recognition (psychology); Symmetric matrix; Artificial intelligence; Hankel matrix","score_opus":0.023325833791601854,"score_gpt":0.2439223227193447,"score_spread":0.22059648892774283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116495282","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022670145,0.00022928407,0.99637467,0.00012357782,0.00006213207,0.00006441623,0.00011890393,0.00036102146,0.0003989363],"genre_scores_gemma":[0.06782885,0.0006272826,0.92689884,0.00026524768,0.00023786334,0.0004906844,0.0015529998,0.00028316124,0.0018150305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972263,0.0011073423,0.00017056469,0.00063248695,0.00070152077,0.0001617815],"domain_scores_gemma":[0.99479103,0.0025363294,0.0005099628,0.0007608738,0.0012611599,0.00014062475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039959676,0.002564175,0.0026814207,0.0020353738,0.0010347625,0.002260561,0.0017829498,0.0018451993,0.0040020207],"category_scores_gemma":[0.0132402675,0.00109256,0.0022666147,0.0023803385,0.0014026867,0.0023496086,0.0017113106,0.0029450296,0.0025941827],"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":[0.00040387164,0.00023771501,0.0012975353,0.000685854,0.000380608,0.00033686266,0.00029258747,0.34146005,0.012775685,0.060728863,0.0206365,0.5607639],"study_design_scores_gemma":[0.0000391759,0.00008261244,0.00028081168,0.000026901791,0.000027573546,0.00009394216,0.000034255867,0.9573584,0.0027601328,0.034511134,0.0047437483,0.000041372234],"about_ca_topic_score_codex":0.0039288313,"about_ca_topic_score_gemma":0.004506251,"teacher_disagreement_score":0.0040020207,"about_ca_system_score_codex":0.0009547345,"about_ca_system_score_gemma":0.0020216992,"threshold_uncertainty_score":0.021132946},"labels":[],"label_agreement":null},{"id":"W2118271890","doi":"10.1109/fuzzy.2010.5584450","title":"Enhanced weakly trained frontal face detector for surveillance purposes","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Toronto","funders":"","keywords":"Detector; Artificial intelligence; Histogram; Computer science; Face (sociological concept); Face detection; False positive rate; Pattern recognition (psychology); Computer vision; Histogram of oriented gradients; Haar-like features; Object-class detection; Facial recognition system; Image (mathematics); Telecommunications","score_opus":0.010149526827849278,"score_gpt":0.23852792689550537,"score_spread":0.22837840006765608,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118271890","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.081606805,0.0009296673,0.9095025,0.00019018017,0.00021978735,0.00009054262,0.00033151679,0.00287476,0.0042541036],"genre_scores_gemma":[0.5696519,0.00079859194,0.414613,0.0003230364,0.000121426536,0.00008132415,0.0012896575,0.0001098837,0.013011126],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996526,0.00005044182,0.00001298174,0.00008414594,0.00014924636,0.000050635863],"domain_scores_gemma":[0.9995127,0.00011094912,0.000027283564,0.000074658965,0.00024645083,0.000028056396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071576535,0.0004719473,0.0007201418,0.0006588921,0.000237592,0.00050338736,0.0007069272,0.0006377523,0.0035891095],"category_scores_gemma":[0.0010697335,0.00024792334,0.00047074855,0.00032693785,0.00019100777,0.0005669665,0.00043847048,0.00062054856,0.0022423954],"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":[0.0004889257,0.00026525906,0.0034233595,0.00010299969,0.000099331955,0.00017850312,0.000040185518,0.018163765,0.32889646,0.0016867415,0.007423808,0.6392306],"study_design_scores_gemma":[0.000018846438,0.00021641958,0.005523104,0.00001443895,0.00007199536,0.0006335486,0.000024227063,0.83155996,0.15487795,0.00077877555,0.0062479745,0.000032889093],"about_ca_topic_score_codex":0.0014544437,"about_ca_topic_score_gemma":0.0022949446,"teacher_disagreement_score":0.0035891095,"about_ca_system_score_codex":0.00039033938,"about_ca_system_score_gemma":0.0005935701,"threshold_uncertainty_score":0.012006819},"labels":[],"label_agreement":null},{"id":"W2118536426","doi":"10.1109/icassp.2012.6288280","title":"A heteroscedastic extension of LDA based on multi-class matusita affinity","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Toronto","funders":"","keywords":"Homoscedasticity; Heteroscedasticity; Linear discriminant analysis; Pairwise comparison; Pattern recognition (psychology); Artificial intelligence; Generalization; Mathematics; Computational complexity theory; Computer science; Extension (predicate logic); Feature extraction; Algorithm; Statistics","score_opus":0.0455086727436239,"score_gpt":0.2721668663433713,"score_spread":0.22665819359974737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118536426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007454277,0.00015756462,0.99103254,0.00007706693,0.00003260911,0.000022638846,0.00003469137,0.0002468401,0.00094174955],"genre_scores_gemma":[0.4049789,0.0004211005,0.58486867,0.00032587268,0.00027446492,0.0002066582,0.0004277123,0.00020946765,0.008287127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979208,0.0005523235,0.00008019412,0.0005235888,0.0007857983,0.00013734188],"domain_scores_gemma":[0.99795425,0.0006818491,0.00017394671,0.00052091357,0.0005640105,0.00010502576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015614659,0.00056726293,0.0010620665,0.0010887774,0.00061768026,0.0011125898,0.0012970374,0.0008222986,0.0020289922],"category_scores_gemma":[0.0032456734,0.00042806703,0.0010146449,0.0011138433,0.0007968142,0.0016127748,0.0017823712,0.0015212754,0.0010860354],"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":[0.00027078195,0.00019331307,0.0026860512,0.00020202235,0.00020196129,0.00022882374,0.0003541497,0.096788384,0.06855173,0.067311905,0.004616757,0.75859404],"study_design_scores_gemma":[0.000010473056,0.000079212696,0.0020026006,0.000009926363,0.000018950823,0.00024272256,0.000020830543,0.9696028,0.0080235535,0.015615356,0.0043230853,0.000050400424],"about_ca_topic_score_codex":0.0015184877,"about_ca_topic_score_gemma":0.0022787622,"teacher_disagreement_score":0.0020289922,"about_ca_system_score_codex":0.0006187762,"about_ca_system_score_gemma":0.00070550805,"threshold_uncertainty_score":0.0082579255},"labels":[],"label_agreement":null},{"id":"W2119586505","doi":"10.1109/tsmcb.2004.825930","title":"Facial Expression Recognition Using Constructive Feedforward Neural Networks","year":2004,"lang":"en","type":"letter","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":246,"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":"Instituto de Telecomunicações","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Facial expression; Sadness; Artificial neural network; Feedforward neural network; Facial recognition system; Feature (linguistics); Feed forward; Speech recognition; Anger; Psychology","score_opus":0.031808206359679334,"score_gpt":0.23696929931130678,"score_spread":0.20516109295162743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119586505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013840036,0.00019503986,0.9836024,0.000058781647,0.000054711883,0.00004405436,0.000020396781,0.0008655984,0.0013188638],"genre_scores_gemma":[0.51096565,0.00053276593,0.48392493,0.00025104586,0.00008833535,0.0002174396,0.0002119265,0.000081316815,0.0037265732],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942005,0.00014515867,0.00002945783,0.00009593552,0.00025833884,0.00005116219],"domain_scores_gemma":[0.9993099,0.0002969374,0.00008046774,0.00006753147,0.00022783552,0.000017361343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075959926,0.00096688786,0.00053031486,0.0005988551,0.0002408125,0.0004624043,0.0013793815,0.00052188593,0.0009814636],"category_scores_gemma":[0.002177757,0.00040345156,0.00067692774,0.00042378678,0.0005032315,0.0006837457,0.000688465,0.0006984444,0.0004154037],"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":[0.00021102758,0.00015284943,0.0010283852,0.00016273855,0.0001233183,0.0002762042,0.0001245128,0.25088614,0.08238865,0.004778752,0.001861046,0.65800637],"study_design_scores_gemma":[0.0000066844295,0.00006776979,0.00031472737,0.000010355536,0.000021867794,0.000081004306,0.000007804463,0.9818148,0.015932398,0.0011532153,0.0005792414,0.000010150661],"about_ca_topic_score_codex":0.0016439345,"about_ca_topic_score_gemma":0.0022397744,"teacher_disagreement_score":0.0016439345,"about_ca_system_score_codex":0.00035004326,"about_ca_system_score_gemma":0.00033571402,"threshold_uncertainty_score":0.004017234},"labels":[],"label_agreement":null},{"id":"W2119762490","doi":"10.1109/ijcnn.2007.4371309","title":"Face Recognition in Video Using a What-and-Where Fusion Neural Network","year":2007,"lang":"en","type":"article","venue":"IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Pattern recognition (psychology); Fuzzy logic; Classifier (UML); k-nearest neighbors algorithm; Facial recognition system; Frame (networking); Computer vision","score_opus":0.0936471231002817,"score_gpt":0.32736879406145536,"score_spread":0.23372167096117366,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119762490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.082813434,0.00052874605,0.9121902,0.00025494982,0.00012622602,0.00005330773,0.00007312498,0.0009830883,0.0029768997],"genre_scores_gemma":[0.7635868,0.0003478785,0.23326278,0.00019531902,0.000057448335,0.00006883178,0.00010539154,0.000023615861,0.0023519641],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972767,0.000050899132,0.000015129238,0.00007788484,0.00008605878,0.0000423013],"domain_scores_gemma":[0.9997271,0.00009021673,0.00002421675,0.000027697786,0.00011944129,0.00001140375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008517319,0.00034844177,0.00059509726,0.00039441654,0.0004268334,0.00066277385,0.0006726429,0.0007980342,0.0009096538],"category_scores_gemma":[0.0014118595,0.00024032577,0.00047521113,0.00037543772,0.00031876963,0.0011286465,0.00039316478,0.0005033898,0.00023900613],"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":[0.0005030117,0.00019046286,0.0022547008,0.00007076577,0.000124835,0.00015256615,0.00011267173,0.27135077,0.044817444,0.0043765036,0.0018701863,0.674176],"study_design_scores_gemma":[0.0000049807186,0.0000428723,0.0005781345,0.000005532493,0.000020776144,0.000040400402,0.000014701713,0.98784816,0.009991874,0.0010395416,0.00040403253,0.000008976612],"about_ca_topic_score_codex":0.007340953,"about_ca_topic_score_gemma":0.006273198,"teacher_disagreement_score":0.007340953,"about_ca_system_score_codex":0.00071587,"about_ca_system_score_gemma":0.00040714108,"threshold_uncertainty_score":0.014596462},"labels":[],"label_agreement":null},{"id":"W2119933351","doi":"10.1109/ipta.2008.4743776","title":"Score Fusion of SVD and DCT-RLDA for Face Recognition","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Normalization (sociology); Discrete cosine transform; Singular value decomposition; Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Computer science; Linear discriminant analysis; Biometrics; Fusion; Feature extraction; Face (sociological concept); Fusion rules; Mathematics; Image fusion; Image (mathematics)","score_opus":0.061271773459986766,"score_gpt":0.24665273043433428,"score_spread":0.1853809569743475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119933351","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021095501,0.00047814092,0.97606224,0.000071542876,0.000089855435,0.0000602101,0.00007845003,0.000677753,0.0013863677],"genre_scores_gemma":[0.35470214,0.00055547286,0.64051443,0.000065423585,0.00011850748,0.00011826271,0.0005465555,0.00008356112,0.003295636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832255,0.0003441413,0.00009413587,0.00024168816,0.00089994364,0.00009755962],"domain_scores_gemma":[0.99913675,0.00018389134,0.00005853032,0.0001519795,0.0004221524,0.000046841447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016338768,0.0006442371,0.0009276749,0.001372636,0.00031528177,0.00090759725,0.000727997,0.00048341893,0.0021048477],"category_scores_gemma":[0.0029847168,0.00023650556,0.0009072164,0.001298321,0.00042589373,0.00093196094,0.0010164928,0.0005869184,0.0017545427],"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":[0.00054691936,0.00011240045,0.0013333844,0.00015981878,0.00012362612,0.00008055943,0.00007252213,0.027505023,0.09225756,0.009930071,0.0018769173,0.8660012],"study_design_scores_gemma":[0.00003199094,0.00050128234,0.0036247934,0.000025214653,0.00009816143,0.0004861376,0.000065630265,0.88458824,0.09675802,0.007230857,0.006503208,0.00008643183],"about_ca_topic_score_codex":0.0013215761,"about_ca_topic_score_gemma":0.0015148433,"teacher_disagreement_score":0.0021048477,"about_ca_system_score_codex":0.00035606028,"about_ca_system_score_gemma":0.00055952,"threshold_uncertainty_score":0.008640885},"labels":[],"label_agreement":null},{"id":"W2120766769","doi":"10.1109/imtc.2010.5488048","title":"Applying Contrast-limited Adaptive Histogram Equalization and integral projection for facial feature enhancement and detection","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"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":"Adaptive histogram equalization; Artificial intelligence; Computer science; Pattern recognition (psychology); Histogram equalization; Computer vision; Facial recognition system; Projection (relational algebra); Feature (linguistics); Histogram; Noise (video); Face (sociological concept); Filter (signal processing); Contrast (vision); Image (mathematics)","score_opus":0.01752618911732602,"score_gpt":0.2551136238968415,"score_spread":0.2375874347795155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120766769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025144419,0.00031646385,0.97241026,0.000046274505,0.000055623426,0.0000690346,0.000016156402,0.0008612205,0.0010806067],"genre_scores_gemma":[0.2042417,0.0004516612,0.79270124,0.000086680586,0.00005296687,0.00007526538,0.00007918805,0.00008030463,0.0022310396],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995772,0.00005899839,0.000025503472,0.00009291119,0.00020540663,0.000039888495],"domain_scores_gemma":[0.9995969,0.00017616767,0.000032426477,0.000058676684,0.000117275325,0.000018560146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059922936,0.00043634293,0.0005008011,0.0008633602,0.00020342213,0.00041433782,0.00059741084,0.00045162736,0.0013729956],"category_scores_gemma":[0.0010993854,0.00030267172,0.0003465353,0.000499838,0.00044177353,0.0007914433,0.00062943576,0.00050525594,0.00058917794],"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":[0.00022226409,0.00012795026,0.0013102586,0.00015610762,0.00006378637,0.0001314063,0.000067879126,0.0027836321,0.43019912,0.001420901,0.0007369081,0.5627797],"study_design_scores_gemma":[0.000057899546,0.00046402623,0.009652235,0.000025521138,0.000096186996,0.0023436048,0.000060976192,0.23194881,0.7430063,0.0018888987,0.010371209,0.00008422953],"about_ca_topic_score_codex":0.0006054236,"about_ca_topic_score_gemma":0.001130185,"teacher_disagreement_score":0.0013729956,"about_ca_system_score_codex":0.00016389388,"about_ca_system_score_gemma":0.0003481557,"threshold_uncertainty_score":0.004593134},"labels":[],"label_agreement":null},{"id":"W2120922739","doi":"10.1109/icassp.2005.1415344","title":"Statistical Non-Uniform Sampling of Gabor Wavelet Coefficients for Face Recongnition","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of British Columbia","funders":"","keywords":"Gabor wavelet; Pattern recognition (psychology); Artificial intelligence; Principal component analysis; Sampling (signal processing); Face (sociological concept); Mathematics; Curse of dimensionality; Wavelet; Computer science; Facial recognition system; Gabor transform; Computer vision; Wavelet transform; Discrete wavelet transform; Time–frequency analysis","score_opus":0.024982531911697636,"score_gpt":0.27996536241062625,"score_spread":0.2549828304989286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120922739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026337622,0.00044716365,0.9721814,0.000042176478,0.000041134444,0.000021748056,0.000025741892,0.00027854083,0.00062459806],"genre_scores_gemma":[0.48462507,0.0010802245,0.5119573,0.0000716469,0.00013609747,0.000077048935,0.00025023447,0.00014266485,0.001659705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995359,0.00015271668,0.000021991254,0.00006207904,0.00019762215,0.000029592804],"domain_scores_gemma":[0.99920636,0.00034182414,0.00007809258,0.0001879383,0.00015545456,0.000030365509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058276975,0.00033883276,0.00047809805,0.00059765286,0.00019574373,0.00034418367,0.00034094643,0.00022235699,0.00083445833],"category_scores_gemma":[0.0022263974,0.00020989431,0.0004238235,0.0007140886,0.00030472834,0.00049425423,0.00028981827,0.00037924876,0.0004234189],"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":[0.0004218946,0.00013709647,0.0021622377,0.00014680614,0.00005894872,0.00016052402,0.00007204421,0.08976944,0.14817306,0.01313641,0.0020784459,0.7436831],"study_design_scores_gemma":[0.000013394211,0.00015935057,0.004199478,0.000010066705,0.000036224294,0.00032601308,0.000022105698,0.9320669,0.056842156,0.0028269198,0.0034712532,0.000026059251],"about_ca_topic_score_codex":0.0007860295,"about_ca_topic_score_gemma":0.0013325898,"teacher_disagreement_score":0.00083445833,"about_ca_system_score_codex":0.00022669186,"about_ca_system_score_gemma":0.00031642933,"threshold_uncertainty_score":0.003082037},"labels":[],"label_agreement":null},{"id":"W2121317230","doi":"10.1109/icis.2012.62","title":"Initial Investigation into Using Two-Level Regional Voting Approach for Face Verification","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Northern British Columbia","funders":"","keywords":"Face (sociological concept); Computer science; Benchmark (surveying); Linear discriminant analysis; Voting; Facial recognition system; Identification (biology); Artificial intelligence; A priori and a posteriori; Embedding; Similarity (geometry); Baseline (sea); Biometrics; Identity (music); Data mining; Machine learning; Pattern recognition (psychology); Algorithm; Image (mathematics)","score_opus":0.20592556755567729,"score_gpt":0.3415556428932812,"score_spread":0.13563007533760393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121317230","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064931534,0.0009072159,0.929064,0.00009029019,0.000064397405,0.00027080922,0.000046656598,0.00047702304,0.0041480917],"genre_scores_gemma":[0.5610448,0.0007477992,0.43205065,0.00007753921,0.000044799286,0.00010747815,0.00014468381,0.000059449336,0.005722836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990087,0.00031717587,0.000047225116,0.0002597104,0.00028778866,0.00007935759],"domain_scores_gemma":[0.9989837,0.00034849532,0.000026372609,0.00018309956,0.00043765787,0.000020760595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019170602,0.00035033555,0.0007896846,0.0006039913,0.00046629456,0.0006737823,0.0008607309,0.0006687272,0.0028255482],"category_scores_gemma":[0.0025435246,0.00020930695,0.0006279578,0.00046784262,0.00045661026,0.0011164146,0.0004988206,0.00052134425,0.00083005015],"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":[0.00052964105,0.00014070689,0.0034340236,0.00027620784,0.000113482994,0.00012930979,0.00022464083,0.022834437,0.12571955,0.014883325,0.00079614494,0.83091855],"study_design_scores_gemma":[0.00006503467,0.0020433022,0.0074540544,0.000053527183,0.00021726862,0.0014536558,0.00026078362,0.747,0.21755747,0.0049193827,0.018827837,0.0001477654],"about_ca_topic_score_codex":0.0022102199,"about_ca_topic_score_gemma":0.0035522203,"teacher_disagreement_score":0.0028255482,"about_ca_system_score_codex":0.0005096508,"about_ca_system_score_gemma":0.00055533065,"threshold_uncertainty_score":0.010138512},"labels":[],"label_agreement":null},{"id":"W2121340607","doi":"10.4304/jmm.1.1.9-15","title":"Invariant Robust 3-D Face Recognition based on the Hilbert Transform in Spectral Space","year":2006,"lang":"en","type":"article","venue":"Journal of Multimedia","topic":"Face and Expression Recognition","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":"National Research Council Canada","funders":"","keywords":"Invariant (physics); Computer science; Facial recognition system; Hilbert space; Artificial intelligence; Hilbert transform; Pattern recognition (psychology); Mathematics; Computer vision; Pure mathematics","score_opus":0.02207023508301608,"score_gpt":0.21919479021360738,"score_spread":0.1971245551305913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121340607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015466625,0.00022140976,0.9823924,0.00007079212,0.000033048134,0.000027293889,0.000059592257,0.0006296559,0.0010991673],"genre_scores_gemma":[0.3570515,0.0007061119,0.6389788,0.00014682129,0.00008186274,0.00012226243,0.00032770223,0.00011080263,0.0024741974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996897,0.00007131892,0.000013541796,0.0000442068,0.00015610886,0.000025067451],"domain_scores_gemma":[0.9996736,0.00013725717,0.00003604299,0.0000588721,0.00007987813,0.000014429321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004310534,0.00025862904,0.00046752786,0.0005889744,0.00015698744,0.0004315521,0.0004125849,0.000335329,0.0019495487],"category_scores_gemma":[0.0010857509,0.0001442402,0.00041819748,0.0004692403,0.00044143468,0.00077738264,0.00044448374,0.00040307152,0.00084418576],"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":[0.00025733229,0.0000964853,0.0011062016,0.00015963755,0.00007423567,0.00019639725,0.00014431875,0.039017577,0.23441678,0.02119494,0.004071703,0.6992644],"study_design_scores_gemma":[0.000024542955,0.00023155563,0.0045329756,0.000024980953,0.000035995974,0.0012277947,0.00006683375,0.85931236,0.1107218,0.014809232,0.008918113,0.00009383644],"about_ca_topic_score_codex":0.0008049123,"about_ca_topic_score_gemma":0.00067677355,"teacher_disagreement_score":0.0019495487,"about_ca_system_score_codex":0.00025249564,"about_ca_system_score_gemma":0.0002928176,"threshold_uncertainty_score":0.0065218806},"labels":[],"label_agreement":null},{"id":"W2122090912","doi":"","title":"Maximum-Margin Matrix Factorization","year":2004,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":960,"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":"Margin (machine learning); Generalization; Matrix decomposition; Matrix norm; Computer science; Factorization; Norm (philosophy); Generalization error; Rank (graph theory); Matrix (chemical analysis); Mathematics; Algorithm; Artificial intelligence; Algebra over a field; Machine learning; Combinatorics; Pure mathematics; Artificial neural network","score_opus":0.0113121206388473,"score_gpt":0.24761233986231668,"score_spread":0.2363002192234694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122090912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00089929707,0.00010230372,0.99770564,0.00010770991,0.00003973415,0.000018042032,0.00004796617,0.00024304977,0.0008363203],"genre_scores_gemma":[0.1628631,0.00043578263,0.82777786,0.00035341608,0.00042137798,0.00028704124,0.0007910975,0.00024930038,0.006821033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752754,0.00087325217,0.00010190252,0.0006109174,0.00071475806,0.00017155275],"domain_scores_gemma":[0.9969441,0.0014107683,0.00029076583,0.0006911799,0.00052599044,0.00013713876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025179056,0.0016003089,0.0018903365,0.00084775925,0.00080883893,0.0018079637,0.0025791004,0.001998149,0.0068134144],"category_scores_gemma":[0.00890879,0.0006659819,0.001021689,0.0013007009,0.0015090584,0.0034845832,0.0025847135,0.0028513002,0.0039352034],"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":[0.00032032942,0.00027406492,0.00080408825,0.0004425821,0.0001630528,0.00024309328,0.00025145846,0.35878688,0.010340038,0.16824035,0.02886597,0.43126816],"study_design_scores_gemma":[0.000017931032,0.000053027212,0.00008087998,0.000016630356,0.000011061874,0.00005397246,0.00001763384,0.9135706,0.0022959285,0.07895756,0.0049111187,0.00001369797],"about_ca_topic_score_codex":0.0010690475,"about_ca_topic_score_gemma":0.0014504816,"teacher_disagreement_score":0.0068134144,"about_ca_system_score_codex":0.00067684904,"about_ca_system_score_gemma":0.0011908892,"threshold_uncertainty_score":0.022793114},"labels":[],"label_agreement":null},{"id":"W2122598626","doi":"10.1186/1687-5281-2012-17","title":"Gauss–Laguerre wavelet textural feature fusion with geometrical information for facial expression identification","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":97,"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 British Columbia; University of Calgary","funders":"","keywords":"Artificial intelligence; Biometrics; Computer science; Facial expression; Pattern recognition (psychology); Wavelet; Computer vision; Feature extraction; Facial Action Coding System; Face (sociological concept); Feature (linguistics); Gabor wavelet; Expression (computer science); Fiducial marker; Identification (biology); Wavelet transform; Discrete wavelet transform","score_opus":0.015420196842654178,"score_gpt":0.2675989143781633,"score_spread":0.25217871753550913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122598626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11364656,0.0005705036,0.8828296,0.00017094308,0.00007041833,0.000046985104,0.00013445054,0.00066787034,0.0018625933],"genre_scores_gemma":[0.62670404,0.00069157797,0.36942273,0.000075127406,0.00008480452,0.000060872353,0.00049336127,0.000087681605,0.0023798323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997067,0.000065279724,0.000016761862,0.0000392512,0.00014245464,0.00002956917],"domain_scores_gemma":[0.9997073,0.00007146349,0.00003936999,0.000053535066,0.00011402519,0.0000142705385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006442453,0.0003517153,0.00042577396,0.001183322,0.00014033847,0.00038570294,0.0003253828,0.00031120837,0.0011862472],"category_scores_gemma":[0.0013854785,0.00012285192,0.00046872915,0.0010662159,0.00022242483,0.0007817947,0.0003842908,0.00033733787,0.0006936299],"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":[0.00046935532,0.000120436256,0.0015661621,0.000100149184,0.000047157442,0.00012378738,0.00008610574,0.02342042,0.2108996,0.0032756128,0.0022703381,0.7576208],"study_design_scores_gemma":[0.000028778848,0.00025580492,0.009325976,0.000021381935,0.00008696106,0.0003268249,0.000086464395,0.89404464,0.08741381,0.003021292,0.005336153,0.00005191781],"about_ca_topic_score_codex":0.0006126092,"about_ca_topic_score_gemma":0.00066465884,"teacher_disagreement_score":0.0011862472,"about_ca_system_score_codex":0.00021888912,"about_ca_system_score_gemma":0.00023195431,"threshold_uncertainty_score":0.0039684176},"labels":[],"label_agreement":null},{"id":"W2123247936","doi":"10.1109/mmsp.2009.5293308","title":"Automatic fiducial points detection for facial expressions using scale invariant feature","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Fiducial marker; Computer science; Computer vision; Pattern recognition (psychology); Facial recognition system; Face detection; AdaBoost; Normalization (sociology); Feature extraction; Feature (linguistics); Face (sociological concept); Detector; Object-class detection; Facial expression; Classifier (UML)","score_opus":0.023508422655093952,"score_gpt":0.27295541989872235,"score_spread":0.2494469972436284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123247936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0584501,0.00025819434,0.938821,0.000047631103,0.00005454385,0.000066410794,0.00006342942,0.0013994281,0.00083930616],"genre_scores_gemma":[0.39395127,0.0003481436,0.60329145,0.000038109134,0.000038520164,0.00011282311,0.00027693078,0.00013930931,0.0018034586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947196,0.000098574776,0.0000243679,0.00010193517,0.00025851862,0.000044630608],"domain_scores_gemma":[0.99940586,0.0001520348,0.00007840251,0.00008455565,0.00025235783,0.000026774076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056213635,0.00049351,0.0006077613,0.0015164807,0.00027140084,0.00035848498,0.0006552622,0.00042411938,0.0012909208],"category_scores_gemma":[0.001752038,0.00023713417,0.00044647686,0.000552031,0.00034908365,0.00067360257,0.0002746997,0.00045243057,0.0007729675],"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":[0.00027454278,0.000098871074,0.0031308886,0.0001112263,0.000046640005,0.00011994301,0.00009768769,0.008607485,0.24117841,0.0011868201,0.002137176,0.7430102],"study_design_scores_gemma":[0.000050952753,0.00033785013,0.020348562,0.0000396825,0.00007180982,0.0012939303,0.00014295164,0.6591766,0.3088384,0.0020617642,0.0075101196,0.00012741318],"about_ca_topic_score_codex":0.001698664,"about_ca_topic_score_gemma":0.0017928324,"teacher_disagreement_score":0.001698664,"about_ca_system_score_codex":0.0002990758,"about_ca_system_score_gemma":0.00038038407,"threshold_uncertainty_score":0.0043185353},"labels":[],"label_agreement":null},{"id":"W2123515711","doi":"10.1007/978-3-642-01818-3_10","title":"An Iterative Hybrid Filter-Wrapper Approach to Feature Selection for Document Clustering","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"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 New Brunswick","funders":"","keywords":"Computer science; Feature selection; Artificial intelligence; Cluster analysis; Maximization; Feature (linguistics); Filter (signal processing); Pattern recognition (psychology); Data mining; Greedy algorithm; Set (abstract data type); Selection (genetic algorithm); Machine learning; Algorithm; Mathematics; Mathematical optimization","score_opus":0.017006082730740172,"score_gpt":0.2600075452697171,"score_spread":0.24300146253897692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123515711","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002260362,0.0001478726,0.99585176,0.00002771624,0.00002784675,0.000043146843,0.00006000781,0.001422638,0.00015877644],"genre_scores_gemma":[0.034099124,0.00013875842,0.96204126,0.00008653957,0.00007473301,0.00021544023,0.0006357776,0.00033467833,0.0023736868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974033,0.0006352381,0.00025045697,0.0005280191,0.0009389007,0.00024400526],"domain_scores_gemma":[0.99652207,0.0014452054,0.00013907327,0.0004695908,0.0013231094,0.000100889374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027417787,0.0016597568,0.0035398973,0.003186904,0.0013541585,0.0020042993,0.0042263325,0.0022249636,0.004189105],"category_scores_gemma":[0.0050069527,0.001051703,0.0026824817,0.0043211663,0.0007338798,0.0018688597,0.00181277,0.0015809647,0.0032268493],"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":[0.00033650894,0.0002002197,0.0005105997,0.00015349883,0.00025837022,0.00008992126,0.00011720709,0.05120145,0.018786412,0.002011724,0.008037466,0.91829675],"study_design_scores_gemma":[0.0000452791,0.00010515485,0.0006529574,0.000012726935,0.000086561806,0.00013881235,0.000042189913,0.97995305,0.012239929,0.0038311805,0.0028502622,0.000041909243],"about_ca_topic_score_codex":0.012265312,"about_ca_topic_score_gemma":0.0139333205,"teacher_disagreement_score":0.012265312,"about_ca_system_score_codex":0.0009826808,"about_ca_system_score_gemma":0.001828633,"threshold_uncertainty_score":0.024387836},"labels":[],"label_agreement":null},{"id":"W2123776888","doi":"10.1109/wacv.2007.39","title":"Local Graph Matching for Face Recognition","year":2007,"lang":"en","type":"article","venue":"Proceedings","topic":"Face and Expression Recognition","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":"Pattern recognition (psychology); Artificial intelligence; Computer science; Facial recognition system; Classifier (UML); Feature vector; Feature extraction; Graph; Theoretical computer science","score_opus":0.024427581095193416,"score_gpt":0.2607327016284507,"score_spread":0.23630512053325728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123776888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051134927,0.00046018139,0.9905612,0.00011292997,0.00002880383,0.000045447432,0.0001236475,0.002186633,0.0013676509],"genre_scores_gemma":[0.26527065,0.0010308638,0.7251599,0.00028530447,0.00010689126,0.00020189561,0.0012377528,0.00040227777,0.006304571],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932325,0.00018978439,0.000024342165,0.00019672124,0.00021252267,0.000053397045],"domain_scores_gemma":[0.99961686,0.000106532716,0.000042811964,0.00015122016,0.000065779284,0.000016750959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005324404,0.0005715289,0.0008808694,0.002185016,0.00045011687,0.00073099154,0.0013595588,0.00092597795,0.0054533156],"category_scores_gemma":[0.0015768223,0.0002807466,0.00088544714,0.0021819398,0.00063323043,0.0016093126,0.000881355,0.0008470026,0.0026758132],"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":[0.0001285455,0.00009854008,0.00054224086,0.00014986726,0.00008843966,0.00007023549,0.00006393818,0.089366056,0.024599846,0.028174313,0.008715808,0.8480021],"study_design_scores_gemma":[0.00001544015,0.00006174258,0.0007112566,0.000019170824,0.000025056604,0.00019119291,0.000050247083,0.91510206,0.015506459,0.060474265,0.007817032,0.000026081545],"about_ca_topic_score_codex":0.003634341,"about_ca_topic_score_gemma":0.0046477565,"teacher_disagreement_score":0.0054533156,"about_ca_system_score_codex":0.000825857,"about_ca_system_score_gemma":0.00060803187,"threshold_uncertainty_score":0.018243194},"labels":[],"label_agreement":null},{"id":"W2125126592","doi":"10.1162/089976602753633402","title":"A Parallel Mixture of SVMs for Very Large Scale Problems","year":2002,"lang":"en","type":"article","venue":"Neural Computation","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":368,"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","funders":"","keywords":"Support vector machine; Generalization; Benchmark (surveying); Set (abstract data type); Computer science; Artificial intelligence; Machine learning; Data set; Quadratic equation; Training set; Scale (ratio); Pattern recognition (psychology); Mathematics","score_opus":0.028763387988349154,"score_gpt":0.2530030794397367,"score_spread":0.22423969145138756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125126592","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009092386,0.0004818035,0.9855989,0.00029666637,0.00019365094,0.00008460362,0.000051592622,0.002385554,0.0018147923],"genre_scores_gemma":[0.16942324,0.00045371224,0.82200617,0.00022011242,0.0002578101,0.0003616885,0.00034432113,0.00036057454,0.006572462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869245,0.0003089684,0.00008552375,0.00020121413,0.00060162833,0.00011026845],"domain_scores_gemma":[0.99842846,0.00039174798,0.00007613366,0.0003829393,0.00056007254,0.00016056267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020433713,0.00094998203,0.001310436,0.00083837437,0.0008195142,0.0013218782,0.0020871717,0.0010400262,0.0061538755],"category_scores_gemma":[0.004693801,0.0008720816,0.0010767931,0.0012525974,0.00051766826,0.0029724767,0.0026462309,0.0023496666,0.0037770206],"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":[0.0010322147,0.0004162339,0.0018511222,0.00021292525,0.0002369294,0.00017984923,0.00014409001,0.2315269,0.017506938,0.028097566,0.016903903,0.7018913],"study_design_scores_gemma":[0.000045651363,0.000058407975,0.00013437045,0.0000056565696,0.000018874032,0.00005267721,0.000009442134,0.98580265,0.0022949576,0.007893907,0.0036727584,0.000010502958],"about_ca_topic_score_codex":0.0018516593,"about_ca_topic_score_gemma":0.0025452347,"teacher_disagreement_score":0.0061538755,"about_ca_system_score_codex":0.0006000824,"about_ca_system_score_gemma":0.0013245525,"threshold_uncertainty_score":0.020586789},"labels":[],"label_agreement":null},{"id":"W2125403238","doi":"10.1109/cibim.2011.5949217","title":"Using fuzzy adaptive fusion in face detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Artificial intelligence; Computer science; Face (sociological concept); Face detection; Computer vision; Object-class detection; Facial recognition system; Detector; Pattern recognition (psychology); Fuzzy logic; Set (abstract data type); Fuzzy set; Process (computing); Image (mathematics)","score_opus":0.10222985889662728,"score_gpt":0.2638070988943621,"score_spread":0.16157723999773482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125403238","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05491821,0.00042466895,0.9422256,0.00006887042,0.000057542293,0.000040309224,0.000013088235,0.00025789306,0.0019937432],"genre_scores_gemma":[0.7746689,0.00027428608,0.22355884,0.00006131197,0.000045972425,0.00004930423,0.000032075288,0.000017359434,0.0012919534],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939907,0.000104727806,0.000030835992,0.0001351596,0.00027830238,0.000051932206],"domain_scores_gemma":[0.99960035,0.0001550411,0.000038502865,0.000037905244,0.00015187755,0.000016305235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011872997,0.00035921606,0.0005551364,0.0007434092,0.00039956567,0.00052888884,0.0006902872,0.0006653779,0.0005955154],"category_scores_gemma":[0.0016099174,0.0002565978,0.00054093346,0.00056398916,0.00046398697,0.00076198997,0.00064460735,0.00052150426,0.00021558198],"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":[0.00046481553,0.00018454592,0.002236884,0.00015832088,0.00014616421,0.00023718075,0.00027523577,0.2003225,0.12250061,0.009463947,0.0010127623,0.66299707],"study_design_scores_gemma":[0.000013027161,0.00015725815,0.0013037887,0.000014667966,0.00003858911,0.00012934222,0.000026175572,0.9711264,0.022266924,0.0036650233,0.0012262787,0.000032531134],"about_ca_topic_score_codex":0.0020705468,"about_ca_topic_score_gemma":0.0017555059,"teacher_disagreement_score":0.0020705468,"about_ca_system_score_codex":0.00046241292,"about_ca_system_score_gemma":0.0002792759,"threshold_uncertainty_score":0.006279111},"labels":[],"label_agreement":null},{"id":"W2125602612","doi":"10.5430/air.v3n2p41","title":"Partitioning trees: A global multiclass classification technique for SVMs","year":2014,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","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":"","keywords":"Directed acyclic graph; Multiclass classification; Computer science; Support vector machine; Machine learning; Classifier (UML); Artificial intelligence; Decision tree; Node (physics); Binary classification; Binary decision diagram; Graph; Binary number; Pattern recognition (psychology); Data mining; Theoretical computer science; Mathematics; Algorithm","score_opus":0.28185344579650184,"score_gpt":0.4575787784853236,"score_spread":0.17572533268882173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125602612","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002906577,0.00041726994,0.994987,0.000080086334,0.000058945057,0.000051314826,0.00009574591,0.0008168031,0.00058630673],"genre_scores_gemma":[0.15313426,0.0009466318,0.84085697,0.00019775642,0.00023708603,0.00027936677,0.0010315307,0.00049683964,0.0028195733],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983736,0.00050889107,0.000096436415,0.00031982458,0.0005710609,0.0001301701],"domain_scores_gemma":[0.9984485,0.0004859725,0.00015908027,0.00042108545,0.00040405814,0.00008122655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019536149,0.0017379457,0.0016095059,0.002590808,0.00080160185,0.0012377936,0.0014923838,0.0010424869,0.0030474379],"category_scores_gemma":[0.0035573875,0.0005455813,0.0013349182,0.0025960773,0.00061374326,0.0024668386,0.0018668666,0.0023797455,0.0017796888],"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":[0.00018279463,0.00009470082,0.0012852926,0.00028006558,0.00019903497,0.0001226212,0.0002437995,0.04859384,0.017315345,0.017088192,0.008162126,0.9064323],"study_design_scores_gemma":[0.00004328077,0.0002955575,0.0015161824,0.00010687807,0.00016005053,0.00043222093,0.000118481636,0.8960129,0.015970694,0.0570523,0.028226383,0.00006512933],"about_ca_topic_score_codex":0.0014804115,"about_ca_topic_score_gemma":0.0017724506,"teacher_disagreement_score":0.0030474379,"about_ca_system_score_codex":0.0004548125,"about_ca_system_score_gemma":0.0006948771,"threshold_uncertainty_score":0.0103318095},"labels":[],"label_agreement":null},{"id":"W2125618387","doi":"10.1109/nafips.2004.1336295","title":"Cluster validation indices for fMRI data: Fuzzy C-Means with feature partitions versus cluster merging strategies","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Face and Expression Recognition","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":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Pattern recognition (psychology); Computer science; Centroid; Artificial intelligence; Fuzzy logic; False positive paradox; Cluster analysis; Data mining; Feature (linguistics); Fuzzy set; Cluster (spacecraft); Fuzzy clustering","score_opus":0.022099809126403996,"score_gpt":0.272613496844768,"score_spread":0.250513687718364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125618387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0418605,0.000772679,0.9549921,0.00022771618,0.00004086115,0.0003475529,0.0001602694,0.0007455679,0.0008527244],"genre_scores_gemma":[0.16146888,0.00021901858,0.83674526,0.000083310944,0.000037437796,0.00046673455,0.0003994435,0.00022508933,0.00035472904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9941736,0.0024749306,0.0005494205,0.0008092395,0.0017887878,0.00020405184],"domain_scores_gemma":[0.98262066,0.011283692,0.0011725442,0.0013616305,0.0033202637,0.00024116771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01762937,0.0013070169,0.0019603923,0.004673014,0.0018138369,0.0022519939,0.0024988805,0.001923516,0.0009636704],"category_scores_gemma":[0.046245065,0.0005181373,0.0012564438,0.0037130322,0.001375545,0.0025042861,0.0018324441,0.0019700432,0.000356246],"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":[0.0011628357,0.00023035349,0.006913924,0.0004150988,0.00051387877,0.00007916295,0.000978459,0.26089045,0.0062858826,0.017334854,0.0033991467,0.70179594],"study_design_scores_gemma":[0.000055151806,0.00017600138,0.0033671386,0.00006911084,0.000079060555,0.00007240006,0.00014006824,0.9793936,0.006077186,0.009463068,0.0010420136,0.00006512455],"about_ca_topic_score_codex":0.0067672413,"about_ca_topic_score_gemma":0.007024892,"teacher_disagreement_score":0.01762937,"about_ca_system_score_codex":0.0020377957,"about_ca_system_score_gemma":0.0023347693,"threshold_uncertainty_score":0.09323412},"labels":[],"label_agreement":null},{"id":"W2125952675","doi":"10.1109/iscas.2000.857051","title":"Fast modular neural nets for face detection","year":2000,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Modular design; Computer science; Artificial intelligence; Artificial neural network; Process (computing); Face (sociological concept); Pixel; Computational complexity theory; Division (mathematics); Image (mathematics); Computation; Simple (philosophy); Pattern recognition (psychology); Face detection; Facial recognition system; Modular neural network; Machine learning; Algorithm; Time delay neural network; Arithmetic","score_opus":0.012558965991766664,"score_gpt":0.22763034748417546,"score_spread":0.2150713814924088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125952675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011791196,0.000593807,0.985306,0.000056012144,0.000053192914,0.00003113784,0.000039591683,0.0010937752,0.0010352385],"genre_scores_gemma":[0.2688592,0.00056892604,0.72609097,0.0001295622,0.00009018275,0.00015308861,0.00019658296,0.00012899912,0.0037824593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996805,0.00007690361,0.000015797912,0.0000642019,0.00012150646,0.000040988078],"domain_scores_gemma":[0.9994104,0.00028563477,0.000058420414,0.0000659374,0.0001573511,0.000022313343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007192797,0.0008281427,0.0005681217,0.0007137011,0.00026491363,0.00047521834,0.0010570821,0.0006264091,0.0022748455],"category_scores_gemma":[0.0014322475,0.0003498526,0.00042235263,0.0006281871,0.00036867804,0.0012489829,0.0007410658,0.00075497275,0.00080257363],"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":[0.00038380097,0.000089308465,0.00090521504,0.00019931415,0.00009959204,0.0001239819,0.000050666295,0.2738428,0.036698293,0.016936226,0.003886643,0.6667841],"study_design_scores_gemma":[0.000015391606,0.000065803906,0.0003320895,0.0000084446,0.000018098854,0.000051758954,0.0000064759347,0.98494035,0.0073742694,0.0052083954,0.0019679095,0.000011043242],"about_ca_topic_score_codex":0.0020983256,"about_ca_topic_score_gemma":0.003051134,"teacher_disagreement_score":0.0022748455,"about_ca_system_score_codex":0.0006512052,"about_ca_system_score_gemma":0.0004185265,"threshold_uncertainty_score":0.0076100826},"labels":[],"label_agreement":null},{"id":"W2126246733","doi":"10.1109/fuzz.2001.1009088","title":"Fuzzy measures for vehicle detection","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Fuzzy logic; Artificial intelligence; Computer science; Computer vision; Object detection; Boundary (topology); Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.02218935355063498,"score_gpt":0.24826391291020708,"score_spread":0.2260745593595721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126246733","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008533077,0.0014412649,0.98776,0.000101894264,0.000057273235,0.000020963738,0.000033125525,0.0001309222,0.0019215057],"genre_scores_gemma":[0.41968334,0.001532935,0.5748016,0.000108360466,0.00027978278,0.00009154781,0.00014729168,0.00005952217,0.0032954884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999102,0.00016743362,0.000054955708,0.00015516774,0.0004684154,0.000052123098],"domain_scores_gemma":[0.99814916,0.0011837131,0.00015739026,0.0001410381,0.00031185747,0.000056839846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011869017,0.00052978593,0.00077499764,0.0024350237,0.0005353742,0.0012139197,0.00090091786,0.00093313196,0.0016835828],"category_scores_gemma":[0.005246946,0.00024020871,0.00079571403,0.0011320021,0.000969794,0.0015177109,0.0006312052,0.0013597077,0.00049560773],"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":[0.00016507784,0.00008726627,0.0017101965,0.00033080098,0.00015269272,0.00017000445,0.00023108548,0.19619057,0.015659038,0.28889275,0.002471149,0.49393937],"study_design_scores_gemma":[0.000012639567,0.00008278624,0.0010020839,0.00005039902,0.00003465873,0.00015297385,0.000034431967,0.84067345,0.0056175613,0.14647791,0.005815263,0.00004580759],"about_ca_topic_score_codex":0.0022180702,"about_ca_topic_score_gemma":0.0013746911,"teacher_disagreement_score":0.0024350237,"about_ca_system_score_codex":0.0011136864,"about_ca_system_score_gemma":0.00037563764,"threshold_uncertainty_score":0.008080363},"labels":[],"label_agreement":null},{"id":"W2126552487","doi":"10.1109/tmm.2012.2189550","title":"Kernel Cross-Modal Factor Analysis for Information Fusion With Application to Bimodal Emotion Recognition","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":155,"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","funders":"","keywords":"Computer science; Kernel (algebra); Artificial intelligence; Kernel principal component analysis; Pattern recognition (psychology); Tree kernel; Kernel embedding of distributions; Kernel method; Canonical correlation; Polynomial kernel; Radial basis function kernel; Domain (mathematical analysis); Support vector machine; Machine learning; Mathematics","score_opus":0.020322360921013995,"score_gpt":0.2726646795590411,"score_spread":0.25234231863802714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126552487","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030014464,0.00025960954,0.99619883,0.000052437037,0.000012403231,0.000017557175,0.000018690513,0.00015175737,0.00028737716],"genre_scores_gemma":[0.330544,0.001077449,0.66610324,0.00010729593,0.00008747014,0.00020816328,0.00026147513,0.00014112165,0.0014697321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872524,0.0005152548,0.000074483214,0.00023888126,0.00036187656,0.00008410204],"domain_scores_gemma":[0.9977416,0.0012864094,0.00021163197,0.0002616683,0.00044306036,0.000055753266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002896997,0.0012534747,0.001071203,0.0014342682,0.00042336583,0.0012012831,0.00090971706,0.0008691458,0.002208557],"category_scores_gemma":[0.007367339,0.00033654732,0.001439069,0.0016667228,0.00084268366,0.0016175082,0.0012558163,0.001341139,0.0007351888],"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":[0.00033721828,0.00017459401,0.0015321819,0.00040840203,0.00031408496,0.00018245875,0.00040615816,0.33941054,0.028415319,0.06031026,0.0025399528,0.5659689],"study_design_scores_gemma":[0.000006186633,0.00004972866,0.000460433,0.000012955707,0.000025563453,0.000048017628,0.000030489657,0.9817323,0.0038993726,0.012535487,0.0011709112,0.000028548353],"about_ca_topic_score_codex":0.0019698762,"about_ca_topic_score_gemma":0.0013228089,"teacher_disagreement_score":0.002896997,"about_ca_system_score_codex":0.0007019566,"about_ca_system_score_gemma":0.0006953985,"threshold_uncertainty_score":0.015320957},"labels":[],"label_agreement":null},{"id":"W2128253627","doi":"10.1007/978-3-540-35488-8_28","title":"Spectral Dimensionality Reduction","year":2006,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Face and Expression Recognition","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":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Isomap; Dimensionality reduction; Nonlinear dimensionality reduction; Mathematics; Spectral clustering; Embedding; Multidimensional scaling; Kernel (algebra); Kernel principal component analysis; Pattern recognition (psychology); Principal component analysis; Artificial intelligence; Generalization; Cluster analysis; Laplace operator; Linear map; Metric (unit); Diffusion map; Algorithm; Computer science; Kernel method; Combinatorics; Statistics; Pure mathematics; Support vector machine","score_opus":0.046795988655360846,"score_gpt":0.2922817282576733,"score_spread":0.24548573960231246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128253627","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009934336,0.0024920565,0.9504835,0.00074229715,0.00045471382,0.000073186435,0.00034299225,0.0007140614,0.034762938],"genre_scores_gemma":[0.21066116,0.004942167,0.6925543,0.00057905365,0.0007798809,0.0002541861,0.0025838267,0.00060717703,0.08703827],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999376,0.00012337301,0.000029271456,0.00014762452,0.00028971283,0.000034000328],"domain_scores_gemma":[0.9995339,0.00009470717,0.000020549165,0.00019469787,0.00013872968,0.000017489032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049988943,0.0006518858,0.00078269927,0.001015125,0.000686989,0.0014586964,0.000576795,0.0004578745,0.0077884695],"category_scores_gemma":[0.0017235399,0.00027102357,0.00069403125,0.0012401207,0.00087933434,0.0014773281,0.0013619569,0.0013961783,0.004933566],"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":[0.00005208309,0.00006491805,0.0003552702,0.00018012754,0.000057560188,0.00003919499,0.00014471596,0.019640476,0.012288117,0.30673844,0.024300229,0.63613886],"study_design_scores_gemma":[0.000013400214,0.00007190497,0.0019450582,0.000089472844,0.00004380235,0.00041969828,0.00017307536,0.31101173,0.02224867,0.530493,0.13343057,0.000059639347],"about_ca_topic_score_codex":0.0005176355,"about_ca_topic_score_gemma":0.00078095327,"teacher_disagreement_score":0.0077884695,"about_ca_system_score_codex":0.00036946632,"about_ca_system_score_gemma":0.0004841992,"threshold_uncertainty_score":0.026055038},"labels":[],"label_agreement":null},{"id":"W2128507168","doi":"10.1109/icdm.2011.22","title":"An Efficient Greedy Method for Unsupervised Feature Selection","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":89,"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":"Computer science; Feature selection; Greedy algorithm; Artificial intelligence; Selection (genetic algorithm); Machine learning; Pattern recognition (psychology); Feature (linguistics); Unsupervised learning; Feature extraction; Task (project management); Data mining; Feature learning; Algorithm","score_opus":0.03664982792937564,"score_gpt":0.2908666649722158,"score_spread":0.25421683704284015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128507168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017952184,0.00007544732,0.99752265,0.00003002232,0.000017541064,0.000037205155,0.000022751383,0.00031805492,0.00018122757],"genre_scores_gemma":[0.049534652,0.00013056876,0.94780064,0.00011508738,0.00006197324,0.00034253136,0.0003565003,0.0001547303,0.0015033848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983765,0.00046686266,0.00010572092,0.00031184818,0.00061886845,0.00012025593],"domain_scores_gemma":[0.9989291,0.0005211549,0.00007922173,0.00013930179,0.00029803516,0.000033219174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015069922,0.0012774051,0.0016802626,0.002016648,0.0007980243,0.00073977315,0.0016678815,0.0011590112,0.0018302561],"category_scores_gemma":[0.0033794828,0.000549552,0.0013196303,0.0022539983,0.0008082315,0.00091137725,0.0011375363,0.0009273284,0.0012087655],"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":[0.00025978842,0.0001280635,0.00082518515,0.00014760994,0.00017449106,0.00020500629,0.00010345266,0.14270662,0.02607913,0.012993455,0.0074378517,0.8089393],"study_design_scores_gemma":[0.000062742074,0.00010078931,0.00049015385,0.000012817699,0.00003508135,0.0003615119,0.000024167755,0.9744031,0.008435827,0.011100435,0.004934477,0.000039013245],"about_ca_topic_score_codex":0.002048744,"about_ca_topic_score_gemma":0.0030748656,"teacher_disagreement_score":0.002048744,"about_ca_system_score_codex":0.00053572125,"about_ca_system_score_gemma":0.0015729683,"threshold_uncertainty_score":0.007969856},"labels":[],"label_agreement":null},{"id":"W2129316059","doi":"10.1109/crv.2008.43","title":"Thermal Faceprint: A New Thermal Face Signature Extraction for Infrared Face Recognition","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":41,"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":"Face (sociological concept); Infrared; Artificial intelligence; Facial recognition system; Computer science; Thermal infrared; Feature extraction; Computer vision; Pattern recognition (psychology); Signature (topology); Face detection; Optics; Mathematics; Physics","score_opus":0.04028860312127667,"score_gpt":0.2629824102760043,"score_spread":0.2226938071547276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129316059","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015105073,0.0007531175,0.97877526,0.00009327681,0.00015032585,0.000120875266,0.00032602414,0.0024405604,0.0022354443],"genre_scores_gemma":[0.1360401,0.0010387275,0.84905964,0.00021717045,0.0001789393,0.00027283188,0.0010608442,0.00030294468,0.011828751],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999335,0.00006287021,0.000024701749,0.000106827996,0.00041889618,0.000051737326],"domain_scores_gemma":[0.99968493,0.00005847214,0.000043255834,0.00007039539,0.00012004296,0.000022967608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004246186,0.00058578746,0.0008301214,0.0013645646,0.00033763677,0.00063019653,0.0009319324,0.000731003,0.006299112],"category_scores_gemma":[0.00093990227,0.00026960456,0.00046264858,0.0007456466,0.0003202943,0.0014609555,0.0007235097,0.0006131387,0.0037456644],"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":[0.00024125275,0.000108774846,0.0010374847,0.00022816533,0.00005995889,0.00014184305,0.00004162164,0.002843573,0.21894826,0.001905475,0.0054762634,0.76896733],"study_design_scores_gemma":[0.000092263545,0.0007307318,0.015745627,0.00009231573,0.00020183042,0.0054234387,0.00009990282,0.31237632,0.5960001,0.0049145925,0.0640724,0.00025048517],"about_ca_topic_score_codex":0.00052779325,"about_ca_topic_score_gemma":0.00077409827,"teacher_disagreement_score":0.006299112,"about_ca_system_score_codex":0.00025402632,"about_ca_system_score_gemma":0.000318649,"threshold_uncertainty_score":0.021072626},"labels":[],"label_agreement":null},{"id":"W2129785219","doi":"10.1109/tsmcb.2009.2018137","title":"Improved Face Representation by Nonuniform Multilevel Selection of Gabor Convolution Features","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"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 British Columbia; University of British Columbia Hospital","funders":"","keywords":"Gabor wavelet; Pattern recognition (psychology); Artificial intelligence; Computer science; Face (sociological concept); Principal component analysis; Facial recognition system; Curse of dimensionality; Dimensionality reduction; Gabor filter; Dimension (graph theory); Representation (politics); Linear discriminant analysis; Wavelet; Mathematics; Computer vision; Feature extraction; Wavelet transform; Discrete wavelet transform","score_opus":0.015201415605786616,"score_gpt":0.2477303234335937,"score_spread":0.23252890782780708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129785219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17335688,0.00053588906,0.82322854,0.000091029964,0.000054755532,0.000039325205,0.00012824875,0.0007063819,0.0018589143],"genre_scores_gemma":[0.74215716,0.00040947145,0.25451458,0.0000710505,0.000059908856,0.0000574722,0.00039776912,0.000073082796,0.0022595366],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976987,0.000034538007,0.000011789701,0.000051181225,0.000098766126,0.000033788772],"domain_scores_gemma":[0.99980885,0.00004085861,0.000031145995,0.000045365065,0.00006328764,0.000010596476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022838273,0.000331791,0.00058034086,0.0005683805,0.00014659936,0.00029830865,0.00033678097,0.00019757572,0.0009187383],"category_scores_gemma":[0.00068069284,0.00012419042,0.00050333585,0.00064728817,0.00014523484,0.000492853,0.0003925287,0.00026317107,0.0003430237],"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":[0.00036331953,0.0001057544,0.0025184548,0.0000676054,0.00005027491,0.00015297541,0.000057799993,0.044124585,0.20282806,0.0033559243,0.0027993333,0.7435759],"study_design_scores_gemma":[0.000016297114,0.00018751473,0.0056701223,0.000007711659,0.00005055667,0.0003843771,0.00002659438,0.9348841,0.055345383,0.0010421822,0.0023652068,0.00002007564],"about_ca_topic_score_codex":0.0015133519,"about_ca_topic_score_gemma":0.0014067328,"teacher_disagreement_score":0.0015133519,"about_ca_system_score_codex":0.00019756083,"about_ca_system_score_gemma":0.000242113,"threshold_uncertainty_score":0.0030735135},"labels":[],"label_agreement":null},{"id":"W2129943987","doi":"10.1109/iccet.2009.36","title":"Features Selection Using Fuzzy ESVDF for Data Dimensionality Reduction","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"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":"Feature selection; Computer science; Dimensionality reduction; Fuzzy logic; Artificial intelligence; Curse of dimensionality; Data mining; Fuzzy set; Selection (genetic algorithm); Reduction (mathematics); Weight; Fuzzy classification; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.08150717625596637,"score_gpt":0.33896809960981245,"score_spread":0.25746092335384607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129943987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073301243,0.00013888843,0.9919986,0.000035523797,0.0000187917,0.00004561928,0.000023805098,0.00024244597,0.00016618393],"genre_scores_gemma":[0.11460148,0.00014265552,0.8843041,0.00004507976,0.00003822157,0.00019399042,0.00017319876,0.0000373627,0.0004638762],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998131,0.00042692863,0.00016965666,0.00032437846,0.000849569,0.00009847915],"domain_scores_gemma":[0.99835396,0.00086364936,0.00011281005,0.00013013936,0.0005048949,0.000034651628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002145638,0.0009860229,0.0015833186,0.0026973088,0.0006596495,0.0009938452,0.0011706459,0.0007648195,0.0008413338],"category_scores_gemma":[0.0050786394,0.00035928527,0.0010684477,0.0015342846,0.0004838675,0.00088066247,0.00065616646,0.00092719105,0.0003540738],"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":[0.00014154689,0.000105163526,0.0015794139,0.0001052157,0.00010348445,0.00013868752,0.0001179778,0.064451024,0.01745282,0.0040985793,0.0013378654,0.9103682],"study_design_scores_gemma":[0.000034266097,0.00010713708,0.0010504958,0.00001811426,0.000032759457,0.00019812386,0.00003445984,0.97798616,0.013529254,0.0047778394,0.002196329,0.00003508997],"about_ca_topic_score_codex":0.0021015443,"about_ca_topic_score_gemma":0.0017483982,"teacher_disagreement_score":0.0026973088,"about_ca_system_score_codex":0.00047662423,"about_ca_system_score_gemma":0.00077237294,"threshold_uncertainty_score":0.011347353},"labels":[],"label_agreement":null},{"id":"W2130105873","doi":"10.1109/tpami.2012.107","title":"Iterative Closest Normal Point for 3D Face Recognition","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":129,"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":"Face (sociological concept); Computer science; Facial recognition system; Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Point (geometry); Iterative closest point; Set (abstract data type); Discriminant; Class (philosophy); Computer vision; Mathematics; Point cloud; Geometry","score_opus":0.02425297454398013,"score_gpt":0.2649859309135449,"score_spread":0.24073295636956477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130105873","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021776105,0.0003027629,0.9943745,0.000049918377,0.000042068415,0.000062582236,0.00007604475,0.0021224294,0.0007920418],"genre_scores_gemma":[0.08131718,0.00048568915,0.9141693,0.000078076635,0.000047048332,0.00029038993,0.0005601012,0.00027172375,0.0027804046],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655354,0.00068057847,0.0001272358,0.000543174,0.0019856128,0.000109869754],"domain_scores_gemma":[0.9987031,0.0003766649,0.00010697121,0.0003449337,0.00042965822,0.000038571492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013429098,0.0012416111,0.0015396645,0.0022416445,0.0005648303,0.0011368337,0.002217338,0.0015111552,0.0047740866],"category_scores_gemma":[0.0055907867,0.00064976077,0.0012320076,0.0021620267,0.0008984058,0.0015187003,0.0020748144,0.0017412887,0.0044699884],"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":[0.00014803155,0.000075208496,0.0007868219,0.0001279387,0.00007612515,0.000117664225,0.00014949538,0.10295063,0.019584969,0.012557902,0.0068475734,0.85657763],"study_design_scores_gemma":[0.000017961309,0.000059651382,0.0007275569,0.000019872696,0.0000093159215,0.00032310092,0.00004340769,0.9609099,0.014706434,0.014213573,0.008919312,0.00004992918],"about_ca_topic_score_codex":0.0050035566,"about_ca_topic_score_gemma":0.0038169571,"teacher_disagreement_score":0.0050035566,"about_ca_system_score_codex":0.0008207256,"about_ca_system_score_gemma":0.0009930378,"threshold_uncertainty_score":0.015970945},"labels":[],"label_agreement":null},{"id":"W2130280024","doi":"10.1109/ccece.2005.1557134","title":"3D characteristic facial contours","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"University of Regina","funders":"University of Regina","keywords":"Computer science; Face (sociological concept); Matching (statistics); Facial recognition system; Artificial intelligence; Computer vision; Set (abstract data type); Pattern recognition (psychology); Mathematics","score_opus":0.007860165607440047,"score_gpt":0.2092987743805206,"score_spread":0.20143860877308056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130280024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.083198905,0.00059180136,0.90693486,0.000113399605,0.000064912936,0.00014509355,0.0013505835,0.0012562934,0.0063440963],"genre_scores_gemma":[0.5242436,0.0008194036,0.4686505,0.000078517,0.000027937236,0.00015402114,0.002717826,0.00022150275,0.0030867325],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995265,0.00006381613,0.000026693551,0.00013192653,0.00022119594,0.000029922856],"domain_scores_gemma":[0.99954283,0.000110551744,0.00005114628,0.00016028709,0.00011729382,0.00001799483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049313396,0.00045509267,0.00061805715,0.0013985011,0.00026693154,0.0010583663,0.00066072476,0.0007641209,0.0036504292],"category_scores_gemma":[0.0017634061,0.0003958681,0.000787362,0.0011721886,0.00035179392,0.0009914199,0.0007707914,0.0005005968,0.0016828985],"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":[0.00035854056,0.0000824464,0.008273196,0.00024612798,0.0000450789,0.0003277029,0.0002893342,0.0521411,0.12160335,0.014326859,0.004749943,0.79755634],"study_design_scores_gemma":[0.00004610465,0.0002128891,0.028309485,0.00009328631,0.000071505594,0.0036854607,0.00023674735,0.8206434,0.09466979,0.017716596,0.03417475,0.0001399256],"about_ca_topic_score_codex":0.0010839945,"about_ca_topic_score_gemma":0.0009995607,"teacher_disagreement_score":0.0036504292,"about_ca_system_score_codex":0.00030120678,"about_ca_system_score_gemma":0.00028721805,"threshold_uncertainty_score":0.012211919},"labels":[],"label_agreement":null},{"id":"W2130288511","doi":"10.1109/ccece.2007.334","title":"Face Recognition Under Significant Pose Variation","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Artificial intelligence; Computer science; Facial recognition system; Computer vision; Face (sociological concept); Pattern recognition (psychology); AdaBoost; Three-dimensional face recognition; Pose; Face detection; Active appearance model; Variation (astronomy); Image (mathematics); Support vector machine","score_opus":0.028394351318182506,"score_gpt":0.25347855858548785,"score_spread":0.22508420726730533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130288511","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41052023,0.0011098589,0.5809858,0.00027127142,0.00022834219,0.000062355866,0.00024217134,0.0015024842,0.0050774366],"genre_scores_gemma":[0.9460778,0.0004124136,0.04914949,0.00012072853,0.0001261215,0.000044490782,0.00038989418,0.00007349434,0.0036056342],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990502,0.00011914411,0.000029595523,0.00027245973,0.0003892925,0.00013926052],"domain_scores_gemma":[0.9995004,0.0001450754,0.00007233533,0.00012243971,0.00013544055,0.000024241712],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057625433,0.0004275762,0.0010020025,0.0004982048,0.00027265714,0.00057392864,0.00047263905,0.00061587733,0.0011941589],"category_scores_gemma":[0.0015402494,0.00020152383,0.00042714132,0.00043085497,0.000358659,0.0006639061,0.0005628815,0.0004070045,0.00070055487],"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":[0.0005715129,0.00012360358,0.0073416815,0.00010213281,0.00011142345,0.0008684949,0.00015750552,0.053056374,0.27755836,0.002395068,0.0028232965,0.65489054],"study_design_scores_gemma":[0.000023787166,0.00042142777,0.030673526,0.000014823423,0.00006925085,0.0034102348,0.00017933651,0.8212513,0.13452283,0.005842353,0.0035321976,0.000058999136],"about_ca_topic_score_codex":0.0008647199,"about_ca_topic_score_gemma":0.00074672815,"teacher_disagreement_score":0.0011941589,"about_ca_system_score_codex":0.00024622015,"about_ca_system_score_gemma":0.00024296237,"threshold_uncertainty_score":0.0039948225},"labels":[],"label_agreement":null},{"id":"W2130573243","doi":"10.1109/ijcnn.2005.1556171","title":"Estimating accurate multi-class probabilities with support vector machines","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Softmax function; Support vector machine; Computer science; Pattern recognition (psychology); Artificial intelligence; Probabilistic logic; Class (philosophy); Function (biology); Machine learning; Algorithm; Artificial neural network","score_opus":0.04290289209975255,"score_gpt":0.2723164471426226,"score_spread":0.22941355504287003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130573243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007749911,0.00017110797,0.991088,0.000037319012,0.000027757585,0.000018901103,0.000026168162,0.00075855246,0.00012233865],"genre_scores_gemma":[0.2731012,0.00031243058,0.72469693,0.00008048037,0.00014020453,0.0001105174,0.00036750358,0.00022544536,0.0009652498],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962173,0.0009975557,0.00025824236,0.00065202586,0.0016385113,0.00023634455],"domain_scores_gemma":[0.9911957,0.0051175463,0.00077926245,0.0012539587,0.0015149246,0.00013857473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003611436,0.0016215685,0.0015569584,0.0017212926,0.0004849122,0.0017248876,0.002038879,0.0017787676,0.0017824637],"category_scores_gemma":[0.01896494,0.00090212334,0.00085240783,0.0011637072,0.000593464,0.0035166447,0.0016447048,0.0023380592,0.0016662318],"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":[0.00054318283,0.00014580386,0.0029890104,0.00019152382,0.0001935074,0.00015356576,0.00011381638,0.16321146,0.019730233,0.004185695,0.0017139702,0.80682826],"study_design_scores_gemma":[0.000015968963,0.00007671969,0.0011772411,0.000014728396,0.00002372091,0.00010463751,0.000019686135,0.97724986,0.015427303,0.0047598197,0.0010972983,0.00003301325],"about_ca_topic_score_codex":0.0007338945,"about_ca_topic_score_gemma":0.0007989046,"teacher_disagreement_score":0.003611436,"about_ca_system_score_codex":0.0003666869,"about_ca_system_score_gemma":0.00055063545,"threshold_uncertainty_score":0.019099295},"labels":[],"label_agreement":null},{"id":"W2131332843","doi":"10.1109/icassp.2006.1661338","title":"An Optimal Basis for Feature Extraction With Support Vector Machine Classification Using The Radius–Margin Bound","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"McMaster University","funders":"","keywords":"Support vector machine; Basis (linear algebra); Gradient descent; Margin (machine learning); Feature extraction; Computer science; Pattern recognition (psychology); Loop (graph theory); RADIUS; Artificial intelligence; Feature vector; Descent (aeronautics); Structured support vector machine; Feature (linguistics); Relevance vector machine; Algorithm; Mathematics; Machine learning; Artificial neural network; Engineering","score_opus":0.025668695088371784,"score_gpt":0.28520087066827304,"score_spread":0.25953217557990127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131332843","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013004288,0.000068822665,0.9982167,0.00003189107,0.000009079096,0.000011865079,0.000012228648,0.00013073979,0.00021816716],"genre_scores_gemma":[0.07095535,0.00020483494,0.9270699,0.000059700025,0.00005761524,0.00022222921,0.00025097426,0.00020224438,0.0009770409],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817,0.00059235876,0.00013019731,0.00027226796,0.0007116825,0.00012345491],"domain_scores_gemma":[0.99801433,0.0008692751,0.00014760894,0.00027575903,0.00062902045,0.00006402415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021951695,0.0008326283,0.0015475817,0.0012824049,0.0007051021,0.0013167585,0.0011435552,0.0011823559,0.0023546966],"category_scores_gemma":[0.0074418327,0.0006964988,0.001010894,0.001252993,0.0010145401,0.0020496782,0.0016299868,0.0021597615,0.0022298691],"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":[0.00021083502,0.00013850213,0.00074164825,0.00019966924,0.00007038129,0.0001327369,0.00011357226,0.32687676,0.024403311,0.06792934,0.0062019536,0.57298124],"study_design_scores_gemma":[0.00000912218,0.00004071364,0.00012242413,0.000019981151,0.00000759631,0.00005328322,0.000011534045,0.9741906,0.004088009,0.019427242,0.002012438,0.000017137134],"about_ca_topic_score_codex":0.0013382056,"about_ca_topic_score_gemma":0.0009522004,"teacher_disagreement_score":0.0023546966,"about_ca_system_score_codex":0.00062797905,"about_ca_system_score_gemma":0.0012045864,"threshold_uncertainty_score":0.011609256},"labels":[],"label_agreement":null},{"id":"W2131484288","doi":"10.1109/pacrim.2009.5291376","title":"Histogram-enhanced principal component analysis for face recognition","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Principal component analysis; Artificial intelligence; Facial recognition system; Preprocessor; Pattern recognition (psychology); Computer science; Face (sociological concept); Computer vision; Histogram matching; Eigenface; Gaussian; Image (mathematics)","score_opus":0.03431095305592775,"score_gpt":0.2728443802774793,"score_spread":0.23853342722155157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131484288","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00424669,0.0013660254,0.99009234,0.00013558766,0.000106605185,0.00006344577,0.00013691382,0.0019223159,0.0019300302],"genre_scores_gemma":[0.11574991,0.0023223844,0.8758397,0.00013155007,0.00017614411,0.00022220098,0.00066278403,0.00021408309,0.004681236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944896,0.0001275077,0.0000204989,0.000082559054,0.00028740466,0.00003305127],"domain_scores_gemma":[0.9995401,0.00016009768,0.000028447084,0.00008139737,0.0001769087,0.000012986841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054063834,0.0005147353,0.0005191477,0.0009796385,0.00030688578,0.00047536107,0.0006749211,0.0004886113,0.005478282],"category_scores_gemma":[0.0015635466,0.00023808591,0.0004733516,0.0015872953,0.0003574779,0.0006728756,0.0003977493,0.00085098034,0.0036856863],"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":[0.00010830386,0.00007438733,0.0004503611,0.00020735773,0.000060160375,0.00008296504,0.000037062917,0.016374363,0.04306457,0.007510523,0.008448028,0.923582],"study_design_scores_gemma":[0.000044879194,0.00021064075,0.0061822757,0.00006188765,0.00009794098,0.00072588323,0.000053820222,0.8464269,0.08375359,0.0166623,0.04566062,0.00011918773],"about_ca_topic_score_codex":0.001995456,"about_ca_topic_score_gemma":0.0021250066,"teacher_disagreement_score":0.005478282,"about_ca_system_score_codex":0.00028793886,"about_ca_system_score_gemma":0.0005363232,"threshold_uncertainty_score":0.0183267},"labels":[],"label_agreement":null},{"id":"W2132319264","doi":"10.1111/j.1467-8640.2012.00439.x","title":"ROBUSTNESS INSTEAD OF ACCURACY SHOULD BE THE PRIMARY OBJECTIVE FOR SUBJECTIVE PATTERN RECOGNITION RESEARCH: STABILITY ANALYSIS ON MULTICANDIDATE ELECTORAL COLLEGE VERSUS DIRECT POPULAR VOTE","year":2012,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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 Northern British Columbia","funders":"","keywords":"Robustness (evolution); Computer science; Facial recognition system; Artificial intelligence; Pattern recognition (psychology); Machine learning; Stability (learning theory); Metric (unit); Algorithm","score_opus":0.26024305123216745,"score_gpt":0.39922220223903987,"score_spread":0.13897915100687241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132319264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17152087,0.0018149817,0.7944003,0.0070390697,0.00028956562,0.00017161971,0.0003900677,0.00035149293,0.024021985],"genre_scores_gemma":[0.9543107,0.0004491942,0.040198307,0.00050130737,0.000232926,0.00021442336,0.00021173395,0.00014525224,0.0037362054],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935808,0.0024973326,0.00033211047,0.0012486085,0.0018918889,0.00044920703],"domain_scores_gemma":[0.9396713,0.040890932,0.0059627597,0.00796559,0.0045686555,0.0009407466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011179386,0.00067570666,0.0013504454,0.0022453673,0.0010992645,0.0033508807,0.0018967703,0.001918358,0.0053679785],"category_scores_gemma":[0.09432036,0.0003748753,0.0014244289,0.001541104,0.0054523973,0.008385107,0.0025393122,0.0033508374,0.0008979231],"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":[0.00030488928,0.00010731304,0.010660937,0.00025576874,0.0001981739,0.0001207072,0.0009534339,0.12930575,0.003953116,0.787024,0.0032574523,0.063858405],"study_design_scores_gemma":[0.000032072552,0.00024298327,0.008050722,0.00012442438,0.00005001272,0.00015718685,0.00042880344,0.45676744,0.0037703137,0.52636,0.0039323703,0.00008378247],"about_ca_topic_score_codex":0.001415556,"about_ca_topic_score_gemma":0.00066815194,"teacher_disagreement_score":0.011179386,"about_ca_system_score_codex":0.0022886645,"about_ca_system_score_gemma":0.000946122,"threshold_uncertainty_score":0.05912298},"labels":[],"label_agreement":null},{"id":"W2132450737","doi":"10.1109/iccsa.2011.69","title":"PCA Based Geometric Modeling for Automatic Face Detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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 Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Face detection; Face (sociological concept); Pattern recognition (psychology); Feature extraction; Geometric transformation; Edge detection; Object-class detection; Principal component analysis; Facial recognition system; Pixel; Feature (linguistics); Image (mathematics); Image processing","score_opus":0.06905308257624158,"score_gpt":0.24701788020956478,"score_spread":0.17796479763332318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132450737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017353426,0.00024904817,0.99586475,0.000047945698,0.000040193034,0.000020617255,0.000047915255,0.0008882996,0.001105832],"genre_scores_gemma":[0.1660032,0.0016911621,0.8243188,0.00011823483,0.00014141102,0.0001985872,0.0008342928,0.00038915672,0.00630522],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927324,0.000114405186,0.000019372825,0.00014070522,0.00041344916,0.000038720922],"domain_scores_gemma":[0.99970454,0.000081268256,0.000032210723,0.000070402406,0.000102944345,0.000008566118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037978622,0.000846767,0.00069712626,0.0011800021,0.0003678323,0.0006016905,0.00090538524,0.00059297524,0.0036789682],"category_scores_gemma":[0.0010263846,0.00046845083,0.0010567326,0.0010698345,0.00040221884,0.0008446017,0.00048514354,0.00084145006,0.002807331],"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":[0.000089792644,0.00005199184,0.00080991694,0.00021063909,0.000091082904,0.00018734146,0.000088303226,0.18750246,0.06679377,0.03290789,0.0088132825,0.70245355],"study_design_scores_gemma":[0.0000040438426,0.000041529016,0.0008379389,0.000012467109,0.00001582421,0.0002596569,0.00001245292,0.9670459,0.013983634,0.005487014,0.012270325,0.000029110784],"about_ca_topic_score_codex":0.0029700496,"about_ca_topic_score_gemma":0.0020449988,"teacher_disagreement_score":0.0036789682,"about_ca_system_score_codex":0.00040773107,"about_ca_system_score_gemma":0.0005796333,"threshold_uncertainty_score":0.0123074055},"labels":[],"label_agreement":null},{"id":"W2132820034","doi":"","title":"Maximum Margin Clustering","year":2004,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":457,"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; University of Waterloo","funders":"","keywords":"Cluster analysis; Margin (machine learning); Correlation clustering; Computer science; Constrained clustering; Fuzzy clustering; Artificial intelligence; Pattern recognition (psychology); CURE data clustering algorithm; Mathematics; Mathematical optimization; Machine learning","score_opus":0.014905955973929245,"score_gpt":0.22861220771125287,"score_spread":0.21370625173732363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132820034","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010726292,0.00019745882,0.9966613,0.000103382954,0.00003746801,0.000051686424,0.00008618229,0.00045217268,0.0013378232],"genre_scores_gemma":[0.06849448,0.00040363826,0.92373514,0.00027871827,0.00018968241,0.00040122384,0.0012778782,0.0005185819,0.004700533],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9934976,0.0019092294,0.00036558017,0.0016235841,0.002256115,0.00034783265],"domain_scores_gemma":[0.996443,0.0008820386,0.0003775443,0.0011947448,0.00094830897,0.00015452206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035174985,0.0020701617,0.002993495,0.003425052,0.002010724,0.0034607782,0.005944159,0.0031984732,0.00561652],"category_scores_gemma":[0.009780735,0.0010848199,0.0021651913,0.00374436,0.0022838644,0.0046930634,0.00485068,0.0033739223,0.0052316575],"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":[0.00031654368,0.00022910818,0.001440229,0.0005174668,0.0003129103,0.0001611076,0.00047326172,0.23007861,0.008329256,0.13136142,0.028918594,0.59786147],"study_design_scores_gemma":[0.00003782637,0.000079231344,0.00040678872,0.00007130927,0.000039603186,0.00018113852,0.000100333906,0.8449415,0.0072244895,0.12639573,0.02046314,0.000058931197],"about_ca_topic_score_codex":0.0016321781,"about_ca_topic_score_gemma":0.0018286714,"teacher_disagreement_score":0.005944159,"about_ca_system_score_codex":0.0012815355,"about_ca_system_score_gemma":0.0019868738,"threshold_uncertainty_score":0.018789113},"labels":[],"label_agreement":null},{"id":"W2133901274","doi":"10.1109/ccece.2008.4564607","title":"Boosting chromatic information for face recognition","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Boosting (machine learning); Computer science; Artificial intelligence; Facial recognition system; AdaBoost; Pattern recognition (psychology); Chromatic scale; Curse of dimensionality; Face detection; Face (sociological concept); Machine learning; Computer vision; Support vector machine; Mathematics","score_opus":0.02606498616448898,"score_gpt":0.19429135556007926,"score_spread":0.16822636939559027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133901274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033775814,0.0010468306,0.96011615,0.00016869114,0.00013805037,0.000065625005,0.000055303335,0.0014343759,0.003199128],"genre_scores_gemma":[0.60020757,0.00086261064,0.3891968,0.00026632534,0.00021405423,0.00008823185,0.0003042645,0.00018887095,0.008671293],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995449,0.00009841871,0.000010548254,0.00010066198,0.00018561084,0.00005970564],"domain_scores_gemma":[0.999542,0.000108581946,0.000036131885,0.00006669709,0.00021685066,0.000029802197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012896636,0.0005162777,0.0006101781,0.0009370338,0.00049694174,0.00062503864,0.0008835256,0.0005426787,0.0022379286],"category_scores_gemma":[0.0015572269,0.00026894195,0.00061880186,0.00064712594,0.00037894776,0.0007888933,0.00069090124,0.00086988404,0.0013943831],"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":[0.00032567052,0.0002055936,0.00214511,0.00009670214,0.00010218293,0.000059965387,0.000072319795,0.13904853,0.06745715,0.0067008543,0.0038332674,0.77995265],"study_design_scores_gemma":[0.000010227345,0.00011894748,0.0015122715,0.000010981237,0.000033597425,0.000077189194,0.000015214012,0.9655325,0.02524911,0.0032568248,0.0041618454,0.000021213165],"about_ca_topic_score_codex":0.0022538032,"about_ca_topic_score_gemma":0.002963653,"teacher_disagreement_score":0.0022538032,"about_ca_system_score_codex":0.00057482615,"about_ca_system_score_gemma":0.0004821876,"threshold_uncertainty_score":0.007486582},"labels":[],"label_agreement":null},{"id":"W2134047262","doi":"10.1109/icdim.2010.5664217","title":"A new combined KSVM and KFD model for classification and recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Linear discriminant analysis; Artificial intelligence; Kernel Fisher discriminant analysis; Support vector machine; Decision boundary; Computer science; Kernel (algebra); Pattern recognition (psychology); Kernel method; Classifier (UML); Discriminant; Machine learning; Linear classifier; Mathematics","score_opus":0.04616374652636641,"score_gpt":0.262021876163825,"score_spread":0.2158581296374586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134047262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00434702,0.00047033612,0.9933955,0.00020394659,0.00009738942,0.000027260023,0.00010233117,0.0004486962,0.0009075131],"genre_scores_gemma":[0.4566989,0.0011665688,0.52518445,0.00051539857,0.00033138934,0.00031524766,0.0009720828,0.0002438288,0.014572152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880266,0.00023438943,0.00008830572,0.0002633849,0.0004935868,0.00011769207],"domain_scores_gemma":[0.99912554,0.00028583698,0.00006936955,0.00014187276,0.0003297738,0.000047633497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011689217,0.00086459046,0.001504074,0.0010551935,0.00042034616,0.001439269,0.0022732571,0.0017165821,0.0022847676],"category_scores_gemma":[0.0023706544,0.0005098702,0.0011572344,0.0011550384,0.0005746369,0.0025053502,0.0012099585,0.0014536703,0.0014296426],"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":[0.00015543097,0.00014510797,0.0013023699,0.00025329733,0.00015490002,0.00015804135,0.00007728714,0.5312527,0.012077326,0.021385375,0.007917264,0.42512098],"study_design_scores_gemma":[0.0000029134844,0.000013289264,0.000081185266,0.0000040496543,0.0000056850567,0.00003563647,0.0000029823311,0.9956054,0.00073278986,0.0022984785,0.0012104971,0.0000071549907],"about_ca_topic_score_codex":0.0038623142,"about_ca_topic_score_gemma":0.0037570964,"teacher_disagreement_score":0.0038623142,"about_ca_system_score_codex":0.0011031107,"about_ca_system_score_gemma":0.0010637003,"threshold_uncertainty_score":0.008003712},"labels":[],"label_agreement":null},{"id":"W2136171901","doi":"10.1109/ccece.2008.4564802","title":"Color face recognition under various learning scenarios","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"YCbCr; Artificial intelligence; Computer science; Facial recognition system; Computer vision; Color space; Chromatic scale; Face detection; Face (sociological concept); Pattern recognition (psychology); Color image; Mathematics; Image processing; Image (mathematics)","score_opus":0.024434361334663823,"score_gpt":0.19459801357335138,"score_spread":0.17016365223868757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136171901","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8590949,0.00045116793,0.13247055,0.0002248182,0.000060270104,0.00012797824,0.0002052069,0.00065490534,0.006710158],"genre_scores_gemma":[0.9625849,0.0003101988,0.035270087,0.00006901151,0.000029273528,0.000049711678,0.0002542956,0.000026338223,0.0014062205],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988594,0.00037900184,0.00004102016,0.00018575971,0.00038476946,0.00015019182],"domain_scores_gemma":[0.99785835,0.0012072639,0.00012788338,0.00022088806,0.000513491,0.000072129624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015520775,0.0005148197,0.0004681366,0.00047123412,0.00041140703,0.0006398692,0.00047275866,0.0006194273,0.0011559355],"category_scores_gemma":[0.005933589,0.0001423444,0.00027097474,0.00035706052,0.00050298305,0.0010608152,0.0004940689,0.00040845294,0.0005052121],"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":[0.0037758593,0.00077370164,0.01687739,0.0005225438,0.00019879988,0.0010571184,0.00073695375,0.3465874,0.15817797,0.0055365358,0.002909601,0.46284604],"study_design_scores_gemma":[0.00004802788,0.00093435094,0.010141259,0.00003350473,0.00007144758,0.0007596593,0.00038386392,0.85539395,0.12640682,0.004559758,0.0011880238,0.000079376405],"about_ca_topic_score_codex":0.0016310426,"about_ca_topic_score_gemma":0.0017364575,"teacher_disagreement_score":0.0016310426,"about_ca_system_score_codex":0.00046783214,"about_ca_system_score_gemma":0.0003010724,"threshold_uncertainty_score":0.008208275},"labels":[],"label_agreement":null},{"id":"W2136235502","doi":"10.1109/icpr.2000.906242","title":"Invariant neural-network based face detection with orthogonal Fourier-Mellin moments","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Université Laval","funders":"","keywords":"Chrominance; Artificial intelligence; Computer science; Pattern recognition (psychology); Invariant (physics); Robustness (evolution); Computer vision; Mathematics; Luminance","score_opus":0.020883681176709835,"score_gpt":0.2024119719458,"score_spread":0.18152829076909016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136235502","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21812908,0.00065387646,0.7762107,0.00021029722,0.000108285596,0.00005970514,0.000115994684,0.0016185819,0.0028933566],"genre_scores_gemma":[0.74370444,0.0002355735,0.25382045,0.00006819728,0.000050108538,0.000050412717,0.00013816847,0.00004094493,0.0018917088],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997631,0.00004816525,0.000009227139,0.00003826933,0.000084304644,0.00005707636],"domain_scores_gemma":[0.99959546,0.0002106842,0.000054209777,0.00004186299,0.000079091544,0.000018647659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072145415,0.0004967189,0.0005244306,0.0007110145,0.0001926163,0.00040409822,0.00058145425,0.00047147676,0.0011477601],"category_scores_gemma":[0.0017257918,0.0001993889,0.00033556554,0.00049361464,0.0003055449,0.00067028246,0.00043115858,0.00033452502,0.00030853038],"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":[0.0008038885,0.00020515262,0.0042327708,0.00011406882,0.00016338014,0.000170556,0.00006545126,0.17972414,0.0586129,0.00517349,0.002067164,0.748667],"study_design_scores_gemma":[0.0000074626378,0.000042908407,0.0012071525,0.0000035826622,0.000013351507,0.000055335076,0.0000069225175,0.9852681,0.012225135,0.00093782187,0.00022333261,0.000008901523],"about_ca_topic_score_codex":0.0021497859,"about_ca_topic_score_gemma":0.0029396787,"teacher_disagreement_score":0.0021497859,"about_ca_system_score_codex":0.00043703686,"about_ca_system_score_gemma":0.00027911563,"threshold_uncertainty_score":0.0042744875},"labels":[],"label_agreement":null},{"id":"W2138588234","doi":"10.1109/vecims.2011.6053844","title":"An enhanced Mean-Shift and LBP-based face tracking method","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Mean-shift; Local binary patterns; Histogram; Artificial intelligence; Face (sociological concept); Computer science; Tracking (education); Computer vision; Pattern recognition (psychology); Facial motion capture; Histogram of oriented gradients; Facial recognition system; Face detection; Image (mathematics); Psychology","score_opus":0.04925588214031818,"score_gpt":0.3013708154464269,"score_spread":0.25211493330610873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138588234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017809365,0.00061575347,0.97860956,0.0001312347,0.00022436894,0.00005120204,0.00006703932,0.00087418285,0.0016171513],"genre_scores_gemma":[0.2688507,0.000875881,0.7185236,0.00028516323,0.00028956882,0.00015893702,0.00033480715,0.00014105567,0.01054027],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919266,0.00008718686,0.00003588407,0.00014719162,0.00048624384,0.00005088553],"domain_scores_gemma":[0.99949384,0.000066401175,0.00003211371,0.00009391711,0.0002790823,0.00003460607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005364702,0.0003435657,0.00077198143,0.0009218718,0.00037007494,0.00043820805,0.0010324661,0.0007707077,0.0020316541],"category_scores_gemma":[0.0011501608,0.00037195272,0.00070890354,0.00080723164,0.00026167787,0.000961818,0.00079753436,0.0006581044,0.0012302423],"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":[0.000321548,0.0001334385,0.0011062972,0.00013464574,0.0000843389,0.00011439796,0.00006974186,0.022181502,0.17536454,0.002901948,0.0036750997,0.7939125],"study_design_scores_gemma":[0.000072044444,0.00023549158,0.004181051,0.000024345709,0.00011906156,0.0011393087,0.00002147268,0.9140782,0.06299297,0.0017513831,0.015293554,0.00009118473],"about_ca_topic_score_codex":0.0023380516,"about_ca_topic_score_gemma":0.0023620136,"teacher_disagreement_score":0.0023380516,"about_ca_system_score_codex":0.00032884,"about_ca_system_score_gemma":0.0007537018,"threshold_uncertainty_score":0.006796539},"labels":[],"label_agreement":null},{"id":"W2138882494","doi":"10.1016/j.patcog.2005.03.011","title":"Automatic model selection for the optimization of SVM kernels","year":2005,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":183,"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":"Structural risk minimization; Support vector machine; Computer science; Classifier (UML); Generalization; Kernel (algebra); Selection (genetic algorithm); Artificial intelligence; Minification; Model selection; VC dimension; Pattern recognition (psychology); Algorithm; Mathematics; Mathematical optimization; Machine learning","score_opus":0.03615936389211299,"score_gpt":0.26257979915406,"score_spread":0.226420435261947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138882494","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012600698,0.00015205095,0.9850904,0.000107470325,0.000033090546,0.00003004894,0.000060417307,0.0014370171,0.0004889111],"genre_scores_gemma":[0.49756438,0.00018970855,0.49657917,0.000120692865,0.000064646294,0.00022078134,0.000871168,0.0008220595,0.003567404],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929,0.00026035524,0.000043278924,0.000116350035,0.00019555817,0.00009445849],"domain_scores_gemma":[0.9988759,0.00047612857,0.00007703109,0.00014815613,0.00037191497,0.000050908075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012494624,0.000977491,0.0012604161,0.0007132449,0.00049509876,0.0010731096,0.0011265572,0.0010065712,0.002421226],"category_scores_gemma":[0.0045112404,0.00065935915,0.0009910421,0.0005666985,0.00031839087,0.0011254278,0.00092528923,0.001779201,0.0015854299],"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":[0.00041219452,0.00021667767,0.0013190374,0.00014484285,0.00010115692,0.000069821675,0.000081585604,0.38348442,0.02269047,0.007985515,0.010027974,0.57346624],"study_design_scores_gemma":[0.000008243529,0.000015253716,0.00014801853,0.0000028613035,0.000005919782,0.000010138128,0.000004831391,0.9952577,0.0025361562,0.0016069943,0.00039939364,0.0000045470165],"about_ca_topic_score_codex":0.0035271386,"about_ca_topic_score_gemma":0.004536457,"teacher_disagreement_score":0.0035271386,"about_ca_system_score_codex":0.00069632125,"about_ca_system_score_gemma":0.0013954332,"threshold_uncertainty_score":0.008099794},"labels":[],"label_agreement":null},{"id":"W2138917496","doi":"10.1109/crv.2006.48","title":"Local Feature Matching For Face Recognition","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"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":"Artificial intelligence; Facial recognition system; Computer science; Pattern recognition (psychology); Feature (linguistics); Face (sociological concept); Matching (statistics); Feature extraction; Three-dimensional face recognition; Computer vision; Face detection; Mathematics","score_opus":0.015035115929421966,"score_gpt":0.2366748681578751,"score_spread":0.22163975222845314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138917496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049231504,0.0009489779,0.99089545,0.00008047789,0.00007027317,0.00005485871,0.000104776635,0.0015026757,0.0014193059],"genre_scores_gemma":[0.17423657,0.0014136698,0.8180672,0.00022974699,0.00018408088,0.00024110754,0.0007758825,0.00025103087,0.0046007694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990175,0.00019838275,0.00004641168,0.00025415895,0.0004099327,0.00007363812],"domain_scores_gemma":[0.9994887,0.00014767793,0.000050308605,0.00017294251,0.00012235483,0.000018015544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087808096,0.0005411684,0.0012277389,0.0015154615,0.00038867438,0.0006655012,0.0012437553,0.0009793409,0.0052160323],"category_scores_gemma":[0.00182994,0.00028007448,0.00078959577,0.0017036062,0.0005313645,0.0013385644,0.00087708666,0.00069945946,0.003765199],"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":[0.00018766793,0.000079647056,0.00050457014,0.00022897142,0.000080911275,0.0001182412,0.00005178375,0.015469501,0.07123467,0.011206048,0.0062304283,0.89460754],"study_design_scores_gemma":[0.00006367754,0.0003701126,0.0042645247,0.0000859012,0.00013277918,0.0014433573,0.00012228808,0.74941814,0.14996459,0.044526897,0.049497616,0.00011011203],"about_ca_topic_score_codex":0.0010558526,"about_ca_topic_score_gemma":0.0009470395,"teacher_disagreement_score":0.0052160323,"about_ca_system_score_codex":0.0005324074,"about_ca_system_score_gemma":0.00043210207,"threshold_uncertainty_score":0.017449379},"labels":[],"label_agreement":null},{"id":"W2139139435","doi":"10.1109/icassp.2012.6288355","title":"Face recognition from video: An MMV recovery approach","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Facial recognition system; Artificial intelligence; Face (sociological concept); Pattern recognition (psychology); Machine learning; Class (philosophy); Contextual image classification; Image (mathematics)","score_opus":0.044909642670233105,"score_gpt":0.24681331985511878,"score_spread":0.20190367718488567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139139435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053663235,0.0002793842,0.9933407,0.00012274402,0.000032718424,0.000025333236,0.000028907385,0.0002235323,0.0005803801],"genre_scores_gemma":[0.2441945,0.0010428425,0.7499464,0.0002729458,0.0002608362,0.00014038956,0.00042197577,0.000078530684,0.0036416068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993344,0.00014538119,0.000030546355,0.00013072103,0.00029440215,0.000064552456],"domain_scores_gemma":[0.9994404,0.00020448981,0.00006801214,0.00013325608,0.00013257211,0.000021238842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005064732,0.000474562,0.0008214442,0.0011282052,0.00028515828,0.000630753,0.0011873217,0.00094288634,0.0013927028],"category_scores_gemma":[0.0017390911,0.00027330677,0.000638581,0.00073807186,0.00043723415,0.0012788275,0.0009262375,0.0010201416,0.00090295495],"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":[0.00014755913,0.000112164285,0.0008222256,0.00015645663,0.00006804724,0.00012391737,0.00009570307,0.047953665,0.055769365,0.010201534,0.0022326377,0.88231677],"study_design_scores_gemma":[0.000013595828,0.0001086228,0.0007451818,0.000016910617,0.00002996674,0.00036355623,0.000054525342,0.9623733,0.02506371,0.00771691,0.0034886089,0.000025022015],"about_ca_topic_score_codex":0.0017120725,"about_ca_topic_score_gemma":0.0019809608,"teacher_disagreement_score":0.0017120725,"about_ca_system_score_codex":0.00029982522,"about_ca_system_score_gemma":0.00038149933,"threshold_uncertainty_score":0.004658997},"labels":[],"label_agreement":null},{"id":"W2140113562","doi":"10.1109/biotechno.2008.29","title":"Towards Better Outliers Detection for Gene Expression Datasets","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Cluster analysis; Outlier; Computer science; Anomaly detection; Data mining; CURE data clustering algorithm; Pattern recognition (psychology); Task (project management); Medoid; Artificial intelligence; Correlation clustering; Engineering","score_opus":0.030113288743014457,"score_gpt":0.2549994400524776,"score_spread":0.22488615130946316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140113562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09440715,0.00070547295,0.90014577,0.00033429876,0.000055517477,0.00010899233,0.00044075435,0.003396246,0.00040583365],"genre_scores_gemma":[0.19179401,0.00029012724,0.8044621,0.0001006413,0.00003923125,0.00020172662,0.0022840567,0.00041969368,0.00040842808],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9900746,0.004019325,0.000819809,0.0017332307,0.0030134663,0.00033958332],"domain_scores_gemma":[0.9785131,0.012159802,0.0022775824,0.0022797794,0.0044639464,0.00030575338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.014722961,0.0016462518,0.002523771,0.005158057,0.0010093417,0.002821495,0.0019161655,0.0025996554,0.00058505044],"category_scores_gemma":[0.044200525,0.00063746946,0.0015427719,0.0038067813,0.0011642056,0.0028991892,0.0016950454,0.0018862068,0.00084078434],"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":[0.002832261,0.0005946539,0.03159031,0.0012563636,0.0008540302,0.00031115394,0.0013116141,0.23860762,0.1382495,0.0062221917,0.004617916,0.57355237],"study_design_scores_gemma":[0.00012084812,0.00043086943,0.019348048,0.00007734566,0.00013437272,0.00046036264,0.0005766015,0.85558605,0.107231036,0.009078515,0.0067442916,0.00021165171],"about_ca_topic_score_codex":0.0020533956,"about_ca_topic_score_gemma":0.0017223425,"teacher_disagreement_score":0.014722961,"about_ca_system_score_codex":0.0011274924,"about_ca_system_score_gemma":0.0010084978,"threshold_uncertainty_score":0.077863395},"labels":[],"label_agreement":null},{"id":"W2140327639","doi":"10.1109/icdar.2001.953808","title":"A multi-net local learning framework for pattern recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Concordia University","funders":"","keywords":"MNIST database; Computer science; Classifier (UML); Artificial intelligence; Machine learning; Pattern recognition (psychology); Ensemble learning; Digit recognition; Divide and conquer algorithms; Quantization (signal processing); Word error rate; Deep learning; Artificial neural network; Algorithm","score_opus":0.059704977382468265,"score_gpt":0.2705413175188081,"score_spread":0.21083634013633984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140327639","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010078534,0.00023029327,0.99741215,0.000045941215,0.000016792446,0.000014864647,0.00003088421,0.0006042134,0.00063693494],"genre_scores_gemma":[0.15167049,0.0006240701,0.8385118,0.00023317839,0.00016886723,0.0002657805,0.00048301608,0.00024902492,0.0077937674],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995252,0.00011243493,0.000026190151,0.00014358995,0.0001491727,0.00004338673],"domain_scores_gemma":[0.9996916,0.000077145036,0.0000329617,0.000074852935,0.00009469664,0.000028688659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009336847,0.0007564866,0.0010845166,0.0012262331,0.0005343199,0.0012885617,0.002300883,0.0010131182,0.0043843156],"category_scores_gemma":[0.0011384652,0.0003449063,0.0009025924,0.001238615,0.0007197113,0.0018629434,0.001325533,0.0010155375,0.0022752287],"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":[0.00011363547,0.00010918392,0.0007687462,0.00025023214,0.00013262648,0.00017165203,0.000095519434,0.28114277,0.013880514,0.06442095,0.0069414675,0.63197273],"study_design_scores_gemma":[0.0000064365204,0.000044383924,0.0001857939,0.000010537743,0.000015626247,0.000056909106,0.00001106657,0.9737428,0.0024821651,0.019124398,0.0043086326,0.00001127263],"about_ca_topic_score_codex":0.0028442822,"about_ca_topic_score_gemma":0.0038997114,"teacher_disagreement_score":0.0043843156,"about_ca_system_score_codex":0.0008041755,"about_ca_system_score_gemma":0.00064630155,"threshold_uncertainty_score":0.014666975},"labels":[],"label_agreement":null},{"id":"W2140389641","doi":"10.1109/tnn.2004.841784","title":"Optimizing the Kernel in the Empirical Feature Space","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":318,"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":"Kernel (algebra); Measure (data warehouse); Kernel embedding of distributions; Feature (linguistics); Kernel method; Computer science; Artificial intelligence; Feature vector; Pattern recognition (psychology); Euclidean space; Variable kernel density estimation; Graph kernel; Mathematics; Tree kernel; Algorithm; Data mining; Support vector machine","score_opus":0.043998601912425904,"score_gpt":0.27858648263350055,"score_spread":0.23458788072107464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140389641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075516575,0.000050249022,0.9921036,0.00003451089,0.0000043449613,0.000006237382,0.000007704769,0.00009251134,0.00014919222],"genre_scores_gemma":[0.37805235,0.00023167266,0.61960375,0.000058119676,0.0000425704,0.00010709099,0.00019840631,0.00022218715,0.0014838123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984621,0.0005115204,0.000106263615,0.00034882897,0.0004733462,0.00009789715],"domain_scores_gemma":[0.99794203,0.000967649,0.00019423287,0.00041950657,0.00042728317,0.00004922722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022081009,0.0005967037,0.0010731688,0.0005352602,0.0003160085,0.000961961,0.0010145367,0.0007949364,0.0005942536],"category_scores_gemma":[0.00806758,0.00036219155,0.00056944415,0.0007285956,0.001124608,0.0025350028,0.0012477522,0.0010868199,0.0004178924],"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":[0.00022905454,0.00012902184,0.0014776714,0.00018402329,0.000118531985,0.00008569728,0.00014640238,0.6702343,0.028633274,0.07052343,0.0011829411,0.22705558],"study_design_scores_gemma":[0.0000059797817,0.000025455538,0.00024308651,0.0000029277735,0.0000062768295,0.000028757064,0.000007564885,0.9843715,0.004596109,0.010144764,0.0005589854,0.0000086245855],"about_ca_topic_score_codex":0.0009213217,"about_ca_topic_score_gemma":0.000539956,"teacher_disagreement_score":0.0022081009,"about_ca_system_score_codex":0.000693208,"about_ca_system_score_gemma":0.00081432983,"threshold_uncertainty_score":0.011677682},"labels":[],"label_agreement":null},{"id":"W2140886528","doi":"10.1109/ccece.1993.332440","title":"A weighted minimum distance classifier for pattern recognition","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"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":"Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Computer science; Mathematics","score_opus":0.048279440508506265,"score_gpt":0.23967139755636274,"score_spread":0.19139195704785647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140886528","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022833457,0.000816571,0.9947864,0.00012050489,0.00013311379,0.000056527086,0.00008126428,0.0006161163,0.001106245],"genre_scores_gemma":[0.0492751,0.0010039315,0.9421478,0.0001989716,0.00022988363,0.0002176374,0.0007362105,0.00020074914,0.005989743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99679214,0.00047883147,0.00024004927,0.0005745103,0.0017712322,0.000143195],"domain_scores_gemma":[0.99854916,0.00043570052,0.000090636866,0.00020409735,0.0006648403,0.000055630328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020234373,0.0009091535,0.0016280287,0.0025292665,0.0006804444,0.00172063,0.0020120256,0.0019662506,0.0041035814],"category_scores_gemma":[0.004913904,0.0005522618,0.0010133644,0.0025012314,0.0006364244,0.0027743499,0.0011517071,0.0018784362,0.0043622577],"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":[0.00014776154,0.0001227614,0.00055574876,0.00019598535,0.00012277604,0.00009482837,0.000045547516,0.037787743,0.020342525,0.01659372,0.009406221,0.91458434],"study_design_scores_gemma":[0.000027165584,0.000112506874,0.0006216346,0.00004209627,0.00003844089,0.00029916322,0.00001825727,0.94456756,0.01476371,0.017458841,0.02200347,0.00004722645],"about_ca_topic_score_codex":0.002343246,"about_ca_topic_score_gemma":0.0019879795,"teacher_disagreement_score":0.0041035814,"about_ca_system_score_codex":0.0008700436,"about_ca_system_score_gemma":0.0008981901,"threshold_uncertainty_score":0.013727784},"labels":[],"label_agreement":null},{"id":"W2140896141","doi":"10.1007/978-3-642-15883-4_2","title":"Learning an Affine Transformation for Non-linear Dimensionality Reduction","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Dimensionality reduction; Affine transformation; Diffusion map; Embedding; Curse of dimensionality; Computer science; Transformation (genetics); Data point; Algorithm; Linear map; Feature vector; Reduction (mathematics); Space (punctuation); Affine space; Nearest neighbor search; Geometric transformation; Artificial intelligence; Nonlinear dimensionality reduction; Mathematics; Image (mathematics)","score_opus":0.020143282381011842,"score_gpt":0.26878885763192967,"score_spread":0.24864557525091782,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140896141","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004945944,0.00032120294,0.9930033,0.00009990214,0.00006448488,0.00003228575,0.00011081544,0.000853568,0.000568465],"genre_scores_gemma":[0.1406076,0.0009811613,0.84632754,0.00020531217,0.00018500799,0.0002496373,0.0017064561,0.00027920533,0.00945811],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993119,0.00013131296,0.00005396458,0.00021793133,0.00021892852,0.0000659038],"domain_scores_gemma":[0.99943954,0.0001710941,0.000045340046,0.00019997873,0.000121438825,0.00002254445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061366573,0.0010345811,0.0011427837,0.0006198836,0.0004701873,0.00088744715,0.0013296987,0.00086041726,0.0041074106],"category_scores_gemma":[0.002244151,0.0004851387,0.0012253077,0.0012466147,0.0006262121,0.001607371,0.0018447847,0.0018309226,0.0030034475],"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":[0.000090174944,0.00009377256,0.0004482614,0.00013296145,0.000070144575,0.00006186673,0.00006716691,0.051851362,0.014817045,0.011641216,0.009394604,0.9113313],"study_design_scores_gemma":[0.000013265048,0.0001259667,0.00064578437,0.000019427547,0.000030079502,0.00017353854,0.00005243247,0.95853657,0.014537649,0.019031648,0.0068103652,0.000023257806],"about_ca_topic_score_codex":0.0018094684,"about_ca_topic_score_gemma":0.0023799865,"teacher_disagreement_score":0.0041074106,"about_ca_system_score_codex":0.00036879175,"about_ca_system_score_gemma":0.0006667967,"threshold_uncertainty_score":0.013740659},"labels":[],"label_agreement":null},{"id":"W2140941844","doi":"10.1109/icecs.2013.6815445","title":"Interpolation of low resolution images for improved accuracy in human face recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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 Windsor","funders":"","keywords":"Interpolation (computer graphics); Artificial intelligence; Computer vision; Facial recognition system; Face (sociological concept); Computer science; Image resolution; Resolution (logic); Pattern recognition (psychology); Image (mathematics)","score_opus":0.023553775293901826,"score_gpt":0.27718281653760857,"score_spread":0.2536290412437067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2140941844","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091592215,0.002452461,0.90070784,0.00019774953,0.00012839722,0.00007795421,0.00015204842,0.002001438,0.002689963],"genre_scores_gemma":[0.4067945,0.0015311684,0.587842,0.000099037105,0.000061986124,0.000053129683,0.00038710638,0.00019494469,0.0030362299],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943703,0.00013617991,0.000026452302,0.00006677957,0.00029140315,0.000042167987],"domain_scores_gemma":[0.9992142,0.0003234028,0.00006219414,0.00019877031,0.00018022078,0.000021174885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007898374,0.00036295285,0.0005103608,0.00067893515,0.00025955957,0.0004132019,0.0004552944,0.00035642757,0.003940768],"category_scores_gemma":[0.0026238058,0.00020099248,0.0003224328,0.00077330414,0.00022623908,0.00065272907,0.00034655692,0.0005398595,0.0015390078],"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":[0.00060686946,0.00012790044,0.0013701543,0.00041975552,0.000037508606,0.00013535969,0.0001270783,0.00799687,0.3341395,0.0020213393,0.0015435354,0.6514742],"study_design_scores_gemma":[0.00005681298,0.0007073947,0.014087147,0.00009529268,0.00012130522,0.002001248,0.00014377147,0.36314648,0.592725,0.0033940244,0.023431046,0.00009046797],"about_ca_topic_score_codex":0.00088549184,"about_ca_topic_score_gemma":0.0012147091,"teacher_disagreement_score":0.003940768,"about_ca_system_score_codex":0.00016389515,"about_ca_system_score_gemma":0.0002603594,"threshold_uncertainty_score":0.0131831765},"labels":[],"label_agreement":null},{"id":"W2141457144","doi":"10.1109/icdsp.2011.6004975","title":"Correspondence normal difference: An aligned representation of 3D faces to apply discriminant analysis methods","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Linear discriminant analysis; Artificial intelligence; Discriminative model; Pattern recognition (psychology); Discriminant; Computer science; Facial recognition system; Face (sociological concept); Representation (politics); Three-dimensional face recognition; Expression (computer science); Facial expression; Face Recognition Grand Challenge; Computer vision; Face detection","score_opus":0.08351908611627076,"score_gpt":0.3680376147981437,"score_spread":0.2845185286818729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141457144","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008806894,0.00010857258,0.98898447,0.00008926262,0.00010233064,0.00006825126,0.00015033485,0.00048457365,0.0012052949],"genre_scores_gemma":[0.15256214,0.0003018351,0.8425345,0.00011627439,0.00010527612,0.0003088366,0.00084740843,0.00021101387,0.00301266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990854,0.0001963972,0.000039361606,0.00016717447,0.00045532614,0.000056344998],"domain_scores_gemma":[0.9993667,0.00014423343,0.000052672956,0.00018835122,0.00021655762,0.000031504904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009930198,0.0006645034,0.00054273824,0.0014038974,0.0003387798,0.0008058428,0.00091582583,0.0005073797,0.004140955],"category_scores_gemma":[0.0026436045,0.00025009114,0.00066290743,0.0011568536,0.00064667984,0.0010381255,0.0010861445,0.00091072917,0.001834817],"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":[0.00031288632,0.00019242433,0.0021981513,0.00017814647,0.00007151057,0.00015845748,0.00018570859,0.05470589,0.07810473,0.031803384,0.010064292,0.8220244],"study_design_scores_gemma":[0.00004261457,0.0002310849,0.0034034192,0.000032423657,0.000030775987,0.000697796,0.000110853434,0.9024759,0.04121528,0.023656605,0.028031703,0.00007149397],"about_ca_topic_score_codex":0.0011273448,"about_ca_topic_score_gemma":0.0010178647,"teacher_disagreement_score":0.004140955,"about_ca_system_score_codex":0.00038555422,"about_ca_system_score_gemma":0.00075619324,"threshold_uncertainty_score":0.013852835},"labels":[],"label_agreement":null},{"id":"W2141721864","doi":"10.5539/mas.v3n5p31","title":"Research on Dynamic Facial Expressions Recognition","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Face and Expression Recognition","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":"Program for Changjiang Scholars and Innovative Research Team in University; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Facial expression; Computer science; Surprise; Hidden Markov model; Artificial intelligence; Pattern recognition (psychology); Mixture model; Vector quantization; Gaussian; Disgust; Facial expression recognition; Speech recognition; Expression (computer science); Facial recognition system; Psychology","score_opus":0.0766306965758321,"score_gpt":0.3589253986170575,"score_spread":0.2822947020412254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2141721864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050000504,0.018710002,0.91748947,0.00084790995,0.00033333414,0.00011234079,0.00013047007,0.0009578254,0.011418127],"genre_scores_gemma":[0.5125634,0.027640667,0.4411783,0.0006131933,0.00068280206,0.00021232064,0.0007563522,0.00020864194,0.01614435],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99877614,0.00019415565,0.000071609,0.00047089587,0.0004025992,0.00008467549],"domain_scores_gemma":[0.99894863,0.00034845815,0.00007851955,0.000117491916,0.00046784087,0.000039124305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011851743,0.0005724115,0.0009586406,0.0012719035,0.00028134495,0.0010014203,0.0011227496,0.00078072224,0.0020454514],"category_scores_gemma":[0.0023997587,0.00033422536,0.0005628279,0.0018193377,0.0007159625,0.0023526442,0.00038666985,0.000668682,0.00087677414],"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":[0.00013847496,0.000102525104,0.001835547,0.00042800495,0.000053252767,0.00008470563,0.00024218825,0.009378505,0.08301215,0.013992247,0.0022150904,0.88851726],"study_design_scores_gemma":[0.00007089612,0.0011219868,0.016471304,0.0002635548,0.0002528512,0.0018055897,0.00069592963,0.6600934,0.210797,0.029775944,0.07843904,0.0002125054],"about_ca_topic_score_codex":0.0023822112,"about_ca_topic_score_gemma":0.00069793,"teacher_disagreement_score":0.0023822112,"about_ca_system_score_codex":0.00054451835,"about_ca_system_score_gemma":0.000500598,"threshold_uncertainty_score":0.0068427324},"labels":[],"label_agreement":null},{"id":"W2142414446","doi":"10.1109/nnsp.2001.943141","title":"Face recognition using feature optimization and ν-support vector learning","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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":"Support vector machine; Computer science; Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Feature selection; Classifier (UML); Face (sociological concept); Feature (linguistics); Feature vector; Feature extraction; Machine learning","score_opus":0.037183441916346746,"score_gpt":0.23611481214047209,"score_spread":0.19893137022412533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142414446","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015266467,0.00024703745,0.98233795,0.00004667989,0.000037422342,0.000030488223,0.000023557024,0.000972066,0.0010383701],"genre_scores_gemma":[0.3365338,0.00037881205,0.659448,0.00007534335,0.0000684143,0.00013582605,0.00016838596,0.0000847736,0.0031066942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996395,0.00005292978,0.000022680691,0.00006827214,0.00018441427,0.000032206506],"domain_scores_gemma":[0.9997476,0.00007846284,0.000039402752,0.000030934687,0.00009424569,0.00000928756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038850584,0.00036474323,0.0006867524,0.0004225107,0.0002256955,0.0004986333,0.0006438427,0.00037882364,0.0014514799],"category_scores_gemma":[0.0009195454,0.00021162552,0.00041416567,0.00045139543,0.0002635668,0.0009283292,0.00045658913,0.00043825293,0.00084426627],"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":[0.0002090693,0.000091712325,0.0020859898,0.00013640657,0.00007705244,0.00011803947,0.00006629426,0.06123001,0.066054456,0.007394203,0.0017666562,0.86077017],"study_design_scores_gemma":[0.000016601036,0.0002117458,0.0017998383,0.000013183398,0.0000325173,0.00034927065,0.00002014767,0.94744307,0.04090321,0.0031991939,0.0059828376,0.000028324734],"about_ca_topic_score_codex":0.0008876514,"about_ca_topic_score_gemma":0.000735767,"teacher_disagreement_score":0.0014514799,"about_ca_system_score_codex":0.0002993759,"about_ca_system_score_gemma":0.00029873243,"threshold_uncertainty_score":0.004855752},"labels":[],"label_agreement":null},{"id":"W2143304877","doi":"10.1109/tnn.2002.806629","title":"Face recognition using kernel direct discriminant analysis algorithms","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":608,"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":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Kernel principal component analysis; Pattern recognition (psychology); Facial recognition system; Kernel (algebra); Artificial intelligence; Computer science; Principal component analysis; Face (sociological concept); Discriminant; Kernel method; Feature extraction; Word error rate; Algorithm; Mathematics; Support vector machine","score_opus":0.04051875161713008,"score_gpt":0.26509096159119266,"score_spread":0.22457220997406258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143304877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008391825,0.00041852234,0.9894302,0.00007201613,0.000031055923,0.000027483657,0.000038297487,0.0007600924,0.0008304678],"genre_scores_gemma":[0.2506072,0.00080778793,0.74291587,0.0000855559,0.0000707794,0.00014719063,0.0003058316,0.0001027272,0.004957079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994087,0.00013916893,0.000037444985,0.0001264352,0.00024400579,0.000044290577],"domain_scores_gemma":[0.9994049,0.00021544589,0.000066161025,0.00012246071,0.00017550388,0.00001553225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077979657,0.00053277315,0.00093684,0.00097109255,0.00027023378,0.0007171172,0.00063550216,0.0006219426,0.0020582362],"category_scores_gemma":[0.0023202754,0.0002516876,0.0006052064,0.0008685622,0.00030640283,0.0011410726,0.0007398154,0.000756678,0.0019079398],"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":[0.00012641255,0.00008806463,0.00093227177,0.0001198352,0.00007354218,0.00006174284,0.000054875432,0.06299437,0.016291285,0.0108252475,0.0034268736,0.90500546],"study_design_scores_gemma":[0.000020438994,0.000051190334,0.0012344957,0.000011480435,0.000018050583,0.00018310593,0.000022256592,0.9734298,0.010471735,0.009704977,0.0048278584,0.00002466874],"about_ca_topic_score_codex":0.0012693669,"about_ca_topic_score_gemma":0.0009248243,"teacher_disagreement_score":0.0020582362,"about_ca_system_score_codex":0.00034730192,"about_ca_system_score_gemma":0.00037293977,"threshold_uncertainty_score":0.006885469},"labels":[],"label_agreement":null},{"id":"W2143340430","doi":"10.1016/j.patrec.2004.09.014","title":"Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition","year":2004,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":306,"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 Toronto; Bell (Canada)","funders":"National Institute of Standards and Technology","keywords":"Linear discriminant analysis; Pattern recognition (psychology); Artificial intelligence; Computer science; Regularization (linguistics); Facial recognition system; Curse of dimensionality; Sample size determination; Face (sociological concept); Eigenface; Principal component analysis; Mathematics; Machine learning; Statistics","score_opus":0.0353282354177338,"score_gpt":0.26474892325301047,"score_spread":0.22942068783527667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143340430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028545005,0.0016632824,0.96668154,0.00090610696,0.0000741654,0.000043856307,0.000033897155,0.00013480715,0.0019173569],"genre_scores_gemma":[0.61767846,0.0031180885,0.36908183,0.00044783793,0.00074484776,0.00030888655,0.0002721992,0.00032574934,0.00802207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960563,0.0028350696,0.000116884905,0.00031758586,0.0005239467,0.0001501771],"domain_scores_gemma":[0.947729,0.0462763,0.0013160316,0.0019979882,0.0022105942,0.000470045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013984638,0.0012795506,0.0018089871,0.0012479888,0.00076218386,0.0012417211,0.0014036162,0.0018311946,0.0014819469],"category_scores_gemma":[0.05066415,0.0007088347,0.00109973,0.0011098101,0.003066656,0.0028561484,0.0018565693,0.0028651634,0.00025982675],"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":[0.0007622048,0.00031768513,0.0032931408,0.0006446409,0.0004051596,0.00043461937,0.00058185344,0.5148312,0.013393628,0.33200258,0.005215927,0.12811743],"study_design_scores_gemma":[0.000020129808,0.000038133345,0.00047071493,0.000016121117,0.000022451799,0.000047779326,0.00002126882,0.96654344,0.00082667544,0.03137037,0.00060245395,0.000020517362],"about_ca_topic_score_codex":0.0021761928,"about_ca_topic_score_gemma":0.0014753964,"teacher_disagreement_score":0.013984638,"about_ca_system_score_codex":0.00087197236,"about_ca_system_score_gemma":0.00085107685,"threshold_uncertainty_score":0.073958695},"labels":[],"label_agreement":null},{"id":"W2143379662","doi":"10.1007/s10994-009-5154-2","title":"Particle swarm optimizer for variable weighting in clustering high-dimensional data","year":2009,"lang":"en","type":"article","venue":"Machine Learning","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Cluster analysis; k-medians clustering; Weighting; Particle swarm optimization; Correlation clustering; Variable (mathematics); Mathematical optimization; Algorithm; CURE data clustering algorithm; Mathematics; Computer science; Fuzzy clustering; Jaccard index; Data mining; Artificial intelligence","score_opus":0.027126925669503376,"score_gpt":0.2695101599614228,"score_spread":0.24238323429191944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143379662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003952246,0.00016437599,0.99522877,0.00005453382,0.000044259297,0.00003252015,0.000016257352,0.00020602232,0.00030106658],"genre_scores_gemma":[0.14651868,0.00022299848,0.8502004,0.00009069671,0.00007832076,0.00034100504,0.00017080759,0.0001462475,0.0022307935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992053,0.00035710772,0.00006478023,0.000114764895,0.00019704142,0.000061032548],"domain_scores_gemma":[0.99824464,0.0010621642,0.00009767898,0.00014104365,0.00039471168,0.000059702565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036457912,0.0012173807,0.0021166992,0.0009501459,0.0008044078,0.001058393,0.0018639358,0.0017483542,0.0020139357],"category_scores_gemma":[0.0064967154,0.00085443223,0.0009064125,0.0014362009,0.0008952002,0.0010678692,0.0012155384,0.0018066576,0.00055879593],"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":[0.00023342832,0.000101488775,0.0004904973,0.00013998483,0.00011834311,0.000050330415,0.0001269307,0.7975883,0.0025413583,0.009193185,0.0034673512,0.18594874],"study_design_scores_gemma":[0.0000123206455,0.00001260694,0.00004545249,0.00000281848,0.0000041087956,0.0000031551906,0.000003680014,0.9987423,0.000238199,0.0007383066,0.00019450359,0.0000025899863],"about_ca_topic_score_codex":0.010651174,"about_ca_topic_score_gemma":0.006525383,"teacher_disagreement_score":0.010651174,"about_ca_system_score_codex":0.000900137,"about_ca_system_score_gemma":0.0013026809,"threshold_uncertainty_score":0.021178365},"labels":[],"label_agreement":null},{"id":"W2143484718","doi":"10.1109/icsmc.2009.5346252","title":"A robust wavelet based feature extraction method for face recognition","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Feature extraction; Artificial intelligence; Pattern recognition (psychology); Computer science; Facial recognition system; Wavelet; White noise; Robustness (evolution); Classifier (UML); Additive white Gaussian noise; Face (sociological concept); Feature (linguistics); Hidden Markov model; Wavelet transform; Gaussian; Speech recognition","score_opus":0.054710471287895135,"score_gpt":0.3083666133238127,"score_spread":0.2536561420359176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143484718","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032622817,0.00035295862,0.99500245,0.000048571033,0.00008193857,0.000045028304,0.00007787832,0.0006081718,0.0005206513],"genre_scores_gemma":[0.044659737,0.00082030817,0.9490317,0.000099969824,0.00009974451,0.00017315047,0.00043801992,0.00018314758,0.0044941143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994312,0.000059673515,0.00003556716,0.000101402016,0.00033528326,0.00003679788],"domain_scores_gemma":[0.9996706,0.000084034065,0.00004099745,0.00006432757,0.00012487077,0.000015080047],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005289365,0.00058860483,0.00083259976,0.0009680977,0.00028378863,0.00038959403,0.0006469132,0.0007689939,0.0027379573],"category_scores_gemma":[0.0012058532,0.0003733704,0.0009066876,0.00092082407,0.00029610275,0.0007218716,0.00042898717,0.0008622881,0.002909586],"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":[0.00011556417,0.00006710607,0.00024782136,0.0002480984,0.000065202104,0.00012132524,0.00003706603,0.006317201,0.29060787,0.0027409059,0.0033729896,0.69605887],"study_design_scores_gemma":[0.000069026,0.00059261185,0.0052198307,0.00009728008,0.00020127301,0.002869853,0.00005017952,0.48734006,0.44137278,0.003938786,0.058066525,0.00018174364],"about_ca_topic_score_codex":0.00053523644,"about_ca_topic_score_gemma":0.0005444942,"teacher_disagreement_score":0.0027379573,"about_ca_system_score_codex":0.00020982148,"about_ca_system_score_gemma":0.00034716522,"threshold_uncertainty_score":0.009159446},"labels":[],"label_agreement":null},{"id":"W2143797877","doi":"","title":"Deep Supervised t-Distributed Embedding","year":2010,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":48,"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":"Embedding; Computer science; Deep learning; Pairwise comparison; Scalability; Artificial intelligence; Parametric statistics; Visualization; Architecture; Class (philosophy); Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.025390876167961204,"score_gpt":0.2998689291950903,"score_spread":0.2744780530271291,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143797877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02741076,0.00013273058,0.9703068,0.00012329001,0.000028998118,0.000037785598,0.00007981274,0.00094216043,0.00093768217],"genre_scores_gemma":[0.71134293,0.0001467146,0.28163296,0.0001786502,0.000080509046,0.00021747415,0.0006342787,0.00023070817,0.005535922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989135,0.00037868434,0.00006066521,0.00032216855,0.00024266246,0.000082384075],"domain_scores_gemma":[0.9967739,0.0012056788,0.0003756083,0.00083092327,0.0006771923,0.00013669467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013839047,0.0010299055,0.0010338933,0.0006860639,0.0004515583,0.00081667694,0.0014791235,0.001140783,0.0026812383],"category_scores_gemma":[0.0055802376,0.00034544812,0.00084262947,0.00079319754,0.001093056,0.002325168,0.0016279481,0.0016338667,0.0008410881],"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":[0.00022630523,0.00020498194,0.0018951711,0.0001236444,0.00009488059,0.000088547386,0.00013077747,0.62550324,0.009303288,0.02649603,0.0035417944,0.33239135],"study_design_scores_gemma":[0.0000041120998,0.000031968902,0.00009510534,0.0000022726113,0.0000025172458,0.000015107047,0.00000552189,0.992417,0.0015961801,0.0055871033,0.00023884502,0.0000042866045],"about_ca_topic_score_codex":0.0016600514,"about_ca_topic_score_gemma":0.0030441626,"teacher_disagreement_score":0.0026812383,"about_ca_system_score_codex":0.0009937155,"about_ca_system_score_gemma":0.0009743678,"threshold_uncertainty_score":0.008969605},"labels":[],"label_agreement":null},{"id":"W2144567651","doi":"10.1109/cisp.2008.479","title":"Human Face Recognition Using Different Moment Invariants: A Comparative Study","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":40,"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 Windsor","funders":"","keywords":"Zernike polynomials; Artificial intelligence; Invariant (physics); Pattern recognition (psychology); Moment (physics); Facial recognition system; Velocity Moments; Computer science; Face (sociological concept); Computer vision; Mathematics; Physics; Optics","score_opus":0.19478208455059884,"score_gpt":0.3334798240477524,"score_spread":0.13869773949715355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144567651","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9178536,0.006755375,0.06363766,0.00014699856,0.0001248515,0.00006359113,0.00023841432,0.00062736636,0.01055229],"genre_scores_gemma":[0.9878321,0.0013654316,0.009181752,0.000016676007,0.000060152233,0.000011602638,0.00028524635,0.000038114762,0.0012089552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939203,0.00010167538,0.00003497005,0.00008521976,0.00032929695,0.000056748195],"domain_scores_gemma":[0.9990269,0.00051385263,0.000062884756,0.00009054102,0.000270204,0.0000356182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010531422,0.0002852826,0.00053208094,0.0020722698,0.0001804759,0.0003559476,0.00022241232,0.0002934965,0.0020143269],"category_scores_gemma":[0.0023483827,0.00009910253,0.0005538701,0.00094022014,0.0002357882,0.0007664695,0.00023935779,0.00014690035,0.00046208376],"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":[0.0020040618,0.0002609622,0.025474438,0.00053656666,0.00041875878,0.00035625757,0.00026059864,0.008644882,0.09524228,0.0012100443,0.0024859228,0.86310524],"study_design_scores_gemma":[0.00008101169,0.0046778065,0.5151461,0.00010835983,0.0010639007,0.0082908515,0.0011025561,0.25665927,0.199235,0.0025202718,0.010873129,0.00024170865],"about_ca_topic_score_codex":0.0006443043,"about_ca_topic_score_gemma":0.0004883631,"teacher_disagreement_score":0.0020722698,"about_ca_system_score_codex":0.00018251917,"about_ca_system_score_gemma":0.00009345314,"threshold_uncertainty_score":0.0067386627},"labels":[],"label_agreement":null},{"id":"W2144910496","doi":"10.1109/icpr.2014.225","title":"Generic Subclass Ensemble: A Novel Approach to Ensemble Classification","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Dalhousie University","funders":"","keywords":"Subclass; Classifier (UML); Computer science; Artificial intelligence; Ensemble learning; Perceptron; Machine learning; Pattern recognition (psychology); Benchmark (surveying); One-class classification; Statistical classification; Multiclass classification; Data mining; Support vector machine; Artificial neural network","score_opus":0.05217494820695119,"score_gpt":0.24831377500362636,"score_spread":0.19613882679667516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144910496","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007330605,0.0009115709,0.98885393,0.00011520676,0.0001018951,0.00007541472,0.00011632182,0.0006835074,0.0018115194],"genre_scores_gemma":[0.323623,0.0020330593,0.66633743,0.00039676562,0.0006329706,0.00030685792,0.0014114691,0.0003241007,0.0049342597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978927,0.0005897389,0.00011040155,0.000462926,0.00080447434,0.00013976135],"domain_scores_gemma":[0.99764144,0.0006636423,0.00020036142,0.0006478877,0.0007372808,0.00010946125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025238132,0.0012688066,0.0025607757,0.0027335316,0.00083098165,0.0014801634,0.0020372614,0.0012741874,0.001808257],"category_scores_gemma":[0.004304059,0.0004326053,0.001389438,0.0026381,0.0005475398,0.0025879275,0.0019475428,0.002082083,0.00095200486],"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":[0.000120185694,0.00016875289,0.0060457583,0.000219854,0.0006323579,0.00016630291,0.00018793062,0.10141695,0.010120907,0.01952067,0.009181899,0.8522184],"study_design_scores_gemma":[0.000011360618,0.00011290591,0.001325224,0.000040020434,0.00013704223,0.0002597828,0.000052432068,0.9594858,0.004115927,0.023042347,0.01138264,0.000034560628],"about_ca_topic_score_codex":0.0018668507,"about_ca_topic_score_gemma":0.0025580577,"teacher_disagreement_score":0.0027335316,"about_ca_system_score_codex":0.000458091,"about_ca_system_score_gemma":0.0007512215,"threshold_uncertainty_score":0.013347328},"labels":[],"label_agreement":null},{"id":"W2144935315","doi":"","title":"Neighbourhood Components Analysis","year":2004,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1729,"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":"Mahalanobis distance; Computer science; Dimensionality reduction; Artificial intelligence; Pattern recognition (psychology); Parametric statistics; Embedding; Visualization; Metric (unit); Measure (data warehouse); Curse of dimensionality; Data set; Data mining; Mathematics; Statistics","score_opus":0.01554007278522379,"score_gpt":0.23072732080815195,"score_spread":0.21518724802292816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144935315","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043133907,0.0007600177,0.9899082,0.000098273296,0.000120283,0.00007784607,0.00019310076,0.00058674806,0.003942202],"genre_scores_gemma":[0.21837036,0.0019174707,0.76109767,0.00019067684,0.00033741514,0.0003371594,0.0020473746,0.0007000338,0.015001722],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985598,0.00030669634,0.000070857786,0.000402685,0.00055756344,0.000102460064],"domain_scores_gemma":[0.9988656,0.0003642,0.00008529876,0.00020493355,0.0004237826,0.00005625384],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010800379,0.0010225484,0.0012585052,0.004040984,0.0009978171,0.0022053847,0.0014168851,0.001049521,0.0051629385],"category_scores_gemma":[0.004857699,0.00050950557,0.001169562,0.0033713502,0.00085443066,0.0016965178,0.0014926838,0.0011287476,0.0034127044],"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":[0.00016729151,0.0000704856,0.003277954,0.00037001335,0.00023805391,0.00014540418,0.0002790031,0.06755815,0.00990304,0.10604416,0.014895522,0.79705095],"study_design_scores_gemma":[0.000023483966,0.00007628219,0.004422097,0.00009748,0.00012276201,0.00042048495,0.00018837738,0.82506925,0.0084343245,0.10840409,0.052644286,0.00009701943],"about_ca_topic_score_codex":0.00518076,"about_ca_topic_score_gemma":0.0045076394,"teacher_disagreement_score":0.00518076,"about_ca_system_score_codex":0.0006938305,"about_ca_system_score_gemma":0.0010210862,"threshold_uncertainty_score":0.017271698},"labels":[],"label_agreement":null},{"id":"W2146297874","doi":"10.1109/icbake.2009.48","title":"Multi-objective Evolutionary Approach for Biometric Fusion","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Biometrics; Computer science; Artificial intelligence; AdaBoost; Face (sociological concept); Machine learning; Word error rate; Facial recognition system; Genetic algorithm; Domain (mathematical analysis); Pattern recognition (psychology); Set (abstract data type); Class (philosophy); Support vector machine; Mathematics","score_opus":0.031090252283046366,"score_gpt":0.2697131275872095,"score_spread":0.23862287530416312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146297874","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005741354,0.0005107396,0.9907829,0.00011588782,0.000037802485,0.000028713506,0.000009027199,0.00005169195,0.0027219413],"genre_scores_gemma":[0.33827028,0.0009701892,0.65230733,0.00016673602,0.00009374511,0.00035648252,0.00007316978,0.00006368041,0.007698308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995788,0.00013896395,0.00002105197,0.00005208644,0.00018057771,0.000028613125],"domain_scores_gemma":[0.9997266,0.00011516287,0.000028160926,0.000018450624,0.00009571706,0.000015981088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010033333,0.0007078545,0.000770741,0.0010161104,0.0003752992,0.0006811559,0.0010537374,0.0009759473,0.0017126472],"category_scores_gemma":[0.0013307001,0.00030772312,0.00081965287,0.00074418244,0.0004859753,0.00057289214,0.00080683065,0.0008469264,0.0002762256],"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":[0.000022482229,0.00004927411,0.00045969035,0.00009527626,0.00012653244,0.00009954518,0.00007507811,0.8588209,0.0046303533,0.03968424,0.00053701364,0.095399536],"study_design_scores_gemma":[0.000005846974,0.000025317735,0.0001109284,0.000008682471,0.000009693758,0.000028178061,0.0000073577203,0.9916419,0.0004984595,0.0064061144,0.001249622,0.000007835107],"about_ca_topic_score_codex":0.001607801,"about_ca_topic_score_gemma":0.0013927652,"teacher_disagreement_score":0.0017126472,"about_ca_system_score_codex":0.00067296764,"about_ca_system_score_gemma":0.00047443033,"threshold_uncertainty_score":0.0057293773},"labels":[],"label_agreement":null},{"id":"W2146572393","doi":"10.1109/tpami.2005.15","title":"On utilizing search methods to select subspace dimensions for kernel-based nonlinear subspace classifiers","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Korea Science and Engineering Foundation","keywords":"Subspace topology; Random subspace method; Linear subspace; Kernel (algebra); Pattern recognition (psychology); Dimension (graph theory); Kernel method; Artificial intelligence; Computer science; Mathematics; Classifier (UML); Nonlinear system; Algorithm; Mathematical optimization; Support vector machine; Discrete mathematics; Combinatorics","score_opus":0.04758193827027168,"score_gpt":0.35186657639173163,"score_spread":0.30428463812145995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146572393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011493572,0.00022573015,0.9872677,0.00004898256,0.000010338795,0.000059863953,0.000010995728,0.00020472035,0.0006782708],"genre_scores_gemma":[0.17157046,0.00033536993,0.8262754,0.000113392365,0.00004812006,0.0003336762,0.0001183807,0.00008609817,0.0011190725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990814,0.00036781898,0.00007781134,0.00011918022,0.00029899194,0.000054907112],"domain_scores_gemma":[0.99762183,0.0014626379,0.0002035474,0.00023134668,0.00042646308,0.000054065502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002245171,0.0010131024,0.0011602156,0.0013728223,0.0006563497,0.0007605862,0.00089882826,0.00095555204,0.0012288146],"category_scores_gemma":[0.0065045864,0.00037580368,0.00048933184,0.0017624067,0.0009455142,0.0017132253,0.0009900706,0.000703129,0.00051505136],"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":[0.00022224916,0.00021101932,0.0016091279,0.0001688428,0.00007848596,0.00007815061,0.00026919565,0.38912418,0.013817581,0.04013227,0.001488339,0.5528006],"study_design_scores_gemma":[0.00002914206,0.00012343674,0.00021496975,0.00001599232,0.000015555495,0.00006244049,0.000028273938,0.98851925,0.0034083747,0.006381221,0.001182989,0.000018257533],"about_ca_topic_score_codex":0.0016094315,"about_ca_topic_score_gemma":0.0025638773,"teacher_disagreement_score":0.002245171,"about_ca_system_score_codex":0.0004032745,"about_ca_system_score_gemma":0.0009694476,"threshold_uncertainty_score":0.011873722},"labels":[],"label_agreement":null},{"id":"W2146785254","doi":"10.1109/icpr.2002.1047860","title":"KMOD - a two-parameter SVM kernel for pattern recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"","keywords":"Support vector machine; Pattern recognition (psychology); Kernel (algebra); Artificial intelligence; Computer science; NIST; Benchmark (surveying); Kernel method; Radial basis function kernel; Polynomial kernel; Smoothness; Machine learning; Mathematics; Speech recognition","score_opus":0.03989522105805321,"score_gpt":0.27130896040246333,"score_spread":0.23141373934441012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2146785254","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064265723,0.00047594344,0.99033326,0.000106419015,0.0000500793,0.000042149644,0.00015779799,0.0015472973,0.0008604376],"genre_scores_gemma":[0.23066396,0.0006216353,0.7613353,0.00012194223,0.000055386885,0.0001583528,0.0012430114,0.00040389402,0.00539649],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990439,0.00023603663,0.00009529306,0.00015620631,0.00040109965,0.000067507324],"domain_scores_gemma":[0.9988682,0.00026922335,0.000101673664,0.00029566366,0.00040804545,0.000057123463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011581266,0.000607422,0.00091918313,0.00075999275,0.0003775069,0.001114306,0.0012836631,0.0009589978,0.0019360755],"category_scores_gemma":[0.0037553352,0.0003192069,0.00060433144,0.0010068638,0.0004351066,0.0016598604,0.0011183347,0.0012312821,0.00211279],"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":[0.00043281756,0.00021488537,0.0026006403,0.00053466554,0.00019462085,0.00014631734,0.00008953331,0.08955612,0.05509337,0.028921003,0.012963931,0.809252],"study_design_scores_gemma":[0.000028581979,0.00013208186,0.001654001,0.000023935585,0.000019604337,0.00030554275,0.00002795366,0.93166363,0.030581849,0.016856762,0.018646909,0.000059194837],"about_ca_topic_score_codex":0.000887101,"about_ca_topic_score_gemma":0.0009291118,"teacher_disagreement_score":0.0019360755,"about_ca_system_score_codex":0.0004923062,"about_ca_system_score_gemma":0.0006692921,"threshold_uncertainty_score":0.0064768195},"labels":[],"label_agreement":null},{"id":"W2147252654","doi":"10.1109/icassp.2011.5946778","title":"BEMD for expression transformation in face recognition","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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":"Computer science; Transformation (genetics); Artificial intelligence; Pattern recognition (psychology); Face (sociological concept); Facial recognition system; Expression (computer science); Biometrics; Bivariate analysis; Facial expression; Linear discriminant analysis; Hilbert–Huang transform; Identification (biology); Feature extraction; Image (mathematics); Computer vision; Speech recognition; Machine learning","score_opus":0.07939199057648368,"score_gpt":0.2535728545570106,"score_spread":0.17418086398052696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147252654","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005181192,0.0003764488,0.9927527,0.00008019469,0.000048011043,0.00003492458,0.00007590841,0.00048318174,0.00096758956],"genre_scores_gemma":[0.11725686,0.0008463568,0.8754466,0.000137925,0.000058161673,0.00019781977,0.0005716767,0.00023666534,0.0052479045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999448,0.00014317657,0.000019929454,0.00008561188,0.00026424995,0.00003910148],"domain_scores_gemma":[0.99972147,0.00009770257,0.000028025644,0.000072528805,0.000068133275,0.000012135376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008272148,0.000551845,0.00046690166,0.0005470815,0.0002496523,0.00046486486,0.0005377413,0.00041191955,0.0034093312],"category_scores_gemma":[0.0017570433,0.00018999167,0.00041640954,0.00065228663,0.00037012345,0.00056893966,0.00078849203,0.00088875205,0.0022268856],"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":[0.00022876728,0.00008166281,0.00082709664,0.00014089885,0.0000483168,0.00014082294,0.0001080415,0.02819912,0.1304233,0.023375226,0.0051096464,0.8113171],"study_design_scores_gemma":[0.00002515772,0.00017461671,0.0039151795,0.000054531985,0.000030876137,0.0007093944,0.00007879491,0.8379586,0.09161471,0.019001225,0.046383962,0.00005306958],"about_ca_topic_score_codex":0.0008769584,"about_ca_topic_score_gemma":0.00095297414,"teacher_disagreement_score":0.0034093312,"about_ca_system_score_codex":0.00021089714,"about_ca_system_score_gemma":0.00030853096,"threshold_uncertainty_score":0.011405349},"labels":[],"label_agreement":null},{"id":"W2147461642","doi":"10.1109/ccece.2007.300","title":"Incremental Line Tangent Space Alignment Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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":"Embedding; Intrinsic dimension; Hessian matrix; Dimension (graph theory); Tangent space; Linear subspace; Curse of dimensionality; Projection (relational algebra); Nonlinear dimensionality reduction; Manifold (fluid mechanics); Tangent; Line (geometry); Algorithm; Representation (politics); Space (punctuation); Mathematics; Basis (linear algebra); Computer science; Parallelizable manifold; Dimensionality reduction; Artificial intelligence; Applied mathematics; Geometry; Combinatorics","score_opus":0.01608505589031123,"score_gpt":0.25924496969045363,"score_spread":0.2431599138001424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147461642","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075721326,0.00013013063,0.9872214,0.000058705456,0.00007231659,0.00006856278,0.00009705931,0.0036073208,0.0011723872],"genre_scores_gemma":[0.1935189,0.0001726157,0.7961847,0.0001481449,0.000089594556,0.00022893198,0.0012650237,0.00065648096,0.007735605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989524,0.00015816733,0.000057235597,0.0003353973,0.0003894751,0.00010716875],"domain_scores_gemma":[0.99902654,0.00014030673,0.0000812742,0.00026028755,0.00042495338,0.00006675609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006141467,0.0010154819,0.001340895,0.0016000263,0.0007469901,0.0014504686,0.0022884489,0.0009331106,0.0086461315],"category_scores_gemma":[0.0022378645,0.00053386047,0.0008099801,0.00159243,0.000544794,0.0024507048,0.0016193698,0.0013411709,0.00479276],"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":[0.00027444423,0.00016363624,0.0014319753,0.00010394133,0.00009603766,0.00019825192,0.00014903343,0.059297938,0.019914102,0.01133146,0.013522592,0.8935166],"study_design_scores_gemma":[0.00004173248,0.00022721269,0.00087153085,0.0000127354715,0.000035319943,0.00037103466,0.00008505951,0.95606554,0.019008916,0.0087318225,0.014501852,0.00004726398],"about_ca_topic_score_codex":0.0028722864,"about_ca_topic_score_gemma":0.0029143405,"teacher_disagreement_score":0.0086461315,"about_ca_system_score_codex":0.0005413136,"about_ca_system_score_gemma":0.0009871757,"threshold_uncertainty_score":0.028924167},"labels":[],"label_agreement":null},{"id":"W2147471231","doi":"10.1109/iscas.2013.6572174","title":"Application of neural networks with CSD coefficients for human face recognition","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Windsor","funders":"","keywords":"Artificial neural network; Computer science; Facial recognition system; Classifier (UML); Artificial intelligence; Time delay neural network; Pattern recognition (psychology); Field-programmable gate array; Speech recognition; Computer hardware","score_opus":0.018240816772721276,"score_gpt":0.24590343653760433,"score_spread":0.22766261976488306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147471231","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08495365,0.0025932798,0.90147346,0.00042399642,0.00027238316,0.00008186447,0.00011161441,0.0008541394,0.009235564],"genre_scores_gemma":[0.7235485,0.0018233744,0.26925617,0.0001252018,0.000093072704,0.00006328469,0.00015291509,0.00003902975,0.004898547],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982125,0.000032210828,0.000009471655,0.000025832906,0.00009981871,0.000011528285],"domain_scores_gemma":[0.9998202,0.000063979016,0.000013236742,0.000020437808,0.0000759866,0.000006176963],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002873739,0.000237474,0.00018509263,0.00044659968,0.00014998578,0.0002841005,0.00024621337,0.00028767655,0.0014400232],"category_scores_gemma":[0.0008089715,0.0001307693,0.0001621486,0.00048059993,0.000170915,0.000360367,0.00024570004,0.0003513669,0.00030579205],"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":[0.00021989433,0.000068805115,0.001974259,0.00016207283,0.000049242724,0.00014803611,0.00004649198,0.07077028,0.13679768,0.006778492,0.0029501107,0.78003466],"study_design_scores_gemma":[0.0000131526285,0.00008119495,0.0022203587,0.000017737339,0.000023513825,0.00023798522,0.000018788236,0.9232123,0.06595397,0.0020804114,0.0061185127,0.000022079548],"about_ca_topic_score_codex":0.002109973,"about_ca_topic_score_gemma":0.0026645164,"teacher_disagreement_score":0.002109973,"about_ca_system_score_codex":0.00028025213,"about_ca_system_score_gemma":0.00027555143,"threshold_uncertainty_score":0.0048173666},"labels":[],"label_agreement":null},{"id":"W2147876959","doi":"10.1109/cgiv.2007.6","title":"A Human Face Recognition System Using Neural Classifiers","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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 Windsor","funders":"","keywords":"Eigenface; Facial recognition system; Artificial intelligence; Computer science; Pattern recognition (psychology); Subspace topology; Three-dimensional face recognition; Classifier (UML); Face (sociological concept); Preprocessor; Face detection; Speech recognition","score_opus":0.07259836631807912,"score_gpt":0.29555627804525475,"score_spread":0.22295791172717563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147876959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033548463,0.00054266717,0.94917864,0.00032407336,0.00026171564,0.000274373,0.00019537081,0.005389044,0.010285593],"genre_scores_gemma":[0.38694373,0.00068871817,0.5885526,0.00074166793,0.00020998408,0.00046886792,0.00039576922,0.00008190505,0.021916801],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999574,0.00004384127,0.000013856343,0.00013039137,0.00020558723,0.000032447635],"domain_scores_gemma":[0.9996855,0.0000588773,0.000020451667,0.00004567997,0.0001686915,0.000020634041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006208583,0.0004037307,0.0005986417,0.00056292553,0.000505893,0.0005904605,0.0008822654,0.0010567936,0.0057949997],"category_scores_gemma":[0.0009809887,0.00021638762,0.00032034836,0.00048422234,0.0003333169,0.00096125685,0.00041734663,0.0005008837,0.003161439],"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":[0.0003704048,0.0003413457,0.0015079374,0.00018896951,0.00010588085,0.00014713731,0.00006537092,0.012660597,0.1845748,0.005320471,0.0098962495,0.78482085],"study_design_scores_gemma":[0.000079749254,0.0009005524,0.005029826,0.00006530302,0.00016862707,0.0009954013,0.00006570752,0.76428354,0.18932417,0.006456066,0.03251089,0.000120097226],"about_ca_topic_score_codex":0.0020880399,"about_ca_topic_score_gemma":0.0023360203,"teacher_disagreement_score":0.0057949997,"about_ca_system_score_codex":0.00043457415,"about_ca_system_score_gemma":0.0005672578,"threshold_uncertainty_score":0.019386232},"labels":[],"label_agreement":null},{"id":"W2148293345","doi":"10.1109/iccima.2007.12","title":"Face Recognition by Multi-resolution Curvelet Transform on Bit Quantized Facial Images","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"PricewaterhouseCoopers (Canada)","funders":"","keywords":"Curvelet; Artificial intelligence; Pattern recognition (psychology); Wavelet transform; Computer science; Wavelet; Face (sociological concept); Facial recognition system; Feature (linguistics); Image (mathematics); Computer vision; Set (abstract data type); Mathematics","score_opus":0.034538535779149865,"score_gpt":0.28348507966692366,"score_spread":0.2489465438877738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148293345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16877317,0.00030765482,0.8278982,0.00016796213,0.000053128668,0.000050840637,0.000084620166,0.00096026907,0.0017041959],"genre_scores_gemma":[0.63988733,0.00045942885,0.35694236,0.000070045804,0.00003646607,0.000041928583,0.00025164193,0.000070996364,0.0022397211],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965394,0.00005803174,0.000015128295,0.000044994027,0.00019310202,0.000034895584],"domain_scores_gemma":[0.99966395,0.000108000604,0.000035271467,0.00007322322,0.00010515655,0.000014470414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000631771,0.00027753625,0.000539583,0.0009816535,0.00013179857,0.00043046742,0.00036049122,0.00041587872,0.0013313738],"category_scores_gemma":[0.0015929238,0.00018065477,0.0003998257,0.0009094493,0.0002606619,0.0009246314,0.00031659685,0.00043096012,0.0007086152],"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":[0.00029625205,0.00010465018,0.0017507039,0.00008386534,0.000037428217,0.00013652003,0.00011470294,0.044247556,0.20434918,0.0037998455,0.0013148012,0.74376446],"study_design_scores_gemma":[0.00001606987,0.0001222947,0.0045541027,0.000012501819,0.000022235907,0.00031162432,0.000051380688,0.9204548,0.07005992,0.0029146685,0.0014550352,0.000025315978],"about_ca_topic_score_codex":0.0007116826,"about_ca_topic_score_gemma":0.0005574825,"teacher_disagreement_score":0.0013313738,"about_ca_system_score_codex":0.00022718844,"about_ca_system_score_gemma":0.00018812736,"threshold_uncertainty_score":0.004453838},"labels":[],"label_agreement":null},{"id":"W2148652035","doi":"10.1007/978-3-319-59876-5_29","title":"Development of an Active Shape Model Using the Discrete Cosine Transform","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Discrete cosine transform; Landmark; Artificial intelligence; Computer science; Computer vision; Face (sociological concept); Feature (linguistics); Pattern recognition (psychology); Property (philosophy); Image (mathematics); Set (abstract data type)","score_opus":0.0433817104011967,"score_gpt":0.28764330924108183,"score_spread":0.24426159883988513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148652035","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00086807366,0.00007422893,0.9980198,0.000030182553,0.000035407254,0.000019038076,0.00002498287,0.00024444313,0.000683888],"genre_scores_gemma":[0.0775911,0.0006432484,0.9113208,0.0001253072,0.00007091763,0.00016045706,0.0005900505,0.00048108827,0.009016995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996295,0.000037146663,0.000019335499,0.000067832356,0.00022443665,0.000021764838],"domain_scores_gemma":[0.9995915,0.00010064766,0.000023389981,0.000070733346,0.00018894921,0.000024775974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005364392,0.0006383099,0.00079935015,0.00065483956,0.00029396333,0.001146398,0.0018702095,0.001166988,0.0029086648],"category_scores_gemma":[0.0011019622,0.00072245416,0.00118306,0.0008960271,0.00043236616,0.0015495516,0.00093649153,0.0016066969,0.0037265294],"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":[0.00010184288,0.00014212244,0.00066027837,0.00022674246,0.00008665371,0.0001790266,0.00012476077,0.3340031,0.099678494,0.052584972,0.00567848,0.50653344],"study_design_scores_gemma":[0.000004587811,0.000023393297,0.00009102577,0.000008628785,0.00000961018,0.00009232177,0.0000089409195,0.9823667,0.010011069,0.0020364244,0.005332413,0.000014767396],"about_ca_topic_score_codex":0.0033892086,"about_ca_topic_score_gemma":0.0028421609,"teacher_disagreement_score":0.0033892086,"about_ca_system_score_codex":0.00042469995,"about_ca_system_score_gemma":0.0008651081,"threshold_uncertainty_score":0.009730518},"labels":[],"label_agreement":null},{"id":"W2149753900","doi":"10.1109/tsmcb.2007.907036","title":"Comparing Human and Automatic Face Recognition Performance","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Facial recognition system; Face (sociological concept); Computer science; Artificial intelligence; Computer vision; Speech recognition; Pattern recognition (psychology); Sociology","score_opus":0.03772601261753666,"score_gpt":0.2499326494810211,"score_spread":0.21220663686348443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149753900","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.86102617,0.0023495543,0.11643923,0.00021564229,0.00017459912,0.00015688472,0.0012524845,0.001324769,0.017060632],"genre_scores_gemma":[0.9623224,0.00048346326,0.033327613,0.00009459953,0.00009092438,0.00008639991,0.0013369208,0.000103119106,0.0021545233],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9907803,0.003153334,0.00051392976,0.0010690342,0.0041898796,0.0002935481],"domain_scores_gemma":[0.9820884,0.012136085,0.0010213864,0.0013670652,0.0032166457,0.0001704786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008166412,0.00043462368,0.00051492226,0.0023222188,0.000270284,0.00089539343,0.0005571712,0.0007615004,0.002782888],"category_scores_gemma":[0.020380318,0.00013795093,0.00043022336,0.0010919091,0.00063109095,0.0013782281,0.0007935169,0.00024403264,0.0010175984],"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":[0.0021099523,0.0005626821,0.11452694,0.0006144517,0.0007548852,0.00011860217,0.0006097503,0.047862425,0.059590787,0.003798456,0.003844523,0.7656065],"study_design_scores_gemma":[0.00010656628,0.005294506,0.5897196,0.00010501212,0.00022540169,0.001153694,0.00059796113,0.26834276,0.11999862,0.0040022773,0.010133117,0.00032043585],"about_ca_topic_score_codex":0.0016343178,"about_ca_topic_score_gemma":0.0016563169,"teacher_disagreement_score":0.008166412,"about_ca_system_score_codex":0.00047251652,"about_ca_system_score_gemma":0.000391149,"threshold_uncertainty_score":0.04318863},"labels":[],"label_agreement":null},{"id":"W2150631128","doi":"10.1109/ijcnn.2002.1005434","title":"A hybrid learning RBF neural network for human face recognition with pseudo Zernike moment invariant","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":32,"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 Windsor","funders":"","keywords":"Zernike polynomials; Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Radial basis function; Computer science; Invariant (physics); Artificial neural network; Classifier (UML); Feature extraction; Computer vision; Mathematics; Physics; Optics","score_opus":0.028587987068954435,"score_gpt":0.24220634430932486,"score_spread":0.21361835724037043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150631128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011026765,0.00036792477,0.9866883,0.00006973624,0.000056426754,0.000041500713,0.0000287586,0.0009193012,0.00080135436],"genre_scores_gemma":[0.19518231,0.000493999,0.79823047,0.00013363535,0.00011958813,0.00021432823,0.00016629758,0.00008902848,0.0053703077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962485,0.000079596226,0.000015642696,0.00006859785,0.00017975166,0.000031679738],"domain_scores_gemma":[0.9997516,0.000083384475,0.000021024149,0.00002878817,0.000102519094,0.000012736027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077595457,0.0004254064,0.0006198593,0.000493185,0.00024193393,0.00046839414,0.0009152043,0.00092159864,0.0017136568],"category_scores_gemma":[0.00091680407,0.00025749157,0.00042506974,0.00044006438,0.00026994615,0.0010167834,0.00039642362,0.0006361912,0.0011175936],"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":[0.00031108691,0.00019342039,0.0010233645,0.00018619282,0.0001320534,0.000121833364,0.00006608992,0.07031121,0.10845429,0.004610438,0.0023992248,0.8121909],"study_design_scores_gemma":[0.000028096467,0.0002441763,0.001116831,0.000009922904,0.000031873282,0.0002526173,0.000011810427,0.9583793,0.034646615,0.00089256413,0.004353268,0.000032951888],"about_ca_topic_score_codex":0.0016091411,"about_ca_topic_score_gemma":0.0019306127,"teacher_disagreement_score":0.0017136568,"about_ca_system_score_codex":0.00028799652,"about_ca_system_score_gemma":0.00028536253,"threshold_uncertainty_score":0.0057327747},"labels":[],"label_agreement":null},{"id":"W2150682505","doi":"10.1109/ssd.2008.4632873","title":"Face recognition based on 2DPCA, DIAPCA and DIA2DPCA in DCT domain","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure","funders":"","keywords":"Discrete cosine transform; Artificial intelligence; Facial recognition system; Computer science; Preprocessor; Pattern recognition (psychology); Face (sociological concept); Computer vision; Domain (mathematical analysis); Block (permutation group theory); Pixel; Speech recognition; Image (mathematics); Mathematics","score_opus":0.024883285313281146,"score_gpt":0.21948650316524526,"score_spread":0.1946032178519641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150682505","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013677584,0.0014809177,0.9780633,0.00017554454,0.00032184806,0.00016600611,0.00017732134,0.0010919775,0.004845623],"genre_scores_gemma":[0.13955538,0.0017201906,0.8473805,0.00025411096,0.00032309405,0.00024616203,0.00057788996,0.00014590014,0.009796735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993524,0.00011098741,0.000026611537,0.00012189722,0.00033466908,0.00005355619],"domain_scores_gemma":[0.99945337,0.00015250221,0.000033482786,0.00010219628,0.00023060574,0.000027820219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007196228,0.0006095475,0.0005587669,0.0013313505,0.00030632602,0.0007715226,0.0006783333,0.00061335874,0.0037389444],"category_scores_gemma":[0.0016156236,0.00024489666,0.00053004577,0.0009421558,0.00040638633,0.0010055557,0.00042133845,0.0007520359,0.0016233557],"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":[0.00028510697,0.00010993794,0.0012019788,0.00019390427,0.00008406634,0.00014747871,0.0000618582,0.019526392,0.08586025,0.009093178,0.006261781,0.8771741],"study_design_scores_gemma":[0.00007156846,0.00050715404,0.0063043223,0.00006241442,0.00011504451,0.0017142782,0.00008598819,0.82862204,0.10854978,0.007224598,0.046604298,0.00013861462],"about_ca_topic_score_codex":0.002599339,"about_ca_topic_score_gemma":0.0037427344,"teacher_disagreement_score":0.0037389444,"about_ca_system_score_codex":0.00025781116,"about_ca_system_score_gemma":0.00048760325,"threshold_uncertainty_score":0.012507975},"labels":[],"label_agreement":null},{"id":"W2150954665","doi":"10.1109/icpr.2006.365","title":"Class Separability in Spaces Reduced By Feature Selection","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Feature selection; Curse of dimensionality; Artificial intelligence; Pattern recognition (psychology); Feature vector; Support vector machine; Computer science; Linear classifier; Classifier (UML); Selection (genetic algorithm); Class (philosophy); Feature extraction; Feature (linguistics); Dimensionality reduction; Machine learning","score_opus":0.005994538448580035,"score_gpt":0.22984878022243035,"score_spread":0.22385424177385033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150954665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25836617,0.00044423647,0.73907715,0.00044995054,0.00003175755,0.00007167315,0.000117053496,0.00020071235,0.0012411969],"genre_scores_gemma":[0.8284092,0.00022576205,0.16952498,0.00006917697,0.000091000635,0.00017054843,0.0005004145,0.00008120282,0.0009277568],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961731,0.0017707553,0.00022659851,0.00044593774,0.0011878939,0.00019573509],"domain_scores_gemma":[0.9689623,0.025992231,0.0016925118,0.001774024,0.0012882168,0.00029075393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049318834,0.0007415794,0.0014718854,0.0012366629,0.0005805417,0.0016832398,0.0007666551,0.00076875795,0.0009753821],"category_scores_gemma":[0.028839558,0.00042981547,0.0010719927,0.0014077331,0.0018236431,0.0028066519,0.0017733375,0.001940905,0.00021249516],"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":[0.0008114733,0.00024705185,0.006371542,0.00033207706,0.0001857357,0.0005469837,0.0005532852,0.7578032,0.015766516,0.066982724,0.0015150484,0.14888434],"study_design_scores_gemma":[0.000027191696,0.00011635667,0.0039464803,0.000008904006,0.000013530572,0.00012925678,0.00005150077,0.94425535,0.0031780794,0.047728226,0.00051896187,0.000026154186],"about_ca_topic_score_codex":0.0010703758,"about_ca_topic_score_gemma":0.0006708468,"teacher_disagreement_score":0.0049318834,"about_ca_system_score_codex":0.00092203077,"about_ca_system_score_gemma":0.00065952784,"threshold_uncertainty_score":0.026082575},"labels":[],"label_agreement":null},{"id":"W2151820202","doi":"10.1109/icpr.2014.237","title":"Feature Relevance for Kernel Logistic Regression and Application to Action Classification","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"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 du Québec en Outaouais","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Kernel (algebra); Support vector machine; Weighting; Feature (linguistics); Logistic regression; Relevance (law); Machine learning; Linear classifier; Kernel method; Data mining; Mathematics","score_opus":0.04569691326310274,"score_gpt":0.31806680284707567,"score_spread":0.2723698895839729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151820202","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050111534,0.00022855846,0.9938962,0.00011821393,0.000028748515,0.000028556693,0.000015430132,0.0003884486,0.00028472542],"genre_scores_gemma":[0.37568986,0.0005195732,0.6199243,0.00017694589,0.00026013405,0.00024318579,0.00023604804,0.0003043817,0.0026455927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976421,0.0011664812,0.00012026263,0.00046984182,0.0004739872,0.00012740263],"domain_scores_gemma":[0.99703395,0.0016417698,0.00027655213,0.00037018923,0.00058331405,0.00009420507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033692166,0.0009447208,0.0012920421,0.0011628906,0.0004906034,0.0010263391,0.0013899509,0.0012667973,0.0015242854],"category_scores_gemma":[0.011995928,0.0005063957,0.0010629658,0.0012967897,0.00084120553,0.0013414104,0.0014536261,0.0022890095,0.0010642243],"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":[0.00032166432,0.0003114563,0.002695359,0.00028092795,0.00015605113,0.00030551114,0.00030192937,0.23820645,0.030218223,0.030926047,0.004355396,0.691921],"study_design_scores_gemma":[0.000010076564,0.00004910623,0.0004893643,0.000008864426,0.00001124049,0.000069315174,0.000011096711,0.9875907,0.002978343,0.0073601347,0.0014000989,0.000021588132],"about_ca_topic_score_codex":0.0012852357,"about_ca_topic_score_gemma":0.0009225708,"teacher_disagreement_score":0.0033692166,"about_ca_system_score_codex":0.0005743486,"about_ca_system_score_gemma":0.0006574187,"threshold_uncertainty_score":0.017818332},"labels":[],"label_agreement":null},{"id":"W2151872909","doi":"10.1142/s021812660200046x","title":"A NEURAL BASED HUMAN FACE RECOGNITION SYSTEM USING AN EFFICIENT FEATURE EXTRACTION METHOD WITH PSEUDO ZERNIKE MOMENT","year":2002,"lang":"en","type":"article","venue":"Journal of Circuits Systems and Computers","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Zernike polynomials; Artificial intelligence; Pattern recognition (psychology); Feature extraction; Computer science; Artificial neural network; Facial recognition system; Classifier (UML); Discrete cosine transform; Principal component analysis; Radial basis function; Face (sociological concept); Computer vision; Image (mathematics)","score_opus":0.050146488104429274,"score_gpt":0.2751133698914148,"score_spread":0.22496688178698554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151872909","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023405666,0.00048100174,0.96923494,0.00015242823,0.00014669423,0.00013471772,0.00012989277,0.0026662436,0.0036485135],"genre_scores_gemma":[0.28058314,0.0005698999,0.70366514,0.00032410474,0.00011921487,0.00033817498,0.00039732116,0.000074202784,0.013928776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996754,0.00002530095,0.000012386907,0.00008175499,0.00018314073,0.000021971387],"domain_scores_gemma":[0.9998518,0.000025897487,0.000015717089,0.000019946749,0.00007661991,0.000009964242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039761278,0.00035569997,0.0005712501,0.00047602848,0.00032017706,0.00036797085,0.0008245297,0.000663519,0.0034133724],"category_scores_gemma":[0.00050569006,0.00022032656,0.0003573205,0.0003535793,0.00018691298,0.0006752737,0.0003525542,0.0004323264,0.0017274062],"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":[0.00024412331,0.00017258564,0.0006583797,0.00018410654,0.00006754112,0.00012375508,0.000050961175,0.008925934,0.22746378,0.0023219741,0.0040657427,0.75572103],"study_design_scores_gemma":[0.00009110722,0.00075725664,0.0069751116,0.00004757259,0.0001545553,0.0014984272,0.00004919865,0.7622814,0.19962883,0.002700605,0.025700191,0.00011575282],"about_ca_topic_score_codex":0.0015197912,"about_ca_topic_score_gemma":0.002329301,"teacher_disagreement_score":0.0034133724,"about_ca_system_score_codex":0.00037778445,"about_ca_system_score_gemma":0.0003132943,"threshold_uncertainty_score":0.011418879},"labels":[],"label_agreement":null},{"id":"W2152810608","doi":"10.1109/pacrim.2007.4313230","title":"Person Recognition Using Features of Still Face Images and Text-independent Speeches","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Fuse (electrical); Pattern recognition (psychology); Face (sociological concept); Facial recognition system; Feature vector; Frame (networking); Speech recognition; Probabilistic logic; Feature extraction; Probabilistic neural network; Artificial neural network; Computer vision; Time delay neural network; Engineering","score_opus":0.03973777653828866,"score_gpt":0.27372336089497157,"score_spread":0.2339855843566829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152810608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11484871,0.0005384587,0.88039243,0.000072067305,0.00010667671,0.000081734484,0.00026076555,0.0016796733,0.002019484],"genre_scores_gemma":[0.5525883,0.0004534428,0.44315085,0.00006162979,0.000093703406,0.000072741015,0.0005635335,0.00007922687,0.0029366803],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973434,0.000026861482,0.000012244157,0.00008532191,0.000102751634,0.00003850659],"domain_scores_gemma":[0.99979216,0.000045678702,0.000032274216,0.000033035794,0.00007930856,0.0000176281],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034502402,0.0005044588,0.00064705004,0.0009067644,0.00018963404,0.00035401018,0.00048330697,0.0005384496,0.0012117801],"category_scores_gemma":[0.0008507853,0.0002296179,0.00057950016,0.0007333772,0.00020606164,0.0011159197,0.00038404597,0.00042302636,0.0006183087],"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":[0.00040464828,0.0000979006,0.0020871593,0.0001324921,0.00008614513,0.00019770996,0.000096416996,0.008893382,0.1611664,0.0012346484,0.0010619558,0.82454115],"study_design_scores_gemma":[0.00006022526,0.00062993186,0.030780083,0.00004132067,0.00035362577,0.002050671,0.00018773705,0.687267,0.2677079,0.0053633363,0.005449335,0.00010889039],"about_ca_topic_score_codex":0.0012011669,"about_ca_topic_score_gemma":0.0013898893,"teacher_disagreement_score":0.0012117801,"about_ca_system_score_codex":0.00015714606,"about_ca_system_score_gemma":0.00023131295,"threshold_uncertainty_score":0.004053831},"labels":[],"label_agreement":null},{"id":"W2152836620","doi":"","title":"Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices","year":2004,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":128,"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":"Generalization; Rank (graph theory); Class (philosophy); Matrix (chemical analysis); Mathematics; Generalization error; Applied mathematics; Error analysis; Computer science; Algorithm; Discrete mathematics; Combinatorics; Artificial intelligence; Artificial neural network; Mathematical analysis","score_opus":0.012358558755244291,"score_gpt":0.24368482083653334,"score_spread":0.23132626208128904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152836620","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031478524,0.0007198181,0.96361005,0.00088190427,0.00009423378,0.000075175376,0.00031905062,0.00068336853,0.0021378824],"genre_scores_gemma":[0.72258186,0.0013036076,0.2672576,0.0007779718,0.0005450384,0.00059610663,0.0018858076,0.00041857627,0.004633482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9923274,0.0022689363,0.0004717779,0.0018203321,0.002162026,0.0009494402],"domain_scores_gemma":[0.8985798,0.074237004,0.005739554,0.0130013265,0.006283893,0.0021583955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010361681,0.0029728962,0.003172027,0.0016947502,0.0015234598,0.0025469833,0.004761489,0.0033465615,0.0048026526],"category_scores_gemma":[0.083724745,0.0011046523,0.0018829762,0.0021074936,0.0039402177,0.009987506,0.0065488317,0.0064899265,0.0012474682],"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":[0.0007176962,0.00020754944,0.005158353,0.00024326434,0.00020264044,0.00022070693,0.00028236196,0.82551175,0.0037715407,0.08110076,0.0046912055,0.077892154],"study_design_scores_gemma":[0.000014467227,0.00006204882,0.0003109061,0.000020320427,0.000017270839,0.00004067554,0.000019017354,0.9442622,0.0010809542,0.053885017,0.00027100154,0.000016152539],"about_ca_topic_score_codex":0.00470389,"about_ca_topic_score_gemma":0.004187806,"teacher_disagreement_score":0.010361681,"about_ca_system_score_codex":0.0029780297,"about_ca_system_score_gemma":0.0019027195,"threshold_uncertainty_score":0.054798484},"labels":[],"label_agreement":null},{"id":"W2153385691","doi":"10.1142/s0218001410008391","title":"ROTATION INVARIANT MULTIVIEW FACE DETECTION USING SKIN COLOR REGRESSIVE MODEL AND SUPPORT VECTOR REGRESSION","year":2010,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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 Calgary","funders":"","keywords":"Artificial intelligence; Computer vision; Pattern recognition (psychology); Face detection; Computer science; Support vector machine; Invariant (physics); Luminance; Face (sociological concept); Chromatic scale; Principal component analysis; Facial recognition system; Mathematics","score_opus":0.08453554673862385,"score_gpt":0.3292188132531539,"score_spread":0.24468326651453004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153385691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02018094,0.0001269288,0.97853994,0.000032682587,0.000021595155,0.000017551682,0.000023136294,0.0007310307,0.00032621165],"genre_scores_gemma":[0.47263062,0.0002770473,0.52450526,0.00006511115,0.000048815178,0.000073900796,0.00015503759,0.00011106231,0.002133153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950504,0.000094015566,0.000015880978,0.00012774087,0.00020983543,0.000047516358],"domain_scores_gemma":[0.9996803,0.00009631421,0.000056929748,0.000050350456,0.000098934026,0.000017175227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066015363,0.0004789711,0.00084753346,0.0008669213,0.0001808197,0.00041904964,0.00070569944,0.0005082137,0.0007622935],"category_scores_gemma":[0.0010595133,0.00031328952,0.0007562361,0.00045779572,0.00025235582,0.00059032266,0.0004156855,0.00051542046,0.0005749881],"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":[0.00031884815,0.00018142056,0.0033474376,0.00011563102,0.00017625069,0.00020356562,0.000100030615,0.14211778,0.13954817,0.003530107,0.0019404455,0.7084202],"study_design_scores_gemma":[0.0000049899236,0.000051658324,0.0010095378,0.0000038064654,0.000013111414,0.00014002605,0.000010874378,0.98373806,0.01401412,0.0005736087,0.00042601154,0.0000141305745],"about_ca_topic_score_codex":0.0015542982,"about_ca_topic_score_gemma":0.0013074515,"teacher_disagreement_score":0.0015542982,"about_ca_system_score_codex":0.0002695881,"about_ca_system_score_gemma":0.00028477493,"threshold_uncertainty_score":0.0034912825},"labels":[],"label_agreement":null},{"id":"W2154211011","doi":"10.1109/tpami.2008.48","title":"Tied Factor Analysis for Face Recognition across Large Pose Differences","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":196,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Computer science; Feature vector; Metric (unit); Identity (music); Feature extraction; Transformation (genetics); Face (sociological concept); Feature (linguistics); Pose; Noise (video); Image (mathematics)","score_opus":0.05356022456716487,"score_gpt":0.2980524006241523,"score_spread":0.24449217605698742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154211011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009767214,0.00016682988,0.98912954,0.000071580194,0.000020515425,0.000025972144,0.000062480656,0.0004511464,0.0003046634],"genre_scores_gemma":[0.4237808,0.0004255882,0.5706333,0.00016882618,0.00013527776,0.000265524,0.0009672371,0.0002548193,0.0033687048],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979735,0.00079188874,0.000076047894,0.00054473156,0.00047491104,0.0001389651],"domain_scores_gemma":[0.99718046,0.0015674515,0.0001975125,0.0007293019,0.00024839744,0.00007681018],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031453003,0.0007867732,0.0010063301,0.001112352,0.0007039027,0.00084307237,0.001149357,0.0009196418,0.0026425123],"category_scores_gemma":[0.011091014,0.00048236715,0.0015298859,0.0012231517,0.0011800054,0.0015350415,0.001543428,0.0016238248,0.0016688935],"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":[0.0004435329,0.00020498224,0.003954182,0.00011808107,0.00025884123,0.00018145479,0.0003055639,0.27692318,0.015488092,0.039334487,0.0037654159,0.6590222],"study_design_scores_gemma":[0.000015923735,0.000051508978,0.001956647,0.000007290027,0.00001924649,0.00009190441,0.000022562668,0.95400393,0.002967899,0.0397179,0.001117403,0.000027712831],"about_ca_topic_score_codex":0.0030492623,"about_ca_topic_score_gemma":0.0031199567,"teacher_disagreement_score":0.0031453003,"about_ca_system_score_codex":0.00064359297,"about_ca_system_score_gemma":0.00065896363,"threshold_uncertainty_score":0.016634166},"labels":[],"label_agreement":null},{"id":"W2154332623","doi":"10.1109/icpr.2008.4761091","title":"Help-training for semi-supervised discriminative classifiers. Application to SVM","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Support vector machine; Computer science; Co-training; Machine learning; Classifier (UML); Generative grammar; Pattern recognition (psychology); Training set; Semi-supervised learning; Random subspace method; Labeled data; Supervised learning; Training (meteorology); Artificial neural network","score_opus":0.159852228198708,"score_gpt":0.3218316136727785,"score_spread":0.1619793854740705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154332623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0074369907,0.00018736324,0.9903453,0.00009446407,0.000020980837,0.000054906843,0.000023499215,0.0011553202,0.00068125786],"genre_scores_gemma":[0.39673173,0.00017195,0.59936297,0.00024788314,0.00009652119,0.00024065592,0.00025675583,0.00026774724,0.0026237217],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983138,0.00096867944,0.00006803688,0.00026885714,0.0003115807,0.00006901188],"domain_scores_gemma":[0.9958134,0.002441641,0.00025541437,0.00072709034,0.0006159189,0.00014646401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022444297,0.00074553327,0.00067191856,0.0007303074,0.00042665622,0.0005765815,0.0014629951,0.0012936402,0.002452245],"category_scores_gemma":[0.0070757754,0.00049432187,0.0006053068,0.00057444966,0.0009605551,0.0011324692,0.0012960866,0.001324673,0.0012080997],"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":[0.00026813755,0.0002872066,0.0038122025,0.00037159037,0.00014102594,0.00022266811,0.00034386164,0.15817721,0.031619336,0.021100927,0.007914174,0.77574164],"study_design_scores_gemma":[0.000010381662,0.0000654926,0.00042870248,0.000016500117,0.000010115941,0.00014367297,0.0000150533015,0.9830996,0.007269125,0.006526359,0.002403957,0.00001108315],"about_ca_topic_score_codex":0.00066700677,"about_ca_topic_score_gemma":0.0014528387,"teacher_disagreement_score":0.002452245,"about_ca_system_score_codex":0.0003677396,"about_ca_system_score_gemma":0.00044333658,"threshold_uncertainty_score":0.011869788},"labels":[],"label_agreement":null},{"id":"W2154604394","doi":"10.1109/icpr.2002.1048410","title":"Robust contrast-invariant eigen detection","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Subspace topology; Pattern recognition (psychology); Artificial intelligence; Linear subspace; Outlier; Computer science; Principal component analysis; Pixel; Residual; Mathematics; Object detection; Classifier (UML); Contrast (vision); Invariant (physics); Computer vision; Algorithm","score_opus":0.025221363916942276,"score_gpt":0.20940398104207014,"score_spread":0.18418261712512787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154604394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014814858,0.00018771901,0.9824492,0.00007379011,0.000038424118,0.00002245298,0.00007223869,0.0009700048,0.0013713281],"genre_scores_gemma":[0.30558816,0.0002824692,0.6890934,0.00022271415,0.000102739825,0.00008472743,0.0005409392,0.00029471057,0.00379008],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987399,0.00018885234,0.000049773782,0.00031818677,0.00055887905,0.00014450359],"domain_scores_gemma":[0.9990042,0.00019579088,0.00013291584,0.0003011103,0.00031946393,0.00004654856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009854791,0.0009117351,0.001126817,0.0017164765,0.00035709608,0.0010736408,0.001209251,0.0008479588,0.001630426],"category_scores_gemma":[0.0032688146,0.000363115,0.0008866218,0.00083236094,0.0005766192,0.001258869,0.0014462183,0.0009189099,0.001631569],"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":[0.00029254396,0.00019699337,0.0028215025,0.0000980973,0.00013047441,0.00014768171,0.000080456346,0.044534773,0.21916592,0.017349273,0.0054450966,0.7097372],"study_design_scores_gemma":[0.000015811878,0.00015346172,0.0037488674,0.00001694753,0.000040828538,0.00059245795,0.00004458331,0.84965384,0.12734403,0.01295713,0.0053680823,0.00006383488],"about_ca_topic_score_codex":0.00086820434,"about_ca_topic_score_gemma":0.0012192201,"teacher_disagreement_score":0.0017164765,"about_ca_system_score_codex":0.00042286146,"about_ca_system_score_gemma":0.00049268716,"threshold_uncertainty_score":0.005454302},"labels":[],"label_agreement":null},{"id":"W2154793733","doi":"10.1109/icpr.2006.586","title":"Function Dot Product Kernels for Support Vector Machine","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"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; Canada Research Chairs","keywords":"Dot product; Support vector machine; Computer science; Relevance vector machine; Product (mathematics); Function (biology); Artificial intelligence; Mathematics","score_opus":0.013308773523592143,"score_gpt":0.23042370660983538,"score_spread":0.21711493308624324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154793733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038707298,0.00027865547,0.99480563,0.00005533595,0.000041003295,0.000019741745,0.00003696748,0.00041199423,0.0004798933],"genre_scores_gemma":[0.38146877,0.0013058534,0.6093753,0.00018501485,0.00019356725,0.0002719558,0.00086496474,0.00037256628,0.0059619476],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99822026,0.0005342427,0.00016656058,0.0002596411,0.00069129746,0.0001280092],"domain_scores_gemma":[0.99692756,0.0010179022,0.0002474991,0.0004939313,0.0012080984,0.00010496726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018196668,0.0010188338,0.0010996625,0.0011769655,0.00033322224,0.0014354952,0.0010314608,0.0010172476,0.0019228122],"category_scores_gemma":[0.007119325,0.00038053462,0.00077968143,0.0014671849,0.0008132965,0.0023815336,0.0008793146,0.0015465536,0.0015307459],"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":[0.00040377633,0.00014981143,0.0015121914,0.0003620463,0.00016771781,0.00027337004,0.00014390457,0.2529215,0.024215048,0.13729846,0.008390106,0.57416195],"study_design_scores_gemma":[0.00000888671,0.0000683001,0.00027015325,0.000012221313,0.000013752747,0.00010024664,0.000010149976,0.9726873,0.004962701,0.017204015,0.0046400037,0.000022262895],"about_ca_topic_score_codex":0.00114013,"about_ca_topic_score_gemma":0.00054290635,"teacher_disagreement_score":0.0019228122,"about_ca_system_score_codex":0.0006097999,"about_ca_system_score_gemma":0.0006108426,"threshold_uncertainty_score":0.009623468},"labels":[],"label_agreement":null},{"id":"W2155178156","doi":"10.1109/tnn.2005.860853","title":"Ensemble-based discriminant learning with boosting for face recognition","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":193,"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":"National Institute of Standards and Technology","keywords":"Boosting (machine learning); Linear discriminant analysis; Computer science; Artificial intelligence; Machine learning; Ensemble learning; Facial recognition system; Discriminant; Pairwise comparison; Pattern recognition (psychology)","score_opus":0.02248973911586177,"score_gpt":0.22837046837794747,"score_spread":0.2058807292620857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155178156","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036276667,0.0006289926,0.9944882,0.00006723923,0.000058276233,0.000025399124,0.000017321803,0.00030161958,0.00078538805],"genre_scores_gemma":[0.24279873,0.0013763495,0.75212216,0.00021201643,0.00031932263,0.00020106879,0.00021229037,0.00011690818,0.00264114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987256,0.0004982406,0.000047497873,0.00019129562,0.00045347778,0.00008384356],"domain_scores_gemma":[0.9986319,0.00059426646,0.000089366804,0.00024254822,0.00038809015,0.000053810727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028938253,0.00090876024,0.0018621687,0.0013732179,0.0005198491,0.00079023716,0.0015348288,0.00088010635,0.0017380377],"category_scores_gemma":[0.0040144892,0.00040989928,0.0011722675,0.0013714926,0.00052245636,0.0014672434,0.0013768052,0.0014699188,0.0013952778],"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":[0.00017799223,0.00014129361,0.0020844676,0.00021184949,0.00021669616,0.0001114444,0.00009967981,0.2231408,0.010777577,0.025793627,0.0049102833,0.7323342],"study_design_scores_gemma":[0.000012243992,0.00006718114,0.00041646408,0.000014016282,0.000033520824,0.000086417596,0.000010844597,0.97851914,0.0036923673,0.012415116,0.0047123046,0.00002029383],"about_ca_topic_score_codex":0.0006531214,"about_ca_topic_score_gemma":0.00073672755,"teacher_disagreement_score":0.0028938253,"about_ca_system_score_codex":0.00039657086,"about_ca_system_score_gemma":0.0004305113,"threshold_uncertainty_score":0.015304148},"labels":[],"label_agreement":null},{"id":"W2155590375","doi":"10.1109/icpr.2002.1048336","title":"A tied-mixture 2D HMM face recognition system","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Hidden Markov model; Robustness (evolution); Tying; Computer science; Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Speech recognition; Face (sociological concept); Machine learning","score_opus":0.017565654930520553,"score_gpt":0.2192848525702358,"score_spread":0.20171919763971524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155590375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016634725,0.00024456286,0.9767578,0.00012722518,0.00016441205,0.00006237718,0.00018450648,0.003880025,0.0019443334],"genre_scores_gemma":[0.4584227,0.00026163773,0.5314217,0.0003242738,0.000115985626,0.0002199066,0.0007386031,0.00011821628,0.008376929],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995757,0.00007205826,0.000023237577,0.00016980713,0.00012414058,0.000035005873],"domain_scores_gemma":[0.99968123,0.00006797386,0.000020053734,0.00011333618,0.00008569836,0.0000317175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004961705,0.00031317366,0.0008917319,0.00032467843,0.0004214859,0.000647266,0.0011086647,0.001002757,0.0041338936],"category_scores_gemma":[0.0009480037,0.0005065215,0.00052988867,0.00035557212,0.00026260837,0.0008877356,0.0009218055,0.0007219707,0.0026531743],"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":[0.00087832817,0.00030478812,0.003530023,0.00026447792,0.00021730848,0.00054392836,0.00028853814,0.14388223,0.16436903,0.0129157575,0.009235567,0.6635701],"study_design_scores_gemma":[0.00002517108,0.00006800419,0.0009406221,0.000008256582,0.00003347352,0.00019833946,0.000010856591,0.982812,0.010717719,0.0019250285,0.003226632,0.00003391635],"about_ca_topic_score_codex":0.0027539348,"about_ca_topic_score_gemma":0.0030500235,"teacher_disagreement_score":0.0041338936,"about_ca_system_score_codex":0.0003564748,"about_ca_system_score_gemma":0.0005155489,"threshold_uncertainty_score":0.013829231},"labels":[],"label_agreement":null},{"id":"W2156387284","doi":"10.1109/icip.2005.1530215","title":"Wavelet-based illumination normalization for face recognition","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":178,"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 British Columbia","funders":"Yale University","keywords":"Artificial intelligence; Normalization (sociology); Computer science; Computer vision; Facial recognition system; Histogram equalization; Pattern recognition (psychology); Histogram; Pixel; Wavelet; Three-dimensional face recognition; Face (sociological concept); Wavelet transform; Face detection; Image (mathematics)","score_opus":0.024674795670707583,"score_gpt":0.25183039402944885,"score_spread":0.22715559835874127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156387284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014634355,0.00083460216,0.98124295,0.00009992324,0.00008959163,0.000040018935,0.00008854828,0.0011122086,0.0018577728],"genre_scores_gemma":[0.17605156,0.002614244,0.8145584,0.00012708672,0.00016020502,0.00016594023,0.00072615145,0.0002811747,0.0053152884],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995851,0.00007785369,0.000021124464,0.00007015889,0.00021388082,0.000031972304],"domain_scores_gemma":[0.9997311,0.00006512562,0.000023195023,0.00006265709,0.000105496605,0.000012365264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005721446,0.00039156128,0.00056083343,0.0005925371,0.0002735122,0.000430963,0.0005471131,0.00036881477,0.0025253345],"category_scores_gemma":[0.0012096565,0.000254402,0.0005461643,0.0009327466,0.00032402412,0.0007043298,0.000479683,0.0007055001,0.0017330024],"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":[0.00020374332,0.00008968525,0.00047596855,0.00015536026,0.000052872336,0.00009211307,0.000042329906,0.02173494,0.18915412,0.0072204573,0.005083306,0.77569515],"study_design_scores_gemma":[0.00003554092,0.00016222184,0.003945703,0.0000404671,0.0000710736,0.00061732304,0.00003381785,0.6966212,0.2680911,0.007042484,0.023265766,0.00007319376],"about_ca_topic_score_codex":0.0007789592,"about_ca_topic_score_gemma":0.00089285546,"teacher_disagreement_score":0.0025253345,"about_ca_system_score_codex":0.00032499494,"about_ca_system_score_gemma":0.00040250193,"threshold_uncertainty_score":0.008448064},"labels":[],"label_agreement":null},{"id":"W2157051384","doi":"10.1109/icif.2007.4408078","title":"Face detection using information fusion","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":30,"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":"Detector; Face (sociological concept); Artificial intelligence; Pixel; Computer science; Face detection; Computer vision; Pattern recognition (psychology); Artificial neural network; Object-class detection; Facial recognition system; Telecommunications","score_opus":0.0163123974614167,"score_gpt":0.24802156675336448,"score_spread":0.2317091692919478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157051384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017875675,0.00069161854,0.97782576,0.00013155458,0.00006775778,0.000040848303,0.00007248753,0.00090881804,0.0023854377],"genre_scores_gemma":[0.45275712,0.0008661677,0.54303026,0.00017781279,0.00013005464,0.00007970684,0.0003194428,0.0000689216,0.0025704952],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985123,0.00029058455,0.00008593457,0.00034967656,0.000617272,0.00014421763],"domain_scores_gemma":[0.99872357,0.00049700256,0.000125418,0.00027852412,0.00034362316,0.00003186573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018822388,0.0009328888,0.0013565536,0.0022570514,0.00046523943,0.0011897751,0.0012036156,0.0012670181,0.0017129309],"category_scores_gemma":[0.0042927363,0.0004686523,0.0011156423,0.0012465833,0.0007047629,0.0022868142,0.0017408635,0.00087934814,0.0011861987],"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":[0.00031171003,0.0001270367,0.0021422913,0.0002027448,0.00021225722,0.00014866663,0.00014221549,0.07772007,0.05428872,0.010552237,0.0028768347,0.85127527],"study_design_scores_gemma":[0.000019257908,0.00019491783,0.002979957,0.000042486907,0.00011440371,0.00041193192,0.00005036013,0.8924416,0.0815265,0.017773258,0.0043639457,0.000081485865],"about_ca_topic_score_codex":0.0015653543,"about_ca_topic_score_gemma":0.0013422078,"teacher_disagreement_score":0.0022570514,"about_ca_system_score_codex":0.00071325107,"about_ca_system_score_gemma":0.0005790529,"threshold_uncertainty_score":0.009954333},"labels":[],"label_agreement":null},{"id":"W2157894025","doi":"10.1007/978-3-540-35488-8_23","title":"Information Gain, Correlation and Support Vector Machines","year":2008,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":100,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Imperial Bank of Commerce (Canada)","funders":"","keywords":"Feature selection; Benchmark (surveying); Correlation; Feature (linguistics); Information gain; Selection (genetic algorithm); Support vector machine; Artificial intelligence; Computer science; Set (abstract data type); Machine learning; Pattern recognition (psychology); Data mining; Mathematics; Geography","score_opus":0.03005900661737723,"score_gpt":0.2679775509179859,"score_spread":0.2379185443006087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157894025","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023080038,0.23978943,0.7037739,0.0040296256,0.0012150573,0.000042365486,0.00022593793,0.00028910444,0.02755455],"genre_scores_gemma":[0.5996122,0.14044912,0.2250432,0.0008609035,0.005411545,0.0002072515,0.00051851117,0.00015135366,0.02774589],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998701,0.0003841746,0.00008539457,0.00020860397,0.0005659417,0.00005491359],"domain_scores_gemma":[0.9944935,0.004597982,0.00022509997,0.0002873499,0.00035789656,0.000038133927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026486572,0.0010580911,0.0017217762,0.0018397324,0.0004123348,0.0025883056,0.0011315203,0.0018289691,0.002189366],"category_scores_gemma":[0.012558437,0.00043031687,0.00048310016,0.0047205375,0.0031179904,0.0054403306,0.000761385,0.002476206,0.00045394685],"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":[0.00010411239,0.000084598236,0.0010273069,0.0006893274,0.00010921774,0.00010560548,0.0001892264,0.0745066,0.0010517191,0.6449966,0.011865926,0.2652698],"study_design_scores_gemma":[0.000010278981,0.000047105703,0.0010260006,0.000103902574,0.000035756864,0.00016061253,0.000053901476,0.2069811,0.0007051205,0.7812149,0.009621145,0.00004016424],"about_ca_topic_score_codex":0.002293877,"about_ca_topic_score_gemma":0.0014642292,"teacher_disagreement_score":0.0026486572,"about_ca_system_score_codex":0.0013790821,"about_ca_system_score_gemma":0.0006829177,"threshold_uncertainty_score":0.014007568},"labels":[],"label_agreement":null},{"id":"W2158019970","doi":"","title":"K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms","year":2001,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":184,"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","funders":"","keywords":"Hyperplane; k-nearest neighbors algorithm; Best bin first; Support vector machine; Tangent; Intuition; Margin (machine learning); Regular polygon; Artificial intelligence; Computer science; Algorithm; Tangent space; Pattern recognition (psychology); Nearest neighbor search; Mathematics; Machine learning; Combinatorics","score_opus":0.012337462008172302,"score_gpt":0.228698276451739,"score_spread":0.21636081444356672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158019970","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026321178,0.0006257059,0.9949462,0.00010088403,0.000052216797,0.000027707441,0.000043959422,0.0003169673,0.0012541895],"genre_scores_gemma":[0.2202448,0.0011215225,0.77045596,0.00018974995,0.0003119751,0.00023122833,0.0005659012,0.00027260522,0.0066062924],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972435,0.00072647823,0.00017418094,0.0006016654,0.0011074386,0.00014669883],"domain_scores_gemma":[0.99692434,0.001057282,0.00033524638,0.0006641336,0.00089853164,0.000120428056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021024062,0.001046133,0.001871596,0.0017037805,0.00073319796,0.0019522569,0.0031511786,0.0015203202,0.0033981397],"category_scores_gemma":[0.010296695,0.0004995584,0.0009031292,0.0025627546,0.0014811751,0.0039844965,0.0024334316,0.001999318,0.0030100413],"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":[0.00018594405,0.000100586236,0.001151378,0.00024391752,0.000116986266,0.000111442285,0.00014547614,0.3508713,0.0027701927,0.11428225,0.007712943,0.52230763],"study_design_scores_gemma":[0.000011359641,0.000044083972,0.00029715325,0.000017087124,0.000010202848,0.00009031058,0.000025012443,0.93467236,0.0013578192,0.059651997,0.0037944496,0.000028200775],"about_ca_topic_score_codex":0.0023200137,"about_ca_topic_score_gemma":0.00197765,"teacher_disagreement_score":0.0033981397,"about_ca_system_score_codex":0.00096692337,"about_ca_system_score_gemma":0.0007548886,"threshold_uncertainty_score":0.011367917},"labels":[],"label_agreement":null},{"id":"W2158247472","doi":"10.1109/tnn.2002.1000134","title":"Face recognition with radial basis function (RBF) neural networks","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":661,"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":"Radial basis function; Overfitting; Artificial intelligence; Computer science; Pattern recognition (psychology); Linear discriminant analysis; Artificial neural network; Facial recognition system; Principal component analysis; Radial basis function network; Hierarchical RBF; Classifier (UML); Machine learning","score_opus":0.025577086661139226,"score_gpt":0.20844895168478164,"score_spread":0.18287186502364242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158247472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009692232,0.00060865,0.9877082,0.00008244381,0.000036754947,0.000023019596,0.000024806468,0.00078835245,0.0010354858],"genre_scores_gemma":[0.26635873,0.0009397913,0.7282901,0.00015184483,0.00008093079,0.00011612396,0.00014069947,0.000064826556,0.0038569034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994875,0.00014275983,0.000023718541,0.00008657616,0.00021748853,0.00004190656],"domain_scores_gemma":[0.9996551,0.00010334915,0.00004361001,0.00005324379,0.0001347386,0.0000098261635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083069113,0.0005012134,0.0008263616,0.0005019392,0.00026400443,0.0005410794,0.0007954354,0.0010675961,0.0011163454],"category_scores_gemma":[0.0016384154,0.000281031,0.00049195276,0.0006019613,0.00029485085,0.0010503898,0.00050199183,0.0006917403,0.0011462856],"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":[0.00026446747,0.00010858133,0.00081661146,0.00020375801,0.0000823044,0.0001576031,0.00007942608,0.10277275,0.07638782,0.0076911957,0.0036712992,0.80776423],"study_design_scores_gemma":[0.000013036225,0.000084709136,0.0005573887,0.000016191509,0.000019116487,0.00024535818,0.0000116560905,0.9717808,0.021059597,0.00289949,0.003286164,0.000026594735],"about_ca_topic_score_codex":0.0017810465,"about_ca_topic_score_gemma":0.0013682839,"teacher_disagreement_score":0.0017810465,"about_ca_system_score_codex":0.0003058314,"about_ca_system_score_gemma":0.00023258457,"threshold_uncertainty_score":0.0043931603},"labels":[],"label_agreement":null},{"id":"W2159088388","doi":"10.1109/crv.2013.17","title":"Online Facial Expression Recognition Based on Finite Beta-Liouville Mixture Models","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Facial expression; Computer science; BETA (programming language); Mixture model; Facial expression recognition; Expression (computer science); Artificial intelligence; Data modeling; Pattern recognition (psychology); Facial recognition system; Machine learning","score_opus":0.03488059973764252,"score_gpt":0.24041405540811764,"score_spread":0.20553345567047512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159088388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0107352305,0.000099058365,0.98856807,0.000040933344,0.000010150891,0.000010352333,0.000011704716,0.00019106825,0.00033351633],"genre_scores_gemma":[0.6280207,0.0004469269,0.36728153,0.00016573776,0.00005271675,0.0001536232,0.0002971088,0.00014869975,0.003432961],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993492,0.00024520492,0.000026699865,0.00015888657,0.00017127453,0.000048736714],"domain_scores_gemma":[0.9993437,0.00040747732,0.00004854695,0.00006482401,0.00010768743,0.000027663516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011410926,0.00056158233,0.0009396045,0.0005295029,0.00025534557,0.0007512766,0.0014375205,0.00076391955,0.0011946503],"category_scores_gemma":[0.0031099294,0.00051785755,0.000933164,0.00036789334,0.00060035696,0.0013577744,0.000998371,0.0011348131,0.00059153157],"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":[0.00032360334,0.00013128346,0.0027498496,0.00008150985,0.00014214404,0.0001404189,0.00021353188,0.56693757,0.052982654,0.018975066,0.0012062946,0.35611618],"study_design_scores_gemma":[0.0000017299632,0.000009458371,0.00012555695,0.0000013367826,0.0000031950983,0.000017394235,0.0000033375404,0.9968188,0.0013455813,0.0015412974,0.0001269625,0.0000053729545],"about_ca_topic_score_codex":0.0026494358,"about_ca_topic_score_gemma":0.0025206867,"teacher_disagreement_score":0.0026494358,"about_ca_system_score_codex":0.0005346763,"about_ca_system_score_gemma":0.00045546718,"threshold_uncertainty_score":0.006034732},"labels":[],"label_agreement":null},{"id":"W2159786793","doi":"10.1109/tpami.2011.104","title":"Probabilistic Models for Inference about Identity","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":221,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Artificial intelligence; Pattern recognition (psychology); Identity (music); Computer science; Face (sociological concept); Generative model; Facial recognition system; Feature (linguistics); Probabilistic logic; Feature vector; Noise (video); Inference; Subspace topology; Bayesian probability; Machine learning; Image (mathematics); Generative grammar","score_opus":0.05955989066811044,"score_gpt":0.29598860830975415,"score_spread":0.23642871764164372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159786793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046107657,0.0013618023,0.98771405,0.001372294,0.00011852973,0.000060489314,0.00075754884,0.000454419,0.003550047],"genre_scores_gemma":[0.51917595,0.006537586,0.44927385,0.0014037888,0.0016922618,0.0010800423,0.005249209,0.00046352742,0.015123851],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955123,0.0018345936,0.00024806088,0.0012602815,0.0008695644,0.00027523222],"domain_scores_gemma":[0.97968346,0.016517652,0.0009968716,0.001656977,0.0008971678,0.00024791743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007153785,0.0018879125,0.0026504074,0.0035819293,0.0015055155,0.0044671777,0.006092599,0.0037872833,0.009689616],"category_scores_gemma":[0.030878862,0.0021087534,0.0027989366,0.0032720633,0.00354633,0.008695776,0.0026104169,0.0060296943,0.0023283039],"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":[0.00013807994,0.00008079327,0.003177494,0.000237408,0.0002664223,0.00022740167,0.00027643802,0.30551592,0.00041094626,0.6260129,0.006450265,0.057205945],"study_design_scores_gemma":[0.000027306134,0.000012171631,0.0003841035,0.000038466675,0.000032988944,0.00007857918,0.000022866427,0.5004749,0.00012094574,0.49618742,0.0025900027,0.000030343866],"about_ca_topic_score_codex":0.011792982,"about_ca_topic_score_gemma":0.011050952,"teacher_disagreement_score":0.011792982,"about_ca_system_score_codex":0.0026904626,"about_ca_system_score_gemma":0.0014745158,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2160164899","doi":"10.1109/tip.2010.2093906","title":"Boosting Color Feature Selection for Color Face Recognition","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":69,"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":"Samsung; National Institute of Standards and Technology","keywords":"Artificial intelligence; Boosting (machine learning); Computer science; Pattern recognition (psychology); Color normalization; Facial recognition system; Computer vision; Color space; Color histogram; Feature extraction; Face (sociological concept); Color image; Image processing; Image (mathematics)","score_opus":0.01858296260573422,"score_gpt":0.2693187808625067,"score_spread":0.2507358182567725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160164899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011096931,0.0004152358,0.9864768,0.000054446,0.000055145294,0.000034086937,0.000047144793,0.0009351475,0.0008850194],"genre_scores_gemma":[0.40915188,0.0006478176,0.58649695,0.00024044316,0.00018038138,0.00015127994,0.00046691182,0.00021028904,0.0024541614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993905,0.00017008293,0.00001936106,0.000106924126,0.00024651122,0.00006660461],"domain_scores_gemma":[0.9994978,0.00012889048,0.000036223024,0.00006992559,0.00024142432,0.000025752342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009807929,0.00077852246,0.0009929697,0.0011025629,0.00036956093,0.0004658934,0.0008548582,0.00041582342,0.0015607205],"category_scores_gemma":[0.0016753896,0.00023581139,0.00080678787,0.0009428271,0.0003084992,0.00057575223,0.0005170282,0.00062925013,0.0008795447],"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":[0.00021759799,0.00011603986,0.001616913,0.00009547974,0.00009122974,0.000085652726,0.0000484487,0.06673005,0.06490464,0.005382116,0.0062990556,0.8544128],"study_design_scores_gemma":[0.000015670475,0.00008768844,0.0015136009,0.000008649553,0.000043138774,0.00020647453,0.000014603523,0.9569662,0.03249857,0.0037758627,0.004839889,0.000029624569],"about_ca_topic_score_codex":0.0019330233,"about_ca_topic_score_gemma":0.001500847,"teacher_disagreement_score":0.0019330233,"about_ca_system_score_codex":0.00037067712,"about_ca_system_score_gemma":0.000400915,"threshold_uncertainty_score":0.0052211285},"labels":[],"label_agreement":null},{"id":"W2160174167","doi":"10.1109/icip.2007.4378970","title":"A Robust Approach for Eye Localization Under Variable Illuminations","year":2007,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Face and Expression Recognition","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 British Columbia","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Initialization; Face (sociological concept); Pattern recognition (psychology); Facial recognition system; Feature (linguistics); Robustness (evolution); Orientation (vector space); Wavelet transform; Image resolution; Wavelet; Mathematics","score_opus":0.06253956214579244,"score_gpt":0.30750681510494166,"score_spread":0.24496725295914923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160174167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009085006,0.00019933381,0.9884675,0.000037892925,0.000031570544,0.00002820309,0.00005327339,0.0013483561,0.00074892567],"genre_scores_gemma":[0.20281413,0.00038124024,0.7910479,0.00009437385,0.00008136341,0.00013055511,0.0003210789,0.0002967793,0.0048326273],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938166,0.00007413235,0.000021769945,0.00021107128,0.00024698672,0.000064286716],"domain_scores_gemma":[0.99947923,0.00009182185,0.00007102551,0.0001309195,0.00020636208,0.000020657877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004382822,0.00074610254,0.001053796,0.0011676212,0.00041150313,0.00080191623,0.0011706187,0.0010777634,0.0018341909],"category_scores_gemma":[0.001320762,0.00046605105,0.0010419586,0.00057064276,0.00045656197,0.0008416297,0.0010418415,0.0007998732,0.0014674397],"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":[0.00025130308,0.00008314792,0.0008458269,0.00015235145,0.00014095905,0.00028757373,0.00012880645,0.04702174,0.38320282,0.005883362,0.0027405731,0.5592615],"study_design_scores_gemma":[0.000030376736,0.00021445175,0.0031756666,0.000020331057,0.00010319322,0.0007828871,0.000053143438,0.7960464,0.18725461,0.003641037,0.008585174,0.000092804],"about_ca_topic_score_codex":0.0015212131,"about_ca_topic_score_gemma":0.0015974081,"teacher_disagreement_score":0.0018341909,"about_ca_system_score_codex":0.00038858474,"about_ca_system_score_gemma":0.00051934,"threshold_uncertainty_score":0.0061359406},"labels":[],"label_agreement":null},{"id":"W2161366100","doi":"","title":"Manifold Parzen Windows","year":2002,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":100,"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","funders":"","keywords":"Kernel density estimation; Density estimation; Estimator; Kernel (algebra); Pattern recognition (psychology); Mathematics; Artificial intelligence; Parametric statistics; Variable kernel density estimation; Covariance; Classifier (UML); Manifold (fluid mechanics); Multivariate kernel density estimation; Kernel method; Computer science; Algorithm; Support vector machine; Statistics","score_opus":0.027354015565530243,"score_gpt":0.20591556715744494,"score_spread":0.1785615515919147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161366100","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015061305,0.0012248058,0.979042,0.0002487184,0.0000785664,0.00006564043,0.00022492645,0.000465394,0.0035887677],"genre_scores_gemma":[0.5139032,0.0030610096,0.4629365,0.00036734794,0.00055598,0.00039403766,0.0012249282,0.0005505455,0.017006516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9986749,0.00030207104,0.00006480441,0.00041670658,0.00040036748,0.000141163],"domain_scores_gemma":[0.99757594,0.0011745704,0.0002710735,0.00053501874,0.00031560383,0.00012790402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021236946,0.00083879585,0.0015938675,0.0018973643,0.0008389969,0.002647175,0.0017043096,0.0013627873,0.0077883955],"category_scores_gemma":[0.008641555,0.00068216864,0.0016116911,0.0014939765,0.0018149924,0.0048733964,0.001766669,0.0022439032,0.0017180779],"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":[0.00020973764,0.00006873783,0.0015253377,0.00027202265,0.00019795465,0.00015999669,0.00031342864,0.114690416,0.006123149,0.68328834,0.0070646857,0.1860862],"study_design_scores_gemma":[0.000015777428,0.00004640332,0.0014129274,0.00002802328,0.000033450928,0.00017164506,0.000044907236,0.752425,0.0020572988,0.23496625,0.008761887,0.00003646819],"about_ca_topic_score_codex":0.0022489415,"about_ca_topic_score_gemma":0.0021112033,"teacher_disagreement_score":0.0077883955,"about_ca_system_score_codex":0.0011330815,"about_ca_system_score_gemma":0.0007723104,"threshold_uncertainty_score":0.02605474},"labels":[],"label_agreement":null},{"id":"W2161596294","doi":"","title":"Boosting on Manifolds: Adaptive Regularization of Base Classifiers","year":2004,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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","funders":"","keywords":"Boosting (machine learning); AdaBoost; Machine learning; Artificial intelligence; Computer science; Classifier (UML); Manifold alignment; Nonlinear dimensionality reduction; Pattern recognition (psychology)","score_opus":0.028206116121128045,"score_gpt":0.22936666055137442,"score_spread":0.20116054443024636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161596294","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044233645,0.00038138859,0.9936999,0.0001120562,0.00006631321,0.000031021325,0.00001797124,0.00030835255,0.00095969264],"genre_scores_gemma":[0.36553794,0.0015734633,0.6261677,0.0005465144,0.0006363635,0.00031785484,0.00032066813,0.00041527272,0.0044841967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814844,0.0007966979,0.000059233374,0.00025322326,0.00059781276,0.0001446035],"domain_scores_gemma":[0.9975793,0.00092372746,0.00021560267,0.0005148408,0.00062469044,0.00014186221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035093299,0.0012778036,0.0024916704,0.0015698726,0.00068351184,0.0013941807,0.0019503996,0.0015657129,0.0017593716],"category_scores_gemma":[0.007051575,0.00079584133,0.0011126562,0.0014758622,0.0012582451,0.00227296,0.0019246807,0.0021600022,0.0015778892],"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":[0.00024171606,0.00015789103,0.0019524407,0.00025078942,0.00023486758,0.00012937462,0.00019443891,0.4864976,0.016189732,0.11665451,0.010444539,0.3670522],"study_design_scores_gemma":[0.000011929721,0.00007221592,0.00025612852,0.000013455531,0.000016145468,0.000036882593,0.00000905322,0.95545745,0.0019870056,0.037903972,0.0042221267,0.00001370329],"about_ca_topic_score_codex":0.00070674194,"about_ca_topic_score_gemma":0.00058362295,"teacher_disagreement_score":0.0035093299,"about_ca_system_score_codex":0.0005912534,"about_ca_system_score_gemma":0.00058003975,"threshold_uncertainty_score":0.018559337},"labels":[],"label_agreement":null},{"id":"W2161627023","doi":"","title":"Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":30,"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":"Computer science; Embedding; Classifier (UML); Artificial intelligence; k-nearest neighbors algorithm; Pattern recognition (psychology); Metric (unit); Unsupervised learning; Homogeneous; Machine learning; Mathematics; Combinatorics","score_opus":0.011282401167336096,"score_gpt":0.2158653646872579,"score_spread":0.2045829635199218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161627023","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020643051,0.00022583974,0.99674606,0.000088494206,0.000019833238,0.000023783457,0.000050067265,0.00030087982,0.00048077365],"genre_scores_gemma":[0.16894962,0.00054814696,0.8262928,0.00017336206,0.00016235335,0.00034930505,0.0005987705,0.000288988,0.0026366478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99725586,0.0011465319,0.00015514652,0.0006603261,0.000704757,0.000077365614],"domain_scores_gemma":[0.9963993,0.0017918011,0.00030386908,0.00071704277,0.00069231377,0.00009564254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023793448,0.0008255702,0.0013604421,0.0010867391,0.0007559641,0.0012080404,0.0019097688,0.0014083339,0.0018550536],"category_scores_gemma":[0.009690064,0.0005723122,0.0010728244,0.0015393016,0.0014738005,0.0019274465,0.0015878569,0.0015859386,0.0010571687],"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":[0.00012069786,0.00008548123,0.0021748221,0.00029673966,0.00017809073,0.00012728786,0.0001885158,0.5214631,0.005915085,0.1764424,0.0068893903,0.28611836],"study_design_scores_gemma":[0.0000033861465,0.000017871373,0.00019940843,0.0000116491365,0.0000059959066,0.000027899687,0.00000775391,0.96197647,0.00095269945,0.03512635,0.001660374,0.000010102382],"about_ca_topic_score_codex":0.0038313626,"about_ca_topic_score_gemma":0.0052287425,"teacher_disagreement_score":0.0038313626,"about_ca_system_score_codex":0.001415864,"about_ca_system_score_gemma":0.001343149,"threshold_uncertainty_score":0.012583315},"labels":[],"label_agreement":null},{"id":"W2161895106","doi":"10.1109/icme.2006.262861","title":"Selecting Kernel Eigenfaces for Face Recognition with One Training Sample Per Subject","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Facial recognition system; Kernel Fisher discriminant analysis; Kernel principal component analysis; Computer science; Kernel (algebra); Eigenface; Principal component analysis; Subspace topology; Feature (linguistics); Curse of dimensionality; Feature extraction; Sample (material); Feature vector; Face (sociological concept); Feature selection; Kernel method; Mathematics; Support vector machine","score_opus":0.05173743025900296,"score_gpt":0.2512673951621029,"score_spread":0.19952996490309993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161895106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08265705,0.0003871055,0.91486436,0.00009901308,0.00003673343,0.000044782773,0.0000643486,0.00086813304,0.0009784971],"genre_scores_gemma":[0.44067883,0.00042477887,0.5548826,0.000057858113,0.00004447878,0.00013828784,0.0003314333,0.00009891953,0.0033428848],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959046,0.0001567735,0.000018635194,0.00008482668,0.000107591535,0.00004177273],"domain_scores_gemma":[0.9996681,0.00012542281,0.000028612736,0.00006925515,0.000092731796,0.000015866264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007303781,0.00038312498,0.0006867468,0.000628654,0.00020813478,0.00029797407,0.00034377322,0.0003561112,0.0010702946],"category_scores_gemma":[0.001624462,0.00020706332,0.00041332835,0.00054079504,0.00025919036,0.00047699735,0.00034498653,0.0003289958,0.0010051769],"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":[0.00037225473,0.00017390639,0.0022434646,0.00007917681,0.00006594306,0.00012523009,0.00012931775,0.030959614,0.10548699,0.005098246,0.004217111,0.8510488],"study_design_scores_gemma":[0.000034070537,0.00014812968,0.0067225834,0.00001289524,0.000040611612,0.00047506034,0.00007486899,0.9290722,0.052456316,0.0061091613,0.0048147114,0.000039262402],"about_ca_topic_score_codex":0.000703261,"about_ca_topic_score_gemma":0.0011923738,"teacher_disagreement_score":0.0010702946,"about_ca_system_score_codex":0.00018987614,"about_ca_system_score_gemma":0.0003585547,"threshold_uncertainty_score":0.003862679},"labels":[],"label_agreement":null},{"id":"W2162496653","doi":"10.1109/pacrim.2007.4313229","title":"Face Recognition Using Multiscale Gabor Wavelet","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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 Calgary","funders":"","keywords":"Gabor wavelet; Eigenface; Artificial intelligence; Gabor filter; Facial recognition system; Pattern recognition (psychology); Gabor transform; Computer science; Face (sociological concept); Computer vision; Wavelet; Fourier transform; Wavelet transform; Discrete wavelet transform; Filter (signal processing); Time–frequency analysis; Feature extraction; Mathematics","score_opus":0.045164166536698164,"score_gpt":0.2853379105392214,"score_spread":0.24017374400252325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162496653","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08845992,0.0017558676,0.904956,0.00015001038,0.000109678374,0.00003236035,0.00008762078,0.00095839374,0.0034901318],"genre_scores_gemma":[0.5781291,0.002130098,0.41568843,0.00010682787,0.00012536976,0.000053978896,0.0002637948,0.0001045013,0.0033978967],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997806,0.000026437581,0.000010101035,0.00004511377,0.00011498044,0.000022770244],"domain_scores_gemma":[0.9998716,0.000037132068,0.000015912732,0.000026608,0.000040492112,0.000008078812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025592124,0.0002204825,0.00048798294,0.0006268334,0.000121779936,0.0003012418,0.00022980955,0.0002919064,0.0010143332],"category_scores_gemma":[0.0005538965,0.0001410896,0.00043091268,0.00045619172,0.00016546981,0.0005577339,0.0003070002,0.00021087019,0.00063069287],"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":[0.00010885862,0.000047274963,0.001988543,0.00014852613,0.000055161607,0.00018058787,0.000068311434,0.011640692,0.27388775,0.0058341445,0.0019896473,0.7040505],"study_design_scores_gemma":[0.000022736409,0.0002958764,0.019262016,0.00004858846,0.00012422202,0.0015723404,0.00009783346,0.76032954,0.18900706,0.0076849298,0.021483451,0.00007131995],"about_ca_topic_score_codex":0.0005011372,"about_ca_topic_score_gemma":0.00050721917,"teacher_disagreement_score":0.0010143332,"about_ca_system_score_codex":0.00012412648,"about_ca_system_score_gemma":0.00012625323,"threshold_uncertainty_score":0.0033932328},"labels":[],"label_agreement":null},{"id":"W2163097154","doi":"10.1109/have.2008.4685307","title":"Combining integral projection and Gabor transformation for automatic facial feature detection and extraction","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Artificial intelligence; Computer science; Feature extraction; Computer vision; Pattern recognition (psychology); Robustness (evolution); Transformation (genetics); Projection (relational algebra); Pixel; Facial recognition system; Local binary patterns; Feature vector; Facial expression; Face (sociological concept); Image (mathematics); Histogram; Algorithm","score_opus":0.020096009419276684,"score_gpt":0.2592010973426208,"score_spread":0.23910508792334412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163097154","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011657435,0.0001616779,0.9859658,0.000030877018,0.0000256828,0.000040407722,0.000023043,0.0014268945,0.00066813576],"genre_scores_gemma":[0.102642745,0.00035380444,0.89488214,0.000048608064,0.00005778035,0.00010225674,0.00019431919,0.00021020674,0.001508128],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989967,0.00014922413,0.000052243504,0.00018035814,0.00053663977,0.00008480976],"domain_scores_gemma":[0.99943,0.0002361657,0.00003953377,0.00010519241,0.00016868685,0.000020394542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000911345,0.00071042083,0.0011562223,0.0015157271,0.00024235436,0.0006996698,0.0006177539,0.0004399788,0.0017155029],"category_scores_gemma":[0.0014655497,0.00047075195,0.00058056106,0.0013049671,0.0004349146,0.0010983889,0.0007722007,0.00057755725,0.0014962659],"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":[0.00017793964,0.0000844967,0.0008014363,0.000119230266,0.000055440352,0.0001308592,0.00006113299,0.0038839397,0.25026375,0.001373716,0.0012773542,0.74177074],"study_design_scores_gemma":[0.000088100984,0.000570832,0.011364416,0.00004009227,0.00017188625,0.0028361706,0.00011307633,0.4540332,0.50882274,0.0058525293,0.015953004,0.00015395107],"about_ca_topic_score_codex":0.0007606369,"about_ca_topic_score_gemma":0.0010197156,"teacher_disagreement_score":0.0017155029,"about_ca_system_score_codex":0.00016805304,"about_ca_system_score_gemma":0.0004387968,"threshold_uncertainty_score":0.0057389736},"labels":[],"label_agreement":null},{"id":"W2163182675","doi":"10.1109/tpami.2013.187","title":"A Fair Comparison Should Be Based on the Same Protocol--Comments on \"Trainable Convolution Filters and Their Application to Face Recognition\"","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Wenzhou University; University of Northern British Columbia","keywords":"Computer science; Facial recognition system; Artificial intelligence; Kernel (algebra); Classifier (UML); Pattern recognition (psychology); Face (sociological concept); Protocol (science); Convolution (computer science); Three-dimensional face recognition; Face detection; Computer vision; Mathematics; Artificial neural network","score_opus":0.04953457564605248,"score_gpt":0.2984483917661311,"score_spread":0.2489138161200786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163182675","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004804964,0.0026603248,0.103629224,0.7234887,0.14099589,0.00091053755,0.001151158,0.0020909763,0.02026829],"genre_scores_gemma":[0.068692714,0.0030535401,0.131515,0.6902614,0.043174848,0.0038540885,0.0010883528,0.0016761326,0.056683965],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.96724963,0.011803683,0.0037745691,0.0021246367,0.013438396,0.0016091288],"domain_scores_gemma":[0.86453676,0.055903997,0.004529902,0.0145711545,0.058590297,0.0018678496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.035773136,0.0013802055,0.0018661043,0.0016241156,0.0037866842,0.0047635324,0.0072390186,0.016167596,0.015325549],"category_scores_gemma":[0.1965264,0.00086374325,0.002481626,0.0013221867,0.0055367975,0.010684258,0.003813818,0.015511481,0.012926015],"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":[0.0006714024,0.00024003371,0.0012115616,0.00069451623,0.00016567198,0.00075777614,0.00080967514,0.0018483537,0.00554577,0.11715799,0.8194449,0.05145231],"study_design_scores_gemma":[0.00014271168,0.0007296674,0.0043658298,0.0010760445,0.00013877913,0.0009212985,0.0012573937,0.0092005795,0.022571387,0.16540694,0.7936379,0.00055149355],"about_ca_topic_score_codex":0.0040294225,"about_ca_topic_score_gemma":0.0028555442,"teacher_disagreement_score":0.035773136,"about_ca_system_score_codex":0.0025622963,"about_ca_system_score_gemma":0.0027367899,"threshold_uncertainty_score":0.18918872},"labels":[],"label_agreement":null},{"id":"W2163922914","doi":"10.1109/tpami.2013.50","title":"Representation Learning: A Review and New Perspectives","year":2013,"lang":"en","type":"review","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13002,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Feature learning; Computer science; Machine learning; Representation (politics); Inference; Nonlinear dimensionality reduction; Unsupervised learning; Deep learning; Prior probability; External Data Representation; Probabilistic logic; Feature (linguistics); Domain knowledge; Active learning (machine learning); Semi-supervised learning; Bayesian probability; Dimensionality reduction","score_opus":0.06847167730100265,"score_gpt":0.3494132673853828,"score_spread":0.28094159008438013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163922914","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000067368994,0.9967295,0.00069293566,0.00095101364,0.000266763,0.0000032636983,0.000016315988,0.00000897669,0.0012639356],"genre_scores_gemma":[0.0007938418,0.99714714,0.00054022827,0.00035520818,0.0007208217,0.0000061897226,0.000032216965,0.000004151709,0.00040028358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99954945,0.00010028356,0.000060054022,0.00009186578,0.00016347339,0.000034893335],"domain_scores_gemma":[0.9978435,0.0013582581,0.00013424557,0.0000748744,0.00049220363,0.000096862714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001428543,0.0010253481,0.0016333681,0.004018942,0.00048632722,0.0024931617,0.0016890214,0.0018808014,0.0057194848],"category_scores_gemma":[0.002940873,0.0005683017,0.0006376241,0.0067651034,0.0015016447,0.004687501,0.0011964206,0.0029245636,0.003371411],"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":[0.000052370167,0.00009529666,0.00028651624,0.012211101,0.0000921124,0.00015864558,0.00014655758,0.0010204007,0.00045187454,0.037106533,0.07957305,0.8688056],"study_design_scores_gemma":[0.000009100431,0.00004935389,0.00045174372,0.0036712114,0.00004673909,0.0007002932,0.00009836286,0.00032310217,0.00014985679,0.015614534,0.9788563,0.000029340294],"about_ca_topic_score_codex":0.0023093082,"about_ca_topic_score_gemma":0.0025790622,"teacher_disagreement_score":0.0057194848,"about_ca_system_score_codex":0.0013478564,"about_ca_system_score_gemma":0.0018987601,"threshold_uncertainty_score":0.019133568},"labels":[],"label_agreement":null},{"id":"W2164804261","doi":"10.1109/icmla.2008.32","title":"Regularized Minimum Volume Ellipsoid Metric for Query-Based Learning","year":2008,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Metric (unit); Manifold (fluid mechanics); Benchmark (surveying); Context (archaeology); Computer science; Ellipsoid; Point (geometry); Metric space; Volume (thermodynamics); Artificial intelligence; Mathematics; Mathematical analysis; Geometry; Physics","score_opus":0.02359283868455642,"score_gpt":0.23525141968442817,"score_spread":0.21165858099987175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164804261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006973156,0.00038507054,0.9914484,0.00012521945,0.000030869178,0.00004940795,0.00012265178,0.00033113293,0.00053409743],"genre_scores_gemma":[0.36403117,0.0009158831,0.63046294,0.00020503855,0.00017417136,0.00044114378,0.0016359215,0.0002849503,0.0018488093],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99697185,0.0010751052,0.00017495228,0.0005243913,0.0011412577,0.00011237232],"domain_scores_gemma":[0.99623376,0.0013573731,0.0003399081,0.0009947615,0.0009160031,0.00015821839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00262171,0.0009918965,0.0020597538,0.0014497201,0.00053492276,0.0015861968,0.0028866425,0.0013924171,0.0017828429],"category_scores_gemma":[0.014887651,0.0003770278,0.00081585563,0.002171528,0.0012496635,0.003642692,0.002213221,0.0019678578,0.0010420579],"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":[0.00036443234,0.000157761,0.0015356314,0.00031955485,0.00011111968,0.00008982969,0.00020576658,0.55670464,0.01244569,0.07521715,0.010036238,0.34281215],"study_design_scores_gemma":[0.0000073658825,0.00007130212,0.00020193012,0.0000069512084,0.00000438541,0.00005299982,0.000015241475,0.97468877,0.0021406475,0.0212192,0.0015737803,0.00001748779],"about_ca_topic_score_codex":0.0030235886,"about_ca_topic_score_gemma":0.002130261,"teacher_disagreement_score":0.0030235886,"about_ca_system_score_codex":0.0011999672,"about_ca_system_score_gemma":0.0010170465,"threshold_uncertainty_score":0.013865113},"labels":[],"label_agreement":null},{"id":"W2165273325","doi":"10.1109/icassp.2011.5946963","title":"Kernel cross-modal factor analysis for multimodal information fusion","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"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","funders":"","keywords":"Concatenation (mathematics); Kernel (algebra); Computer science; Canonical correlation; Modal; Artificial intelligence; Kernel principal component analysis; Pattern recognition (psychology); Kernel method; Domain (mathematical analysis); Kernel embedding of distributions; Factor (programming language); Fusion; Algorithm; Mathematics; Support vector machine; Discrete mathematics; Arithmetic","score_opus":0.03728439493003565,"score_gpt":0.276246892539148,"score_spread":0.23896249760911237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165273325","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002150038,0.0002919444,0.9969675,0.000042689055,0.00001745914,0.000013774381,0.000020651583,0.00015511646,0.00034083973],"genre_scores_gemma":[0.29824892,0.0011927652,0.6980404,0.00011370992,0.00013382366,0.00019774912,0.0003272924,0.00015564256,0.0015897455],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988329,0.00046809926,0.000067290675,0.00022283793,0.0003437624,0.00006515851],"domain_scores_gemma":[0.9987036,0.00060319365,0.00012665988,0.00019654658,0.00033328455,0.000036762394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00204836,0.0012272923,0.0009687869,0.0014905971,0.00048071172,0.0010582624,0.00081998605,0.0008164809,0.0021786802],"category_scores_gemma":[0.0049480093,0.00029179873,0.001403117,0.0015921072,0.000836537,0.001709063,0.001359285,0.0013077256,0.00076468685],"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":[0.00030698505,0.000115955365,0.0009589134,0.00042757927,0.00037978645,0.00019378538,0.00031591379,0.2270316,0.032126658,0.08367147,0.003760022,0.65071136],"study_design_scores_gemma":[0.000011314619,0.0000816144,0.0006471775,0.000023600589,0.00006014398,0.000104966406,0.0000439566,0.9578088,0.007907825,0.029691111,0.0035636763,0.00005591256],"about_ca_topic_score_codex":0.0014835896,"about_ca_topic_score_gemma":0.0011058669,"teacher_disagreement_score":0.0021786802,"about_ca_system_score_codex":0.00066375616,"about_ca_system_score_gemma":0.0006681997,"threshold_uncertainty_score":0.010832906},"labels":[],"label_agreement":null},{"id":"W2165408289","doi":"10.1109/icassp.2005.1416341","title":"Two-Stage Classification Using Selective Attention for Fast Face Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Computer science; Artificial intelligence; Face detection; Classifier (UML); Pattern recognition (psychology); Linear discriminant analysis; Facial recognition system; Support vector machine; Discriminant; Classification scheme; Computational complexity theory; Nonlinear system; Face (sociological concept); Test set; Computer vision; Machine learning; Algorithm","score_opus":0.036300016767106785,"score_gpt":0.2860772817874295,"score_spread":0.24977726502032274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165408289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061433416,0.00042342514,0.93251145,0.000101608755,0.00013139384,0.00012470623,0.00007051004,0.0026999195,0.0025036046],"genre_scores_gemma":[0.40503454,0.00021094999,0.5889955,0.0002036604,0.000086068205,0.00022488234,0.00025092918,0.00009934999,0.004894037],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997588,0.000031505755,0.000009228332,0.000059383794,0.00008683899,0.000054266427],"domain_scores_gemma":[0.9996526,0.00014539476,0.000021952854,0.00004703139,0.00010615398,0.000026882195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040734862,0.00048883166,0.00050099444,0.0004971031,0.0003644844,0.00049317913,0.0010623428,0.0006420191,0.0030033647],"category_scores_gemma":[0.00074960926,0.00026480303,0.00032849342,0.00040641517,0.00021119678,0.00069764635,0.00066158833,0.00052922394,0.0013248767],"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":[0.00037138382,0.0002043724,0.00096445874,0.000106346895,0.000039678154,0.00009843588,0.000053642463,0.005233672,0.30794784,0.0020766791,0.0028398754,0.68006355],"study_design_scores_gemma":[0.00007653622,0.00063392514,0.0049377885,0.000016085913,0.00007269401,0.00053888053,0.000024971489,0.805856,0.17965427,0.0026383903,0.005497994,0.000052423988],"about_ca_topic_score_codex":0.0019228706,"about_ca_topic_score_gemma":0.0032744359,"teacher_disagreement_score":0.0030033647,"about_ca_system_score_codex":0.00034053356,"about_ca_system_score_gemma":0.000465343,"threshold_uncertainty_score":0.010047257},"labels":[],"label_agreement":null},{"id":"W2165570403","doi":"10.1007/11550518_40","title":"Separable Linear Discriminant Classification","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"York University","funders":"","keywords":"Linear discriminant analysis; Separable space; Computer science; Discriminant; Artificial intelligence; Pattern recognition (psychology); Object (grammar); Optimal discriminant analysis; Binary classification; Sample (material); Binary number; Machine learning; Support vector machine; Mathematics","score_opus":0.032904859021884635,"score_gpt":0.2738893529282073,"score_spread":0.24098449390632268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165570403","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010873287,0.003939394,0.93630725,0.0005611406,0.00070524815,0.000093482864,0.00078097614,0.0051280744,0.04161112],"genre_scores_gemma":[0.14612453,0.0042515662,0.64928085,0.000436853,0.0007446518,0.0002092808,0.0075918417,0.0011685553,0.1901919],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99960846,0.000052903408,0.000019075056,0.00011348703,0.00016344548,0.00004257761],"domain_scores_gemma":[0.9996171,0.00006719845,0.00001826059,0.00012510749,0.00014910365,0.000023248205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056488416,0.0009143536,0.000978647,0.0012914626,0.0005358347,0.0011849429,0.00084003684,0.00067188876,0.020166159],"category_scores_gemma":[0.00097460585,0.00039993704,0.0006267949,0.0013722111,0.00041358965,0.0011787539,0.0011248629,0.0012919725,0.021492224],"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":[0.00007180713,0.000063872554,0.00020457002,0.00008797776,0.000022202063,0.00002757332,0.00001956461,0.004421927,0.011089536,0.010784515,0.031287648,0.9419188],"study_design_scores_gemma":[0.00003461568,0.00017926288,0.0029906768,0.00012016504,0.0000992678,0.0008113957,0.000096219344,0.5861792,0.071589544,0.07399604,0.2638167,0.00008681484],"about_ca_topic_score_codex":0.0008284122,"about_ca_topic_score_gemma":0.0016527884,"teacher_disagreement_score":0.020166159,"about_ca_system_score_codex":0.00035291622,"about_ca_system_score_gemma":0.0005296019,"threshold_uncertainty_score":0.067462504},"labels":[],"label_agreement":null},{"id":"W2165796360","doi":"","title":"Distance metric learning vs. Fisher discriminant analysis","year":2008,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":49,"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":"Metric (unit); Linear discriminant analysis; Discriminant; Computer science; Semidefinite programming; Artificial intelligence; Class (philosophy); Kernel Fisher discriminant analysis; Iterative method; Machine learning; Optimal discriminant analysis; Mathematical optimization; Algorithm; Mathematics; Pattern recognition (psychology)","score_opus":0.13735980511357815,"score_gpt":0.3309964840608148,"score_spread":0.19363667894723663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165796360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005193089,0.0034193308,0.98592037,0.0008671437,0.00013547318,0.000028834718,0.000053355427,0.00026752948,0.0041148625],"genre_scores_gemma":[0.31284168,0.0058017173,0.6712173,0.0006229928,0.00090233167,0.0002180251,0.00038725894,0.0004046761,0.0076039326],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996958,0.0014810375,0.00014650502,0.00056258624,0.0007278983,0.00012395844],"domain_scores_gemma":[0.9956749,0.002664893,0.0002944985,0.000557862,0.00064184295,0.00016595567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031266925,0.0012092815,0.0014358391,0.0015375993,0.00046995076,0.001575761,0.0016279496,0.0016009094,0.0027969847],"category_scores_gemma":[0.01155181,0.00034480472,0.00054591184,0.0021120734,0.0018544648,0.0037613446,0.0020493036,0.0024184885,0.0012224754],"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":[0.00020377092,0.000088870016,0.0008327196,0.00045319906,0.000076429475,0.000063824875,0.00011748825,0.07249051,0.002337742,0.45753583,0.007998928,0.4578007],"study_design_scores_gemma":[0.000037543243,0.000102308346,0.00049359485,0.000055045293,0.000023112194,0.00017596409,0.000057473135,0.5568587,0.0025336149,0.42800587,0.01160968,0.000047042875],"about_ca_topic_score_codex":0.001141701,"about_ca_topic_score_gemma":0.0008237031,"teacher_disagreement_score":0.0031266925,"about_ca_system_score_codex":0.0009464068,"about_ca_system_score_gemma":0.000798373,"threshold_uncertainty_score":0.016535759},"labels":[],"label_agreement":null},{"id":"W2166016566","doi":"10.1109/crv.2006.34","title":"Expression-Invariant Face Recognition with Expression Classification","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Facial recognition system; Classifier (UML); Pattern recognition (psychology); Computer science; Invariant (physics); Three-dimensional face recognition; Computer vision; Face (sociological concept); Facial expression; Expression (computer science); Face hallucination; Facial expression recognition; Face detection; Mathematics","score_opus":0.025074085508575202,"score_gpt":0.2260188255239831,"score_spread":0.2009447400154079,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166016566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039150454,0.0002772254,0.9536498,0.00014601342,0.000118195094,0.00011551207,0.00010565729,0.0014334406,0.005003779],"genre_scores_gemma":[0.5488234,0.00040599453,0.44036666,0.00013514972,0.000106864514,0.00023272652,0.00051747146,0.0001877023,0.00922421],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921787,0.00013299233,0.000025284475,0.0001497469,0.00037938758,0.00009476404],"domain_scores_gemma":[0.9996458,0.000054891785,0.000027955217,0.00010352101,0.00015195445,0.000015767519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064142834,0.00046756177,0.0007277903,0.0005021998,0.00025231516,0.00052164873,0.0009666156,0.00034149326,0.0027993906],"category_scores_gemma":[0.0015451566,0.0001611773,0.000514895,0.0007192951,0.000398207,0.00083409145,0.0005521075,0.0005609556,0.0014803875],"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":[0.00019171461,0.00015681723,0.0015651402,0.00008043366,0.00003732612,0.00011667913,0.000049957398,0.02990021,0.105257496,0.0065512294,0.0034117408,0.8526812],"study_design_scores_gemma":[0.000013179751,0.00011216493,0.0035870518,0.0000081738535,0.000030277499,0.00030857374,0.000025581528,0.91263413,0.07510454,0.004074468,0.004075593,0.00002626193],"about_ca_topic_score_codex":0.0012060588,"about_ca_topic_score_gemma":0.00079120084,"teacher_disagreement_score":0.0027993906,"about_ca_system_score_codex":0.00038270414,"about_ca_system_score_gemma":0.00028858415,"threshold_uncertainty_score":0.009364903},"labels":[],"label_agreement":null},{"id":"W2166857861","doi":"10.1109/cib.2009.4925689","title":"A facial presence monitoring system for information security","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina; Defence Research and Development Canada","funders":"Defence Research and Development Canada","keywords":"Computer science; Eigenface; Biometrics; Facial recognition system; Session (web analytics); Human–computer interaction; Graphical user interface; Face detection; User interface; Identity (music); Face (sociological concept); Information security; Computer security; Feature extraction; Artificial intelligence; Operating system; World Wide Web","score_opus":0.014526832703074753,"score_gpt":0.25166244704915136,"score_spread":0.2371356143460766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166857861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10837617,0.0007028065,0.8412982,0.00067832426,0.00043363406,0.0010180426,0.00084939215,0.023742137,0.022901323],"genre_scores_gemma":[0.553708,0.00045226447,0.41939524,0.00054163905,0.0001313073,0.0007675924,0.0008627529,0.00032123065,0.023820015],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995396,0.00007235914,0.000018401435,0.00011197171,0.00022880122,0.00002894519],"domain_scores_gemma":[0.9995726,0.000080099126,0.000036903708,0.00007612806,0.00018976384,0.00004449506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005793339,0.00042094904,0.00050361943,0.00046573594,0.0004566653,0.00048073538,0.00080056407,0.0006778451,0.013780122],"category_scores_gemma":[0.0013157618,0.00020211423,0.0002336501,0.00025380697,0.0001903841,0.00075059925,0.0005382031,0.00052257336,0.0032129865],"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":[0.0011738293,0.0003731534,0.004123105,0.00028875453,0.00004826461,0.00037975184,0.00021946873,0.0020892129,0.48034748,0.0034709338,0.018347492,0.48913857],"study_design_scores_gemma":[0.0003156185,0.0026398327,0.03697223,0.00017505857,0.00031082696,0.004739471,0.000104157385,0.36645752,0.4789959,0.0026539445,0.10638757,0.00024795847],"about_ca_topic_score_codex":0.0010133968,"about_ca_topic_score_gemma":0.0013071276,"teacher_disagreement_score":0.013780122,"about_ca_system_score_codex":0.00045224733,"about_ca_system_score_gemma":0.00049741165,"threshold_uncertainty_score":0.046099067},"labels":[],"label_agreement":null},{"id":"W2166962601","doi":"10.1109/imtc.2009.5168537","title":"Facial expression recognition by applying multi-step integral projection and SVMs","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Artificial intelligence; Computer science; Support vector machine; Pattern recognition (psychology); Feature extraction; Robustness (evolution); Transformation (genetics); Facial recognition system; Projection (relational algebra); Computer vision; Facial expression; Face (sociological concept); Pixel; Algorithm","score_opus":0.03121224882530641,"score_gpt":0.26718688111524597,"score_spread":0.23597463228993956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166962601","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074234545,0.00023045666,0.92221856,0.00012153841,0.000048822047,0.000060709684,0.000042720018,0.0018335562,0.0012090328],"genre_scores_gemma":[0.55098283,0.00024224773,0.4464407,0.00005569663,0.000043297765,0.000089135814,0.00016842312,0.000103048114,0.0018746165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992914,0.00017881679,0.00004970893,0.00011044912,0.00031645727,0.000053081774],"domain_scores_gemma":[0.9994691,0.00018893922,0.000044042463,0.000069602705,0.00020488143,0.00002336478],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008613524,0.0004291584,0.0007965948,0.000577254,0.00021341413,0.000562388,0.00052386004,0.00034385078,0.0010173891],"category_scores_gemma":[0.0018391405,0.0002497463,0.00049402204,0.0005302517,0.00026750806,0.0007944461,0.0005447608,0.0007052164,0.000606486],"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":[0.00025699806,0.00013252399,0.0022176253,0.00008060616,0.00007845103,0.00008622299,0.00008798499,0.027317556,0.071662545,0.0017397163,0.0011994372,0.8951404],"study_design_scores_gemma":[0.0000136217095,0.00009252141,0.0035128507,0.000006971209,0.000020221516,0.0001739634,0.000034411183,0.96026486,0.033073172,0.001695765,0.0010924916,0.000019150022],"about_ca_topic_score_codex":0.00097618153,"about_ca_topic_score_gemma":0.0008106719,"teacher_disagreement_score":0.0010173891,"about_ca_system_score_codex":0.0002033655,"about_ca_system_score_gemma":0.00028607514,"threshold_uncertainty_score":0.004555285},"labels":[],"label_agreement":null},{"id":"W2167080239","doi":"10.1109/have.2009.5356139","title":"Part-based PCA for facial feature extraction and classification","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Feature extraction; Principal component analysis; Computer science; Projection (relational algebra); Facial recognition system; Face (sociological concept); Feature (linguistics); Similarity (geometry); Computer vision; Image (mathematics); Algorithm","score_opus":0.03734792689751818,"score_gpt":0.2917562934482483,"score_spread":0.2544083665507301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167080239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034653037,0.00040689023,0.99416417,0.000058385223,0.00004561009,0.00005347785,0.00011936864,0.0010813775,0.00060547254],"genre_scores_gemma":[0.10339551,0.0013906063,0.8893896,0.00009143938,0.00012520936,0.0003332068,0.0010565997,0.00029384342,0.0039239586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884146,0.00028574016,0.000051919324,0.00020090188,0.00055363605,0.00006633177],"domain_scores_gemma":[0.9991535,0.0002754933,0.000055825716,0.00021147719,0.00027905114,0.000024606585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085995987,0.001298857,0.0013623579,0.001949339,0.00055892963,0.00077311666,0.00094237435,0.0006429425,0.0044783223],"category_scores_gemma":[0.002646207,0.0005207349,0.0012754459,0.0025715015,0.0004979178,0.0010446243,0.0005600159,0.0010370214,0.0029649537],"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":[0.00015901367,0.000058624744,0.00051973556,0.00014630149,0.00007938723,0.00008766147,0.000063322426,0.031242136,0.050734885,0.004559641,0.0063614626,0.9059878],"study_design_scores_gemma":[0.00002318468,0.00015348323,0.004563176,0.000026114689,0.00007758275,0.0005621695,0.000034869714,0.9230718,0.045328464,0.007905742,0.018151931,0.000101422906],"about_ca_topic_score_codex":0.0029743847,"about_ca_topic_score_gemma":0.002108653,"teacher_disagreement_score":0.0044783223,"about_ca_system_score_codex":0.00038529534,"about_ca_system_score_gemma":0.0006781187,"threshold_uncertainty_score":0.014981508},"labels":[],"label_agreement":null},{"id":"W2168056397","doi":"10.1109/icip.2002.1039899","title":"Boosting face recognition on a large-scale database","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"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":"Computer science; Cluster analysis; Partition (number theory); Boosting (machine learning); Facial recognition system; Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Data mining; Face (sociological concept); Computational complexity theory; Similarity (geometry); Database; Algorithm; Mathematics","score_opus":0.05494082662572857,"score_gpt":0.3066130968816479,"score_spread":0.2516722702559193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168056397","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14922272,0.000799137,0.845252,0.000274475,0.000097705924,0.00013132807,0.00013188935,0.0014753415,0.002615377],"genre_scores_gemma":[0.6126522,0.00059159833,0.38269585,0.0002129638,0.00017514541,0.00017622928,0.0005919355,0.00007661175,0.0028274776],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881065,0.0003116228,0.000050506056,0.00025254788,0.00047930647,0.000095393814],"domain_scores_gemma":[0.99834836,0.0007743276,0.00009871491,0.00029337322,0.00043723904,0.00004805496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002231544,0.000623719,0.0014579721,0.0009921778,0.00040481187,0.0007934939,0.0010331741,0.0008014731,0.0014374885],"category_scores_gemma":[0.0041764174,0.00032076612,0.00057907857,0.0008564395,0.00032633403,0.0014669913,0.000975101,0.00070230727,0.0013336049],"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":[0.00035755103,0.00032803143,0.002522641,0.0001243762,0.000110779365,0.00010185725,0.00006403535,0.13036111,0.06227196,0.0029760564,0.0045050243,0.79627657],"study_design_scores_gemma":[0.000016604003,0.00009730083,0.0022373472,0.00000461384,0.000025244426,0.00009374845,0.000024787432,0.9829554,0.011539564,0.0019667542,0.0010292802,0.00000934369],"about_ca_topic_score_codex":0.0015329359,"about_ca_topic_score_gemma":0.0016829498,"teacher_disagreement_score":0.002231544,"about_ca_system_score_codex":0.0003657861,"about_ca_system_score_gemma":0.00038401742,"threshold_uncertainty_score":0.01180166},"labels":[],"label_agreement":null},{"id":"W2169388877","doi":"10.5539/mas.v8n1p11","title":"A Wrapper-Based Combined Recursive Orthogonal Array and Support Vector Machine for Classification and Feature Selection","year":2013,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Face and Expression Recognition","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":"Support vector machine; Feature selection; Computer science; Pattern recognition (psychology); Classifier (UML); Artificial intelligence; Feature (linguistics); Data mining; Filter (signal processing); Selection (genetic algorithm); Feature vector; Machine learning","score_opus":0.014978857684791938,"score_gpt":0.23516052004158358,"score_spread":0.22018166235679165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169388877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009112206,0.00022776898,0.9888576,0.000032041924,0.000035699737,0.00003736843,0.000033332286,0.0012394147,0.00042454773],"genre_scores_gemma":[0.25974694,0.00030502616,0.7360806,0.00015130723,0.00012551385,0.00030312905,0.0004793877,0.00015126915,0.0026568484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828655,0.00040858096,0.00013723492,0.00037881266,0.000662703,0.00012607877],"domain_scores_gemma":[0.99910814,0.00028795018,0.00008138686,0.00011662022,0.00037313235,0.00003275516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011921619,0.0010552563,0.0016607273,0.0015330774,0.00043533376,0.00086769945,0.0011263073,0.00079003576,0.0015793105],"category_scores_gemma":[0.0028185009,0.00046058637,0.0012882819,0.0017271173,0.00031793254,0.0012204115,0.0007627517,0.0007066493,0.0012026443],"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":[0.00036647075,0.00018218547,0.002061177,0.00015681378,0.00024350527,0.00018727797,0.00008973269,0.089005366,0.02874355,0.0033680457,0.0029337478,0.8726621],"study_design_scores_gemma":[0.00002324725,0.00017786089,0.00093234755,0.000010658705,0.000057301597,0.00014919636,0.000016883856,0.98207927,0.012450985,0.0014982268,0.0025754715,0.000028507393],"about_ca_topic_score_codex":0.0011579897,"about_ca_topic_score_gemma":0.0009385443,"teacher_disagreement_score":0.0016607273,"about_ca_system_score_codex":0.00028008918,"about_ca_system_score_gemma":0.00049903867,"threshold_uncertainty_score":0.00630486},"labels":[],"label_agreement":null},{"id":"W2170018389","doi":"10.1109/cvpr.2004.389","title":"Learning with the Optimized Data-Dependent Kernel","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Concordia University","funders":"","keywords":"Kernel (algebra); Computer science; Kernel method; Kernel embedding of distributions; Tree kernel; Euclidean space; Feature (linguistics); Artificial intelligence; Measure (data warehouse); Variable kernel density estimation; Feature vector; Euclidean distance; Graph kernel; Pattern recognition (psychology); Space (punctuation); Algorithm; Data mining; Mathematics; Support vector machine","score_opus":0.02413321905973083,"score_gpt":0.2504121136505171,"score_spread":0.22627889459078626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170018389","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006268501,0.00004544101,0.99324846,0.000037576407,0.000007673204,0.000008844396,0.000014532576,0.00020383533,0.00016520474],"genre_scores_gemma":[0.31158313,0.0001755161,0.68492705,0.00010560066,0.000054225497,0.00013702699,0.0004375734,0.00030493684,0.0022749526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987979,0.00033067545,0.000100392965,0.00028840126,0.0003801903,0.00010243173],"domain_scores_gemma":[0.9983139,0.0005523643,0.00016518947,0.00042581453,0.0004872829,0.0000553508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016600799,0.00073565275,0.0012782515,0.00061116787,0.0003472304,0.0010720008,0.001434965,0.0010741327,0.0007813903],"category_scores_gemma":[0.007012224,0.0005707442,0.0009293128,0.0008466226,0.0008926955,0.0022952121,0.0016323079,0.0012481712,0.0006566572],"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":[0.00024298968,0.00013455053,0.0014132,0.00013811409,0.00018588579,0.00007131873,0.00012472166,0.7171295,0.01480961,0.035151057,0.0023829509,0.22821613],"study_design_scores_gemma":[0.000008103423,0.00001930137,0.000133261,0.0000025524046,0.000007597151,0.000020277123,0.000003912562,0.99133897,0.002680481,0.0053423103,0.00043396116,0.000009288813],"about_ca_topic_score_codex":0.0017008545,"about_ca_topic_score_gemma":0.0011260798,"teacher_disagreement_score":0.0017008545,"about_ca_system_score_codex":0.00077852356,"about_ca_system_score_gemma":0.0011177033,"threshold_uncertainty_score":0.008779466},"labels":[],"label_agreement":null},{"id":"W2170080599","doi":"10.1109/icpr.2008.4760972","title":"Face recognition using curvelet based PCA","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":56,"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 Windsor","funders":"","keywords":"Curvelet; Artificial intelligence; Pattern recognition (psychology); Computer science; Wavelet; Wavelet transform; Feature extraction; Principal component analysis; Feature vector; Facial recognition system; Feature (linguistics); Face (sociological concept); Set (abstract data type); Computer vision","score_opus":0.15214857972335602,"score_gpt":0.3101005234448106,"score_spread":0.15795194372145457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170080599","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034256056,0.00060708204,0.9605357,0.00012938022,0.00008909378,0.000049951257,0.00013784542,0.0011807985,0.003014122],"genre_scores_gemma":[0.3843404,0.0016169876,0.60688275,0.00013767705,0.00017877838,0.000112544614,0.00068566814,0.00015121025,0.005893864],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995497,0.000058672344,0.000015586469,0.00008169514,0.0002592743,0.00003501348],"domain_scores_gemma":[0.9995871,0.00013601435,0.000035208803,0.00005826853,0.00016855271,0.000014836719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038412123,0.000378434,0.00068389624,0.0013096243,0.00023030683,0.0005685195,0.0004193112,0.00048225222,0.0016465262],"category_scores_gemma":[0.00118809,0.00017900135,0.0004915894,0.0012194292,0.00029404712,0.0008839766,0.00037870306,0.0005334988,0.0011837358],"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":[0.00020212935,0.000078123056,0.0010367213,0.00012235237,0.000048687674,0.0001562137,0.00009509684,0.031338345,0.10549951,0.0050182724,0.002898534,0.8535059],"study_design_scores_gemma":[0.000017973001,0.00016539475,0.0047527235,0.000022242599,0.000039684328,0.0008156149,0.00005214676,0.9037072,0.074659705,0.0054004616,0.01031174,0.000055101806],"about_ca_topic_score_codex":0.0008661494,"about_ca_topic_score_gemma":0.00055472704,"teacher_disagreement_score":0.0016465262,"about_ca_system_score_codex":0.0002702768,"about_ca_system_score_gemma":0.00023853964,"threshold_uncertainty_score":0.005508125},"labels":[],"label_agreement":null},{"id":"W2170093232","doi":"10.1109/cvpr.2009.5206613","title":"Expression-insensitive 3D face recognition using sparse representation","year":2009,"lang":"en","type":"article","venue":"2009 IEEE Conference on Computer Vision and Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Facial recognition system; Pattern recognition (psychology); Artificial intelligence; Facial expression; Sparse approximation; Pooling; Face (sociological concept); Representation (politics); Three-dimensional face recognition; Expression (computer science); Set (abstract data type); Feature (linguistics); Polygon mesh; Face Recognition Grand Challenge; Ranking (information retrieval); Face detection","score_opus":0.09432256142549554,"score_gpt":0.3144741806189615,"score_spread":0.22015161919346593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170093232","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02151313,0.000090097885,0.97646105,0.00008972308,0.000025080051,0.000037092504,0.00013200406,0.0008333181,0.0008185004],"genre_scores_gemma":[0.28396347,0.00032364507,0.7118764,0.0001833777,0.00006777254,0.00014866378,0.0009601059,0.00011310281,0.0023635118],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99943155,0.000096878466,0.00002181351,0.00009363769,0.0003016079,0.000054468674],"domain_scores_gemma":[0.9995877,0.00011201481,0.00005819188,0.00011037845,0.00011314621,0.000018552226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004138756,0.0005027561,0.0007898314,0.0009550697,0.00020465204,0.00052985863,0.0007178628,0.00058844354,0.0014985343],"category_scores_gemma":[0.0015309331,0.00026927984,0.00066896115,0.00072640134,0.00032932282,0.0007449694,0.0006998248,0.0005889702,0.0008890773],"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":[0.00020166353,0.00010436803,0.0013035757,0.00010317355,0.000073883835,0.00023343824,0.00010478661,0.069976,0.2228522,0.0051243184,0.004885105,0.6950376],"study_design_scores_gemma":[0.000012630059,0.00008144619,0.0016230561,0.000009413248,0.000017201139,0.0004053208,0.000028840712,0.93676,0.05583706,0.0032048614,0.0019880475,0.000032124804],"about_ca_topic_score_codex":0.0015493865,"about_ca_topic_score_gemma":0.0019750718,"teacher_disagreement_score":0.0015493865,"about_ca_system_score_codex":0.0002515928,"about_ca_system_score_gemma":0.0003191202,"threshold_uncertainty_score":0.0050130486},"labels":[],"label_agreement":null},{"id":"W2170372557","doi":"10.1109/icme.2009.5202807","title":"Toward natural and efficient human computer interaction","year":2009,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Computer science; Human–computer interaction; Emotion recognition; Face detection; Facial expression; Focus (optics); Face (sociological concept); Natural (archaeology); Facial recognition system; Artificial intelligence; Emotion detection; Sketch recognition; Pattern recognition (psychology); Gesture recognition","score_opus":0.019818605323379288,"score_gpt":0.2697615556744895,"score_spread":0.24994295035111022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170372557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070649427,0.0034001523,0.9669083,0.003367,0.00014438991,0.0001944571,0.00006996825,0.0014680724,0.017382832],"genre_scores_gemma":[0.10327231,0.0041457647,0.8760346,0.0011786629,0.0001951137,0.0005722364,0.0003097001,0.00026631213,0.014025136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99759823,0.00077801157,0.000099957884,0.00034168042,0.0010828581,0.00009909472],"domain_scores_gemma":[0.9987669,0.00040088483,0.00008351592,0.00024866473,0.00041052228,0.00008953314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023354415,0.0008399068,0.0005513981,0.00051932863,0.00056332245,0.0034064713,0.001884252,0.0019071248,0.005872667],"category_scores_gemma":[0.0047014034,0.00052059715,0.00030700868,0.00040573176,0.0022000805,0.0038546824,0.0032507249,0.0016424953,0.0044953595],"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":[0.00015751089,0.00037966043,0.0011292284,0.0014231416,0.00006935197,0.00021063925,0.0020976411,0.01225609,0.13494788,0.33652717,0.041901425,0.4689002],"study_design_scores_gemma":[0.000075771444,0.00041422452,0.0025684524,0.0004662673,0.000042779644,0.0010573114,0.0014332407,0.14836085,0.03235482,0.31443265,0.49869788,0.00009577494],"about_ca_topic_score_codex":0.00084101874,"about_ca_topic_score_gemma":0.00095797045,"teacher_disagreement_score":0.005872667,"about_ca_system_score_codex":0.00051920116,"about_ca_system_score_gemma":0.0009841162,"threshold_uncertainty_score":0.019646049},"labels":[],"label_agreement":null},{"id":"W2170406903","doi":"10.5555/1390681.1390699","title":"An Information Criterion for Variable Selection in Support Vector Machines","year":2008,"lang":"en","type":"article","venue":"Lirias (KU Leuven)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":37,"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":"Support vector machine; Benchmark (surveying); Generalization; Curse of dimensionality; Computer science; Dimension (graph theory); Variable (mathematics); Feature selection; Dimensionality reduction; Relevance vector machine; Selection (genetic algorithm); Data mining; Artificial intelligence; Machine learning; Mathematics","score_opus":0.01761514711537405,"score_gpt":0.25885471139631905,"score_spread":0.241239564280945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170406903","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007617811,0.0011714166,0.9893232,0.00038694238,0.000101250596,0.00012489766,0.00012881786,0.00022903507,0.0009166218],"genre_scores_gemma":[0.36279225,0.0014746507,0.6304433,0.00059189677,0.0010203882,0.0009054227,0.0011776898,0.00021103157,0.001383312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9846986,0.007614851,0.0012102963,0.0010267127,0.0049919607,0.00045765616],"domain_scores_gemma":[0.95713145,0.033112045,0.0017758923,0.0016824888,0.0058172042,0.0004809957],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016302684,0.0014481947,0.0024784175,0.0043201814,0.0009201369,0.0029127034,0.0019034713,0.0033602957,0.001530799],"category_scores_gemma":[0.066413626,0.00056252116,0.0012651683,0.0035642395,0.002295769,0.0038242328,0.0021791302,0.002802367,0.00079522014],"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":[0.00054450886,0.00026583963,0.00609129,0.0010147543,0.00044688102,0.0004957479,0.00026331554,0.47383726,0.008195116,0.11672289,0.010629355,0.3814931],"study_design_scores_gemma":[0.000058851947,0.000280303,0.0012598156,0.00013487875,0.000046540452,0.00014519341,0.000026874299,0.9394046,0.0031772626,0.053313028,0.0020840853,0.00006860453],"about_ca_topic_score_codex":0.0009968095,"about_ca_topic_score_gemma":0.0007284043,"teacher_disagreement_score":0.016302684,"about_ca_system_score_codex":0.0013040171,"about_ca_system_score_gemma":0.0014817887,"threshold_uncertainty_score":0.08621788},"labels":[],"label_agreement":null},{"id":"W2170414574","doi":"10.1109/tpami.2008.233","title":"Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":110,"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":"Artificial intelligence; Pattern recognition (psychology); Support vector machine; Viewpoints; Invariant (physics); Computer science; Computer vision; Bayesian probability; Classifier (UML); Contextual image classification; Naive Bayes classifier; Mathematics; Image (mathematics)","score_opus":0.025917114165222067,"score_gpt":0.2560439646949041,"score_spread":0.23012685052968201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170414574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25653392,0.0008016101,0.7379926,0.00013538463,0.00007800375,0.00008773226,0.00034628005,0.0011434061,0.0028810902],"genre_scores_gemma":[0.7588979,0.0005679105,0.23620899,0.00008568011,0.00011874652,0.00007353392,0.0008157301,0.00008961389,0.0031418155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993973,0.000088872366,0.000015625732,0.00013372263,0.00027128166,0.00009321591],"domain_scores_gemma":[0.9995378,0.000105708416,0.000085233325,0.000079341415,0.00015464466,0.000037281985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005644288,0.00060752005,0.00073189003,0.0014909698,0.0003447122,0.0004326688,0.00077281584,0.00046662605,0.0011782454],"category_scores_gemma":[0.001348221,0.00021802347,0.0005544823,0.00043511367,0.00035538338,0.00060167536,0.0007284074,0.00032409828,0.0007790379],"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":[0.00039724418,0.000118695105,0.021746997,0.00010102741,0.000061542574,0.00031526765,0.00024846496,0.012369766,0.11344814,0.0020833975,0.0023150374,0.8467943],"study_design_scores_gemma":[0.000044462155,0.0005128754,0.07136349,0.000041985826,0.00012977034,0.0043531777,0.0006201586,0.7743291,0.13533266,0.0072085084,0.0059558903,0.00010789194],"about_ca_topic_score_codex":0.0023139932,"about_ca_topic_score_gemma":0.0031243346,"teacher_disagreement_score":0.0023139932,"about_ca_system_score_codex":0.00028417312,"about_ca_system_score_gemma":0.00036446375,"threshold_uncertainty_score":0.0046010017},"labels":[],"label_agreement":null},{"id":"W2170950676","doi":"10.1109/icassp.2005.1415607","title":"Recognizing Human Emotion from Audiovisual Informaiton","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":55,"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","funders":"","keywords":"Mel-frequency cepstrum; Computer science; Formant; Mahalanobis distance; Speech recognition; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Face (sociological concept); Emotion classification; Feature selection; Facial expression; Hidden Markov model; Feature (linguistics); Cepstrum; Facial recognition system; Vowel","score_opus":0.013025267570274356,"score_gpt":0.23735368139250124,"score_spread":0.22432841382222687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170950676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.45911744,0.006784105,0.4979801,0.0008212052,0.001051467,0.00021120346,0.0015980124,0.0033473712,0.029089067],"genre_scores_gemma":[0.8832094,0.0032977194,0.100442976,0.00040151385,0.0004710204,0.0000976293,0.0014659369,0.00011253923,0.010501221],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998466,0.000017399912,0.0000067939377,0.000032003,0.000076123084,0.000020985399],"domain_scores_gemma":[0.99974436,0.00007399304,0.000028632936,0.000019513083,0.00011838562,0.000015128927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016989233,0.0003252311,0.0002866638,0.0005854333,0.00011540132,0.0006745025,0.00022761451,0.00032446155,0.0020593326],"category_scores_gemma":[0.0010851191,0.0000771776,0.00017229338,0.00031398982,0.0001651514,0.0004527164,0.00028621702,0.00016087804,0.0011592777],"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":[0.00046709288,0.00007906668,0.0036639925,0.00037190595,0.00004899694,0.00031415347,0.0001829087,0.0024944262,0.36791578,0.00088647363,0.0036596328,0.6199155],"study_design_scores_gemma":[0.00011397241,0.0010760981,0.11436045,0.00037734504,0.000504981,0.0036304654,0.0014249452,0.23309723,0.57247484,0.0071741515,0.065577276,0.00018826406],"about_ca_topic_score_codex":0.00066644535,"about_ca_topic_score_gemma":0.00068241055,"teacher_disagreement_score":0.0020593326,"about_ca_system_score_codex":0.0001296003,"about_ca_system_score_gemma":0.000089469984,"threshold_uncertainty_score":0.0068891644},"labels":[],"label_agreement":null},{"id":"W2172038329","doi":"10.1007/978-3-540-76858-6_50","title":"Comparing a Transferable Belief Model Capable of Recognizing Facial Expressions with the Latest Human Data","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Université de Montréal","funders":"","keywords":"Facial expression; Computer science; Artificial intelligence; Observer (physics); Task (project management); Pattern recognition (psychology); Gaussian; Mixture model; Computer vision; Machine learning; Speech recognition; Engineering","score_opus":0.09897695843539747,"score_gpt":0.2903498281996686,"score_spread":0.19137286976427112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172038329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44966635,0.00086051575,0.54319566,0.00060889137,0.00030323985,0.00010517545,0.0005013471,0.0019857646,0.002773029],"genre_scores_gemma":[0.9514789,0.00021354684,0.046084426,0.00010738875,0.000037328802,0.000044873363,0.00060386903,0.00007415864,0.0013555889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993549,0.00019970279,0.00003417356,0.00018499511,0.0001485355,0.00007763367],"domain_scores_gemma":[0.99733883,0.0018470167,0.0000898113,0.00031903572,0.00031157277,0.00009374653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026885786,0.00065310276,0.00065707945,0.00062324485,0.00029513912,0.0012445949,0.0012469657,0.0011738082,0.0023563157],"category_scores_gemma":[0.0077777095,0.00035247422,0.0008913174,0.00051943085,0.00044487455,0.0017185115,0.0009405579,0.0012119337,0.0005738124],"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":[0.0023599193,0.00045111313,0.008171078,0.00015925795,0.00045354676,0.00015146409,0.0001741035,0.6284198,0.013328518,0.002572089,0.002396902,0.34136218],"study_design_scores_gemma":[0.000012533877,0.000105648396,0.0014598143,0.000005021918,0.000026255715,0.000021190017,0.00002338318,0.9945311,0.002116019,0.0015813451,0.000105091895,0.000012611501],"about_ca_topic_score_codex":0.016686574,"about_ca_topic_score_gemma":0.010045826,"teacher_disagreement_score":0.016686574,"about_ca_system_score_codex":0.0010236504,"about_ca_system_score_gemma":0.0008339845,"threshold_uncertainty_score":0.033178866},"labels":[],"label_agreement":null},{"id":"W2179228901","doi":"10.1016/j.jvcir.2015.09.007","title":"A novel approach for pain intensity detection based on facial feature deformations","year":2015,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Department of Science and Technology, Ministry of Science and Technology, India; McMaster University; University of Northern British Columbia","keywords":"Discriminative model; Facial expression; Artificial intelligence; Intensity (physics); Metric (unit); Feature vector; Pattern recognition (psychology); Feature (linguistics); Computer science; Support vector machine; Classifier (UML); Mathematics; Physics; Engineering","score_opus":0.061812186935857674,"score_gpt":0.3472417915356212,"score_spread":0.2854296045997635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2179228901","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027353965,0.00067840016,0.96800596,0.00015598933,0.00017108582,0.00018007764,0.00019452976,0.0008807176,0.0023792647],"genre_scores_gemma":[0.2852116,0.0014233808,0.70395744,0.00024076244,0.00022968478,0.00032409283,0.00059957925,0.00012488829,0.007888562],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971837,0.000025349013,0.000013235824,0.0000759727,0.0001308959,0.000036153288],"domain_scores_gemma":[0.99984336,0.000027036614,0.000013573372,0.000017903682,0.00008249671,0.000015654443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028815636,0.0006563588,0.00090400176,0.0010700985,0.0002577163,0.0005389346,0.0007945593,0.00058863824,0.0022369162],"category_scores_gemma":[0.00042618165,0.0002942967,0.00070554443,0.0007733429,0.00020760775,0.0006705048,0.00069571857,0.00059088273,0.0011406259],"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":[0.00028050452,0.00014633992,0.0020878334,0.0001606609,0.000099323086,0.00019153517,0.000068495654,0.003272362,0.2726233,0.0016807799,0.0029439363,0.71644485],"study_design_scores_gemma":[0.000090827634,0.0006118165,0.024243413,0.000049608603,0.00033640748,0.0027195578,0.00020174452,0.76936954,0.18500899,0.0039742975,0.013250597,0.00014313906],"about_ca_topic_score_codex":0.0013025985,"about_ca_topic_score_gemma":0.0023693633,"teacher_disagreement_score":0.0022369162,"about_ca_system_score_codex":0.00021078375,"about_ca_system_score_gemma":0.00048584698,"threshold_uncertainty_score":0.0074831843},"labels":[],"label_agreement":null},{"id":"W2181058803","doi":"10.1007/s11227-015-1433-9","title":"Incomplete high-dimensional data imputation algorithm using feature selection and clustering analysis on cloud","year":2015,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Imputation (statistics); Cluster analysis; Data mining; Missing data; Feature selection; Data set; Algorithm; Cloud computing; Hierarchical clustering; Pattern recognition (psychology); Artificial intelligence; Machine learning","score_opus":0.05964736565991257,"score_gpt":0.2945540920085865,"score_spread":0.23490672634867393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2181058803","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021722594,0.00017246832,0.9757873,0.00022393427,0.00009120873,0.00007148999,0.00041592485,0.0010353135,0.0004796753],"genre_scores_gemma":[0.32986298,0.00026584393,0.6634223,0.00015331153,0.0001506489,0.0002455012,0.0034526675,0.00020899218,0.0022377488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803346,0.00042794837,0.00017101428,0.00045151086,0.0005961027,0.00032000509],"domain_scores_gemma":[0.9965509,0.00082984776,0.00024616014,0.0009798006,0.0012460633,0.00014715022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027958509,0.00066589867,0.0023083086,0.0014425231,0.001984297,0.0017131902,0.00376934,0.0010241639,0.002528891],"category_scores_gemma":[0.0067359773,0.0006743428,0.0022834972,0.0034875297,0.00053937803,0.0021469237,0.0019568577,0.0019654918,0.0012394949],"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":[0.0008843162,0.00063059496,0.01926575,0.0001886605,0.00059420965,0.0005054164,0.0002721013,0.37956402,0.005086749,0.013779659,0.020185327,0.5590432],"study_design_scores_gemma":[0.000020602032,0.00002596913,0.0012339255,0.0000069790453,0.00002753286,0.000056263154,0.000043374588,0.99218047,0.001302932,0.0041534943,0.0009355322,0.000012915755],"about_ca_topic_score_codex":0.013225959,"about_ca_topic_score_gemma":0.01074453,"teacher_disagreement_score":0.013225959,"about_ca_system_score_codex":0.00088587246,"about_ca_system_score_gemma":0.0035627394,"threshold_uncertainty_score":0.026297987},"labels":[],"label_agreement":null},{"id":"W2183238361","doi":"","title":"CSC2515 Fall 2011 Boosting with Perceptrons and Decision Stumps","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Boosting (machine learning); Artificial intelligence; Machine learning; Perceptron; Computer science; Gradient boosting; Pattern recognition (psychology); Artificial neural network; Random forest","score_opus":0.03116262240661633,"score_gpt":0.22191102173042931,"score_spread":0.190748399323813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183238361","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12507454,0.012019028,0.77538174,0.004106665,0.0039553964,0.0008029437,0.0013591121,0.006347097,0.07095341],"genre_scores_gemma":[0.5507685,0.0029198986,0.36575678,0.0007930838,0.0007330228,0.00051152066,0.0034729308,0.00059546897,0.07444888],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99725085,0.0010986216,0.00014288853,0.00028714805,0.00095703517,0.00026350867],"domain_scores_gemma":[0.9946149,0.0018464209,0.0001339759,0.0011795769,0.0019730367,0.00025201493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0086440835,0.0013454651,0.0020550722,0.0018057413,0.0010631016,0.002229118,0.0014701582,0.0018063696,0.012978631],"category_scores_gemma":[0.009365083,0.0005019375,0.0010682207,0.0021785798,0.00084062736,0.0018675252,0.0010366278,0.0016447408,0.010274167],"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":[0.0011770547,0.0003794696,0.0038183883,0.00019248601,0.0002966101,0.000121277204,0.0000702776,0.13017774,0.0027033004,0.016126081,0.051791944,0.79314524],"study_design_scores_gemma":[0.00016634223,0.00035555073,0.0027713834,0.00007250755,0.00011765278,0.000117328054,0.000032491647,0.9223707,0.0076253135,0.022651546,0.043676246,0.00004297307],"about_ca_topic_score_codex":0.0076360567,"about_ca_topic_score_gemma":0.009879211,"teacher_disagreement_score":0.012978631,"about_ca_system_score_codex":0.0015972826,"about_ca_system_score_gemma":0.002105677,"threshold_uncertainty_score":0.045714855},"labels":[],"label_agreement":null},{"id":"W2184443690","doi":"","title":"A Learning Model for Fuzzy Classification System","year":2011,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Face and Expression Recognition","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":"Computer science; Artificial intelligence; Fuzzy logic; Robustness (evolution); Facial expression; Feature (linguistics); Neuro-fuzzy; Fuzzy rule; Machine learning; Fuzzy control system","score_opus":0.13547088171282815,"score_gpt":0.3099863491532288,"score_spread":0.17451546744040067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184443690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011459078,0.00089004927,0.97517234,0.00046723904,0.00020235752,0.00015404947,0.00021363555,0.001137154,0.010303993],"genre_scores_gemma":[0.7642923,0.0014051171,0.20021267,0.00032045972,0.00021951334,0.0009817496,0.0007422316,0.00008628643,0.03173965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999356,0.00011310197,0.000051743005,0.000216314,0.00018799548,0.00007490456],"domain_scores_gemma":[0.999548,0.0001338681,0.000035099274,0.000033967382,0.00023387281,0.000015056299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008291652,0.000792859,0.0012050776,0.0007421418,0.001017582,0.0016096276,0.0017736929,0.0020760086,0.0068904143],"category_scores_gemma":[0.0014991396,0.0003220666,0.00084856985,0.00083958195,0.00054001337,0.0013222276,0.00065450725,0.0014631699,0.002619877],"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":[0.0002115243,0.0001275423,0.0019072158,0.0003438506,0.00012630054,0.00041096544,0.00030815782,0.7269853,0.007829363,0.03767376,0.0050086756,0.21906744],"study_design_scores_gemma":[0.000010505132,0.000044976194,0.000158426,0.000013842067,0.000014634785,0.0000624335,0.000011106758,0.99178785,0.0006942,0.004411932,0.002777785,0.000012249437],"about_ca_topic_score_codex":0.010673638,"about_ca_topic_score_gemma":0.0063541466,"teacher_disagreement_score":0.010673638,"about_ca_system_score_codex":0.0011305204,"about_ca_system_score_gemma":0.00093771704,"threshold_uncertainty_score":0.023050725},"labels":[],"label_agreement":null},{"id":"W2185267597","doi":"","title":"Semi Supervised Learning in Wild Faces and Videos","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Margin (machine learning); Probabilistic logic; Face (sociological concept); Property (philosophy); Machine learning; Labeled data; Pattern recognition (psychology)","score_opus":0.029690421968792208,"score_gpt":0.2190930345831911,"score_spread":0.1894026126143989,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185267597","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02540841,0.00014592952,0.9720846,0.00019133584,0.000031548003,0.00005973716,0.00015504262,0.00096166413,0.0009616269],"genre_scores_gemma":[0.56691575,0.00019084374,0.4263473,0.00048553635,0.00024215739,0.00031643303,0.0016507133,0.00017063251,0.0036806287],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979387,0.0008457017,0.00008755817,0.0005711656,0.00041344986,0.00014350894],"domain_scores_gemma":[0.99437535,0.0028918462,0.0005200999,0.001387234,0.0006549996,0.00017042842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002419187,0.0010804109,0.0014805271,0.00094574917,0.000482832,0.0010205124,0.0027991775,0.0015596865,0.0016872366],"category_scores_gemma":[0.008478975,0.0006159099,0.0008975262,0.00071458996,0.0015638979,0.002797376,0.0022256412,0.0017744259,0.0009474755],"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":[0.00055946904,0.0005467537,0.0039720167,0.00023988228,0.00018561697,0.00023049484,0.00026156227,0.4127672,0.01687665,0.01646373,0.008136967,0.5397597],"study_design_scores_gemma":[0.000010687876,0.00005442944,0.00038617186,0.000006363601,0.0000064189576,0.000047812064,0.000022177264,0.98540026,0.0028681876,0.010672135,0.0005153222,0.000010127303],"about_ca_topic_score_codex":0.0022427847,"about_ca_topic_score_gemma":0.0032391113,"teacher_disagreement_score":0.0027991775,"about_ca_system_score_codex":0.000645373,"about_ca_system_score_gemma":0.00077137846,"threshold_uncertainty_score":0.012794018},"labels":[],"label_agreement":null},{"id":"W2187611299","doi":"10.21611/qirt.2010.003","title":"Multispectral Infrared Face Recognition: a comparative study","year":2010,"lang":"en","type":"article","venue":"Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"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":"Multispectral image; Computer science; Face (sociological concept); Artificial intelligence; Computer vision; Facial recognition system; Infrared; Remote sensing; Pattern recognition (psychology); Geology; Optics; Physics; Sociology","score_opus":0.07676616981944305,"score_gpt":0.32122205689574057,"score_spread":0.2444558870762975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2187611299","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7638512,0.0866198,0.06787663,0.0006874696,0.00047962548,0.0001705892,0.0007273476,0.0006551937,0.07893211],"genre_scores_gemma":[0.9569637,0.015630199,0.018315164,0.00011896889,0.00031765152,0.000040056748,0.0008591908,0.0000839919,0.0076710815],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99911076,0.00018140154,0.000045876906,0.00013669238,0.00048363965,0.00004155066],"domain_scores_gemma":[0.9986613,0.0005985955,0.00008288425,0.00009910888,0.0005180346,0.000040159448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013017704,0.0004078551,0.00042697298,0.0031435548,0.00025443206,0.0006291344,0.00041013988,0.00043498096,0.0042948546],"category_scores_gemma":[0.0023775727,0.000086388856,0.00059504993,0.0013191415,0.00020188156,0.0010365034,0.00032772083,0.00016652077,0.001136189],"study_design_candidate":"observational","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":[0.001525299,0.00029676792,0.0248794,0.0009601197,0.00048096816,0.00024449418,0.00035384958,0.002584641,0.028099563,0.0013435131,0.0027464405,0.93648493],"study_design_scores_gemma":[0.000114525,0.0054199873,0.7180804,0.0007178184,0.0028655862,0.01476769,0.0031223132,0.106211275,0.07949012,0.0065941806,0.06235539,0.00026074657],"about_ca_topic_score_codex":0.00075015094,"about_ca_topic_score_gemma":0.0009539475,"teacher_disagreement_score":0.0042948546,"about_ca_system_score_codex":0.00021449287,"about_ca_system_score_gemma":0.00009122306,"threshold_uncertainty_score":0.0143677},"labels":[],"label_agreement":null},{"id":"W2189262813","doi":"10.1016/j.fss.2015.11.018","title":"A comparative study of feature extraction methods and their application to P-RBF NNs in face recognition problem","year":2015,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Ministry of Science, ICT and Future Planning","keywords":"Artificial intelligence; Mathematics; Pattern recognition (psychology); Facial recognition system; Feature extraction; Face (sociological concept); Feature (linguistics); Computer science","score_opus":0.08422997214156418,"score_gpt":0.3700377007801251,"score_spread":0.2858077286385609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189262813","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14816982,0.01028098,0.83293545,0.00025284456,0.0001990876,0.0001064994,0.00010198778,0.00055326807,0.0074000754],"genre_scores_gemma":[0.675181,0.0055505033,0.31550932,0.0000632054,0.00012840338,0.00008510458,0.0001664653,0.0000758582,0.0032401884],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993529,0.00020015235,0.00006131315,0.00009940667,0.00024645214,0.0000397024],"domain_scores_gemma":[0.9978446,0.0013243626,0.000074772164,0.000118733595,0.00060832687,0.000029206845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014614926,0.00043182285,0.00079643616,0.0010914224,0.00037918135,0.0006113866,0.00055456394,0.00067383656,0.001695273],"category_scores_gemma":[0.0039773313,0.00018410133,0.0005821274,0.0013250281,0.0002523332,0.0011869961,0.0002773561,0.00042428434,0.0003705556],"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":[0.0005051386,0.00016927868,0.002831865,0.00042448472,0.000111668705,0.00010842559,0.00011336256,0.043917205,0.020381005,0.0027412781,0.0010581119,0.9276381],"study_design_scores_gemma":[0.000030005385,0.0005702877,0.01044509,0.000060442435,0.00012683719,0.0004566489,0.00016776776,0.96451813,0.01828069,0.0018988275,0.0033993265,0.000045896923],"about_ca_topic_score_codex":0.0034947444,"about_ca_topic_score_gemma":0.0025889284,"teacher_disagreement_score":0.0034947444,"about_ca_system_score_codex":0.0002584417,"about_ca_system_score_gemma":0.00033979336,"threshold_uncertainty_score":0.0077291727},"labels":[],"label_agreement":null},{"id":"W2200116677","doi":"","title":"1 - Algorithmes séquentiels pour l'analyse de données par méthodes à noyau","year":2004,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","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":"Kernel principal component analysis; Kernel Fisher discriminant analysis; Kernel (algebra); Linear discriminant analysis; Mathematics; Kernel method; Artificial intelligence; Principal component analysis; Pattern recognition (psychology); Reproducing kernel Hilbert space; Computer science; Algorithm; Support vector machine; Hilbert space; Discrete mathematics; Pure mathematics","score_opus":0.05456027940168405,"score_gpt":0.2770723711338478,"score_spread":0.22251209173216374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2200116677","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008341497,0.00032266244,0.9976209,0.00008988434,0.000047454654,0.000091594586,0.000026156862,0.0004686569,0.00049858587],"genre_scores_gemma":[0.01277423,0.00029963124,0.98253894,0.0000930871,0.00005592931,0.0004981917,0.00014601857,0.00016437551,0.0034295395],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966839,0.0009963786,0.00029430055,0.00066503114,0.0011729953,0.00018730176],"domain_scores_gemma":[0.99548435,0.0029283625,0.00017107469,0.00045834214,0.00085908425,0.00009879747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005186698,0.0016559188,0.0018340757,0.0020155562,0.001172966,0.002559429,0.0024152915,0.0022461945,0.010044082],"category_scores_gemma":[0.012175835,0.0008905122,0.0015346826,0.0014484953,0.0017553488,0.002372397,0.0026398103,0.0028647522,0.006505755],"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":[0.00048158382,0.00015264573,0.0010929087,0.00053704454,0.00017476085,0.00031533965,0.00039654732,0.08451698,0.0104909735,0.07931704,0.006990594,0.8155335],"study_design_scores_gemma":[0.00009533979,0.00013410924,0.0006489496,0.00012715129,0.000044419437,0.0003347439,0.00010758504,0.89317465,0.008371112,0.06375674,0.03315087,0.000054426117],"about_ca_topic_score_codex":0.0054262793,"about_ca_topic_score_gemma":0.005116047,"teacher_disagreement_score":0.010044082,"about_ca_system_score_codex":0.0012371565,"about_ca_system_score_gemma":0.0025562723,"threshold_uncertainty_score":0.033600748},"labels":[],"label_agreement":null},{"id":"W2222674814","doi":"10.3968/7830","title":"Application of Support Vector Machines to a Small-Sample Prediction","year":2015,"lang":"en","type":"article","venue":"Advances in petroleum exploration and development","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":38,"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; Measure (data warehouse); Sample (material); Sample size determination; Fraction (chemistry); Sample space; Computer science; Artificial intelligence; Machine learning; Relevance vector machine; Computation; Nonlinear system; Data mining; Structured support vector machine; Function (biology); Mathematics; Pattern recognition (psychology); Algorithm; Statistics","score_opus":0.02988777132959098,"score_gpt":0.27017783190445577,"score_spread":0.2402900605748648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2222674814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011635452,0.0008909804,0.9855482,0.00029645936,0.000091429494,0.000031047923,0.000048475755,0.0003823661,0.0010754899],"genre_scores_gemma":[0.58755094,0.0017616728,0.40697965,0.0001723946,0.0003815695,0.00014154635,0.0002639517,0.00010788261,0.0026403603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985422,0.0004884923,0.00010147369,0.00031151765,0.00049588195,0.000060451934],"domain_scores_gemma":[0.99374515,0.00464372,0.00027763992,0.00042917277,0.0007887118,0.00011559921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019141784,0.0007220491,0.0011417653,0.001082177,0.0003747492,0.0011141187,0.0007916259,0.0009752143,0.0013155662],"category_scores_gemma":[0.012613281,0.0003919403,0.0006304152,0.0012342692,0.0007816215,0.0010484933,0.0009087655,0.0018702167,0.00046632567],"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":[0.00022411914,0.00015050781,0.0035816426,0.00027502788,0.00016239652,0.00044446558,0.00018246789,0.5278754,0.009034013,0.03482317,0.003234097,0.42001274],"study_design_scores_gemma":[0.0000055625046,0.000031768628,0.0003544308,0.0000095936,0.000008714687,0.00004123149,0.000008591549,0.9853837,0.0017396637,0.011125281,0.001280606,0.000010882134],"about_ca_topic_score_codex":0.0018344782,"about_ca_topic_score_gemma":0.0009642274,"teacher_disagreement_score":0.0019141784,"about_ca_system_score_codex":0.0004960946,"about_ca_system_score_gemma":0.0006495509,"threshold_uncertainty_score":0.010123253},"labels":[],"label_agreement":null},{"id":"W2232381307","doi":"10.1007/978-3-642-38067-9_12","title":"Single Classifier Based Multiple Classifications","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Classifier (UML); Computer science; Classification scheme; Artificial intelligence; Pattern recognition (psychology); Voting; Quadratic classifier; Machine learning; Data mining","score_opus":0.03900620790117855,"score_gpt":0.24153856043686922,"score_spread":0.20253235253569069,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2232381307","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015335454,0.0021199933,0.9654934,0.0002749018,0.0013830777,0.0002031411,0.0005126322,0.005061467,0.009615925],"genre_scores_gemma":[0.21948822,0.0013824804,0.72179794,0.0004867238,0.0008370162,0.0003311959,0.0036711858,0.00082492485,0.051180266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996298,0.0004202234,0.00020663526,0.0013215723,0.0013663262,0.0003871918],"domain_scores_gemma":[0.99635315,0.000585569,0.00012930657,0.0012262628,0.0015825223,0.0001231535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021410424,0.0018426647,0.003724505,0.0024783504,0.0014800415,0.0036604116,0.00413239,0.0030132614,0.016955383],"category_scores_gemma":[0.003727565,0.0011184083,0.002551967,0.002434096,0.00066386786,0.004270523,0.002970856,0.0026233415,0.01759696],"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":[0.00037777584,0.00019156128,0.00123467,0.00015983394,0.00025050435,0.000118994496,0.00006433643,0.0129714785,0.016281435,0.0037692906,0.013100548,0.9514796],"study_design_scores_gemma":[0.000029589915,0.000282323,0.0026158064,0.00008154727,0.0002819506,0.0007718594,0.00013189262,0.9305934,0.033991884,0.011923231,0.019193271,0.00010331403],"about_ca_topic_score_codex":0.003465292,"about_ca_topic_score_gemma":0.005617287,"teacher_disagreement_score":0.016955383,"about_ca_system_score_codex":0.0008329557,"about_ca_system_score_gemma":0.0013294511,"threshold_uncertainty_score":0.05672139},"labels":[],"label_agreement":null},{"id":"W2237028625","doi":"10.1007/978-1-4614-3501-3_7","title":"Face Orientation Detection Using Histogram of Optimized Local Binary Pattern","year":2012,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Histogram; Orientation (vector space); Artificial intelligence; Local binary patterns; Face (sociological concept); Pattern recognition (psychology); Face detection; Pixel; Computer science; Binary number; Computer vision; Set (abstract data type); Facial recognition system; Mathematics; Image (mathematics)","score_opus":0.031360941192337746,"score_gpt":0.25097203070307295,"score_spread":0.2196110895107352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2237028625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013567082,0.0016640619,0.971369,0.00010888935,0.00016886469,0.000079970465,0.0003638198,0.0032097173,0.009468554],"genre_scores_gemma":[0.14206572,0.002514362,0.8233308,0.00021590415,0.00010905528,0.000121595695,0.0015331242,0.00048338887,0.029626014],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999843,0.000011018065,0.0000057675693,0.00003261778,0.000087082015,0.000020521948],"domain_scores_gemma":[0.99991906,0.0000164558,0.000007350028,0.000013020515,0.00003763618,0.000006351147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017099406,0.00033562438,0.0005241765,0.0010855524,0.00014092139,0.00059605495,0.0007305664,0.0003493826,0.007256925],"category_scores_gemma":[0.0003083451,0.00026526419,0.0003664154,0.00092210236,0.00015299443,0.0006167852,0.00039486025,0.00033081404,0.004108201],"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":[0.0000667425,0.00003592859,0.0005474819,0.00011145077,0.000025744437,0.000035130968,0.00001755397,0.0028649117,0.07168261,0.0028957026,0.009193623,0.91252303],"study_design_scores_gemma":[0.00004727591,0.00025549854,0.019106211,0.00011542544,0.00016511146,0.002073445,0.000115672396,0.54025203,0.34479204,0.014068314,0.07884997,0.00015897721],"about_ca_topic_score_codex":0.0012563253,"about_ca_topic_score_gemma":0.0021109902,"teacher_disagreement_score":0.007256925,"about_ca_system_score_codex":0.00023000859,"about_ca_system_score_gemma":0.0003064034,"threshold_uncertainty_score":0.024276853},"labels":[],"label_agreement":null},{"id":"W2240173575","doi":"10.1109/smc.2015.324","title":"Weighted Fusion of Bit Plane-Specific Local Image Descriptors for Facial Expression Recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Local binary patterns; Pattern recognition (psychology); Computer science; Facial expression; Linear discriminant analysis; Bit plane; Expression (computer science); Facial recognition system; Computer vision; Image (mathematics); Histogram","score_opus":0.05628681041215869,"score_gpt":0.25142332051010385,"score_spread":0.19513651009794516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2240173575","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093588,0.000729316,0.90268457,0.00008385153,0.00008496141,0.00011572528,0.00023797608,0.0006444252,0.001831115],"genre_scores_gemma":[0.6602664,0.0010173362,0.33379883,0.00009967146,0.0000780311,0.0001827346,0.0013017494,0.00012400211,0.0031312036],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947506,0.00007613966,0.0000338192,0.00007727395,0.0002865009,0.00005112701],"domain_scores_gemma":[0.9995395,0.00007174887,0.00005156633,0.000074957694,0.00023442974,0.000027887914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064092263,0.00056809105,0.0007797606,0.0013597531,0.00017102601,0.00048139293,0.0004823391,0.00024615656,0.0015052503],"category_scores_gemma":[0.0014806194,0.00012652861,0.00059189304,0.0013123457,0.00024652414,0.001084122,0.0008185741,0.00046817452,0.00071404845],"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":[0.0004990203,0.00014040012,0.0022960154,0.00014214985,0.0001024539,0.00006714056,0.000084881394,0.022408042,0.16855438,0.0029558672,0.002209671,0.80054],"study_design_scores_gemma":[0.00003990868,0.0006769446,0.01509536,0.000040425697,0.00021215432,0.000522474,0.00022603641,0.85152745,0.11920196,0.0053434186,0.0070104436,0.00010341558],"about_ca_topic_score_codex":0.001196526,"about_ca_topic_score_gemma":0.0015201633,"teacher_disagreement_score":0.0015052503,"about_ca_system_score_codex":0.00033233676,"about_ca_system_score_gemma":0.0003470059,"threshold_uncertainty_score":0.005035579},"labels":[],"label_agreement":null},{"id":"W2257550357","doi":"10.1145/2976744","title":"Scalable and Accurate Online Feature Selection for Big Data","year":2016,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Pairwise comparison; Big data; Computer science; Scalability; Feature (linguistics); Benchmark (surveying); Curse of dimensionality; Selection (genetic algorithm); Artificial intelligence; Data mining; Dimensionality reduction; Set (abstract data type); Machine learning; Pattern recognition (psychology); Database","score_opus":0.10563085290882107,"score_gpt":0.31964763730881357,"score_spread":0.2140167843999925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2257550357","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026696641,0.0010495348,0.9671476,0.0003862286,0.00008806078,0.00013457613,0.0005054205,0.0033554619,0.00063641113],"genre_scores_gemma":[0.44404927,0.00053376175,0.54866105,0.0003536175,0.0003011262,0.00045786085,0.0034438216,0.000294214,0.00190533],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806803,0.00042963427,0.000119553486,0.00048530646,0.00072079245,0.00017671539],"domain_scores_gemma":[0.99589765,0.0019991188,0.00038502357,0.0009573074,0.00059052877,0.0001704668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019229997,0.0019029467,0.0019351295,0.0024027308,0.0010014442,0.0012643882,0.0022734725,0.0010345303,0.0018370845],"category_scores_gemma":[0.008684577,0.00064960506,0.0014017412,0.0032108189,0.0006331607,0.0030545418,0.0015338536,0.0018086745,0.0010738402],"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":[0.000654714,0.00045799895,0.006358625,0.00025094787,0.00027282952,0.00041856183,0.00018571745,0.22503857,0.016939938,0.0045510354,0.017457651,0.72741336],"study_design_scores_gemma":[0.00005028029,0.00009853363,0.0011674414,0.000008767111,0.000025477457,0.000116445524,0.000056084387,0.98212147,0.0032215067,0.010835889,0.0022798304,0.000018232851],"about_ca_topic_score_codex":0.0039437097,"about_ca_topic_score_gemma":0.0054232692,"teacher_disagreement_score":0.0039437097,"about_ca_system_score_codex":0.00072211726,"about_ca_system_score_gemma":0.0013591968,"threshold_uncertainty_score":0.010169864},"labels":[],"label_agreement":null},{"id":"W2268210699","doi":"10.1049/ic.2015.0100","title":"Multimodal 2D-3D Face Recognition Using Structural Context and Pyramidal Shape Index","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Histogram; Scale-invariant feature transform; Facial recognition system; Computer science; Shape context; Pyramid (geometry); Context (archaeology); Computer vision; Face (sociological concept); Feature extraction; Feature (linguistics); Mathematics; Image (mathematics)","score_opus":0.057754459599328976,"score_gpt":0.28116409889044774,"score_spread":0.22340963929111876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2268210699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08598806,0.00066940516,0.9076158,0.00009428992,0.0000651314,0.00009326689,0.00027327545,0.0022429847,0.0029577978],"genre_scores_gemma":[0.5298324,0.0006091244,0.4664454,0.00011840124,0.000055835688,0.000097716686,0.00051880436,0.00010720253,0.002215208],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996495,0.000028456005,0.000013341158,0.000087283064,0.0001856632,0.000035779245],"domain_scores_gemma":[0.99983037,0.00002988147,0.000024772844,0.0000426488,0.00005788615,0.000014365481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024466516,0.0004627061,0.00070451945,0.0013928541,0.00021901062,0.00062323624,0.0005472077,0.0004528711,0.0023217383],"category_scores_gemma":[0.0005981592,0.00027319606,0.00075979583,0.0007485342,0.00023939589,0.0010001285,0.0010241937,0.00032380983,0.0011585541],"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":[0.0002369771,0.00006672819,0.0038168808,0.00013438136,0.000080672384,0.00019336287,0.000068163536,0.010802357,0.2056858,0.0017266143,0.0019570552,0.77523094],"study_design_scores_gemma":[0.000027340624,0.00038975867,0.020770697,0.000039465347,0.00012406017,0.003019027,0.000184324,0.807758,0.15612355,0.005542817,0.0059288614,0.00009207147],"about_ca_topic_score_codex":0.0012376562,"about_ca_topic_score_gemma":0.002628723,"teacher_disagreement_score":0.0023217383,"about_ca_system_score_codex":0.00022939076,"about_ca_system_score_gemma":0.00030147855,"threshold_uncertainty_score":0.0077670217},"labels":[],"label_agreement":null},{"id":"W2274755597","doi":"10.1016/j.isatra.2015.12.011","title":"Discriminative sparse subspace learning and its application to unsupervised feature selection","year":2016,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"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":"Discriminative model; Subspace topology; Artificial intelligence; Pattern recognition (psychology); Computer science; Random subspace method; Feature selection; Feature (linguistics); Machine learning; Feature learning","score_opus":0.013487181814078325,"score_gpt":0.25192304777159275,"score_spread":0.23843586595751443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2274755597","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004449558,0.00026843508,0.99455124,0.000093587354,0.000018843022,0.000009963428,0.00002619772,0.00018128425,0.00040087936],"genre_scores_gemma":[0.24621795,0.0010699793,0.747524,0.00012364636,0.000177269,0.00011741296,0.00038351645,0.00014692389,0.004239216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994599,0.00021298658,0.000027380443,0.00008541001,0.00017985454,0.00003434149],"domain_scores_gemma":[0.9981108,0.0011447989,0.000101516365,0.00026081447,0.00032324877,0.00005879044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009978965,0.00043847715,0.0008726332,0.00077107456,0.00039945124,0.0005949555,0.0008136193,0.0006621873,0.0014949242],"category_scores_gemma":[0.0045319907,0.00042689854,0.0005028026,0.0017733561,0.0008237244,0.0008733939,0.0011474388,0.0011084235,0.00064077484],"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":[0.00017501085,0.00015694051,0.0010184377,0.00011692062,0.00007737332,0.00008615638,0.00013641588,0.18968745,0.020809647,0.05816272,0.0048164013,0.72475654],"study_design_scores_gemma":[0.0000063560456,0.00002799179,0.0003300355,0.0000035013688,0.000006261134,0.000048559174,0.000010103054,0.9798996,0.0020735282,0.016111966,0.0014717581,0.000010280168],"about_ca_topic_score_codex":0.0029377467,"about_ca_topic_score_gemma":0.0033572246,"teacher_disagreement_score":0.0029377467,"about_ca_system_score_codex":0.00027843204,"about_ca_system_score_gemma":0.0006324259,"threshold_uncertainty_score":0.005841255},"labels":[],"label_agreement":null},{"id":"W2279375033","doi":"10.5281/zenodo.30927","title":"stackr: New in v.0.1.4: haplo2fstat with Map-independent imputations using Random Forest","year":2015,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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 Laval","funders":"","keywords":"Random forest; Mathematics; Statistics; Remote sensing; Computer science; Geography; Artificial intelligence","score_opus":0.06351688921128552,"score_gpt":0.26036700548121666,"score_spread":0.19685011626993115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279375033","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003227266,0.0006746426,0.2415326,0.0006223201,0.0012013554,0.00017964932,0.19172952,0.55688345,0.0039492766],"genre_scores_gemma":[0.021293469,0.00060497253,0.15814267,0.0012321846,0.0005372748,0.0017322764,0.27818498,0.52290523,0.015366941],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973248,0.000825085,0.0002451663,0.0008448519,0.00048609683,0.00027394787],"domain_scores_gemma":[0.9939266,0.0036021841,0.0003132058,0.0013940575,0.00044406374,0.00032001216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075982255,0.0044134185,0.0040645865,0.0034978248,0.0016146024,0.003864237,0.007365875,0.0027749478,0.18370286],"category_scores_gemma":[0.018281873,0.004230816,0.0047892905,0.0027738488,0.00091551215,0.0026816693,0.004201717,0.004991779,0.13205346],"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":[0.001120578,0.00017069292,0.0064702393,0.0015945601,0.0014511837,0.00066986517,0.00046278522,0.0061157425,0.0043820557,0.004309069,0.9012489,0.07200453],"study_design_scores_gemma":[0.0023581756,0.00026749205,0.0072050486,0.00055912853,0.0009394176,0.0016218002,0.00018369318,0.052451156,0.020411331,0.038973927,0.8742281,0.0008005824],"about_ca_topic_score_codex":0.0032953122,"about_ca_topic_score_gemma":0.006057195,"teacher_disagreement_score":0.18370286,"about_ca_system_score_codex":0.00077192223,"about_ca_system_score_gemma":0.0018655369,"threshold_uncertainty_score":0.61454725},"labels":[],"label_agreement":null},{"id":"W2279713460","doi":"","title":"A Dimension-Independent Generalization Bound for Kernel Supervised Principal Component Analysis","year":2015,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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":"Principal component analysis; Dimension (graph theory); Kernel principal component analysis; Generalization; Kernel (algebra); Mathematics; Upper and lower bounds; Sample complexity; Pattern recognition (psychology); Artificial intelligence; Kernel method; Computer science; Dimensionality reduction; Sample (material); Support vector machine; Discrete mathematics; Combinatorics","score_opus":0.04234872702388897,"score_gpt":0.27453572817249733,"score_spread":0.23218700114860835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279713460","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068203355,0.001878941,0.9859409,0.0010821385,0.00015687996,0.000047225894,0.00018419673,0.00044220753,0.0034471967],"genre_scores_gemma":[0.4604353,0.0074484046,0.51091695,0.002214813,0.0024228555,0.0010119845,0.0017504176,0.001166172,0.012633104],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9943311,0.0018702981,0.0002831337,0.0011246944,0.0019287847,0.0004619634],"domain_scores_gemma":[0.9640322,0.02223328,0.0018623611,0.006005475,0.0048874826,0.0009792362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008882251,0.002706147,0.0020037338,0.0019452188,0.0012555029,0.0023936294,0.0033302924,0.0026626065,0.004689201],"category_scores_gemma":[0.055787943,0.0008861514,0.0019818349,0.0025764643,0.0041106204,0.006741687,0.006704254,0.010478996,0.0021840846],"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":[0.00039532094,0.00019917809,0.0029000144,0.00056563696,0.0002664879,0.00042385585,0.00043778055,0.33600786,0.010859163,0.46128124,0.02117761,0.16548581],"study_design_scores_gemma":[0.000017872751,0.00007778762,0.0007445809,0.00007179204,0.000033841206,0.00017591592,0.00003430621,0.83113456,0.0019312985,0.16162455,0.0041039293,0.000049488175],"about_ca_topic_score_codex":0.0025032156,"about_ca_topic_score_gemma":0.0027005034,"teacher_disagreement_score":0.008882251,"about_ca_system_score_codex":0.0025523033,"about_ca_system_score_gemma":0.002153159,"threshold_uncertainty_score":0.04697442},"labels":[],"label_agreement":null},{"id":"W2282526122","doi":"10.1109/reconfig.2015.7393281","title":"A real-time reconfigurable architecture for face detection","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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; Field-programmable gate array; Reconfigurability; Face detection; AdaBoost; Object detection; Block (permutation group theory); Artificial intelligence; Reconfigurable computing; Viola–Jones object detection framework; Architecture; Face (sociological concept); Virtex; Facial recognition system; Feature extraction; Computer vision; Computer hardware; Computer architecture; Pattern recognition (psychology); Classifier (UML)","score_opus":0.02526150608637937,"score_gpt":0.2500891206889213,"score_spread":0.22482761460254194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2282526122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12186208,0.0023164374,0.82903737,0.0003153696,0.00061758497,0.00019900792,0.00033380516,0.017457116,0.027861217],"genre_scores_gemma":[0.6599742,0.000639149,0.31094274,0.00032425777,0.00012964249,0.00016168974,0.00058565225,0.00023292228,0.027009752],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984026,0.00001799284,0.000008396609,0.00004249567,0.00005903052,0.00003182915],"domain_scores_gemma":[0.99990046,0.000016278678,0.00001289555,0.00002510875,0.000032735836,0.000012438306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012288743,0.00035828084,0.0001886508,0.0004664717,0.0001845891,0.0003734674,0.0009866031,0.00032172046,0.006344336],"category_scores_gemma":[0.00018261823,0.00019543343,0.0002615073,0.00025361488,0.000111386165,0.0003614122,0.00022515687,0.00036428642,0.0020215644],"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":[0.0006674422,0.00013484959,0.0014120026,0.00031657048,0.00009258279,0.00064933806,0.00011133354,0.013343934,0.38622698,0.0069211163,0.012405107,0.57771873],"study_design_scores_gemma":[0.000268216,0.00227215,0.010817933,0.00013322257,0.00032563994,0.004766361,0.00010176599,0.32810274,0.48613435,0.0039738966,0.16294831,0.00015552735],"about_ca_topic_score_codex":0.0008872806,"about_ca_topic_score_gemma":0.0011303164,"teacher_disagreement_score":0.006344336,"about_ca_system_score_codex":0.0003306539,"about_ca_system_score_gemma":0.00024749018,"threshold_uncertainty_score":0.021223962},"labels":[],"label_agreement":null},{"id":"W2287458595","doi":"10.1109/sitis.2015.19","title":"Partial Face Recognition Based on Template Matching","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"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 Windsor","funders":"","keywords":"Artificial intelligence; Face (sociological concept); Facial recognition system; Pattern recognition (psychology); Computer science; Template matching; Feature extraction; Matching (statistics); Image (mathematics); Feature (linguistics); Computer vision; Three-dimensional face recognition; Face detection; Mathematics; Statistics","score_opus":0.0707048636881823,"score_gpt":0.2753486301982899,"score_spread":0.20464376651010763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2287458595","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019868037,0.00060764607,0.97415787,0.000049345923,0.00011869407,0.00009357882,0.00012009194,0.001931973,0.003052716],"genre_scores_gemma":[0.32480535,0.0011850253,0.6652667,0.00019642348,0.00012191955,0.00019740168,0.000918603,0.00021514425,0.0070934393],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99919766,0.00009409823,0.000042003703,0.00021360962,0.00038558865,0.00006696891],"domain_scores_gemma":[0.9995577,0.00006546387,0.000033761342,0.00016344014,0.0001606491,0.000019119343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004368267,0.000578512,0.0011851111,0.0012606905,0.00027474397,0.00068478455,0.001189714,0.0005758187,0.0030587208],"category_scores_gemma":[0.0010085261,0.00031807082,0.0011556955,0.0012084823,0.00035900174,0.0012492244,0.0007606865,0.0004387504,0.0019568112],"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":[0.00018373353,0.00006267743,0.0007864754,0.00015185776,0.000094006275,0.00016145161,0.000051040377,0.012198798,0.13499305,0.004143201,0.003182168,0.8439916],"study_design_scores_gemma":[0.000032073556,0.00053313555,0.004401182,0.000035548,0.00016768475,0.0038047144,0.00006531684,0.7474073,0.2203286,0.0056529413,0.017463975,0.00010758516],"about_ca_topic_score_codex":0.001193812,"about_ca_topic_score_gemma":0.0008767525,"teacher_disagreement_score":0.0030587208,"about_ca_system_score_codex":0.00020346112,"about_ca_system_score_gemma":0.0004229941,"threshold_uncertainty_score":0.010232449},"labels":[],"label_agreement":null},{"id":"W2293986576","doi":"10.1109/icip.2015.7350956","title":"Evolutionary fusion of local texture patterns for facial expression recognition","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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 Calgary","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Face (sociological concept); Feature (linguistics); Facial recognition system; Genetic programming; Texture (cosmology); Set (abstract data type); Fusion; Feature extraction; Computer vision; Image (mathematics)","score_opus":0.04336576213782884,"score_gpt":0.2619949445292504,"score_spread":0.21862918239142154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293986576","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09157113,0.0003961461,0.9053908,0.00006212975,0.000054425254,0.000047624857,0.00007523109,0.00039700008,0.0020054688],"genre_scores_gemma":[0.74218667,0.0003588071,0.2544222,0.000050867493,0.000032992735,0.00008152342,0.0002483593,0.00009841371,0.0025200935],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997874,0.000028945738,0.000010979168,0.00004405195,0.00010752359,0.000021116572],"domain_scores_gemma":[0.9998354,0.000045480763,0.000026628715,0.000025388248,0.00005310839,0.000014029149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032716015,0.00034339246,0.0004882416,0.00073345785,0.00016016189,0.00029726812,0.00039899795,0.00022949949,0.00089830434],"category_scores_gemma":[0.0008018784,0.00011798714,0.0004579179,0.0007269564,0.00023135288,0.000464712,0.00042806417,0.00033229936,0.00021751042],"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":[0.0001881339,0.00010144263,0.0019392413,0.00012013385,0.00006940545,0.00012741679,0.00008535086,0.07277384,0.23468173,0.0051064,0.0008553385,0.6839515],"study_design_scores_gemma":[0.000016749564,0.00020349992,0.0042134267,0.0000119815995,0.00006326327,0.0002529394,0.000059542388,0.93983215,0.049345665,0.0029802641,0.0029876817,0.00003285851],"about_ca_topic_score_codex":0.001149726,"about_ca_topic_score_gemma":0.0011170023,"teacher_disagreement_score":0.001149726,"about_ca_system_score_codex":0.00030195806,"about_ca_system_score_gemma":0.00019138056,"threshold_uncertainty_score":0.003005147},"labels":[],"label_agreement":null},{"id":"W2295024663","doi":"10.2316/journal.206.2008.1.206-2940","title":"FACE ALIGNMENT BASED ON STATISTICAL MODELS AND GABOR WAVELETS","year":2008,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Face and Expression Recognition","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":"Gabor wavelet; Artificial intelligence; Wavelet; Pattern recognition (psychology); Face (sociological concept); Computer science; Computer vision; Wavelet transform; Discrete wavelet transform; Sociology","score_opus":0.02436635618174059,"score_gpt":0.2666760283122777,"score_spread":0.2423096721305371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295024663","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027427026,0.00006206292,0.99672383,0.000016568803,0.000018437111,0.000005530347,0.0000063535276,0.00017343643,0.00025115887],"genre_scores_gemma":[0.2869918,0.0005547989,0.70936066,0.00007393183,0.00013548786,0.00010932461,0.0001463782,0.00022537155,0.002402258],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99928194,0.0001309536,0.000029846004,0.00013955022,0.00037389266,0.00004374891],"domain_scores_gemma":[0.9994281,0.00018178772,0.00012102815,0.00011683794,0.00012532623,0.000026910166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006434449,0.00048643225,0.0006923645,0.0008699309,0.0002757317,0.00074516854,0.0008251726,0.0005735149,0.00078627],"category_scores_gemma":[0.002441249,0.00052096846,0.00082812645,0.0008962845,0.00044701746,0.0013812484,0.00067862554,0.0008646165,0.0009055328],"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":[0.00018746109,0.000088352026,0.0017546176,0.0001080044,0.000102393235,0.00017862908,0.0001323694,0.27035865,0.068729624,0.04902741,0.001908918,0.60742354],"study_design_scores_gemma":[0.0000053747244,0.000045494813,0.0006272865,0.0000065853696,0.000010934468,0.00012032786,0.000010932758,0.9816232,0.009276111,0.0065155886,0.0017426129,0.000015485562],"about_ca_topic_score_codex":0.0013049247,"about_ca_topic_score_gemma":0.0011100041,"teacher_disagreement_score":0.0013049247,"about_ca_system_score_codex":0.0003890547,"about_ca_system_score_gemma":0.00060618954,"threshold_uncertainty_score":0.0034028888},"labels":[],"label_agreement":null},{"id":"W2295367484","doi":"","title":"A Novel Kernelized Classifier Based on the Combination of Partially Global and Local Characteristics.","year":2015,"lang":"en","type":"article","venue":"LWA","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Decision boundary; Kernel method; Artificial intelligence; Support vector machine; Pattern recognition (psychology); Kernel (algebra); Computer science; Linear discriminant analysis; Classifier (UML); Nonparametric statistics; Normality; Mathematics; Data mining; Kernel Fisher discriminant analysis; Machine learning; Statistics","score_opus":0.04040891613615394,"score_gpt":0.25530651244933456,"score_spread":0.21489759631318062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295367484","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016544437,0.0007847426,0.9794931,0.00018938215,0.00014976432,0.000053953787,0.00016091764,0.0012777519,0.0013459155],"genre_scores_gemma":[0.5672623,0.0006323459,0.41686425,0.00029459092,0.0002566414,0.00016988686,0.0014867643,0.00021859053,0.012814684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920636,0.00012034097,0.000044758617,0.00019402156,0.00034732264,0.000087170425],"domain_scores_gemma":[0.9992724,0.0001563124,0.00008538983,0.00010459766,0.00032857776,0.000052765015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000739302,0.0005403076,0.0010153677,0.0009640202,0.00032020605,0.0011231541,0.0011766786,0.0009510235,0.0014352835],"category_scores_gemma":[0.0017461028,0.00029908778,0.0006840306,0.00079834374,0.00035172398,0.0016239316,0.0008203491,0.0010133109,0.0015427375],"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":[0.00025511652,0.00020315933,0.0031110633,0.00014069068,0.00015011063,0.0001259093,0.00004742244,0.10074497,0.04174226,0.009278376,0.0137854135,0.83041567],"study_design_scores_gemma":[0.0000063524567,0.000036883142,0.0005275686,0.0000045288907,0.000013386756,0.000084727704,0.0000074750933,0.99006116,0.0045928243,0.0017509647,0.0029015918,0.000012559106],"about_ca_topic_score_codex":0.0024596832,"about_ca_topic_score_gemma":0.0026349716,"teacher_disagreement_score":0.0024596832,"about_ca_system_score_codex":0.00058896653,"about_ca_system_score_gemma":0.00080142467,"threshold_uncertainty_score":0.0048907995},"labels":[],"label_agreement":null},{"id":"W2295568617","doi":"10.1007/978-3-319-20801-5_22","title":"Head Pose Classification Using a Bidimensional Correlation Filter","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Université de Sherbrooke","funders":"","keywords":"Artificial intelligence; Optimal distinctiveness theory; Pattern recognition (psychology); Robustness (evolution); Computer science; Filter (signal processing); Computer vision; Correlation; Face (sociological concept); Pose; Composite image filter; Facial recognition system; Image (mathematics); Mathematics","score_opus":0.08099457627857419,"score_gpt":0.30500302149790764,"score_spread":0.22400844521933344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295568617","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017502727,0.0005279449,0.9780783,0.00009646654,0.00019600076,0.0000834904,0.0002599611,0.0012181978,0.0020369003],"genre_scores_gemma":[0.18720393,0.0015028926,0.79603475,0.00034100178,0.0002678387,0.00024336344,0.0014684449,0.0002704826,0.012667343],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993455,0.00008767416,0.000033286116,0.00018423541,0.00025677538,0.00009266112],"domain_scores_gemma":[0.9993591,0.00011010889,0.000039094346,0.00010225107,0.00035253935,0.00003692867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007623988,0.0009506449,0.0010931862,0.0015719052,0.00046291656,0.00083888264,0.0006201932,0.0007820566,0.005682398],"category_scores_gemma":[0.0011782118,0.0004406359,0.00085230835,0.0020285936,0.00027385217,0.00074594724,0.00095964083,0.00060047914,0.004977858],"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":[0.00040594727,0.0001242851,0.0018056656,0.00011121788,0.000102817365,0.00007626839,0.000051121082,0.0054749106,0.11964301,0.0018554301,0.0068935226,0.86345583],"study_design_scores_gemma":[0.00006393593,0.0004922758,0.020560637,0.00006166022,0.0003303175,0.0011383728,0.00013060242,0.8053661,0.15290454,0.002376258,0.01646981,0.00010549669],"about_ca_topic_score_codex":0.0030429603,"about_ca_topic_score_gemma":0.005131104,"teacher_disagreement_score":0.005682398,"about_ca_system_score_codex":0.00032489208,"about_ca_system_score_gemma":0.0011854791,"threshold_uncertainty_score":0.01900953},"labels":[],"label_agreement":null},{"id":"W2296592649","doi":"10.1007/978-3-319-20801-5_23","title":"Illumination Robust Facial Feature Detection via Decoupled Illumination and Texture Features","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Feature (linguistics); Face (sociological concept); Facial recognition system","score_opus":0.016422926645250954,"score_gpt":0.23839591052588566,"score_spread":0.2219729838806347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296592649","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026857184,0.000519238,0.96718985,0.0000895566,0.00008623333,0.000046671852,0.00017602253,0.0012650389,0.00377017],"genre_scores_gemma":[0.310139,0.0012281758,0.6718278,0.00019655553,0.00013516139,0.00012750387,0.0008789339,0.00045357732,0.015013244],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966705,0.000026709196,0.000010101603,0.000065242275,0.00017616559,0.00005479948],"domain_scores_gemma":[0.9997781,0.000052538784,0.000020432915,0.00005055329,0.000084857646,0.000013554686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024819057,0.00061450445,0.0007956556,0.00062136765,0.00016903029,0.0006526417,0.00068716647,0.00048316052,0.0034369016],"category_scores_gemma":[0.00071455183,0.00040779292,0.0007177445,0.00056397927,0.00025587928,0.0007471275,0.00085470994,0.0005489063,0.0025256863],"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":[0.00027020884,0.00006394648,0.0006393718,0.000086004846,0.000058891663,0.000078725374,0.00002576199,0.006866176,0.39568627,0.0014971935,0.003073803,0.59165376],"study_design_scores_gemma":[0.000045650217,0.0002401688,0.00863201,0.000041367584,0.00015108562,0.0011477707,0.0000548091,0.58922064,0.38614044,0.0043682237,0.00988587,0.00007202336],"about_ca_topic_score_codex":0.0010131134,"about_ca_topic_score_gemma":0.0019418944,"teacher_disagreement_score":0.0034369016,"about_ca_system_score_codex":0.00021335656,"about_ca_system_score_gemma":0.00034672846,"threshold_uncertainty_score":0.011497557},"labels":[],"label_agreement":null},{"id":"W2327768015","doi":"","title":"Eigenvector-based dimensionality reduction for human activity recognition and data classification","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Kernel (algebra); Mathematics; Dimensionality reduction; Computer science; Kernel principal component analysis; Kernel method; Support vector machine","score_opus":0.2130753780556418,"score_gpt":0.3390191764716207,"score_spread":0.1259437984159789,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327768015","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035871947,0.00024474712,0.9952636,0.000113303664,0.000024615709,0.000028331533,0.00007429629,0.00037425777,0.00028970352],"genre_scores_gemma":[0.11952231,0.00069914904,0.8768664,0.000087488654,0.00008719329,0.00024005167,0.00063761533,0.00011469718,0.0017451568],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981218,0.00055752357,0.00019648454,0.00038714297,0.00062915846,0.00010784736],"domain_scores_gemma":[0.9984427,0.0005381857,0.00015896092,0.00041141827,0.00039858592,0.00005006408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001803629,0.0008615296,0.0013135793,0.0016109723,0.00054322183,0.0013551307,0.0008134891,0.00055878796,0.0015646993],"category_scores_gemma":[0.005218546,0.00033896993,0.0013400217,0.002355241,0.0007771157,0.0013603371,0.0011980075,0.0013129271,0.0011850479],"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":[0.00015007317,0.00017599277,0.0018231979,0.0002503655,0.00013139418,0.000104731895,0.00026389956,0.092303455,0.0335336,0.05266937,0.0054503796,0.8131436],"study_design_scores_gemma":[0.00000923803,0.00008266205,0.0014419921,0.000025211606,0.000021159798,0.00013245424,0.00006241123,0.9432169,0.01187371,0.03645808,0.006630141,0.000046060602],"about_ca_topic_score_codex":0.0023057677,"about_ca_topic_score_gemma":0.0019483912,"teacher_disagreement_score":0.0023057677,"about_ca_system_score_codex":0.0007927967,"about_ca_system_score_gemma":0.0013001449,"threshold_uncertainty_score":0.009538591},"labels":[],"label_agreement":null},{"id":"W2332703577","doi":"10.11159/jmta.2014.001","title":"Multi-scale Analysis of Local Phase and Local Orientation for Dynamic Facial Expression Recognition","year":2014,"lang":"en","type":"article","venue":"Journal of Multimedia Theory and Applications","topic":"Face and Expression Recognition","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":"Agency for Science, Technology and Research","keywords":"Facial expression; Scale (ratio); Orientation (vector space); Facial expression recognition; Computer science; Expression (computer science); Pattern recognition (psychology); Artificial intelligence; Computer vision; Facial recognition system; Mathematics; Geography; Cartography; Geometry","score_opus":0.013624157184420336,"score_gpt":0.3039169409405475,"score_spread":0.29029278375612716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332703577","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064824805,0.0016074018,0.9294403,0.00011927761,0.00014093153,0.00013711942,0.00040379044,0.00085986464,0.0024665873],"genre_scores_gemma":[0.6945046,0.0017599339,0.2982178,0.00013086722,0.00012157843,0.0002194531,0.0012936669,0.00014575699,0.0036063627],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996946,0.000047832247,0.00001909665,0.000060587823,0.00014363426,0.000034350567],"domain_scores_gemma":[0.9997496,0.000057026675,0.000036441594,0.000041421845,0.0000972205,0.000018255783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004347534,0.00032244602,0.00048851187,0.0012952313,0.00014845053,0.00035181793,0.00036020146,0.0002496723,0.0019141592],"category_scores_gemma":[0.00088432536,0.00010266339,0.00046077848,0.0012159322,0.00022237204,0.00059372315,0.0003626796,0.00032324568,0.00090792676],"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":[0.0003280827,0.00013025249,0.0025979443,0.0002601909,0.000073027615,0.00012866996,0.00007639745,0.011369637,0.2360661,0.0026976736,0.0037926845,0.7424793],"study_design_scores_gemma":[0.000056941346,0.00056409393,0.03719014,0.00006487154,0.00020048286,0.0012397622,0.00030858594,0.7896641,0.14747569,0.005075606,0.018042177,0.00011745957],"about_ca_topic_score_codex":0.001512069,"about_ca_topic_score_gemma":0.0021962016,"teacher_disagreement_score":0.0019141592,"about_ca_system_score_codex":0.0002510175,"about_ca_system_score_gemma":0.00031713882,"threshold_uncertainty_score":0.0064035654},"labels":[],"label_agreement":null},{"id":"W2333899602","doi":"10.5815/ijmecs.2016.04.03","title":"GCSTLPP: Face Recognition using Gabor Center-Symmetric Tensor Locality Preservative Projection Approach in Video","year":2016,"lang":"en","type":"article","venue":"International Journal of Modern Education and Computer Science","topic":"Face and Expression Recognition","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":"Computer science; Facial recognition system; Artificial intelligence; Locality; Computer vision; Pattern recognition (psychology); Face (sociological concept); Classifier (UML); Feature (linguistics); Support vector machine","score_opus":0.049299599452376705,"score_gpt":0.31335504837727285,"score_spread":0.26405544892489613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2333899602","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04026349,0.0011765278,0.94589305,0.00028444536,0.00025645667,0.0002587448,0.0010355874,0.007479579,0.0033520905],"genre_scores_gemma":[0.28487545,0.0017307801,0.69750184,0.00022344035,0.00021308425,0.00033611053,0.005122511,0.00032521898,0.00967149],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959177,0.000047229434,0.000015301366,0.00009574131,0.00018570347,0.000064355576],"domain_scores_gemma":[0.99981254,0.000022451231,0.000017020093,0.00004237697,0.00008379421,0.000021874603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036516663,0.00064457604,0.0007709717,0.0012758042,0.00042774683,0.0006565718,0.0009613466,0.0005574201,0.0031750451],"category_scores_gemma":[0.00066326105,0.00019175174,0.0006264604,0.0011502638,0.00031698585,0.00091466773,0.0008930295,0.00089316553,0.0018074465],"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":[0.0003019332,0.00015707665,0.0010569404,0.0001403413,0.000060754137,0.000173718,0.00007096648,0.018381778,0.05627197,0.00369565,0.019111393,0.9005775],"study_design_scores_gemma":[0.000027173586,0.0002060124,0.0030243625,0.000025445797,0.000034379307,0.00052600645,0.00011019722,0.9215415,0.05963623,0.0038718847,0.010956341,0.000040439398],"about_ca_topic_score_codex":0.008823974,"about_ca_topic_score_gemma":0.007812254,"teacher_disagreement_score":0.008823974,"about_ca_system_score_codex":0.00042026682,"about_ca_system_score_gemma":0.0007921027,"threshold_uncertainty_score":0.017545223},"labels":[],"label_agreement":null},{"id":"W2336127679","doi":"","title":"Face Detecting by Skin-Color Using a New Technique in Personnel Photographs with Colorful and White Backgrounds","year":2014,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Face and Expression Recognition","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":"White (mutation); Skin color; Artificial intelligence; Computer vision; Face (sociological concept); Computer science; Communication; Psychology; Linguistics; Biology; Philosophy; Genetics","score_opus":0.023103131196844668,"score_gpt":0.27164444088236467,"score_spread":0.24854130968552,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2336127679","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.69484776,0.00086215185,0.29790568,0.00014666794,0.00019754763,0.00006766497,0.00014707189,0.0004469056,0.0053785094],"genre_scores_gemma":[0.8501281,0.00091726385,0.14526281,0.000053818178,0.000062277766,0.000035393685,0.000090477006,0.000058380483,0.0033914486],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998092,0.000041026364,0.0000075316643,0.000044801982,0.00007388641,0.000023568651],"domain_scores_gemma":[0.99964464,0.00009763899,0.000027222082,0.000041731113,0.00016413213,0.00002468759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030888384,0.00022998336,0.00020667752,0.00055283814,0.00025108262,0.00024611948,0.0002181341,0.0002939053,0.0018382112],"category_scores_gemma":[0.0004354321,0.0001764848,0.0003017195,0.00040552448,0.00023012857,0.0004810271,0.00024748547,0.00034502815,0.00044790359],"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":[0.00042043527,0.00010922877,0.007096155,0.0002119249,0.000043810567,0.00019695147,0.00025560707,0.0006336882,0.8107733,0.00048680409,0.00056993985,0.17920221],"study_design_scores_gemma":[0.00005253033,0.00077858305,0.11253758,0.000045912944,0.00041354218,0.006148937,0.00071127,0.061971754,0.8098127,0.0005824587,0.0068511455,0.00009350649],"about_ca_topic_score_codex":0.00056327117,"about_ca_topic_score_gemma":0.0011823486,"teacher_disagreement_score":0.0018382112,"about_ca_system_score_codex":0.00010942675,"about_ca_system_score_gemma":0.00017879631,"threshold_uncertainty_score":0.006149411},"labels":[],"label_agreement":null},{"id":"W2343281977","doi":"10.1109/vcip.2015.7457912","title":"Active appearance model search using partial least squares regression","year":2015,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"National Natural Science Foundation of China","keywords":"Partial least squares regression; Canonical correlation; Covariance matrix; Latent variable; Covariance; Mathematics; Regression analysis; Pattern recognition (psychology); Context (archaeology); Computer science; Active appearance model; Artificial intelligence; Statistics; Image (mathematics)","score_opus":0.14982228331199923,"score_gpt":0.34046592267426984,"score_spread":0.1906436393622706,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343281977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033244337,0.00007006879,0.9956392,0.00003278816,0.000016541493,0.000013723376,0.000013397345,0.0005253599,0.00036440068],"genre_scores_gemma":[0.20145251,0.00015930297,0.79333216,0.00016128011,0.00005708655,0.00020137973,0.00030839132,0.00039384817,0.0039339885],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992613,0.000211637,0.000028070108,0.00019664472,0.00024408034,0.000058215333],"domain_scores_gemma":[0.9989743,0.00046726209,0.00010220742,0.00013384614,0.00026847064,0.000053937398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092128396,0.001184952,0.0017405231,0.0008964126,0.00045912858,0.0010467811,0.0021000009,0.0014208448,0.002471123],"category_scores_gemma":[0.0029826306,0.0007988856,0.0013809359,0.0010616265,0.00064259424,0.0013271455,0.0013120257,0.001546516,0.0015916905],"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":[0.00017058857,0.00013545208,0.0008651874,0.00013984964,0.00014148925,0.00014258918,0.00012305843,0.54692227,0.021885293,0.010060359,0.005139831,0.41427407],"study_design_scores_gemma":[0.0000048388456,0.000013056738,0.000042359097,0.0000018251923,0.000004111742,0.000018942479,0.0000032873018,0.9976834,0.0009842846,0.000882703,0.00035716058,0.0000040532177],"about_ca_topic_score_codex":0.0030129205,"about_ca_topic_score_gemma":0.0030403663,"teacher_disagreement_score":0.0030129205,"about_ca_system_score_codex":0.0003745804,"about_ca_system_score_gemma":0.0010839924,"threshold_uncertainty_score":0.008266687},"labels":[],"label_agreement":null},{"id":"W2345061659","doi":"10.5220/0005710403090316","title":"Learning of Graph Compressed Dictionaries for Sparse Representation Classification","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 du Québec","funders":"","keywords":"Computer science; Sparse approximation; Artificial intelligence; Robustness (evolution); Neural coding; Pattern recognition (psychology); Matrix decomposition; Sparse matrix; Graph; Matching pursuit; Data compression; Compressed sensing; Theoretical computer science","score_opus":0.056231402821363956,"score_gpt":0.2883965974120433,"score_spread":0.23216519459067936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2345061659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013341304,0.00014331458,0.9852594,0.000119291544,0.000027370308,0.000028468652,0.00011314661,0.0003246056,0.0006431753],"genre_scores_gemma":[0.4247025,0.00064554426,0.5687819,0.0002641009,0.00013900704,0.00016650441,0.0016088198,0.00013180166,0.0035598446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996457,0.00010167655,0.000019444657,0.00008610155,0.0001105647,0.000036474732],"domain_scores_gemma":[0.9985128,0.0008177584,0.00014038109,0.00022404121,0.00026665934,0.00003843864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055711996,0.0005580617,0.0006570535,0.00061183836,0.00020392159,0.0005049538,0.000677415,0.00061702356,0.0018343383],"category_scores_gemma":[0.0035853055,0.00024062864,0.00038392408,0.0008842903,0.00044883235,0.0009858058,0.0006478559,0.0009827384,0.0006937985],"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":[0.00023315604,0.00012662905,0.0011466366,0.0001841957,0.00006192672,0.00011456238,0.00014789337,0.3791617,0.030062571,0.019687483,0.0075795166,0.5614937],"study_design_scores_gemma":[0.000006190458,0.000035811823,0.00018540249,0.000005035841,0.0000049413648,0.00003056369,0.000013494615,0.99302435,0.0028685934,0.003057381,0.0007629913,0.0000052301825],"about_ca_topic_score_codex":0.0027855227,"about_ca_topic_score_gemma":0.0032318623,"teacher_disagreement_score":0.0027855227,"about_ca_system_score_codex":0.00036693693,"about_ca_system_score_gemma":0.00047897385,"threshold_uncertainty_score":0.006136477},"labels":[],"label_agreement":null},{"id":"W2346012296","doi":"10.1049/iet-cvi.2015.0406","title":"Adaboost modular tensor locality preservative projection: face detection in video using Adaboost modular‐based tensor locality preservative projections","year":2016,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"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":"AdaBoost; Artificial intelligence; Computer science; Pattern recognition (psychology); Facial recognition system; Locality; Computer vision; Subspace topology; Face detection; Face (sociological concept); Block (permutation group theory); Classifier (UML); Mathematics","score_opus":0.035752776413940035,"score_gpt":0.2902369967304005,"score_spread":0.25448422031646045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346012296","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020159597,0.00024061585,0.9769711,0.00009684043,0.000055872802,0.00007875158,0.000050845312,0.0014896551,0.00085668365],"genre_scores_gemma":[0.45218036,0.0004140872,0.53897595,0.00020461372,0.00010706964,0.000273619,0.00051736576,0.00021644858,0.0071105203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999154,0.00017996521,0.000032554857,0.00019042297,0.0003342945,0.00010884008],"domain_scores_gemma":[0.99930763,0.00013958417,0.00007752331,0.0000895518,0.0003344713,0.000051187522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013857603,0.0012134607,0.0012655599,0.0011141268,0.0005762129,0.00083818345,0.001625083,0.0009043075,0.0018722073],"category_scores_gemma":[0.0017661419,0.00045477282,0.00085583585,0.0011001508,0.0006551735,0.0012122379,0.00094276055,0.0012775067,0.0010022469],"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":[0.0003865393,0.0003535318,0.0018785648,0.00010951685,0.00016272537,0.00007765789,0.000073098316,0.16242252,0.028071541,0.0039996747,0.0061331885,0.79633135],"study_design_scores_gemma":[0.00000803396,0.000056710607,0.00046214816,0.0000036462964,0.000010213553,0.00003193591,0.000011178883,0.99233115,0.005357558,0.0010327357,0.0006853531,0.000009204079],"about_ca_topic_score_codex":0.008474066,"about_ca_topic_score_gemma":0.0070458213,"teacher_disagreement_score":0.008474066,"about_ca_system_score_codex":0.0007782172,"about_ca_system_score_gemma":0.001505684,"threshold_uncertainty_score":0.016849458},"labels":[],"label_agreement":null},{"id":"W2346141776","doi":"10.1109/mmul.2016.27","title":"A Novel Semi-Supervised Dimensionality Reduction Framework","year":2016,"lang":"en","type":"article","venue":"IEEE Multimedia","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Dimensionality reduction; Nonlinear dimensionality reduction; Manifold alignment; Cluster analysis; Manifold (fluid mechanics); Computer science; Pattern recognition (psychology); Artificial intelligence; Curse of dimensionality; Exploit; Dimension (graph theory); Class (philosophy); Reduction (mathematics); Machine learning; Mathematics","score_opus":0.030374307188547854,"score_gpt":0.26611805705391767,"score_spread":0.2357437498653698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346141776","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022966876,0.00020092937,0.9965299,0.00008916697,0.000024108269,0.00003271453,0.000059510614,0.00035627146,0.00041082397],"genre_scores_gemma":[0.13966466,0.000460483,0.85482645,0.00023100326,0.00018509115,0.0003752456,0.0009335267,0.00017870171,0.0031448053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979122,0.0007689625,0.00009186571,0.0004925616,0.00062457565,0.000109835775],"domain_scores_gemma":[0.99913675,0.00020450453,0.000087056855,0.0002297647,0.0002899259,0.000052004503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014638654,0.0011229529,0.0017607346,0.0013659012,0.0006761134,0.0010289052,0.002250911,0.00097254483,0.0012695622],"category_scores_gemma":[0.0019617071,0.00050588336,0.0015558966,0.0015123745,0.0010130102,0.0017923597,0.0019588368,0.0016309905,0.00074845046],"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":[0.00017484133,0.0002822594,0.001376413,0.00030333953,0.00036275538,0.00019769405,0.000383041,0.23465969,0.019201348,0.10582078,0.014373213,0.6228646],"study_design_scores_gemma":[0.000013104228,0.00007134508,0.00025256738,0.000008937552,0.000017343733,0.00009526745,0.000023539633,0.96288466,0.0020842713,0.030237796,0.004287698,0.000023436682],"about_ca_topic_score_codex":0.0021174015,"about_ca_topic_score_gemma":0.0025024929,"teacher_disagreement_score":0.002250911,"about_ca_system_score_codex":0.00059740426,"about_ca_system_score_gemma":0.0012633355,"threshold_uncertainty_score":0.0077417493},"labels":[],"label_agreement":null},{"id":"W2346181499","doi":"10.1162/neco_a_00837","title":"Robust Support Vector Machines for Classification with Nonconvex and Smooth Losses","year":2016,"lang":"en","type":"article","venue":"Neural Computation","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":54,"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":"Support vector machine; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Machine learning","score_opus":0.04856399756352703,"score_gpt":0.2679607977473139,"score_spread":0.21939680018378688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346181499","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005763487,0.0002841519,0.99328387,0.000121703146,0.000017670682,0.000018858877,0.000024249755,0.00016382351,0.00032220164],"genre_scores_gemma":[0.4961038,0.0008188636,0.4980954,0.00026008915,0.0002720652,0.00031335172,0.00047990773,0.00024434103,0.0034121564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975854,0.00082384027,0.00014856097,0.0005244699,0.0007482767,0.00016945804],"domain_scores_gemma":[0.9952136,0.0029229047,0.00057728897,0.000510204,0.0006814014,0.00009446686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038828698,0.0016170787,0.0018020759,0.0012579748,0.00043296363,0.0016763438,0.0017368832,0.0020168687,0.0012752959],"category_scores_gemma":[0.014797693,0.00061264774,0.0011315963,0.0013548881,0.0015285455,0.0024240434,0.0016457504,0.0026007325,0.00082181056],"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":[0.00019036036,0.00007347526,0.00069043075,0.00019734558,0.00010814201,0.00013356966,0.000095657146,0.7955816,0.0070952494,0.048752356,0.0020040274,0.14507776],"study_design_scores_gemma":[0.0000031710636,0.000020761636,0.00005621311,0.0000054849406,0.0000028218392,0.000010754767,0.000003492001,0.99020666,0.00072536274,0.008702366,0.0002576921,0.00000510118],"about_ca_topic_score_codex":0.0011142784,"about_ca_topic_score_gemma":0.00074284547,"teacher_disagreement_score":0.0038828698,"about_ca_system_score_codex":0.0010261753,"about_ca_system_score_gemma":0.0009157285,"threshold_uncertainty_score":0.020534813},"labels":[],"label_agreement":null},{"id":"W2348555218","doi":"","title":"Robust face recognition based on low frequency DCT coefficients retransforming optimized by CLAHE","year":2014,"lang":"en","type":"article","venue":"Computer Engineering and Applications Journal","topic":"Face and Expression Recognition","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":"Institute for Biological Sciences","funders":"","keywords":"Adaptive histogram equalization; Discrete cosine transform; Pattern recognition (psychology); Artificial intelligence; Computer science; Histogram; Facial recognition system; Classifier (UML); Histogram equalization; Kernel (algebra); Contrast (vision); Computer vision; Mathematics; Image (mathematics)","score_opus":0.01087108710243183,"score_gpt":0.1975140228717637,"score_spread":0.18664293576933186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2348555218","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06631729,0.000708696,0.92827463,0.00012335555,0.00013662915,0.00008724199,0.000097572956,0.0014181018,0.0028363632],"genre_scores_gemma":[0.45716506,0.0006197368,0.5329858,0.0001349328,0.00008505942,0.00012145972,0.0004995993,0.00013840097,0.008249912],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995926,0.000040594274,0.0000238633,0.00010246368,0.000205873,0.00003458659],"domain_scores_gemma":[0.99972945,0.000058622263,0.000029356659,0.000052131167,0.00011749654,0.000012954896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031677922,0.00038193745,0.0005482994,0.00066418905,0.00025215186,0.00044258282,0.0006434384,0.00032029033,0.0017210157],"category_scores_gemma":[0.0008242249,0.00018325578,0.0003752359,0.0006619731,0.0002592556,0.0006592196,0.00034331146,0.00043689023,0.00073009345],"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":[0.00031025585,0.000118913085,0.00092792924,0.0000871282,0.00003618833,0.000120606586,0.000050488103,0.02266708,0.31616884,0.0031230033,0.0028155025,0.65357417],"study_design_scores_gemma":[0.000042581927,0.00031053007,0.0037572116,0.000013167511,0.000047132096,0.00092740665,0.000043136824,0.7046816,0.2807564,0.001311634,0.008055097,0.00005412046],"about_ca_topic_score_codex":0.0019301765,"about_ca_topic_score_gemma":0.0022231827,"teacher_disagreement_score":0.0019301765,"about_ca_system_score_codex":0.00024622676,"about_ca_system_score_gemma":0.00044537164,"threshold_uncertainty_score":0.0057573915},"labels":[],"label_agreement":null},{"id":"W2351533079","doi":"","title":"A Security Surveillance System Based on Face Detection","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Face and Expression Recognition","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":"Computer science; Face detection; AdaBoost; Face (sociological concept); Rectangle; Feature (linguistics); Artificial intelligence; Object-class detection; Haar-like features; Facial recognition system; Computer vision; Pattern recognition (psychology); Support vector machine","score_opus":0.005245295722015244,"score_gpt":0.21490639160306055,"score_spread":0.2096610958810453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2351533079","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10098724,0.0024032847,0.8474268,0.0007765278,0.0007210611,0.0004864429,0.00042891825,0.021038912,0.02573086],"genre_scores_gemma":[0.5211263,0.0018123259,0.4467916,0.00087844866,0.0005674518,0.00045924465,0.0011456829,0.0002943455,0.026924672],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996574,0.00003864555,0.000019105555,0.00008782172,0.00016105923,0.000035933164],"domain_scores_gemma":[0.9997434,0.000047977446,0.000018871344,0.000034127395,0.000115303614,0.000040468927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003494999,0.00048453675,0.00063724746,0.0007549404,0.00053140835,0.00056282955,0.0006233227,0.000739986,0.0045481576],"category_scores_gemma":[0.00038048328,0.00033233422,0.00036864585,0.00028324506,0.00022771477,0.0011038024,0.0005271881,0.0005524063,0.002151861],"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":[0.0006749374,0.0003457603,0.005567268,0.0003158525,0.00010615319,0.0006900343,0.0003400623,0.0039562904,0.33018395,0.0063003786,0.026337812,0.6251815],"study_design_scores_gemma":[0.00036447262,0.0025741619,0.016735375,0.0001994839,0.00058933126,0.0071478486,0.00021380467,0.37853476,0.38407612,0.0039030004,0.20533745,0.00032409726],"about_ca_topic_score_codex":0.0013339822,"about_ca_topic_score_gemma":0.0010259247,"teacher_disagreement_score":0.0045481576,"about_ca_system_score_codex":0.0003093486,"about_ca_system_score_gemma":0.00035888152,"threshold_uncertainty_score":0.015215099},"labels":[],"label_agreement":null},{"id":"W2355329649","doi":"10.1109/tpami.2015.2481396","title":"Hierarchical Spatio-Temporal Probabilistic Graphical Model with Multiple Feature Fusion for Binary Facial Attribute Classification in Real-World Face Videos","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"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":"Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Face (sociological concept); Feature (linguistics); Probabilistic logic; Feature extraction; Facial recognition system; Face hallucination; Three-dimensional face recognition; Face detection; Local binary patterns; Image (mathematics); Histogram","score_opus":0.05239917336192016,"score_gpt":0.29616120824342007,"score_spread":0.24376203488149992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2355329649","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0137986,0.00058288337,0.9838064,0.000266993,0.000035796424,0.000042271757,0.00022361013,0.00074821117,0.0004951612],"genre_scores_gemma":[0.7665012,0.00078369357,0.2285783,0.00032648433,0.00016134734,0.00018774325,0.0009987238,0.00012192872,0.0023405438],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915326,0.00025253647,0.000035182915,0.00025004268,0.00019537298,0.00011366547],"domain_scores_gemma":[0.9992168,0.0003839257,0.00013297924,0.00008532353,0.00013492287,0.000046032193],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016239106,0.000799981,0.0011461993,0.0013453689,0.00033448677,0.0007972733,0.0019468728,0.0010811177,0.0015237384],"category_scores_gemma":[0.003171416,0.00046936743,0.0013987182,0.0011791779,0.0006364156,0.001223009,0.0009296147,0.0013696247,0.0005523078],"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":[0.00025433843,0.00012844396,0.0015926078,0.0000980318,0.000112427464,0.00009475164,0.000101937534,0.78595346,0.0042045605,0.009876856,0.0026675789,0.19491506],"study_design_scores_gemma":[0.0000019254053,0.000008816413,0.00013580319,0.0000018243649,0.000005411563,0.00000829392,0.0000025317615,0.99774677,0.00017608814,0.0018182796,0.00009102008,0.0000032816558],"about_ca_topic_score_codex":0.016455213,"about_ca_topic_score_gemma":0.014165924,"teacher_disagreement_score":0.016455213,"about_ca_system_score_codex":0.0012853642,"about_ca_system_score_gemma":0.0008821919,"threshold_uncertainty_score":0.032718837},"labels":[],"label_agreement":null},{"id":"W2358256276","doi":"","title":"Recognition of Human Face in Different Pose","year":2006,"lang":"en","type":"article","venue":"Computer Technology and Development","topic":"Face and Expression Recognition","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":"L'Alliance Boviteq","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Principal component analysis; Face (sociological concept); Facial recognition system; Pattern recognition (psychology); Three-dimensional face recognition; Facial expression; Transformation (genetics); Representation (politics); Pose; Image (mathematics); Face hallucination; Face detection","score_opus":0.015122910325838818,"score_gpt":0.22308546322248216,"score_spread":0.20796255289664334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2358256276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11530117,0.0013885816,0.8678968,0.0002804334,0.00046629642,0.000120482066,0.00034566276,0.0017574866,0.012443112],"genre_scores_gemma":[0.6067046,0.0017946162,0.37710267,0.00032833527,0.00032861563,0.00014492698,0.00083787,0.00015916204,0.0125992065],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970704,0.00004266803,0.000010417095,0.00008901546,0.000117815376,0.00003306513],"domain_scores_gemma":[0.9998511,0.000026129474,0.000016069003,0.000034941168,0.00005596612,0.000015631262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023310652,0.00040019368,0.0005215001,0.00057002174,0.00020783019,0.00039143697,0.0003500144,0.00043571595,0.0032404342],"category_scores_gemma":[0.0006139035,0.00013791793,0.00041119082,0.0003134625,0.00028063607,0.00066534115,0.00037146563,0.00028431864,0.001829927],"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":[0.00025446364,0.00006131152,0.0028212084,0.00017685094,0.00005140701,0.00030284945,0.00023481343,0.003685987,0.3214458,0.004227565,0.0038462218,0.6628915],"study_design_scores_gemma":[0.000085247266,0.00150893,0.06390836,0.00012559802,0.0002694286,0.013258455,0.00093408517,0.27782044,0.5693503,0.015743632,0.05671292,0.0002826357],"about_ca_topic_score_codex":0.00042756603,"about_ca_topic_score_gemma":0.0004736382,"teacher_disagreement_score":0.0032404342,"about_ca_system_score_codex":0.000103313134,"about_ca_system_score_gemma":0.00013204727,"threshold_uncertainty_score":0.010840356},"labels":[],"label_agreement":null},{"id":"W2358736845","doi":"","title":"Face Detection Based on Skin Color In Complex Background","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Face and Expression Recognition","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":"Computer science; Artificial intelligence; Face detection; Computer vision; Face (sociological concept); Feature (linguistics); Pattern recognition (psychology); Skin color; Facial expression; Object-class detection; Facial recognition system","score_opus":0.015224444295765338,"score_gpt":0.244377954310203,"score_spread":0.22915351001443768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2358736845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31790617,0.00043015642,0.6767244,0.00008266843,0.00008380386,0.000047836376,0.00009159677,0.001098928,0.0035345173],"genre_scores_gemma":[0.8489321,0.0003858325,0.14827581,0.00005643797,0.00003080903,0.000031056647,0.00010286203,0.00009756397,0.0020876057],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974686,0.000036655438,0.0000062163094,0.00006478839,0.000104172956,0.000041380787],"domain_scores_gemma":[0.999634,0.0001211441,0.00003753714,0.000039720922,0.00013684186,0.000030756302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025492135,0.000326697,0.00037374377,0.0007760822,0.0001928903,0.0004195933,0.00040751425,0.00027385453,0.0015382818],"category_scores_gemma":[0.0008025821,0.00016414131,0.00021207555,0.00023943334,0.0003065916,0.0005781876,0.00032742225,0.0002789318,0.00049449573],"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":[0.0005459286,0.00006530546,0.0056613535,0.00015048824,0.000050151724,0.00046276386,0.00013651789,0.009309916,0.65370464,0.0021005415,0.00085186923,0.32696047],"study_design_scores_gemma":[0.000023973449,0.00034175508,0.023444178,0.000022885333,0.000083023915,0.0020904131,0.00014003145,0.41758707,0.55017,0.0018973568,0.0041436017,0.00005564481],"about_ca_topic_score_codex":0.0009896137,"about_ca_topic_score_gemma":0.0010607146,"teacher_disagreement_score":0.0015382818,"about_ca_system_score_codex":0.00021004405,"about_ca_system_score_gemma":0.00018478933,"threshold_uncertainty_score":0.0051460266},"labels":[],"label_agreement":null},{"id":"W2362693384","doi":"","title":"Robust Face Recognition in Complex Background","year":2003,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Discovery Air (Canada)","funders":"","keywords":"Eigenface; Computer science; Face (sociological concept); Facial recognition system; Artificial intelligence; Matching (statistics); Pattern recognition (psychology); Key (lock); Three-dimensional face recognition; Computer vision; Face detection; Mathematics; Statistics; Computer security","score_opus":0.1474783044942904,"score_gpt":0.27363777434511705,"score_spread":0.12615946985082666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2362693384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06567446,0.00056117476,0.9288271,0.00012774623,0.00007437087,0.000030726005,0.00009527859,0.0014850023,0.0031241828],"genre_scores_gemma":[0.6146515,0.0011035362,0.37602985,0.00023175363,0.00012299951,0.00007818124,0.00066949165,0.00037481176,0.0067379028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999302,0.000077378994,0.000023905512,0.00018103856,0.00030295044,0.00011271351],"domain_scores_gemma":[0.9995809,0.00013308329,0.000052802356,0.00010294468,0.00010703327,0.000023215327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056128076,0.0005741699,0.00090816565,0.0009836626,0.00029190362,0.00090787746,0.0008238166,0.0007652354,0.0022443198],"category_scores_gemma":[0.0016991721,0.00025658385,0.00049762503,0.00068606745,0.0005713818,0.0013408088,0.0008169868,0.00057140284,0.0018623911],"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":[0.00047193808,0.00011066747,0.0011688741,0.00015425336,0.00009313892,0.0003774066,0.000087005246,0.06957352,0.274195,0.010204532,0.0025456308,0.641018],"study_design_scores_gemma":[0.000022552696,0.00018274422,0.004277347,0.000026030633,0.000057450117,0.0010060244,0.00007164117,0.7717722,0.2041969,0.011116374,0.0072065825,0.000064221786],"about_ca_topic_score_codex":0.0010031253,"about_ca_topic_score_gemma":0.0006372529,"teacher_disagreement_score":0.0022443198,"about_ca_system_score_codex":0.00026077975,"about_ca_system_score_gemma":0.00020974365,"threshold_uncertainty_score":0.0075080395},"labels":[],"label_agreement":null},{"id":"W2364053654","doi":"","title":"The Software Solving of Multiple Regression and Correlation Analysis and Case Explanation","year":2000,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Discovery Air (Canada)","funders":"","keywords":"Regression analysis; Computer science; Software; Regression; Linear regression; Statistical analysis; Regression diagnostic; Correlation; Statistics; Data mining; Polynomial regression; Machine learning; Mathematics; Programming language","score_opus":0.01015478045673392,"score_gpt":0.2286462663993454,"score_spread":0.21849148594261147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2364053654","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038233674,0.0005326867,0.9656411,0.0012561566,0.0003128217,0.00049285364,0.00031051933,0.0063048727,0.021325596],"genre_scores_gemma":[0.03700841,0.0010127218,0.9457056,0.00030932136,0.00022854871,0.0007218368,0.0007643849,0.0007754399,0.013473758],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9919709,0.0025100883,0.001096238,0.0012003207,0.0028593123,0.00036310137],"domain_scores_gemma":[0.9909588,0.004650482,0.0006895798,0.001248956,0.002221566,0.00023061316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063189114,0.0016504414,0.00094130787,0.00612886,0.001402817,0.0034979428,0.0027140288,0.001711287,0.017065527],"category_scores_gemma":[0.02425888,0.0008338439,0.0019565048,0.0035845935,0.0014208651,0.0033963171,0.0024775288,0.0019425747,0.0063744104],"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":[0.00012407494,0.00017488595,0.003800584,0.0008479015,0.00008446486,0.0019459692,0.0013294907,0.0074297725,0.0039326516,0.17101918,0.058122084,0.7511889],"study_design_scores_gemma":[0.0001770845,0.00022088326,0.004385005,0.00095238426,0.00022356884,0.009798034,0.0014854093,0.16954096,0.027384896,0.27729198,0.50824726,0.00029257397],"about_ca_topic_score_codex":0.0026061398,"about_ca_topic_score_gemma":0.0015904118,"teacher_disagreement_score":0.017065527,"about_ca_system_score_codex":0.0010930804,"about_ca_system_score_gemma":0.0035051939,"threshold_uncertainty_score":0.057089865},"labels":[],"label_agreement":null},{"id":"W2364913443","doi":"10.48550/arxiv.1605.03072","title":"Semi-Supervised Representation Learning based on Probabilistic Labeling","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Probabilistic logic; Artificial intelligence; Representation (politics); Computer science; Machine learning; Supervised learning; Natural language processing; Pattern recognition (psychology); Artificial neural network; Political science","score_opus":0.08014613218572131,"score_gpt":0.20610007512537803,"score_spread":0.12595394293965673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2364913443","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001475235,0.000060758255,0.9975018,0.000080302,0.000014497812,0.00004004765,0.000033257456,0.00050291885,0.00029118246],"genre_scores_gemma":[0.1469347,0.00023006233,0.84854,0.000275651,0.00017443714,0.0005749412,0.0010615512,0.00029231491,0.0019163709],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9935323,0.0027661405,0.0003154212,0.0015030162,0.0015965565,0.0002866153],"domain_scores_gemma":[0.9880989,0.006546021,0.000982377,0.0024610716,0.0016639202,0.00024768672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0050984686,0.001428401,0.0025732329,0.0024716824,0.0010320492,0.0022602365,0.0047486373,0.0022853294,0.0025071024],"category_scores_gemma":[0.015922574,0.00097019365,0.0017616585,0.0023751424,0.0024220115,0.00498366,0.0035541637,0.004098273,0.0017235678],"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":[0.00024111844,0.00028413135,0.001210895,0.00035054435,0.00019471288,0.00012217645,0.00028891393,0.36647925,0.0051288074,0.06265347,0.009958618,0.5530873],"study_design_scores_gemma":[0.000013024426,0.00003084624,0.00007876555,0.000014485164,0.0000083207005,0.000043721848,0.000013653621,0.9646649,0.0013500642,0.03286604,0.0009023835,0.000013887535],"about_ca_topic_score_codex":0.0018187213,"about_ca_topic_score_gemma":0.001785128,"teacher_disagreement_score":0.0050984686,"about_ca_system_score_codex":0.0015159126,"about_ca_system_score_gemma":0.0019030718,"threshold_uncertainty_score":0.026963592},"labels":[],"label_agreement":null},{"id":"W2383764705","doi":"","title":"Extension Samples Methods of Face Recognition with One Training Image Per Person","year":2010,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Face and Expression Recognition","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":"Computer science; Artificial intelligence; Mirroring; Facial recognition system; Pattern recognition (psychology); Image (mathematics); Face (sociological concept); Computer vision; Extension (predicate logic); Scaling; Training (meteorology); Shearing (physics); Mathematics; Geometry","score_opus":0.06297416329684055,"score_gpt":0.30651396758384813,"score_spread":0.24353980428700758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2383764705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013506477,0.0009238438,0.9829275,0.00006155931,0.00009579152,0.00013872022,0.00013161276,0.0006884858,0.0015260419],"genre_scores_gemma":[0.2033795,0.0017993241,0.7742681,0.00017382992,0.00033983827,0.0008284226,0.0010604599,0.0002501485,0.017900404],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988281,0.00025536484,0.00006971586,0.0003514081,0.00043585134,0.00005952273],"domain_scores_gemma":[0.99914074,0.00028830805,0.00005957805,0.00029442058,0.00019268913,0.00002418875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013358747,0.00079851376,0.00131911,0.0008258003,0.00035144598,0.00040052956,0.0016348173,0.00046821783,0.0059212907],"category_scores_gemma":[0.002103406,0.00044233399,0.0009950025,0.0008064562,0.0005402744,0.0010170954,0.0010143607,0.0010107134,0.0031431853],"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":[0.00028207662,0.00009979576,0.0012517915,0.00020534158,0.00009884866,0.000061690356,0.00011188871,0.019094605,0.01994185,0.005160724,0.0030330317,0.9506583],"study_design_scores_gemma":[0.00008198722,0.0007605076,0.014321392,0.000102876016,0.00017843829,0.0017487595,0.00014641794,0.864405,0.052112114,0.01834571,0.047687836,0.00010897836],"about_ca_topic_score_codex":0.0016244845,"about_ca_topic_score_gemma":0.0015744915,"teacher_disagreement_score":0.0059212907,"about_ca_system_score_codex":0.000297798,"about_ca_system_score_gemma":0.00038298755,"threshold_uncertainty_score":0.01980871},"labels":[],"label_agreement":null},{"id":"W2389416714","doi":"","title":"Multi-Model and SVM Used in Face Tracking","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Face and Expression Recognition","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":"Computer science; Artificial intelligence; Computer vision; Face (sociological concept); Histogram; Support vector machine; Face detection; Tracking (education); Facial recognition system; Chromatic scale; Facial motion capture; Pattern recognition (psychology); Matching (statistics); Image (mathematics)","score_opus":0.019977275406628168,"score_gpt":0.25670641399366084,"score_spread":0.23672913858703268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2389416714","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013742496,0.000742299,0.9821846,0.00015281087,0.00018316925,0.000037271195,0.00008156569,0.0012676123,0.0016081185],"genre_scores_gemma":[0.6322104,0.0008728371,0.35645065,0.0001814256,0.0001336968,0.00013302667,0.0005046035,0.00018801265,0.009325382],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99909365,0.0001756359,0.00005807155,0.00025345705,0.000322743,0.00009642556],"domain_scores_gemma":[0.999432,0.00013558585,0.00004007562,0.000114265145,0.00024413626,0.000033892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008834385,0.00071255397,0.001202075,0.001026456,0.00063133554,0.0010911069,0.0009561363,0.0011967683,0.0023727291],"category_scores_gemma":[0.0019148162,0.00039144332,0.0011317881,0.0008363592,0.00033817586,0.0016659494,0.00074245833,0.0012467372,0.0017122591],"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":[0.00029113505,0.00018907592,0.0038702975,0.0001670052,0.00020225116,0.00020368141,0.00009162466,0.20425093,0.026402961,0.01059291,0.0044037043,0.7493344],"study_design_scores_gemma":[0.00000573814,0.000032441785,0.0007672316,0.000005265899,0.000013712593,0.00008236161,0.000009668296,0.99099535,0.004704839,0.001902825,0.0014670242,0.000013534418],"about_ca_topic_score_codex":0.005965331,"about_ca_topic_score_gemma":0.0036897298,"teacher_disagreement_score":0.005965331,"about_ca_system_score_codex":0.00065427215,"about_ca_system_score_gemma":0.0005733808,"threshold_uncertainty_score":0.011861205},"labels":[],"label_agreement":null},{"id":"W2393655905","doi":"","title":"View-Based Active Shape Model and Application","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Face and Expression Recognition","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":"Computer science; Active shape model; Artificial intelligence; Face (sociological concept); Computer vision; Active appearance model; Machine learning; Pattern recognition (psychology); Image (mathematics); Segmentation","score_opus":0.014962624559825435,"score_gpt":0.23726093730481776,"score_spread":0.22229831274499232,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2393655905","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021018332,0.00009330266,0.9966138,0.000052094936,0.000029064411,0.000008075854,0.000018865125,0.00028244103,0.00080055103],"genre_scores_gemma":[0.5168099,0.0010152915,0.4705549,0.00022050897,0.00016433283,0.00017088253,0.00039343277,0.000294733,0.010376027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966645,0.00005280556,0.00001228063,0.000083278195,0.00016689897,0.000018191737],"domain_scores_gemma":[0.99960893,0.000110607565,0.000024534855,0.0000965758,0.00013211594,0.000027268581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004043348,0.0006037586,0.0007191518,0.0006745225,0.00022285046,0.0007620324,0.0013282456,0.001056789,0.0026027039],"category_scores_gemma":[0.0012083574,0.00045744152,0.0009499324,0.000596867,0.0005412272,0.0012277277,0.00075254816,0.00090354064,0.001805547],"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":[0.000103719474,0.00008444582,0.0009073627,0.00011796594,0.00006430067,0.0001482107,0.00014941474,0.4785549,0.05778528,0.034762215,0.002325447,0.4249968],"study_design_scores_gemma":[0.0000029134121,0.000018389623,0.00009138426,0.0000038030396,0.000005652377,0.00007077283,0.000005804337,0.98917073,0.004898066,0.0038569581,0.0018657543,0.000009727902],"about_ca_topic_score_codex":0.0013061191,"about_ca_topic_score_gemma":0.0009798645,"teacher_disagreement_score":0.0026027039,"about_ca_system_score_codex":0.00034507798,"about_ca_system_score_gemma":0.00032155853,"threshold_uncertainty_score":0.008706868},"labels":[],"label_agreement":null},{"id":"W2396491491","doi":"","title":"An Effective L0 - SVM Classifier For Face Recognition Based on Haar Feature","year":2016,"lang":"en","type":"article","venue":"Advances in natural science/Advances in natural sciences","topic":"Face and Expression Recognition","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":"","keywords":"Haar; Pattern recognition (psychology); Artificial intelligence; Support vector machine; Classifier (UML); Facial recognition system; Haar-like features; Computer science; Face detection","score_opus":0.010447998900796897,"score_gpt":0.31449358151471996,"score_spread":0.30404558261392306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2396491491","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010167543,0.00083594327,0.98695487,0.00011031064,0.00013510554,0.00005852919,0.000083903265,0.0006550306,0.0009987124],"genre_scores_gemma":[0.2905337,0.0015236703,0.7003379,0.0002859024,0.000335415,0.00025421934,0.0009690909,0.00011369555,0.0056463946],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990183,0.00015179058,0.00007433339,0.00022462608,0.0004487424,0.00008216719],"domain_scores_gemma":[0.99911577,0.0001727048,0.00004368652,0.00007507104,0.0005492833,0.000043423093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010221209,0.00066210114,0.0011371353,0.0011314404,0.00065062183,0.00084230874,0.0010114618,0.0010407899,0.0019804188],"category_scores_gemma":[0.0020103487,0.00028912805,0.0006620418,0.0012940272,0.00033447304,0.0015846179,0.00058935286,0.0011018577,0.0018430022],"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":[0.00017548837,0.00015818713,0.001601363,0.00016728362,0.00007306185,0.00007501261,0.00005260927,0.018084595,0.05041756,0.0057884613,0.0064440346,0.9169623],"study_design_scores_gemma":[0.000020843549,0.00015743526,0.0016770513,0.000016785103,0.000037758633,0.00026411552,0.000028146027,0.9710571,0.019067632,0.0022233238,0.0054170196,0.000032746444],"about_ca_topic_score_codex":0.0028567486,"about_ca_topic_score_gemma":0.0023800398,"teacher_disagreement_score":0.0028567486,"about_ca_system_score_codex":0.0005088581,"about_ca_system_score_gemma":0.0008776769,"threshold_uncertainty_score":0.0066251755},"labels":[],"label_agreement":null},{"id":"W2400826247","doi":"10.1109/icassp.2016.7472109","title":"Multiview learning via deep discriminative canonical correlation analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":29,"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","funders":"","keywords":"Discriminative model; Canonical correlation; Artificial intelligence; Computer science; Pattern recognition (psychology); Correlation; Transformation (genetics); Class (philosophy); Deep learning; Speech recognition; Mathematics","score_opus":0.011897607660011881,"score_gpt":0.25016916802442585,"score_spread":0.23827156036441396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2400826247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007777502,0.00032243054,0.99025226,0.00010793821,0.000044199765,0.000021603273,0.000068585694,0.00068735937,0.0007181867],"genre_scores_gemma":[0.53386056,0.00089801004,0.45623952,0.0005080235,0.0002686018,0.00020958927,0.0014688026,0.0004136217,0.0061331512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985997,0.0003906608,0.00005548553,0.00044640523,0.00034422567,0.00016349556],"domain_scores_gemma":[0.9986166,0.00046045127,0.00014946697,0.00029997213,0.00037679696,0.0000966798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001708871,0.0014080564,0.0016363927,0.001106564,0.000488758,0.0010507667,0.0015464188,0.0007638861,0.0021927194],"category_scores_gemma":[0.004054361,0.0007207927,0.001280218,0.0015856487,0.0010262951,0.0015797918,0.0018342598,0.00194283,0.0010381215],"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":[0.0002663024,0.00017193449,0.0030244314,0.0001684844,0.00034837043,0.0001450873,0.00017007308,0.33959094,0.013450237,0.03610944,0.010121832,0.5964329],"study_design_scores_gemma":[0.0000058424434,0.000033501303,0.00029426356,0.000006240935,0.000017046861,0.000034599838,0.000009947339,0.9901424,0.0016439634,0.0068561933,0.00094239897,0.000013691387],"about_ca_topic_score_codex":0.0061243973,"about_ca_topic_score_gemma":0.008350964,"teacher_disagreement_score":0.0061243973,"about_ca_system_score_codex":0.00082650804,"about_ca_system_score_gemma":0.0018293207,"threshold_uncertainty_score":0.012177527},"labels":[],"label_agreement":null},{"id":"W2404360944","doi":"","title":"Revisiting the Performance of Weighted k-Nearest Centroid Neighbor Classifiers.","year":2013,"lang":"en","type":"article","venue":"Software Engineering and Knowledge Engineering","topic":"Face and Expression Recognition","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 Regina","funders":"","keywords":"Centroid; k-nearest neighbors algorithm; Kernel (algebra); Pattern recognition (psychology); Computer science; Artificial intelligence; Weighted voting; Rank (graph theory); Voting; Data mining; Mathematics; Algorithm; Combinatorics","score_opus":0.005989387078223789,"score_gpt":0.1835266774161679,"score_spread":0.1775372903379441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404360944","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122580394,0.011120779,0.85109514,0.0006105327,0.0011833255,0.0003470926,0.00037214987,0.002203514,0.010487098],"genre_scores_gemma":[0.69776905,0.0022039318,0.29353333,0.0002753105,0.00022518198,0.0001108236,0.0011524415,0.0002215425,0.0045084464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99276054,0.0014103774,0.00056901854,0.0012723873,0.0036159572,0.00037174247],"domain_scores_gemma":[0.98870975,0.0034662085,0.00058514084,0.0014276031,0.0056301113,0.00018116414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055719325,0.0013318413,0.0018375436,0.0020976088,0.0012119123,0.002223369,0.0022228071,0.0016398084,0.0015905444],"category_scores_gemma":[0.02225501,0.0003464932,0.0007689074,0.002168798,0.000776912,0.0043475926,0.0012143938,0.0011961785,0.001365656],"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":[0.00059184426,0.00021808749,0.0073160287,0.0003974561,0.00028416206,0.00009272475,0.00016738339,0.079355046,0.016304407,0.0062269648,0.006747324,0.8822986],"study_design_scores_gemma":[0.000021406251,0.00027156275,0.0028111394,0.000045979345,0.00009097844,0.00026104058,0.00012986513,0.9683424,0.017766714,0.0038112877,0.0063937875,0.00005384334],"about_ca_topic_score_codex":0.012131462,"about_ca_topic_score_gemma":0.010855194,"teacher_disagreement_score":0.012131462,"about_ca_system_score_codex":0.001506619,"about_ca_system_score_gemma":0.0017115005,"threshold_uncertainty_score":0.029467523},"labels":[],"label_agreement":null},{"id":"W2404410302","doi":"10.1109/swste.2016.11","title":"A Software Framework for PCa-Based Face Recognition","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Facial recognition system; Computer science; Principal component analysis; Dimensionality reduction; Software; Face (sociological concept); Pattern recognition (psychology); Artificial intelligence; Process (computing); Principal (computer security); Data mining","score_opus":0.03927196193418325,"score_gpt":0.2727603542776421,"score_spread":0.23348839234345886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404410302","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063922204,0.0000893905,0.9288515,0.000045520814,0.000037449507,0.00014847532,0.00026483205,0.06859053,0.0013331798],"genre_scores_gemma":[0.036993787,0.0005148029,0.9337265,0.00031516902,0.00009712458,0.0011535937,0.004352961,0.01569828,0.007147766],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99834335,0.00018162021,0.0001473567,0.0002736153,0.0009092944,0.00014481794],"domain_scores_gemma":[0.9983418,0.00055237394,0.00010409265,0.00037642036,0.00049319345,0.00013202589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018592239,0.0021463996,0.0013066124,0.0021816927,0.00092863565,0.0019247577,0.0040254635,0.0013410436,0.017270304],"category_scores_gemma":[0.005114797,0.0014377033,0.0021009264,0.0013896961,0.0008611308,0.0025305431,0.003434146,0.0029005448,0.0102075655],"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":[0.0006017045,0.00045413172,0.0017800503,0.0007444645,0.0003578806,0.001070008,0.00072644895,0.040306572,0.048466664,0.045753207,0.10504156,0.7546973],"study_design_scores_gemma":[0.0002632215,0.00031853007,0.001988424,0.0002731645,0.00016094098,0.0021214024,0.00014363862,0.62799454,0.056284655,0.054020714,0.25605336,0.00037740386],"about_ca_topic_score_codex":0.0054781805,"about_ca_topic_score_gemma":0.0041049556,"teacher_disagreement_score":0.017270304,"about_ca_system_score_codex":0.000675667,"about_ca_system_score_gemma":0.0015511084,"threshold_uncertainty_score":0.0577749},"labels":[],"label_agreement":null},{"id":"W2406566318","doi":"","title":"A Note on Metric Properties for Some Divergence Measures: The Gaussian Case","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":52,"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":"Bhattacharyya distance; Divergence (linguistics); Metric (unit); Kullback–Leibler divergence; Cluster analysis; Gaussian process; Multivariate normal distribution; Multivariate statistics; Artificial intelligence; Pattern recognition (psychology); Computer science; Gaussian; Measure (data warehouse); Nonlinear dimensionality reduction; Distance measures; Mathematics; Manifold (fluid mechanics); Axiom; Mixture model; Machine learning; Data mining; Dimensionality reduction; Geometry","score_opus":0.07174115009607196,"score_gpt":0.2738666433988506,"score_spread":0.20212549330277862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406566318","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006351281,0.0023712695,0.9850036,0.0015384441,0.0003438331,0.000032809643,0.00007067067,0.00012045275,0.00416756],"genre_scores_gemma":[0.25534594,0.004483564,0.7323345,0.002029086,0.0014305016,0.00038023616,0.00028999487,0.00053849275,0.0031676516],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98694694,0.005650677,0.0011714131,0.0018375153,0.0039005126,0.0004929337],"domain_scores_gemma":[0.95953906,0.027295554,0.0019982827,0.005146898,0.0051132035,0.00090701313],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0149234785,0.0016511707,0.0020169762,0.002971462,0.001832573,0.003667364,0.0021231268,0.003829699,0.0019122552],"category_scores_gemma":[0.067702726,0.00070009136,0.0022050133,0.0038281057,0.008091242,0.0112708025,0.0057997652,0.00937855,0.0010815493],"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":[0.000053774977,0.000041373645,0.00093241583,0.00018008403,0.000043506338,0.00025626045,0.00040393727,0.009267196,0.0036229743,0.9240885,0.0038492829,0.057260696],"study_design_scores_gemma":[0.000018255423,0.00017742487,0.00086574734,0.00006855058,0.000026237718,0.00091343705,0.000098807024,0.08856447,0.0031387438,0.8883221,0.017711578,0.00009469295],"about_ca_topic_score_codex":0.0013635372,"about_ca_topic_score_gemma":0.0007995714,"teacher_disagreement_score":0.0149234785,"about_ca_system_score_codex":0.0015015333,"about_ca_system_score_gemma":0.0009956947,"threshold_uncertainty_score":0.07892388},"labels":[],"label_agreement":null},{"id":"W2408859795","doi":"","title":"A hybrid manifold learning algorithm for the diagnosis and prognostication of Alzheimer's disease.","year":2017,"lang":"en","type":"article","venue":"PubMed","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"","keywords":"Isomap; Artificial intelligence; Principal component analysis; Pattern recognition (psychology); Nonlinear dimensionality reduction; Support vector machine; Computer science; Neuroimaging; Pairwise comparison; Alzheimer's Disease Neuroimaging Initiative; Machine learning; Subspace topology; Feature (linguistics); Dimensionality reduction; Cognition; Cognitive impairment; Psychology; Neuroscience","score_opus":0.046744783621755624,"score_gpt":0.25823622519954453,"score_spread":0.2114914415777889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2408859795","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013373638,0.001025647,0.9833597,0.00026217345,0.00007910103,0.00007380533,0.00009444726,0.0011101101,0.00062138436],"genre_scores_gemma":[0.20894973,0.0005716298,0.7868368,0.00019443996,0.00013042049,0.00024635062,0.0006910033,0.000117472,0.0022621555],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992449,0.00023672194,0.000052626074,0.00017180253,0.00024600144,0.000047906808],"domain_scores_gemma":[0.99936384,0.00021458525,0.000054431806,0.000083795436,0.00024884657,0.00003452589],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017515144,0.00091036526,0.0012305574,0.0020626464,0.00070005964,0.00066373305,0.0013468805,0.0012525592,0.0013638436],"category_scores_gemma":[0.003078333,0.00029873272,0.0010292566,0.001337254,0.00054143334,0.0012208488,0.0011927123,0.0010767935,0.0007626353],"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":[0.0001737368,0.00010805046,0.0024072765,0.00007778116,0.00018802006,0.000075786505,0.00010813809,0.09991377,0.0042141434,0.0071765473,0.0083681345,0.8771885],"study_design_scores_gemma":[0.000017780265,0.0000590656,0.0006157181,0.000008617412,0.00001877107,0.00010064468,0.000024480854,0.98882896,0.0011170611,0.007380202,0.0018162602,0.000012389302],"about_ca_topic_score_codex":0.0042631305,"about_ca_topic_score_gemma":0.003515442,"teacher_disagreement_score":0.0042631305,"about_ca_system_score_codex":0.0006461651,"about_ca_system_score_gemma":0.00090336415,"threshold_uncertainty_score":0.009262979},"labels":[],"label_agreement":null},{"id":"W2413405683","doi":"10.1007/s00500-016-2199-6","title":"Incorporating neighbors’ distribution knowledge into support vector machines","year":2016,"lang":"en","type":"article","venue":"Soft Computing","topic":"Face and Expression Recognition","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":"University of Regina; University of Calgary","funders":"","keywords":"Support vector machine; Computer science; Vector (molecular biology); Artificial intelligence; Biology","score_opus":0.012738929730563137,"score_gpt":0.2601791387055705,"score_spread":0.24744020897500738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2413405683","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022584327,0.00043755397,0.9751975,0.0001867472,0.000075449025,0.000030131763,0.000053605654,0.00055054395,0.0008841961],"genre_scores_gemma":[0.77073795,0.00040699207,0.22360803,0.00019432863,0.0002192445,0.000093225215,0.00044622304,0.00013341664,0.004160639],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909735,0.00028731974,0.00007441048,0.00020480016,0.0002640769,0.00007210344],"domain_scores_gemma":[0.9958455,0.0026886389,0.0001734193,0.0004017082,0.00080014864,0.00009057319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018206768,0.0006670328,0.0013645478,0.0009325396,0.0004913943,0.00096875697,0.0019071465,0.0013612671,0.002273067],"category_scores_gemma":[0.0072195833,0.00067329826,0.0006927559,0.0009968987,0.0005365576,0.0026242826,0.0010036922,0.0016549408,0.0010742224],"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":[0.00027563283,0.00027113306,0.0020542804,0.000105094165,0.00013909167,0.00006348264,0.000063669504,0.49687457,0.0023015034,0.005711273,0.002542708,0.4895976],"study_design_scores_gemma":[0.000005442581,0.000014765601,0.00010360715,0.0000036448816,0.000006248024,0.000007711823,0.000004376674,0.996503,0.0004352842,0.0027533711,0.00015961486,0.0000029652495],"about_ca_topic_score_codex":0.0063858577,"about_ca_topic_score_gemma":0.007194483,"teacher_disagreement_score":0.0063858577,"about_ca_system_score_codex":0.0004815455,"about_ca_system_score_gemma":0.00070444023,"threshold_uncertainty_score":0.012697399},"labels":[],"label_agreement":null},{"id":"W2461866071","doi":"","title":"4 - Localisation et reconnaissance de visages en temps réels avec un réseau de neurones RBF: algorithme et architecture","year":2003,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","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; Robustness (evolution); Field-programmable gate array; Artificial intelligence; Computer vision; Image processing; Pattern recognition (psychology); Computer hardware; Image (mathematics)","score_opus":0.02469003388496174,"score_gpt":0.26953570746612876,"score_spread":0.24484567358116702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2461866071","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055951267,0.00033609197,0.9405394,0.00009932748,0.000029559978,0.000030880634,0.000046096997,0.0019293724,0.0010380147],"genre_scores_gemma":[0.47951734,0.0003189131,0.5142435,0.000052061136,0.000037833837,0.000076707285,0.00009687448,0.00014235891,0.0055144387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976045,0.0000387977,0.000010895719,0.00007381175,0.0000786588,0.0000374372],"domain_scores_gemma":[0.9997577,0.000070190676,0.00002397908,0.0000296364,0.00010568903,0.000012853218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006076101,0.00059967506,0.0005680854,0.0005044535,0.0002727825,0.00068882696,0.0008569632,0.0012392494,0.0017159701],"category_scores_gemma":[0.0010337703,0.00027657658,0.000402416,0.00032565216,0.0003910172,0.00078566105,0.00036151166,0.000525982,0.0012417631],"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":[0.00065504917,0.000106266285,0.0024461488,0.00018325307,0.00009762421,0.00017387768,0.00016224053,0.14564599,0.19551675,0.0037262056,0.0016967892,0.64958984],"study_design_scores_gemma":[0.000040277715,0.0002175174,0.0027185036,0.000021572414,0.00003533494,0.0003116319,0.000036893573,0.90091133,0.09115357,0.0012405913,0.0032766045,0.000036196554],"about_ca_topic_score_codex":0.005766766,"about_ca_topic_score_gemma":0.004453529,"teacher_disagreement_score":0.005766766,"about_ca_system_score_codex":0.0005351176,"about_ca_system_score_gemma":0.00036749808,"threshold_uncertainty_score":0.011466444},"labels":[],"label_agreement":null},{"id":"W2467311320","doi":"10.20381/ruor-6160","title":"Human Emotion Recognition from Body Language of the Head using Soft Computing Techniques","year":2012,"lang":"en","type":"dissertation","venue":"uO Research (University of Ottawa)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"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":"Gaze; Facial expression; Movement (music); Head (geology); Computer science; Eye tracking; Computer vision; Artificial intelligence; Expression (computer science); Eye movement; Communication; Human–computer interaction; Psychology; Cognitive psychology","score_opus":0.07674602896116631,"score_gpt":0.3520465884852115,"score_spread":0.2753005595240452,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2467311320","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26545897,0.0009241753,0.727111,0.00031689613,0.00015644198,0.00015084763,0.00019417323,0.000521103,0.005166477],"genre_scores_gemma":[0.8651958,0.0008350829,0.12965515,0.0001291998,0.00007214771,0.0001967026,0.00020901955,0.000046191228,0.0036606784],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975353,0.0000594707,0.000020855226,0.000052578696,0.000085294014,0.000028189792],"domain_scores_gemma":[0.9997459,0.000105062514,0.000045768025,0.000017879314,0.00007145828,0.000013890084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026498057,0.0003983969,0.00036142685,0.0006922441,0.00017818123,0.0007433822,0.0002111746,0.00032243435,0.0013682187],"category_scores_gemma":[0.00098725,0.00014384669,0.0006368774,0.0005115133,0.0003250842,0.00052725547,0.0004693325,0.00031576876,0.00035788232],"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":[0.00037715305,0.00010775937,0.0061716074,0.0005603386,0.00013221854,0.0003768255,0.0006836925,0.02558994,0.45901114,0.0029162632,0.0015995433,0.5024734],"study_design_scores_gemma":[0.00004969084,0.0005711665,0.05282253,0.00015147979,0.00024407031,0.0007250236,0.0011560228,0.7995462,0.13099119,0.008217017,0.005412386,0.00011327354],"about_ca_topic_score_codex":0.0005496971,"about_ca_topic_score_gemma":0.0006163333,"teacher_disagreement_score":0.0013682187,"about_ca_system_score_codex":0.00016889608,"about_ca_system_score_gemma":0.00021257477,"threshold_uncertainty_score":0.00457716},"labels":[],"label_agreement":null},{"id":"W2499161046","doi":"10.1007/978-3-642-28258-4","title":"Partially Supervised Learning","year":2012,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":21,"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":"Computer science; Artificial intelligence; Machine learning","score_opus":0.018327215123471786,"score_gpt":0.24362950601024758,"score_spread":0.22530229088677578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2499161046","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040313113,0.0022413672,0.97222704,0.0005257879,0.00032485445,0.00006781052,0.00089332636,0.0044430126,0.0152455075],"genre_scores_gemma":[0.15894362,0.0022985488,0.73063725,0.00077839283,0.0008923561,0.00040395375,0.010979193,0.0016750845,0.09339147],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990971,0.00020257285,0.000045398097,0.00036479914,0.00023415487,0.00005597898],"domain_scores_gemma":[0.99861825,0.00044245593,0.00006152858,0.00053912436,0.00028368327,0.000054991422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009608636,0.0012006286,0.0012469218,0.0007605862,0.0004872455,0.0010905701,0.0016961084,0.0012220651,0.015298781],"category_scores_gemma":[0.0027594431,0.00057388487,0.0009795112,0.00086348975,0.0006592802,0.0018668789,0.0019367572,0.0018896636,0.010547991],"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":[0.00009655909,0.000098709505,0.00042438542,0.0001906439,0.000104145125,0.000045364708,0.00003527654,0.04656648,0.0029075185,0.018785542,0.068252064,0.8624933],"study_design_scores_gemma":[0.000025364066,0.000103220285,0.0006691112,0.00006897897,0.0000503043,0.00022261842,0.000026424052,0.8621156,0.0065168277,0.085483946,0.04469016,0.000027547874],"about_ca_topic_score_codex":0.0010965817,"about_ca_topic_score_gemma":0.0023017894,"teacher_disagreement_score":0.015298781,"about_ca_system_score_codex":0.00044212994,"about_ca_system_score_gemma":0.00081500865,"threshold_uncertainty_score":0.05117953},"labels":[],"label_agreement":null},{"id":"W2500190401","doi":"10.1007/978-3-319-39378-0_50","title":"Linguistic Descriptors and Analytic Hierarchy Process in Face Recognition Realized by Humans","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Computer science; Voting; Identification (biology); Facial recognition system; Analytic hierarchy process; Artificial intelligence; Face (sociological concept); Process (computing); Suspect; Hierarchy; Machine learning; Parametric statistics; Natural language processing; Pattern recognition (psychology); Linguistics; Operations research; Mathematics; Statistics","score_opus":0.02179805180015682,"score_gpt":0.2619041789382045,"score_spread":0.2401061271380477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500190401","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12888925,0.005194061,0.8461552,0.0011077366,0.00015885287,0.00005459427,0.00022470421,0.0004385817,0.017776899],"genre_scores_gemma":[0.79203725,0.0019345249,0.19681634,0.00009331,0.00013094836,0.00005565969,0.00022463435,0.000059732978,0.008647625],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99981564,0.00005796783,0.000010240072,0.00003907206,0.000053139618,0.000023920855],"domain_scores_gemma":[0.99973804,0.00014143056,0.000027393626,0.000030511603,0.000047759797,0.0000147575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005001082,0.000158951,0.00025314116,0.0005796203,0.00034022037,0.0011969245,0.00051097386,0.00031854206,0.0018844339],"category_scores_gemma":[0.0017114778,0.00016930887,0.0002954705,0.00075202656,0.0007590077,0.001627821,0.0004142993,0.00067615794,0.00042476394],"study_design_candidate":"observational","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":[0.00015215509,0.00006839833,0.0015274312,0.00022116944,0.000037136953,0.000118794174,0.001281907,0.025605617,0.015856186,0.6422226,0.0047295233,0.30817914],"study_design_scores_gemma":[0.000015154351,0.0000622625,0.0029248158,0.00004465446,0.000028377284,0.00008233462,0.00048360016,0.34299308,0.005053402,0.6407472,0.0075288867,0.000036143807],"about_ca_topic_score_codex":0.004520363,"about_ca_topic_score_gemma":0.0032764766,"teacher_disagreement_score":0.004520363,"about_ca_system_score_codex":0.00079556997,"about_ca_system_score_gemma":0.0006086853,"threshold_uncertainty_score":0.008988082},"labels":[],"label_agreement":null},{"id":"W2500256459","doi":"10.5539/mas.v10n9p245","title":"One Method to Reduce Data Classification Using Weighting Technique in SVM +","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Face and Expression Recognition","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":"Support vector machine; Weighting; Replicate; Computer science; Pattern recognition (psychology); Function (biology); Artificial intelligence; Interval (graph theory); Data mining; Mathematics; Statistics","score_opus":0.1603792813823001,"score_gpt":0.3680402540780572,"score_spread":0.2076609726957571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2500256459","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009632248,0.0002806603,0.9882735,0.000081049686,0.00008974163,0.0000667607,0.000048351445,0.0009022043,0.0006254911],"genre_scores_gemma":[0.17344594,0.00044538852,0.82029307,0.0001438635,0.000100917576,0.00021945353,0.00048150268,0.00023025065,0.004639639],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989863,0.00014309652,0.00011343337,0.00023870844,0.00045160396,0.00006685457],"domain_scores_gemma":[0.9990337,0.00018184168,0.000093483286,0.00015993479,0.00049857254,0.000032572705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085480977,0.0009430068,0.0009087083,0.0009758078,0.00041418837,0.0008910418,0.0010675804,0.00072847964,0.0024492282],"category_scores_gemma":[0.0018533433,0.00032277143,0.0007872585,0.00087512744,0.00026784034,0.0012694111,0.000704961,0.00095079973,0.0013146764],"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":[0.0002055385,0.00019858783,0.0022908123,0.00035186223,0.00015265108,0.00007565969,0.00009144049,0.020054745,0.0934994,0.0062261443,0.005200149,0.8716531],"study_design_scores_gemma":[0.00004996996,0.00036129003,0.0032684724,0.000045009543,0.000110639914,0.00050896255,0.00007916435,0.84067583,0.12703037,0.006284051,0.021531736,0.000054535456],"about_ca_topic_score_codex":0.0013267879,"about_ca_topic_score_gemma":0.0014046924,"teacher_disagreement_score":0.0024492282,"about_ca_system_score_codex":0.00036493415,"about_ca_system_score_gemma":0.00056538044,"threshold_uncertainty_score":0.008193433},"labels":[],"label_agreement":null},{"id":"W2506488269","doi":"10.1007/11362197_4","title":"A New Theoretical Framework for K-Means-Type Clustering","year":2005,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Semidefinite programming; Cluster analysis; Mathematics; Heuristics; Kernel (algebra); Positive-definite matrix; Spectral clustering; Matrix (chemical analysis); Algorithm; Mathematical optimization; Computer science; Theoretical computer science; Discrete mathematics; Eigenvalues and eigenvectors","score_opus":0.050355809767088096,"score_gpt":0.3245408982911945,"score_spread":0.2741850885241064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2506488269","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008626528,0.0007051979,0.99203765,0.0006883669,0.0001145625,0.000028342796,0.000054998465,0.00008100226,0.005427106],"genre_scores_gemma":[0.11488808,0.0025089462,0.86989015,0.0010609856,0.0010118969,0.0005089194,0.00033835467,0.00024612786,0.009546568],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99620134,0.0013810672,0.00021827348,0.0007340131,0.0012830836,0.00018230824],"domain_scores_gemma":[0.99681395,0.0017025499,0.00016986477,0.00047299048,0.00073535246,0.000105426785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041332203,0.0010851072,0.0019078909,0.003066287,0.0025057958,0.0055490253,0.0053297654,0.0036618481,0.0057997713],"category_scores_gemma":[0.011874032,0.0008563261,0.0019763939,0.0053113787,0.0070941946,0.0074425964,0.0039954004,0.005490204,0.0018528504],"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":[0.000005331564,0.0000076246174,0.00006174771,0.000050007588,0.000013650859,0.000013980752,0.00012328802,0.0077640447,0.00016833645,0.9796196,0.0014884322,0.010683945],"study_design_scores_gemma":[0.000005821917,0.000008778032,0.00007135532,0.000023346707,0.000007430313,0.00004425266,0.00003936758,0.06558299,0.00017927696,0.9264712,0.0075518284,0.0000144139185],"about_ca_topic_score_codex":0.0030381398,"about_ca_topic_score_gemma":0.002728577,"teacher_disagreement_score":0.0057997713,"about_ca_system_score_codex":0.0031285048,"about_ca_system_score_gemma":0.002014905,"threshold_uncertainty_score":0.022698939},"labels":[],"label_agreement":null},{"id":"W2507645579","doi":"10.1109/ssp.2016.7551818","title":"Order-based generalized multivariate regression","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"McGill University","funders":"","keywords":"Multivariate statistics; Mathematics; Monotonic function; Estimator; Bayesian multivariate linear regression; Rate of convergence; Algorithm; Transformation (genetics); Applied mathematics; Parametric statistics; Mathematical optimization; Computer science; Linear regression; Statistics","score_opus":0.026097330308692785,"score_gpt":0.270606059712865,"score_spread":0.2445087294041722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507645579","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00756752,0.00012275076,0.99153906,0.000084673666,0.000014409774,0.000020272184,0.00002980577,0.00023696393,0.00038458363],"genre_scores_gemma":[0.33424738,0.00043108413,0.6590067,0.00022677823,0.0001185937,0.00015932614,0.0003893484,0.00034873615,0.005071994],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99814296,0.0010232776,0.000067642526,0.00032818445,0.00030399382,0.00013402867],"domain_scores_gemma":[0.9973187,0.0014938171,0.00028412882,0.000503793,0.00030667058,0.000092975824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030295611,0.0012581724,0.0016668593,0.0007535115,0.0004187039,0.0008491219,0.0018314351,0.0011840273,0.0021731278],"category_scores_gemma":[0.007055646,0.000518577,0.0010660504,0.0013985676,0.00113517,0.0014023507,0.0016307381,0.0016315737,0.0006928094],"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":[0.00010489223,0.000045150817,0.00094771414,0.000086432214,0.000086909706,0.00006294016,0.00007005297,0.8471902,0.0032907634,0.035309725,0.0012312632,0.11157401],"study_design_scores_gemma":[0.0000064217834,0.00001944292,0.0001412399,0.0000027543672,0.0000048531847,0.000013921159,0.0000042868387,0.991115,0.0004429816,0.007832733,0.00041000877,0.0000064008173],"about_ca_topic_score_codex":0.0046217702,"about_ca_topic_score_gemma":0.0054595824,"teacher_disagreement_score":0.0046217702,"about_ca_system_score_codex":0.00096939265,"about_ca_system_score_gemma":0.0012945542,"threshold_uncertainty_score":0.016022027},"labels":[],"label_agreement":null},{"id":"W2508202655","doi":"10.1109/iscas.2016.7527309","title":"Multiview emotion recognition via multi-set locality preserving canonical correlation analysis","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"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","funders":"","keywords":"Canonical correlation; Locality; Computer science; Correlation; Pattern recognition (psychology); Emotion recognition; Set (abstract data type); Artificial intelligence; Basis (linear algebra); Data correlation; Data set; Fusion; Data mining; Mathematics","score_opus":0.05873250954835924,"score_gpt":0.2916485848936467,"score_spread":0.23291607534528747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508202655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0136327,0.00031587644,0.98480856,0.000095284,0.0000381636,0.000027975908,0.000052954292,0.00034739354,0.00068104465],"genre_scores_gemma":[0.5005321,0.00081493106,0.49537167,0.0002504797,0.00019379564,0.00018307302,0.00053605787,0.00022521846,0.0018925961],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989054,0.00030819338,0.000045207304,0.00029693698,0.00034186122,0.0001023657],"domain_scores_gemma":[0.9990694,0.00023188283,0.00011314773,0.00018549283,0.0003323324,0.00006786945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010790162,0.00089817983,0.0010181342,0.0013391344,0.0005268287,0.00097390835,0.0009276086,0.00053019734,0.0014392869],"category_scores_gemma":[0.002750251,0.00035408276,0.0012098477,0.0014775003,0.0007237604,0.0014973832,0.0014667838,0.0010234388,0.0006060673],"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":[0.00042624568,0.00015981082,0.0034970688,0.00023818172,0.0003658338,0.00023835101,0.0003923097,0.10516304,0.077607654,0.02121874,0.0066447244,0.7840481],"study_design_scores_gemma":[0.000010491128,0.00009857622,0.0016857523,0.000013940859,0.000053410102,0.00016509702,0.00007286149,0.9762842,0.012768062,0.006772532,0.0020240285,0.000050949162],"about_ca_topic_score_codex":0.0021401036,"about_ca_topic_score_gemma":0.002797526,"teacher_disagreement_score":0.0021401036,"about_ca_system_score_codex":0.0004213652,"about_ca_system_score_gemma":0.0007778091,"threshold_uncertainty_score":0.005706489},"labels":[],"label_agreement":null},{"id":"W2508514794","doi":"10.1049/iet-bmt.2015.0120","title":"Multimodal 2D–3D face recognition using local descriptors: pyramidal shape map and structural context","year":2016,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Histogram; Shape context; Pyramid (geometry); Context (archaeology); Facial recognition system; Face (sociological concept); Computer vision; Gaussian; Image (mathematics); Mathematics","score_opus":0.04587877145520918,"score_gpt":0.2646333710308942,"score_spread":0.218754599575685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508514794","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08898624,0.00092056935,0.9060688,0.00013451226,0.00007770205,0.00009051583,0.00019611197,0.0010649806,0.0024605189],"genre_scores_gemma":[0.7106353,0.00078838127,0.28427315,0.00014633946,0.00011182259,0.00010234228,0.0006067997,0.00009273407,0.0032429986],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996364,0.000043431388,0.000016627047,0.00007437526,0.00019103417,0.000038075155],"domain_scores_gemma":[0.99973506,0.000046080193,0.00003766997,0.00006706182,0.000096855954,0.000017214576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036436509,0.00047639073,0.0006820841,0.0013466617,0.0001763329,0.0005854032,0.00055624684,0.0003967094,0.0015774124],"category_scores_gemma":[0.0007858386,0.00017331411,0.00069964014,0.0010611886,0.0002865922,0.0009914747,0.0008848154,0.00032136793,0.0009604156],"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":[0.00031374127,0.00013372961,0.002854263,0.00018363725,0.00012181957,0.00016331229,0.00007761776,0.020788392,0.16537468,0.0031470307,0.0026319616,0.8042099],"study_design_scores_gemma":[0.000028889888,0.0005380798,0.013678495,0.000028519347,0.00016940551,0.0013893436,0.00016133153,0.86790824,0.10449909,0.0042699305,0.007236203,0.000092489536],"about_ca_topic_score_codex":0.0012011504,"about_ca_topic_score_gemma":0.0017353963,"teacher_disagreement_score":0.0015774124,"about_ca_system_score_codex":0.0002605951,"about_ca_system_score_gemma":0.00034301553,"threshold_uncertainty_score":0.005276978},"labels":[],"label_agreement":null},{"id":"W2510588791","doi":"","title":"Metric learning revisited new approaches for supervised and unsupervised metric learning with analysis and algorithms","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Metric (unit); Equivalence of metrics; Metric space; Euclidean distance; Mathematics; Artificial intelligence; Set (abstract data type); Supervised learning; Intrinsic metric; Unsupervised learning; Semi-supervised learning; Space (punctuation); Similarity (geometry); Euclidean space; Convex metric space; Algorithm; Computer science; Discrete mathematics; Combinatorics; Artificial neural network","score_opus":0.043809689760648494,"score_gpt":0.24721855012685656,"score_spread":0.20340886036620806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510588791","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00061889424,0.011217023,0.98195535,0.0019490304,0.0005401975,0.00006795295,0.00008957633,0.00013964092,0.003422291],"genre_scores_gemma":[0.037673417,0.013564616,0.9368213,0.0018954859,0.0038548852,0.0006387425,0.0003929265,0.00029717505,0.004861511],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9809157,0.009378274,0.0015259199,0.0035271544,0.004331628,0.00032135448],"domain_scores_gemma":[0.97949517,0.01366071,0.0010830318,0.0026151545,0.0026024822,0.0005435594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015045505,0.0038907863,0.0034606024,0.0065706205,0.0016530014,0.008550428,0.005225214,0.005071705,0.0033808225],"category_scores_gemma":[0.034072652,0.0015502594,0.00371096,0.008718037,0.012089914,0.014718625,0.008362023,0.013783478,0.0021458482],"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":[0.000036983238,0.000040391165,0.0004285778,0.00041021383,0.00012978222,0.000078451216,0.00033309733,0.012252231,0.0004523736,0.9021488,0.006087274,0.07760179],"study_design_scores_gemma":[0.000021515109,0.00007716033,0.00025664177,0.00014909384,0.000030240812,0.00019377601,0.00007742954,0.06495604,0.00053514604,0.88344556,0.050193742,0.000063641404],"about_ca_topic_score_codex":0.0022834407,"about_ca_topic_score_gemma":0.0014624449,"teacher_disagreement_score":0.015045505,"about_ca_system_score_codex":0.00432291,"about_ca_system_score_gemma":0.002664765,"threshold_uncertainty_score":0.07956922},"labels":[],"label_agreement":null},{"id":"W2514735102","doi":"10.1049/iet-bmt.2015.0103","title":"Hyperspectral face recognition with log‐polar Fourier features and collaborative representation based voting classifiers","year":2016,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Face and Expression Recognition","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":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Computer science; Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Computer vision","score_opus":0.028587643627251705,"score_gpt":0.26127712222124844,"score_spread":0.23268947859399675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2514735102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042118117,0.0002801244,0.95434886,0.00016092115,0.000090274945,0.00010909111,0.000036366357,0.00039922868,0.0024569717],"genre_scores_gemma":[0.58728683,0.00022446929,0.4072534,0.00018230366,0.00013029201,0.0001874043,0.00024007901,0.0000598533,0.004435331],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979843,0.000509161,0.000100554855,0.0004042468,0.0008166921,0.00018499172],"domain_scores_gemma":[0.99858934,0.00043410502,0.00014319552,0.00026671385,0.00051495136,0.000051614337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021868963,0.0007319089,0.0013995696,0.0014101462,0.00048456283,0.001002307,0.0016366022,0.0013368024,0.0014384832],"category_scores_gemma":[0.0034052087,0.0004109404,0.0011598555,0.0010089157,0.00061772706,0.0019438241,0.0012060106,0.0009486304,0.0007183963],"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":[0.00045069715,0.0004663325,0.0025092622,0.000089431895,0.00019065326,0.00008265531,0.00010921953,0.08879706,0.042398535,0.0077277413,0.002970421,0.854208],"study_design_scores_gemma":[0.000013584169,0.000104055,0.00061231747,0.0000049488094,0.000026892909,0.00007557049,0.000017365488,0.9849439,0.0119802905,0.0014599958,0.0007443032,0.000016824806],"about_ca_topic_score_codex":0.0016672347,"about_ca_topic_score_gemma":0.0016925555,"teacher_disagreement_score":0.0021868963,"about_ca_system_score_codex":0.0005631069,"about_ca_system_score_gemma":0.00040048567,"threshold_uncertainty_score":0.011565566},"labels":[],"label_agreement":null},{"id":"W2544435399","doi":"10.1109/apeie.2006.4292568","title":"Feature Spaces Combination in Face Recognition Task using Support Vector Machines","year":2006,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Memorial University of Newfoundland","funders":"","keywords":"Support vector machine; Computer science; Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Face (sociological concept); Task (project management); Feature (linguistics); Feature vector; Feature extraction; Speech recognition; Engineering","score_opus":0.017194012103537742,"score_gpt":0.24871618433399445,"score_spread":0.2315221722304567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544435399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3492179,0.0021392284,0.6416869,0.00043037863,0.00033751965,0.00011873368,0.00030704917,0.002404667,0.0033576034],"genre_scores_gemma":[0.8685897,0.00039778097,0.12707172,0.00008963837,0.00010517955,0.000074962976,0.00035023826,0.00006555007,0.00325536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931693,0.00018778011,0.0000451394,0.000107062064,0.00022950323,0.000113545175],"domain_scores_gemma":[0.99929094,0.00027869668,0.00003767795,0.0000680505,0.00027778363,0.00004679224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009428005,0.00058700424,0.0010062942,0.0008494739,0.0003501978,0.0006318438,0.0005396404,0.00066614687,0.0026473678],"category_scores_gemma":[0.0019507591,0.00030268903,0.00069111754,0.00097221776,0.00018333476,0.00093975494,0.0005118301,0.00078607263,0.0010694136],"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":[0.00091707724,0.0002884688,0.0032041178,0.00006975455,0.00011851364,0.000062563005,0.000035403325,0.016677702,0.035077337,0.00052047364,0.002814008,0.9402145],"study_design_scores_gemma":[0.00005872232,0.00044758042,0.007134918,0.000013228867,0.00018640977,0.00020733115,0.000083835694,0.9301102,0.057969883,0.001687803,0.0020705562,0.000029550816],"about_ca_topic_score_codex":0.0020030758,"about_ca_topic_score_gemma":0.0018676397,"teacher_disagreement_score":0.0026473678,"about_ca_system_score_codex":0.00022323345,"about_ca_system_score_gemma":0.00044197487,"threshold_uncertainty_score":0.008856356},"labels":[],"label_agreement":null},{"id":"W2545851563","doi":"10.1007/s10618-016-0481-y","title":"Multiple Bayesian discriminant functions for high-dimensional massive data classification","year":2016,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Université Pierre et Marie Curie; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Artificial intelligence; Pattern recognition (psychology); Weighting; Naive Bayes classifier; Machine learning; Computer science; Linear discriminant analysis; Discriminant; Bayesian probability; Classifier (UML); Bayesian inference; Feature (linguistics); Clustering high-dimensional data; Data mining; Mathematics; Support vector machine; Cluster analysis","score_opus":0.09958120661649893,"score_gpt":0.30668627265241966,"score_spread":0.20710506603592072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545851563","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006363886,0.00089245796,0.99112844,0.00039550304,0.000052076295,0.000035464418,0.00021350566,0.0006317725,0.00028688685],"genre_scores_gemma":[0.22347498,0.001134077,0.77018535,0.00027783457,0.00034174332,0.00042273547,0.0014551259,0.00017829092,0.0025297862],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99684876,0.0012952127,0.00025078747,0.0004791727,0.0009120027,0.00021411279],"domain_scores_gemma":[0.99252653,0.0047547715,0.00044967755,0.0011806673,0.00086789945,0.00022045204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004874636,0.0011857338,0.0029473337,0.0028370586,0.0012770194,0.0019068879,0.0027534056,0.0017751312,0.0020128377],"category_scores_gemma":[0.017747335,0.00086731877,0.0014789841,0.0033284018,0.0010762301,0.002652093,0.003052948,0.0033046266,0.0016813002],"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":[0.000317114,0.00030285734,0.0037761582,0.0002740857,0.00025407798,0.00013084797,0.00015787478,0.21328717,0.0031697312,0.04055574,0.011318442,0.7264558],"study_design_scores_gemma":[0.000013620056,0.000016668779,0.00048177107,0.000016938848,0.000015617155,0.000035204375,0.000023942295,0.9545681,0.0005560768,0.043141875,0.0011148663,0.000015383761],"about_ca_topic_score_codex":0.0037328922,"about_ca_topic_score_gemma":0.004748477,"teacher_disagreement_score":0.004874636,"about_ca_system_score_codex":0.0010339589,"about_ca_system_score_gemma":0.001680133,"threshold_uncertainty_score":0.025779843},"labels":[],"label_agreement":null},{"id":"W2549186529","doi":"10.1109/fuzz-ieee.2016.7737913","title":"Imputation of missing data using fuzzy neighborhood density-based clustering","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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 Windsor","funders":"","keywords":"Imputation (statistics); Missing data; Data mining; Cluster analysis; Computer science; Fuzzy logic; Fuzzy clustering; Artificial intelligence; Statistics; Pattern recognition (psychology); Machine learning; Mathematics","score_opus":0.06652938555001926,"score_gpt":0.2981647245585485,"score_spread":0.23163533900852923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549186529","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012471375,0.00013174582,0.98672706,0.00006132543,0.000021446902,0.00003356039,0.00006781105,0.00018513043,0.0003005574],"genre_scores_gemma":[0.32614076,0.00025423247,0.6718091,0.00006457101,0.00004112541,0.00015929835,0.0005930466,0.00005974258,0.0008781029],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806374,0.00064039987,0.00011891426,0.00040955076,0.00065088563,0.000116545096],"domain_scores_gemma":[0.9961332,0.0018758548,0.00036020292,0.0005452488,0.0010127992,0.00007275115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041714306,0.0005153656,0.0016769615,0.0016252556,0.0011989122,0.0008646194,0.002242643,0.0011280709,0.0007839731],"category_scores_gemma":[0.011543641,0.00041892708,0.0013900099,0.0018642924,0.0005981985,0.0014466763,0.0009824857,0.0011186934,0.0003297897],"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":[0.00033247087,0.00018415823,0.008997515,0.00027595364,0.00033735004,0.00033860226,0.00057508936,0.601953,0.005765102,0.018347843,0.0030540857,0.35983878],"study_design_scores_gemma":[0.000010838598,0.000041042065,0.0010855128,0.000023327906,0.000028172175,0.000112241025,0.0000800935,0.985762,0.0034478945,0.008339697,0.0010383277,0.000030792606],"about_ca_topic_score_codex":0.0054869866,"about_ca_topic_score_gemma":0.004696669,"teacher_disagreement_score":0.0054869866,"about_ca_system_score_codex":0.0007646311,"about_ca_system_score_gemma":0.001565121,"threshold_uncertainty_score":0.022060871},"labels":[],"label_agreement":null},{"id":"W2549394911","doi":"10.1109/ijcnn.2016.7727477","title":"Relational Fisher Analysis: A general framework for dimensionality reduction","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Dimensionality reduction; Linear discriminant analysis; Computer science; Kernel (algebra); Reduction (mathematics); Artificial intelligence; Curse of dimensionality; Pattern recognition (psychology); Nonlinear dimensionality reduction; Facial recognition system; Machine learning; Representation (politics); Kernel Fisher discriminant analysis; Principal component analysis; Relational database; Data mining; Mathematics","score_opus":0.0430556779222243,"score_gpt":0.28202217501788907,"score_spread":0.23896649709566475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2549394911","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007763841,0.00050533697,0.9976023,0.00017268411,0.00003631896,0.000030834708,0.00009930392,0.0001387123,0.0006382017],"genre_scores_gemma":[0.076803915,0.0025365737,0.915466,0.0004132761,0.0004357504,0.00041249135,0.00083812454,0.00021975067,0.0028741679],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99510974,0.0018340197,0.0003337005,0.0010473704,0.0014438432,0.00023140763],"domain_scores_gemma":[0.99683726,0.0011554463,0.00032450934,0.0009124816,0.0006552497,0.000114976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060223853,0.0018018186,0.0018765945,0.004136018,0.0012739998,0.0034342958,0.0023446812,0.0010436787,0.0034147205],"category_scores_gemma":[0.010204006,0.0007216653,0.0026178104,0.004404142,0.002939929,0.0052944687,0.0044311034,0.0031104865,0.0016745117],"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":[0.00007571066,0.00006500571,0.0018637172,0.00042813603,0.0002991786,0.00017638008,0.00047905312,0.04274144,0.005028391,0.64143574,0.008421793,0.29898545],"study_design_scores_gemma":[0.0000189067,0.00007848202,0.0011681198,0.00010934164,0.00008878103,0.0003832096,0.00014345493,0.27038944,0.0029477403,0.69277334,0.031781398,0.00011789348],"about_ca_topic_score_codex":0.0036881368,"about_ca_topic_score_gemma":0.00294134,"teacher_disagreement_score":0.0060223853,"about_ca_system_score_codex":0.0011675813,"about_ca_system_score_gemma":0.0020698898,"threshold_uncertainty_score":0.031849742},"labels":[],"label_agreement":null},{"id":"W2554546292","doi":"10.1109/fuzz-ieee.2016.7737813","title":"Linguistic descriptors and fuzzy sets in face recognition realized by humans","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Computer science; Face (sociological concept); Artificial intelligence; Facial recognition system; Fuzzy set; Pattern recognition (psychology); Hierarchy; Fuzzy logic; Natural language processing; Exploit; Process (computing); Speech recognition; Linguistics","score_opus":0.025679345779008475,"score_gpt":0.2541220460585242,"score_spread":0.2284427002795157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554546292","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2374344,0.004193867,0.7328465,0.001846827,0.00014422064,0.00013062879,0.00013198066,0.0002668797,0.023004655],"genre_scores_gemma":[0.8237004,0.0008543969,0.1729834,0.0001360113,0.000076812976,0.0000750346,0.00007726798,0.0000206259,0.0020760081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994228,0.000227595,0.000029709458,0.00008799558,0.00020275544,0.000029200752],"domain_scores_gemma":[0.99909973,0.000605823,0.00009564036,0.000073378964,0.000094069626,0.00003136846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00136529,0.00021229264,0.00023204346,0.0010335501,0.00036818243,0.0010408276,0.00038176257,0.0005844438,0.0014561871],"category_scores_gemma":[0.004563687,0.0001581793,0.0002622223,0.00057553995,0.0014217584,0.0018299993,0.0003918432,0.00055471197,0.0002382812],"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":[0.0005020174,0.00014460279,0.0045852326,0.00048239512,0.00007608733,0.00042150714,0.0026172863,0.05941325,0.042881247,0.43330443,0.0023052557,0.4532667],"study_design_scores_gemma":[0.00006826745,0.00026703812,0.008707454,0.00018209357,0.000057204375,0.0003931215,0.0013131886,0.3437327,0.013761132,0.6206908,0.0106897745,0.00013717606],"about_ca_topic_score_codex":0.002199411,"about_ca_topic_score_gemma":0.0016456187,"teacher_disagreement_score":0.002199411,"about_ca_system_score_codex":0.000596615,"about_ca_system_score_gemma":0.00040242902,"threshold_uncertainty_score":0.007220447},"labels":[],"label_agreement":null},{"id":"W2563145168","doi":"10.1609/aaai.v30i1.10229","title":"Multitask Generalized Eigenvalue Program","year":2016,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Subspace topology; Computer science; Principal component analysis; Multi-task learning; Eigenvalues and eigenvectors; Machine learning; Linear discriminant analysis; Artificial intelligence; Unsupervised learning; Benchmark (surveying); Pattern recognition (psychology); Task (project management)","score_opus":0.08226779278877065,"score_gpt":0.3105908612917288,"score_spread":0.22832306850295814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563145168","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0050634113,0.00013171727,0.9916871,0.0002813229,0.000043697873,0.000045914625,0.0000753715,0.00011373813,0.0025576614],"genre_scores_gemma":[0.44880113,0.00059086905,0.533134,0.00058640225,0.0003061126,0.0010366491,0.0006917762,0.00039027436,0.014462839],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99845695,0.0007316429,0.000050899973,0.00024002085,0.0003265183,0.00019392691],"domain_scores_gemma":[0.99786276,0.0011795403,0.00017804539,0.0001765689,0.00040886365,0.00019417873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022297811,0.0013547067,0.0013288484,0.0007353944,0.0005747763,0.0014900047,0.0016266815,0.0015113457,0.005867438],"category_scores_gemma":[0.006031258,0.0005330609,0.0011211412,0.00092423754,0.0011948501,0.0019207753,0.002328683,0.0016707487,0.00090348086],"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":[0.000114019196,0.00012984421,0.0009518337,0.0001937901,0.00012400484,0.00030618816,0.00014859039,0.7000418,0.0018166575,0.22543982,0.009915114,0.060818326],"study_design_scores_gemma":[0.000009325284,0.000022489337,0.00006440787,0.0000052768055,0.0000038734124,0.000022139491,0.000012788108,0.939972,0.00015449629,0.058677413,0.0010489998,0.0000067980245],"about_ca_topic_score_codex":0.002475637,"about_ca_topic_score_gemma":0.0029361758,"teacher_disagreement_score":0.005867438,"about_ca_system_score_codex":0.0006998309,"about_ca_system_score_gemma":0.001975399,"threshold_uncertainty_score":0.019628584},"labels":[],"label_agreement":null},{"id":"W2564304793","doi":"10.1049/iet-ipr.2016.0722","title":"Hyperspectral face recognition via feature extraction and CRC‐based classifier","year":2016,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":28,"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":"Hong Kong Polytechnic University","keywords":"Hyperspectral imaging; Artificial intelligence; Pattern recognition (psychology); Computer science; Feature extraction; Facial recognition system; Classifier (UML); Three-dimensional face recognition; Computer vision; Face detection","score_opus":0.02124485896515857,"score_gpt":0.26771509180495234,"score_spread":0.24647023283979377,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564304793","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17718996,0.00068276515,0.81143314,0.00028145805,0.00025478593,0.00034259798,0.0003660917,0.0036548802,0.005794293],"genre_scores_gemma":[0.5384898,0.00041689727,0.4511466,0.00025953792,0.00010096615,0.00031072073,0.0010437388,0.00009087685,0.008140915],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999111,0.00007465117,0.00004413674,0.00016525073,0.00051563897,0.00008925191],"domain_scores_gemma":[0.9993018,0.00011694166,0.000074195385,0.000101388505,0.00038149214,0.000024226772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006205518,0.00054662465,0.00092405535,0.0016724603,0.00032344725,0.0006233699,0.0008995296,0.0008780412,0.0023051472],"category_scores_gemma":[0.0014552029,0.00025668638,0.0006819255,0.0009542615,0.0002628862,0.0011426504,0.00067819277,0.00049962697,0.0013985158],"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":[0.0003267495,0.00034715346,0.0019825788,0.0000666423,0.000058326077,0.000067095265,0.000032264154,0.007285099,0.15645118,0.001227482,0.004770764,0.82738465],"study_design_scores_gemma":[0.00005169966,0.00060963264,0.013048027,0.000021902413,0.00009402587,0.000777477,0.00005789629,0.7407211,0.23743993,0.0009870037,0.006091959,0.00009933527],"about_ca_topic_score_codex":0.0025570807,"about_ca_topic_score_gemma":0.0024780652,"teacher_disagreement_score":0.0025570807,"about_ca_system_score_codex":0.00047132847,"about_ca_system_score_gemma":0.0004450474,"threshold_uncertainty_score":0.00771147},"labels":[],"label_agreement":null},{"id":"W2567193860","doi":"","title":"Hair Color Classification in Face Recognition using Machine Learning Algorithms","year":2016,"lang":"en","type":"article","venue":"American Scientific Research Journal for Engineering, Technology, and Sciences (Global Society of Scientific Research and Researchers)","topic":"Face and Expression Recognition","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":"McMaster University","funders":"","keywords":"Artificial intelligence; Support vector machine; Computer science; Pattern recognition (psychology); Facial recognition system; Feature extraction; Biometrics; Dimensionality reduction; Face detection; Face (sociological concept); Radial basis function kernel; Feature (linguistics); Machine learning; Identification (biology); Kernel method","score_opus":0.18667714296118001,"score_gpt":0.41902737604169193,"score_spread":0.23235023308051192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567193860","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09130641,0.002680953,0.9017469,0.00023114236,0.00015241733,0.0000951924,0.000063011896,0.001013764,0.0027102863],"genre_scores_gemma":[0.7060227,0.0013237994,0.2897225,0.00011452382,0.00010323055,0.000093357405,0.00013095206,0.00004934499,0.0024395008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936336,0.0001870956,0.000040336046,0.000126929,0.00022226143,0.00005990152],"domain_scores_gemma":[0.99945205,0.00022519968,0.00004546111,0.000051748666,0.00021299222,0.000012437685],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011214387,0.00044072742,0.0006218363,0.00095658726,0.00040809624,0.000560927,0.00060527766,0.00060795306,0.0009647241],"category_scores_gemma":[0.0017749952,0.00022079259,0.0005527818,0.00075906306,0.00038113838,0.00071881985,0.00030263708,0.0006151528,0.0006403127],"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":[0.00016973184,0.00021862758,0.0036776764,0.0001693509,0.00009253134,0.00010314115,0.00009539625,0.14169502,0.03096894,0.003004023,0.0015969918,0.81820863],"study_design_scores_gemma":[0.0000056335607,0.00007548663,0.0020127376,0.000015876241,0.00001795699,0.00007807291,0.000033524408,0.9821607,0.01283747,0.001496309,0.0012509648,0.000015403935],"about_ca_topic_score_codex":0.002823829,"about_ca_topic_score_gemma":0.0024924409,"teacher_disagreement_score":0.002823829,"about_ca_system_score_codex":0.00038910258,"about_ca_system_score_gemma":0.00032477317,"threshold_uncertainty_score":0.0059307814},"labels":[],"label_agreement":null},{"id":"W2573597659","doi":"10.1109/mmsp.2016.7813403","title":"Generalized dirichlet mixture matching projection for supervised linear dimensionality reduction of proportional data","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Concordia University","funders":"","keywords":"Dimensionality reduction; Projection (relational algebra); Dirichlet distribution; Divergence (linguistics); Mathematics; Pattern recognition (psychology); Reduction (mathematics); Computer science; Preprocessor; Algorithm; Artificial intelligence","score_opus":0.06380959374351942,"score_gpt":0.3115505121241675,"score_spread":0.2477409183806481,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2573597659","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031087592,0.00011656419,0.99608946,0.000061554965,0.000024326788,0.00003031707,0.00003506834,0.0002650299,0.00026899873],"genre_scores_gemma":[0.105550244,0.00024933217,0.8902814,0.00013543124,0.000097562996,0.00035153123,0.00066779467,0.0002070566,0.002459625],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99615675,0.0013575723,0.00017881433,0.00090608455,0.0011769522,0.00022377941],"domain_scores_gemma":[0.9982462,0.0007540142,0.00013694934,0.00040685886,0.00037281192,0.000083208586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029335706,0.0010165147,0.002086952,0.0017725772,0.0011527456,0.0014825941,0.001955252,0.0011655649,0.0022599902],"category_scores_gemma":[0.006423052,0.0007719635,0.0018397375,0.001963025,0.0013715866,0.0022431156,0.002808605,0.002633174,0.0012336576],"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":[0.00032961415,0.00027166898,0.001636634,0.00031421482,0.00023485307,0.00016341096,0.00047029895,0.1651606,0.01527795,0.06719547,0.0071042594,0.741841],"study_design_scores_gemma":[0.000012614644,0.00004004554,0.000360979,0.0000133529375,0.000015040383,0.000090696034,0.0000463527,0.9641608,0.004079432,0.028605841,0.002545481,0.00002945722],"about_ca_topic_score_codex":0.0026290645,"about_ca_topic_score_gemma":0.0029793398,"teacher_disagreement_score":0.0029335706,"about_ca_system_score_codex":0.00092699716,"about_ca_system_score_gemma":0.0016993855,"threshold_uncertainty_score":0.015514433},"labels":[],"label_agreement":null},{"id":"W2584047031","doi":"10.1109/tip.2017.2766446","title":"Object Classification With Joint Projection and Low-Rank Dictionary Learning","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":47,"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":"Computer science; Artificial intelligence; Pattern recognition (psychology); Outlier; Graph; Redundancy (engineering); Data mining; Machine learning; Theoretical computer science","score_opus":0.026387083760742634,"score_gpt":0.2632947758162877,"score_spread":0.23690769205554504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2584047031","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008240649,0.000245088,0.9895147,0.00013306271,0.000042582113,0.00003930698,0.00008041627,0.0010716303,0.00063250883],"genre_scores_gemma":[0.29516092,0.00071247545,0.69432634,0.0005542238,0.0002701737,0.0003402666,0.0022159917,0.00032006908,0.006099495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99821585,0.00034059607,0.00008781207,0.0005494895,0.0006177977,0.00018832712],"domain_scores_gemma":[0.9983398,0.00044729846,0.00018117912,0.00046109516,0.00044847178,0.00012224949],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010257575,0.0015159183,0.0024028472,0.0014348535,0.0005484708,0.001603483,0.0023859285,0.0015351475,0.0022228158],"category_scores_gemma":[0.003850876,0.00066509703,0.0012042732,0.0024169188,0.0011186161,0.0024582879,0.0022446432,0.0028434857,0.0022396946],"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":[0.00031353053,0.00024725884,0.0010179763,0.00020201954,0.00014079317,0.00014453681,0.00013606637,0.18737474,0.016056683,0.011718513,0.011308966,0.7713389],"study_design_scores_gemma":[0.000016317574,0.000045503435,0.00012433087,0.0000053188382,0.000008436367,0.000053059877,0.000015963495,0.991327,0.0027227635,0.004914859,0.00075291307,0.000013414267],"about_ca_topic_score_codex":0.0052743396,"about_ca_topic_score_gemma":0.004650307,"teacher_disagreement_score":0.0052743396,"about_ca_system_score_codex":0.00064239156,"about_ca_system_score_gemma":0.001200712,"threshold_uncertainty_score":0.010487258},"labels":[],"label_agreement":null},{"id":"W2585958134","doi":"10.1177/1536867x1601600407","title":"Support Vector Machines","year":2016,"lang":"en","type":"article","venue":"The Stata Journal Promoting communications on statistics and Stata","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":218,"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":"Categorical variable; Support vector machine; Multinomial distribution; Computer science; Multinomial logistic regression; Artificial intelligence; Binary classification; Statistical learning; Relevance vector machine; Machine learning; Binary number; Data mining; Mathematics; Statistics; Arithmetic","score_opus":0.03801075982839052,"score_gpt":0.30926332236989607,"score_spread":0.27125256254150554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585958134","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070191356,0.006191555,0.89180344,0.002282594,0.0014084073,0.000980667,0.022447312,0.026008084,0.041858893],"genre_scores_gemma":[0.11992268,0.009430719,0.76135045,0.0019579432,0.001539773,0.0015502889,0.05794678,0.0038053198,0.0424961],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9931011,0.0015684641,0.0006257274,0.0013316949,0.003054767,0.00031834008],"domain_scores_gemma":[0.99038786,0.0038524622,0.000848364,0.0017817554,0.0028031378,0.00032630892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036125206,0.0025603264,0.001969579,0.004217264,0.00075864256,0.00545293,0.0040311967,0.0019011,0.053278327],"category_scores_gemma":[0.030670868,0.0005749654,0.0015560492,0.004194704,0.00061419705,0.003968704,0.0027024688,0.0022286207,0.057106316],"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":[0.00017253433,0.00013757881,0.0031203893,0.0010556433,0.0003031603,0.00017176339,0.00012104279,0.014941825,0.0019705037,0.02204494,0.18201023,0.77395034],"study_design_scores_gemma":[0.00013231424,0.00027145585,0.0043407655,0.0011560763,0.00022184642,0.0010034719,0.0003286225,0.18826616,0.009760085,0.145554,0.6487219,0.0002434265],"about_ca_topic_score_codex":0.0015475892,"about_ca_topic_score_gemma":0.0013461765,"teacher_disagreement_score":0.053278327,"about_ca_system_score_codex":0.0008250323,"about_ca_system_score_gemma":0.0016365328,"threshold_uncertainty_score":0.17823374},"labels":[],"label_agreement":null},{"id":"W2588393813","doi":"10.1109/ssci.2016.7850120","title":"Utility functions as aggregation functions in face recognition","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canada Research Chairs","keywords":"Computer science; Classifier (UML); Artificial intelligence; Facial recognition system; Machine learning; Pattern recognition (psychology); Face (sociological concept); Operator (biology); Witness; Data mining","score_opus":0.03313023782430877,"score_gpt":0.2495597440367413,"score_spread":0.21642950621243254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2588393813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012954086,0.0037899942,0.9787089,0.00042181363,0.000076413904,0.0000515174,0.00006234474,0.000095471856,0.0038394905],"genre_scores_gemma":[0.68335295,0.0069780075,0.3003812,0.00022120516,0.0004968168,0.0003497857,0.0002913291,0.00013212179,0.007796476],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949955,0.0029005404,0.00024768207,0.00056064705,0.0010009556,0.00029464235],"domain_scores_gemma":[0.9957159,0.002906129,0.00033694942,0.00037330954,0.0005455617,0.0001221653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005923806,0.0011488922,0.0015229661,0.0021652314,0.0006197051,0.0030417168,0.001226774,0.0012643514,0.0013876336],"category_scores_gemma":[0.011542068,0.00044090414,0.0013858626,0.0034108676,0.0021336286,0.0038959363,0.0015756898,0.0018011265,0.00041501402],"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":[0.0001082788,0.0000798468,0.0016155606,0.00022315422,0.00014120858,0.0002540635,0.00034277464,0.26951167,0.0013802368,0.5931018,0.0026422744,0.13059919],"study_design_scores_gemma":[0.0000057832135,0.000041502088,0.000549918,0.00003517386,0.000025649728,0.000089118825,0.000056885463,0.70560557,0.0004669183,0.2900675,0.003030797,0.000025219984],"about_ca_topic_score_codex":0.0029070696,"about_ca_topic_score_gemma":0.0013026511,"teacher_disagreement_score":0.005923806,"about_ca_system_score_codex":0.0022635488,"about_ca_system_score_gemma":0.0008354003,"threshold_uncertainty_score":0.03132844},"labels":[],"label_agreement":null},{"id":"W2591555609","doi":"10.1109/icci-cc.2016.7862022","title":"Image-to-image face recognition using Dual Linear Regression based Classification and Electoral College voting","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Face (sociological concept); Artificial intelligence; Voting; Pattern recognition (psychology); Computer science; Facial recognition system; Image (mathematics); Similarity (geometry); Benchmark (surveying); Pixel; Contextual image classification; Dual (grammatical number); Computer vision; Geography","score_opus":0.0542428880676587,"score_gpt":0.295144665666524,"score_spread":0.24090177759886527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591555609","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06925654,0.0004295455,0.9236862,0.00025930625,0.00011431962,0.00013244446,0.00008355,0.0021219295,0.0039161905],"genre_scores_gemma":[0.6239384,0.00021588604,0.36744246,0.00018137365,0.00012225255,0.00012208501,0.0005088351,0.000104961895,0.0073637255],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815804,0.00039771263,0.00009695481,0.00044862714,0.00067291054,0.00022579913],"domain_scores_gemma":[0.9990736,0.00018493668,0.00012093875,0.00018179952,0.00038186688,0.000056854034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013632054,0.00059119,0.0014573538,0.0016004156,0.00054471433,0.0011676236,0.0017437191,0.00088935305,0.0021791137],"category_scores_gemma":[0.0028193132,0.00028886026,0.00083858095,0.0011424925,0.00044637025,0.001032745,0.0010905613,0.00096714264,0.001954676],"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":[0.00035926525,0.00036124056,0.0043274,0.000064771004,0.0000946517,0.00010781509,0.00006936196,0.036576018,0.028702024,0.0025809328,0.002735673,0.9240209],"study_design_scores_gemma":[0.000019495244,0.00014152387,0.002740753,0.000006969451,0.000025919162,0.00023304453,0.000050351184,0.9696473,0.023844378,0.0013548252,0.001910708,0.000024645404],"about_ca_topic_score_codex":0.0025756117,"about_ca_topic_score_gemma":0.0022961458,"teacher_disagreement_score":0.0025756117,"about_ca_system_score_codex":0.00047566812,"about_ca_system_score_gemma":0.00054241286,"threshold_uncertainty_score":0.0072898865},"labels":[],"label_agreement":null},{"id":"W2592327898","doi":"10.1109/icspis.2016.7869885","title":"Facial expression recognition with discriminatory graphical models","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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","funders":"","keywords":"CRFS; Conditional random field; Discriminative model; Graphical model; Artificial intelligence; Pattern recognition (psychology); Computer science; Sequence labeling; Hidden Markov model; Feature (linguistics); Hidden variable theory; Probabilistic logic; Sequence (biology); Histogram; Statistical model; Feature vector; Local binary patterns; Machine learning; Task (project management); Image (mathematics)","score_opus":0.03047639171738409,"score_gpt":0.2266802257807653,"score_spread":0.1962038340633812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2592327898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027268268,0.0001565643,0.9667419,0.00025578061,0.00003233322,0.00005904222,0.00032128525,0.0032461165,0.001918642],"genre_scores_gemma":[0.67161,0.00023483083,0.3205529,0.00029555752,0.00003412364,0.0002178142,0.0013048864,0.00036625474,0.005383681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993467,0.00020968617,0.000026738886,0.0002223474,0.00012824865,0.00006624947],"domain_scores_gemma":[0.9990689,0.00050794554,0.000081640894,0.00016880175,0.00013699633,0.000035804358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010078398,0.0007411117,0.0007463273,0.00074689434,0.000254558,0.0007452562,0.0013458957,0.00073333347,0.0039318595],"category_scores_gemma":[0.0032761404,0.00059497904,0.0013321368,0.0006370094,0.0004969448,0.0013663657,0.00069150265,0.0013507644,0.0012954688],"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":[0.00021017628,0.00012160543,0.0013861088,0.00007311577,0.000068577916,0.00007160196,0.00008362547,0.7822261,0.010361773,0.008771667,0.0024627098,0.19416295],"study_design_scores_gemma":[0.0000040440245,0.000010072361,0.00015226786,0.0000027682963,0.0000038109413,0.000013132564,0.000003383769,0.99621165,0.00068127504,0.0027064446,0.00020628795,0.0000048860798],"about_ca_topic_score_codex":0.010173013,"about_ca_topic_score_gemma":0.010897752,"teacher_disagreement_score":0.010173013,"about_ca_system_score_codex":0.0011593126,"about_ca_system_score_gemma":0.0005468602,"threshold_uncertainty_score":0.020227611},"labels":[],"label_agreement":null},{"id":"W2597643657","doi":"10.1109/icip.2017.8296448","title":"Face recognition using multi-modal low-rank dictionary learning","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Alberta","funders":"","keywords":"Artificial intelligence; Computer science; Robustness (evolution); Discriminative model; Pattern recognition (psychology); Facial recognition system; Modal; Hyperspectral imaging; Computer vision; Feature extraction; Pixel; Face (sociological concept)","score_opus":0.06800087206521177,"score_gpt":0.3031511949018057,"score_spread":0.23515032283659393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597643657","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009665067,0.00029621876,0.9884913,0.000108324886,0.000041592408,0.000034631696,0.0000932588,0.00050723547,0.00076231523],"genre_scores_gemma":[0.32267857,0.00092158595,0.67061746,0.00034714825,0.00018520471,0.00017811608,0.0011712617,0.00013012685,0.0037704497],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992505,0.00016876885,0.000036810598,0.00020208773,0.00027471248,0.00006709698],"domain_scores_gemma":[0.99926704,0.00020113378,0.000094871604,0.00019949507,0.00019694463,0.000040571147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006413174,0.00069669663,0.001209937,0.00081499893,0.00031326592,0.00075979327,0.0009995846,0.0008572915,0.0022084257],"category_scores_gemma":[0.002282206,0.0002682206,0.00076480495,0.00091150665,0.0005101171,0.0012147889,0.0010317272,0.0012177618,0.001581677],"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":[0.00024412524,0.00022667408,0.0011574039,0.0002490751,0.00013482454,0.00011753731,0.00010394674,0.10470788,0.06398602,0.0095666265,0.008344657,0.81116116],"study_design_scores_gemma":[0.000015809397,0.00008572887,0.00050710706,0.000010825725,0.000019310426,0.00014615287,0.000027075966,0.97875494,0.013973529,0.004867005,0.0015699485,0.000022502834],"about_ca_topic_score_codex":0.0016263113,"about_ca_topic_score_gemma":0.0020401292,"teacher_disagreement_score":0.0022084257,"about_ca_system_score_codex":0.00029881275,"about_ca_system_score_gemma":0.00040305327,"threshold_uncertainty_score":0.0073878765},"labels":[],"label_agreement":null},{"id":"W2598046210","doi":"","title":"Face Detection with Improved Local Binary Patterns in CUDA","year":2012,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Computer science; CUDA; Leverage (statistics); Interactivity; Local binary patterns; Face detection; Binary number; Facial recognition system; Artificial intelligence; Parallel computing; Pattern recognition (psychology); Histogram; Operating system","score_opus":0.010166875588354796,"score_gpt":0.2178731925031776,"score_spread":0.2077063169148228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598046210","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05234446,0.0003528625,0.9182972,0.00023513044,0.00016296237,0.00012360809,0.00015634707,0.024475109,0.003852349],"genre_scores_gemma":[0.25941423,0.0001555128,0.73431325,0.00010484359,0.000038322385,0.00022983126,0.0003001355,0.00075997383,0.0046837935],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987446,0.00028021567,0.0001045155,0.00016811216,0.0005472728,0.00015523595],"domain_scores_gemma":[0.9982261,0.00035380377,0.00013566812,0.0004936379,0.00068941404,0.000101313475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010322222,0.00082593295,0.0009623858,0.0012978914,0.0005885019,0.0017078285,0.0019573336,0.00066258025,0.0043872246],"category_scores_gemma":[0.004073067,0.0005513432,0.0004609081,0.0014518456,0.0006570372,0.0017397113,0.00093194493,0.000957553,0.0013771678],"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":[0.0022024424,0.00041117167,0.0061162007,0.00039830807,0.0002497358,0.00069725147,0.00066278595,0.11563106,0.10049932,0.030725049,0.025464013,0.7169427],"study_design_scores_gemma":[0.0001075323,0.00010629755,0.0010819142,0.000020530886,0.000030895964,0.00015047134,0.000041070332,0.9360443,0.048501868,0.004108483,0.009754826,0.000051934203],"about_ca_topic_score_codex":0.010754179,"about_ca_topic_score_gemma":0.009424618,"teacher_disagreement_score":0.010754179,"about_ca_system_score_codex":0.001083192,"about_ca_system_score_gemma":0.0010469176,"threshold_uncertainty_score":0.021383166},"labels":[],"label_agreement":null},{"id":"W2600806505","doi":"","title":"Evaluating a Branch-and-Bound RLT-Based Algorithm for Minimum Sum-of-Squares Clustering","year":2008,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Face and Expression Recognition","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":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematics; Convex hull; Combinatorics; Cluster analysis; Centroid; Triangle inequality; Least-squares function approximation; Set (abstract data type); Data point; Algorithm; Regular polygon; Statistics; Computer science; Geometry","score_opus":0.03339265974949093,"score_gpt":0.2845295203447807,"score_spread":0.2511368605952897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600806505","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.063328244,0.0008224339,0.9260699,0.0005579227,0.00011251962,0.0002447389,0.00015032856,0.002260987,0.006452887],"genre_scores_gemma":[0.2896654,0.00024625176,0.7055253,0.00021793281,0.00005907985,0.0003576633,0.00068780547,0.00043329754,0.0028072312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812037,0.00082042033,0.00008877041,0.00029444293,0.00047138397,0.0002045794],"domain_scores_gemma":[0.9941712,0.0043403176,0.0001945672,0.00027569206,0.0008493914,0.00016882735],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038844284,0.0017938159,0.0023279684,0.0012673554,0.0007837531,0.0018878586,0.0023689407,0.0027498847,0.00691869],"category_scores_gemma":[0.012681374,0.00064842333,0.0010148082,0.0015764245,0.000956058,0.0019319941,0.0011806407,0.001696677,0.0018571076],"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":[0.0004399125,0.00016624463,0.0008239051,0.00014842997,0.00006747122,0.00004500794,0.000053136293,0.89161384,0.001449448,0.004068677,0.0026781643,0.098445766],"study_design_scores_gemma":[0.000024569985,0.000042676427,0.000066589964,0.0000043766845,0.000005879153,0.000006854907,0.000009892563,0.998365,0.0003644755,0.00094181666,0.00016475753,0.0000031112452],"about_ca_topic_score_codex":0.009347638,"about_ca_topic_score_gemma":0.008687669,"teacher_disagreement_score":0.009347638,"about_ca_system_score_codex":0.0023068695,"about_ca_system_score_gemma":0.0029995237,"threshold_uncertainty_score":0.023145378},"labels":[],"label_agreement":null},{"id":"W2602628422","doi":"10.1109/crv.2017.20","title":"Multi-path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained \"Hard Faces\"","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Pixel; Face detection; Classifier (UML); Face (sociological concept); Deep learning; Facial recognition system; Feature (linguistics); Convolution (computer science); Path (computing); Feature extraction; Artificial neural network","score_opus":0.06537923013406781,"score_gpt":0.2879696959416588,"score_spread":0.22259046580759095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2602628422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14315635,0.00215741,0.84371996,0.0002292976,0.00014369647,0.00012431659,0.00045963028,0.0053693936,0.0046399347],"genre_scores_gemma":[0.7130414,0.00074595545,0.27621326,0.0002191315,0.000050892955,0.000096202246,0.0011095734,0.00017736488,0.0083461935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995814,0.000053628883,0.0000137587,0.0001419463,0.00014357227,0.000065694825],"domain_scores_gemma":[0.99969363,0.00009218992,0.000038424176,0.000069418515,0.00008865734,0.000017749991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060863834,0.00080980954,0.00053683127,0.00072139746,0.00024614154,0.00041589956,0.0013311843,0.0006009151,0.003037656],"category_scores_gemma":[0.0011765081,0.00030809714,0.00051334524,0.00046385278,0.00029380474,0.0010926463,0.0007171145,0.0007273451,0.0012351519],"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":[0.000635012,0.00021526394,0.0036456839,0.00014426936,0.00016208563,0.00025876175,0.00008119936,0.12329899,0.08002504,0.0032785211,0.008080794,0.78017443],"study_design_scores_gemma":[0.000009228013,0.000058690086,0.0017158408,0.000010373078,0.000030304675,0.0001494331,0.000013066985,0.9761019,0.01927879,0.0012547704,0.0013656276,0.000012022898],"about_ca_topic_score_codex":0.006218993,"about_ca_topic_score_gemma":0.010216395,"teacher_disagreement_score":0.006218993,"about_ca_system_score_codex":0.0006386586,"about_ca_system_score_gemma":0.00073436013,"threshold_uncertainty_score":0.01236558},"labels":[],"label_agreement":null},{"id":"W2603242634","doi":"10.1109/tnnls.2017.2676101","title":"Logistic Localized Modeling of the Sample Space for Feature Selection and Classification","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":24,"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; McMaster University","funders":"","keywords":"Pattern recognition (psychology); Discriminative model; Feature selection; Sample space; Feature vector; Disjoint sets; Feature (linguistics); Artificial intelligence; Mathematics; Computer science; Algorithm","score_opus":0.04827772332104714,"score_gpt":0.27388614541062006,"score_spread":0.22560842208957294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603242634","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028509435,0.00014875515,0.9964946,0.00006495695,0.0000085098845,0.000025738338,0.000024825058,0.0001177079,0.0002639721],"genre_scores_gemma":[0.43199363,0.0010191821,0.55985636,0.00017574313,0.00014351234,0.00084050186,0.0006303109,0.00017171133,0.005168958],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985561,0.00056011375,0.00005904839,0.00030262588,0.0004335019,0.00008858855],"domain_scores_gemma":[0.9986848,0.0007422907,0.00015529223,0.00016553981,0.00021521347,0.000036867452],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017574506,0.0011022661,0.0010645016,0.0009143968,0.00042073682,0.0010270297,0.0016566734,0.00078167586,0.0022320165],"category_scores_gemma":[0.003900734,0.00046060458,0.0012180036,0.0018646253,0.0010146179,0.0015102796,0.0013380437,0.0019401152,0.0011322065],"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":[0.0002482786,0.00009729437,0.0019062306,0.00021177299,0.00010226785,0.00021038382,0.00019200475,0.74817204,0.012122472,0.041365664,0.0028974675,0.19247411],"study_design_scores_gemma":[0.0000048510497,0.00003210067,0.00015849854,0.0000049091773,0.000005132743,0.000024246574,0.000008187415,0.99265265,0.0008622299,0.0051732,0.0010657669,0.000008172718],"about_ca_topic_score_codex":0.0028079841,"about_ca_topic_score_gemma":0.0022011716,"teacher_disagreement_score":0.0028079841,"about_ca_system_score_codex":0.0009179823,"about_ca_system_score_gemma":0.00082162657,"threshold_uncertainty_score":0.00929445},"labels":[],"label_agreement":null},{"id":"W2604343608","doi":"10.1007/s11063-017-9615-5","title":"A Brain-Inspired Method of Facial Expression Generation Using Chaotic Feature Extracting Bidirectional Associative Memory","year":2017,"lang":"en","type":"article","venue":"Neural Processing Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"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","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Chaotic; Feature (linguistics); Content-addressable memory; Context (archaeology); Attractor; Feature extraction; Artificial neural network; Mathematics","score_opus":0.06399533764518658,"score_gpt":0.33089566297902234,"score_spread":0.2669003253338358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2604343608","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073806785,0.00033249203,0.91979325,0.00009540339,0.00009260855,0.000048854406,0.000042411615,0.0004572411,0.0053309747],"genre_scores_gemma":[0.81056,0.00019133263,0.18366532,0.000054232267,0.000027921837,0.00006574501,0.000054318443,0.000041451967,0.0053396616],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999584,0.0000055755477,0.0000027518013,0.000013289373,0.000013740662,0.0000062617482],"domain_scores_gemma":[0.99995255,0.000011572198,0.000004656163,0.000012358265,0.000013996781,0.0000048615016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008553324,0.00017259855,0.00021991061,0.0001534652,0.00021723662,0.0002008781,0.00040924238,0.00019150616,0.0013921141],"category_scores_gemma":[0.00018712535,0.000090882444,0.0002371871,0.00019763809,0.00018961325,0.0003218772,0.00030021797,0.00019881656,0.0002371482],"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":[0.00021575288,0.00009350562,0.0010876,0.00015031613,0.00008730574,0.00022051466,0.00014281871,0.06506806,0.4766496,0.02572532,0.0019176294,0.42864153],"study_design_scores_gemma":[0.000017920227,0.00011713739,0.00082273054,0.000006873453,0.00003598747,0.00024253198,0.000021786387,0.91985977,0.071254365,0.0047447863,0.002855893,0.000020214015],"about_ca_topic_score_codex":0.0006472061,"about_ca_topic_score_gemma":0.0008278752,"teacher_disagreement_score":0.0013921141,"about_ca_system_score_codex":0.00015685697,"about_ca_system_score_gemma":0.00017247286,"threshold_uncertainty_score":0.0046571493},"labels":[],"label_agreement":null},{"id":"W2606536620","doi":"","title":"Sparse Kernel Canonical Correlation Analysis","year":2013,"lang":"en","type":"article","venue":"National University of Singapore","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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","funders":"","keywords":"Canonical correlation; Kernel (algebra); Mathematics; Kernel principal component analysis; Kernel method; Transformation (genetics); Computer science; Pattern recognition (psychology); Statistics; Artificial intelligence; Support vector machine; Discrete mathematics","score_opus":0.015514445197622068,"score_gpt":0.2057438676853904,"score_spread":0.19022942248776833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606536620","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013830105,0.0006822162,0.9802572,0.00025783805,0.00019792977,0.000036538466,0.00019031088,0.00061170204,0.003936086],"genre_scores_gemma":[0.5038938,0.0024269153,0.46409106,0.00034030783,0.0005142005,0.00016553898,0.0023538147,0.00088738254,0.025327004],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911493,0.0002674448,0.000038345464,0.00019729398,0.0002890386,0.0000929196],"domain_scores_gemma":[0.9979925,0.0004723518,0.0001978912,0.0005302757,0.00070511823,0.00010191969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009160286,0.00085756846,0.0011590293,0.0017521674,0.0006064085,0.0019166588,0.0006739988,0.0008682643,0.0058419155],"category_scores_gemma":[0.0040957704,0.00053088914,0.0009361798,0.0028851263,0.0010804047,0.001520541,0.0016254699,0.0014776553,0.0031943077],"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":[0.00025659602,0.00017799094,0.0025047862,0.00020629045,0.00019405104,0.00023683459,0.00017105699,0.1338747,0.013370704,0.23606436,0.032853797,0.58008885],"study_design_scores_gemma":[0.000008909487,0.000042867785,0.0012908123,0.000020707517,0.000035522855,0.00019422204,0.00004557068,0.9542216,0.004513185,0.02861703,0.010964968,0.00004461755],"about_ca_topic_score_codex":0.002875336,"about_ca_topic_score_gemma":0.0034193892,"teacher_disagreement_score":0.0058419155,"about_ca_system_score_codex":0.00048695487,"about_ca_system_score_gemma":0.0014381867,"threshold_uncertainty_score":0.019543111},"labels":[],"label_agreement":null},{"id":"W2607738548","doi":"10.1002/cjs.11321","title":"A new algorithm for computation of a regularization solution path for reinforced multicategory support vector machines","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Face and Expression Recognition","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":true,"ca_institutions":"","funders":"","keywords":"Algorithm; Regularization (linguistics); Mathematics; Path (computing); Computation; Computer science; Combinatorics; Mathematical optimization; Applied mathematics; Artificial intelligence","score_opus":0.02528299646803576,"score_gpt":0.2645628737910798,"score_spread":0.23927987732304404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607738548","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024516273,0.000051672752,0.9966229,0.00006905537,0.000026919888,0.000037944053,0.000021913407,0.00041904018,0.00029896558],"genre_scores_gemma":[0.0449682,0.00004849786,0.95282227,0.000099719284,0.00003109403,0.00026508278,0.00016922815,0.00021977203,0.0013760632],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99930394,0.00019978403,0.00005620794,0.00015857708,0.00022035826,0.00006102356],"domain_scores_gemma":[0.9980799,0.0009118011,0.00014941997,0.0001692214,0.00059456704,0.00009511816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001602275,0.00081418274,0.0008383117,0.0011210568,0.0005832209,0.000906049,0.0018823785,0.0016548574,0.006430536],"category_scores_gemma":[0.0066493535,0.00057456654,0.0007753684,0.00095267675,0.00076279166,0.0013380616,0.002058431,0.0021877997,0.0015556967],"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":[0.00017731386,0.00011421308,0.0011491943,0.00017767999,0.00006904348,0.00012442401,0.00013403129,0.36121768,0.008556718,0.047236077,0.009611302,0.5714324],"study_design_scores_gemma":[0.000015135297,0.000026351028,0.00007266558,0.000007987533,0.0000033843407,0.000025178568,0.0000071696327,0.98946,0.0007780644,0.008431227,0.0011649822,0.000007857767],"about_ca_topic_score_codex":0.0026302827,"about_ca_topic_score_gemma":0.0031311275,"teacher_disagreement_score":0.006430536,"about_ca_system_score_codex":0.0008226861,"about_ca_system_score_gemma":0.0022497196,"threshold_uncertainty_score":0.02151227},"labels":[],"label_agreement":null},{"id":"W2608385747","doi":"10.1090/amsip/025/21","title":"A wavelet transform based face recognition system and its applications","year":2002,"lang":"en","type":"book-chapter","venue":"AMS/IP studies in advanced mathematics","topic":"Face and Expression Recognition","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 Alberta","funders":"","keywords":"Artificial intelligence; Facial recognition system; Computer science; Pattern recognition (psychology); Wavelet transform; Face (sociological concept); Computer vision; Wavelet; Speech recognition; Sociology","score_opus":0.07241086849949642,"score_gpt":0.29226037290024165,"score_spread":0.21984950440074524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608385747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041262507,0.0029939297,0.935311,0.00034881535,0.00023647425,0.00014866261,0.00020203985,0.002955514,0.016541028],"genre_scores_gemma":[0.21576391,0.004927458,0.7264571,0.00047568892,0.0002847414,0.00027799502,0.0006258423,0.00032768614,0.05085956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998685,0.000015119181,0.000007691679,0.000028384526,0.00006675185,0.000013441047],"domain_scores_gemma":[0.99984825,0.00004647952,0.000006681886,0.000027036178,0.00006187711,0.000009729321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026759403,0.00035288307,0.0005439597,0.00074501795,0.0002566115,0.00043363665,0.0006884728,0.0010934346,0.007893336],"category_scores_gemma":[0.00040663607,0.00032250967,0.0003040414,0.00094249385,0.00020327477,0.00075619965,0.00033008066,0.00047163042,0.003407891],"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":[0.00019186798,0.00008812079,0.0003336782,0.00011852748,0.000019039382,0.0002324749,0.000087694825,0.0033697234,0.18135247,0.004938346,0.0055688587,0.80369925],"study_design_scores_gemma":[0.000077820194,0.0011349203,0.007364988,0.00012695664,0.00022867086,0.007314534,0.0001736689,0.48477244,0.3795238,0.010915722,0.10819956,0.00016695651],"about_ca_topic_score_codex":0.00054930453,"about_ca_topic_score_gemma":0.0005066591,"teacher_disagreement_score":0.007893336,"about_ca_system_score_codex":0.0001511474,"about_ca_system_score_gemma":0.00016508969,"threshold_uncertainty_score":0.026405811},"labels":[],"label_agreement":null},{"id":"W2609430174","doi":"10.1007/s11634-017-0286-x","title":"Local generalized quadratic distance metrics: application to the k-nearest neighbors classifier","year":2017,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Alberta","funders":"","keywords":"k-nearest neighbors algorithm; Metric (unit); Curse of dimensionality; Computer science; Mathematics; Artificial intelligence; Algorithm; Pattern recognition (psychology)","score_opus":0.041089338011552366,"score_gpt":0.3371891365524899,"score_spread":0.29609979854093754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609430174","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020610504,0.0012125961,0.97573256,0.00021441591,0.00007803053,0.000058022877,0.00012368667,0.00038832778,0.0015819187],"genre_scores_gemma":[0.3620388,0.0008857284,0.63253397,0.000093335184,0.00014553058,0.00014823739,0.0004802608,0.00025354858,0.0034205841],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971367,0.00088959874,0.00018458518,0.00034106668,0.0013623815,0.00008564015],"domain_scores_gemma":[0.9954803,0.0016909585,0.00031844457,0.00042318052,0.0019371179,0.00014996194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028962395,0.00056358724,0.0017831712,0.0019323354,0.0008106453,0.0016402502,0.0016555893,0.0010578195,0.0015112004],"category_scores_gemma":[0.011252664,0.00026116392,0.00064171356,0.0027304695,0.000857917,0.001989581,0.0016607273,0.0010845425,0.0007494597],"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":[0.00023572728,0.00018975082,0.0036253112,0.00033011285,0.00010912197,0.00010449317,0.0003008085,0.17984177,0.00680084,0.042275626,0.007809927,0.7583765],"study_design_scores_gemma":[0.000009836979,0.00006229608,0.0010517448,0.000015714038,0.000013929435,0.0001032698,0.00004887582,0.97965425,0.0012411542,0.015347973,0.0024270175,0.000023847131],"about_ca_topic_score_codex":0.007449297,"about_ca_topic_score_gemma":0.0075800205,"teacher_disagreement_score":0.007449297,"about_ca_system_score_codex":0.0010950367,"about_ca_system_score_gemma":0.0015963317,"threshold_uncertainty_score":0.015316963},"labels":[],"label_agreement":null},{"id":"W2610476332","doi":"10.48550/arxiv.1704.08265","title":"Pruning variable selection ensembles","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":false,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Ensemble learning; Sorting; Selection (genetic algorithm); Pruning; Stability (learning theory); Context (archaeology); Artificial intelligence; Machine learning; Feature selection; Process (computing); Variable (mathematics); Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.08180838361224178,"score_gpt":0.19177144084695433,"score_spread":0.10996305723471254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610476332","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032475922,0.0008791441,0.9637411,0.0001447816,0.00008837713,0.00008559976,0.00012717147,0.00055607804,0.0019017977],"genre_scores_gemma":[0.5466523,0.0009156793,0.44592252,0.00042590173,0.0002438829,0.0004582804,0.0015125816,0.00022184434,0.0036470147],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99662375,0.0012910158,0.00017689538,0.0005860566,0.0010910142,0.00023128257],"domain_scores_gemma":[0.99458885,0.003114049,0.00032196165,0.00072974176,0.001103724,0.00014162117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004118303,0.0012902048,0.0020768829,0.0023776258,0.0010137932,0.0011904412,0.0017529532,0.0010969186,0.0016120763],"category_scores_gemma":[0.012889155,0.0004380364,0.0010783513,0.0020160559,0.00065895804,0.0013918749,0.0021366095,0.0012230678,0.0006905978],"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":[0.0002239129,0.00013290453,0.008222218,0.00020291349,0.0003860995,0.00028849076,0.00022002053,0.39499053,0.010863712,0.019620867,0.00647336,0.558375],"study_design_scores_gemma":[0.000017265906,0.000097550655,0.00095820497,0.000031684744,0.000065670414,0.00013768254,0.000040454164,0.9781506,0.004350838,0.012897385,0.0032363485,0.000016381287],"about_ca_topic_score_codex":0.0015222823,"about_ca_topic_score_gemma":0.0025870982,"teacher_disagreement_score":0.004118303,"about_ca_system_score_codex":0.00050239847,"about_ca_system_score_gemma":0.0011341047,"threshold_uncertainty_score":0.021779954},"labels":[],"label_agreement":null},{"id":"W2610514569","doi":"10.1109/tbdata.2017.2701816","title":"PPHOPCM: Privacy-Preserving High-Order Possibilistic c-Means Algorithm for Big Data Clustering with Cloud Computing","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Big Data","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":123,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Cluster analysis; Computer science; Big data; Cloud computing; Data mining; Fuzzy clustering; Scheme (mathematics); Encryption; CURE data clustering algorithm; Clustering high-dimensional data; Algorithm; Artificial intelligence; Mathematics","score_opus":0.1454192020572554,"score_gpt":0.3165568846312504,"score_spread":0.17113768257399503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610514569","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066608675,0.00012095397,0.99179804,0.00015581936,0.000030243798,0.00006342822,0.000059190545,0.00053606473,0.0005753694],"genre_scores_gemma":[0.29160276,0.00019396048,0.70521307,0.0002617986,0.00005840112,0.00025477735,0.0003470405,0.00014337664,0.0019248503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983021,0.00039442317,0.000090467045,0.00032860858,0.000725151,0.00015934209],"domain_scores_gemma":[0.99846554,0.00043353572,0.00014772666,0.0004917432,0.00035448666,0.000106992884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013772807,0.0007341663,0.001004658,0.0010354266,0.0016043643,0.0013330049,0.0025643725,0.0012803619,0.0014780711],"category_scores_gemma":[0.004182975,0.00044553363,0.0011723137,0.0019189204,0.0010758548,0.0019104177,0.0023250454,0.002060341,0.00047263788],"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":[0.0005114248,0.00021557377,0.0017132121,0.00024944922,0.00020963265,0.0002746088,0.00037964384,0.55189526,0.01711908,0.06774383,0.0089333085,0.350755],"study_design_scores_gemma":[0.000020774658,0.00003653839,0.00014857468,0.000005522251,0.000007811357,0.00009630439,0.000025851323,0.9723141,0.004068251,0.021655012,0.0016042019,0.000017061671],"about_ca_topic_score_codex":0.00663188,"about_ca_topic_score_gemma":0.0063149356,"teacher_disagreement_score":0.00663188,"about_ca_system_score_codex":0.0015733264,"about_ca_system_score_gemma":0.0038274897,"threshold_uncertainty_score":0.013186574},"labels":[],"label_agreement":null},{"id":"W2612977878","doi":"10.1145/3005347","title":"A Load-Balancing Divide-and-Conquer SVM Solver","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Embedded Computing Systems","topic":"Face and Expression Recognition","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 British Columbia","funders":"","keywords":"Computer science; Support vector machine; Divide and conquer algorithms; Kernel (algebra); Cluster analysis; Partition (number theory); Solver; Kernel method; Computation; Artificial intelligence; Machine learning; Data mining; Algorithm; Mathematics","score_opus":0.025050258575256643,"score_gpt":0.2755864711538491,"score_spread":0.25053621257859243,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612977878","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010969899,0.00019126898,0.9851076,0.00027271739,0.000070100556,0.0000810873,0.0000400169,0.0011992301,0.0020681343],"genre_scores_gemma":[0.22932811,0.00020719405,0.76238734,0.00032461315,0.00022010598,0.00034634536,0.0004046377,0.00035733066,0.0064243716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990908,0.00015718545,0.00006250885,0.0002398308,0.0003032876,0.0001464955],"domain_scores_gemma":[0.998923,0.0004059156,0.000066809094,0.00011839864,0.00040017697,0.000085560954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001067882,0.0011615492,0.0015238987,0.0007085652,0.00087773,0.0011676857,0.0021352386,0.0017006116,0.0048417714],"category_scores_gemma":[0.0029146716,0.0006105074,0.0007702135,0.0011429741,0.0005634997,0.0016625815,0.0016349372,0.0020728873,0.0015131163],"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":[0.0003506476,0.00031034995,0.0013625829,0.00017169975,0.00008845495,0.00017691927,0.0001725655,0.5422078,0.009916449,0.013450221,0.013360951,0.41843143],"study_design_scores_gemma":[0.00001789867,0.000011969876,0.000030870346,0.0000016762634,0.0000034941925,0.000011263064,0.000008297459,0.9976997,0.00052901707,0.0012047673,0.00047892818,0.0000020738844],"about_ca_topic_score_codex":0.0059685046,"about_ca_topic_score_gemma":0.006790347,"teacher_disagreement_score":0.0059685046,"about_ca_system_score_codex":0.0008839496,"about_ca_system_score_gemma":0.0017378664,"threshold_uncertainty_score":0.016197324},"labels":[],"label_agreement":null},{"id":"W2615931486","doi":"10.1109/ceit.2016.7929118","title":"A Novel Kernelized Face Recognition System","year":2016,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Computer science; Kernel Fisher discriminant analysis; Linear discriminant analysis; Support vector machine; Robustness (evolution); Feature extraction; Feature vector; Classifier (UML); Kernel (algebra); Biometrics; Mathematics","score_opus":0.03101872414014333,"score_gpt":0.23058730372240407,"score_spread":0.19956857958226074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2615931486","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044130526,0.00072506914,0.9418125,0.0002085771,0.000182643,0.00010443441,0.00014584973,0.008390484,0.004299893],"genre_scores_gemma":[0.72942585,0.00045944477,0.25409427,0.0002650783,0.00013806076,0.0001413887,0.0004593854,0.00012305332,0.014893492],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995084,0.000044872642,0.000028040951,0.00015939408,0.00020179956,0.000057561923],"domain_scores_gemma":[0.9997689,0.000023296507,0.000023888211,0.00005123328,0.00011479492,0.000017914013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002957526,0.00042042308,0.0009633252,0.00048045523,0.00040851135,0.00067319727,0.0012334313,0.00072983885,0.004292323],"category_scores_gemma":[0.00047024086,0.00024848394,0.00038467656,0.00033667922,0.00024042293,0.0012523297,0.0007384796,0.0005325604,0.0029564647],"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":[0.0006060572,0.00024121765,0.0012047446,0.00017885902,0.00007443996,0.00033689165,0.00010315291,0.021277564,0.20913413,0.004948297,0.008368764,0.7535258],"study_design_scores_gemma":[0.000048327787,0.00026839442,0.0020815993,0.00001223792,0.00005421232,0.00095356605,0.000024137924,0.92468745,0.0564426,0.0014329671,0.013934433,0.000060123395],"about_ca_topic_score_codex":0.0015544343,"about_ca_topic_score_gemma":0.0010221502,"teacher_disagreement_score":0.004292323,"about_ca_system_score_codex":0.00039862082,"about_ca_system_score_gemma":0.00039984705,"threshold_uncertainty_score":0.014359176},"labels":[],"label_agreement":null},{"id":"W2618393310","doi":"10.1007/978-3-319-59063-9_60","title":"An Evaluation of Fuzzy Measure for Face Recognition","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"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":"Computer science; Measure (data warehouse); Face (sociological concept); Facial recognition system; Fuzzy logic; Artificial intelligence; Pattern recognition (psychology); Data mining","score_opus":0.07926603880853486,"score_gpt":0.3165934306683353,"score_spread":0.23732739185980042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618393310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.069610834,0.005060709,0.9142105,0.00018851703,0.00030243362,0.000117231866,0.00018948894,0.00041733216,0.009902899],"genre_scores_gemma":[0.66869485,0.0011577795,0.32564858,0.00007291638,0.00017669753,0.00011028077,0.00035211386,0.00008129043,0.0037054652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961061,0.00086366496,0.00018868512,0.0003596497,0.00232574,0.00015614048],"domain_scores_gemma":[0.99608755,0.0018360232,0.00014027592,0.00028736342,0.0015372819,0.00011162822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003542175,0.00061488966,0.0011120797,0.002740418,0.0005691029,0.0016749119,0.0012093167,0.0010072312,0.0024571235],"category_scores_gemma":[0.007598696,0.00016973526,0.0008606202,0.0014733458,0.0007208964,0.0015101051,0.000859588,0.00053203607,0.00039147114],"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":[0.0009227015,0.00018293488,0.0036025345,0.0006108688,0.0001933499,0.00012385847,0.00024074814,0.045437463,0.051265754,0.06591258,0.004251768,0.8272555],"study_design_scores_gemma":[0.000029329318,0.0010887831,0.0074043353,0.000094280025,0.00016791162,0.0004900313,0.00016077404,0.9220406,0.03480818,0.02714694,0.006486471,0.00008250374],"about_ca_topic_score_codex":0.0018975033,"about_ca_topic_score_gemma":0.0014306001,"teacher_disagreement_score":0.003542175,"about_ca_system_score_codex":0.0018120635,"about_ca_system_score_gemma":0.0006051712,"threshold_uncertainty_score":0.018733025},"labels":[],"label_agreement":null},{"id":"W2618451668","doi":"10.11591/ijece.v7i4.pp1915-1922","title":"Unimodal Multi-Feature Fusion and one-dimensional Hidden Markov Models for Low-Resolution Face Recognition","year":2017,"lang":"en","type":"article","venue":"International Journal of Electrical and Computer Engineering (IJECE)","topic":"Face and Expression Recognition","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":"Université de Moncton","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Histogram of oriented gradients; Histogram; Facial recognition system; Linear discriminant analysis; Hidden Markov model; Face (sociological concept); Feature extraction; Feature (linguistics); Canonical correlation; Face hallucination; Computer vision; Image (mathematics); Face detection","score_opus":0.019216086063022084,"score_gpt":0.24108049744906118,"score_spread":0.2218644113860391,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2618451668","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022393622,0.00068125373,0.9752524,0.00011003048,0.00004977392,0.000020079839,0.000066303954,0.0008104803,0.0006161262],"genre_scores_gemma":[0.762525,0.0007142926,0.23368163,0.0001202948,0.00006731883,0.00006280523,0.00034551873,0.000066819484,0.0024162894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995121,0.00014099626,0.000030731615,0.000117558746,0.0001365229,0.0000620401],"domain_scores_gemma":[0.9995183,0.00023014746,0.000058026413,0.000081140985,0.0000927056,0.000019756522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009476227,0.0003391372,0.0007287328,0.00054644985,0.00025184985,0.0004584658,0.00061688334,0.00047000515,0.0012917104],"category_scores_gemma":[0.0016942846,0.0002928472,0.00094376,0.00052473135,0.00029761466,0.0008871014,0.0005748903,0.0008087565,0.0005365146],"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":[0.00044429224,0.00023315799,0.002288311,0.00014554714,0.00021357839,0.0002026241,0.00018053706,0.41078126,0.03274888,0.013094116,0.0019831974,0.5376845],"study_design_scores_gemma":[0.0000025086536,0.000027257352,0.00042382328,0.0000037070263,0.000011812034,0.000027277454,0.000007042063,0.99456364,0.0026685284,0.0019709577,0.00028259913,0.000010857311],"about_ca_topic_score_codex":0.0057629566,"about_ca_topic_score_gemma":0.004408619,"teacher_disagreement_score":0.0057629566,"about_ca_system_score_codex":0.00060805475,"about_ca_system_score_gemma":0.00047780466,"threshold_uncertainty_score":0.011458814},"labels":[],"label_agreement":null},{"id":"W2620830516","doi":"10.5430/air.v6n2p69","title":"The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification","year":2017,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","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; Preprocessor; Artificial intelligence; Representation (politics); Face (sociological concept); Pattern recognition (psychology); Set (abstract data type); Class (philosophy); Facial recognition system; Image (mathematics); Residual; Computer vision; Algorithm","score_opus":0.26655912910634716,"score_gpt":0.45925236974329414,"score_spread":0.19269324063694698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2620830516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021369906,0.00022100085,0.9767775,0.000079233956,0.00006520343,0.000042818625,0.000040449995,0.00046559726,0.0009383329],"genre_scores_gemma":[0.35246503,0.00047180607,0.64448994,0.0001067215,0.00008487615,0.00010110927,0.00026520432,0.000097072116,0.0019181523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999012,0.00016228635,0.000056890825,0.0002333349,0.0004099121,0.00012547312],"domain_scores_gemma":[0.9995011,0.00009035199,0.0000617158,0.00015789708,0.00016293461,0.000026072297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009784222,0.0007166591,0.0009990142,0.0011527973,0.0003745904,0.000804619,0.0010828391,0.0006865076,0.0014991324],"category_scores_gemma":[0.0017622279,0.0003018657,0.0011665613,0.0011874592,0.0006689073,0.0014707953,0.0011242758,0.000834671,0.00079823233],"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":[0.00027977402,0.00013881356,0.0011549835,0.00017187513,0.00007297068,0.00013596195,0.00016652275,0.032288376,0.17827505,0.007408419,0.0020335526,0.7778736],"study_design_scores_gemma":[0.0000183803,0.000400193,0.003641998,0.000027145552,0.00011306224,0.000703913,0.00016246096,0.8223063,0.1606503,0.0065677892,0.005335976,0.00007255151],"about_ca_topic_score_codex":0.0010637469,"about_ca_topic_score_gemma":0.00096778554,"teacher_disagreement_score":0.0014991324,"about_ca_system_score_codex":0.00033551513,"about_ca_system_score_gemma":0.00055855844,"threshold_uncertainty_score":0.005174458},"labels":[],"label_agreement":null},{"id":"W2698259589","doi":"10.1109/icassp.2017.7952668","title":"Dirichlet Mixture Matching Projection for supervised linear dimensionality reduction of proportional data","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Dimensionality reduction; Divergence (linguistics); Projection (relational algebra); Dirichlet distribution; Preprocessor; Computer science; Pattern recognition (psychology); Matching (statistics); Mathematics; Reduction (mathematics); Artificial intelligence; Algorithm; Statistics","score_opus":0.0918194876975221,"score_gpt":0.3463753254222018,"score_spread":0.2545558377246797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2698259589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004409118,0.00009438467,0.99474806,0.00005442343,0.000022773467,0.000028316374,0.000029002698,0.00036492216,0.00024891796],"genre_scores_gemma":[0.11711364,0.00018001863,0.87898237,0.00011049895,0.00007180354,0.00030644448,0.00063709915,0.00020292279,0.0023951514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99711156,0.0009245359,0.00015830767,0.00069071277,0.00088020833,0.0002345887],"domain_scores_gemma":[0.99842536,0.0006692599,0.00011609459,0.00032676308,0.00037578327,0.00008682265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022960552,0.001087273,0.0022153333,0.001852458,0.0011568018,0.0014219392,0.0018175695,0.001145079,0.0020471078],"category_scores_gemma":[0.004955624,0.0007157162,0.0018724886,0.0022861625,0.0012809383,0.00213012,0.002977979,0.0026999756,0.001229081],"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":[0.00030491914,0.0002575888,0.0013501554,0.00017495311,0.00016429649,0.00012017507,0.00040333864,0.12701637,0.017166222,0.02682008,0.005923331,0.8202986],"study_design_scores_gemma":[0.000012950593,0.00004201518,0.0003184482,0.000008538223,0.00001303239,0.000081397775,0.000049037877,0.9772235,0.005550251,0.015004504,0.0016721908,0.000024023717],"about_ca_topic_score_codex":0.002951448,"about_ca_topic_score_gemma":0.0027609854,"teacher_disagreement_score":0.002951448,"about_ca_system_score_codex":0.0007402855,"about_ca_system_score_gemma":0.001659974,"threshold_uncertainty_score":0.012142897},"labels":[],"label_agreement":null},{"id":"W2707606089","doi":"","title":"Expression, pose, and illumination invariant face recognition using lower order pseudo Zernike moments","year":2015,"lang":"en","type":"article","venue":"Computer Vision Theory and Applications (VISAPP), 2014 International Conference on","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Zernike polynomials; Artificial intelligence; Pattern recognition (psychology); Invariant (physics); Normalization (sociology); Facial expression; Computer vision; Facial recognition system; Computer science; Gabor wavelet; Wavelet; Expression (computer science); Orientation (vector space); Face (sociological concept); Mathematics; Wavelet transform; Discrete wavelet transform; Geometry; Optics; Physics","score_opus":0.04528622607631284,"score_gpt":0.30861333972573785,"score_spread":0.263327113649425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2707606089","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.090195306,0.0009406412,0.9039527,0.00017559995,0.00020713397,0.00008827229,0.00028754302,0.0011297753,0.0030230277],"genre_scores_gemma":[0.6070007,0.0017385469,0.38351834,0.00014731691,0.00017976208,0.00013502235,0.0010626714,0.00019289389,0.0060247164],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996099,0.000034916116,0.000015780639,0.00006584931,0.00023375326,0.000039821913],"domain_scores_gemma":[0.99975854,0.000046556794,0.000043249864,0.000045523393,0.00009261079,0.000013455983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024425052,0.00041192345,0.0006635199,0.0009146346,0.00017515503,0.00042051126,0.00053908513,0.00028382544,0.001129189],"category_scores_gemma":[0.00081396516,0.00016049408,0.0006126766,0.0007628956,0.00024943007,0.00083920796,0.00040747327,0.00045573516,0.0006840993],"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":[0.00017305397,0.00008567503,0.0017658466,0.00016257318,0.000050739287,0.00019783842,0.000059747017,0.0076182024,0.31634286,0.003253142,0.002568645,0.66772175],"study_design_scores_gemma":[0.00003931464,0.0006152089,0.03110243,0.00004178103,0.00016904865,0.0033567646,0.00021203331,0.5386605,0.4043706,0.0065134205,0.014749464,0.00016936233],"about_ca_topic_score_codex":0.00077294983,"about_ca_topic_score_gemma":0.0009435279,"teacher_disagreement_score":0.001129189,"about_ca_system_score_codex":0.00023302584,"about_ca_system_score_gemma":0.00027994235,"threshold_uncertainty_score":0.003777504},"labels":[],"label_agreement":null},{"id":"W2740643007","doi":"","title":"Deep Spectral Clustering Learning.","year":2017,"lang":"en","type":"article","venue":"International Conference on Machine Learning","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":107,"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 Toronto","funders":"","keywords":"Cluster analysis; Artificial intelligence; Computer science; Deep learning; Pattern recognition (psychology)","score_opus":0.0414787561868741,"score_gpt":0.3124472869121453,"score_spread":0.2709685307252712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740643007","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016324064,0.0035296702,0.9545465,0.0011435215,0.0005354834,0.00009191301,0.0013688849,0.0075272913,0.014932685],"genre_scores_gemma":[0.30506477,0.0018716531,0.6290821,0.0009107671,0.000276931,0.00021077888,0.007227057,0.0018327262,0.05352336],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939024,0.0001371993,0.000030354708,0.00018144347,0.0001780087,0.000082710394],"domain_scores_gemma":[0.99900705,0.00018123348,0.000063416985,0.00031824366,0.0003471696,0.00008291628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009822854,0.0011390814,0.0013591608,0.0013906765,0.0008927734,0.0015014705,0.002346037,0.0017021562,0.010748044],"category_scores_gemma":[0.0026310734,0.0008136243,0.0010031717,0.0015189094,0.0007741444,0.0017339737,0.0017309841,0.002022675,0.007750046],"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":[0.00026264988,0.00028404396,0.0013406108,0.00027413663,0.00027115818,0.00008009445,0.000107664,0.12252087,0.008534096,0.05087933,0.11057176,0.7048736],"study_design_scores_gemma":[0.000016522341,0.000022813185,0.0004783623,0.000028927869,0.000028355425,0.00004686031,0.000038174956,0.9402713,0.005417664,0.039729267,0.013903134,0.000018564737],"about_ca_topic_score_codex":0.008069969,"about_ca_topic_score_gemma":0.01919611,"teacher_disagreement_score":0.010748044,"about_ca_system_score_codex":0.0010531422,"about_ca_system_score_gemma":0.0013392874,"threshold_uncertainty_score":0.035955846},"labels":[],"label_agreement":null},{"id":"W2740792888","doi":"10.24963/ijcai.2017/463","title":"Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"National Natural Science Foundation of China","keywords":"Mahalanobis distance; Metric (unit); Artificial intelligence; Computer science; Feature (linguistics); Visualization; Pattern recognition (psychology); Similarity (geometry); Machine learning; Engineering","score_opus":0.02185502056054667,"score_gpt":0.25751470267909765,"score_spread":0.23565968211855098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740792888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028157432,0.0003606797,0.96950924,0.00020506878,0.00009352825,0.000051454306,0.000081925136,0.00072853465,0.0008121332],"genre_scores_gemma":[0.6270442,0.0005209671,0.3692429,0.0001876777,0.00015812836,0.00013986822,0.00072954275,0.00023558822,0.0017411506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976126,0.0004935105,0.00022236646,0.00075802376,0.00078722014,0.0001263067],"domain_scores_gemma":[0.9963834,0.0010458058,0.00039000215,0.000965533,0.0010447212,0.00017045108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016433812,0.0013575869,0.0015758789,0.0012554611,0.0006480514,0.0019086248,0.0018008462,0.0014388168,0.0010208791],"category_scores_gemma":[0.012830361,0.00037076834,0.00074214354,0.0021084887,0.00097069616,0.0033683234,0.0014492748,0.0017491566,0.00064689683],"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":[0.00031467504,0.00018198357,0.009965271,0.0003063955,0.00024961156,0.0002671494,0.00038607407,0.17947386,0.019983131,0.01564648,0.0056726434,0.7675527],"study_design_scores_gemma":[0.000013684321,0.00017317692,0.0026608284,0.00002256462,0.000047843663,0.0003702013,0.00011148561,0.9617172,0.01324189,0.017526977,0.004060081,0.0000541182],"about_ca_topic_score_codex":0.0026084671,"about_ca_topic_score_gemma":0.0019182604,"teacher_disagreement_score":0.0026084671,"about_ca_system_score_codex":0.00067387737,"about_ca_system_score_gemma":0.0009899782,"threshold_uncertainty_score":0.008691072},"labels":[],"label_agreement":null},{"id":"W2745595611","doi":"10.1109/icip.2017.8296795","title":"High-order local normal derivative pattern (LNDP) for 3D face recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Local binary patterns; Discriminative model; Pattern recognition (psychology); Artificial intelligence; Histogram; Face (sociological concept); Facial recognition system; Derivative (finance); Computer science; Orientation (vector space); Normal; Surface (topology); Computer vision; Directional derivative; Component (thermodynamics); Mathematics; Image (mathematics); Physics; Geometry","score_opus":0.0306713859144712,"score_gpt":0.2674498345644894,"score_spread":0.2367784486500182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745595611","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041657884,0.001266946,0.9515327,0.00015867688,0.00015277104,0.00009466521,0.0005346473,0.0018361667,0.0027655682],"genre_scores_gemma":[0.5723185,0.0017063426,0.41813862,0.0001869044,0.00011765956,0.00022023352,0.0024767849,0.00018359322,0.00465136],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965596,0.000035206525,0.000015072005,0.000051389066,0.00021542357,0.00002693199],"domain_scores_gemma":[0.9998116,0.000038639642,0.000022746552,0.000050162565,0.00006150995,0.000015368661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023070615,0.000360616,0.0005234745,0.0012780738,0.00018570357,0.00043684256,0.0006001107,0.00035910925,0.0019195636],"category_scores_gemma":[0.0006963929,0.00014513823,0.00041937857,0.0013213533,0.00034091342,0.0007288911,0.00057046115,0.00039766225,0.0009318277],"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":[0.00015345996,0.00006314631,0.0021712924,0.0001615194,0.000038944483,0.00013739135,0.000047731206,0.015951455,0.082537144,0.0057996707,0.0077683036,0.88517],"study_design_scores_gemma":[0.00003127733,0.00025131096,0.012795755,0.0000402099,0.0000627694,0.0016241259,0.000102193575,0.86399615,0.08457155,0.009604622,0.026826192,0.00009379115],"about_ca_topic_score_codex":0.0019456513,"about_ca_topic_score_gemma":0.0020085273,"teacher_disagreement_score":0.0019456513,"about_ca_system_score_codex":0.00040437875,"about_ca_system_score_gemma":0.00042574372,"threshold_uncertainty_score":0.006421566},"labels":[],"label_agreement":null},{"id":"W2748843925","doi":"","title":"Appearance-based face recognition under small sample size scenario","year":2007,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Facial recognition system; Pattern recognition (psychology); Kernel (algebra); Machine learning; Face (sociological concept); Quadratic classifier; Linear discriminant analysis; Identification (biology); Mathematics; Support vector machine","score_opus":0.039954973938573175,"score_gpt":0.24871926631875932,"score_spread":0.20876429238018615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2748843925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018152665,0.0003807491,0.9800106,0.000098621145,0.000027268827,0.000028653296,0.000026282738,0.00028662465,0.000988442],"genre_scores_gemma":[0.71048206,0.0013761452,0.2836673,0.00019476628,0.00018084094,0.00011807766,0.00029838525,0.00007561749,0.003606819],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988858,0.00024791833,0.000045885216,0.00030439187,0.00041329244,0.00010270516],"domain_scores_gemma":[0.9987413,0.0004987226,0.00014263258,0.00027550707,0.00030333074,0.00003845866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012766308,0.0005106121,0.0009854378,0.0006002594,0.00025818901,0.0005882033,0.0011396366,0.0010219568,0.00079785974],"category_scores_gemma":[0.0045860666,0.00018296977,0.00057918153,0.0005615625,0.000760642,0.0013938454,0.0009273377,0.0009825984,0.0004667644],"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":[0.00033034894,0.000105772226,0.0031157874,0.00024841746,0.00008553679,0.00037748125,0.00022963095,0.19914493,0.05411358,0.030549018,0.0036051285,0.7080945],"study_design_scores_gemma":[0.0000048712964,0.000053721982,0.0011507971,0.0000051986094,0.000011509907,0.00023191934,0.000019155692,0.98543084,0.0067952676,0.0053303614,0.0009546632,0.000011676631],"about_ca_topic_score_codex":0.0011098767,"about_ca_topic_score_gemma":0.0006748051,"teacher_disagreement_score":0.0012766308,"about_ca_system_score_codex":0.00035367723,"about_ca_system_score_gemma":0.00034236556,"threshold_uncertainty_score":0.0067515373},"labels":[],"label_agreement":null},{"id":"W2750994910","doi":"10.1167/17.10.826","title":"Spatial frequency utilization during the recognition of static, dynamic and dynamic random facial expressions.","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face and Expression Recognition","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":"Université de Montréal; Université du Québec en Outaouais; Université du Québec à Montréal","funders":"","keywords":"Speech recognition; Computer science; Pattern recognition (psychology); Artificial intelligence","score_opus":0.02005342271295631,"score_gpt":0.3005070880270525,"score_spread":0.2804536653140962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750994910","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9898615,0.00062110665,0.00546579,0.00006983039,0.00005975195,0.000022774626,0.00038245477,0.00006108929,0.0034557283],"genre_scores_gemma":[0.9963546,0.00029832433,0.0015839529,0.000040984272,0.000025718258,0.000022746752,0.00025588242,0.000034792756,0.0013830609],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984217,0.000032499764,0.000006808748,0.00003620393,0.000042131567,0.00004020264],"domain_scores_gemma":[0.9994247,0.000365499,0.00005850181,0.000029563787,0.00008455797,0.00003717839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024697464,0.00015442402,0.0002099972,0.00040095265,0.00011015016,0.0004051601,0.00013601921,0.00021614203,0.0018116857],"category_scores_gemma":[0.003062396,0.000119569384,0.00010482529,0.00029495123,0.00015486951,0.00028133206,0.0002428848,0.00020794812,0.00040765025],"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":[0.00403775,0.00010762281,0.02400015,0.0002140178,0.00008025652,0.00021119583,0.0005043179,0.0017909036,0.79234576,0.0006378311,0.0019528,0.1741173],"study_design_scores_gemma":[0.000048881793,0.00047573252,0.8730827,0.00007037609,0.00018095919,0.0017861922,0.00065780693,0.03895874,0.08127397,0.0010996427,0.0023056513,0.000059455873],"about_ca_topic_score_codex":0.0022212383,"about_ca_topic_score_gemma":0.001986509,"teacher_disagreement_score":0.0022212383,"about_ca_system_score_codex":0.000130585,"about_ca_system_score_gemma":0.00016253947,"threshold_uncertainty_score":0.00606066},"labels":[],"label_agreement":null},{"id":"W2753315806","doi":"10.1109/tbdata.2017.2742530","title":"Euler Clustering on Large-Scale Dataset","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Big Data","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"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":"National Key Research and Development Program of China; Guangdong Science and Technology Department; Sun Yat-sen University; National Natural Science Foundation of China","keywords":"Cluster analysis; Euler's formula; Kernel (algebra); Spectral clustering; Mathematics; Variable kernel density estimation; Algorithm; Pattern recognition (psychology); Computer science; Kernel method; Artificial intelligence; Discrete mathematics; Mathematical analysis; Support vector machine","score_opus":0.129188872121244,"score_gpt":0.3208625784651777,"score_spread":0.1916737063439337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753315806","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07280857,0.0025744312,0.889791,0.0015909952,0.0009778094,0.00054197834,0.0139322765,0.013407832,0.004375204],"genre_scores_gemma":[0.19961864,0.00093615113,0.73096657,0.0007182951,0.00024191086,0.00067259587,0.06153864,0.0010524386,0.0042548506],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99498737,0.0010879346,0.00046953475,0.0018536098,0.0012686074,0.00033300652],"domain_scores_gemma":[0.9924655,0.0015346914,0.00044477172,0.003025896,0.0022574903,0.0002717482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055030305,0.0018385226,0.0016591944,0.00416259,0.0018823437,0.0025194115,0.0030622198,0.002520881,0.002404041],"category_scores_gemma":[0.01977602,0.0004751506,0.0021822439,0.004460905,0.0010865601,0.0035856445,0.0029579266,0.0026146201,0.00252265],"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":[0.0010522248,0.0005698679,0.015086383,0.0015611215,0.00086036406,0.00087429746,0.00063828717,0.30745742,0.015611745,0.029033495,0.14416155,0.48309326],"study_design_scores_gemma":[0.000060979146,0.00009808082,0.005879808,0.00010595057,0.00006466235,0.00035800954,0.0003935019,0.91983366,0.009294522,0.036136653,0.027675547,0.00009853871],"about_ca_topic_score_codex":0.008343728,"about_ca_topic_score_gemma":0.014323688,"teacher_disagreement_score":0.008343728,"about_ca_system_score_codex":0.0016338014,"about_ca_system_score_gemma":0.0023131114,"threshold_uncertainty_score":0.02910316},"labels":[],"label_agreement":null},{"id":"W2755558024","doi":"10.1109/icip.2017.8296583","title":"Facial expression recognition using SVM classification on mic-macro patterns","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"","keywords":"Support vector machine; Computer science; Classifier (UML); Salient; Pattern recognition (psychology); Artificial intelligence; Facial expression; Facial expression recognition; Computation; Macro; Visualization; Facial recognition system; Feature extraction; Contextual image classification; Pixel; Feature (linguistics); Machine learning; Speech recognition; Image (mathematics)","score_opus":0.1277042241825967,"score_gpt":0.3248762195957593,"score_spread":0.1971719954131626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755558024","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5221089,0.0007847673,0.46447182,0.00032145323,0.00035376218,0.00027907465,0.0010124805,0.002348139,0.008319578],"genre_scores_gemma":[0.9201213,0.0002910187,0.07473694,0.00005856203,0.000054978947,0.000111503614,0.0010382432,0.0000619305,0.003525505],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996486,0.000045044617,0.00002805197,0.00009292669,0.00011249654,0.0000729971],"domain_scores_gemma":[0.999699,0.000045704113,0.000025975742,0.00003763963,0.00016501655,0.000026632091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032745034,0.0005540332,0.00062251935,0.0008481911,0.0002153365,0.00048269168,0.00034588246,0.00028358446,0.0021307606],"category_scores_gemma":[0.0008932805,0.000101517566,0.00046341127,0.00069470226,0.0001441111,0.00046000016,0.00043258388,0.00040916025,0.0010817508],"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":[0.00049107755,0.00028595363,0.009626838,0.00009068035,0.0000653166,0.00012888915,0.00006391084,0.0124857845,0.10242515,0.000743926,0.0045317654,0.86906075],"study_design_scores_gemma":[0.000014630405,0.00018308485,0.023231337,0.000017478267,0.000046216417,0.00018299145,0.00014505914,0.9482389,0.024915874,0.00082352984,0.0021800674,0.00002082553],"about_ca_topic_score_codex":0.0020551065,"about_ca_topic_score_gemma":0.0016854649,"teacher_disagreement_score":0.0021307606,"about_ca_system_score_codex":0.00019848355,"about_ca_system_score_gemma":0.0002638884,"threshold_uncertainty_score":0.00712806},"labels":[],"label_agreement":null},{"id":"W2765401959","doi":"10.1002/cjs.11329","title":"Linear operator‐based statistical analysis: A useful paradigm for big data","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Operator (biology); Computer science; Functional data analysis; Kernel principal component analysis; Covariance; Covariance operator; Linear map; Nonparametric statistics; Mathematics; Artificial intelligence; Machine learning; Kernel method; Statistics","score_opus":0.17922659278961964,"score_gpt":0.31833346952491753,"score_spread":0.1391068767352979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765401959","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00088102417,0.00055671,0.9956677,0.0016228416,0.00008185514,0.000026439184,0.00007916721,0.00008219275,0.0010020504],"genre_scores_gemma":[0.12171642,0.0023727887,0.86977226,0.0015183908,0.0015390599,0.0005521305,0.0003433927,0.0002145963,0.0019709126],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9900081,0.006346752,0.00054271193,0.0009666194,0.0019503783,0.00018532861],"domain_scores_gemma":[0.967229,0.023853537,0.0015971229,0.0042877155,0.0024905188,0.00054197764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016742246,0.0014125697,0.0014853934,0.0031814475,0.001049765,0.0047607976,0.0021400973,0.0015198078,0.0026483561],"category_scores_gemma":[0.03206036,0.00070079364,0.001930724,0.0037905858,0.008961999,0.006004686,0.0043983753,0.0065306625,0.00072320126],"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":[0.000014761085,0.000018875882,0.00046269342,0.0001096693,0.000045116056,0.00006808125,0.00018592714,0.01089629,0.00042490812,0.9679336,0.0021233931,0.01771666],"study_design_scores_gemma":[0.000007660891,0.000020275456,0.00017871821,0.00003215595,0.000007845362,0.00003488106,0.000041406292,0.07715203,0.00022383005,0.9168972,0.0053885975,0.00001555248],"about_ca_topic_score_codex":0.0025060126,"about_ca_topic_score_gemma":0.0017530742,"teacher_disagreement_score":0.016742246,"about_ca_system_score_codex":0.0022164688,"about_ca_system_score_gemma":0.003407075,"threshold_uncertainty_score":0.08854252},"labels":[],"label_agreement":null},{"id":"W2765744821","doi":"10.1007/978-3-662-56006-8_2","title":"KINECT Face Recognition Using Occluded Area Localization Method","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Facial recognition system; Face (sociological concept); Orientation (vector space); Three-dimensional face recognition; Occlusion; Local binary patterns; Pattern recognition (psychology); Identification (biology); Image (mathematics); Face detection; Histogram; Mathematics","score_opus":0.06203110054676792,"score_gpt":0.3083443143584312,"score_spread":0.24631321381166327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765744821","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028931312,0.0013086625,0.9503112,0.00009462197,0.00027673633,0.00016485504,0.0018454096,0.0069512315,0.010115946],"genre_scores_gemma":[0.35517997,0.0020380158,0.59841186,0.00025096702,0.000113797105,0.00040344344,0.0048065167,0.0005249384,0.038270514],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994968,0.000024797218,0.000021043781,0.00013309697,0.0002852057,0.000038917595],"domain_scores_gemma":[0.9998871,0.000011228803,0.0000126826135,0.000024408359,0.00005540975,0.00000915323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018330837,0.000768814,0.0008477204,0.0011400882,0.0002400141,0.000627589,0.00085935165,0.0005858358,0.0082813585],"category_scores_gemma":[0.00030811856,0.00041096684,0.00052856654,0.000812875,0.0001776284,0.000789504,0.00074720156,0.000468231,0.0050614583],"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":[0.00025443526,0.00012348071,0.0017821739,0.00034925577,0.00006234521,0.00017914701,0.0000798248,0.0057992563,0.2738206,0.0024634842,0.010988349,0.7040976],"study_design_scores_gemma":[0.00006155676,0.00032077634,0.025151076,0.00016476978,0.00014692277,0.002311595,0.00015420784,0.46313435,0.4559106,0.002743111,0.04975726,0.00014375849],"about_ca_topic_score_codex":0.0020188016,"about_ca_topic_score_gemma":0.0035328667,"teacher_disagreement_score":0.0082813585,"about_ca_system_score_codex":0.00022803352,"about_ca_system_score_gemma":0.00039493802,"threshold_uncertainty_score":0.027703881},"labels":[],"label_agreement":null},{"id":"W2771063748","doi":"10.4018/ijcini.2017100102","title":"Regression-Based Automated Facial Image Quality Model","year":2017,"lang":"en","type":"article","venue":"International Journal of Cognitive Informatics and Natural Intelligence","topic":"Face and Expression Recognition","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 British Columbia; University of Calgary","funders":"","keywords":"Computer science; Biometrics; Quality (philosophy); Artificial intelligence; Facial recognition system; Image quality; Regression analysis; Sample (material); Regression; Face (sociological concept); Linear regression; Linear model; Pattern recognition (psychology); Authentication (law); Computer vision; Machine learning; Image (mathematics); Statistics; Mathematics","score_opus":0.03785301755140954,"score_gpt":0.37929687889197894,"score_spread":0.3414438613405694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2771063748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06776789,0.0003095148,0.9284383,0.0001210982,0.000033197997,0.000101061276,0.00019667053,0.0012238979,0.0018083451],"genre_scores_gemma":[0.8813067,0.00037733637,0.11364964,0.00005659981,0.000029756708,0.00015294939,0.00048794944,0.000120199365,0.003818979],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946874,0.00010816214,0.000027157996,0.0001528237,0.00019445785,0.000048688657],"domain_scores_gemma":[0.9992211,0.0002470387,0.00012177489,0.0000618934,0.00033172985,0.000016485454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009812013,0.0006588161,0.00062307407,0.00095808593,0.0001669951,0.000610138,0.00095390156,0.0005398204,0.001638435],"category_scores_gemma":[0.0027782326,0.00028904428,0.0009569233,0.0005706093,0.0002875844,0.00065178925,0.0003778486,0.00071115175,0.00073583185],"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":[0.00019988799,0.000106822874,0.004755065,0.000059996597,0.000100923,0.0000701974,0.00004712498,0.82569486,0.013629591,0.0015976032,0.0010633338,0.15267457],"study_design_scores_gemma":[0.0000025321958,0.000014586045,0.00079464243,0.0000015066696,0.000007559531,0.000012707138,0.0000019516915,0.9981729,0.00079699827,0.00011169594,0.00007934619,0.0000035649116],"about_ca_topic_score_codex":0.01176814,"about_ca_topic_score_gemma":0.005534611,"teacher_disagreement_score":0.01176814,"about_ca_system_score_codex":0.0007938127,"about_ca_system_score_gemma":0.00041768016,"threshold_uncertainty_score":0.023399293},"labels":[],"label_agreement":null},{"id":"W2772060786","doi":"10.1109/cw.2017.35","title":"Adaptive Face Recognition Based on Image Quality","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Facial recognition system; Normalization (sociology); Pattern recognition (psychology); Discrete cosine transform; Computer vision; Discrete wavelet transform; Histogram; Adaptive histogram equalization; Face (sociological concept); Histogram equalization; Wavelet transform; Wavelet; Image (mathematics)","score_opus":0.09066360916732008,"score_gpt":0.3315434691153908,"score_spread":0.2408798599480707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772060786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23443821,0.001230285,0.75848466,0.00015884324,0.00015028396,0.00014184174,0.00012608508,0.00107258,0.004197327],"genre_scores_gemma":[0.8671441,0.0009181021,0.12896882,0.00007269794,0.000072141425,0.00006991687,0.00018922254,0.000069153604,0.0024958935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993556,0.000078296805,0.000027064098,0.00014364911,0.00034846654,0.000046954814],"domain_scores_gemma":[0.99907756,0.00024268148,0.00010428447,0.00009494764,0.0004512726,0.000029189858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000677485,0.00031600308,0.0004524793,0.000769686,0.00013625938,0.00046824839,0.00042555464,0.0002658962,0.0010814191],"category_scores_gemma":[0.0024830773,0.00014470873,0.00034901858,0.0004815891,0.0003097399,0.00072740996,0.00039893138,0.00035214593,0.00044895455],"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":[0.0003630321,0.00008941927,0.00657771,0.00016210356,0.00007846842,0.0001335605,0.00009272037,0.031598885,0.30298883,0.0011231205,0.0011638823,0.6556283],"study_design_scores_gemma":[0.000033894517,0.0003395005,0.04636224,0.00003492845,0.00012777979,0.00093307055,0.0000797087,0.7940296,0.15409155,0.001499357,0.0023856054,0.00008278598],"about_ca_topic_score_codex":0.0011609237,"about_ca_topic_score_gemma":0.001086869,"teacher_disagreement_score":0.0011609237,"about_ca_system_score_codex":0.0003036029,"about_ca_system_score_gemma":0.00019935583,"threshold_uncertainty_score":0.0036176443},"labels":[],"label_agreement":null},{"id":"W2772759524","doi":"10.15353/vsnl.v3i1.172","title":"Ensembles of Random Projections for Nonlinear Dimensionality Reduction","year":2017,"lang":"en","type":"article","venue":"Journal of Computational Vision and Imaging Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Nvidia","keywords":"Dimensionality reduction; Random projection; Generalization; Nonlinear system; Embedding; Computer science; Curse of dimensionality; Algorithm; Reduction (mathematics); Artificial intelligence; Mathematics; Pattern recognition (psychology)","score_opus":0.021659844421247267,"score_gpt":0.32386730766506533,"score_spread":0.3022074632438181,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772759524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005127195,0.0007237195,0.9928514,0.0001349944,0.000046244226,0.000033735407,0.00010588962,0.00027915955,0.00069763995],"genre_scores_gemma":[0.22152153,0.002988283,0.7687678,0.00020577703,0.00032966078,0.00047276772,0.0013664763,0.00022643442,0.004121271],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818474,0.0007701953,0.000091544585,0.00035544368,0.0005208575,0.00007722676],"domain_scores_gemma":[0.9980888,0.00084876816,0.0001748037,0.00046772938,0.00035399114,0.00006594936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019464013,0.0013806886,0.0011498426,0.0010213519,0.0005202214,0.0010094167,0.00093720475,0.0008525728,0.0024464175],"category_scores_gemma":[0.0058239894,0.00046559056,0.0012098138,0.0013719283,0.0008523494,0.0023291162,0.0019059305,0.0020179255,0.0012945129],"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":[0.00017269337,0.00012704148,0.001777401,0.00041466474,0.0002895851,0.00015690057,0.00019494476,0.3607309,0.01249227,0.10833076,0.008164817,0.5071481],"study_design_scores_gemma":[0.000008624594,0.000071889954,0.0006357715,0.00003370433,0.000025469935,0.00012607385,0.000029907234,0.9501819,0.0038758353,0.03975565,0.0052245324,0.00003065436],"about_ca_topic_score_codex":0.0012416443,"about_ca_topic_score_gemma":0.0016793589,"teacher_disagreement_score":0.0024464175,"about_ca_system_score_codex":0.00042998616,"about_ca_system_score_gemma":0.0007294195,"threshold_uncertainty_score":0.010293722},"labels":[],"label_agreement":null},{"id":"W2775543769","doi":"10.1109/pacrim.2017.8121906","title":"Handwritten digits recognition using PCA of histogram of oriented gradient","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"University of Victoria","funders":"","keywords":"MNIST database; Pattern recognition (psychology); Artificial intelligence; Principal component analysis; Histogram; Computer science; Classifier (UML); Histogram of oriented gradients; Feature extraction; Feature (linguistics); Image (mathematics); Artificial neural network","score_opus":0.055789861452927934,"score_gpt":0.28282573982526044,"score_spread":0.2270358783723325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775543769","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032065805,0.0014328205,0.9547152,0.00016045902,0.00037388512,0.00014117835,0.00045122413,0.0051661315,0.005493262],"genre_scores_gemma":[0.28518343,0.0022195433,0.7001907,0.0001770333,0.0002597194,0.00012916901,0.0012422501,0.00027235041,0.010325868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946076,0.000042679407,0.000025303938,0.00013778395,0.000288333,0.00004519109],"domain_scores_gemma":[0.99965405,0.00005804304,0.000044851204,0.00004785408,0.00017476373,0.000020474969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035031233,0.00051520526,0.0007667303,0.0015833428,0.0002754621,0.0006895928,0.0004341721,0.00036222575,0.0020513956],"category_scores_gemma":[0.0008394371,0.00019742169,0.00052498275,0.0014686092,0.0002746366,0.00084126927,0.0003364158,0.00048874307,0.0019879814],"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":[0.00007675268,0.0000682543,0.0010354673,0.0001477329,0.000052261174,0.00011536302,0.000026994112,0.0067051454,0.084141284,0.002063943,0.0056453026,0.8999215],"study_design_scores_gemma":[0.000033169083,0.00034537344,0.021857843,0.00007863805,0.00012357291,0.0015983771,0.00008614875,0.656393,0.26479444,0.0067811026,0.047755063,0.00015326613],"about_ca_topic_score_codex":0.0020496508,"about_ca_topic_score_gemma":0.00216264,"teacher_disagreement_score":0.0020513956,"about_ca_system_score_codex":0.00023805755,"about_ca_system_score_gemma":0.00040854892,"threshold_uncertainty_score":0.006862581},"labels":[],"label_agreement":null},{"id":"W2781609257","doi":"10.1007/s00521-017-3316-x","title":"Face recognition using AMVP and WSRC under variable illumination and pose","year":2018,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"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 Windsor","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Histogram; Face (sociological concept); Facial recognition system; Local binary patterns; Principal component analysis; Histogram of oriented gradients; Computer vision; Image (mathematics)","score_opus":0.03433440184906231,"score_gpt":0.285613616885854,"score_spread":0.25127921503679174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2781609257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20217165,0.00043347335,0.79062635,0.00015654115,0.00017532278,0.00006195053,0.00020156335,0.0012792031,0.004893878],"genre_scores_gemma":[0.6855286,0.0004994939,0.30858913,0.00012074779,0.00010756281,0.000084390296,0.0004828116,0.000104815095,0.0044824868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994868,0.00009351845,0.000018742348,0.00009772743,0.00023182202,0.0000713654],"domain_scores_gemma":[0.9997303,0.00006184161,0.00002134201,0.00006787678,0.00010283298,0.00001583614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004133082,0.0004008116,0.0005106445,0.0004891819,0.0002599754,0.000569162,0.00045655784,0.0004845806,0.0015978644],"category_scores_gemma":[0.00084976933,0.0001710813,0.00045008663,0.00062101445,0.0002724698,0.0006140545,0.0004102204,0.0004008315,0.0007617057],"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":[0.0004937035,0.000114386865,0.0025147311,0.000105294734,0.000101857586,0.000119329365,0.0000891979,0.017545054,0.31368116,0.0020158004,0.0016496147,0.6615699],"study_design_scores_gemma":[0.000029476778,0.00021165614,0.00949005,0.000013171182,0.000096310105,0.0006014722,0.000105912426,0.78639174,0.19861335,0.0015629423,0.002848775,0.00003522795],"about_ca_topic_score_codex":0.00177017,"about_ca_topic_score_gemma":0.0019894198,"teacher_disagreement_score":0.00177017,"about_ca_system_score_codex":0.00016038395,"about_ca_system_score_gemma":0.000397155,"threshold_uncertainty_score":0.0053453445},"labels":[],"label_agreement":null},{"id":"W2787191314","doi":"10.1007/978-3-642-39479-9_46","title":"Illumination Invariant Face Recognition","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Fast Fourier transform; Facial recognition system; Complex wavelet transform; Face (sociological concept); Pattern recognition (psychology); Computer vision; Invariant (physics); Classifier (UML); Face detection; Three-dimensional face recognition; Wavelet; Wavelet transform; Discrete wavelet transform; Algorithm; Mathematics","score_opus":0.023696995919841552,"score_gpt":0.23375939710388333,"score_spread":0.21006240118404176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787191314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008779741,0.0075984057,0.878019,0.00025539196,0.00084458943,0.00012774147,0.00070608006,0.009187659,0.094481386],"genre_scores_gemma":[0.13637392,0.009656125,0.49493665,0.0008904878,0.0005155894,0.00016541808,0.006199175,0.0014292055,0.3498335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971217,0.00001603105,0.000010622323,0.00008354077,0.00013798405,0.000039633433],"domain_scores_gemma":[0.99985945,0.0000150716805,0.000008138195,0.00005925518,0.000049527313,0.000008476988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020833849,0.00069650507,0.000601655,0.0008699715,0.0002962188,0.00078654767,0.0012035083,0.0006824684,0.022486068],"category_scores_gemma":[0.0002995986,0.000329641,0.00054911297,0.0007935839,0.0002930265,0.00080635014,0.00081446016,0.000747042,0.025157044],"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":[0.00004262853,0.000052952033,0.0001341697,0.00009828036,0.000017465523,0.00004997673,0.000016024193,0.0014562693,0.07440038,0.004124375,0.022689376,0.8969181],"study_design_scores_gemma":[0.000017601316,0.000217661,0.0053655524,0.00013309892,0.00009772473,0.0026549597,0.0000759706,0.12068332,0.49051723,0.014680476,0.36545646,0.00009998116],"about_ca_topic_score_codex":0.0007657486,"about_ca_topic_score_gemma":0.0013709376,"teacher_disagreement_score":0.022486068,"about_ca_system_score_codex":0.00028652718,"about_ca_system_score_gemma":0.00029918674,"threshold_uncertainty_score":0.07522339},"labels":[],"label_agreement":null},{"id":"W2789439234","doi":"10.1109/tie.2018.2815997","title":"Variational Inference based Automatic Relevance Determination Kernel for Embedded Feature Selection of Noisy Industrial Data","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":50,"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":"National Natural Science Foundation of China","keywords":"Feature selection; Kernel (algebra); Artificial intelligence; Computer science; Pointwise; Relevance vector machine; Support vector machine; Pattern recognition (psychology); Prior probability; Multivariate normal distribution; Benchmark (surveying); Feature (linguistics); Algorithm; Mathematics; Machine learning; Bayesian probability; Multivariate statistics","score_opus":0.0697939172603343,"score_gpt":0.3110790750905012,"score_spread":0.2412851578301669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789439234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048600724,0.00013419856,0.9946274,0.00003372418,0.000008915845,0.000014495352,0.000015205667,0.00018131593,0.00012460699],"genre_scores_gemma":[0.4947164,0.00037650674,0.50181377,0.00014049976,0.00009667436,0.00018045207,0.00044580337,0.00024384772,0.0019860256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982193,0.0006421229,0.00009161082,0.00047649787,0.00042280724,0.00014762601],"domain_scores_gemma":[0.9983765,0.0009198478,0.00016761372,0.00018194107,0.0003068542,0.000047354017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002515697,0.0009301149,0.0017735283,0.0010565851,0.00041532825,0.0008897519,0.002103327,0.0010939066,0.0010448298],"category_scores_gemma":[0.0067813518,0.0006452337,0.00130821,0.0011057588,0.00072633283,0.001651908,0.0012848899,0.0015125053,0.0004474594],"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":[0.00017558534,0.0001384511,0.0022343013,0.0001922471,0.0001616214,0.00017099887,0.00017108336,0.65734845,0.014095338,0.020959249,0.002236048,0.3021167],"study_design_scores_gemma":[0.0000040377267,0.000010053233,0.000115913725,0.0000021204926,0.0000042027937,0.000015561356,0.0000033582487,0.996218,0.00076042814,0.0026636666,0.00019746344,0.0000051065267],"about_ca_topic_score_codex":0.0034327984,"about_ca_topic_score_gemma":0.0026590582,"teacher_disagreement_score":0.0034327984,"about_ca_system_score_codex":0.0006876932,"about_ca_system_score_gemma":0.0010809168,"threshold_uncertainty_score":0.013304472},"labels":[],"label_agreement":null},{"id":"W2790230334","doi":"10.5220/0006658002330239","title":"Distributed Clustering using Semi-supervised Fusion and Feature Reduction Preprocessing","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"McMaster University","funders":"","keywords":"Computer science; Cluster analysis; Preprocessor; Artificial intelligence; Fusion; Reduction (mathematics); Pattern recognition (psychology); Feature (linguistics); Sensor fusion; Data mining; Mathematics","score_opus":0.024041878750405754,"score_gpt":0.2642852600164684,"score_spread":0.24024338126606265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790230334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012862465,0.000072444476,0.98399884,0.000054584125,0.00005386322,0.00006141684,0.00010533152,0.0019538782,0.0008372096],"genre_scores_gemma":[0.31280813,0.000093766976,0.6803197,0.00007401742,0.00009688443,0.00022177724,0.0013675874,0.0003294286,0.0046886727],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983602,0.00022329354,0.000093967195,0.00052335096,0.0006022472,0.00019686502],"domain_scores_gemma":[0.9984877,0.00016579813,0.000086180466,0.00043199107,0.00076938793,0.000058900707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011196854,0.001042241,0.0019494736,0.001188108,0.0011231588,0.0011968673,0.0019927146,0.0009101016,0.002663218],"category_scores_gemma":[0.0019875623,0.0005730393,0.0014728381,0.0016971381,0.000596654,0.0012730586,0.0016914658,0.0011331717,0.002637321],"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":[0.0006236038,0.00039911552,0.0017260093,0.00014398598,0.00020515121,0.00007034804,0.00022853559,0.075137004,0.089424334,0.0035023114,0.0065768682,0.82196283],"study_design_scores_gemma":[0.000027189291,0.00015317925,0.0033769521,0.000009526642,0.00006470504,0.00011726305,0.00011191336,0.9440747,0.043164227,0.0056277174,0.0032234353,0.000049195103],"about_ca_topic_score_codex":0.004229131,"about_ca_topic_score_gemma":0.007883405,"teacher_disagreement_score":0.004229131,"about_ca_system_score_codex":0.00075990916,"about_ca_system_score_gemma":0.0018174207,"threshold_uncertainty_score":0.008909404},"labels":[],"label_agreement":null},{"id":"W2790829116","doi":"10.1007/978-3-662-56672-5_6","title":"Image Quality-Based Illumination-Invariant Face Recognition","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Normalization (sociology); Facial recognition system; Biometrics; Pattern recognition (psychology); Computer vision; Discrete wavelet transform; Three-dimensional face recognition; Invariant (physics); Face (sociological concept); Wavelet; Wavelet transform; Face detection; Mathematics","score_opus":0.03140662278290552,"score_gpt":0.27706904167022406,"score_spread":0.24566241888731855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2790829116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04445726,0.0023041577,0.94340914,0.00011352542,0.00017213037,0.00009709695,0.0003181979,0.0018841008,0.00724441],"genre_scores_gemma":[0.533922,0.0039522946,0.4392326,0.0002659052,0.00023987461,0.00010666036,0.001982952,0.00071344344,0.019584294],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995302,0.00003711642,0.0000148756,0.00008455908,0.00027930332,0.000053930213],"domain_scores_gemma":[0.9996038,0.00006562059,0.000044639688,0.0000858004,0.0001818816,0.000018275627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039887626,0.0004186031,0.0006093732,0.00062932627,0.00012186997,0.0005236833,0.0009681936,0.00034016152,0.0044930335],"category_scores_gemma":[0.00089839415,0.00019787568,0.00053177366,0.0006259102,0.0002740128,0.0006184118,0.00057198666,0.00053806097,0.0020990288],"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":[0.000306569,0.00011776927,0.0007979604,0.00018874097,0.00006674722,0.00004183783,0.000024748035,0.009536801,0.2743713,0.0015587044,0.0040075015,0.70898134],"study_design_scores_gemma":[0.00004017884,0.0004052234,0.0146610215,0.000045107896,0.00015904942,0.0009266081,0.000039149443,0.5705432,0.40104815,0.0021596598,0.009898389,0.000074221054],"about_ca_topic_score_codex":0.0010403795,"about_ca_topic_score_gemma":0.0015000027,"teacher_disagreement_score":0.0044930335,"about_ca_system_score_codex":0.00032759117,"about_ca_system_score_gemma":0.00023460956,"threshold_uncertainty_score":0.015030682},"labels":[],"label_agreement":null},{"id":"W2794904811","doi":"10.1049/iet-bmt.2016.0195","title":"Filter‐based face recognition under varying illumination","year":2018,"lang":"en","type":"article","venue":"IET Biometrics","topic":"Face and Expression Recognition","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":"Yale University","keywords":"Computer science; Artificial intelligence; Face (sociological concept); Filter (signal processing); Computer vision; Convolution (computer science); Facial recognition system; Image (mathematics); Pattern recognition (psychology); Noise reduction; Artificial neural network","score_opus":0.06441198556389974,"score_gpt":0.2841099578405247,"score_spread":0.21969797227662494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794904811","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1718905,0.0009986467,0.81733817,0.00011722976,0.00022747222,0.000106706546,0.00032773748,0.002388966,0.0066045094],"genre_scores_gemma":[0.66408247,0.00088779296,0.32462257,0.0001591731,0.00008369679,0.00011146696,0.0007931734,0.0001870682,0.009072672],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954104,0.00004112315,0.000023091723,0.00014240686,0.00020380852,0.000048641774],"domain_scores_gemma":[0.99967635,0.000084974374,0.000026708094,0.00006513602,0.00013617432,0.000010604756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046065944,0.00047429602,0.00081615185,0.0007868789,0.0003052031,0.00065939745,0.00064263097,0.0006530427,0.0020531162],"category_scores_gemma":[0.0010206773,0.00021560646,0.0006478019,0.0006265733,0.00024010956,0.0009243807,0.00028240835,0.0004278445,0.0014592153],"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":[0.0005186745,0.00013999004,0.002320979,0.00017079024,0.00015352202,0.00013531625,0.000099675904,0.026800051,0.21584287,0.0016568617,0.0020655545,0.7500957],"study_design_scores_gemma":[0.00002375623,0.00022808756,0.0145541355,0.000020060499,0.00011557746,0.0008561191,0.000061356746,0.6914069,0.28562853,0.0010800275,0.0059550777,0.00007036289],"about_ca_topic_score_codex":0.0028175868,"about_ca_topic_score_gemma":0.003126701,"teacher_disagreement_score":0.0028175868,"about_ca_system_score_codex":0.00044403467,"about_ca_system_score_gemma":0.00030712198,"threshold_uncertainty_score":0.0068683624},"labels":[],"label_agreement":null},{"id":"W2795588493","doi":"10.1007/978-3-319-89656-4_19","title":"Dimensionality Reduction and Visualization by Doubly Kernelized Unit Ball Embedding","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Dalhousie University","funders":"","keywords":"Cluster analysis; Dimensionality reduction; Computer science; Embedding; Visualization; Graph embedding; Kernel (algebra); Pattern recognition (psychology); Gaussian function; Algorithm; Gaussian; Artificial intelligence; Data mining; Mathematics; Discrete mathematics","score_opus":0.021881989167738893,"score_gpt":0.2866329445835294,"score_spread":0.2647509554157905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795588493","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010276916,0.00028619563,0.9851009,0.00023269493,0.00009327511,0.000038423623,0.00029445626,0.0023019263,0.0013752668],"genre_scores_gemma":[0.20606814,0.0008146744,0.7812262,0.00014419711,0.00010506212,0.00020485865,0.0017618135,0.0011766864,0.008498248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956554,0.000106607644,0.000026853388,0.000085632,0.00016256051,0.000052812065],"domain_scores_gemma":[0.999453,0.00011079193,0.000040371222,0.00017690053,0.00017473123,0.000044180328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003643998,0.0007895416,0.0009283074,0.00096185785,0.0003270666,0.0015867754,0.00087683223,0.0005313379,0.0061052185],"category_scores_gemma":[0.001900609,0.00036237843,0.00074930605,0.0010430609,0.0004500654,0.0015514187,0.001961936,0.0013560569,0.0027742907],"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":[0.00034168715,0.00012398562,0.00060561486,0.00024666186,0.00007208935,0.00009650578,0.0002685181,0.040314283,0.05426578,0.039915383,0.036401715,0.82734776],"study_design_scores_gemma":[0.000018515912,0.00006048584,0.0006134105,0.00002396882,0.000015371501,0.0001504494,0.00008897602,0.9355072,0.023202525,0.027077107,0.013205786,0.000036200367],"about_ca_topic_score_codex":0.0019382631,"about_ca_topic_score_gemma":0.0016670957,"teacher_disagreement_score":0.0061052185,"about_ca_system_score_codex":0.00029434136,"about_ca_system_score_gemma":0.0006506159,"threshold_uncertainty_score":0.020423949},"labels":[],"label_agreement":null},{"id":"W2801120821","doi":"10.1007/978-3-319-91262-2_13","title":"An Application of Graphic Tools and Analytic Hierarchy Process to the Description of Biometric Features","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Computer science; Analytic hierarchy process; Consistency (knowledge bases); Biometrics; Process (computing); Particle swarm optimization; Context (archaeology); Basis (linear algebra); Pairwise comparison; Interface (matter); Artificial intelligence; Graphical user interface; Data mining; Hierarchy; Transformation (genetics); Machine learning; Operations research; Mathematics; Programming language","score_opus":0.0232410764384563,"score_gpt":0.2711687848015181,"score_spread":0.24792770836306183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2801120821","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007823856,0.0003905183,0.99575114,0.00012497879,0.000037970596,0.00006405844,0.0001360633,0.0009233696,0.0017895207],"genre_scores_gemma":[0.011131259,0.0004330025,0.9867409,0.000050643743,0.000035539742,0.00014897194,0.0002048712,0.00014390987,0.001110831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980451,0.00075793196,0.00019631325,0.0002050028,0.0007199436,0.00007575756],"domain_scores_gemma":[0.996696,0.002374041,0.00016243395,0.00032440707,0.0003912749,0.000051849616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026506444,0.0011798469,0.001278201,0.004288375,0.00087358867,0.0036017527,0.0019449948,0.0010734214,0.009389875],"category_scores_gemma":[0.009991123,0.00073379447,0.0018535524,0.0055038556,0.00206225,0.0026452937,0.0017616409,0.0018960066,0.0021892658],"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":[0.00011482633,0.000097987046,0.0008794017,0.00094401743,0.000121852914,0.00044064157,0.001169696,0.032527816,0.005163427,0.40814725,0.014582007,0.53581125],"study_design_scores_gemma":[0.00004247438,0.0000901912,0.0010360506,0.00022737838,0.00009245434,0.00047445818,0.0003379715,0.38142526,0.0036621774,0.5522035,0.060315132,0.00009289139],"about_ca_topic_score_codex":0.0067467852,"about_ca_topic_score_gemma":0.0045094136,"teacher_disagreement_score":0.009389875,"about_ca_system_score_codex":0.0012600842,"about_ca_system_score_gemma":0.0017761751,"threshold_uncertainty_score":0.031412244},"labels":[],"label_agreement":null},{"id":"W2802472748","doi":"10.1007/978-3-319-78196-9_11","title":"Combining Feature Extraction and Clustering for Better Face Recognition","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Cluster analysis; Computer science; Pattern recognition (psychology); Artificial intelligence; Feature extraction; Face (sociological concept); Feature (linguistics); Fuzzy clustering; Facial recognition system; Rank (graph theory); Correlation clustering; Data mining; Mathematics","score_opus":0.02713523786856239,"score_gpt":0.26977091293884464,"score_spread":0.24263567507028225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802472748","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010213606,0.00082549214,0.9819089,0.00017059574,0.00019972338,0.00009400191,0.0003326673,0.003846649,0.0024083785],"genre_scores_gemma":[0.07718622,0.00092587783,0.91164654,0.00023721701,0.00017578344,0.0001604653,0.0016100955,0.0008078626,0.007249892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991702,0.00008961286,0.000048280406,0.00027037883,0.00027736835,0.00014425156],"domain_scores_gemma":[0.9993149,0.0001775643,0.000030967687,0.00017865121,0.000276444,0.000021384532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008200067,0.001705976,0.0019393963,0.0024820075,0.00072375796,0.0015449653,0.0015146427,0.0015557589,0.008409764],"category_scores_gemma":[0.0014960989,0.0006741709,0.0018334526,0.0031098374,0.00034754522,0.0020134442,0.0010008357,0.0011400975,0.008443467],"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":[0.0001082131,0.00013430207,0.0005037058,0.00017102143,0.000084784,0.000059106183,0.000047082056,0.006798612,0.16506991,0.0011661242,0.009423428,0.81643367],"study_design_scores_gemma":[0.000038602422,0.00023071817,0.008044773,0.00009929767,0.00028576452,0.00087419257,0.00016834112,0.5843192,0.36569357,0.009849521,0.030236732,0.00015934947],"about_ca_topic_score_codex":0.0033934421,"about_ca_topic_score_gemma":0.005052143,"teacher_disagreement_score":0.008409764,"about_ca_system_score_codex":0.0005170955,"about_ca_system_score_gemma":0.00060005236,"threshold_uncertainty_score":0.028133452},"labels":[],"label_agreement":null},{"id":"W2803106003","doi":"10.1137/16m1107863","title":"Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization","year":2018,"lang":"en","type":"article","venue":"SIAM Journal on Matrix Analysis and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"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","funders":"Hong Kong Baptist University","keywords":"Mathematics; Factorization; Matrix decomposition; Matrix norm; Sparse matrix; Coefficient matrix; Non-negative matrix factorization; Matrix (chemical analysis); Incomplete LU factorization; Norm (philosophy); Algorithm; Applied mathematics; Mathematical optimization","score_opus":0.00649217373800545,"score_gpt":0.2568799767331522,"score_spread":0.2503878029951468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803106003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002046901,0.00020985614,0.99682873,0.00011377768,0.000030828167,0.000018744578,0.000037596405,0.000048345824,0.0006652064],"genre_scores_gemma":[0.23020083,0.0019613463,0.76224947,0.0003095506,0.0003876419,0.0003546887,0.00082302245,0.00016869359,0.0035447986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982255,0.0009139301,0.00006455232,0.000277534,0.0004101362,0.00010833401],"domain_scores_gemma":[0.99679774,0.0019301048,0.00030914426,0.00029037206,0.00059266353,0.00007986123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027379014,0.0013942125,0.0012257057,0.00085674686,0.0004172495,0.0011555915,0.0010701413,0.0009723688,0.001882744],"category_scores_gemma":[0.009920716,0.00048521085,0.0008424573,0.0016413814,0.0017896241,0.0023623337,0.0013920909,0.0017018255,0.00094514305],"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":[0.00014942871,0.00009174037,0.0011845769,0.0005142864,0.000121836,0.00028409366,0.00018514153,0.5537032,0.008793919,0.25512406,0.008803943,0.17104378],"study_design_scores_gemma":[0.000007144735,0.000029848876,0.000105087885,0.000015875461,0.000006811107,0.000048069625,0.000015885762,0.9521193,0.0009109551,0.04512249,0.0016048054,0.000013764904],"about_ca_topic_score_codex":0.0021966603,"about_ca_topic_score_gemma":0.0017033776,"teacher_disagreement_score":0.0027379014,"about_ca_system_score_codex":0.0005325571,"about_ca_system_score_gemma":0.0008363445,"threshold_uncertainty_score":0.014479518},"labels":[],"label_agreement":null},{"id":"W2803390963","doi":"10.1002/wics.1434","title":"A review of quadratic discriminant analysis for high‐dimensional data","year":2018,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quadratic classifier; Exploratory data analysis; Linear discriminant analysis; Curse of dimensionality; Clustering high-dimensional data; Cluster analysis; Mathematics; Covariance; Artificial intelligence; Bayesian probability; Graphical model; Quadratic equation; Machine learning; Computer science; Pattern recognition (psychology); Data mining; Statistics; Support vector machine","score_opus":0.14764106280167305,"score_gpt":0.42556221373641756,"score_spread":0.2779211509347445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2803390963","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008685178,0.89379734,0.09288817,0.0022943986,0.0014212815,0.000053713997,0.00029438245,0.0002452401,0.008137021],"genre_scores_gemma":[0.014413733,0.9117988,0.062295258,0.0014088979,0.0034167722,0.00014697443,0.0007587232,0.00015963816,0.0056012515],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99841607,0.00036096503,0.00018719102,0.0002916412,0.0006794144,0.00006478613],"domain_scores_gemma":[0.9966846,0.002012803,0.00017650342,0.00014360635,0.00091248675,0.000069893424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029168872,0.0013524205,0.0016740304,0.00428921,0.000599411,0.002091795,0.0016267318,0.0014288729,0.005643396],"category_scores_gemma":[0.0062005343,0.0006072628,0.0011975248,0.006497836,0.0011366772,0.0026501378,0.0011151752,0.0018639216,0.0044828546],"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":[0.0000417239,0.00005977345,0.0007793828,0.0061723352,0.00012706668,0.00014223046,0.00014272165,0.002702157,0.001181253,0.027992222,0.045758374,0.9149008],"study_design_scores_gemma":[0.000016388532,0.00012287158,0.0028751749,0.002600047,0.00015316659,0.0014303272,0.0001637798,0.011482392,0.0016118727,0.04760956,0.93179226,0.00014216997],"about_ca_topic_score_codex":0.0023774155,"about_ca_topic_score_gemma":0.001853981,"teacher_disagreement_score":0.005643396,"about_ca_system_score_codex":0.0010288684,"about_ca_system_score_gemma":0.0017277572,"threshold_uncertainty_score":0.018879056},"labels":[],"label_agreement":null},{"id":"W2804369000","doi":"10.1109/atsip.2018.8364451","title":"A novel incremental face recognition method based on nonparametric discriminant model","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Windsor","funders":"","keywords":"Facial recognition system; Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Computer science; Feature (linguistics); Face (sociological concept); Gabor wavelet; Nonparametric statistics; Dimension (graph theory); Variance (accounting); Feature extraction; Feature vector; Gabor filter; Wavelet; Discriminant; Mathematics; Wavelet transform; Statistics; Discrete wavelet transform","score_opus":0.06854357935184707,"score_gpt":0.3161507762084522,"score_spread":0.24760719685660515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804369000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014410229,0.00024051935,0.98306125,0.0000757328,0.00009006642,0.000039888295,0.000051815732,0.0008942307,0.001136257],"genre_scores_gemma":[0.28146234,0.00037373474,0.71039546,0.00014510982,0.00014510678,0.00015675098,0.00040917366,0.00012178742,0.006790545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966,0.000045704164,0.000014238176,0.000089826586,0.00015754988,0.00003277053],"domain_scores_gemma":[0.99973696,0.000054864497,0.000019768651,0.00004661577,0.00012352486,0.000018337827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043239974,0.00042230997,0.00090224197,0.0007677967,0.00035303904,0.0004632293,0.0011851708,0.00041611056,0.0019574212],"category_scores_gemma":[0.0008677263,0.00021057062,0.0006372942,0.00055423187,0.00021677128,0.00072701735,0.00060717075,0.00072014524,0.0010825766],"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":[0.00015379314,0.00009205732,0.0011709414,0.000064821455,0.00004342355,0.000087313536,0.00004159646,0.0200161,0.04136264,0.003995416,0.0036250816,0.92934686],"study_design_scores_gemma":[0.000014646527,0.000085549385,0.001418454,0.00000524304,0.000030727428,0.0003237505,0.000016735186,0.9798565,0.012634889,0.0016123593,0.0039699986,0.000031134525],"about_ca_topic_score_codex":0.0018811648,"about_ca_topic_score_gemma":0.001963664,"teacher_disagreement_score":0.0019574212,"about_ca_system_score_codex":0.00028505037,"about_ca_system_score_gemma":0.0005048792,"threshold_uncertainty_score":0.006548226},"labels":[],"label_agreement":null},{"id":"W2806829681","doi":"10.5555/3213200.3213210","title":"Improving support vector machine classification accuracy based on kernel parameters optimization","year":2018,"lang":"en","type":"article","venue":"Communications and Networking Symposium","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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","funders":"","keywords":"Support vector machine; Artificial intelligence; Computer science; Kernel (algebra); Pattern recognition (psychology); Machine learning; Hyperplane; Linear classifier; Multiple kernel learning; Structured support vector machine; Decision boundary; Feature selection; Statistical classification; Relevance vector machine; Kernel method; Mathematics","score_opus":0.03728179761610712,"score_gpt":0.2738774502591773,"score_spread":0.2365956526430702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2806829681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2071244,0.0027469953,0.7844049,0.00036445123,0.00020434074,0.000104655395,0.00020044693,0.002789722,0.002059991],"genre_scores_gemma":[0.8301967,0.00070018147,0.16718581,0.00006414099,0.00006841238,0.00008332447,0.0005816567,0.0001360797,0.0009837719],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972791,0.0005942903,0.00039496348,0.00042301873,0.0010716555,0.00023707758],"domain_scores_gemma":[0.99342465,0.0029543592,0.00064202695,0.0005676783,0.0023319153,0.000079403646],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025229645,0.0009588178,0.0016094283,0.0018968948,0.00037107753,0.0015483061,0.00090831745,0.0010798244,0.00083910406],"category_scores_gemma":[0.014264639,0.00027595027,0.0007338977,0.0017089157,0.00032688392,0.0020908376,0.0005806479,0.0011779828,0.00069204485],"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":[0.0006701527,0.00030331488,0.009927213,0.00035422033,0.00018736001,0.00019911244,0.00016925695,0.22648059,0.031047242,0.002374627,0.0032760366,0.725011],"study_design_scores_gemma":[0.000018202365,0.00014338818,0.0038317724,0.000024142035,0.000045811696,0.000101144265,0.00005158918,0.97646856,0.016897336,0.0011987137,0.001191898,0.00002745334],"about_ca_topic_score_codex":0.0015538661,"about_ca_topic_score_gemma":0.00064997666,"teacher_disagreement_score":0.0025229645,"about_ca_system_score_codex":0.0004990672,"about_ca_system_score_gemma":0.0005623595,"threshold_uncertainty_score":0.013342857},"labels":[],"label_agreement":null},{"id":"W2812241481","doi":"10.3390/e20070519","title":"Projected Affinity Values for Nyström Spectral Clustering","year":2018,"lang":"en","type":"article","venue":"Entropy","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Eigenvalues and eigenvectors; Mathematics; Cluster analysis; Projection (relational algebra); Kernel (algebra); Kernel method; Gaussian function; Gaussian; Similarity (geometry); Quadratic equation; Point (geometry); Support vector machine; Applied mathematics; Combinatorics; Algorithm; Artificial intelligence; Computer science; Statistics","score_opus":0.024861720689817433,"score_gpt":0.278469608701322,"score_spread":0.2536078880115045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2812241481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0061935238,0.00017612302,0.9918948,0.00008414408,0.000042153868,0.000049521917,0.00004410594,0.00030372993,0.0012118098],"genre_scores_gemma":[0.27520654,0.00037421827,0.7202665,0.0001824429,0.00011439481,0.00041257576,0.0004975614,0.00027940093,0.0026663055],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99764025,0.00070535514,0.00014775722,0.0003993149,0.0009841614,0.00012321348],"domain_scores_gemma":[0.9965963,0.0013538494,0.0002923159,0.0005228557,0.0010772726,0.00015745065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023805175,0.0010134609,0.0011373357,0.0015686264,0.0011044382,0.0020001468,0.0017513885,0.0019007454,0.003453911],"category_scores_gemma":[0.012595797,0.00052208547,0.00073164597,0.0015375912,0.0015790692,0.0028972658,0.0021502757,0.0016924032,0.0019649353],"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":[0.00034146168,0.0001924033,0.0020984497,0.00033612805,0.00012848154,0.00014834349,0.00050182285,0.4343833,0.012417168,0.15048508,0.007318451,0.39164892],"study_design_scores_gemma":[0.000011271597,0.000029711631,0.00028901626,0.000025753669,0.000006098824,0.000068499714,0.000041300154,0.95800465,0.002358934,0.036621485,0.0025185654,0.000024695928],"about_ca_topic_score_codex":0.0018367586,"about_ca_topic_score_gemma":0.0015167465,"teacher_disagreement_score":0.003453911,"about_ca_system_score_codex":0.0012593044,"about_ca_system_score_gemma":0.0014179271,"threshold_uncertainty_score":0.012589574},"labels":[],"label_agreement":null},{"id":"W2879929106","doi":"10.1145/3221269.3223037","title":"A unified framework of density-based clustering for semi-supervised classification","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Cluster analysis; Computer science; Data mining; Artificial intelligence; Machine learning; Supervised learning; Big data; Labeled data; Pattern recognition (psychology); Artificial neural network","score_opus":0.04523541358972343,"score_gpt":0.28867403175838535,"score_spread":0.2434386181686619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2879929106","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005597468,0.00008937629,0.99888736,0.000038366245,0.000010906448,0.000025582496,0.000019160021,0.00016270943,0.000206844],"genre_scores_gemma":[0.09621534,0.00052189495,0.90032005,0.0001398766,0.00018088642,0.00045085192,0.00051255355,0.00022504767,0.0014333482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995096,0.0019767578,0.0002492754,0.00086436054,0.0015724116,0.00024117276],"domain_scores_gemma":[0.9954074,0.0014827586,0.0003698619,0.000814353,0.0017180332,0.00020757326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004323056,0.0013075467,0.0025129942,0.0030807778,0.0013697135,0.0020035708,0.004143488,0.0018850518,0.0015481875],"category_scores_gemma":[0.009547407,0.0008759896,0.0019332488,0.00302992,0.0022456003,0.0031495718,0.0036030894,0.0025578567,0.0016661072],"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":[0.00011646034,0.00018713587,0.0017982862,0.00039400236,0.00029535935,0.00021278739,0.0006731747,0.41781595,0.00923957,0.17173757,0.008628938,0.38890076],"study_design_scores_gemma":[0.000005555651,0.00003141379,0.00027181668,0.00001803643,0.000018051609,0.000073324285,0.000030644158,0.9608947,0.0014316556,0.03473504,0.002458268,0.000031445772],"about_ca_topic_score_codex":0.006159048,"about_ca_topic_score_gemma":0.0068222065,"teacher_disagreement_score":0.006159048,"about_ca_system_score_codex":0.0016232375,"about_ca_system_score_gemma":0.0024837803,"threshold_uncertainty_score":0.022862732},"labels":[],"label_agreement":null},{"id":"W2883750810","doi":"10.2478/amcs-2018-0030","title":"Facial Expression Recognition under Difficult Conditions: A Comprehensive Study on Edge Directional Texture Patterns","year":2018,"lang":"en","type":"article","venue":"International Journal of Applied Mathematics and Computer Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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 Calgary","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Feature (linguistics); Facial recognition system; Noise (video); Facial expression; Local binary patterns; Face (sociological concept); Texture (cosmology); Enhanced Data Rates for GSM Evolution; Image (mathematics); Histogram","score_opus":0.02973759199697199,"score_gpt":0.2926573451710637,"score_spread":0.26291975317409166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883750810","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85491,0.0022919858,0.13953057,0.00010187473,0.00005665599,0.00008391174,0.00022973769,0.0001283105,0.0026668306],"genre_scores_gemma":[0.98235303,0.0016706568,0.014756274,0.000029852004,0.000047303613,0.0000220244,0.00035667472,0.000016356473,0.000747894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996568,0.00005331781,0.000023442784,0.00007019377,0.00016665942,0.000029509858],"domain_scores_gemma":[0.99955684,0.00016178671,0.00006510383,0.00006016505,0.00013101149,0.000025228344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003727061,0.0002884942,0.00046093005,0.0008170245,0.00015688958,0.00037796656,0.00021029434,0.00021171231,0.00050401635],"category_scores_gemma":[0.0012644515,0.00008245115,0.00037812695,0.000831579,0.00024426845,0.0006177082,0.00023356058,0.0001888792,0.00015072011],"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":[0.0006250761,0.0002792521,0.033547044,0.0005243188,0.0001390884,0.000772794,0.00024756097,0.026546247,0.23381086,0.0017062811,0.0020633093,0.69973814],"study_design_scores_gemma":[0.000030103738,0.0008822951,0.14998698,0.000062029074,0.00030773148,0.0029381325,0.00061088515,0.7457217,0.09176223,0.0019086421,0.0057175644,0.00007164281],"about_ca_topic_score_codex":0.0015202558,"about_ca_topic_score_gemma":0.00073866366,"teacher_disagreement_score":0.0015202558,"about_ca_system_score_codex":0.000099555,"about_ca_system_score_gemma":0.00014580972,"threshold_uncertainty_score":0.0030228496},"labels":[],"label_agreement":null},{"id":"W2884529360","doi":"10.3233/jifs-171812","title":"Learning regression problems by using classifiers","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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 Rehabilitation Institute; University Health Network","funders":"","keywords":"Random forest; Ensemble learning; Classifier (UML); Computer science; Regression; Artificial intelligence; Random subspace method; Binary classification; Machine learning; Ensemble forecasting; Regression analysis; Pattern recognition (psychology); Binary number; Data mining; Support vector machine; Mathematics; Statistics","score_opus":0.04002840583221206,"score_gpt":0.2838153491085753,"score_spread":0.24378694327636324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884529360","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002137456,0.00058075326,0.9962351,0.00012661412,0.00004206842,0.000020141768,0.000015133657,0.00015645943,0.00068644626],"genre_scores_gemma":[0.163047,0.0030685915,0.8293468,0.00024906883,0.00063973333,0.00025046608,0.0002471256,0.00013594792,0.0030151745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99735236,0.0010409917,0.00018665146,0.00058178284,0.00069541164,0.00014279516],"domain_scores_gemma":[0.99642926,0.0023538826,0.00031977694,0.00035703275,0.00048451076,0.000055558514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003528488,0.0012376887,0.0017609675,0.0017984007,0.00049316266,0.0018098862,0.001279277,0.001531027,0.001288761],"category_scores_gemma":[0.009091981,0.00050885737,0.0014005604,0.0019039542,0.0008809252,0.0023520354,0.00155555,0.002184436,0.0008242763],"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":[0.00006240918,0.0000900949,0.0024229207,0.00031668137,0.0003042522,0.00012500414,0.00021442144,0.39956126,0.005196618,0.07917346,0.004077235,0.5084556],"study_design_scores_gemma":[0.0000053827307,0.000024133944,0.00016374329,0.00003129193,0.000023200779,0.000038626076,0.000015965777,0.97182864,0.0011755622,0.0238344,0.0028484494,0.0000106839725],"about_ca_topic_score_codex":0.0019813087,"about_ca_topic_score_gemma":0.001330586,"teacher_disagreement_score":0.003528488,"about_ca_system_score_codex":0.0006056057,"about_ca_system_score_gemma":0.00061948225,"threshold_uncertainty_score":0.018660665},"labels":[],"label_agreement":null},{"id":"W2887052598","doi":"10.1007/978-3-319-97785-0_19","title":"Supervised Classification Using Feature Space Partitioning","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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; Pattern recognition (psychology); Feature vector; Support vector machine; Artificial intelligence; Class (philosophy); Heuristic; Space partitioning; Feature (linguistics); Algorithm","score_opus":0.042480969466897694,"score_gpt":0.26967200626207166,"score_spread":0.22719103679517397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887052598","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011660202,0.00046972468,0.98297447,0.00011384294,0.00009879986,0.0001124097,0.00022219203,0.0018299664,0.0025183707],"genre_scores_gemma":[0.18425912,0.00048150306,0.8020798,0.00014109623,0.00016923879,0.00037871717,0.0032070628,0.0004477363,0.008835755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989857,0.00022226326,0.00006726061,0.0003156309,0.00031354788,0.000095492374],"domain_scores_gemma":[0.99898094,0.00034616445,0.00006155604,0.00027276605,0.00030281354,0.000035650002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076920056,0.0010346998,0.0016140998,0.001169966,0.0006339206,0.0013546667,0.0016835844,0.0008764426,0.0033632673],"category_scores_gemma":[0.0016297526,0.00045242257,0.00126721,0.0012571376,0.00043544063,0.0013157881,0.0012771429,0.0010933934,0.0025492192],"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":[0.00013836319,0.00015792547,0.00065795006,0.00013927507,0.00008055827,0.000046804333,0.00008284372,0.026736854,0.017055366,0.0056366753,0.010379838,0.93888754],"study_design_scores_gemma":[0.00002064445,0.00010409793,0.0011695443,0.00003147873,0.000041191463,0.00017433555,0.000086360815,0.9545954,0.012960352,0.022495406,0.00829661,0.000024543053],"about_ca_topic_score_codex":0.0018802912,"about_ca_topic_score_gemma":0.0026948673,"teacher_disagreement_score":0.0033632673,"about_ca_system_score_codex":0.00047456176,"about_ca_system_score_gemma":0.000793807,"threshold_uncertainty_score":0.011251271},"labels":[],"label_agreement":null},{"id":"W2891398956","doi":"10.1007/978-3-030-00919-9_18","title":"Brain Status Prediction with Non-negative Projective Dictionary Learning","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; École de Technologie Supérieure; Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Computer science; Discriminative model; Dictionary learning; Artificial intelligence; Neural coding; Machine learning; Projective test; Pattern recognition (psychology); Representation (politics); Sparse approximation; Mathematics","score_opus":0.010803993489296003,"score_gpt":0.23288823474240078,"score_spread":0.22208424125310477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891398956","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08821139,0.0011017438,0.9023544,0.0005011848,0.00027833087,0.0001049448,0.0007903701,0.0013700998,0.005287419],"genre_scores_gemma":[0.8439818,0.00065023184,0.14482282,0.00020160095,0.00016763703,0.000100062745,0.0016170909,0.00014332452,0.008315312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998227,0.000032006417,0.000009477678,0.00007773359,0.00002632101,0.000031766158],"domain_scores_gemma":[0.9996736,0.00012419703,0.000026058426,0.00006382284,0.00008538477,0.000026964772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039898462,0.00067211065,0.00060627615,0.00041925727,0.00023136237,0.0006967643,0.00076941727,0.0005756321,0.0031828678],"category_scores_gemma":[0.0014325326,0.00023952808,0.0004696296,0.00048254742,0.00035896036,0.000935868,0.0010147063,0.0009103245,0.0014197234],"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":[0.00038236106,0.00018029689,0.005997332,0.00011752601,0.00012815958,0.00013865312,0.00006722107,0.033160143,0.015828667,0.00673131,0.013091965,0.92417634],"study_design_scores_gemma":[0.000024429193,0.00009973258,0.0026975963,0.000019037256,0.00005057345,0.0002111808,0.00004560489,0.9745147,0.007914185,0.012586032,0.0018176853,0.00001912542],"about_ca_topic_score_codex":0.0017932148,"about_ca_topic_score_gemma":0.002856158,"teacher_disagreement_score":0.0031828678,"about_ca_system_score_codex":0.0001917051,"about_ca_system_score_gemma":0.000386137,"threshold_uncertainty_score":0.010647714},"labels":[],"label_agreement":null},{"id":"W2892320280","doi":"10.1109/ssci.2018.8628705","title":"Facial Recognition with Encoded Local Projections","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Artificial intelligence; Histogram; Pattern recognition (psychology); Support vector machine; Computer science; Histogram of oriented gradients; Feature (linguistics); Local binary patterns; Image (mathematics); Feature vector; Computer vision; Feature extraction","score_opus":0.024391170107269225,"score_gpt":0.24545549916674902,"score_spread":0.2210643290594798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892320280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21288338,0.0010995955,0.7736485,0.00034297956,0.0002902846,0.00022830376,0.00095383293,0.0032413914,0.0073118126],"genre_scores_gemma":[0.73608583,0.0007604546,0.25496218,0.0001588687,0.00009562801,0.00014038898,0.0014646584,0.0001238397,0.006208109],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991991,0.00012701687,0.000027899428,0.00015683727,0.0004139878,0.00007515603],"domain_scores_gemma":[0.99956375,0.00008125851,0.00003474156,0.000107209904,0.00019036069,0.000022520519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050619856,0.00042019613,0.0005871626,0.0007280161,0.00017079948,0.00062116695,0.00046895572,0.00032051455,0.0026470518],"category_scores_gemma":[0.0017627077,0.00018096097,0.00042940304,0.0007563972,0.00031611093,0.0009055695,0.000912309,0.0006095367,0.0010978412],"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":[0.000504787,0.00014200772,0.0021752878,0.00015721789,0.00009859775,0.0001810632,0.00009062752,0.031321924,0.10407136,0.00327746,0.006301705,0.851678],"study_design_scores_gemma":[0.000042791784,0.0004421227,0.0075004944,0.00003249013,0.00007021296,0.00080438814,0.00015564967,0.8887585,0.09270023,0.0042020343,0.005235482,0.0000556875],"about_ca_topic_score_codex":0.002967388,"about_ca_topic_score_gemma":0.0020825306,"teacher_disagreement_score":0.002967388,"about_ca_system_score_codex":0.00034910813,"about_ca_system_score_gemma":0.00040780942,"threshold_uncertainty_score":0.008855224},"labels":[],"label_agreement":null},{"id":"W2896457526","doi":"10.1109/ijcnn.2018.8489242","title":"Nonnegative Matrix Factorization Using Autoencoders And Exponentiated Gradient Descent","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Autoencoder; Non-negative matrix factorization; Matrix decomposition; Gradient descent; Cluster analysis; Computer science; Factorization; Basis (linear algebra); Stochastic gradient descent; Artificial intelligence; Matrix (chemical analysis); Pattern recognition (psychology); Image (mathematics); Algorithm; Hierarchy; Encoder; Mathematics; Deep learning; Artificial neural network","score_opus":0.02788483744999191,"score_gpt":0.2806034847195265,"score_spread":0.2527186472695346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896457526","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014589347,0.00006817525,0.99789053,0.000036712434,0.0000186512,0.000016401413,0.000015314647,0.00019751917,0.0002977892],"genre_scores_gemma":[0.11260213,0.000275138,0.8832385,0.00013625581,0.00008421009,0.0001745496,0.00025540852,0.0001683175,0.0030654154],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942917,0.0001714569,0.000038229926,0.00013165816,0.00017979997,0.000049693692],"domain_scores_gemma":[0.998728,0.000619339,0.00014250503,0.00018117353,0.00027862441,0.00005043945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012882843,0.0013749609,0.0011721418,0.0006593836,0.00038027077,0.00089166604,0.0013388258,0.0010952302,0.0018307902],"category_scores_gemma":[0.004034796,0.0006841759,0.0010020159,0.0006678302,0.0008202642,0.0015638042,0.00103046,0.002102174,0.0011979053],"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":[0.000078603596,0.000088939836,0.0007150297,0.00013765448,0.00011428991,0.00011843755,0.00010379327,0.7068596,0.008225474,0.035051983,0.0040217605,0.24448448],"study_design_scores_gemma":[0.0000036484205,0.000009278821,0.000040197916,0.000004312514,0.000002470284,0.000012672139,0.0000021274554,0.9950069,0.0006445759,0.0038667808,0.00040328625,0.0000035975615],"about_ca_topic_score_codex":0.0057405955,"about_ca_topic_score_gemma":0.008838123,"teacher_disagreement_score":0.0057405955,"about_ca_system_score_codex":0.0007177561,"about_ca_system_score_gemma":0.0010708342,"threshold_uncertainty_score":0.011414349},"labels":[],"label_agreement":null},{"id":"W2896695955","doi":"10.1109/icci-cc.2018.8482086","title":"Score and Rank-Level Fusion for Emotion Recognition Using Genetic Algorithm","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Rank (graph theory); Artificial intelligence; Genetic algorithm; Task (project management); Identification (biology); Machine learning; Chromosome; Pattern recognition (psychology); Fusion; Data mining; Mathematics; Engineering","score_opus":0.0900983701419351,"score_gpt":0.28284543837631027,"score_spread":0.19274706823437515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896695955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052938025,0.0001613848,0.9444292,0.00012262998,0.000029197732,0.000071564435,0.000051004372,0.0008352102,0.0013617292],"genre_scores_gemma":[0.5845679,0.00009702689,0.4131983,0.00008727862,0.000029470373,0.00014172935,0.0002496282,0.00007157317,0.001557075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987128,0.00038251647,0.0000754564,0.0001888184,0.00048654684,0.00015386296],"domain_scores_gemma":[0.99873954,0.0005032651,0.00012238117,0.00010706115,0.00047729618,0.000050466784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002141556,0.00078885467,0.0010693193,0.0015056145,0.0005396352,0.001117567,0.00094981823,0.00089770276,0.0011341425],"category_scores_gemma":[0.004661944,0.0002564804,0.00072605704,0.0010217061,0.00054608984,0.00077244843,0.00078537024,0.000847445,0.00043458265],"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":[0.0002848446,0.00022320938,0.0031474791,0.000056019522,0.0001257596,0.000061545245,0.00012863513,0.5078484,0.016591204,0.0076787747,0.0013500744,0.46250406],"study_design_scores_gemma":[0.000011760615,0.00008265679,0.0005481512,0.0000037682648,0.000014955687,0.000018567674,0.000014383705,0.9930392,0.0037849715,0.0021747807,0.00029481194,0.000012074097],"about_ca_topic_score_codex":0.0050490657,"about_ca_topic_score_gemma":0.003671176,"teacher_disagreement_score":0.0050490657,"about_ca_system_score_codex":0.001172034,"about_ca_system_score_gemma":0.0011286008,"threshold_uncertainty_score":0.011325717},"labels":[],"label_agreement":null},{"id":"W2897433210","doi":"10.1109/jstsp.2018.2877041","title":"Graph and Sparse-Based Robust Nonnegative Block Value Decomposition for Clustering","year":2018,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Signal Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Robustness (evolution); Outlier; Mathematics; Graph; Sparse approximation; Sparse matrix; Matrix norm; Computer science; Algorithm; Discrete mathematics; Artificial intelligence; Eigenvalues and eigenvectors","score_opus":0.02693701711978837,"score_gpt":0.28109854936478756,"score_spread":0.2541615322449992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897433210","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015677292,0.00013086856,0.9976544,0.00006212383,0.0000186123,0.00001554966,0.00004019678,0.00014372056,0.00036688102],"genre_scores_gemma":[0.101063095,0.000491645,0.8954094,0.000120207216,0.000063551575,0.00011185092,0.0006426009,0.00023264938,0.00186501],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99896026,0.00033118372,0.00005063854,0.0002167354,0.0003718641,0.00006930319],"domain_scores_gemma":[0.99883765,0.00042616838,0.00013792372,0.00021079349,0.00033609167,0.00005132844],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011800083,0.0012024266,0.0011388243,0.0014598985,0.00044617182,0.0012240969,0.0014242309,0.0012809688,0.0017946279],"category_scores_gemma":[0.004409123,0.00044779366,0.0011376645,0.0016844214,0.0008299968,0.0016292459,0.0011388742,0.0013054102,0.000991035],"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":[0.000109870176,0.000054562945,0.00068178284,0.0002742531,0.000100617566,0.00008197612,0.00009944477,0.6609016,0.015382379,0.06265659,0.0057013794,0.25395548],"study_design_scores_gemma":[0.000003190965,0.000009478089,0.000081008366,0.0000059796257,0.0000038787684,0.000019292898,0.0000078513485,0.98908186,0.0015919314,0.008057151,0.0011307566,0.000007756224],"about_ca_topic_score_codex":0.004794655,"about_ca_topic_score_gemma":0.0048350208,"teacher_disagreement_score":0.004794655,"about_ca_system_score_codex":0.00092479074,"about_ca_system_score_gemma":0.0010449771,"threshold_uncertainty_score":0.0095335245},"labels":[],"label_agreement":null},{"id":"W2899000040","doi":"","title":"Discriminative Training of Sum-Product Networks by Extended Baum-Welch.","year":2018,"lang":"en","type":"article","venue":"Probabilistic Graphical Models","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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 Waterloo","funders":"","keywords":"Discriminative model; Training (meteorology); Product (mathematics); Computer science; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Mathematics; Physics","score_opus":0.040581510970118646,"score_gpt":0.26223389838308414,"score_spread":0.2216523874129655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899000040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010744343,0.0004905701,0.98479027,0.00019967585,0.00009366994,0.000051676107,0.00018837639,0.0023438556,0.0010975231],"genre_scores_gemma":[0.485624,0.00047281478,0.49897832,0.00052636827,0.00015368937,0.00040131863,0.0027394854,0.00097293296,0.010131095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984505,0.0007104263,0.0000678782,0.00043289587,0.00019459573,0.00014363485],"domain_scores_gemma":[0.99672604,0.0021424766,0.00015145529,0.00042752145,0.00044016185,0.00011234547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022644158,0.001413933,0.0016980359,0.0011451697,0.00061483984,0.0010839481,0.0032765204,0.0020707825,0.005956697],"category_scores_gemma":[0.009591878,0.0014735998,0.0012997264,0.0017026172,0.0009840801,0.0024849134,0.0019808072,0.0036343858,0.0039709797],"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":[0.00037833268,0.00020143924,0.0009337215,0.00018188282,0.00024179116,0.0000941776,0.00009094648,0.58627594,0.0041004387,0.009990237,0.007973381,0.38953766],"study_design_scores_gemma":[0.000006464543,0.000012095637,0.00007206394,0.000005194401,0.000009061798,0.000013110559,0.000004157721,0.99549603,0.0005607702,0.003510993,0.00030551662,0.000004579617],"about_ca_topic_score_codex":0.011048481,"about_ca_topic_score_gemma":0.021861864,"teacher_disagreement_score":0.011048481,"about_ca_system_score_codex":0.0010019916,"about_ca_system_score_gemma":0.0014678898,"threshold_uncertainty_score":0.021968365},"labels":[],"label_agreement":null},{"id":"W2905162745","doi":"10.4310/cms.2018.v16.n5.a08","title":"Regularized semi-supervised least squares regression with dependent samples","year":2018,"lang":"en","type":"article","venue":"Communications in Mathematical Sciences","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Regression; Statistics; Regression analysis; Partial least squares regression; Least-squares function approximation; Applied mathematics","score_opus":0.08636991048138158,"score_gpt":0.33638073428529974,"score_spread":0.25001082380391815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905162745","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059028342,0.00010893895,0.992902,0.000062773935,0.00003395296,0.000032205426,0.000075290445,0.000648596,0.00023335364],"genre_scores_gemma":[0.3692479,0.00026045294,0.6208186,0.00023463511,0.00022528009,0.00047626506,0.0015853851,0.0005002938,0.0066511147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974153,0.0012525736,0.00014446203,0.00058625644,0.00045728424,0.00014408462],"domain_scores_gemma":[0.9927049,0.0035977087,0.0005431209,0.0016085882,0.0013957395,0.00014992467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031899498,0.0014044457,0.0021421167,0.0004912913,0.00047360506,0.0010879874,0.0025104925,0.0024569281,0.0019871688],"category_scores_gemma":[0.011312122,0.0012121814,0.0014000842,0.00067767233,0.0014084526,0.0017821048,0.0020266273,0.0026825876,0.0019430012],"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":[0.0008677137,0.000275674,0.0011675117,0.00044247063,0.00028848293,0.00012927743,0.00018906673,0.7572049,0.012130445,0.010954496,0.007225212,0.20912477],"study_design_scores_gemma":[0.000012631076,0.000026015423,0.00008389084,0.000005179192,0.000007143953,0.000011814984,0.0000037924244,0.9970203,0.0010187102,0.0014798492,0.00032527174,0.0000054227185],"about_ca_topic_score_codex":0.0028563277,"about_ca_topic_score_gemma":0.0037228174,"teacher_disagreement_score":0.0031899498,"about_ca_system_score_codex":0.00058961107,"about_ca_system_score_gemma":0.0019115966,"threshold_uncertainty_score":0.01687026},"labels":[],"label_agreement":null},{"id":"W2905452426","doi":"10.1007/978-3-030-03000-1_8","title":"Face Recognition with Discrete Orthogonal Moments","year":2018,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":18,"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 Winnipeg","funders":"","keywords":"Biometrics; Robustness (evolution); Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Euclidean geometry; Mathematics; Moment (physics); Computer science; Representation (politics); Face (sociological concept); Feature (linguistics); Euclidean distance; Algorithm; Geometry; Physics","score_opus":0.11419444957335993,"score_gpt":0.34671742431170344,"score_spread":0.2325229747383435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905452426","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051944754,0.0043508327,0.971501,0.0002333427,0.00035181417,0.000028339502,0.00008553325,0.0011496056,0.017105004],"genre_scores_gemma":[0.13381663,0.008982953,0.80409527,0.0003190865,0.00061003934,0.00010054756,0.0006028143,0.0004378789,0.051034793],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996649,0.0000379291,0.000014621018,0.000058559584,0.00019672619,0.000027233542],"domain_scores_gemma":[0.9998173,0.00006557298,0.000014869291,0.000055951525,0.000038686103,0.0000076129236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002999456,0.0005420382,0.00064802484,0.0008180848,0.00016900644,0.0009462621,0.0006765672,0.0004747737,0.0073054433],"category_scores_gemma":[0.00081194984,0.00037400986,0.00048500672,0.0011650249,0.00054492464,0.0014639134,0.0009313381,0.00093780464,0.0041645607],"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":[0.000057436613,0.000035814566,0.0001416422,0.00017047275,0.000029014396,0.00004479375,0.0000426987,0.0093196435,0.037829768,0.053261515,0.012354496,0.8867127],"study_design_scores_gemma":[0.00003241013,0.00017254348,0.0019624508,0.00015215905,0.000075914315,0.001476341,0.00008944483,0.61658674,0.114531435,0.119194806,0.14563133,0.00009433797],"about_ca_topic_score_codex":0.00031615133,"about_ca_topic_score_gemma":0.00043441018,"teacher_disagreement_score":0.0073054433,"about_ca_system_score_codex":0.00027601523,"about_ca_system_score_gemma":0.0002415009,"threshold_uncertainty_score":0.024439156},"labels":[],"label_agreement":null},{"id":"W2905739182","doi":"","title":"Bias Assessment and Reduction in Kernel Smoothing","year":2018,"lang":"en","type":"article","venue":"Scholarship@Western (Western University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"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":"Smoothing; Computer science; Reduction (mathematics); Kernel (algebra); Statistics; Mathematics; Artificial intelligence; Econometrics; Combinatorics","score_opus":0.1370924155891088,"score_gpt":0.3463245486401221,"score_spread":0.20923213305101326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905739182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010446544,0.00025816908,0.98751616,0.00010572345,0.000046893656,0.000055145203,0.000050840772,0.00089713105,0.00062345114],"genre_scores_gemma":[0.2346199,0.00037193563,0.7618917,0.0001463537,0.000074602736,0.00024373135,0.00031685986,0.0007556626,0.001579176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98919743,0.003939268,0.0008610797,0.001455885,0.0041696816,0.00037666646],"domain_scores_gemma":[0.95775706,0.027000543,0.002640028,0.0049896757,0.0073153754,0.0002973588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016934358,0.0010154586,0.0014736827,0.0027979081,0.00070378865,0.0025081888,0.0017387294,0.0016212796,0.0025927026],"category_scores_gemma":[0.08285641,0.0006148536,0.0015098635,0.0017703536,0.000992407,0.002117615,0.0030617733,0.002080129,0.0009515066],"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":[0.00069225195,0.00020923225,0.01160046,0.00091827987,0.00037778862,0.00030265632,0.0011876015,0.14144334,0.03180384,0.045124162,0.005179734,0.76116073],"study_design_scores_gemma":[0.00007410586,0.0003380358,0.008331843,0.00020486843,0.00014510621,0.0004490817,0.00022528585,0.889575,0.04648955,0.043946218,0.010066288,0.00015457439],"about_ca_topic_score_codex":0.0014160372,"about_ca_topic_score_gemma":0.0012533207,"teacher_disagreement_score":0.016934358,"about_ca_system_score_codex":0.00082293,"about_ca_system_score_gemma":0.0011781977,"threshold_uncertainty_score":0.08955848},"labels":[],"label_agreement":null},{"id":"W2906620243","doi":"10.32920/ryerson.14661798.v1","title":"A discriminative analysis framework for multi-modal information fusion","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Canonical correlation; Computer science; Modal; Artificial intelligence; Mutual information; Sensor fusion; Data mining; Pattern recognition (psychology); Machine learning","score_opus":0.05131854744278919,"score_gpt":0.3315410063576179,"score_spread":0.2802224589148287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906620243","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012805134,0.00014727448,0.9977083,0.00005935736,0.000017736398,0.000015092113,0.000022572676,0.00009284765,0.00065628625],"genre_scores_gemma":[0.29265022,0.0011778584,0.70149505,0.00024573892,0.00021266607,0.00024551732,0.0004372024,0.00018730469,0.0033484807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984487,0.0005053624,0.000081588194,0.00030316785,0.0005390203,0.00012216097],"domain_scores_gemma":[0.9986318,0.00044814116,0.0001310945,0.00021367562,0.0005085464,0.00006676446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021452194,0.0011889146,0.0010040044,0.0018126752,0.00064701645,0.00182858,0.0011195153,0.0008536417,0.0019954084],"category_scores_gemma":[0.0039174277,0.00043093137,0.0013615666,0.0019987405,0.0015737016,0.0019831783,0.0021375625,0.0017039932,0.0006338632],"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":[0.00013846144,0.00008261195,0.00092160876,0.00026907757,0.00015556492,0.00025011756,0.00032998584,0.34634057,0.024878595,0.37126884,0.0038988646,0.25146568],"study_design_scores_gemma":[0.0000065226423,0.000048561717,0.00031937825,0.000016624634,0.000024494679,0.000088673885,0.00003334216,0.9379203,0.0030956634,0.05501684,0.0033935914,0.0000360045],"about_ca_topic_score_codex":0.0039435294,"about_ca_topic_score_gemma":0.0031164186,"teacher_disagreement_score":0.0039435294,"about_ca_system_score_codex":0.0010391185,"about_ca_system_score_gemma":0.0015400155,"threshold_uncertainty_score":0.011345148},"labels":[],"label_agreement":null},{"id":"W2909641699","doi":"","title":"Doubly Sparse Regularized Regression Incorporating Graphical Structure Among Predictors","year":2018,"lang":"en","type":"dissertation","venue":"The Atrium (University of Guelph)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Agricultural Adaptation Council; Eisai; Natural Sciences and Engineering Research Council of Canada; BioClinica; Ontario Ministry of Agriculture, Food and Rural Affairs; Bristol-Myers Squibb; Eli Lilly and Company; Genentech; IXICO; Ministry of Agriculture, Food and Rural Affairs; Alzheimer's Drug Discovery Foundation; Biogen; U.S. Department of Defense","keywords":"Regression; Graphical model; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.0114144251968611,"score_gpt":0.2137305148664629,"score_spread":0.20231608966960182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909641699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010989424,0.00022008117,0.9875644,0.0002753217,0.000022657636,0.000023077104,0.00012890968,0.00030143475,0.00047465484],"genre_scores_gemma":[0.48333237,0.0013142696,0.50496435,0.0004685294,0.00023958001,0.000307091,0.001835718,0.00032797834,0.0072101797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757785,0.00146915,0.00007618394,0.0004512032,0.00028712937,0.00013852557],"domain_scores_gemma":[0.9914266,0.0059330235,0.00086186867,0.000986323,0.0006477996,0.00014443311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004707738,0.0011598952,0.0014701434,0.0009326951,0.00033727966,0.0012648442,0.0018770617,0.0012892577,0.0016046147],"category_scores_gemma":[0.014293357,0.0007488171,0.0016397436,0.0012487546,0.0011520957,0.0015758197,0.0014944538,0.0023995987,0.0008466033],"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":[0.00016675929,0.00008986815,0.0052346187,0.00014301612,0.0002091646,0.00013618755,0.0001429211,0.83729595,0.0027399845,0.046455324,0.003384933,0.10400124],"study_design_scores_gemma":[0.000007912866,0.000022788523,0.00034300162,0.000010506345,0.00001447629,0.000017469367,0.000006085015,0.98754346,0.00026874483,0.011199428,0.0005575343,0.000008512842],"about_ca_topic_score_codex":0.005441984,"about_ca_topic_score_gemma":0.0053843227,"teacher_disagreement_score":0.005441984,"about_ca_system_score_codex":0.0007821867,"about_ca_system_score_gemma":0.0013339688,"threshold_uncertainty_score":0.024897218},"labels":[],"label_agreement":null},{"id":"W2911852444","doi":"10.32920/ryerson.14646201.v1","title":"Human emotional state recognition using 3D facial expression features","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Computer science; Isomap; Artificial intelligence; Discriminative model; Feature extraction; Pattern recognition (psychology); Robustness (evolution); Computer vision; Facial expression; Support vector machine; Gesture recognition; Hidden Markov model; Gesture; Dimensionality reduction; Nonlinear dimensionality reduction","score_opus":0.05594265068750391,"score_gpt":0.29896214741734306,"score_spread":0.24301949672983914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911852444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26038688,0.0008321851,0.72752476,0.00025950104,0.00015980704,0.00017003082,0.0011087307,0.002546507,0.0070115724],"genre_scores_gemma":[0.8483852,0.0008939824,0.14534977,0.00012989163,0.00007514268,0.0001475021,0.0012015711,0.00012356037,0.0036933813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996972,0.000058543887,0.000014092199,0.00008133395,0.000115487885,0.00003344547],"domain_scores_gemma":[0.9998385,0.000032662774,0.00002551313,0.000024514198,0.0000685094,0.000010283856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525313,0.00048546042,0.00044203282,0.0008683847,0.000112878726,0.00050188205,0.00024944101,0.00036480304,0.0016363786],"category_scores_gemma":[0.0009020035,0.00016676914,0.00055774185,0.0004970055,0.00017633139,0.0004616798,0.00038762705,0.00024452407,0.0008835676],"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":[0.0003604487,0.00009362595,0.0072239186,0.0001349558,0.00008786442,0.0002956859,0.00022879339,0.0138101075,0.28711742,0.001695778,0.0044066254,0.68454474],"study_design_scores_gemma":[0.000036977428,0.00031942173,0.09582831,0.00006563684,0.00012222718,0.0015657825,0.0003171496,0.7286328,0.15849178,0.0045030415,0.009988773,0.00012818145],"about_ca_topic_score_codex":0.0012562595,"about_ca_topic_score_gemma":0.0011564961,"teacher_disagreement_score":0.0016363786,"about_ca_system_score_codex":0.00019415132,"about_ca_system_score_gemma":0.00012020729,"threshold_uncertainty_score":0.0054742694},"labels":[],"label_agreement":null},{"id":"W2911876654","doi":"10.1080/00949655.2019.1575382","title":"The cluster correlation-network support vector machine for high-dimensional binary classification","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"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 du Québec à Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Mathematics; Cluster analysis; Clustering high-dimensional data; Binary classification; Minification; Pattern recognition (psychology); Binary number; Data mining; Artificial intelligence; Algorithm; Computer science; Statistics; Mathematical optimization","score_opus":0.01713046912501945,"score_gpt":0.2884889762901576,"score_spread":0.2713585071651381,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911876654","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015899574,0.00023242057,0.98219645,0.00028099807,0.000036843743,0.00005222283,0.000107441316,0.00057168776,0.0006222922],"genre_scores_gemma":[0.46995896,0.0002813439,0.52553475,0.00021295453,0.00011808066,0.00050966087,0.00072358834,0.00014148008,0.0025191538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986349,0.00072815677,0.00004889016,0.00021963396,0.00028727797,0.00008112],"domain_scores_gemma":[0.9959877,0.0024732356,0.00030886097,0.00036131582,0.0007458638,0.00012297831],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025995565,0.00078427646,0.0010473097,0.0010063444,0.00061897613,0.0007182337,0.0017358102,0.0012586478,0.001519376],"category_scores_gemma":[0.011330717,0.0003180335,0.0006850865,0.0012426008,0.0008694502,0.0009012307,0.0011128692,0.0018189871,0.000619057],"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":[0.00024097732,0.00009300308,0.0034608662,0.000118401345,0.00012354545,0.00008937436,0.00009214592,0.76714134,0.0023233858,0.025218397,0.0053468407,0.1957517],"study_design_scores_gemma":[0.000002804358,0.000008524085,0.000105301435,0.000002484308,0.000001741543,0.0000065306835,0.0000024917563,0.99670035,0.0002403655,0.002752766,0.00017344061,0.0000032544592],"about_ca_topic_score_codex":0.0048204414,"about_ca_topic_score_gemma":0.0039849887,"teacher_disagreement_score":0.0048204414,"about_ca_system_score_codex":0.0009707467,"about_ca_system_score_gemma":0.0016481322,"threshold_uncertainty_score":0.0137479305},"labels":[],"label_agreement":null},{"id":"W2913686728","doi":"10.1371/journal.pone.0212361","title":"Support vector machine with quantile hyper-spheres for pattern classification","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"State Key Laboratory of Robotics and System; State Key Laboratory of Robotics; Natural Science Foundation of Liaoning Province","keywords":"Quantile; Decision boundary; Hinge loss; Cluster analysis; Robustness (evolution); Pattern recognition (psychology); Support vector machine; Margin (machine learning); Computer science; Artificial intelligence; Quadratic programming; Mathematics; Mathematical optimization; Machine learning; Statistics","score_opus":0.05412361806655518,"score_gpt":0.2329707102329036,"score_spread":0.17884709216634842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913686728","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004396987,0.00039129975,0.99400926,0.00016451419,0.000029017883,0.000022708513,0.000054645658,0.0005845945,0.00034698428],"genre_scores_gemma":[0.5631488,0.001205235,0.4303615,0.00041331653,0.0002770612,0.0003723753,0.0009374107,0.0002859297,0.0029984284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981536,0.0006321876,0.00013936979,0.00045228243,0.0004778321,0.00014468994],"domain_scores_gemma":[0.99804604,0.0008655,0.00019368023,0.000281521,0.0005281097,0.00008514103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017772474,0.0013251954,0.001663525,0.0011358357,0.00044944373,0.0015830481,0.0020261267,0.0015507872,0.0014257235],"category_scores_gemma":[0.006855062,0.00056472665,0.0013904314,0.0016540666,0.0010899298,0.002202741,0.0015646978,0.002450853,0.001009962],"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":[0.00014175507,0.000059314192,0.001260551,0.00012527235,0.00007644998,0.000086748136,0.000085106876,0.773958,0.003390277,0.024463968,0.003702915,0.19264965],"study_design_scores_gemma":[0.0000017342464,0.00000950688,0.000051251765,0.0000026709924,0.0000016025642,0.0000069948746,0.0000027507745,0.99557424,0.00030735228,0.0038371673,0.00020123599,0.000003486659],"about_ca_topic_score_codex":0.0032716542,"about_ca_topic_score_gemma":0.0014845479,"teacher_disagreement_score":0.0032716542,"about_ca_system_score_codex":0.0010409718,"about_ca_system_score_gemma":0.001048856,"threshold_uncertainty_score":0.009399116},"labels":[],"label_agreement":null},{"id":"W2914146692","doi":"10.22111/ijfs.2019.4778","title":"A comprehensive experimental comparison of the aggregation techniques for face recognition","year":2019,"lang":"en","type":"article","venue":"Iranian journal of fuzzy systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"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":"Computer science; Salient; Pattern recognition (psychology); Artificial intelligence; Redundancy (engineering); Facial recognition system; Face (sociological concept); Similarity (geometry); Data mining; Machine learning; Image (mathematics)","score_opus":0.04185824577761649,"score_gpt":0.29827971767184946,"score_spread":0.256421471894233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914146692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6416539,0.0132997,0.32418418,0.00052035676,0.00080067175,0.0010563602,0.0013274845,0.0026240787,0.014533226],"genre_scores_gemma":[0.7411026,0.0037037476,0.24977176,0.0001004438,0.000107393804,0.0005131903,0.0012694206,0.00021970352,0.0032117825],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9962708,0.001055312,0.00035041946,0.00041832763,0.0017015873,0.00020353342],"domain_scores_gemma":[0.994885,0.0026529871,0.00024532407,0.00090019626,0.001229871,0.00008653788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041673146,0.0007597006,0.00089202635,0.0019287586,0.00071702804,0.00071583595,0.0007149684,0.00069708447,0.002556378],"category_scores_gemma":[0.009852038,0.00023591437,0.0008402838,0.0018435189,0.0004298167,0.001172125,0.0008349415,0.0005049232,0.0005887659],"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":[0.002618712,0.0010297792,0.004158116,0.0020302422,0.00038020944,0.0001704164,0.0004820222,0.019555116,0.10851633,0.0023436763,0.0035701755,0.85514516],"study_design_scores_gemma":[0.00023208873,0.012287463,0.084444664,0.00055294426,0.0011267805,0.0022193086,0.0018213094,0.39229065,0.46842802,0.008471561,0.027750922,0.0003743278],"about_ca_topic_score_codex":0.0010585397,"about_ca_topic_score_gemma":0.0009404657,"teacher_disagreement_score":0.0041673146,"about_ca_system_score_codex":0.00043807813,"about_ca_system_score_gemma":0.0003663651,"threshold_uncertainty_score":0.022039175},"labels":[],"label_agreement":null},{"id":"W2915060795","doi":"","title":"A Faster Sampling Algorithm for Spherical $k$-means","year":2018,"lang":"en","type":"article","venue":"Asian Conference on Machine Learning","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Waterloo","funders":"","keywords":"Sampling (signal processing); Algorithm; Computer science; Computer vision","score_opus":0.04289535429729919,"score_gpt":0.30132508077595627,"score_spread":0.2584297264786571,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915060795","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019249804,0.00013050801,0.9952389,0.00005792003,0.00011159919,0.000042531236,0.000065500935,0.0016429288,0.0007850741],"genre_scores_gemma":[0.029302487,0.000109376306,0.9666734,0.00009365494,0.00007264836,0.00016321876,0.00052296545,0.00044003516,0.0026222093],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988771,0.00023333478,0.00007181944,0.0001960881,0.0005354744,0.00008621227],"domain_scores_gemma":[0.9984199,0.00044790807,0.000051700747,0.00035662155,0.0006492781,0.000074726275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008937169,0.0011298126,0.0013431886,0.0010130502,0.0008363469,0.0011227668,0.0023628664,0.0012232634,0.013573887],"category_scores_gemma":[0.004433437,0.00065543485,0.0012681246,0.0015760852,0.00053214486,0.0013572179,0.0017973046,0.0019989565,0.0073036626],"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":[0.0003368303,0.00011703034,0.00064266217,0.00022186735,0.00012362376,0.000067656176,0.00013175214,0.079010025,0.019925619,0.02398093,0.021371126,0.85407084],"study_design_scores_gemma":[0.000057155565,0.00004691107,0.00033414623,0.000010993686,0.000016404261,0.000096564196,0.000030812804,0.97422147,0.0068016862,0.008818421,0.009537759,0.000027702408],"about_ca_topic_score_codex":0.017176483,"about_ca_topic_score_gemma":0.024659567,"teacher_disagreement_score":0.017176483,"about_ca_system_score_codex":0.00080396485,"about_ca_system_score_gemma":0.0019693822,"threshold_uncertainty_score":0.045409143},"labels":[],"label_agreement":null},{"id":"W2920973327","doi":"","title":"A Novel Cluster of Quarter Feature Selection Based on Symmetrical Uncertainty","year":2018,"lang":"en","type":"article","venue":"DergiPark (Istanbul University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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":"Quarter (Canadian coin); Feature selection; Data mining; Feature (linguistics); Computer science; Dimensionality reduction; Filter (signal processing); Curse of dimensionality; Pattern recognition (psychology); Artificial intelligence; Selection (genetic algorithm); Feature vector; Naive Bayes classifier; Machine learning; Support vector machine; Geography","score_opus":0.009705218735710692,"score_gpt":0.20401808121457235,"score_spread":0.19431286247886165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920973327","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052601345,0.00048898754,0.944025,0.00018698235,0.000067368295,0.00013561714,0.00029168665,0.0012112153,0.0009917729],"genre_scores_gemma":[0.58020157,0.00036459032,0.41272599,0.00021390217,0.00014974606,0.0003624482,0.0019791485,0.00015441082,0.0038482056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983069,0.0002903799,0.000105972664,0.00040913594,0.00067663606,0.00021102936],"domain_scores_gemma":[0.9988501,0.00027749484,0.00008350655,0.00015723739,0.000566172,0.00006553454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014429353,0.00070589414,0.0018545865,0.0022285231,0.0008700436,0.0010621485,0.001350376,0.0006196629,0.0018237957],"category_scores_gemma":[0.0024178743,0.00030014897,0.0012344816,0.0025527366,0.00047947335,0.0013178977,0.00089328346,0.000543601,0.0004699142],"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":[0.0004481058,0.00023963588,0.009614085,0.00010029019,0.00016510584,0.00018045623,0.00017607871,0.047679357,0.019733338,0.0042217905,0.0076920064,0.9097497],"study_design_scores_gemma":[0.00006515341,0.00034647802,0.0070008314,0.000016634021,0.00007654065,0.0003925826,0.00015131063,0.9670629,0.013057408,0.0058710612,0.0059074615,0.000051713596],"about_ca_topic_score_codex":0.0042507104,"about_ca_topic_score_gemma":0.0033983062,"teacher_disagreement_score":0.0042507104,"about_ca_system_score_codex":0.00057740783,"about_ca_system_score_gemma":0.0015652939,"threshold_uncertainty_score":0.008451939},"labels":[],"label_agreement":null},{"id":"W2921234600","doi":"","title":"Improved Semi-Supervised Learning with Multiple Graphs","year":2019,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Hyperparameter; Computer science; Artificial intelligence; Benchmark (surveying); Covariance matrix; Covariance; Laplacian matrix; Machine learning; Gaussian; Algorithm; TRACE (psycholinguistics); Graph; Maximum cut; Stochastic gradient descent; Inverse; Gradient descent; Pattern recognition (psychology); Mathematics; Artificial neural network; Theoretical computer science; Statistics","score_opus":0.054614506757747906,"score_gpt":0.29294117695397487,"score_spread":0.23832667019622697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921234600","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004441172,0.00019840774,0.991679,0.00016327706,0.000042727093,0.000041948657,0.00016827906,0.002657094,0.00060810585],"genre_scores_gemma":[0.29816186,0.00039984414,0.6895238,0.0007015178,0.0003202425,0.00045558545,0.0033136632,0.0012412847,0.005882243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9958734,0.0017182427,0.00016788261,0.0011497191,0.0008684235,0.00022236166],"domain_scores_gemma":[0.99165994,0.0033862293,0.0007134634,0.0024073573,0.0015543125,0.00027881894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030717447,0.002179322,0.0025010528,0.0029303422,0.0010806039,0.0019096778,0.0055466862,0.0028987387,0.0027661566],"category_scores_gemma":[0.011697056,0.0011710876,0.0022175952,0.0026527757,0.0016326358,0.0043670507,0.0032237298,0.0033925567,0.0024000178],"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":[0.00017636483,0.00027122145,0.0015911065,0.00020860876,0.00032499895,0.00015949899,0.00022825906,0.5841533,0.0030160788,0.025784897,0.015404278,0.36868137],"study_design_scores_gemma":[0.0000060405982,0.0000104272685,0.000058260168,0.0000052728155,0.0000060274206,0.000014152861,0.0000064642218,0.98697865,0.00038479996,0.01201749,0.0005062499,0.00000617201],"about_ca_topic_score_codex":0.007801721,"about_ca_topic_score_gemma":0.012391222,"teacher_disagreement_score":0.007801721,"about_ca_system_score_codex":0.0013986543,"about_ca_system_score_gemma":0.0020697028,"threshold_uncertainty_score":0.016245127},"labels":[],"label_agreement":null},{"id":"W2922025696","doi":"","title":"Multitask Metric Learning: Theory and Algorithm","year":2019,"lang":"en","type":"article","venue":"International Conference on Artificial Intelligence and Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"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":"Computer science; Boosting (machine learning); Generalization; Multi-task learning; Stability (learning theory); Metric (unit); Benchmark (surveying); Algorithm; Artificial intelligence; Machine learning; Learning to rank; Task (project management); Mathematics; Ranking (information retrieval)","score_opus":0.05850819405852739,"score_gpt":0.3255875308177551,"score_spread":0.2670793367592277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922025696","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001286284,0.0013857048,0.9956246,0.0005090599,0.000057888356,0.000036622678,0.000043329423,0.000117269825,0.0009392342],"genre_scores_gemma":[0.21491283,0.0051525175,0.7714204,0.0008864141,0.0010046321,0.0010612174,0.0006766842,0.00032349888,0.0045617544],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966695,0.0017314446,0.00019861931,0.00062646327,0.0006130293,0.00016092723],"domain_scores_gemma":[0.9899733,0.0073299287,0.0005212786,0.000882451,0.0010046576,0.00028834722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0060099075,0.0016876182,0.0020894282,0.0016684732,0.00079220574,0.0023604836,0.00269302,0.0025137067,0.0027081154],"category_scores_gemma":[0.023066837,0.0007874003,0.0010876867,0.0030670874,0.0024648153,0.003891692,0.0048011052,0.004009623,0.0012940889],"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":[0.00010604409,0.0001421546,0.0014905655,0.00062803196,0.00014877529,0.00016415528,0.00023287277,0.30687484,0.0010063131,0.4301027,0.014890375,0.24421313],"study_design_scores_gemma":[0.000012195254,0.000045048942,0.00014512839,0.000027289943,0.000009618054,0.0000677656,0.00002047387,0.7503044,0.00020866448,0.24568942,0.0034529024,0.000017034505],"about_ca_topic_score_codex":0.002493673,"about_ca_topic_score_gemma":0.0015686541,"teacher_disagreement_score":0.0060099075,"about_ca_system_score_codex":0.0021808832,"about_ca_system_score_gemma":0.001687382,"threshold_uncertainty_score":0.03178382},"labels":[],"label_agreement":null},{"id":"W2923756999","doi":"10.1177/0301006620901671","title":"The McGill Face Database: Validation and Insights Into the Recognition of Facial Expressions of Complex Mental States","year":2020,"lang":"en","type":"article","venue":"Perception","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Sadness; Disgust; Surprise; Valence (chemistry); Facial expression; Happiness; Anger; Psychology; Arousal; Cognitive psychology; Two-alternative forced choice; Computer science; Emotion classification; Face (sociological concept); Artificial intelligence; Social psychology","score_opus":0.06104842194133626,"score_gpt":0.28483595426095143,"score_spread":0.22378753231961518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923756999","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6335477,0.0060836053,0.122854576,0.0012703269,0.0005868481,0.0031280809,0.19245137,0.009284662,0.03079291],"genre_scores_gemma":[0.6813275,0.0013531265,0.11926683,0.00058196683,0.00014027835,0.002701272,0.18299547,0.00086451013,0.010768996],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9985039,0.00031178183,0.00015379206,0.0002631402,0.00066600344,0.000101380814],"domain_scores_gemma":[0.99820244,0.00058458495,0.00010508075,0.0004576128,0.0005419898,0.000108214415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017652812,0.00088467926,0.0005294878,0.0019339537,0.00036259627,0.00060458056,0.0015608937,0.00066838856,0.0073639504],"category_scores_gemma":[0.0056169704,0.00022586147,0.0004939655,0.00085073145,0.00030224118,0.00064155424,0.00094803766,0.0004107111,0.002472182],"study_design_candidate":"observational","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":[0.003330691,0.0005351155,0.037401814,0.001981171,0.0004439255,0.0012781604,0.0005837289,0.0066280123,0.18427548,0.004219225,0.2176662,0.5416565],"study_design_scores_gemma":[0.00072459626,0.001222192,0.5610397,0.000404619,0.00045467337,0.009345043,0.00066993973,0.13748293,0.11404972,0.0028074207,0.17131479,0.00048440314],"about_ca_topic_score_codex":0.035350557,"about_ca_topic_score_gemma":0.066201024,"teacher_disagreement_score":0.035350557,"about_ca_system_score_codex":0.0008511502,"about_ca_system_score_gemma":0.0008367108,"threshold_uncertainty_score":0.07028961},"labels":[],"label_agreement":null},{"id":"W2935865093","doi":"10.1109/icassp.2019.8682786","title":"Discriminative Feature Selection Guided Deep Canonical Correlation Analysis","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"Nvidia","keywords":"Discriminative model; Canonical correlation; Artificial intelligence; Feature selection; Selection (genetic algorithm); Computer science; Pattern recognition (psychology); Feature (linguistics); Representation (politics); Mathematics","score_opus":0.010520372053044083,"score_gpt":0.2565164516068002,"score_spread":0.24599607955375613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2935865093","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008021153,0.00029301763,0.9895258,0.0000939783,0.000039956245,0.000032363445,0.00007235707,0.0008163922,0.0011050195],"genre_scores_gemma":[0.38767216,0.00075463374,0.60069513,0.00056836527,0.00018012164,0.00022719264,0.0012811051,0.00043886807,0.008182407],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987748,0.00026180988,0.00004494434,0.00034247315,0.00038539845,0.00019059413],"domain_scores_gemma":[0.9991617,0.00020818137,0.00009259868,0.00016402107,0.0003051035,0.000068405076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011440319,0.0013535944,0.0018669039,0.0012583055,0.0005940452,0.00091566134,0.0018941523,0.0008055373,0.0029850306],"category_scores_gemma":[0.0024872026,0.0006567896,0.0012641798,0.001906807,0.00079266634,0.0014122967,0.0016520362,0.0015200893,0.0013125796],"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":[0.0001847761,0.00017326024,0.0019837702,0.00010578759,0.00015918598,0.00013151662,0.00010993521,0.18507488,0.019261045,0.02767464,0.012825294,0.7523159],"study_design_scores_gemma":[0.0000067623723,0.00003609639,0.00028101535,0.00000530517,0.00001549816,0.00005088717,0.000009873032,0.9914669,0.0033850712,0.003224562,0.0015055991,0.000012554386],"about_ca_topic_score_codex":0.00981977,"about_ca_topic_score_gemma":0.011973903,"teacher_disagreement_score":0.00981977,"about_ca_system_score_codex":0.0009125533,"about_ca_system_score_gemma":0.002365417,"threshold_uncertainty_score":0.01952523},"labels":[],"label_agreement":null},{"id":"W2943107725","doi":"10.1109/iscas.2019.8702762","title":"Information Fusion via Deep Cross-Modal Factor Analysis","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Deep learning; Artificial intelligence; Computer science; Pattern recognition (psychology); Kernel (algebra); MNIST database; Kernel method; Canonical correlation; Modal; Representation (politics); Nonlinear system; Algorithm; Support vector machine; Mathematics","score_opus":0.006148167601654335,"score_gpt":0.24006972258134474,"score_spread":0.2339215549796904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2943107725","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056084525,0.0003970451,0.992577,0.00012386555,0.00003709268,0.000018981837,0.00006963379,0.0003919799,0.0007758871],"genre_scores_gemma":[0.5184094,0.0011125322,0.47515783,0.0003626906,0.00020121451,0.00014494873,0.0008673401,0.00019028454,0.0035537714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999044,0.00024121231,0.00005925747,0.000270171,0.00028238093,0.00010294405],"domain_scores_gemma":[0.9990701,0.00033219636,0.00010231173,0.00015654298,0.00028795793,0.00005094882],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018666099,0.001506683,0.0011219606,0.0017952039,0.00047944963,0.0013463841,0.0010184238,0.0009284936,0.0021394375],"category_scores_gemma":[0.004086651,0.00041872184,0.0016209139,0.0014337655,0.0008216336,0.0025812492,0.0021701532,0.0015194012,0.0007488475],"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":[0.00034165243,0.00013633633,0.0015350569,0.0002630346,0.0004016341,0.00018811057,0.00025533288,0.24631757,0.025360653,0.03999757,0.0057931636,0.67940986],"study_design_scores_gemma":[0.0000073390274,0.000050595267,0.0004507856,0.000017996412,0.000044258526,0.000051390118,0.000031760694,0.9672126,0.0046955515,0.025556676,0.0018526106,0.000028366301],"about_ca_topic_score_codex":0.0029569229,"about_ca_topic_score_gemma":0.0025315157,"teacher_disagreement_score":0.0029569229,"about_ca_system_score_codex":0.0007835597,"about_ca_system_score_gemma":0.00088337535,"threshold_uncertainty_score":0.009871662},"labels":[],"label_agreement":null},{"id":"W2946218049","doi":"10.2478/jee-2019-0017","title":"Illumination invariant face recognition using dual-tree complex wavelet transform in logarithm domain","year":2019,"lang":"en","type":"article","venue":"Journal of Electrical Engineering","topic":"Face and Expression Recognition","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":"Concordia University","funders":"","keywords":"Complex wavelet transform; Artificial intelligence; Logarithm; Invariant (physics); Pattern recognition (psychology); Facial recognition system; Computer science; Wavelet transform; Face (sociological concept); Wavelet; Mathematics; Computer vision; Discrete wavelet transform; Mathematical analysis","score_opus":0.01935178465361733,"score_gpt":0.22760206484506465,"score_spread":0.2082502801914473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946218049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040689096,0.0001246454,0.957455,0.00006727746,0.00004131454,0.000030815354,0.000041513278,0.00055240514,0.000997882],"genre_scores_gemma":[0.37381962,0.0002614328,0.6227693,0.000112161004,0.000050800394,0.00005995536,0.00025367845,0.00008975293,0.0025832618],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996811,0.000035134195,0.000015710166,0.00006840533,0.00016808152,0.000031641513],"domain_scores_gemma":[0.9997434,0.00005850702,0.000034732893,0.000055073127,0.00008812569,0.000020311247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003565297,0.00028117117,0.00051012833,0.0008345374,0.00020029573,0.0005684367,0.00053502887,0.00039978532,0.0014517129],"category_scores_gemma":[0.00089475204,0.00014414615,0.00048065194,0.0006701643,0.00029651404,0.00074095,0.00048784594,0.00054525427,0.00090990646],"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":[0.00023107797,0.00015558602,0.0017988769,0.00005836546,0.00004003087,0.00014918903,0.00006629403,0.024919497,0.24144961,0.00509966,0.0023016818,0.72373],"study_design_scores_gemma":[0.000021622865,0.000114638315,0.003057322,0.000007120937,0.000024737597,0.00037881167,0.000030678522,0.90836215,0.08310861,0.0023445536,0.0025254511,0.000024252051],"about_ca_topic_score_codex":0.00082087645,"about_ca_topic_score_gemma":0.0006692242,"teacher_disagreement_score":0.0014517129,"about_ca_system_score_codex":0.00023012485,"about_ca_system_score_gemma":0.00031133866,"threshold_uncertainty_score":0.0048564076},"labels":[],"label_agreement":null},{"id":"W2947369712","doi":"10.1109/smc.2019.8914172","title":"Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Domain (mathematical analysis); Face (sociological concept); Process (computing); Expression (computer science); Facial expression recognition; Retraining; Domain adaptation; Fine-tuning; Facial recognition system; Machine learning","score_opus":0.07763707824792546,"score_gpt":0.34085778760050317,"score_spread":0.2632207093525777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947369712","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8594176,0.0069327536,0.11775505,0.0011093626,0.00057457696,0.0002985113,0.001444686,0.0062236614,0.00624388],"genre_scores_gemma":[0.97504574,0.0005627135,0.019440183,0.00028482603,0.00005121945,0.00013082138,0.0021676545,0.00018475288,0.0021319366],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99719113,0.00053553196,0.00038595844,0.0008152717,0.0006916987,0.0003803975],"domain_scores_gemma":[0.99244916,0.0022573648,0.0008270262,0.0031344744,0.0011109501,0.00022108563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00553092,0.0015646799,0.0010091556,0.0010183618,0.0007251305,0.0017047989,0.0025518213,0.0013641566,0.0019480907],"category_scores_gemma":[0.021247849,0.000559344,0.0007724859,0.0008153551,0.0007852026,0.0037309988,0.002489737,0.00162936,0.0008302831],"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":[0.0042493693,0.0007035794,0.03414566,0.00075693324,0.0015072685,0.00080734416,0.0005076777,0.1877996,0.060555313,0.0024020283,0.00938012,0.69718504],"study_design_scores_gemma":[0.00014397984,0.0015344665,0.03235508,0.00018933645,0.0005470941,0.0007850129,0.00046429547,0.7997455,0.15069081,0.0075395335,0.0058860527,0.00011891875],"about_ca_topic_score_codex":0.006434101,"about_ca_topic_score_gemma":0.0062617613,"teacher_disagreement_score":0.006434101,"about_ca_system_score_codex":0.0013597376,"about_ca_system_score_gemma":0.0010153252,"threshold_uncertainty_score":0.029250681},"labels":[],"label_agreement":null},{"id":"W2947785270","doi":"10.1007/978-3-030-20915-5_17","title":"Generalizations of Aggregation Functions for Face Recognition","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"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":"Computer science; Facial recognition system; Voting; Face (sociological concept); Task (project management); Artificial intelligence; Machine learning; Pattern recognition (psychology); Theoretical computer science","score_opus":0.030692061145377363,"score_gpt":0.25119137338744174,"score_spread":0.22049931224206437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947785270","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010265762,0.0015271554,0.9760542,0.00021211088,0.00021923841,0.000038206606,0.00017233234,0.00081132806,0.010699715],"genre_scores_gemma":[0.2607486,0.00414927,0.6976299,0.00050674455,0.0010456506,0.00022967411,0.0010296303,0.0008334846,0.033826977],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999315,0.00017373779,0.00005560306,0.00016619902,0.00020832018,0.000081006205],"domain_scores_gemma":[0.9986987,0.00046880764,0.00006711457,0.00047069055,0.00022921847,0.0000654242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012868041,0.0009702986,0.0012409018,0.0013314297,0.00065242406,0.0016210127,0.0013464681,0.00081010046,0.0042953156],"category_scores_gemma":[0.0035120908,0.00037641462,0.0016537816,0.0018792428,0.0006944336,0.0025455942,0.0015779841,0.0021523251,0.001739886],"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":[0.00010769661,0.00011718094,0.00089748803,0.00017673995,0.0000739022,0.00016568226,0.00020871231,0.025567135,0.01100667,0.4696358,0.018124333,0.47391874],"study_design_scores_gemma":[0.000012555901,0.00004580768,0.0010484093,0.00003562338,0.00004416825,0.00040603508,0.0000597728,0.32346016,0.004820358,0.6426106,0.027429024,0.000027530788],"about_ca_topic_score_codex":0.0017898781,"about_ca_topic_score_gemma":0.0017095382,"teacher_disagreement_score":0.0042953156,"about_ca_system_score_codex":0.0007437546,"about_ca_system_score_gemma":0.00043351867,"threshold_uncertainty_score":0.014369249},"labels":[],"label_agreement":null},{"id":"W2948950091","doi":"10.48550/arxiv.1906.02590","title":"Linear and Quadratic Discriminant Analysis: Tutorial","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":84,"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":"Quadratic classifier; Linear discriminant analysis; Kernel Fisher discriminant analysis; Mathematics; Optimal discriminant analysis; Mahalanobis distance; Artificial intelligence; Pattern recognition (psychology); Principal component analysis; Bayes' theorem; Naive Bayes classifier; Multiple discriminant analysis; Binary classification; Machine learning; Statistics; Classifier (UML); Bayesian probability; Computer science; Support vector machine","score_opus":0.06530002424121407,"score_gpt":0.19328165354632054,"score_spread":0.12798162930510648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2948950091","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025072503,0.38606963,0.5085981,0.0041353595,0.008535441,0.00020284386,0.0019948673,0.0025587883,0.08539771],"genre_scores_gemma":[0.032049768,0.35465032,0.46750814,0.005390574,0.022741377,0.0009155529,0.0058676046,0.0020243793,0.10885228],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990722,0.00023962652,0.00008470334,0.00021050082,0.00033854044,0.000054401236],"domain_scores_gemma":[0.99881256,0.0006856954,0.00005221978,0.000107805965,0.00029122326,0.000050467104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015971025,0.0020551924,0.0012212953,0.0025414955,0.0004630677,0.0019008177,0.00096020324,0.001676426,0.021529568],"category_scores_gemma":[0.0031255956,0.0008428604,0.0011762364,0.0035874986,0.0009054396,0.0027606953,0.0011803366,0.0030347693,0.018299855],"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":[0.000076253855,0.00015113578,0.0006044745,0.0019938492,0.00013255085,0.0002959175,0.0002535492,0.008565055,0.0036142136,0.13794166,0.29471415,0.5516571],"study_design_scores_gemma":[0.000011240969,0.000076727294,0.0010423812,0.00035208793,0.000035261393,0.0008307168,0.00006486696,0.012890669,0.00089735514,0.10841869,0.8753196,0.000060368387],"about_ca_topic_score_codex":0.0014677548,"about_ca_topic_score_gemma":0.001248607,"teacher_disagreement_score":0.021529568,"about_ca_system_score_codex":0.0007529936,"about_ca_system_score_gemma":0.0008202411,"threshold_uncertainty_score":0.07202351},"labels":[],"label_agreement":null},{"id":"W2949209578","doi":"10.48550/arxiv.1703.04853","title":"Face Recognition using Multi-Modal Low-Rank Dictionary Learning","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"Artificial intelligence; Computer science; Discriminative model; Robustness (evolution); Pattern recognition (psychology); Facial recognition system; Modal; Hyperspectral imaging; Computer vision; Face (sociological concept); Invariant (physics); Pixel; Mathematics","score_opus":0.1488796024968731,"score_gpt":0.22352388874847592,"score_spread":0.07464428625160283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949209578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010157983,0.00032078745,0.9875935,0.00013814685,0.0000465984,0.00003623858,0.00011952797,0.00061512797,0.0009721713],"genre_scores_gemma":[0.34874138,0.000960885,0.6433246,0.00040345622,0.00020789377,0.00017838608,0.0014333813,0.00015843986,0.0045916056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991385,0.00020346025,0.000039328756,0.00023888737,0.00030733246,0.000072515206],"domain_scores_gemma":[0.99923646,0.00019713852,0.0000963838,0.0002253962,0.00020171578,0.000042928852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006990636,0.00075231615,0.0012074871,0.00085383485,0.00031759264,0.00087609567,0.0010646705,0.0009266736,0.0025561836],"category_scores_gemma":[0.0025743477,0.00028248996,0.0007787803,0.0009525865,0.0005670751,0.0012776101,0.0011463976,0.0013169051,0.0018982404],"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":[0.0002637699,0.00023930009,0.001323702,0.0002728544,0.00015270709,0.00012841872,0.000117568285,0.10477646,0.06234681,0.012179441,0.011130304,0.80706865],"study_design_scores_gemma":[0.000015770005,0.000073599695,0.00054412516,0.000011964623,0.000018612927,0.00014144469,0.000029828023,0.97726077,0.013364488,0.0067753512,0.0017421254,0.000021884294],"about_ca_topic_score_codex":0.0016809121,"about_ca_topic_score_gemma":0.0020505043,"teacher_disagreement_score":0.0025561836,"about_ca_system_score_codex":0.00032957093,"about_ca_system_score_gemma":0.00040872613,"threshold_uncertainty_score":0.00855124},"labels":[],"label_agreement":null},{"id":"W2949803069","doi":"10.48550/arxiv.1704.02345","title":"Fast Spectral Clustering Using Autoencoders and Landmarks","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Spectral clustering; Adjacency matrix; Cluster analysis; Laplacian matrix; Autoencoder; Computer science; Computational complexity theory; Graph; Adjacency list; Matrix decomposition; Pattern recognition (psychology); Spectral graph theory; Eigenvalues and eigenvectors; Artificial intelligence; Algorithm; Matrix (chemical analysis); Theoretical computer science; Deep learning","score_opus":0.09574453108513345,"score_gpt":0.20590665296913765,"score_spread":0.1101621218840042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949803069","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007841452,0.00011815277,0.9894364,0.00006658864,0.00003502058,0.000029554985,0.000067483335,0.0019018808,0.0005034653],"genre_scores_gemma":[0.15848269,0.00021585627,0.8368425,0.00014715642,0.00006554259,0.00017134428,0.00093594345,0.00046184196,0.002677174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988894,0.00019764205,0.000057631485,0.0003889089,0.00033755493,0.000128856],"domain_scores_gemma":[0.9986186,0.00041470726,0.00013094433,0.00035729684,0.00040905338,0.00006937046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011089112,0.0014919179,0.0015949688,0.001999967,0.00084974914,0.0012000627,0.0020260795,0.0013091224,0.0022888342],"category_scores_gemma":[0.0036276493,0.00094252476,0.0011939802,0.0018298476,0.00087071914,0.0020189793,0.0018493984,0.0019510996,0.0022232058],"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":[0.00024394397,0.00018542516,0.001662127,0.00012376752,0.00017341047,0.00010554109,0.00019415755,0.4100117,0.016765442,0.0160658,0.006892614,0.5475761],"study_design_scores_gemma":[0.000006591992,0.00001704205,0.00025553216,0.00000720239,0.000006752315,0.000029243298,0.0000247193,0.98856956,0.003264699,0.006879983,0.0009274041,0.00001119247],"about_ca_topic_score_codex":0.007918641,"about_ca_topic_score_gemma":0.012500478,"teacher_disagreement_score":0.007918641,"about_ca_system_score_codex":0.0011047549,"about_ca_system_score_gemma":0.0014673999,"threshold_uncertainty_score":0.015745103},"labels":[],"label_agreement":null},{"id":"W2950041186","doi":"10.3390/sym11060787","title":"Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain","year":2019,"lang":"en","type":"article","venue":"Symmetry","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"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 du Québec à Montréal","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Discrete wavelet transform; Wavelet; Mathematics; Facial recognition system; Fuzzy logic; Support vector machine; Computer science; Stationary wavelet transform; Wavelet transform","score_opus":0.012464241379227949,"score_gpt":0.24384641737265017,"score_spread":0.23138217599342223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950041186","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05786443,0.0003852803,0.93823886,0.00009841382,0.0000866948,0.000056751942,0.0000598045,0.0004081513,0.0028016376],"genre_scores_gemma":[0.692253,0.0004794586,0.30354947,0.00011435503,0.0000582628,0.00009442828,0.00022181516,0.000035489717,0.0031938246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996363,0.000040237857,0.000025043892,0.00008603902,0.00018391908,0.00002851795],"domain_scores_gemma":[0.9997285,0.00005848361,0.00003283488,0.00003868178,0.00012955372,0.000011897756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038298973,0.00032178577,0.00048269736,0.0007686104,0.00022399588,0.0005958507,0.0006057789,0.00045383786,0.0011972488],"category_scores_gemma":[0.0010305548,0.000163237,0.00054724683,0.0006994691,0.00029336725,0.0008974352,0.00033661295,0.00045064426,0.0003953514],"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":[0.00036896745,0.00014188327,0.0025924155,0.00022240519,0.00012814271,0.00031406933,0.00015993112,0.12464563,0.0990819,0.013368775,0.0023609535,0.7566149],"study_design_scores_gemma":[0.00001369521,0.00013502438,0.0019582447,0.00001187087,0.000034770947,0.00025901292,0.000041129202,0.97730756,0.015125311,0.003347495,0.0017399988,0.00002583689],"about_ca_topic_score_codex":0.0022999241,"about_ca_topic_score_gemma":0.001689429,"teacher_disagreement_score":0.0022999241,"about_ca_system_score_codex":0.00032599168,"about_ca_system_score_gemma":0.0002830809,"threshold_uncertainty_score":0.004573047},"labels":[],"label_agreement":null},{"id":"W2950302414","doi":"10.1016/j.image.2019.06.003","title":"A novel correlation filter based on variational calculus","year":2019,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Face and Expression Recognition","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":"Université de Sherbrooke","funders":"","keywords":"Discriminative model; Correlation; Smoothness; Filter (signal processing); Mathematics; High-pass filter; Identification (biology); Filter design; Computer science; Artificial intelligence; Algorithm; Control theory (sociology); Low-pass filter; Computer vision; Mathematical analysis","score_opus":0.01920236589864766,"score_gpt":0.2559347901546385,"score_spread":0.23673242425599084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950302414","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010417001,0.00013430664,0.9979387,0.00004670223,0.000074301795,0.000010208691,0.00002172388,0.00014988419,0.0005824942],"genre_scores_gemma":[0.08201638,0.00081178604,0.90428776,0.00029461342,0.00033549962,0.00013422186,0.00030515515,0.00038423808,0.0114303725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941826,0.00009988702,0.000028476485,0.00014019906,0.00025195384,0.000061231025],"domain_scores_gemma":[0.99953663,0.00016512968,0.00003716122,0.000052074505,0.00016385557,0.000045205383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007903998,0.00074881286,0.0014174848,0.0009425114,0.0006820097,0.0013666318,0.0014023461,0.0016133799,0.00420132],"category_scores_gemma":[0.0013439455,0.00061547826,0.0013354808,0.0013038794,0.000564594,0.0014553153,0.0011265422,0.0013048154,0.001887255],"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":[0.00030737312,0.00019182343,0.001069391,0.00033293042,0.0002702068,0.00030515567,0.00013013047,0.1414056,0.105190754,0.24247882,0.012680975,0.4956368],"study_design_scores_gemma":[0.00001734675,0.000042190553,0.0002223412,0.000009340938,0.000034432232,0.00013621082,0.0000054407114,0.9793914,0.0060101273,0.007944062,0.006150899,0.00003621001],"about_ca_topic_score_codex":0.0047378778,"about_ca_topic_score_gemma":0.005018061,"teacher_disagreement_score":0.0047378778,"about_ca_system_score_codex":0.00065257424,"about_ca_system_score_gemma":0.0016199712,"threshold_uncertainty_score":0.014054835},"labels":[],"label_agreement":null},{"id":"W2950449113","doi":"10.3233/jifs-17283","title":"Hyperspectral face recognition with minimum noise fraction, histogram of oriented gradient features and collaborative representation-based classifier","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Face and Expression Recognition","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":"Concordia University","funders":"","keywords":"Hyperspectral imaging; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Computer science; Histogram; Facial recognition system; Representation (politics); Image (mathematics)","score_opus":0.016306120802204435,"score_gpt":0.2528771434521848,"score_spread":0.23657102264998037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950449113","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.046351217,0.0006344091,0.95005363,0.0001961598,0.000090146415,0.00009024319,0.00008102589,0.0009271799,0.0015760006],"genre_scores_gemma":[0.46571934,0.0006443126,0.52836484,0.00026541675,0.00015234569,0.00021245544,0.0004936664,0.00010963362,0.0040380047],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988048,0.000148366,0.000056374312,0.00028909143,0.00061140134,0.00009000827],"domain_scores_gemma":[0.9993703,0.00012742849,0.00009349784,0.000107518514,0.00026521855,0.00003610246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007993802,0.0006013133,0.0011156598,0.001163429,0.0004615878,0.0006902694,0.0008726032,0.00085188495,0.0009844191],"category_scores_gemma":[0.0018754596,0.00029331076,0.0008758452,0.0008571821,0.00043743823,0.0014787208,0.0008823806,0.00071279606,0.0005729792],"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":[0.00019480029,0.00029277953,0.0021314388,0.00008609082,0.00011463498,0.00008358121,0.0000803631,0.038034234,0.06275798,0.0026663425,0.0034235045,0.89013433],"study_design_scores_gemma":[0.00001444882,0.00012635525,0.0034233741,0.000010925697,0.000043740187,0.00025427112,0.00003233601,0.95738214,0.03505579,0.00172376,0.0018944868,0.000038385097],"about_ca_topic_score_codex":0.0033874402,"about_ca_topic_score_gemma":0.0028646814,"teacher_disagreement_score":0.0033874402,"about_ca_system_score_codex":0.0004345203,"about_ca_system_score_gemma":0.0005596618,"threshold_uncertainty_score":0.006735444},"labels":[],"label_agreement":null},{"id":"W2950516059","doi":"10.48550/arxiv.1902.09938","title":"A Feature Selection Based on Perturbation Theory","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Row; Singular value decomposition; Feature selection; Row and column spaces; Perturbation (astronomy); Singular value; Selection (genetic algorithm); Pattern recognition (psychology); Mathematics; Algorithm; Computer science; Artificial intelligence; Physics","score_opus":0.038254467887310926,"score_gpt":0.17247328414480967,"score_spread":0.13421881625749874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2950516059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011754604,0.00025929968,0.9864742,0.00021088394,0.00006409847,0.00010195211,0.00018553434,0.0005451785,0.00040420212],"genre_scores_gemma":[0.34673125,0.00050320156,0.6471484,0.000368745,0.00030760097,0.00052530837,0.0014330233,0.00011891283,0.0028635901],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99821895,0.00059284247,0.00008683458,0.00045908318,0.00052593683,0.00011629507],"domain_scores_gemma":[0.9983621,0.0008319822,0.00011810712,0.00019260048,0.0004229619,0.00007230885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017433182,0.001003532,0.0016050327,0.0020122689,0.00057078147,0.00082733267,0.0011131306,0.0010404062,0.0014040819],"category_scores_gemma":[0.004987213,0.00039499,0.0010761372,0.0018241649,0.000697923,0.0009492845,0.0009496621,0.0010363938,0.00077129394],"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":[0.00085308653,0.00030799842,0.005602877,0.00029646198,0.00042220938,0.00068481243,0.0001550778,0.20944865,0.06228765,0.013724972,0.013852859,0.6923634],"study_design_scores_gemma":[0.000049170234,0.00017986204,0.0015129849,0.000012763637,0.000036408484,0.0002079797,0.00002334547,0.9804864,0.007407051,0.008104169,0.0019504728,0.000029435883],"about_ca_topic_score_codex":0.0012819335,"about_ca_topic_score_gemma":0.00089951244,"teacher_disagreement_score":0.0020122689,"about_ca_system_score_codex":0.00048944616,"about_ca_system_score_gemma":0.00089365547,"threshold_uncertainty_score":0.009219646},"labels":[],"label_agreement":null},{"id":"W2951233147","doi":"10.48550/arxiv.1407.7317","title":"A unified framework for thermal face recognition","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Laval","funders":"","keywords":"Facial recognition system; Computer science; Modality (human–computer interaction); Face (sociological concept); Thermal infrared; Three-dimensional face recognition; Artificial intelligence; Facial expression; Computer vision; Reduction (mathematics); Occlusion; Infrared; Pattern recognition (psychology); Speech recognition; Face detection; Mathematics; Medicine","score_opus":0.10243068422026497,"score_gpt":0.2080633949656835,"score_spread":0.10563271074541852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951233147","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005254113,0.00021311519,0.9974669,0.000052649342,0.00003461219,0.000020689635,0.00003106332,0.00025935707,0.0013962189],"genre_scores_gemma":[0.08944689,0.0011221523,0.8968072,0.00027265947,0.0003177729,0.00032475629,0.00039495237,0.00027798893,0.011035677],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992077,0.00013219043,0.00004072088,0.00022627921,0.00031254772,0.00008064034],"domain_scores_gemma":[0.9996618,0.000056622885,0.00002593814,0.00010469891,0.00012913483,0.000021734386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009856861,0.0008269902,0.0010234334,0.0009416537,0.00073731993,0.00197598,0.0023503106,0.0010508081,0.0053854543],"category_scores_gemma":[0.0014210999,0.00046114493,0.0015191567,0.0009129042,0.001008368,0.0019560067,0.0020611952,0.001632946,0.00288309],"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":[0.00010896465,0.00011789652,0.0005088387,0.00025863963,0.00010970464,0.00024296298,0.00029300275,0.14846814,0.0349299,0.38805333,0.0094220815,0.41748658],"study_design_scores_gemma":[0.000009815602,0.00006499251,0.000447806,0.00004005566,0.000035711386,0.00028695248,0.000063973246,0.832577,0.0055356,0.13833785,0.0225536,0.000046713034],"about_ca_topic_score_codex":0.0029299203,"about_ca_topic_score_gemma":0.0038871157,"teacher_disagreement_score":0.0053854543,"about_ca_system_score_codex":0.0006400781,"about_ca_system_score_gemma":0.0008900889,"threshold_uncertainty_score":0.01801616},"labels":[],"label_agreement":null},{"id":"W2951244377","doi":"10.48550/arxiv.1906.09436","title":"Fisher and Kernel Fisher Discriminant Analysis: Tutorial","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"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":"Kernel Fisher discriminant analysis; Linear discriminant analysis; Fisher kernel; Linear subspace; Kernel (algebra); Projection (relational algebra); Artificial intelligence; Subspace topology; Discriminant; Kernel method; Rank (graph theory); Curse of dimensionality; Pattern recognition (psychology); Computer science; Mathematics; Multiple discriminant analysis; Machine learning; Algorithm; Facial recognition system; Support vector machine; Pure mathematics; Combinatorics","score_opus":0.05463847267436746,"score_gpt":0.18073216903379702,"score_spread":0.12609369635942957,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951244377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002208066,0.27900752,0.642454,0.0028043243,0.005435635,0.00018741899,0.0020004213,0.002500157,0.06340237],"genre_scores_gemma":[0.029337713,0.28294155,0.58080494,0.0041431743,0.01391185,0.0008080917,0.0058637983,0.0018646427,0.08032432],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99910384,0.0002134464,0.000082020226,0.00020083299,0.00033925122,0.0000605961],"domain_scores_gemma":[0.9987484,0.0007194911,0.00006831102,0.000120051576,0.00029721268,0.00004655129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016339031,0.0022508681,0.0012964241,0.0034688949,0.00045241113,0.001728718,0.0011675,0.0020522187,0.02376839],"category_scores_gemma":[0.0036882418,0.0009976028,0.0013965247,0.004442399,0.0009579805,0.0037310624,0.0013543551,0.0032481763,0.01699517],"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":[0.000076136086,0.0001484566,0.0006593326,0.0016787288,0.00010289057,0.00026600072,0.00023784871,0.009626239,0.0033171684,0.12329708,0.20242564,0.6581645],"study_design_scores_gemma":[0.000012033269,0.00008009257,0.0012860489,0.000458217,0.00003430906,0.0011801989,0.00007193412,0.021734556,0.0013289722,0.13189657,0.8418258,0.00009124154],"about_ca_topic_score_codex":0.0018203148,"about_ca_topic_score_gemma":0.0013924874,"teacher_disagreement_score":0.02376839,"about_ca_system_score_codex":0.0008210713,"about_ca_system_score_gemma":0.0008405241,"threshold_uncertainty_score":0.07951319},"labels":[],"label_agreement":null},{"id":"W2953006511","doi":"10.48550/arxiv.0805.0120","title":"Nonnegative Matrix Factorization via Rank-One Downdate","year":2008,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Rank (graph theory); Factorization; Mathematics; Combinatorics; Matrix (chemical analysis); Matrix decomposition; Algebra over a field; Pure mathematics; Algorithm; Physics; Materials science; Composite material","score_opus":0.0663526602286746,"score_gpt":0.1913614736516491,"score_spread":0.12500881342297449,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953006511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030042443,0.00030283857,0.99535745,0.00017097298,0.00007692096,0.00007758891,0.00018504825,0.0003692798,0.00045563214],"genre_scores_gemma":[0.091333315,0.0008664992,0.9017564,0.00037301195,0.00033594694,0.0005831411,0.002346864,0.00030205553,0.0021027403],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99667954,0.0013211648,0.00020994345,0.00077534944,0.00082465576,0.00018930793],"domain_scores_gemma":[0.99381256,0.003034494,0.000616161,0.0009577752,0.0014031538,0.0001758398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046467483,0.0026906622,0.0026449754,0.0021058326,0.0010104424,0.0025470064,0.0018333644,0.0018719798,0.003729317],"category_scores_gemma":[0.015822683,0.0010445173,0.002126716,0.0027241015,0.0015609405,0.0026346005,0.001819613,0.0032439493,0.002629267],"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":[0.00051176216,0.00030971845,0.0017831626,0.00079017004,0.0004336674,0.0003575307,0.00034914192,0.3215492,0.014453809,0.06713272,0.02495996,0.5673691],"study_design_scores_gemma":[0.000042384465,0.000111671485,0.00036828493,0.000034829845,0.000031588126,0.00009959186,0.000045062123,0.9473634,0.002895965,0.043981183,0.004979978,0.000046175195],"about_ca_topic_score_codex":0.0037466784,"about_ca_topic_score_gemma":0.004410955,"teacher_disagreement_score":0.0046467483,"about_ca_system_score_codex":0.001029445,"about_ca_system_score_gemma":0.0021118703,"threshold_uncertainty_score":0.024574637},"labels":[],"label_agreement":null},{"id":"W2953391430","doi":"10.1145/3311747","title":"A Deep Learning System for Recognizing Facial Expression in Real-Time","year":2019,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":49,"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":"Qatar National Research Fund; Fonds National de la Recherche Luxembourg; Qatar Foundation","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Facial expression; Frame (networking); Transfer of learning; Pattern recognition (psychology); Deep learning; Image (mathematics); Facial expression recognition; Frame rate; Computer vision; Facial recognition system","score_opus":0.023700032266459486,"score_gpt":0.28015761440916076,"score_spread":0.25645758214270126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2953391430","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05039609,0.0008429673,0.91092676,0.00027780404,0.00038203545,0.0003120306,0.0016378684,0.027850904,0.007373505],"genre_scores_gemma":[0.5006434,0.0007382024,0.46859798,0.00055015436,0.00011479998,0.00060319575,0.005774678,0.0005579721,0.022419598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968207,0.000026720376,0.000020057576,0.00011913751,0.00010170478,0.00005038619],"domain_scores_gemma":[0.9998648,0.000016313748,0.000013999877,0.00003021895,0.000059674414,0.000014957082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004020201,0.0007787258,0.0004994929,0.0005653142,0.0002443481,0.00043448881,0.0010940217,0.0005091931,0.005103146],"category_scores_gemma":[0.00065475056,0.0003258618,0.00044658818,0.0003478167,0.00019039771,0.000752247,0.00079824065,0.0008102367,0.0031102914],"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":[0.00040744586,0.00036176035,0.0017298284,0.0001356899,0.00010584686,0.00016545989,0.00007270525,0.013928112,0.13116725,0.0019823136,0.024359401,0.8255842],"study_design_scores_gemma":[0.000054366923,0.0003356982,0.005321236,0.000048588943,0.000092290415,0.00040684923,0.00005293721,0.8583681,0.109099306,0.0027624501,0.02338801,0.00007017028],"about_ca_topic_score_codex":0.005546765,"about_ca_topic_score_gemma":0.0064896313,"teacher_disagreement_score":0.005546765,"about_ca_system_score_codex":0.0006402642,"about_ca_system_score_gemma":0.0006502855,"threshold_uncertainty_score":0.017071724},"labels":[],"label_agreement":null},{"id":"W2958005625","doi":"10.1109/fg.2019.8756561","title":"Robust Video Face Recognition From a Single Still Using a Synthetic Plus Variational Model","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Facial recognition system; Sparse approximation; Face (sociological concept); Pattern recognition (psychology); Representation (politics); Computer vision; Set (abstract data type); Synthetic data","score_opus":0.07557871057173782,"score_gpt":0.23640638393529406,"score_spread":0.16082767336355624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958005625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02381431,0.0003521254,0.97376674,0.00014681353,0.000049474187,0.00004211336,0.00014069602,0.00041338473,0.0012743308],"genre_scores_gemma":[0.64777845,0.0008644766,0.3406207,0.0003282582,0.00013811036,0.00018229942,0.0015733631,0.00014578599,0.0083685685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996822,0.000054388063,0.00001214679,0.000103314895,0.000112877104,0.00003509538],"domain_scores_gemma":[0.9997869,0.000076027274,0.000026264051,0.000045987006,0.000050261264,0.000014583766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057251303,0.00058713625,0.00079276686,0.00043048838,0.00017992727,0.00058482715,0.001172894,0.000796815,0.0013380846],"category_scores_gemma":[0.001014182,0.000332274,0.0010100786,0.00037546598,0.0004957545,0.0006909249,0.00066769845,0.0008656296,0.00051569904],"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":[0.0002377712,0.000096563985,0.0008210506,0.00011831763,0.0001143576,0.00011272044,0.00007103524,0.6610702,0.049492724,0.010596841,0.0026585695,0.2746098],"study_design_scores_gemma":[0.0000022978272,0.000021490421,0.00011051371,0.0000019456172,0.000004023228,0.00003282663,0.0000032319222,0.99666744,0.0019913719,0.00074169325,0.0004186904,0.000004496688],"about_ca_topic_score_codex":0.005749519,"about_ca_topic_score_gemma":0.0049181376,"teacher_disagreement_score":0.005749519,"about_ca_system_score_codex":0.0005207793,"about_ca_system_score_gemma":0.00061598537,"threshold_uncertainty_score":0.011432111},"labels":[],"label_agreement":null},{"id":"W2963344792","doi":"","title":"Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis","year":2018,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":45,"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","funders":"","keywords":"Diagonal; Kronecker delta; Leverage (statistics); Gradient descent; Computer science; Covariance; Stochastic gradient descent; Algorithm; Mathematics; Mathematical optimization; Artificial intelligence; Artificial neural network; Statistics; Physics; Geometry","score_opus":0.017205537931037383,"score_gpt":0.24234495920924384,"score_spread":0.22513942127820646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963344792","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006306186,0.000084725245,0.99186397,0.00008073637,0.00004021765,0.000023493069,0.000031448566,0.00062038,0.0009488334],"genre_scores_gemma":[0.22935723,0.00017395655,0.7646148,0.00019754088,0.000050830957,0.00016648433,0.0002814615,0.00041386273,0.004743783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951756,0.00014462424,0.000029174567,0.000107839056,0.000150727,0.000050110335],"domain_scores_gemma":[0.998995,0.0003381471,0.000066159126,0.00023569667,0.00031104873,0.000053831012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012358109,0.0009464217,0.0010921572,0.0004820971,0.0003843441,0.0011726465,0.00097537,0.0012680152,0.0036896563],"category_scores_gemma":[0.0053004664,0.0005737891,0.0005406461,0.00060237566,0.0008748181,0.0017471736,0.00093941874,0.0015642354,0.0017744149],"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":[0.0001689771,0.00010898283,0.0011049671,0.00012515049,0.0000721544,0.00008883585,0.00013690814,0.74157435,0.007732449,0.06457617,0.009242476,0.17506866],"study_design_scores_gemma":[0.000007561085,0.000013484877,0.00006706859,0.000005544558,0.0000024390763,0.000014035982,0.0000056581325,0.9910623,0.00048124066,0.0076101664,0.00072603556,0.0000044728404],"about_ca_topic_score_codex":0.006117841,"about_ca_topic_score_gemma":0.010081132,"teacher_disagreement_score":0.006117841,"about_ca_system_score_codex":0.00077137735,"about_ca_system_score_gemma":0.0016389138,"threshold_uncertainty_score":0.012343109},"labels":[],"label_agreement":null},{"id":"W2963382234","doi":"10.24963/ijcai.2017/345","title":"Exemplar-centered Supervised Shallow Parametric Data Embedding","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Embedding; Computer science; Dimensionality reduction; Scalability; Pairwise comparison; Benchmark (surveying); Metric (unit); Machine learning; Speedup; Parametric statistics; Artificial intelligence; Computational complexity theory; Data point; Algorithm; Mathematics","score_opus":0.14866604966467928,"score_gpt":0.3537607889447627,"score_spread":0.20509473928008343,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963382234","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023693642,0.000121894846,0.9738123,0.000058764097,0.000016429005,0.000062374864,0.00015021072,0.0014873146,0.00059700717],"genre_scores_gemma":[0.5066439,0.00016499226,0.4872308,0.00016785764,0.000036141548,0.00032038958,0.0019280767,0.00032837296,0.0031794375],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9987571,0.0003532755,0.0000785436,0.00038070232,0.00034122955,0.00008907542],"domain_scores_gemma":[0.9980738,0.00045673377,0.00020675852,0.0008336168,0.0003562111,0.000072845345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010488869,0.0012629817,0.0014137157,0.00076691294,0.00034316455,0.0008373249,0.0025221007,0.0011184409,0.0020878064],"category_scores_gemma":[0.00444865,0.00048266057,0.0009799307,0.0008940209,0.0010671599,0.002487529,0.0028517288,0.0017772068,0.0012230356],"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":[0.00019381531,0.0002449109,0.0020847644,0.00019304884,0.00009238523,0.00012724285,0.00019447974,0.383821,0.027997727,0.012541073,0.004893547,0.567616],"study_design_scores_gemma":[0.0000038062267,0.000067006134,0.00021916998,0.0000058418236,0.000005401671,0.00006234797,0.000016814069,0.98830354,0.0052094483,0.005528263,0.0005680994,0.000010350669],"about_ca_topic_score_codex":0.001724171,"about_ca_topic_score_gemma":0.003398836,"teacher_disagreement_score":0.0025221007,"about_ca_system_score_codex":0.00062919554,"about_ca_system_score_gemma":0.00078590855,"threshold_uncertainty_score":0.0069844127},"labels":[],"label_agreement":null},{"id":"W2963517422","doi":"10.24963/ijcai.2018/313","title":"Doubly Aligned Incomplete Multi-view Clustering","year":2018,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":281,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Novelis (Canada)","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Non-negative matrix factorization; Computer science; Matrix decomposition; Basis (linear algebra); Artificial intelligence; Data mining; Matrix (chemical analysis); Norm (philosophy); Consensus clustering; Feature (linguistics); Pattern recognition (psychology); Machine learning; Fuzzy clustering; Mathematics; Canopy clustering algorithm; Eigenvalues and eigenvectors","score_opus":0.0489082669056997,"score_gpt":0.29006482326769906,"score_spread":0.24115655636199934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963517422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053809243,0.0003310859,0.99332994,0.00006225798,0.000031723,0.000028445482,0.00012050613,0.00034830318,0.00036683466],"genre_scores_gemma":[0.2543537,0.000611934,0.7382415,0.00025172275,0.00013894109,0.00022884541,0.003161137,0.0003405052,0.0026717067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99736696,0.00076282327,0.00014276622,0.00087719236,0.0006162539,0.00023400677],"domain_scores_gemma":[0.99689376,0.000846099,0.00035418832,0.0008223541,0.00091304694,0.00017053807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022667127,0.001702917,0.002766209,0.001687961,0.0009909987,0.001526638,0.0028637499,0.00199285,0.0015444955],"category_scores_gemma":[0.0062179854,0.0007073083,0.0023741259,0.0018179069,0.0008350342,0.002255613,0.0017965535,0.002020044,0.0011446375],"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":[0.00046525185,0.00017503265,0.0039263903,0.00045542515,0.0005238488,0.00035748776,0.0004912214,0.46469685,0.019967891,0.021325184,0.01353722,0.47407815],"study_design_scores_gemma":[0.000010815446,0.00003938704,0.0005026725,0.000018325183,0.000026014604,0.000100608064,0.00006558071,0.9852224,0.0026492795,0.0093320925,0.0020054677,0.000027429665],"about_ca_topic_score_codex":0.0061441655,"about_ca_topic_score_gemma":0.008407216,"teacher_disagreement_score":0.0061441655,"about_ca_system_score_codex":0.00093976164,"about_ca_system_score_gemma":0.001498396,"threshold_uncertainty_score":0.012216806},"labels":[],"label_agreement":null},{"id":"W2964072797","doi":"","title":"Hierarchical subtask discovery with non-negative matrix factorization","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"Computer science; Matrix decomposition; Non-negative matrix factorization; Factorization; Artificial intelligence; Theoretical computer science; Algorithm; Physics","score_opus":0.02945049135996103,"score_gpt":0.18319195297718155,"score_spread":0.15374146161722052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964072797","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03099301,0.002300538,0.9565577,0.0011237853,0.00033268455,0.00027504764,0.0017157347,0.0039934316,0.0027080576],"genre_scores_gemma":[0.49867252,0.00095051393,0.47427204,0.0011624463,0.0006713127,0.0007072503,0.009905978,0.0007919363,0.012866053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957039,0.0014690735,0.00019163005,0.0015144455,0.0005962736,0.00052483036],"domain_scores_gemma":[0.99386734,0.0031703697,0.00035358215,0.001295825,0.0008803798,0.0004325635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003295544,0.0030749005,0.0032460454,0.0023956387,0.0016725305,0.0024802834,0.0034672061,0.0029459498,0.0058736354],"category_scores_gemma":[0.011831275,0.0011641907,0.0029575117,0.002331444,0.0013035533,0.0048468676,0.0029897587,0.004362581,0.0045162817],"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":[0.0023154505,0.0023239623,0.005390314,0.00088943104,0.00067190686,0.0006491108,0.0008234118,0.07930415,0.033752877,0.023476055,0.093214296,0.75718904],"study_design_scores_gemma":[0.00010881463,0.00024732377,0.0007753597,0.00004696029,0.00011588892,0.00014682293,0.00015440916,0.9193801,0.0036029315,0.07030365,0.0050688777,0.0000487761],"about_ca_topic_score_codex":0.0081238365,"about_ca_topic_score_gemma":0.016539108,"teacher_disagreement_score":0.0081238365,"about_ca_system_score_codex":0.00119416,"about_ca_system_score_gemma":0.0025374289,"threshold_uncertainty_score":0.019649327},"labels":[],"label_agreement":null},{"id":"W2964276935","doi":"10.1002/sam.11410","title":"Pruning variable selection ensembles","year":2019,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Sorting; Selection (genetic algorithm); Ensemble learning; Lasso (programming language); Context (archaeology); Stability (learning theory); Pruning; Artificial intelligence; Feature selection; Machine learning; Process (computing); Boosting (machine learning); Variable (mathematics); Algorithm; Mathematics","score_opus":0.04718228723840193,"score_gpt":0.3264302161271736,"score_spread":0.27924792888877165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964276935","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03536737,0.0011437994,0.9601498,0.0001497635,0.0001178747,0.00009099136,0.00014646791,0.0005589525,0.0022749596],"genre_scores_gemma":[0.58169585,0.0012718781,0.409479,0.0004239248,0.0003097805,0.00050484913,0.001787316,0.00022190591,0.0043054377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973104,0.00091238116,0.00015339801,0.0004540631,0.0009594172,0.00021037045],"domain_scores_gemma":[0.99625623,0.0019990422,0.00024486484,0.0005240164,0.0008645892,0.00011125089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003497677,0.0013108611,0.0021394722,0.0019017,0.0008811745,0.0010240859,0.001485155,0.0009149514,0.0015329915],"category_scores_gemma":[0.009722313,0.00042544148,0.0010487525,0.0016236394,0.0005038775,0.0011963482,0.0020783322,0.0011523498,0.0006498214],"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":[0.00023899195,0.00013203299,0.008527879,0.00021163239,0.00039220642,0.00032132506,0.00019287805,0.39397752,0.011165014,0.015246093,0.007589722,0.5620047],"study_design_scores_gemma":[0.000017383682,0.00009861111,0.0011253643,0.000035314857,0.00007859714,0.00013869895,0.00003924466,0.9809245,0.003909572,0.009699439,0.003918064,0.000015364838],"about_ca_topic_score_codex":0.0014269366,"about_ca_topic_score_gemma":0.0024504412,"teacher_disagreement_score":0.003497677,"about_ca_system_score_codex":0.00039521218,"about_ca_system_score_gemma":0.0009934779,"threshold_uncertainty_score":0.018497705},"labels":[],"label_agreement":null},{"id":"W2964610636","doi":"10.48550/arxiv.1907.11584","title":"Scalable Semi-Supervised SVM via Triply Stochastic Gradients","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"Western University","funders":"","keywords":"Scalability; Computer science; Support vector machine; Classifier (UML); Kernel (algebra); Convexity; Artificial intelligence; Machine learning; Algorithm; Mathematics; Combinatorics; Database","score_opus":0.050850205342883835,"score_gpt":0.18062572534401422,"score_spread":0.12977552000113038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964610636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013721522,0.00019375699,0.98338413,0.00022390626,0.000045986482,0.0000681265,0.00006846767,0.0016396487,0.0006544343],"genre_scores_gemma":[0.4623121,0.00023453106,0.5312022,0.00045250633,0.00017067014,0.00035395983,0.0011818632,0.00045623895,0.0036358815],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982967,0.00057736126,0.00010645505,0.0003727488,0.0004991845,0.00014751474],"domain_scores_gemma":[0.9970624,0.0012292077,0.00029393818,0.00054357934,0.0006680174,0.00020288263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022015602,0.0012397517,0.0019707258,0.00075316866,0.0005818296,0.0012380565,0.0025859124,0.0017017073,0.0017275623],"category_scores_gemma":[0.0069042305,0.00081674894,0.0010879418,0.0008431495,0.0010666141,0.002195538,0.0021057392,0.0025606211,0.0011735522],"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":[0.00022466085,0.00019871701,0.0015542745,0.00014416341,0.00011109529,0.00012173374,0.0001422023,0.6787197,0.005104663,0.019009275,0.008259765,0.28640983],"study_design_scores_gemma":[0.000005803237,0.000010991865,0.00003721629,0.0000017921494,0.0000014761965,0.000007202642,0.000002859491,0.9962315,0.000281361,0.003265858,0.00015153222,0.0000024129672],"about_ca_topic_score_codex":0.004292514,"about_ca_topic_score_gemma":0.004430834,"teacher_disagreement_score":0.004292514,"about_ca_system_score_codex":0.0010517878,"about_ca_system_score_gemma":0.0021586244,"threshold_uncertainty_score":0.011643112},"labels":[],"label_agreement":null},{"id":"W2972964658","doi":"10.1016/j.patcog.2019.107052","title":"Discriminant component analysis via distance correlation maximization","year":2019,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"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 Waterloo","funders":"","keywords":"Dimensionality reduction; Curse of dimensionality; Linear discriminant analysis; Maximization; Correlation; Principal component analysis; Kernel (algebra); Canonical correlation; Measure (data warehouse); Mathematics; Pattern recognition (psychology); Artificial intelligence; Computer science; Distance correlation; Kernel method; Reduction (mathematics); Algorithm; Mathematical optimization; Data mining; Support vector machine","score_opus":0.01400541918691801,"score_gpt":0.2201418818881271,"score_spread":0.2061364627012091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972964658","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043592583,0.000104089704,0.99387544,0.00007002756,0.000032250347,0.000042996504,0.000065205044,0.0006655473,0.0007851953],"genre_scores_gemma":[0.13418373,0.0002828867,0.85525525,0.000105163854,0.00007270746,0.00027740854,0.0008300543,0.00049237703,0.008500476],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999094,0.00029774682,0.000043330107,0.0001708043,0.00031707622,0.00007701675],"domain_scores_gemma":[0.99899334,0.00032837983,0.000059120855,0.00016331469,0.0004187124,0.000037211463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015166431,0.0010419437,0.001269599,0.0011337633,0.00068329595,0.0010234216,0.00094730535,0.00065808126,0.0046129166],"category_scores_gemma":[0.003136137,0.00047709423,0.0010391304,0.0014009965,0.00049956574,0.000705085,0.0011486614,0.0012419546,0.0038925335],"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":[0.0002638591,0.00017918144,0.0009207638,0.00012592472,0.00014035225,0.0000556225,0.00006693835,0.06187045,0.023997542,0.02136053,0.012331314,0.8786875],"study_design_scores_gemma":[0.000020294337,0.00004423392,0.0011571397,0.000011437523,0.00004444628,0.000071701776,0.000015689564,0.9766194,0.009825889,0.006771792,0.0053952355,0.000022677483],"about_ca_topic_score_codex":0.0032945683,"about_ca_topic_score_gemma":0.0035682737,"teacher_disagreement_score":0.0046129166,"about_ca_system_score_codex":0.00043500564,"about_ca_system_score_gemma":0.0016494744,"threshold_uncertainty_score":0.015431762},"labels":[],"label_agreement":null},{"id":"W2973211100","doi":"","title":"Support Vector Machines with Convex Combination of Kernels","year":2018,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University","keywords":"Support vector machine; Hyperplane; Kernel (algebra); Artificial intelligence; Kernel method; Pattern recognition (psychology); Feature vector; Computer science; Feature selection; Margin (machine learning); Data set; Field (mathematics); Machine learning; Relevance vector machine; Data mining; Mathematics","score_opus":0.020672569472242306,"score_gpt":0.273596884143147,"score_spread":0.25292431467090465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973211100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005158663,0.00030821576,0.99279976,0.0001562194,0.000055864526,0.000037095488,0.000053646978,0.0005451344,0.0008854314],"genre_scores_gemma":[0.37727425,0.00092860044,0.61266136,0.00021580745,0.0002731084,0.00032023992,0.0009196402,0.000373914,0.007033081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99655104,0.0012028707,0.00027816402,0.0005179852,0.0011829081,0.00026711336],"domain_scores_gemma":[0.9945851,0.0022261715,0.0004308989,0.00082486344,0.0017669491,0.00016605487],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026920126,0.0014965786,0.0023170754,0.0010924637,0.00045691765,0.0021350912,0.0018282823,0.0015571668,0.002331709],"category_scores_gemma":[0.015203777,0.0009395043,0.0012646617,0.0020534864,0.0007182731,0.0034699058,0.0016548856,0.0023983812,0.002265501],"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":[0.0003775668,0.00012643237,0.0010571615,0.00021120739,0.00016269901,0.00022321801,0.000069691196,0.65466213,0.004410622,0.022698533,0.008032052,0.30796877],"study_design_scores_gemma":[0.000006261486,0.000020322399,0.00007955236,0.0000044855938,0.0000047718854,0.000025048788,0.000004193292,0.9942246,0.0006387015,0.004320916,0.000664203,0.0000068616655],"about_ca_topic_score_codex":0.0018231961,"about_ca_topic_score_gemma":0.0011857112,"teacher_disagreement_score":0.0026920126,"about_ca_system_score_codex":0.0007153538,"about_ca_system_score_gemma":0.00095099135,"threshold_uncertainty_score":0.014236927},"labels":[],"label_agreement":null},{"id":"W2977898923","doi":"10.22215/etd/2013-08573","title":"Pattern classification using novel order statistics and border identification methods","year":2013,"lang":"en","type":"dissertation","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Identification (biology); Order (exchange); Computer science; Statistics; Mathematics; Humanities; Art; Economics","score_opus":0.0571307982894445,"score_gpt":0.3981246098767338,"score_spread":0.3409938115872893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977898923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030503593,0.00031805155,0.96473485,0.00013888974,0.0001482896,0.0000744824,0.00018798198,0.0018416042,0.0020521912],"genre_scores_gemma":[0.31598595,0.000611381,0.67153394,0.0001419479,0.00024067523,0.00015829287,0.0015312036,0.00047821234,0.009318466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991417,0.0001171257,0.00009264814,0.00019189343,0.00034076147,0.00011585956],"domain_scores_gemma":[0.9982318,0.0005529764,0.00016413134,0.0003382231,0.00060023525,0.00011272935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097044837,0.00068523403,0.0012793362,0.003356958,0.0006090895,0.002424754,0.0010505083,0.0009262648,0.0035656963],"category_scores_gemma":[0.0027068253,0.0003789461,0.0013156522,0.0023394022,0.0005882456,0.0020158188,0.0008607362,0.0011318129,0.0028028924],"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":[0.0003895727,0.0002973331,0.0059048324,0.00012882169,0.00009803416,0.000105947045,0.000104877225,0.030451462,0.03465295,0.0100463405,0.005997711,0.91182214],"study_design_scores_gemma":[0.000026356152,0.00011597581,0.0029208623,0.000017461536,0.00004116107,0.00015969305,0.00006543641,0.9754714,0.013107711,0.004859655,0.0031857647,0.000028605238],"about_ca_topic_score_codex":0.0040515685,"about_ca_topic_score_gemma":0.0058787162,"teacher_disagreement_score":0.0040515685,"about_ca_system_score_codex":0.00073333416,"about_ca_system_score_gemma":0.0016190199,"threshold_uncertainty_score":0.011928439},"labels":[],"label_agreement":null},{"id":"W2979163681","doi":"10.3233/ida-194486","title":"A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification","year":2020,"lang":"en","type":"preprint","venue":"Intelligent Data Analysis","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Artificial intelligence; Dimensionality reduction; Pattern recognition (psychology); Principal component analysis; Linear discriminant analysis; Computer science; Support vector machine; Kernel principal component analysis; Kernel (algebra); Benchmark (surveying); Feature extraction; Local binary patterns; Dimension (graph theory); Machine learning; Kernel method; Histogram; Mathematics; Image (mathematics)","score_opus":0.19200957450223277,"score_gpt":0.4478469634700107,"score_spread":0.2558373889677779,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979163681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10600966,0.026772166,0.8564581,0.001383518,0.0004409014,0.0002219414,0.00023521675,0.0016211076,0.0068572853],"genre_scores_gemma":[0.49000412,0.013820862,0.4896221,0.00019741156,0.00032948193,0.00026412352,0.00073400297,0.00053770316,0.0044901967],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817395,0.00072896545,0.00014711,0.00029557289,0.0005619383,0.000092463735],"domain_scores_gemma":[0.9946549,0.0028442978,0.0002966688,0.00077770266,0.0012994233,0.00012701121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028620416,0.001037425,0.0012610285,0.0019732118,0.0004018668,0.0018927454,0.00082669663,0.0008854179,0.0017408726],"category_scores_gemma":[0.010294236,0.00034524183,0.0011078598,0.0021502555,0.0006905293,0.0021908632,0.001075324,0.0010582276,0.0005619713],"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":[0.00058832776,0.00032296567,0.0033973996,0.0006888522,0.0002419425,0.00007650672,0.00024266716,0.11597912,0.006880614,0.014637139,0.005381838,0.8515626],"study_design_scores_gemma":[0.000027910746,0.0002661897,0.0033728636,0.00006123274,0.000057172765,0.00016316824,0.000089426896,0.9812506,0.0046202946,0.0048678,0.0051693497,0.000053948574],"about_ca_topic_score_codex":0.003339036,"about_ca_topic_score_gemma":0.0017774327,"teacher_disagreement_score":0.003339036,"about_ca_system_score_codex":0.0006902309,"about_ca_system_score_gemma":0.00082233624,"threshold_uncertainty_score":0.015136063},"labels":[],"label_agreement":null},{"id":"W2979398171","doi":"10.1109/ccece.2019.8861751","title":"Emotion Recognition from 2D Facial Expressions","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":24,"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":"Overfitting; Computer science; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Support vector machine; Classifier (UML); Facial expression; Machine learning; Artificial neural network","score_opus":0.022421293758872543,"score_gpt":0.23310666919101133,"score_spread":0.21068537543213878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979398171","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13940442,0.0020430025,0.84151596,0.00048860465,0.0005879236,0.00020974259,0.0018017384,0.0017183769,0.012230177],"genre_scores_gemma":[0.7719047,0.0024513267,0.21077196,0.0004367927,0.00031390568,0.0002836943,0.0024473043,0.0002260094,0.011164284],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99967945,0.000058076796,0.00001131423,0.000088082925,0.00011666799,0.000046431313],"domain_scores_gemma":[0.99989724,0.000022347984,0.000017567048,0.000016852951,0.000038399467,0.0000076217198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026065522,0.0007602173,0.0005488189,0.00046025543,0.00012102424,0.00056008284,0.00037760733,0.0003394105,0.0022935725],"category_scores_gemma":[0.00076456997,0.00019825526,0.0006044334,0.00038936845,0.00019610877,0.00060095236,0.00062639,0.00039551052,0.0012049909],"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":[0.00043185827,0.00010834357,0.0034742113,0.00023866442,0.00012134958,0.00034466537,0.00017664427,0.027431605,0.22382186,0.0026451033,0.009871044,0.7313346],"study_design_scores_gemma":[0.000030693627,0.00033212974,0.029519323,0.0001253341,0.00013505518,0.0012158004,0.00035487648,0.84247255,0.09865233,0.008309265,0.018752506,0.00010014897],"about_ca_topic_score_codex":0.00086002087,"about_ca_topic_score_gemma":0.0011595893,"teacher_disagreement_score":0.0022935725,"about_ca_system_score_codex":0.0002101362,"about_ca_system_score_gemma":0.00018462271,"threshold_uncertainty_score":0.0076727867},"labels":[],"label_agreement":null},{"id":"W2980195282","doi":"10.1109/ccece.2019.8861942","title":"A Discriminant Two-Dimensional Canonical Correlation Analysis","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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","funders":"","keywords":"Canonical correlation; Discriminant; Pattern recognition (psychology); Linear discriminant analysis; Artificial intelligence; Feature extraction; Computer science; Correlation; Facial recognition system; Face (sociological concept); Feature (linguistics); Optimal discriminant analysis; Fusion; Set (abstract data type); Kernel Fisher discriminant analysis; Mathematics","score_opus":0.010007624201605171,"score_gpt":0.24780055122818298,"score_spread":0.2377929270265778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980195282","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010919697,0.0005852936,0.9842754,0.00017798954,0.00019907077,0.000107670996,0.0002278089,0.00081212935,0.0026949537],"genre_scores_gemma":[0.26619777,0.0015977517,0.722229,0.00028308996,0.0002818586,0.00034404258,0.0013373959,0.00023751697,0.007491494],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988569,0.00019731601,0.000047268495,0.00029925504,0.0005106614,0.00008852824],"domain_scores_gemma":[0.9991124,0.00012886547,0.000048145106,0.00011619631,0.00054273795,0.000051596726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009455227,0.0011105245,0.001114897,0.0021410435,0.00092580914,0.0011529756,0.0007124745,0.00065177085,0.0030805464],"category_scores_gemma":[0.0024678232,0.00029361012,0.001092075,0.0027582715,0.00065822835,0.000875436,0.00088268874,0.000826907,0.001363765],"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":[0.00017624098,0.00015383113,0.0042274566,0.00031817908,0.00016960232,0.00030548542,0.00017238838,0.04677305,0.053017918,0.028807132,0.022462018,0.8434166],"study_design_scores_gemma":[0.00003090188,0.00014399526,0.004715895,0.00004543513,0.00008396438,0.000853302,0.000105257095,0.9255324,0.031303048,0.0072960523,0.029750315,0.00013949712],"about_ca_topic_score_codex":0.004452191,"about_ca_topic_score_gemma":0.0042849067,"teacher_disagreement_score":0.004452191,"about_ca_system_score_codex":0.00047281658,"about_ca_system_score_gemma":0.0019982527,"threshold_uncertainty_score":0.010305464},"labels":[],"label_agreement":null},{"id":"W2983445208","doi":"10.1142/s0218213019500209","title":"Incremental Subclass Support Vector Machine","year":2019,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Support vector machine; Computer science; Decision boundary; Classifier (UML); Discriminative model; Artificial intelligence; Convex optimization; Linear classifier; Machine learning; Synthetic data; Regular polygon; Pattern recognition (psychology); Kernel method; Kernel (algebra); Data mining; Mathematics","score_opus":0.04618922746386503,"score_gpt":0.31364248664770045,"score_spread":0.26745325918383545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983445208","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059727684,0.00087247224,0.9318672,0.00017612417,0.00015090394,0.00014182889,0.0003745872,0.0032180822,0.0034710788],"genre_scores_gemma":[0.68336886,0.0007414609,0.30704343,0.00021605694,0.00021039887,0.00021564789,0.0024821812,0.0003582439,0.0053635747],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909806,0.00014378176,0.000072848095,0.00020073033,0.00038379463,0.000100865036],"domain_scores_gemma":[0.99773043,0.0005821749,0.00017740791,0.00050012575,0.000903484,0.00010636048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001008616,0.0007162191,0.0014180331,0.0012316534,0.00037855344,0.0011531371,0.0021913063,0.0006608227,0.002049484],"category_scores_gemma":[0.0046183974,0.00031789648,0.00085748633,0.0011187972,0.00044071823,0.0020531593,0.0012226863,0.0011919853,0.0010466824],"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":[0.00030247538,0.00018975761,0.005059911,0.00020353905,0.00010455243,0.00016708531,0.00015529954,0.120631315,0.009750923,0.0096330615,0.0077925185,0.84600955],"study_design_scores_gemma":[0.000010734127,0.00006896502,0.00104089,0.000012656036,0.000024994775,0.000107772335,0.0000335488,0.9864456,0.003942329,0.005267594,0.0030298631,0.000015083662],"about_ca_topic_score_codex":0.002587199,"about_ca_topic_score_gemma":0.0019612936,"teacher_disagreement_score":0.002587199,"about_ca_system_score_codex":0.0006359936,"about_ca_system_score_gemma":0.0010281996,"threshold_uncertainty_score":0.006856203},"labels":[],"label_agreement":null},{"id":"W2991818756","doi":"10.1142/s0218001420560066","title":"Local Comparative Decimal Pattern for Face Recognition","year":2019,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Facial recognition system; Face (sociological concept); Invariant (physics); Sample (material); Representation (politics); Computer vision; Set (abstract data type); Mathematics","score_opus":0.12691044565685305,"score_gpt":0.341547558349473,"score_spread":0.21463711269261992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991818756","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045412738,0.001998712,0.94103307,0.00024681538,0.00021708116,0.00014995802,0.0005801352,0.002883383,0.007478181],"genre_scores_gemma":[0.40287322,0.0017457908,0.5846255,0.00023490694,0.00013568986,0.00023967262,0.0017942511,0.00015777948,0.008193231],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996731,0.000053133204,0.00002056577,0.00007035026,0.00015086048,0.00003187775],"domain_scores_gemma":[0.9997671,0.000049686423,0.000028518229,0.00007655514,0.000064673965,0.00001340052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002598758,0.00043787653,0.000418048,0.001225002,0.00020009073,0.00046512726,0.0005898165,0.00038336724,0.0049025714],"category_scores_gemma":[0.0009772019,0.00013206036,0.00032533874,0.0017223121,0.0002671423,0.0006515547,0.00040417735,0.00045998045,0.0026300289],"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":[0.00020099491,0.000054951095,0.00080176874,0.00012659906,0.000020014668,0.0001261317,0.000028371556,0.0060257227,0.050688062,0.0033445586,0.0063257646,0.9322571],"study_design_scores_gemma":[0.000051409294,0.0004714434,0.0116178095,0.00008048238,0.0000726679,0.0029719756,0.00017004728,0.7638903,0.15161869,0.014553303,0.054414824,0.00008703756],"about_ca_topic_score_codex":0.00086977094,"about_ca_topic_score_gemma":0.0012853844,"teacher_disagreement_score":0.0049025714,"about_ca_system_score_codex":0.00023305674,"about_ca_system_score_gemma":0.0002861428,"threshold_uncertainty_score":0.016400754},"labels":[],"label_agreement":null},{"id":"W2993949519","doi":"10.9781/ijimai.2016.412","title":"Offline Face Recognition System Based on Gabor- Fisher Descriptors and Hidden Markov Models","year":2016,"lang":"en","type":"article","venue":"International Journal of Interactive Multimedia and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Moncton","funders":"","keywords":"Computer science; Pattern recognition (psychology); Hidden Markov model; Artificial intelligence; Linear discriminant analysis; Facial recognition system; Gabor wavelet; Face (sociological concept); Segmentation; Curse of dimensionality; Wavelet; Wavelet transform; Discrete wavelet transform","score_opus":0.04708530327207906,"score_gpt":0.2831041318276288,"score_spread":0.23601882855554973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993949519","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05835766,0.0011105337,0.9304094,0.00010545311,0.00019893148,0.00014505557,0.0003245732,0.0056064134,0.0037420029],"genre_scores_gemma":[0.52109283,0.0013826143,0.46007183,0.00021835709,0.00020394466,0.0002312034,0.0012219013,0.00014397892,0.015433385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964714,0.000035110657,0.0000171245,0.00009997995,0.00015878805,0.000041888077],"domain_scores_gemma":[0.9998165,0.000043493845,0.000017677097,0.000036892267,0.00007106255,0.000014459639],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035095512,0.00046097508,0.0010632317,0.00062259415,0.00029180513,0.0003808282,0.00072987744,0.00040288808,0.0036095197],"category_scores_gemma":[0.00048415727,0.00027430087,0.0005302067,0.00032573755,0.00014300778,0.00097082753,0.0004567047,0.00053818175,0.0018799199],"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":[0.0005021573,0.00024566424,0.0028125,0.00017374479,0.0000913204,0.00022147568,0.000093884475,0.006910807,0.1372243,0.0023571972,0.0053564953,0.84401053],"study_design_scores_gemma":[0.00009678249,0.0009815039,0.012112768,0.00006827668,0.000274024,0.0023517127,0.00008848669,0.80049795,0.16210303,0.0036408445,0.0176386,0.0001459908],"about_ca_topic_score_codex":0.0020456694,"about_ca_topic_score_gemma":0.002367287,"teacher_disagreement_score":0.0036095197,"about_ca_system_score_codex":0.00028055368,"about_ca_system_score_gemma":0.0004072399,"threshold_uncertainty_score":0.012075007},"labels":[],"label_agreement":null},{"id":"W2997716588","doi":"10.1142/s0219467819500220","title":"Face Identification Based on Discrete Wavelet Transform and Neural Networks","year":2019,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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; Artificial neural network; Artificial intelligence; Facial recognition system; Context (archaeology); Wavelet; Pattern recognition (psychology); Face (sociological concept); Authentication (law); Identification (biology); Relevance (law); Machine learning; Computer vision; Computer security","score_opus":0.006847477560962715,"score_gpt":0.24794817667530256,"score_spread":0.24110069911433984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997716588","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01904478,0.00033291572,0.97861516,0.00007685758,0.000042121508,0.000020486275,0.000020764346,0.00019346425,0.0016533559],"genre_scores_gemma":[0.52435815,0.0010622536,0.4686276,0.000053399886,0.00006483565,0.00008939907,0.00011060684,0.000043716762,0.005589982],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998708,0.000027786487,0.00000570532,0.000022538157,0.000061032326,0.000012100506],"domain_scores_gemma":[0.9998759,0.000057469962,0.000013220147,0.000012849076,0.00003633518,0.0000041482763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026830882,0.00023996767,0.0003065679,0.00045271855,0.00015603993,0.00035857645,0.00030601426,0.00035032086,0.0010546639],"category_scores_gemma":[0.0007808305,0.00015634688,0.00029970377,0.0004662388,0.00020967388,0.00072809757,0.00027742222,0.00047722113,0.0003319577],"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":[0.00016588323,0.00006237534,0.00094275794,0.00012761026,0.00006848631,0.00015885988,0.00008756476,0.25755462,0.04048003,0.029011067,0.001319379,0.6700213],"study_design_scores_gemma":[0.0000020208624,0.000016169082,0.00030372117,0.0000056289728,0.0000060144125,0.00004325581,0.000006454363,0.99186796,0.004047499,0.0030154085,0.0006813338,0.0000044857675],"about_ca_topic_score_codex":0.0012204418,"about_ca_topic_score_gemma":0.0011410835,"teacher_disagreement_score":0.0012204418,"about_ca_system_score_codex":0.00022314048,"about_ca_system_score_gemma":0.00025619983,"threshold_uncertainty_score":0.0035282373},"labels":[],"label_agreement":null},{"id":"W2997999303","doi":"10.1609/aaai.v34i04.6182","title":"Safe Sample Screening for Robust Support Vector Machine","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"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":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Benchmark (surveying); Computer science; Solver; Sample (material); Generalization; Mathematical optimization; Support vector machine; Convex optimization; Machine learning; Artificial intelligence; Regular polygon; Mathematics","score_opus":0.1738317798494326,"score_gpt":0.3038440701750817,"score_spread":0.1300122903256491,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997999303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048492905,0.00013611428,0.9931144,0.00014270953,0.000019016526,0.0000715503,0.000039644314,0.0011768631,0.00045039682],"genre_scores_gemma":[0.32278425,0.00027997806,0.67246675,0.0004966934,0.000108969056,0.00067040446,0.00064568274,0.0008455755,0.0017017464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935236,0.0025615487,0.00042203162,0.0008976044,0.0021387748,0.00045650237],"domain_scores_gemma":[0.9845637,0.009577887,0.0011343422,0.0018326612,0.0024697045,0.00042162905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006180972,0.0019450606,0.0017648628,0.0014324432,0.00096992153,0.001720095,0.002612464,0.002169327,0.003089075],"category_scores_gemma":[0.036593456,0.0009951475,0.0016398107,0.0009810517,0.0022290905,0.0027532657,0.003679341,0.0036181184,0.0012835714],"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":[0.0006066126,0.00020935119,0.0033590272,0.00039466706,0.00014817514,0.0005316241,0.00025413488,0.5930447,0.013494591,0.06365458,0.008081537,0.31622103],"study_design_scores_gemma":[0.000018040348,0.00005621881,0.00009589654,0.000013848448,0.000006288398,0.00003763049,0.0000108624445,0.98222095,0.003649565,0.013275291,0.0006036554,0.000011738586],"about_ca_topic_score_codex":0.0018664072,"about_ca_topic_score_gemma":0.0016102102,"teacher_disagreement_score":0.006180972,"about_ca_system_score_codex":0.0008660091,"about_ca_system_score_gemma":0.0030377256,"threshold_uncertainty_score":0.0326885},"labels":[],"label_agreement":null},{"id":"W2998510732","doi":"10.18280/ts.360605","title":"Gender Classification in Human Face Images for Smart Phone Applications Based on Local Texture Information and Evaluated Kullback-Leibler Divergence","year":2019,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":39,"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":"Kullback–Leibler divergence; Divergence (linguistics); Smart phone; Face (sociological concept); Texture (cosmology); Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Image (mathematics); Sociology; Telecommunications","score_opus":0.02856703316297633,"score_gpt":0.269484926238392,"score_spread":0.24091789307541567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998510732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48583272,0.0020924683,0.5014824,0.00053637545,0.00034580572,0.0002147042,0.00086570444,0.0014897343,0.0071400264],"genre_scores_gemma":[0.91227484,0.00075370225,0.08165703,0.00013684681,0.000071858434,0.00008009411,0.0007217729,0.00006769565,0.004236132],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935013,0.00007089886,0.0000333777,0.00010188465,0.00036740984,0.000076267774],"domain_scores_gemma":[0.99959975,0.00006850951,0.000045828856,0.000031244275,0.0002234791,0.00003126279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000730289,0.00043894345,0.0006463736,0.0015647463,0.00032065023,0.00083529047,0.00034645668,0.0004423167,0.0017000515],"category_scores_gemma":[0.001694759,0.0001014568,0.00056028576,0.0006112631,0.0003174858,0.00074347714,0.00042134325,0.00035727737,0.0005533759],"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":[0.0010585153,0.0002309122,0.021176672,0.000284858,0.00014643138,0.00054839265,0.00022687265,0.025328238,0.11681406,0.0030261085,0.005993801,0.8251651],"study_design_scores_gemma":[0.000030487547,0.0006429955,0.06249351,0.00005710922,0.00010167779,0.0018314829,0.00050023105,0.85897744,0.067744,0.003345196,0.0041592526,0.000116582836],"about_ca_topic_score_codex":0.0030379507,"about_ca_topic_score_gemma":0.003057407,"teacher_disagreement_score":0.0030379507,"about_ca_system_score_codex":0.0005459108,"about_ca_system_score_gemma":0.00041474152,"threshold_uncertainty_score":0.006040573},"labels":[],"label_agreement":null},{"id":"W2998595054","doi":"10.1002/sam.11445","title":"A new method for performance analysis in nonlinear dimensionality reduction","year":2020,"lang":"en","type":"article","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Skewness; Benchmark (surveying); Computer science; Curse of dimensionality; Intrinsic dimension; Measure (data warehouse); Dimension (graph theory); Rank (graph theory); Pattern recognition (psychology); Data mining; Artificial intelligence; Algorithm; Mathematics; Statistics; Geography","score_opus":0.10891862325224824,"score_gpt":0.3994450036008474,"score_spread":0.2905263803485991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998595054","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002649851,0.00034016772,0.994759,0.00011009024,0.00007879936,0.00006681177,0.00010190328,0.0006176147,0.0012756558],"genre_scores_gemma":[0.21857508,0.00063873,0.77299064,0.00022918356,0.0006877313,0.00091881474,0.0007089569,0.00087570876,0.0043751462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9890682,0.004390453,0.0005110121,0.0012453237,0.0044297096,0.0003552926],"domain_scores_gemma":[0.9817077,0.010372656,0.0015737473,0.003036861,0.0030740977,0.00023485256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01021679,0.002168181,0.0017878796,0.005705691,0.00074387615,0.002525827,0.0015791865,0.0011892999,0.0042244326],"category_scores_gemma":[0.035451893,0.00045664335,0.0013879886,0.003139299,0.0020761024,0.0030272824,0.0020573097,0.003058833,0.0015861063],"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":[0.00028954333,0.0002487956,0.0034936802,0.00062862824,0.00041860796,0.00016717466,0.0003268704,0.30477327,0.020841176,0.18875948,0.01309599,0.46695673],"study_design_scores_gemma":[0.000015394597,0.00012306105,0.0011211665,0.000034870653,0.000032952397,0.000102547245,0.000025444559,0.9640777,0.004762654,0.024428776,0.0052177566,0.000057612182],"about_ca_topic_score_codex":0.0019242092,"about_ca_topic_score_gemma":0.00092852773,"teacher_disagreement_score":0.01021679,"about_ca_system_score_codex":0.0016067581,"about_ca_system_score_gemma":0.0014180243,"threshold_uncertainty_score":0.054032207},"labels":[],"label_agreement":null},{"id":"W3004064535","doi":"10.4018/978-1-7998-3038-2.ch014","title":"Quality Estimation for Facial Biometrics","year":2020,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Face and Expression Recognition","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":"University of British Columbia; University of Calgary","funders":"","keywords":"Biometrics; Computer science; Facial recognition system; Quality (philosophy); Face (sociological concept); Artificial intelligence; Sample (material); Set (abstract data type); Identity (music); Pattern recognition (psychology); Regression analysis; Computer vision; Machine learning","score_opus":0.059081257114036875,"score_gpt":0.33563659096144965,"score_spread":0.27655533384741277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004064535","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036748217,0.017501257,0.9544913,0.0003301955,0.0004843406,0.00006931625,0.00029243797,0.0012842055,0.021872152],"genre_scores_gemma":[0.15199934,0.029465482,0.7190638,0.00039392925,0.0005820787,0.0002108489,0.001619929,0.00075453124,0.09591008],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999084,0.00011323629,0.000030800707,0.0001874333,0.00055851176,0.00002603789],"domain_scores_gemma":[0.99946684,0.00016843808,0.000043880238,0.00007336527,0.00023846867,0.000009136453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007203182,0.0008614439,0.00063321233,0.001233032,0.00024837285,0.0010552121,0.0008382342,0.0006565978,0.008048397],"category_scores_gemma":[0.002310994,0.0003710954,0.0006645692,0.0015220831,0.000388861,0.0011652724,0.0005868761,0.0009155644,0.0051900623],"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":[0.00007412507,0.000032224863,0.00083751744,0.0003801292,0.000045591067,0.0000944087,0.00009420984,0.021220183,0.025820367,0.037643224,0.027327621,0.8864304],"study_design_scores_gemma":[0.00001865521,0.0002302001,0.006691138,0.00037580676,0.000094095514,0.0016110364,0.00015054915,0.6479024,0.05161652,0.05009746,0.24106862,0.00014334696],"about_ca_topic_score_codex":0.0022518064,"about_ca_topic_score_gemma":0.0017041699,"teacher_disagreement_score":0.008048397,"about_ca_system_score_codex":0.00091116497,"about_ca_system_score_gemma":0.00028408668,"threshold_uncertainty_score":0.02692455},"labels":[],"label_agreement":null},{"id":"W3005857408","doi":"10.1109/cac48633.2019.8996996","title":"Multi-supervised CML for Small Sample Low-resolution Image Matching","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Metric (unit); Matching (statistics); Artificial intelligence; Pattern recognition (psychology); Transformation (genetics); Computer science; Image (mathematics); Class (philosophy); Sample (material); Function (biology); Image resolution; Machine learning; Data mining; Mathematics; Statistics; Engineering","score_opus":0.02817073665170667,"score_gpt":0.25586097694423304,"score_spread":0.22769024029252638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005857408","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012060114,0.00013801108,0.98678,0.000054772136,0.000012718366,0.000033943823,0.000026643142,0.00048208694,0.00041168657],"genre_scores_gemma":[0.5429258,0.0001855033,0.45329282,0.00021301246,0.000059965634,0.00023122241,0.00036598655,0.00024210972,0.0024835942],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809486,0.00046277876,0.00009852762,0.0006032269,0.000618359,0.00012226767],"domain_scores_gemma":[0.9983114,0.00048471653,0.00026594402,0.0003219848,0.0005196132,0.000096378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020601265,0.00085482927,0.001294825,0.0012493589,0.00046034757,0.0008771155,0.0019411998,0.0011526457,0.0015594978],"category_scores_gemma":[0.0049390234,0.000368623,0.00081972545,0.0011531146,0.0008305023,0.0018555269,0.0019094761,0.0010251923,0.00053647114],"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":[0.00031424846,0.00021281556,0.0021787551,0.0002314359,0.00016540143,0.0001113216,0.00019520636,0.25148726,0.045560982,0.0106899105,0.0033914584,0.68546116],"study_design_scores_gemma":[0.000008001016,0.0000360432,0.0003804772,0.0000033619403,0.000008118794,0.000044517645,0.000011812628,0.99078524,0.005487754,0.0026391887,0.00058598066,0.0000095141895],"about_ca_topic_score_codex":0.0021064228,"about_ca_topic_score_gemma":0.002482573,"teacher_disagreement_score":0.0021064228,"about_ca_system_score_codex":0.0008738168,"about_ca_system_score_gemma":0.0010315179,"threshold_uncertainty_score":0.010895133},"labels":[],"label_agreement":null},{"id":"W3008035954","doi":"10.1109/ssci44817.2019.9002739","title":"Distribution Based Feature Mapping for Classifying Count Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Computer science; Pattern recognition (psychology); Kernel (algebra); Support vector machine; Feature (linguistics); Artificial intelligence; Dirichlet distribution; Maximization; Multinomial distribution; Kernel method; Feature vector; Latent Dirichlet allocation; Machine learning; Data mining; Mathematics; Topic model; Statistics","score_opus":0.059514061722793235,"score_gpt":0.2782676755814202,"score_spread":0.21875361385862696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008035954","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02781902,0.00014523562,0.9704572,0.000105130755,0.00003529111,0.000061560946,0.00019116075,0.0007579271,0.00042748434],"genre_scores_gemma":[0.6288537,0.0002463145,0.3676696,0.00010009092,0.00009748703,0.0003386159,0.0013231073,0.00015813267,0.001213086],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99819094,0.0004996207,0.00016877666,0.00048068282,0.0005117791,0.0001482635],"domain_scores_gemma":[0.99674606,0.0016098542,0.0003390948,0.00056152337,0.0006588261,0.00008458627],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001775204,0.0007101376,0.0010133833,0.0026525913,0.0004249332,0.001003556,0.0013434567,0.0008601505,0.0014981763],"category_scores_gemma":[0.0090254685,0.0002028699,0.0009048928,0.0026179762,0.0007844656,0.0025170173,0.00095109467,0.001176572,0.0008655772],"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":[0.0003914507,0.00029824296,0.012828236,0.0002481625,0.00010337803,0.00027488425,0.0002201319,0.089895755,0.020814113,0.018350484,0.00370896,0.8528661],"study_design_scores_gemma":[0.000013104995,0.000108448214,0.0056922548,0.000019135567,0.000017095917,0.0003582539,0.00009127893,0.9641604,0.008637448,0.018528398,0.0023300825,0.000044113178],"about_ca_topic_score_codex":0.00097034016,"about_ca_topic_score_gemma":0.00063123717,"teacher_disagreement_score":0.0026525913,"about_ca_system_score_codex":0.00057466235,"about_ca_system_score_gemma":0.00049166306,"threshold_uncertainty_score":0.009388268},"labels":[],"label_agreement":null},{"id":"W3011549537","doi":"10.5121/sipij.2020.11101","title":"Applying R-spatiogram in Object Tracking for Occlusion Handling","year":2020,"lang":"en","type":"article","venue":"Signal & Image Processing An International Journal","topic":"Face and Expression Recognition","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":"Occlusion; Computer vision; Tracking (education); Computer science; Artificial intelligence; Object (grammar); Video tracking; Medicine; Psychology; Internal medicine","score_opus":0.03542258035457338,"score_gpt":0.32227684511813753,"score_spread":0.2868542647635641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011549537","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019589415,0.00058971817,0.9777453,0.00006484645,0.000035751826,0.000040084884,0.0001019572,0.0013516122,0.0004813167],"genre_scores_gemma":[0.3656099,0.0010573822,0.6288136,0.00018517654,0.000102903614,0.0001161877,0.0008445161,0.00033272099,0.0029376615],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989255,0.00022072608,0.000073016614,0.00042697438,0.00025318514,0.00010062915],"domain_scores_gemma":[0.9986349,0.00033268845,0.00025057406,0.00047610674,0.00025378412,0.000051885618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013231011,0.00093286525,0.0012937381,0.002397456,0.00046203617,0.0010528358,0.0009683569,0.0011549856,0.0010347221],"category_scores_gemma":[0.003017985,0.00041495034,0.0009401668,0.0026015008,0.0006427088,0.0016343357,0.001066636,0.00063860806,0.0011029085],"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":[0.00076397246,0.00018383926,0.0052699624,0.00021385869,0.00015712193,0.0004401864,0.0002941166,0.11277747,0.07645868,0.0091409115,0.0040187826,0.7902812],"study_design_scores_gemma":[0.00001910165,0.00024867378,0.0042282226,0.000028992741,0.000070058035,0.00062379305,0.00007232645,0.9456623,0.03505246,0.0043866835,0.00957116,0.000036314792],"about_ca_topic_score_codex":0.0031197309,"about_ca_topic_score_gemma":0.0036172366,"teacher_disagreement_score":0.0031197309,"about_ca_system_score_codex":0.0007208662,"about_ca_system_score_gemma":0.00080270524,"threshold_uncertainty_score":0.006997347},"labels":[],"label_agreement":null},{"id":"W3014282585","doi":"10.1007/978-3-030-50516-5_1","title":"Weighted Fisher Discriminant Analysis in the Input and Feature Spaces","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Kernel Fisher discriminant analysis; Computer science; Linear discriminant analysis; Weighting; Pattern recognition (psychology); Subspace topology; Artificial intelligence; Kernel (algebra); Feature (linguistics); Discriminant; Class (philosophy); Fisher kernel; Feature vector; Facial recognition system; Mathematics; Discrete mathematics; Medicine","score_opus":0.01551866158589806,"score_gpt":0.23491461448715234,"score_spread":0.21939595290125427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014282585","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005166577,0.0009945984,0.98956746,0.00010941129,0.00013346544,0.000010231799,0.00009314991,0.00038792886,0.0035371184],"genre_scores_gemma":[0.16544431,0.003191433,0.7870975,0.00012947137,0.00027000153,0.00008626686,0.00069114554,0.00059723767,0.04249256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995796,0.00007857942,0.000023813158,0.00007362533,0.00020910574,0.000035190547],"domain_scores_gemma":[0.9996388,0.00012266319,0.00002645103,0.000065223205,0.0001348427,0.000011948571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006127023,0.00071269553,0.00085012167,0.0007281934,0.00021970291,0.0010262199,0.00070300436,0.00045438152,0.0048489515],"category_scores_gemma":[0.0018078741,0.00027675307,0.00059128186,0.0011106811,0.00046558926,0.0013387665,0.00088976545,0.00089537335,0.0029009737],"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":[0.00014918098,0.000040816187,0.0003772629,0.0002341612,0.000053181873,0.00008765483,0.00005852864,0.03266625,0.03275174,0.074574836,0.0104888,0.8485176],"study_design_scores_gemma":[0.000010170968,0.000060865514,0.0012172036,0.000060217764,0.000052107327,0.0003561803,0.0000420575,0.88836944,0.023205843,0.06740475,0.019173808,0.0000474126],"about_ca_topic_score_codex":0.0009892216,"about_ca_topic_score_gemma":0.0012562219,"teacher_disagreement_score":0.0048489515,"about_ca_system_score_codex":0.00028574886,"about_ca_system_score_gemma":0.0004038741,"threshold_uncertainty_score":0.016221404},"labels":[],"label_agreement":null},{"id":"W3017261075","doi":"10.1109/tpami.2020.2987013","title":"Multiview Feature Selection for Single-View Classification","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; University of Toronto; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Feature selection; Weighting; Pattern recognition (psychology); Unavailability; Data set; Matching (statistics); Feature extraction; Set (abstract data type); Word error rate; Data mining; Selection (genetic algorithm); Mathematics; Statistics","score_opus":0.05509779539595317,"score_gpt":0.2891769794478262,"score_spread":0.23407918405187306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017261075","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010715256,0.0004260603,0.987755,0.00006362814,0.000029564024,0.00003266138,0.00010290496,0.0005156234,0.00035932404],"genre_scores_gemma":[0.48334372,0.0005985312,0.5117194,0.00017022193,0.00020236208,0.00022994407,0.0014583158,0.0002215885,0.002055989],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988463,0.00023187173,0.00006462925,0.00032739912,0.00040284105,0.00012698982],"domain_scores_gemma":[0.9991835,0.00026229944,0.00009690717,0.00013050274,0.00028443403,0.000042305255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016673037,0.0010370002,0.0017636998,0.0017562724,0.00041129743,0.00076950557,0.0011151057,0.0007385306,0.0019017254],"category_scores_gemma":[0.0024704929,0.00033535954,0.0014028536,0.001549152,0.00045238808,0.0008998193,0.00093099754,0.0008875787,0.00096779986],"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":[0.00042212472,0.00017517128,0.0030336978,0.0001486135,0.00019268734,0.00013463089,0.00011080118,0.124546476,0.042797945,0.0034161536,0.0057810335,0.81924075],"study_design_scores_gemma":[0.00001638532,0.000106163665,0.0016990359,0.000014139422,0.00003925666,0.0001256078,0.000037646452,0.9818751,0.010855469,0.0032828995,0.0019257622,0.000022441853],"about_ca_topic_score_codex":0.0026683523,"about_ca_topic_score_gemma":0.002157855,"teacher_disagreement_score":0.0026683523,"about_ca_system_score_codex":0.00048506266,"about_ca_system_score_gemma":0.0006025353,"threshold_uncertainty_score":0.008817673},"labels":[],"label_agreement":null},{"id":"W3021893182","doi":"10.1142/s0218001405004071","title":"OPTIMAL SUBSPACE ANALYSIS FOR FACE RECOGNITION","year":2005,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Concordia University; Hong Kong Baptist University","keywords":"Linear discriminant analysis; Subspace topology; Discriminative model; Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Computer science; Random subspace method; Projection (relational algebra); Face (sociological concept); Linear subspace; Projection method; Principal component analysis; Discriminant; Mathematics; Algorithm; Dykstra's projection algorithm","score_opus":0.09924639405014833,"score_gpt":0.33052922803801593,"score_spread":0.2312828339878676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3021893182","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041335574,0.002344119,0.9898066,0.00021030859,0.00007358241,0.000018853745,0.00006852384,0.0005187275,0.0028256492],"genre_scores_gemma":[0.21859027,0.00418444,0.7698856,0.0001607802,0.00025842834,0.00019009471,0.0005527307,0.00017499007,0.0060025794],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994012,0.00018362315,0.000027483344,0.00009264552,0.0002551916,0.000039858962],"domain_scores_gemma":[0.999818,0.000060189555,0.000012623047,0.000037682727,0.000064385975,0.00000710587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050122495,0.00039915185,0.0006963129,0.00081469375,0.00032725936,0.00062681996,0.00034820536,0.0004343905,0.0031370136],"category_scores_gemma":[0.0010992001,0.0001946221,0.0004187358,0.0009345026,0.00049561175,0.00076036365,0.00055940903,0.0006026869,0.0016570361],"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":[0.00009419244,0.000042839227,0.00042070524,0.00014666595,0.000051298153,0.000056327917,0.00005899316,0.096651904,0.020741086,0.06991337,0.010018193,0.80180454],"study_design_scores_gemma":[0.000010790532,0.00003574961,0.00052896515,0.000024316958,0.000012492074,0.00010045981,0.000029462242,0.905777,0.008073493,0.069183506,0.01619787,0.000025944975],"about_ca_topic_score_codex":0.0014498885,"about_ca_topic_score_gemma":0.0013606171,"teacher_disagreement_score":0.0031370136,"about_ca_system_score_codex":0.00040848332,"about_ca_system_score_gemma":0.00055248605,"threshold_uncertainty_score":0.010494411},"labels":[],"label_agreement":null},{"id":"W3035838478","doi":"10.1016/j.ijar.2020.02.007","title":"Discriminative training of feed-forward and recurrent sum-product networks by extended Baum-Welch","year":2020,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Face and Expression Recognition","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":"Vector Institute; University of Waterloo","funders":"","keywords":"Discriminative model; Gradient descent; Monotonic function; Robustness (evolution); Computer science; Artificial intelligence; Maximization; Artificial neural network; Product (mathematics); Generative grammar; Pattern recognition (psychology); Machine learning; Algorithm; Mathematics; Mathematical optimization","score_opus":0.026599343677927312,"score_gpt":0.27500523731508314,"score_spread":0.24840589363715582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035838478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030411888,0.00065149297,0.96533924,0.00020028975,0.0001410267,0.000044850243,0.00009724761,0.001739994,0.0013739353],"genre_scores_gemma":[0.66430116,0.00038478008,0.32486382,0.00024689877,0.00010436481,0.0002064918,0.00091733056,0.00036413906,0.008611021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991456,0.0002620982,0.00006165068,0.00024200787,0.00016677765,0.00012178231],"domain_scores_gemma":[0.99772173,0.0013354282,0.000098402255,0.00022146567,0.00053573464,0.000087339424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021277133,0.0011979082,0.0017461486,0.00060714094,0.000521466,0.0012250113,0.0025345616,0.0019212023,0.0037585245],"category_scores_gemma":[0.0058346507,0.0011756631,0.0009880654,0.0008995904,0.00076889066,0.0019564526,0.0016416002,0.0027085452,0.001910066],"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":[0.00045876412,0.0001928503,0.00089772494,0.00014028056,0.00018295721,0.000106450265,0.00009453158,0.551399,0.0068032146,0.0053120824,0.0030700625,0.4313421],"study_design_scores_gemma":[0.0000046010327,0.000013746369,0.00005156597,0.000003033615,0.0000063242856,0.00000732751,0.0000028984362,0.99838555,0.0007225238,0.00069110113,0.0001082064,0.000003116818],"about_ca_topic_score_codex":0.009962962,"about_ca_topic_score_gemma":0.014159347,"teacher_disagreement_score":0.009962962,"about_ca_system_score_codex":0.000840907,"about_ca_system_score_gemma":0.0015959988,"threshold_uncertainty_score":0.019809961},"labels":[],"label_agreement":null},{"id":"W3036447434","doi":"10.1142/s0218213020600040","title":"Selecting and Combining Classifiers Based on Centrality Measures","year":2020,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence Tools","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure","funders":"","keywords":"Centrality; Computer science; Classifier (UML); Artificial intelligence; Machine learning; Random subspace method; Cascading classifiers; Feature selection; Pattern recognition (psychology); Data mining; Mathematics; Statistics","score_opus":0.14243321558855435,"score_gpt":0.32723385059914123,"score_spread":0.18480063501058688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036447434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20877978,0.0025327646,0.77569824,0.00058224314,0.00042714778,0.0005489924,0.00038951487,0.001202302,0.009839053],"genre_scores_gemma":[0.7764579,0.0008939664,0.21751288,0.00013156838,0.00040289262,0.00026451328,0.00073270046,0.00015020206,0.0034533546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616444,0.00067112036,0.00028307422,0.00059502834,0.0018986961,0.00038756838],"domain_scores_gemma":[0.9948171,0.0019607686,0.00033798444,0.00030368645,0.0023674727,0.00021300172],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003137795,0.0016190785,0.0018801553,0.0065772478,0.0012753329,0.0020793274,0.0010146633,0.0009567172,0.0017998532],"category_scores_gemma":[0.011705018,0.0003434005,0.0011333384,0.0026455894,0.00048158428,0.0021302304,0.0011624824,0.0008642677,0.0011140953],"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":[0.00060062105,0.0003079294,0.029357314,0.00032607402,0.00050911086,0.00043705283,0.00043380514,0.06110443,0.040189758,0.0053432295,0.006538694,0.854852],"study_design_scores_gemma":[0.00006437351,0.0006429672,0.020008793,0.00016248944,0.0010540716,0.0008227528,0.00070166093,0.9047033,0.04042045,0.017213142,0.014085585,0.00012047229],"about_ca_topic_score_codex":0.003573143,"about_ca_topic_score_gemma":0.0042093354,"teacher_disagreement_score":0.0065772478,"about_ca_system_score_codex":0.0008933605,"about_ca_system_score_gemma":0.0012966878,"threshold_uncertainty_score":0.01659447},"labels":[],"label_agreement":null},{"id":"W3036840050","doi":"10.1007/978-3-030-50347-5_29","title":"Generalized Subspace Learning by Roweis Discriminant Analysis","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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 Waterloo","funders":"","keywords":"Computer science; Subspace topology; Linear discriminant analysis; Artificial intelligence; Discriminant; Pattern recognition (psychology); Optimal discriminant analysis","score_opus":0.016382545383615398,"score_gpt":0.24170796227140673,"score_spread":0.22532541688779134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036840050","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039822734,0.0006988761,0.99154776,0.000088894805,0.000118894735,0.000016617676,0.00007176095,0.00064594834,0.0028289992],"genre_scores_gemma":[0.10351988,0.0015203182,0.87077254,0.00016233299,0.00018049862,0.000092344366,0.00074626255,0.0004591025,0.022546832],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995882,0.00013034076,0.000017383192,0.00008595139,0.00014932401,0.000028781049],"domain_scores_gemma":[0.99975425,0.00006278662,0.000015820246,0.00007485699,0.00007774806,0.000014564234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004791248,0.00071712537,0.00081454514,0.00068855257,0.0002986185,0.0007501453,0.0006912671,0.00044341118,0.0057297274],"category_scores_gemma":[0.00092598726,0.00028357902,0.00062198355,0.001278448,0.0005168203,0.0011239917,0.0011899361,0.0010565383,0.004797015],"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":[0.00009017828,0.00005022706,0.00024282512,0.00015478978,0.00007103614,0.00003624035,0.000045927147,0.03670836,0.024864312,0.053731814,0.016034981,0.8679693],"study_design_scores_gemma":[0.00001777122,0.00008902074,0.0007774975,0.00003146651,0.00003721164,0.00026376176,0.000046312183,0.87866414,0.018003242,0.06538093,0.036634035,0.000054643922],"about_ca_topic_score_codex":0.00072005397,"about_ca_topic_score_gemma":0.0011094486,"teacher_disagreement_score":0.0057297274,"about_ca_system_score_codex":0.00018853319,"about_ca_system_score_gemma":0.00043808043,"threshold_uncertainty_score":0.0191679},"labels":[],"label_agreement":null},{"id":"W3039953684","doi":"10.48550/arxiv.2007.02572","title":"A Novel Random Forest Dissimilarity Measure for Multi-View Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"European Regional Development Fund; Région Normandie; European Commission","keywords":"Random forest; Large margin nearest neighbor; Margin (machine learning); Metric (unit); Machine learning; Computer science; Dimension (graph theory); Artificial intelligence; Exploit; Context (archaeology); Measure (data warehouse); Task (project management); k-nearest neighbors algorithm; Sample (material); Supervised learning; Data mining; Pattern recognition (psychology); Mathematics; Artificial neural network; Geography","score_opus":0.19719137147111004,"score_gpt":0.226092966290718,"score_spread":0.028901594819607968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039953684","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020319989,0.0007835068,0.9769001,0.00014772695,0.00008271587,0.000051916617,0.00018493099,0.00018402933,0.001344975],"genre_scores_gemma":[0.48070616,0.00067273824,0.5144389,0.00022369466,0.00044593314,0.00026984606,0.0013398813,0.00020182125,0.0017009478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99646,0.0008403502,0.00022590008,0.000719799,0.0015759354,0.00017796781],"domain_scores_gemma":[0.99605465,0.0015950609,0.000613812,0.0006189815,0.00081209233,0.00030546714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022983034,0.00072160095,0.0014921132,0.0030297413,0.0007140596,0.0017693461,0.0018974601,0.0017996894,0.0017664235],"category_scores_gemma":[0.011220295,0.00026169233,0.00116347,0.0026560277,0.001161899,0.0031945396,0.002043855,0.0018624531,0.0006540608],"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":[0.000586565,0.0003874345,0.0141294105,0.00062798656,0.0003492613,0.0004501014,0.00033733936,0.15254359,0.03683889,0.17096695,0.008439358,0.6143431],"study_design_scores_gemma":[0.0000331412,0.00043669253,0.0067001116,0.000071330265,0.000057303776,0.001255672,0.00008714947,0.88467884,0.0076017804,0.08849915,0.010491837,0.00008694874],"about_ca_topic_score_codex":0.0009043752,"about_ca_topic_score_gemma":0.0009045398,"teacher_disagreement_score":0.0030297413,"about_ca_system_score_codex":0.0010619906,"about_ca_system_score_gemma":0.0005498037,"threshold_uncertainty_score":0.012154758},"labels":[],"label_agreement":null},{"id":"W3041023245","doi":"10.1007/s11634-020-00408-5","title":"Active learning of constraints for weighted feature selection","year":2020,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Conseil National de la Recherche Scientifique; Agence Universitaire de la Francophonie","keywords":"Pairwise comparison; Feature selection; Laplacian matrix; Computer science; Cluster analysis; Graph; Feature (linguistics); Constraint (computer-aided design); Selection (genetic algorithm); Laplace operator; Data mining; Set (abstract data type); Machine learning; Theoretical computer science; Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.03703733615695371,"score_gpt":0.31498423960111605,"score_spread":0.27794690344416234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041023245","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040808693,0.0002223109,0.99499965,0.00011031195,0.000030189663,0.000028094597,0.000051800973,0.000117211974,0.0003595995],"genre_scores_gemma":[0.43314147,0.0008006272,0.55558234,0.00050603587,0.0003645394,0.0009560666,0.0012160523,0.0004926582,0.0069402466],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99763346,0.0011713335,0.00013991894,0.00038908276,0.0004986331,0.00016749138],"domain_scores_gemma":[0.98822695,0.009264238,0.0004079156,0.0006761056,0.0011424837,0.00028222118],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004850321,0.0014298748,0.00312221,0.0013811048,0.0007099782,0.002044236,0.004445631,0.0022306098,0.004404713],"category_scores_gemma":[0.015616124,0.0014302772,0.0012757854,0.001976438,0.0018350903,0.0034325977,0.002896661,0.0029434059,0.0007639159],"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":[0.0005331479,0.0003062326,0.0009757907,0.0004486448,0.00029534937,0.00012612427,0.00015959454,0.58874047,0.0057930863,0.102790594,0.008514685,0.29131627],"study_design_scores_gemma":[0.000020481459,0.000027246764,0.000051921754,0.000010991714,0.0000092366045,0.000010320316,0.0000045016473,0.9843411,0.00054357847,0.014497549,0.00047702112,0.0000060007856],"about_ca_topic_score_codex":0.0033490686,"about_ca_topic_score_gemma":0.003897906,"teacher_disagreement_score":0.004850321,"about_ca_system_score_codex":0.0009934272,"about_ca_system_score_gemma":0.0014301798,"threshold_uncertainty_score":0.025651276},"labels":[],"label_agreement":null},{"id":"W3041242936","doi":"10.1007/978-3-030-51935-3_34","title":"Considerably Improving Clustering Algorithms Using UMAP Dimensionality Reduction Technique: A Comparative Study","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":238,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Cluster analysis; Dimensionality reduction; Computer science; Hierarchical clustering; Artificial intelligence; Pattern recognition (psychology); CURE data clustering algorithm; Correlation clustering; Clustering high-dimensional data; Nonlinear dimensionality reduction; Data mining; Canopy clustering algorithm; Embedding; Curse of dimensionality; Preprocessor; Algorithm","score_opus":0.06014161457299876,"score_gpt":0.30660490886128866,"score_spread":0.2464632942882899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3041242936","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5313359,0.04640949,0.3843643,0.0008297768,0.0010579312,0.0005944542,0.0015464773,0.0065666675,0.027295101],"genre_scores_gemma":[0.48641738,0.010165869,0.49152002,0.00018952411,0.00027086138,0.00018027556,0.003353304,0.0005051056,0.0073976964],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99644095,0.0011617409,0.00028382812,0.000454148,0.0014563126,0.00020306327],"domain_scores_gemma":[0.9938799,0.0026127358,0.00024194905,0.0012158905,0.0019334588,0.000116025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002644598,0.0010650068,0.0015985424,0.0028852485,0.001257334,0.0019075504,0.0019154869,0.0011019559,0.0030859811],"category_scores_gemma":[0.0077997246,0.00022598187,0.001152481,0.0055088955,0.00042421967,0.0023611428,0.0009073535,0.00060087244,0.0017781206],"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":[0.00086944463,0.00060436514,0.0026497024,0.00072200166,0.0003539862,0.00005737787,0.00016107484,0.024701646,0.008777153,0.0022960398,0.0054590213,0.9533482],"study_design_scores_gemma":[0.00027492593,0.004611699,0.039004754,0.00022623439,0.0015634815,0.0015514527,0.0019210071,0.82268435,0.08563241,0.0074830283,0.034807898,0.00023872951],"about_ca_topic_score_codex":0.0052076974,"about_ca_topic_score_gemma":0.005436957,"teacher_disagreement_score":0.0052076974,"about_ca_system_score_codex":0.00091057085,"about_ca_system_score_gemma":0.0010431489,"threshold_uncertainty_score":0.01398617},"labels":[],"label_agreement":null},{"id":"W3047490124","doi":"10.18280/ria.340304","title":"Performance Evaluation of Machine Learning for Recognizing Human Facial Emotions","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"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; Facial expression; Human–computer interaction; Artificial intelligence; Machine learning; Psychology; Cognitive psychology","score_opus":0.14127255305231423,"score_gpt":0.3237512251745021,"score_spread":0.18247867212218788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3047490124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87904114,0.020734936,0.07191409,0.0011975411,0.0019130154,0.0003710442,0.0037605998,0.0064832643,0.014584285],"genre_scores_gemma":[0.9573606,0.0015535405,0.028112344,0.0001710538,0.0001902441,0.00014428634,0.008397287,0.0001365337,0.0039340686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943299,0.0013431032,0.0006532915,0.0010509124,0.0020603354,0.00056249223],"domain_scores_gemma":[0.9948237,0.002551283,0.00030050895,0.00042393574,0.0016856568,0.00021496508],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005820145,0.0017206202,0.0014758313,0.002704571,0.0006644888,0.0013077516,0.0011822231,0.0014249004,0.0015672252],"category_scores_gemma":[0.00949662,0.00020647189,0.00082774507,0.0015341385,0.0004040092,0.0014668355,0.00086995785,0.0007393542,0.0012600458],"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":[0.0045198454,0.0011029271,0.04017102,0.0010637867,0.0008826149,0.00025830747,0.00015767252,0.10635386,0.014400881,0.0010210852,0.025881328,0.8041867],"study_design_scores_gemma":[0.00010411556,0.0015962019,0.029882869,0.000075348056,0.00020199812,0.00029852078,0.00023102175,0.9401593,0.021882482,0.00077913876,0.0047250395,0.00006397],"about_ca_topic_score_codex":0.007467044,"about_ca_topic_score_gemma":0.0043169335,"teacher_disagreement_score":0.007467044,"about_ca_system_score_codex":0.0013582646,"about_ca_system_score_gemma":0.00074483413,"threshold_uncertainty_score":0.030780196},"labels":[],"label_agreement":null},{"id":"W3048868447","doi":"10.1016/j.neunet.2020.07.036","title":"SVM-Boosting based on Markov resampling: Theory and algorithm","year":2020,"lang":"en","type":"article","venue":"Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Boosting (machine learning); AdaBoost; Support vector machine; Resampling; Artificial intelligence; Markov chain; Computer science; Machine learning; Algorithm; Pattern recognition (psychology); Gradient boosting; Mathematics","score_opus":0.02036005395713799,"score_gpt":0.23712970620898507,"score_spread":0.21676965225184708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048868447","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032257221,0.00047180103,0.9954334,0.00011099227,0.00008372617,0.000027938215,0.000016973072,0.00022676976,0.0004026828],"genre_scores_gemma":[0.28982797,0.0010062067,0.703967,0.00029148118,0.0005066976,0.000221556,0.00025045392,0.00017988408,0.0037487275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981457,0.00091762847,0.000079252146,0.00023099117,0.00048522826,0.00014120746],"domain_scores_gemma":[0.99668485,0.001996539,0.00018251418,0.0003427759,0.000671754,0.00012160454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004558188,0.00052305177,0.002306482,0.0010343993,0.00068398716,0.0010756038,0.002016554,0.001339975,0.0021928896],"category_scores_gemma":[0.0078027723,0.00081007235,0.0011649422,0.001192141,0.0009247335,0.0015486468,0.0014309625,0.0016408087,0.0008875136],"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":[0.00034668247,0.00019853996,0.0019248028,0.00026567193,0.00022504113,0.00007807802,0.000105751446,0.301121,0.006389505,0.123337016,0.010172151,0.55583584],"study_design_scores_gemma":[0.000008414729,0.000035453388,0.00018254376,0.000008477508,0.000013889123,0.00004078882,0.0000034286218,0.98445976,0.0007009536,0.013564816,0.00097254716,0.000008913019],"about_ca_topic_score_codex":0.0025014908,"about_ca_topic_score_gemma":0.0026488677,"teacher_disagreement_score":0.004558188,"about_ca_system_score_codex":0.0008348604,"about_ca_system_score_gemma":0.001262003,"threshold_uncertainty_score":0.024106324},"labels":[],"label_agreement":null},{"id":"W3086013422","doi":"10.48550/arxiv.2009.08136","title":"Multidimensional Scaling, Sammon Mapping, and Isomap: Tutorial and Survey","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"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":"Isomap; Multidimensional scaling; Nonlinear dimensionality reduction; Metric (unit); Kernel (algebra); Pattern recognition (psychology); Artificial intelligence; Embedding; Landmark; Mathematics; Computer science; Dimensionality reduction; Machine learning; Combinatorics","score_opus":0.11237914127842596,"score_gpt":0.19160856232920867,"score_spread":0.0792294210507827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086013422","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033174704,0.4594313,0.5102954,0.0022344834,0.0019902638,0.00008924976,0.00042931788,0.0008619187,0.021350635],"genre_scores_gemma":[0.045347143,0.5791278,0.35366535,0.00115642,0.006022699,0.00034422023,0.0013877606,0.00051626394,0.012432389],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915695,0.00021154812,0.000084100284,0.00019230068,0.00031412183,0.000041027368],"domain_scores_gemma":[0.99894935,0.0005546416,0.00006626552,0.000117466574,0.000263298,0.000049033566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014022633,0.0019435292,0.001528251,0.0045322855,0.0006201902,0.0020072563,0.0010815158,0.0014898514,0.0054961196],"category_scores_gemma":[0.003222457,0.00080391,0.0010725034,0.008774732,0.0014079906,0.0047729183,0.0015804191,0.002222139,0.0036464615],"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":[0.00003406038,0.00008151778,0.00077943987,0.003118012,0.00009563389,0.00015201201,0.00040550128,0.008593571,0.0019579444,0.15035786,0.04961677,0.78480774],"study_design_scores_gemma":[0.000011102339,0.00013983084,0.0017920001,0.00085237675,0.000078162746,0.0018192631,0.00034876424,0.055286016,0.003086071,0.20767519,0.7287558,0.00015539923],"about_ca_topic_score_codex":0.0013090458,"about_ca_topic_score_gemma":0.0010294798,"teacher_disagreement_score":0.0054961196,"about_ca_system_score_codex":0.00078355934,"about_ca_system_score_gemma":0.0010398553,"threshold_uncertainty_score":0.018386364},"labels":[],"label_agreement":null},{"id":"W3087778363","doi":"10.5430/air.v9n1p45","title":"Hybrid approaches to feature subset selection for data classification in high-dimensional feature space","year":2020,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Face and Expression Recognition","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":"Feature selection; Interpretability; Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Support vector machine; Computer science; Feature (linguistics); Intersection (aeronautics); Feature vector; Linear classifier; k-nearest neighbors algorithm; Filter (signal processing); Machine learning; Data mining; High dimensional; Engineering","score_opus":0.584270800689593,"score_gpt":0.4137573621001507,"score_spread":0.17051343858944235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087778363","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015778907,0.00031118354,0.9831661,0.000029004404,0.000019654955,0.00005752865,0.000028180104,0.00036817882,0.00024131195],"genre_scores_gemma":[0.22551899,0.0004997964,0.7716209,0.00007093456,0.00010452759,0.00040885198,0.00041730373,0.00013376502,0.0012248635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978491,0.00057694066,0.00018281733,0.00035004065,0.00094636704,0.00009472416],"domain_scores_gemma":[0.99778384,0.0009677821,0.0002033992,0.00031417486,0.0006756098,0.000055255186],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020901882,0.0012850618,0.0016977757,0.00272105,0.000633028,0.0011246739,0.0013671591,0.0006772672,0.0008359748],"category_scores_gemma":[0.0031952353,0.00036330216,0.0014356293,0.0026617772,0.0005279716,0.001506657,0.0008290274,0.0005674214,0.0006395199],"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":[0.00026364246,0.00029677534,0.0041838805,0.00029021976,0.0005263763,0.00015464194,0.00038908637,0.04093035,0.036142617,0.0029667786,0.0014280418,0.91242766],"study_design_scores_gemma":[0.00006940226,0.0008270442,0.011990053,0.000078191144,0.00030883466,0.0010204669,0.00036010056,0.9190249,0.047692385,0.008600243,0.009872732,0.00015573688],"about_ca_topic_score_codex":0.00081907853,"about_ca_topic_score_gemma":0.0011057878,"teacher_disagreement_score":0.00272105,"about_ca_system_score_codex":0.00031301784,"about_ca_system_score_gemma":0.00048148833,"threshold_uncertainty_score":0.011054099},"labels":[],"label_agreement":null},{"id":"W3088543493","doi":"10.48550/arxiv.2009.10301","title":"Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Embedding; Gaussian; Nonlinear dimensionality reduction; Probabilistic logic; Probability distribution; Dimensionality reduction; Cover (algebra); Space (punctuation); Computer science; Manifold (fluid mechanics); Cauchy distribution; Mathematics; Statistical physics; Artificial intelligence; Physics; Mathematical analysis; Statistics","score_opus":0.06702951026061885,"score_gpt":0.21607531679535383,"score_spread":0.14904580653473498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088543493","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032712985,0.10313069,0.8822473,0.00086177007,0.0006133624,0.00007377358,0.0002845355,0.00042291562,0.009094306],"genre_scores_gemma":[0.12728493,0.2536333,0.596369,0.0007979328,0.0031171243,0.00041755295,0.002009039,0.0005572979,0.015813773],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99870336,0.00039147522,0.00011844035,0.00030354204,0.0004325705,0.000050574563],"domain_scores_gemma":[0.99846965,0.00094763585,0.0000723797,0.0001562026,0.0003050411,0.000049092116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019830936,0.0011896454,0.0015413484,0.0019577658,0.00034852052,0.0016743268,0.0012657016,0.0014212724,0.003196575],"category_scores_gemma":[0.0051021636,0.00069943146,0.0010038189,0.0037724874,0.00094744435,0.0041756798,0.0013984399,0.0019197388,0.0022343858],"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":[0.00005142901,0.0001187434,0.0017118442,0.001377116,0.00012881879,0.00011598763,0.00020106297,0.034514513,0.001158184,0.16703665,0.018248644,0.77533704],"study_design_scores_gemma":[0.000019729247,0.00016251534,0.0022739202,0.00045504022,0.00008783874,0.0012712782,0.00018198388,0.52675694,0.002971922,0.25097018,0.21471046,0.0001382008],"about_ca_topic_score_codex":0.0022755426,"about_ca_topic_score_gemma":0.0018351105,"teacher_disagreement_score":0.003196575,"about_ca_system_score_codex":0.0006087013,"about_ca_system_score_gemma":0.0007582245,"threshold_uncertainty_score":0.01069361},"labels":[],"label_agreement":null},{"id":"W3091206037","doi":"10.15353/jcvis.v6i1.3534","title":"Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling","year":2021,"lang":"en","type":"preprint","venue":"Journal of Computational Vision and Imaging Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Large margin nearest neighbor; Metric (unit); Margin (machine learning); MNIST database; k-nearest neighbors algorithm; Computer science; Artificial intelligence; Subspace topology; Nearest neighbor graph; Pattern recognition (psychology); Nearest neighbor search; Acceleration; Sampling (signal processing); Machine learning; Scalability; Deep learning; Physics; Filter (signal processing); Engineering; Computer vision","score_opus":0.07010865272972298,"score_gpt":0.35527759555818356,"score_spread":0.28516894282846056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091206037","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016899448,0.00025761637,0.9795554,0.00014575315,0.00007389045,0.00011929778,0.00014986929,0.001974512,0.0008242065],"genre_scores_gemma":[0.22774306,0.00016581669,0.76723087,0.00021797293,0.000102241014,0.00030901772,0.002103599,0.00035650187,0.0017708344],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99718285,0.00093760696,0.00019487391,0.0005468565,0.0009452302,0.00019261973],"domain_scores_gemma":[0.9956995,0.0013847542,0.00029538674,0.0012343286,0.0011403888,0.0002455903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030108157,0.0014821958,0.0022348578,0.0015761775,0.00075528974,0.001230809,0.0026978527,0.0014035037,0.0031070432],"category_scores_gemma":[0.012101361,0.00054879923,0.0015208501,0.0020783956,0.00077607,0.003017971,0.002607965,0.0023740726,0.002276864],"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":[0.00041262782,0.00064968487,0.004560108,0.00015983275,0.00015555996,0.00013537974,0.00021412862,0.260164,0.012065522,0.013333488,0.014152075,0.6939976],"study_design_scores_gemma":[0.000013252952,0.000040568153,0.00024594791,0.0000033436027,0.0000047404983,0.000034922872,0.000015313291,0.99358785,0.0012853731,0.0041718003,0.0005902221,0.0000065860695],"about_ca_topic_score_codex":0.0059769405,"about_ca_topic_score_gemma":0.00830936,"teacher_disagreement_score":0.0059769405,"about_ca_system_score_codex":0.0010853921,"about_ca_system_score_gemma":0.0015823066,"threshold_uncertainty_score":0.015922844},"labels":[],"label_agreement":null},{"id":"W3091725097","doi":"10.1109/iscas45731.2020.9180979","title":"Negative Label Guided Discriminative Canonical Correlation Analysis for Semi-Supervised and Semi-Paired Learning","year":2020,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Discriminative model; Artificial intelligence; Computer science; Canonical correlation; Pattern recognition (psychology); Class (philosophy); Semi-supervised learning; Exploit; Correlation; Machine learning; Process (computing); Supervised learning; Mathematics; Artificial neural network","score_opus":0.059658392864696236,"score_gpt":0.2867759447777587,"score_spread":0.22711755191306246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091725097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038453853,0.00010869783,0.9947943,0.00006399737,0.000028882927,0.00003892246,0.000040145274,0.00052922656,0.0005503615],"genre_scores_gemma":[0.36316568,0.00042453912,0.6294134,0.0003934621,0.00032935524,0.0005640639,0.0014391312,0.00053769635,0.0037326782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9943678,0.002445462,0.00022561911,0.0014761762,0.0012083728,0.000276592],"domain_scores_gemma":[0.991738,0.0028619827,0.0009084746,0.00199013,0.002133378,0.00036807035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046268734,0.0017331428,0.0021040617,0.0021213586,0.0012589553,0.002006458,0.0029414168,0.0015618637,0.002820831],"category_scores_gemma":[0.014082635,0.0007489777,0.0014268047,0.0024712237,0.0030337828,0.0026176637,0.0031579277,0.0031005356,0.002043069],"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":[0.00040812805,0.00042092765,0.0045297793,0.00041046873,0.00028508864,0.0003323492,0.0006898491,0.28789166,0.011722282,0.07423906,0.015705446,0.603365],"study_design_scores_gemma":[0.0000068798026,0.000039292474,0.00019794378,0.000013403008,0.000011764281,0.00007025267,0.000028825174,0.9827408,0.0018648918,0.013921157,0.0010846223,0.000020053365],"about_ca_topic_score_codex":0.0022758197,"about_ca_topic_score_gemma":0.0031056334,"teacher_disagreement_score":0.0046268734,"about_ca_system_score_codex":0.0009364014,"about_ca_system_score_gemma":0.002406786,"threshold_uncertainty_score":0.024469495},"labels":[],"label_agreement":null},{"id":"W3093091831","doi":"10.1007/s10463-020-00766-z","title":"High-dimensional sign-constrained feature selection and grouping","year":2020,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Face and Expression Recognition","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":"York University","funders":"","keywords":"Coordinate descent; Mathematics; Feature selection; Convex optimization; Bounded function; Feature (linguistics); Oracle; Estimator; Mathematical optimization; Regular polygon; Sign (mathematics); Pattern recognition (psychology); Algorithm; Artificial intelligence; Applied mathematics; Computer science; Statistics","score_opus":0.04443399232188285,"score_gpt":0.27983773584236327,"score_spread":0.2354037435204804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093091831","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067334976,0.0001225133,0.9920277,0.000081571925,0.000035217134,0.00003003927,0.000065049564,0.0005025982,0.00040181653],"genre_scores_gemma":[0.19031775,0.00029294702,0.80226326,0.00013404049,0.000111208494,0.00022586147,0.0010421164,0.0003914117,0.0052214167],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987495,0.00034814922,0.00009506413,0.0002592229,0.00041015336,0.00013791575],"domain_scores_gemma":[0.99826294,0.00055168546,0.00013013357,0.00050151756,0.000462554,0.000091084104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013737581,0.0008946191,0.0027320907,0.001549124,0.0009665259,0.0016091075,0.0021000388,0.0011569401,0.0037018936],"category_scores_gemma":[0.004448059,0.0006026553,0.0015330345,0.0028097748,0.0011322616,0.0014366953,0.0020279305,0.0012564325,0.0019360293],"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":[0.00049409806,0.00021488007,0.0012086664,0.00016991442,0.00013010135,0.00017261654,0.00015207373,0.08907942,0.03938433,0.021117577,0.009543265,0.8383331],"study_design_scores_gemma":[0.000028774133,0.000087030014,0.0011446045,0.000013897818,0.000030202176,0.0001274175,0.000043123637,0.9696623,0.009944042,0.015561109,0.0033214968,0.000035984376],"about_ca_topic_score_codex":0.0025605275,"about_ca_topic_score_gemma":0.0036373239,"teacher_disagreement_score":0.0037018936,"about_ca_system_score_codex":0.00042994987,"about_ca_system_score_gemma":0.0015052524,"threshold_uncertainty_score":0.012384057},"labels":[],"label_agreement":null},{"id":"W3094276816","doi":"10.18280/ria.340402","title":"Improvements on Learning Kernel Extended Dictionary for Face Recognition","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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":"Artificial intelligence; Sparse approximation; Kernel (algebra); Pattern recognition (psychology); K-SVD; Face (sociological concept); Occlusion; Computer science; Representation (politics); Set (abstract data type); Facial recognition system; Image (mathematics); Dictionary learning; Mathematics; Computer vision","score_opus":0.0686789065295118,"score_gpt":0.28179711400758894,"score_spread":0.21311820747807714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094276816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008908531,0.0005537951,0.9868508,0.00014019583,0.0000882021,0.00004365467,0.00013715103,0.0017839001,0.0014937331],"genre_scores_gemma":[0.31444094,0.0013571122,0.66941947,0.0005163684,0.0001997107,0.00025925305,0.002414665,0.00046109347,0.010931322],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896204,0.00017868097,0.0000639244,0.00025326226,0.0004457779,0.00009628633],"domain_scores_gemma":[0.99920636,0.0001842834,0.000049247396,0.0002218496,0.0003018769,0.0000365033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074412115,0.0008270717,0.0014214417,0.00091057643,0.0003905227,0.00083648437,0.0015846948,0.0008931243,0.004343545],"category_scores_gemma":[0.0026138283,0.0003849521,0.001167212,0.0010962209,0.0004418403,0.002009465,0.001598783,0.001592261,0.0026908095],"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":[0.00027053469,0.00017026746,0.0012868536,0.000178992,0.00014099617,0.0001289391,0.00010633991,0.16612722,0.017751392,0.016102372,0.0112125985,0.7865236],"study_design_scores_gemma":[0.000012844773,0.0000394119,0.000184502,0.000005604117,0.000010611294,0.000085746186,0.0000129736945,0.99149686,0.0029346275,0.0027748481,0.0024293468,0.000012623631],"about_ca_topic_score_codex":0.0071788295,"about_ca_topic_score_gemma":0.0055213417,"teacher_disagreement_score":0.0071788295,"about_ca_system_score_codex":0.00050436886,"about_ca_system_score_gemma":0.0008836767,"threshold_uncertainty_score":0.014530599},"labels":[],"label_agreement":null},{"id":"W3094327244","doi":"","title":"Mutual information deep regularization for semi-supervised segmentation","year":2020,"lang":"en","type":"article","venue":"Espace ÉTS (ETS)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Artificial intelligence; Segmentation; Regularization (linguistics); Computer science; Mutual information; Pattern recognition (psychology); Mathematics","score_opus":0.014744169508351166,"score_gpt":0.23501544270991592,"score_spread":0.22027127320156475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094327244","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066229603,0.000305975,0.9892574,0.00022381703,0.00003861225,0.000044736178,0.0002214518,0.002055129,0.001229896],"genre_scores_gemma":[0.32632414,0.0006332816,0.6447244,0.0007329928,0.00025150174,0.00048343072,0.004224715,0.0017028896,0.020922743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874204,0.0003987446,0.00006359703,0.0003741043,0.00026086494,0.0001607225],"domain_scores_gemma":[0.9977597,0.0011623242,0.00018012742,0.0004015311,0.0003843891,0.00011189465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023910895,0.0017863486,0.002246342,0.0012908014,0.00076692685,0.0017102129,0.0033398024,0.0036567473,0.0044143633],"category_scores_gemma":[0.004423709,0.0015054919,0.0019622908,0.0012903609,0.0013792247,0.0019248851,0.0028314942,0.0035124077,0.002507936],"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":[0.00048143783,0.00024851263,0.0006308352,0.0002299356,0.00021895145,0.00010764595,0.0001526573,0.5056913,0.016681883,0.021030808,0.017152606,0.43737343],"study_design_scores_gemma":[0.000005108576,0.000015393174,0.000080641934,0.000008266999,0.000006252053,0.000013301676,0.0000043976734,0.99329597,0.0014298619,0.004611371,0.0005237382,0.0000057446196],"about_ca_topic_score_codex":0.009377637,"about_ca_topic_score_gemma":0.015576525,"teacher_disagreement_score":0.009377637,"about_ca_system_score_codex":0.0015777338,"about_ca_system_score_gemma":0.0023761617,"threshold_uncertainty_score":0.018646061},"labels":[],"label_agreement":null},{"id":"W3094612621","doi":"10.3390/math8101846","title":"Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM","year":2020,"lang":"en","type":"article","venue":"Mathematics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Shanghai University of Finance and Economics","keywords":"Support vector machine; Pattern recognition (psychology); Computer science; Artificial intelligence; Feature selection; Kernel (algebra); Penalty method; Binary classification; Mathematics; Mathematical optimization","score_opus":0.09249794952969265,"score_gpt":0.29931925164717943,"score_spread":0.20682130211748678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094612621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007480379,0.00015586583,0.9917041,0.000078174126,0.000023475173,0.000027519458,0.000018636214,0.00026783868,0.000243909],"genre_scores_gemma":[0.4974241,0.00031375716,0.4977779,0.00020368466,0.0001535479,0.00038239965,0.00042971576,0.00021272867,0.0031022425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99794394,0.0006198593,0.00013778154,0.00044090717,0.00067432516,0.00018326116],"domain_scores_gemma":[0.9973943,0.0012696515,0.0002747168,0.00029536878,0.00067024137,0.00009567115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025830285,0.001119886,0.0015967564,0.0010745274,0.0004766712,0.0010593932,0.0013661725,0.0012326203,0.0013008367],"category_scores_gemma":[0.0060673803,0.00056852686,0.0011227438,0.001370621,0.0007168507,0.0013301623,0.0015771699,0.0017263958,0.0005910308],"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":[0.00044110982,0.00020944703,0.0035158198,0.00022633497,0.00019961395,0.00021999225,0.00017044788,0.4226026,0.020184852,0.01402047,0.0043523484,0.53385705],"study_design_scores_gemma":[0.000005428711,0.000024587374,0.00023769107,0.0000036906306,0.000005921185,0.000023633973,0.000005471591,0.99667823,0.001025918,0.0015955443,0.00038845564,0.0000054160982],"about_ca_topic_score_codex":0.00190885,"about_ca_topic_score_gemma":0.0016642498,"teacher_disagreement_score":0.0025830285,"about_ca_system_score_codex":0.0005520673,"about_ca_system_score_gemma":0.00090805005,"threshold_uncertainty_score":0.0136604905},"labels":[],"label_agreement":null},{"id":"W3094820928","doi":"10.18280/ts.370411","title":"A Facial Expression Recognition Model Based on Texture and Shape Features","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 intelligence; Pattern recognition (psychology); Computer science; Support vector machine; Feature extraction; Classifier (UML); Computer vision; Facial expression; Facial recognition system; Three-dimensional face recognition; Convolutional neural network; Face detection","score_opus":0.030246144244297665,"score_gpt":0.23202053798382113,"score_spread":0.20177439373952347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094820928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037474483,0.00043959866,0.9548287,0.0002467892,0.00013603606,0.00007897144,0.00015849264,0.0007767792,0.005860329],"genre_scores_gemma":[0.84322566,0.0011473319,0.13878879,0.0001608938,0.000098972756,0.00022280047,0.00040625528,0.00010680269,0.015842425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988604,0.000012329145,0.0000052094224,0.00004013614,0.000042396456,0.000013864886],"domain_scores_gemma":[0.9999547,0.000007831099,0.00000557398,0.000004880391,0.00002288649,0.0000041547755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019644451,0.00044725678,0.00036339552,0.00033521268,0.00017098345,0.00038192418,0.0005714774,0.0002976768,0.0014202908],"category_scores_gemma":[0.0003322417,0.00018955595,0.00065953104,0.00034219932,0.00022377112,0.00059736613,0.00022926765,0.00048365194,0.00066596695],"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":[0.00037358358,0.00016827432,0.0038395731,0.00019287647,0.00014619228,0.00043138242,0.00023630277,0.28792012,0.181484,0.015510357,0.0060520344,0.50364536],"study_design_scores_gemma":[0.000005451045,0.00004444953,0.000987967,0.000004773923,0.000025573412,0.00012676642,0.00001049799,0.99224466,0.0043194876,0.0009548143,0.0012649874,0.000010500454],"about_ca_topic_score_codex":0.0044200756,"about_ca_topic_score_gemma":0.0030687877,"teacher_disagreement_score":0.0044200756,"about_ca_system_score_codex":0.00032827552,"about_ca_system_score_gemma":0.0003103104,"threshold_uncertainty_score":0.008788705},"labels":[],"label_agreement":null},{"id":"W3095658164","doi":"10.3390/e22111257","title":"Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions","year":2020,"lang":"en","type":"article","venue":"Entropy","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Feature selection; Penalty method; Consistency (knowledge bases); Lasso (programming language); Uniqueness; Classifier (UML); Pattern recognition (psychology); Mathematical optimization; Operator (biology); Algorithm; Artificial intelligence; Computer science","score_opus":0.022393146004731176,"score_gpt":0.24150804849279606,"score_spread":0.21911490248806487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3095658164","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035511766,0.00015640614,0.9631036,0.00013519517,0.00003255874,0.000021679778,0.000047909438,0.00015800912,0.0008328851],"genre_scores_gemma":[0.6834053,0.000188418,0.3127962,0.00023102746,0.00007150947,0.00010898538,0.00034860757,0.00008769515,0.0027622783],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991848,0.00029037375,0.00003653379,0.00016109429,0.0002528558,0.00007433996],"domain_scores_gemma":[0.99840814,0.0007692507,0.00020202815,0.00028584836,0.00024924646,0.000085539534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015834407,0.00042733294,0.0008333959,0.0005699315,0.00042564818,0.00087851984,0.0011139537,0.0007116524,0.001147237],"category_scores_gemma":[0.0034685046,0.00021334416,0.00045915946,0.00071318843,0.0009727585,0.001099633,0.0015048913,0.0012341759,0.00027788812],"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":[0.00032841935,0.00019627374,0.008520476,0.00034525458,0.00016801365,0.00035809766,0.00030981548,0.3882197,0.038645405,0.10528719,0.006710549,0.45091078],"study_design_scores_gemma":[0.000008054462,0.00005979985,0.0008319774,0.000007507957,0.0000057923953,0.000091278904,0.00003347275,0.9702701,0.0030591767,0.024360891,0.0012553753,0.000016604585],"about_ca_topic_score_codex":0.0008096463,"about_ca_topic_score_gemma":0.0010830233,"teacher_disagreement_score":0.0015834407,"about_ca_system_score_codex":0.00035798945,"about_ca_system_score_gemma":0.00050263054,"threshold_uncertainty_score":0.008374155},"labels":[],"label_agreement":null},{"id":"W3097254004","doi":"10.1167/jov.20.11.1384","title":"Spatial frequencies for detection of pain facial expressions revealed by reverse correlation","year":2020,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Face and Expression Recognition","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 du Québec en Outaouais; Université du Québec à Montréal","funders":"","keywords":"Happiness; Facial expression; Stimulus (psychology); Correlation; Psychology; Expression (computer science); Audiology; Cognitive psychology; Computer science; Communication; Social psychology; Mathematics; Medicine","score_opus":0.018737871111875797,"score_gpt":0.25947215996836653,"score_spread":0.24073428885649073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097254004","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90039736,0.00089190603,0.08799312,0.00017178999,0.00008751818,0.000124888,0.0006466053,0.0004119177,0.009274837],"genre_scores_gemma":[0.96945804,0.0003203673,0.02877462,0.00008790823,0.000028421662,0.00009088594,0.0003688003,0.00008735841,0.000783584],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99948394,0.00011052425,0.000027988888,0.0001303605,0.00017346663,0.00007362846],"domain_scores_gemma":[0.9986607,0.0006599944,0.00019275476,0.00011310156,0.00033274497,0.000040731473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060422544,0.0003685083,0.0002401448,0.00093695876,0.00017861433,0.00046090744,0.0002005149,0.0003268914,0.0029949339],"category_scores_gemma":[0.0052357637,0.00022819142,0.0003037647,0.00059272326,0.00037863123,0.0004914159,0.00046000016,0.0005440782,0.00089222105],"study_design_candidate":"observational","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":[0.0014004334,0.00011555012,0.0196612,0.00040001722,0.000056226683,0.00021112844,0.0005458766,0.0021135034,0.85580665,0.001780362,0.0014636851,0.116445385],"study_design_scores_gemma":[0.00010973036,0.0008133743,0.4938183,0.0001497831,0.0003014883,0.002808528,0.0006332246,0.09240379,0.3961968,0.0059635937,0.006625943,0.00017549317],"about_ca_topic_score_codex":0.00082941446,"about_ca_topic_score_gemma":0.0008512877,"teacher_disagreement_score":0.0029949339,"about_ca_system_score_codex":0.00018371484,"about_ca_system_score_gemma":0.0002352235,"threshold_uncertainty_score":0.010019004},"labels":[],"label_agreement":null},{"id":"W3097505059","doi":"10.1007/978-3-030-60799-9_20","title":"Noise Robust Illumination Invariant Face Recognition via Contourlet Transform in Logarithm Domain","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Contourlet; Invariant (physics); Artificial intelligence; Logarithm; Computer science; Facial recognition system; Pattern recognition (psychology); Face (sociological concept); Computer vision; Classifier (UML); Mathematics; Wavelet transform; Wavelet; Mathematical analysis","score_opus":0.021977339834546177,"score_gpt":0.22294847466531717,"score_spread":0.200971134830771,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097505059","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02537648,0.00044986224,0.9687487,0.00008833258,0.00009028919,0.000025103554,0.00008222367,0.0009922397,0.004146731],"genre_scores_gemma":[0.4059362,0.0011875044,0.5687663,0.00022145697,0.00014320902,0.00007924706,0.00077955826,0.00034734706,0.022539182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997956,0.00002238577,0.000007347964,0.000041281935,0.000115693285,0.00001760088],"domain_scores_gemma":[0.99985313,0.000038458864,0.000014473565,0.000043617278,0.000041971853,0.000008345272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019699222,0.00031565863,0.00044396354,0.00032564532,0.00014033954,0.00055510574,0.0005639577,0.0004096155,0.0026359402],"category_scores_gemma":[0.0005421403,0.0001730428,0.0003930662,0.00044007695,0.00024439028,0.0006679876,0.00049474044,0.0005804064,0.002070021],"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":[0.00020882826,0.00010557256,0.0004699348,0.00007399457,0.000028703247,0.00014294896,0.000033412103,0.0158577,0.33959123,0.0060636066,0.0033203666,0.63410366],"study_design_scores_gemma":[0.000013680648,0.0001652391,0.0023649845,0.00001927653,0.000039617276,0.0008183366,0.000029335562,0.7261621,0.2579065,0.005061121,0.007390591,0.000029253848],"about_ca_topic_score_codex":0.0003552609,"about_ca_topic_score_gemma":0.0004351344,"teacher_disagreement_score":0.0026359402,"about_ca_system_score_codex":0.00017719872,"about_ca_system_score_gemma":0.00018403286,"threshold_uncertainty_score":0.00881803},"labels":[],"label_agreement":null},{"id":"W3099113948","doi":"10.1109/wacv48630.2021.00307","title":"Temporal Stochastic Softmax for 3D CNNs: An Application in Facial Expression Recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Compute Canada","keywords":"Softmax function; Computer science; Artificial intelligence; Convolutional neural network; Pooling; Inference; Weighting; Pattern recognition (psychology); Sampling (signal processing); Speech recognition; Deep learning; Machine learning; Computer vision","score_opus":0.04416350885950689,"score_gpt":0.2943202847817362,"score_spread":0.25015677592222935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099113948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024529954,0.0004691845,0.9711274,0.0002917373,0.00007928619,0.00004910041,0.0003090601,0.0017767167,0.0013675297],"genre_scores_gemma":[0.57913804,0.00093818555,0.4117593,0.00033582738,0.00009467597,0.00019297308,0.0010339734,0.0002980028,0.0062089902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997044,0.00007343648,0.000015612268,0.000084622705,0.000080234015,0.0000416324],"domain_scores_gemma":[0.99969304,0.0001464544,0.00003425983,0.000042690797,0.00005945899,0.000024231105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007705358,0.0009933264,0.0005949138,0.0004502808,0.000239945,0.00062106986,0.00092462427,0.00074653426,0.0025131674],"category_scores_gemma":[0.0017295346,0.00045903694,0.00084095023,0.00070424454,0.00040624998,0.0006579298,0.0009324485,0.0010660803,0.0007381647],"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":[0.00035459912,0.0001466397,0.0015574311,0.00015943735,0.00015387345,0.00019431218,0.000076399214,0.4792331,0.044489093,0.0067625046,0.0060551763,0.46081746],"study_design_scores_gemma":[0.0000025743648,0.000011523272,0.0002166045,0.0000039570396,0.0000058222495,0.00001794818,0.0000044312474,0.9942238,0.003847973,0.0012784768,0.00038223123,0.0000046563246],"about_ca_topic_score_codex":0.0075923917,"about_ca_topic_score_gemma":0.008918213,"teacher_disagreement_score":0.0075923917,"about_ca_system_score_codex":0.0008647804,"about_ca_system_score_gemma":0.00072623213,"threshold_uncertainty_score":0.015096366},"labels":[],"label_agreement":null},{"id":"W3106974175","doi":"10.18280/ria.340501","title":"Novel Descriptors for Effective Recognition of Face and Facial Expressions","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Facial recognition system; Support vector machine; Histogram; Histogram of oriented gradients; Facial expression; Classifier (UML); Three-dimensional face recognition; Face (sociological concept); Feature (linguistics); Computer vision; Face detection; Image (mathematics)","score_opus":0.07607479668900234,"score_gpt":0.27828670296553826,"score_spread":0.2022119062765359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106974175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024549607,0.0038792738,0.9643168,0.0002076072,0.00053231185,0.00021444385,0.0010072141,0.0014986235,0.0037940822],"genre_scores_gemma":[0.31120065,0.006233124,0.6630522,0.00042697467,0.0006436457,0.00063806947,0.006924468,0.00022184495,0.010659053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992834,0.00007105115,0.000056612243,0.00013581816,0.00039532557,0.000057873578],"domain_scores_gemma":[0.9995908,0.00007683168,0.000052820105,0.00008205183,0.00017538226,0.000022071801],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000615647,0.000535377,0.0008278716,0.0014937859,0.00020908394,0.00071015046,0.0008889079,0.0004684446,0.0020792414],"category_scores_gemma":[0.0012013464,0.00017409555,0.00045539532,0.0017944665,0.0003875005,0.0015244307,0.0007926558,0.00070659345,0.0014952337],"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":[0.00014556259,0.00017619407,0.001764644,0.000529161,0.00005936818,0.00014252325,0.00007015116,0.004925676,0.107039474,0.015138121,0.01483152,0.8551776],"study_design_scores_gemma":[0.00017605905,0.0011927189,0.024327416,0.00030477482,0.0003137443,0.0047656484,0.0006312775,0.49205437,0.250448,0.036515918,0.18898083,0.00028916355],"about_ca_topic_score_codex":0.0010225654,"about_ca_topic_score_gemma":0.0011951585,"teacher_disagreement_score":0.0020792414,"about_ca_system_score_codex":0.00036139795,"about_ca_system_score_gemma":0.00047667738,"threshold_uncertainty_score":0.0069558024},"labels":[],"label_agreement":null},{"id":"W3109649745","doi":"10.48550/arxiv.2011.10925","title":"Locally Linear Embedding and its Variants: Tutorial and Survey","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":31,"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":"Isomap; Mathematics; Nonlinear dimensionality reduction; Pattern recognition (psychology); Artificial intelligence; Embedding; Kernel principal component analysis; Dimensionality reduction; Kernel method; Computer science; Support vector machine","score_opus":0.10297623285568448,"score_gpt":0.21201998627460694,"score_spread":0.10904375341892246,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109649745","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002200437,0.38701484,0.5833777,0.0016070248,0.0018072781,0.00006324513,0.0004042613,0.0010853048,0.0224399],"genre_scores_gemma":[0.053578023,0.5189618,0.38807568,0.0017856756,0.007331743,0.00037100032,0.0019386043,0.00126374,0.026693791],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999193,0.00018203605,0.00008001009,0.00023463316,0.00026653658,0.00004375194],"domain_scores_gemma":[0.9988024,0.0006868305,0.000067859786,0.00018505688,0.00021469718,0.000043156688],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012734388,0.0017477184,0.0013970834,0.0024555274,0.0003849396,0.0020196193,0.0012355562,0.0015291228,0.007852188],"category_scores_gemma":[0.0028772398,0.00088553736,0.0012311806,0.0052436,0.0012461737,0.0045549916,0.0016037448,0.0029801612,0.006265014],"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":[0.00005630347,0.00010396076,0.00059283746,0.0029560826,0.00012260197,0.0001846081,0.00025248752,0.014377378,0.0030854745,0.12071059,0.055171132,0.8023866],"study_design_scores_gemma":[0.000014781262,0.00019383711,0.0015747045,0.00086559507,0.000090518915,0.001935341,0.00019258966,0.082866386,0.0036331383,0.15416417,0.75430006,0.00016892592],"about_ca_topic_score_codex":0.0015266191,"about_ca_topic_score_gemma":0.0010885203,"teacher_disagreement_score":0.007852188,"about_ca_system_score_codex":0.0007508977,"about_ca_system_score_gemma":0.00072705373,"threshold_uncertainty_score":0.026268244},"labels":[],"label_agreement":null},{"id":"W3111876614","doi":"10.1016/j.neucom.2020.12.014","title":"Learning performance of LapSVM based on Markov subsampling","year":2020,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Machine learning; Classifier (UML); Artificial intelligence; Probabilistic logic; Markov chain; Labeled data; Support vector machine; Data mining; Supervised learning; Pattern recognition (psychology); Artificial neural network","score_opus":0.020901691009508665,"score_gpt":0.22185480644606187,"score_spread":0.2009531154365532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111876614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4166795,0.0045458013,0.56660753,0.00082511845,0.0004911186,0.00010972497,0.00041657884,0.005858747,0.0044659856],"genre_scores_gemma":[0.89386755,0.0005646609,0.100188464,0.00029935024,0.00013045195,0.0000767382,0.0014689722,0.00014826658,0.003255668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994242,0.00014644323,0.000038488477,0.00016415055,0.00012473574,0.000101989426],"domain_scores_gemma":[0.9990435,0.00041504976,0.00004716204,0.000104548475,0.00033266848,0.0000570873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001398974,0.0007096021,0.0011697069,0.00053969567,0.00039193078,0.00080644357,0.0009161531,0.00080931577,0.00243861],"category_scores_gemma":[0.002994084,0.00023018035,0.000525761,0.00045244826,0.00025356413,0.00095886533,0.000634172,0.000889871,0.0007338955],"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":[0.0010042154,0.0003114601,0.0064705177,0.00015448345,0.00021477624,0.00007680566,0.000049360908,0.13045534,0.014046423,0.0017194338,0.0064991326,0.83899796],"study_design_scores_gemma":[0.000010206907,0.00007812176,0.00097571214,0.000005340308,0.000018241406,0.000032477972,0.000009817908,0.99555546,0.0027257046,0.00033139382,0.000251594,0.0000059290683],"about_ca_topic_score_codex":0.008271774,"about_ca_topic_score_gemma":0.006396886,"teacher_disagreement_score":0.008271774,"about_ca_system_score_codex":0.00047888004,"about_ca_system_score_gemma":0.0014477873,"threshold_uncertainty_score":0.016447246},"labels":[],"label_agreement":null},{"id":"W3114039679","doi":"10.1109/isncc49221.2020.9297189","title":"Probabilistic Features on Simplex Manifold in Predictive Data Modelling","year":2020,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"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":"Probabilistic logic; Discriminative model; Pattern recognition (psychology); Computer science; Artificial intelligence; Dirichlet distribution; Support vector machine; Feature (linguistics); Latent Dirichlet allocation; Simplex; Feature vector; Statistical model; Hierarchical Dirichlet process; Data modeling; Data mining; Topic model; Mathematics","score_opus":0.113077489354312,"score_gpt":0.2771842105952082,"score_spread":0.1641067212408962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3114039679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004583699,0.00021862195,0.9942866,0.0001861448,0.000020749472,0.000019078976,0.00008663855,0.00017309055,0.00042536418],"genre_scores_gemma":[0.6335729,0.0010092743,0.35952964,0.00018675982,0.00022312014,0.00040256034,0.00089635485,0.0002744087,0.0039050246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99786645,0.0009609579,0.0001187183,0.0004415588,0.0004965379,0.00011581347],"domain_scores_gemma":[0.9937801,0.0040859235,0.00049879146,0.0008263334,0.0006623636,0.00014659687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029897941,0.0008476481,0.0015726943,0.0015438065,0.00066560844,0.0019074696,0.002134626,0.0013176397,0.0022183468],"category_scores_gemma":[0.017113231,0.0007075516,0.0013449346,0.0022613464,0.002003914,0.004307381,0.0022920803,0.0027390355,0.0006022649],"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":[0.000059264756,0.000037012876,0.0011420884,0.00008329994,0.000061173894,0.00009832821,0.00015178275,0.7936858,0.0004760075,0.14928912,0.0013011304,0.053614996],"study_design_scores_gemma":[0.0000016837887,0.000006774263,0.00006336776,0.0000045413763,0.0000018855488,0.0000099231,0.0000060721204,0.952067,0.00008605219,0.047383945,0.00036316377,0.0000057173875],"about_ca_topic_score_codex":0.006132454,"about_ca_topic_score_gemma":0.0038806165,"teacher_disagreement_score":0.006132454,"about_ca_system_score_codex":0.0014127203,"about_ca_system_score_gemma":0.0010011056,"threshold_uncertainty_score":0.015811741},"labels":[],"label_agreement":null},{"id":"W3118254750","doi":"","title":"Improving Object Detection with MatrixNets","year":2020,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Waterloo","keywords":"Computer science; Artificial intelligence; Information retrieval","score_opus":0.006729335994791593,"score_gpt":0.1783486197140839,"score_spread":0.1716192837192923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3118254750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034955394,0.0024835265,0.9244307,0.0007809809,0.00065727124,0.00018631281,0.00093728874,0.025389867,0.010178706],"genre_scores_gemma":[0.34531343,0.0014051377,0.62067634,0.0016619712,0.0004450279,0.00022187644,0.0055799503,0.0018364748,0.022859847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901223,0.0001137826,0.000045551446,0.00035575771,0.00034392448,0.00012881827],"domain_scores_gemma":[0.9989176,0.0003162635,0.00009040419,0.00030857418,0.00029803257,0.000069104084],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00114763,0.002836968,0.0019390318,0.0019214053,0.0007100195,0.002008638,0.0030618445,0.0017192871,0.011824275],"category_scores_gemma":[0.00398773,0.0010680112,0.0018245121,0.0013016578,0.0007052258,0.0047556288,0.0026984776,0.0019326583,0.0071358667],"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":[0.00055298186,0.00033232794,0.0028716666,0.0004060688,0.0002824161,0.00024792322,0.00010947219,0.11264343,0.030139526,0.022555087,0.03687032,0.7929887],"study_design_scores_gemma":[0.00003476833,0.000104760235,0.00048546316,0.000021116999,0.00005219783,0.0001427764,0.00003050247,0.9676357,0.008670401,0.014992493,0.0078123556,0.000017467883],"about_ca_topic_score_codex":0.0070100157,"about_ca_topic_score_gemma":0.011740544,"teacher_disagreement_score":0.011824275,"about_ca_system_score_codex":0.0012760594,"about_ca_system_score_gemma":0.0011225386,"threshold_uncertainty_score":0.039556146},"labels":[],"label_agreement":null},{"id":"W3119526997","doi":"10.1145/3428077","title":"Context-Based Evaluation of Dimensionality Reduction Algorithms—Experiments and Statistical Significance Analysis","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Face and Expression Recognition","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":"IBM (Canada); University of Alberta","funders":"","keywords":"Dimensionality reduction; Computer science; Data mining; Curse of dimensionality; Algorithm; Redundancy (engineering); Context (archaeology); Generalizability theory; Parametric statistics; Machine learning; Reduction (mathematics); Artificial intelligence; Mathematics; Statistics","score_opus":0.10026200597180086,"score_gpt":0.3783655596835932,"score_spread":0.27810355371179235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119526997","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48105308,0.010566731,0.49179637,0.0010280301,0.00092398736,0.0031424754,0.0032426026,0.0022629434,0.0059837564],"genre_scores_gemma":[0.62534034,0.0014857595,0.36517912,0.00039612432,0.00029481048,0.0030957975,0.0033506497,0.00028408066,0.0005732671],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9557993,0.028911203,0.004028373,0.0039207404,0.006690117,0.00065024156],"domain_scores_gemma":[0.880228,0.08487849,0.0063193473,0.015542696,0.012070875,0.0009605265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.029753583,0.0019666755,0.0012087304,0.0034269502,0.00120524,0.0018859548,0.0015803485,0.001726404,0.0011354246],"category_scores_gemma":[0.12699865,0.00037139925,0.0017848383,0.003211533,0.002067736,0.0027539977,0.0023221823,0.0017453001,0.0004276221],"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":[0.0054697706,0.0051478054,0.06422506,0.0073135,0.004243555,0.0005568605,0.0021038267,0.27871054,0.035479415,0.03219227,0.016555851,0.5480015],"study_design_scores_gemma":[0.0011130575,0.011800224,0.046745036,0.0011502225,0.0015710675,0.0011375225,0.0018019337,0.7949512,0.0742805,0.043782156,0.02111079,0.00055621256],"about_ca_topic_score_codex":0.0012637608,"about_ca_topic_score_gemma":0.0012244647,"teacher_disagreement_score":0.029753583,"about_ca_system_score_codex":0.001007082,"about_ca_system_score_gemma":0.0014962109,"threshold_uncertainty_score":0.15735388},"labels":[],"label_agreement":null},{"id":"W3121483733","doi":"10.15353/jcvis.v6i1.3534","title":"Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"Metric (unit); Margin (machine learning); Large margin nearest neighbor; Subspace topology; k-nearest neighbors algorithm; Computer science; Artificial intelligence; Nearest neighbor search; Acceleration; Pattern recognition (psychology); Sampling (signal processing); Machine learning; Projection (relational algebra); Scalability; Nearest neighbor graph; Nonlinear dimensionality reduction; Data mining; Algorithm; Physics; Dimensionality reduction; Filter (signal processing); Computer vision; Engineering","score_opus":0.19319849326204325,"score_gpt":0.24934151068679034,"score_spread":0.05614301742474709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121483733","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023482224,0.00043645035,0.9684614,0.0002467947,0.00018010414,0.00016108889,0.00025169467,0.004864941,0.0019152414],"genre_scores_gemma":[0.33608973,0.00023220673,0.65415496,0.00036482417,0.00019997006,0.00039516762,0.0034358685,0.00051258213,0.0046146805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757177,0.0007734697,0.00016496956,0.00042950604,0.0008403364,0.00021999098],"domain_scores_gemma":[0.9951475,0.0018593529,0.00024079569,0.0011339065,0.0013501523,0.000268209],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025849985,0.0011982082,0.0027318283,0.001347626,0.0009085784,0.0014916976,0.0033142825,0.001398567,0.009508769],"category_scores_gemma":[0.012825336,0.0006024715,0.0013345166,0.0018376863,0.00059255917,0.0026013167,0.0032415988,0.0021668246,0.006293351],"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":[0.0007403897,0.00057101576,0.0032453595,0.00017831713,0.00017454344,0.00013785208,0.00018996555,0.11766509,0.006861638,0.0108693745,0.01716585,0.84220064],"study_design_scores_gemma":[0.000032193682,0.000092587696,0.00030366733,0.000007718766,0.00001065659,0.000046828005,0.00003338435,0.9904626,0.0010076135,0.0069998363,0.0009946795,0.000008267769],"about_ca_topic_score_codex":0.009178911,"about_ca_topic_score_gemma":0.012739297,"teacher_disagreement_score":0.009508769,"about_ca_system_score_codex":0.0010104828,"about_ca_system_score_gemma":0.002342108,"threshold_uncertainty_score":0.031809986},"labels":[],"label_agreement":null},{"id":"W3124122716","doi":"10.1049/ell2.12083","title":"Stable ant‐antlion optimiser for feature selection on high‐dimensional data","year":2021,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Face and Expression Recognition","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Feature (linguistics); ANT; Ecology; Artificial intelligence; Biology; Computer network","score_opus":0.01937424667944825,"score_gpt":0.2503677449615346,"score_spread":0.23099349828208632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124122716","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044281602,0.00031483985,0.95340776,0.00012479376,0.00003865739,0.00006115172,0.000026504553,0.0003475295,0.0013971267],"genre_scores_gemma":[0.65285367,0.000235111,0.34304518,0.0001504683,0.000045386325,0.00027476763,0.0001499679,0.0001177399,0.0031277079],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994066,0.00018519584,0.00003527577,0.00011456109,0.00019966188,0.000058686437],"domain_scores_gemma":[0.9992724,0.00038599054,0.00008421267,0.000041727348,0.00018729226,0.000028351456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013341189,0.000942493,0.0010554706,0.0009977549,0.00049343775,0.0008709191,0.00097323774,0.00082612975,0.0010636891],"category_scores_gemma":[0.0026321458,0.0004158732,0.00079130265,0.0009696554,0.0006324976,0.0006808816,0.0006033795,0.0007327589,0.0002803347],"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":[0.00018690515,0.00007585249,0.0014937924,0.00010040863,0.000117471325,0.00009258743,0.00010885313,0.8287795,0.008942178,0.0035906637,0.0014020014,0.15510984],"study_design_scores_gemma":[0.000008449218,0.00002744937,0.00013194018,0.0000024014814,0.000005329846,0.000009682919,0.000006442923,0.998599,0.0005269869,0.0005341521,0.00014480198,0.0000034887407],"about_ca_topic_score_codex":0.0032039708,"about_ca_topic_score_gemma":0.002416913,"teacher_disagreement_score":0.0032039708,"about_ca_system_score_codex":0.0004914023,"about_ca_system_score_gemma":0.0008065362,"threshold_uncertainty_score":0.0070555806},"labels":[],"label_agreement":null},{"id":"W3124489589","doi":"","title":"Empirical Bayes Regression Analysis with Many Regressors but Fewer Observations","year":2004,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Face and Expression Recognition","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","funders":"","keywords":"Mathematics; Statistics; Estimator; Linear regression; Covariance matrix; Regression analysis; Bayesian multivariate linear regression; Generalized least squares; Least-squares function approximation; Bayes' theorem; Econometrics; Bayesian probability","score_opus":0.07138516360403893,"score_gpt":0.3436721959299099,"score_spread":0.2722870323258709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124489589","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009725338,0.0013454645,0.9868261,0.0006531824,0.000083245344,0.000032139942,0.000054179418,0.0001787342,0.0011016116],"genre_scores_gemma":[0.35449708,0.0030215434,0.6309876,0.0007964917,0.0009811469,0.0003446905,0.0003966819,0.0001789332,0.008795817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991093,0.0054414305,0.00027645516,0.0012613189,0.0017032761,0.00022449637],"domain_scores_gemma":[0.97161037,0.023555046,0.0015073356,0.001624546,0.0015336636,0.00016907619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0140054235,0.0013737908,0.002866026,0.001397122,0.0008231927,0.0022908312,0.0022044163,0.0020780119,0.0026970778],"category_scores_gemma":[0.04740593,0.0009720641,0.0009899018,0.0015753717,0.0016266311,0.003277863,0.001350785,0.002342818,0.00096938934],"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":[0.00025544994,0.00019144162,0.010219177,0.00050137023,0.00058678474,0.00046863745,0.0002587541,0.5432997,0.0016752792,0.20753329,0.004106353,0.23090376],"study_design_scores_gemma":[0.000032452626,0.0000398342,0.0007141055,0.00005389886,0.00005488838,0.00008380884,0.000023292501,0.92148256,0.0005132964,0.07495828,0.0020230578,0.000020629403],"about_ca_topic_score_codex":0.004900716,"about_ca_topic_score_gemma":0.003133498,"teacher_disagreement_score":0.0140054235,"about_ca_system_score_codex":0.0010203078,"about_ca_system_score_gemma":0.0016882208,"threshold_uncertainty_score":0.074068666},"labels":[],"label_agreement":null},{"id":"W3128355817","doi":"10.1016/j.ins.2021.01.033","title":"Multi-view subspace clustering via partition fusion","year":2021,"lang":"en","type":"article","venue":"Information Sciences","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Partition (number theory); Robustness (evolution); Graph partition; Subspace topology; Data mining; Artificial intelligence; Machine learning; Graph; Constrained clustering; Correlation clustering; Theoretical computer science; CURE data clustering algorithm; Mathematics","score_opus":0.043537893848246745,"score_gpt":0.29065973307289417,"score_spread":0.24712183922464742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128355817","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006073409,0.00020801356,0.9917412,0.00006569477,0.00003885047,0.00004678045,0.00014265702,0.000930088,0.00075326354],"genre_scores_gemma":[0.201849,0.00044459038,0.7911341,0.00014597923,0.00009237604,0.00021243055,0.0023098295,0.00048457604,0.0033269906],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981225,0.000431268,0.00009010338,0.00048884825,0.0006075474,0.0002596981],"domain_scores_gemma":[0.99869424,0.00025805918,0.00008202601,0.00032144724,0.000563817,0.00008043841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011047749,0.0016022278,0.0026126378,0.0023020534,0.0013032858,0.0019493344,0.0018190866,0.0013111987,0.003077663],"category_scores_gemma":[0.0029326447,0.0006736321,0.0027733424,0.0030633032,0.00087264227,0.0018494715,0.0033421968,0.0015439271,0.0031999757],"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":[0.0005126262,0.00021111296,0.0011902236,0.0002273065,0.00041649796,0.00008449339,0.00035849382,0.09293773,0.032249823,0.008847025,0.01027568,0.85268897],"study_design_scores_gemma":[0.000026405029,0.000114390925,0.0012979365,0.00002229076,0.000104433115,0.0001304202,0.00017287214,0.9716431,0.010009605,0.012779219,0.0036375711,0.00006165533],"about_ca_topic_score_codex":0.007895047,"about_ca_topic_score_gemma":0.008139413,"teacher_disagreement_score":0.007895047,"about_ca_system_score_codex":0.0006824271,"about_ca_system_score_gemma":0.0016267673,"threshold_uncertainty_score":0.015698195},"labels":[],"label_agreement":null},{"id":"W3131860561","doi":"10.1016/j.cosrev.2021.100378","title":"Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE)","year":2021,"lang":"en","type":"article","venue":"Computer Science Review","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":824,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ericsson (Canada); University of Regina","funders":"","keywords":"Isomap; Dimensionality reduction; Computer science; Pattern recognition (psychology); Artificial intelligence; Nonlinear dimensionality reduction; Random projection; Feature extraction; Projection pursuit; Projection (relational algebra); Curse of dimensionality; Principal component analysis; Algorithm; Machine learning","score_opus":0.07235552708127922,"score_gpt":0.35827154172759895,"score_spread":0.2859160146463197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131860561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07354222,0.2593361,0.61009187,0.008611487,0.0013331712,0.00022834136,0.0011300027,0.00070566696,0.045021143],"genre_scores_gemma":[0.44904298,0.15178756,0.38803333,0.0009422177,0.0015062864,0.00030888076,0.0026050943,0.00030294544,0.0054706656],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99546295,0.0014492417,0.00042874465,0.00043102016,0.0021404885,0.00008745631],"domain_scores_gemma":[0.98025686,0.011194509,0.0008518864,0.0011227722,0.0064368825,0.00013713745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008149647,0.0007622071,0.00074901484,0.0035058048,0.00049535936,0.0036634947,0.0011101131,0.00076658453,0.0028646023],"category_scores_gemma":[0.031150391,0.00022089915,0.00067729456,0.0046038367,0.0017453184,0.0034257306,0.00085614575,0.0010250102,0.0006629277],"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":[0.00024647458,0.00014688351,0.007290751,0.0021587275,0.00030994046,0.00005202754,0.00021367143,0.0148535995,0.0019337288,0.09301163,0.010631296,0.86915135],"study_design_scores_gemma":[0.00023078393,0.0022546644,0.09366713,0.0045256414,0.0010347428,0.0033791915,0.0027630785,0.36954656,0.026958773,0.22092386,0.27427012,0.0004455],"about_ca_topic_score_codex":0.0014961752,"about_ca_topic_score_gemma":0.0016060538,"teacher_disagreement_score":0.008149647,"about_ca_system_score_codex":0.0011373981,"about_ca_system_score_gemma":0.0014153564,"threshold_uncertainty_score":0.0431},"labels":[],"label_agreement":null},{"id":"W3132426513","doi":"10.1371/journal.pone.0246159","title":"HDSI: High dimensional selection with interactions algorithm on feature selection and testing","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada","keywords":"Lasso (programming language); Feature selection; Leverage (statistics); Statistical hypothesis testing; Computer science; Selection (genetic algorithm); Statistical model; Feature (linguistics); Artificial intelligence; Statistical inference; Model selection; Machine learning; Algorithm; Pattern recognition (psychology); Data mining; Mathematics; Statistics","score_opus":0.030509158293298467,"score_gpt":0.2177722235576707,"score_spread":0.18726306526437222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3132426513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037293634,0.00019324826,0.9940447,0.00013611697,0.00003739246,0.00015290358,0.0001066448,0.0010194636,0.000580162],"genre_scores_gemma":[0.11469043,0.00028943195,0.8788706,0.00041098538,0.00016799223,0.0014136257,0.0014608877,0.0003240368,0.0023720101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9935236,0.0036145921,0.0003230977,0.00070850604,0.0015826519,0.00024749734],"domain_scores_gemma":[0.99254805,0.005409343,0.00033477604,0.0005664868,0.0009476876,0.00019375258],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007514585,0.0019872077,0.002685463,0.0023643821,0.0011027944,0.0014737794,0.0026525168,0.0015523894,0.004871348],"category_scores_gemma":[0.012512933,0.0007155491,0.002425919,0.0019664716,0.0010994714,0.0013268308,0.002964817,0.0033049833,0.0014253603],"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":[0.0007180394,0.0005144711,0.008338381,0.00040350156,0.0009603326,0.00036549338,0.0003152761,0.19295332,0.0064523513,0.023973312,0.015508105,0.74949735],"study_design_scores_gemma":[0.0001082118,0.00027538542,0.0018828764,0.00003436238,0.00009158452,0.00014586718,0.000051846222,0.97330993,0.0028966714,0.016401421,0.004757128,0.000044648743],"about_ca_topic_score_codex":0.0023443387,"about_ca_topic_score_gemma":0.002293841,"teacher_disagreement_score":0.007514585,"about_ca_system_score_codex":0.00069877785,"about_ca_system_score_gemma":0.0022254877,"threshold_uncertainty_score":0.039741397},"labels":[],"label_agreement":null},{"id":"W3143330991","doi":"10.18280/ria.350111","title":"A Novel Fast Searching Algorithm Based on Least Square Regression","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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":"Binary search algorithm; Computer science; Set (abstract data type); Algorithm; Search algorithm; Interpolation (computer graphics); Linear subspace; Ordinary least squares; Feature (linguistics); Binary number; Data set; Linear search; Simple (philosophy); Data mining; Mathematics; Artificial intelligence; Machine learning","score_opus":0.048700159308747576,"score_gpt":0.2895871373497173,"score_spread":0.24088697804096973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143330991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032741197,0.00026955002,0.9941817,0.00007171393,0.000053223426,0.00002819777,0.000027478347,0.001286407,0.0008075881],"genre_scores_gemma":[0.07848676,0.00041978774,0.914656,0.0001378548,0.0000712249,0.0001720236,0.00032087817,0.0003731132,0.0053623207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895287,0.00011690217,0.000050319577,0.00026063027,0.0005443229,0.0000749087],"domain_scores_gemma":[0.9992429,0.00024333977,0.00007483954,0.00008504923,0.0003204427,0.000033481803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076103024,0.0009658861,0.0015886273,0.0014921487,0.0007435335,0.0010843755,0.0021460636,0.0012639577,0.00489049],"category_scores_gemma":[0.0026200477,0.0005360296,0.0009283357,0.0024206191,0.00055810105,0.002200918,0.0010621424,0.001199822,0.0027920802],"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":[0.0002331965,0.00010745012,0.00096717564,0.00023064767,0.00006762101,0.0001376387,0.00011901462,0.13249487,0.026408609,0.013595495,0.0076579717,0.81798035],"study_design_scores_gemma":[0.000034308036,0.00007922664,0.00024234796,0.000011312282,0.000016943442,0.00022260236,0.000019437648,0.9835302,0.00672316,0.0029435582,0.0061510946,0.000025865],"about_ca_topic_score_codex":0.004344306,"about_ca_topic_score_gemma":0.0022411176,"teacher_disagreement_score":0.00489049,"about_ca_system_score_codex":0.00058774656,"about_ca_system_score_gemma":0.0017582473,"threshold_uncertainty_score":0.016360343},"labels":[],"label_agreement":null},{"id":"W3148199450","doi":"10.1007/978-0-387-73003-5_299","title":"Support Vector Machine","year":2009,"lang":"en","type":"article","venue":"Encyclopedia of Biometrics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Hyperplane; Support vector machine; Structural risk minimization; Margin classifier; Artificial intelligence; Structured support vector machine; Maximization; Classifier (UML); Machine learning; Pattern recognition (psychology); Computer science; Margin (machine learning); Generalization; Minification; Relevance vector machine; Linear classifier; Mathematics; Mathematical optimization; Combinatorics","score_opus":0.01119250399503802,"score_gpt":0.24593907158498451,"score_spread":0.2347465675899465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3148199450","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012965135,0.0046237474,0.94424677,0.0007914634,0.0010712355,0.00028533934,0.002936031,0.011079138,0.022001237],"genre_scores_gemma":[0.34412292,0.0052900715,0.56328315,0.0007531248,0.0009136532,0.000584776,0.01600655,0.0005958298,0.068449974],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991642,0.00012616556,0.00006738102,0.00023112987,0.00034639175,0.00006470676],"domain_scores_gemma":[0.99911696,0.00021188005,0.00006884842,0.0001758847,0.00038818514,0.000038262762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005479094,0.0010788814,0.001129664,0.0010798712,0.00034161992,0.0013001882,0.0010402432,0.0008834153,0.013626897],"category_scores_gemma":[0.0029383723,0.0002679788,0.0005772133,0.001313819,0.00026607144,0.0012872982,0.0007189281,0.0011391981,0.015085424],"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":[0.000095627816,0.00010624042,0.0005609965,0.00015258505,0.000049684164,0.000056029254,0.000012088182,0.017183347,0.004181094,0.0043677054,0.026000151,0.94723445],"study_design_scores_gemma":[0.000042489442,0.00025975346,0.0021868772,0.00011787944,0.000060171566,0.00034357636,0.000052489784,0.86956674,0.01768175,0.022569036,0.087051526,0.00006771642],"about_ca_topic_score_codex":0.00149685,"about_ca_topic_score_gemma":0.0012007304,"teacher_disagreement_score":0.013626897,"about_ca_system_score_codex":0.00023449775,"about_ca_system_score_gemma":0.0007173342,"threshold_uncertainty_score":0.045586526},"labels":[],"label_agreement":null},{"id":"W3150295303","doi":"10.18280/ts.380105","title":"Face Recognition by Using 2D Orthogonal Subspace Projections","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"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":"Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Support vector machine; Subspace topology; Computer science; Projection (relational algebra); Face (sociological concept); Convolutional neural network; Feature (linguistics); Matrix (chemical analysis); Feature vector; Algorithm","score_opus":0.04206772428975267,"score_gpt":0.26084002035983356,"score_spread":0.2187722960700809,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3150295303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024463536,0.00060055015,0.97161657,0.00007871601,0.0000765827,0.000063587155,0.0001817524,0.00082198356,0.00209684],"genre_scores_gemma":[0.24379185,0.0012077917,0.7510978,0.000101076446,0.000090207046,0.00016169954,0.00079317356,0.00007718084,0.002679157],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921083,0.00015380849,0.000028054212,0.00018463586,0.0003663105,0.000056326277],"domain_scores_gemma":[0.99967384,0.00008348906,0.000031524178,0.000078939185,0.00011836994,0.000013876202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059763517,0.0006378201,0.00074381666,0.0013752707,0.0003251459,0.0008518087,0.0004463713,0.00043040627,0.0020425061],"category_scores_gemma":[0.0009949579,0.00026641667,0.00088603655,0.0014138402,0.00035623377,0.0012752285,0.0007768018,0.00048825768,0.0011566536],"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":[0.00012244874,0.00007545178,0.0015611228,0.00012464334,0.00011399519,0.00007562049,0.00009290123,0.044920865,0.0526204,0.006351651,0.0021484713,0.8917924],"study_design_scores_gemma":[0.000010556355,0.00015580011,0.0034546573,0.000019877989,0.000029202149,0.0004763171,0.00007234642,0.9518016,0.03277373,0.0050125998,0.006147651,0.00004569359],"about_ca_topic_score_codex":0.0019237181,"about_ca_topic_score_gemma":0.0021429535,"teacher_disagreement_score":0.0020425061,"about_ca_system_score_codex":0.0002456527,"about_ca_system_score_gemma":0.00047638392,"threshold_uncertainty_score":0.006832838},"labels":[],"label_agreement":null},{"id":"W3152048897","doi":"10.18280/ria.350106","title":"Video Based Sub-Categorized Facial Emotion Detection Using LBP and Edge Computing","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"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":"Sadness; Discriminative model; Happiness; Facial expression; Computer science; Feeling; Artificial intelligence; Face (sociological concept); Isolation (microbiology); Sight; Psychology; Affective computing; Cognitive psychology; Matching (statistics); Computer vision; Social psychology; Mathematics; Anger","score_opus":0.04730915288367296,"score_gpt":0.2710179695726276,"score_spread":0.22370881668895462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152048897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21114898,0.00053604366,0.7780417,0.00021112083,0.00022951067,0.0002563103,0.00091886695,0.003445081,0.0052123303],"genre_scores_gemma":[0.6828229,0.00059456466,0.30761436,0.00017243426,0.00010798896,0.00020206785,0.0014774543,0.00017155916,0.0068367217],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996965,0.000024362918,0.000013174809,0.0000879841,0.00011873985,0.00005930861],"domain_scores_gemma":[0.99977845,0.000023593992,0.000021342245,0.000032416097,0.000121005134,0.00002318816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002467313,0.0005419974,0.00052821776,0.0014035478,0.00020538863,0.00050683686,0.00053968275,0.00042133324,0.0032783677],"category_scores_gemma":[0.0005703679,0.0001447816,0.00041888852,0.0006531516,0.00014413749,0.0006065747,0.00047624815,0.00035685292,0.0014502648],"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":[0.0005588563,0.00019585624,0.002064729,0.00013919381,0.000041553194,0.00012412545,0.0000653847,0.0025178976,0.3121765,0.0005479441,0.0036726024,0.6778954],"study_design_scores_gemma":[0.000063440035,0.0007301301,0.03239698,0.000046278383,0.00013264854,0.0009783218,0.00027642705,0.6874397,0.2679094,0.0018131147,0.008135723,0.000077768695],"about_ca_topic_score_codex":0.0015491843,"about_ca_topic_score_gemma":0.0018235093,"teacher_disagreement_score":0.0032783677,"about_ca_system_score_codex":0.00019073677,"about_ca_system_score_gemma":0.00022120347,"threshold_uncertainty_score":0.010967255},"labels":[],"label_agreement":null},{"id":"W3153657223","doi":"10.1007/978-3-030-73103-8_51","title":"Performance Evaluation of Weighted Entropy Based Fusion Technique for Face Recognition with Different Pre-processing Techniques","year":2021,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"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 Windsor","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Preprocessor; Histogram; Adaptive histogram equalization; Facial recognition system; Entropy (arrow of time); Histogram equalization; Computer vision; Image (mathematics)","score_opus":0.030098567790735067,"score_gpt":0.28497874547546365,"score_spread":0.2548801776847286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153657223","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57888585,0.004787009,0.4076081,0.00019134472,0.00038492313,0.00010932698,0.00030738334,0.0019010018,0.00582502],"genre_scores_gemma":[0.88219446,0.0010130331,0.11185024,0.00006262606,0.00006485425,0.000053578766,0.0006706994,0.000059837246,0.0040305457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937385,0.000120282886,0.000037799415,0.00009034721,0.00031270616,0.00006504561],"domain_scores_gemma":[0.99923897,0.00028313263,0.000036193203,0.000058329344,0.0003591612,0.000024219567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010502287,0.0004930367,0.0006052612,0.0006504293,0.00029914253,0.00044031028,0.00069121836,0.00058549305,0.0021514087],"category_scores_gemma":[0.001370719,0.00014762631,0.00054637075,0.0005399215,0.00017481399,0.0007636029,0.00046853346,0.00033151716,0.00047391505],"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":[0.004599248,0.0005398584,0.004543583,0.00037231017,0.00037229757,0.000207008,0.00015393259,0.050188687,0.23429576,0.001505095,0.0026903525,0.70053196],"study_design_scores_gemma":[0.000035824334,0.001610196,0.009781204,0.000021381613,0.00025195102,0.0004993564,0.000095091076,0.76505405,0.22076112,0.000540435,0.001288115,0.00006137686],"about_ca_topic_score_codex":0.0016928827,"about_ca_topic_score_gemma":0.0013900594,"teacher_disagreement_score":0.0021514087,"about_ca_system_score_codex":0.00026487777,"about_ca_system_score_gemma":0.00029484186,"threshold_uncertainty_score":0.0071971416},"labels":[],"label_agreement":null},{"id":"W3159390462","doi":"10.1109/iccike51210.2021.9410789","title":"Batch Image Processing in Facial Detection Applications","year":2021,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Computer science; Artificial intelligence; Image processing; Computer vision; Batch processing; Face detection; Pattern recognition (psychology); Image (mathematics); Facial recognition system","score_opus":0.011306706059409588,"score_gpt":0.2542571518686583,"score_spread":0.24295044580924874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159390462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2129504,0.0017402475,0.73932993,0.00045979046,0.00043443832,0.0006138441,0.0005777491,0.03410658,0.009787091],"genre_scores_gemma":[0.5323108,0.00057754235,0.45712373,0.00029176997,0.00010734354,0.00042411085,0.0010110084,0.0014455188,0.0067082075],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989052,0.00014620645,0.000069476584,0.00030035633,0.0004223465,0.00015642686],"domain_scores_gemma":[0.9967077,0.001366493,0.00019636004,0.00072257465,0.00085462607,0.00015219164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010439478,0.0011372854,0.00086273515,0.00071517896,0.0006708637,0.0013025072,0.0021819668,0.000872563,0.007121985],"category_scores_gemma":[0.0043061296,0.0006577088,0.0005623274,0.0009899574,0.0005404183,0.0017845426,0.0008865796,0.0011547497,0.0030394068],"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":[0.0038206999,0.0006804424,0.00875968,0.00079888955,0.00020845259,0.00091951276,0.00063662603,0.06562726,0.37328652,0.0054255216,0.03240279,0.50743365],"study_design_scores_gemma":[0.0001852417,0.0011800602,0.009214945,0.00006452349,0.00011108351,0.00092059467,0.00026913336,0.6581902,0.30159864,0.005373367,0.022755431,0.0001367662],"about_ca_topic_score_codex":0.0065067257,"about_ca_topic_score_gemma":0.0053819306,"teacher_disagreement_score":0.007121985,"about_ca_system_score_codex":0.0009357357,"about_ca_system_score_gemma":0.0010760198,"threshold_uncertainty_score":0.023825407},"labels":[],"label_agreement":null},{"id":"W3159542162","doi":"10.1109/icpr48806.2021.9412961","title":"A Novel Random Forest Dissimilarity Measure for Multi-View Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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); École de Technologie Supérieure; Université du Québec à Montréal","funders":"European Regional Development Fund; European Commission","keywords":"Random forest; Margin (machine learning); Computer science; Machine learning; Artificial intelligence; Metric (unit); Large margin nearest neighbor; Dimension (graph theory); Context (archaeology); Measure (data warehouse); Exploit; Task (project management); k-nearest neighbors algorithm; Supervised learning; Data mining; Sample (material); Pattern recognition (psychology); Mathematics; Artificial neural network; Geography","score_opus":0.07591680524562223,"score_gpt":0.29855669766563575,"score_spread":0.22263989242001353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159542162","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02062939,0.0007935098,0.97659636,0.00013357082,0.00008628513,0.00005486699,0.0001865313,0.00019180984,0.0013277604],"genre_scores_gemma":[0.47869995,0.00066117855,0.5165439,0.00020592115,0.00041648187,0.00026838147,0.0013469919,0.00018601243,0.0016710886],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99676114,0.0007164712,0.00021472089,0.00065616856,0.0014833598,0.00016819965],"domain_scores_gemma":[0.9966282,0.0013045324,0.0005253817,0.0005071315,0.00077069283,0.0002640634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002110962,0.0007049059,0.0014509181,0.0029611122,0.0006806675,0.0016518444,0.0018018022,0.0016533541,0.0017465167],"category_scores_gemma":[0.009839068,0.00023515712,0.0011140604,0.0025270274,0.0010171308,0.002967416,0.0018186979,0.0015990471,0.0006384219],"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":[0.0005730048,0.00038039018,0.013723192,0.0005953275,0.00035017062,0.0004371564,0.00030000223,0.15161131,0.037896197,0.13353682,0.008243927,0.65235245],"study_design_scores_gemma":[0.0000315744,0.00043841792,0.006983262,0.00006610762,0.00005746587,0.0012310108,0.00008364217,0.9080728,0.0076387464,0.06522676,0.010082227,0.00008794713],"about_ca_topic_score_codex":0.00097493967,"about_ca_topic_score_gemma":0.0009956326,"teacher_disagreement_score":0.0029611122,"about_ca_system_score_codex":0.0010024938,"about_ca_system_score_gemma":0.0005559575,"threshold_uncertainty_score":0.01116395},"labels":[],"label_agreement":null},{"id":"W3162853854","doi":"10.32604/cmc.2021.014840","title":"A New Hybrid Feature Selection Method Using T-test and Fitness Function","year":2021,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"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":"Feature selection; Selection (genetic algorithm); Test (biology); Fitness function; Function (biology); Computer science; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Machine learning; Biology; Genetic algorithm; Evolutionary biology; Ecology","score_opus":0.013656534972271747,"score_gpt":0.2486228614086436,"score_spread":0.23496632643637183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162853854","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013549153,0.00015663453,0.98437864,0.000060981187,0.000054306212,0.00009641887,0.00007502252,0.0011262933,0.00050256593],"genre_scores_gemma":[0.28253925,0.00016114111,0.7111125,0.00018159203,0.00011393731,0.00072343,0.00080468506,0.00036651842,0.0039969487],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978898,0.00045530326,0.00016392091,0.00048069895,0.00089009834,0.00012023858],"domain_scores_gemma":[0.99777323,0.000929251,0.00018965796,0.00015257695,0.00088843907,0.00006684283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021710682,0.0014601842,0.0019960087,0.0030872447,0.00059933844,0.0010661863,0.0017887905,0.001140351,0.0027437026],"category_scores_gemma":[0.0048764707,0.0003914329,0.0016206577,0.002334023,0.00047866785,0.0012061427,0.00069049536,0.0007590707,0.0008881193],"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":[0.00035066964,0.00023041974,0.0052529993,0.00016593901,0.00040059414,0.00021777643,0.000095568874,0.0874106,0.023878803,0.002228198,0.004147882,0.8756206],"study_design_scores_gemma":[0.000058435577,0.00024567384,0.003076019,0.00001117104,0.000065497865,0.00024239105,0.000025805997,0.98672664,0.006252185,0.0009939531,0.0022621541,0.000040080835],"about_ca_topic_score_codex":0.0039243256,"about_ca_topic_score_gemma":0.002927642,"teacher_disagreement_score":0.0039243256,"about_ca_system_score_codex":0.0006407089,"about_ca_system_score_gemma":0.0009985118,"threshold_uncertainty_score":0.011481822},"labels":[],"label_agreement":null},{"id":"W3165082637","doi":"10.1109/ismvl51352.2021.00031","title":"Hierarchical Subspace Learning for Dimensionality Reduction to Improve Classification Accuracy in Large Data Sets","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Dimensionality reduction; Linear discriminant analysis; Subspace topology; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Nonlinear dimensionality reduction; Projection (relational algebra); Computer science; Random subspace method; Mathematics; Machine learning; Curse of dimensionality; Algorithm","score_opus":0.08935147809219048,"score_gpt":0.3623168508731528,"score_spread":0.27296537278096233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165082637","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029703945,0.00040835972,0.9662936,0.0001740541,0.000055513534,0.00011846457,0.00016869644,0.0020902525,0.00098721],"genre_scores_gemma":[0.28510776,0.00024323764,0.71206254,0.000111450274,0.000056202563,0.0002695843,0.00079450844,0.00018231735,0.0011723808],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699736,0.0010836022,0.00019265903,0.00042546497,0.0010891658,0.00021179907],"domain_scores_gemma":[0.9969836,0.001067521,0.00020489044,0.00084036484,0.00081490586,0.00008865848],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026697908,0.000951313,0.0009949285,0.001980842,0.0008537178,0.000866234,0.0008286724,0.00058626983,0.0020338558],"category_scores_gemma":[0.008173362,0.00023526273,0.0011230743,0.00248076,0.0007182532,0.0013688079,0.0013174496,0.001247135,0.001106087],"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":[0.00014625343,0.00036613626,0.004176334,0.00018366941,0.00019744544,0.00008873714,0.00029687202,0.09547964,0.028799416,0.01066054,0.009246019,0.8503589],"study_design_scores_gemma":[0.000021148524,0.00014967808,0.0026487703,0.000020436853,0.000031801024,0.000080428785,0.00008331332,0.9647352,0.018642876,0.010520428,0.0030323071,0.000033479715],"about_ca_topic_score_codex":0.00375337,"about_ca_topic_score_gemma":0.006552366,"teacher_disagreement_score":0.00375337,"about_ca_system_score_codex":0.00063974026,"about_ca_system_score_gemma":0.0012755971,"threshold_uncertainty_score":0.014119387},"labels":[],"label_agreement":null},{"id":"W3165249856","doi":"10.48550/arxiv.2105.12703","title":"Exploring dual information in distance metric learning for clustering","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"Polytechnique Montréal","funders":"","keywords":"Dual (grammatical number); Cluster analysis; Metric (unit); Computer science; Artificial intelligence; Mathematics; Business; Marketing","score_opus":0.15309940658697152,"score_gpt":0.19761058592968894,"score_spread":0.04451117934271742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165249856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010240838,0.00061132497,0.9872602,0.00039998943,0.000029691273,0.000031300806,0.000078343895,0.00014618682,0.00120207],"genre_scores_gemma":[0.35512432,0.0010958398,0.63955235,0.0004152927,0.00022839347,0.00024296099,0.00088303414,0.00023244359,0.0022254144],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925608,0.0038340592,0.00038002917,0.0012191385,0.0017733844,0.00023251718],"domain_scores_gemma":[0.98722804,0.007937101,0.00092767563,0.0018626273,0.0015440426,0.00050040364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068424237,0.001411039,0.0025388955,0.0033631378,0.0012858938,0.0034429433,0.002547876,0.0026807955,0.00182911],"category_scores_gemma":[0.03169442,0.00089534005,0.0012711848,0.0038012443,0.0028804934,0.005568647,0.0061088456,0.0034657184,0.000859303],"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":[0.00050977897,0.00026978174,0.003386895,0.0005306859,0.0002868077,0.00021223792,0.0008442748,0.3901214,0.004439287,0.3256132,0.0056272326,0.26815838],"study_design_scores_gemma":[0.000019314622,0.000059541584,0.00021221735,0.00003125642,0.000016149303,0.0000656596,0.000048634633,0.7871178,0.0007870193,0.20976147,0.0018527412,0.000028250968],"about_ca_topic_score_codex":0.00223543,"about_ca_topic_score_gemma":0.0020183807,"teacher_disagreement_score":0.0068424237,"about_ca_system_score_codex":0.0021023965,"about_ca_system_score_gemma":0.0016666449,"threshold_uncertainty_score":0.036186576},"labels":[],"label_agreement":null},{"id":"W3171402832","doi":"10.48550/arxiv.2106.02154","title":"Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Spectral clustering; Laplacian matrix; Diffusion map; Adjacency matrix; Dimensionality reduction; Laplace operator; Nonlinear dimensionality reduction; Mathematics; Cluster analysis; Adjacency list; Spectral graph theory; Pattern recognition (psychology); Embedding; Locality; Graph; Computer science; Artificial intelligence; Algorithm; Combinatorics; Voltage graph; Line graph","score_opus":0.07931015357348536,"score_gpt":0.22842597062404543,"score_spread":0.14911581705056007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3171402832","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0045230533,0.1677406,0.8106923,0.0020784826,0.0009010337,0.00008622205,0.00030046486,0.0011150872,0.012562719],"genre_scores_gemma":[0.085884854,0.30766097,0.58054054,0.0015167379,0.0041972334,0.0004013528,0.0017854451,0.00091350864,0.017099326],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99921453,0.00016624636,0.00006779708,0.00021007935,0.00030300685,0.00003828683],"domain_scores_gemma":[0.9991253,0.00044509157,0.00004957768,0.00011489434,0.00022864486,0.000036467565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011114993,0.0016079901,0.001698888,0.0030529206,0.0005113198,0.0018329691,0.0013006942,0.0013281913,0.0037594244],"category_scores_gemma":[0.0027206568,0.0007218792,0.0013486005,0.005982017,0.0012249962,0.0034275576,0.0014645661,0.00202745,0.002364129],"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":[0.000042440897,0.0001266452,0.0006959995,0.0023692783,0.00017299964,0.00012190621,0.00027434574,0.017646613,0.004538532,0.08879223,0.035733163,0.8494858],"study_design_scores_gemma":[0.000029581724,0.00029512958,0.0031030134,0.0006654134,0.00020196805,0.0020216703,0.00041515168,0.25602442,0.011795218,0.3347339,0.39046398,0.00025067182],"about_ca_topic_score_codex":0.0015322792,"about_ca_topic_score_gemma":0.0013607582,"teacher_disagreement_score":0.0037594244,"about_ca_system_score_codex":0.0007714891,"about_ca_system_score_gemma":0.0008837887,"threshold_uncertainty_score":0.01257652},"labels":[],"label_agreement":null},{"id":"W3174966384","doi":"10.1109/tpami.2021.3091682","title":"Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Mathematics; Combinatorics; Diagonal; Eigenvalues and eigenvectors; Metric (unit); Diagonal matrix; Symmetric matrix; Discrete mathematics; Algorithm; Geometry","score_opus":0.016301989375481805,"score_gpt":0.25511640761510246,"score_spread":0.23881441823962066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174966384","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019874526,0.0004431013,0.9715427,0.00060997444,0.00009487327,0.00007779199,0.00030172293,0.0015202019,0.005535094],"genre_scores_gemma":[0.5586247,0.0007110118,0.4228844,0.0006478357,0.00017481219,0.00033931088,0.0023645218,0.0007403175,0.013513128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909544,0.00024651602,0.000044822973,0.00029005387,0.00023759747,0.00008561572],"domain_scores_gemma":[0.9987814,0.00046109987,0.00016691859,0.0002554837,0.00021316617,0.0001220285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096614927,0.0014637175,0.0016408561,0.0011538418,0.00059354806,0.0019045033,0.0020447993,0.0019781957,0.007984802],"category_scores_gemma":[0.0071321647,0.00046728147,0.0006993188,0.001234899,0.0011451473,0.0027276312,0.002914676,0.0018518682,0.0032425558],"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":[0.00022893715,0.000108170505,0.001122843,0.0002932071,0.00007317172,0.00024938304,0.00014851941,0.4519275,0.005162729,0.12000585,0.015978873,0.4047008],"study_design_scores_gemma":[0.000011599704,0.000037040158,0.00012694357,0.0000148963745,0.0000044656617,0.000058410726,0.0000156837,0.9566829,0.001191026,0.040017843,0.0018263678,0.000012834327],"about_ca_topic_score_codex":0.003705772,"about_ca_topic_score_gemma":0.004629193,"teacher_disagreement_score":0.007984802,"about_ca_system_score_codex":0.0013374067,"about_ca_system_score_gemma":0.0018422559,"threshold_uncertainty_score":0.026711762},"labels":[],"label_agreement":null},{"id":"W3175244446","doi":"10.1145/3409264","title":"Pinball Loss Twin Support Vector Clustering","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; IXICO; National Institutes of Health; H. Lundbeck A/S; Pfizer; Novartis Pharmaceuticals Corporation; Servier; Indian Institute of Technology Indore; National Institute on Aging; Alzheimer's Association; Merck; GE Healthcare; BioClinica; Eli Lilly and Company","keywords":"Cluster analysis; Fuzzy clustering; Computer science; Correlation clustering; Data stream clustering; CURE data clustering algorithm; Hinge loss; Canopy clustering algorithm; Noise (video); Benchmark (surveying); Artificial intelligence; Pattern recognition (psychology); Data mining; Stability (learning theory); Clustering high-dimensional data; Support vector machine; Machine learning","score_opus":0.029534637409793313,"score_gpt":0.29204650658054704,"score_spread":0.26251186917075375,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175244446","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013544923,0.00035627355,0.984382,0.0001290778,0.0000528378,0.000078431476,0.0001111281,0.0006074687,0.0007378359],"genre_scores_gemma":[0.54460907,0.00057988055,0.44734192,0.00029971238,0.000107240936,0.00032585918,0.0015919238,0.000292587,0.004851849],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973272,0.0005580651,0.00018212863,0.0006116448,0.0010704579,0.0002504049],"domain_scores_gemma":[0.9971655,0.00064931327,0.0002582307,0.00046794958,0.0013086126,0.0001504195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024250352,0.0013005857,0.0019336251,0.0018963801,0.000927921,0.0021474794,0.003548076,0.0018649927,0.0019363425],"category_scores_gemma":[0.008429892,0.0004919457,0.0010949152,0.002452083,0.0012148845,0.0027926988,0.0023185196,0.002009159,0.00090778753],"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":[0.000392116,0.000112175134,0.0023379363,0.00016770343,0.00014525099,0.00014563826,0.0001609609,0.6184476,0.006039714,0.016176408,0.005707308,0.35016719],"study_design_scores_gemma":[0.0000044969124,0.000029110677,0.00013525056,0.000005576668,0.000004994452,0.00002583642,0.000012143007,0.99588203,0.0011222995,0.0022902072,0.00047987932,0.000008228916],"about_ca_topic_score_codex":0.006394583,"about_ca_topic_score_gemma":0.0037948862,"teacher_disagreement_score":0.006394583,"about_ca_system_score_codex":0.0013764388,"about_ca_system_score_gemma":0.0019573607,"threshold_uncertainty_score":0.012824953},"labels":[],"label_agreement":null},{"id":"W3176678760","doi":"10.36227/techrxiv.14852652.v1","title":"Deep Clustering with Self-supervision using Pairwise Data Similarities","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Cluster analysis; Pairwise comparison; Similarity (geometry); Data mining; Computer science; Autoencoder; Embedding; Data point; Clustering high-dimensional data; Correlation clustering; Pattern recognition (psychology); Artificial intelligence; Single-linkage clustering; CURE data clustering algorithm; Deep learning; Image (mathematics)","score_opus":0.07504930655030552,"score_gpt":0.28189275257814117,"score_spread":0.20684344602783566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176678760","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016736206,0.00010691356,0.9817091,0.00007801632,0.0000123947475,0.00003100707,0.00006005412,0.000662834,0.00060352986],"genre_scores_gemma":[0.5479146,0.0002454777,0.44681674,0.00016355148,0.00005324937,0.00015023178,0.0008519204,0.00027679562,0.0035274315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988279,0.00023906444,0.00006817899,0.0004550924,0.0003044003,0.00010538601],"domain_scores_gemma":[0.9982476,0.00038551827,0.00030611662,0.0005674311,0.00038316107,0.00011016868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013549887,0.0012882119,0.001330837,0.0013691963,0.00072890776,0.0013001238,0.0024808436,0.0014581068,0.0016605209],"category_scores_gemma":[0.0038272277,0.0008675416,0.0012146842,0.0014150896,0.0015653638,0.003310509,0.002876017,0.002062503,0.0008680608],"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":[0.00020898669,0.00019928113,0.0031515493,0.00019280723,0.0002175832,0.00009508736,0.00031384933,0.63331455,0.018663015,0.02162965,0.0032675697,0.3187461],"study_design_scores_gemma":[0.000005749973,0.00003146025,0.00028891844,0.000007739148,0.000009089129,0.000029054854,0.000020371413,0.9864846,0.0035155506,0.009157144,0.00044121718,0.00000913205],"about_ca_topic_score_codex":0.0049297167,"about_ca_topic_score_gemma":0.008233322,"teacher_disagreement_score":0.0049297167,"about_ca_system_score_codex":0.0014624699,"about_ca_system_score_gemma":0.0014433551,"threshold_uncertainty_score":0.010611057},"labels":[],"label_agreement":null},{"id":"W3176743755","doi":"10.48550/arxiv.2106.15379","title":"Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Dimensionality reduction; Kernel (algebra); Computer science; Kernel principal component analysis; Kernel method; Kernelization; Embedding; Nonlinear dimensionality reduction; Semidefinite programming; Mathematics; Artificial intelligence; Graph; Pattern recognition (psychology); Mathematical optimization; Theoretical computer science; Support vector machine; Combinatorics","score_opus":0.06307209418987432,"score_gpt":0.2147962610266682,"score_spread":0.1517241668367939,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176743755","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008281136,0.0142922085,0.9796123,0.00065275683,0.00017481552,0.000023496097,0.000115163064,0.00024590292,0.0040552802],"genre_scores_gemma":[0.09054825,0.06406181,0.82760113,0.0015544973,0.0028514138,0.0006797809,0.0014589578,0.00095107604,0.010293048],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99819785,0.0006899127,0.0001283268,0.00039020518,0.0005035793,0.00009018549],"domain_scores_gemma":[0.9987,0.00068853796,0.00008590007,0.0002157457,0.0002424133,0.00006728211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026515017,0.002211012,0.0020534084,0.0019446607,0.00055296964,0.0026135163,0.0019648138,0.0016199203,0.0048253965],"category_scores_gemma":[0.0038708835,0.000998665,0.0016758506,0.0040219417,0.0019661142,0.004995937,0.002542764,0.005082409,0.0023931877],"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":[0.00003379597,0.00015099603,0.0003990525,0.001051469,0.000114788374,0.00011346502,0.00022628634,0.04334287,0.0014830495,0.68557215,0.023068422,0.24444366],"study_design_scores_gemma":[0.000015408896,0.000098874814,0.00032347042,0.00023585268,0.00003787511,0.0002792074,0.00007587121,0.28563175,0.001142669,0.64203256,0.070063,0.00006347789],"about_ca_topic_score_codex":0.0011286476,"about_ca_topic_score_gemma":0.0010410636,"teacher_disagreement_score":0.0048253965,"about_ca_system_score_codex":0.0013747986,"about_ca_system_score_gemma":0.001409763,"threshold_uncertainty_score":0.016142488},"labels":[],"label_agreement":null},{"id":"W3176826571","doi":"10.1016/j.mlwa.2021.100088","title":"Quantile–Quantile Embedding for distribution transformation and manifold embedding with ability to choose the embedding distribution","year":2021,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Embedding; Quantile; Mathematics; Nonlinear dimensionality reduction; Metric (unit); Distribution (mathematics); Dimensionality reduction; Transformation (genetics); Quantile function; Pattern recognition (psychology); Artificial intelligence; Computer science; Probability distribution; Statistics; Mathematical analysis","score_opus":0.01080910474451487,"score_gpt":0.2766837636730309,"score_spread":0.26587465892851603,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176826571","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004123896,0.00008516136,0.99461055,0.00008373846,0.000024336348,0.000017557188,0.00009011738,0.00040945556,0.000555172],"genre_scores_gemma":[0.35521844,0.00055154803,0.6362984,0.0002580269,0.00018473952,0.0003090097,0.0013209345,0.0008417858,0.005017187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987972,0.00041947467,0.00007241035,0.00035026748,0.00027730525,0.00008340503],"domain_scores_gemma":[0.99811435,0.0005897279,0.00022736707,0.0005514074,0.00042559305,0.0000914856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012979957,0.0009348624,0.0006845089,0.0013506069,0.0004176003,0.0012758382,0.0009705746,0.00071094744,0.0029351052],"category_scores_gemma":[0.006419949,0.00034999137,0.0008942224,0.0013220784,0.0011457687,0.002644959,0.0014139392,0.0018405374,0.0012389106],"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":[0.00020525216,0.00014395111,0.00520106,0.00023193903,0.00011796113,0.00023318546,0.00046084836,0.1685642,0.020378299,0.20537612,0.011736233,0.5873509],"study_design_scores_gemma":[0.000017231267,0.00006626007,0.0016756332,0.000022613513,0.000016424669,0.00019907625,0.00007632104,0.89144236,0.006305419,0.0897389,0.010380616,0.000059058388],"about_ca_topic_score_codex":0.0013151986,"about_ca_topic_score_gemma":0.0011284057,"teacher_disagreement_score":0.0029351052,"about_ca_system_score_codex":0.00050777785,"about_ca_system_score_gemma":0.0005426495,"threshold_uncertainty_score":0.009818912},"labels":[],"label_agreement":null},{"id":"W3179031429","doi":"10.1007/s12561-023-09398-2","title":"Nonnegative Matrix Factorization with Group and Basis Restrictions","year":2023,"lang":"en","type":"article","venue":"Statistics in Biosciences","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Non-negative matrix factorization; Matrix (chemical analysis); Matrix decomposition; Row; Mathematics; Nonnegative matrix; Group (periodic table); Row and column spaces; Rank (graph theory); Basis (linear algebra); Set (abstract data type); Dimensionality reduction; Extension (predicate logic); Curse of dimensionality; Combinatorics; Algorithm; Symmetric matrix; Computer science; Artificial intelligence; Eigenvalues and eigenvectors; Statistics","score_opus":0.018939194728306217,"score_gpt":0.283996933655035,"score_spread":0.26505773892672874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179031429","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0051531736,0.00015140284,0.9918331,0.00023677212,0.00015479546,0.00005577321,0.00023229397,0.00024556482,0.0019369965],"genre_scores_gemma":[0.19365354,0.00078255543,0.78966975,0.00041209583,0.00060001144,0.00052095484,0.0021485938,0.00038969613,0.011822868],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983588,0.0007818019,0.00006999066,0.00026478566,0.00037347354,0.00015115837],"domain_scores_gemma":[0.99688727,0.0011767974,0.00024901688,0.001019991,0.0004844483,0.00018241828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016838562,0.0012164777,0.0011006645,0.00067724905,0.0005999111,0.0011837453,0.001189727,0.0010241147,0.0055087595],"category_scores_gemma":[0.00733575,0.000526152,0.0014793602,0.0010667219,0.0011646831,0.0019496973,0.0018910842,0.002162154,0.0023796253],"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":[0.0003796961,0.0003258587,0.001057809,0.00038696217,0.00019030397,0.00040681445,0.00024899704,0.1373857,0.016055962,0.4703955,0.04192585,0.33124053],"study_design_scores_gemma":[0.00004296291,0.000095487245,0.0004128668,0.000032342607,0.000028833783,0.00017084798,0.000057407615,0.6771978,0.0025469884,0.30998355,0.00939349,0.000037370526],"about_ca_topic_score_codex":0.002192634,"about_ca_topic_score_gemma":0.0033200067,"teacher_disagreement_score":0.0055087595,"about_ca_system_score_codex":0.0002763051,"about_ca_system_score_gemma":0.0013504976,"threshold_uncertainty_score":0.018428624},"labels":[],"label_agreement":null},{"id":"W3180593546","doi":"10.1109/access.2021.3095844","title":"Mobile-Optimized Facial Expression Recognition Techniques","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems","keywords":"Computer science; Facial expression recognition; Facial expression; Facial recognition system; Artificial intelligence; Speech recognition; Pattern recognition (psychology); Computer vision","score_opus":0.039899974639153464,"score_gpt":0.312319647267947,"score_spread":0.2724196726287935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180593546","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025850698,0.00058691634,0.9604522,0.00017843045,0.00018602502,0.00010597162,0.00036887827,0.003596828,0.008673937],"genre_scores_gemma":[0.48638746,0.0010887145,0.47839358,0.000320126,0.0001688763,0.0003417748,0.002181431,0.00039785844,0.030720128],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999699,0.00002800511,0.000010131438,0.00008723337,0.00013195394,0.000043787208],"domain_scores_gemma":[0.9999187,0.000012292611,0.0000086229065,0.000015441963,0.000039977956,0.0000049957366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024689885,0.0006245176,0.00040575073,0.00040512055,0.00018495202,0.00035501955,0.0008917983,0.00031831107,0.0057181483],"category_scores_gemma":[0.0005794841,0.00018593638,0.000364089,0.00033376494,0.00012736618,0.0005691211,0.0005242022,0.00046771622,0.0026276421],"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":[0.00022506193,0.000119300814,0.0013212224,0.000088172346,0.000062783176,0.00014345741,0.000053630818,0.04325929,0.105524145,0.003908358,0.011754868,0.83353984],"study_design_scores_gemma":[0.000025363739,0.00017271406,0.0036923562,0.0000229675,0.0000394001,0.00056155905,0.00005968269,0.8944819,0.076368995,0.0024902236,0.022043638,0.000041090767],"about_ca_topic_score_codex":0.0026488109,"about_ca_topic_score_gemma":0.00505349,"teacher_disagreement_score":0.0057181483,"about_ca_system_score_codex":0.000323645,"about_ca_system_score_gemma":0.00030115998,"threshold_uncertainty_score":0.019129097},"labels":[],"label_agreement":null},{"id":"W3183593932","doi":"","title":"Efficient Greedy Coordinate Descent via Variable Partitioning","year":2021,"lang":"en","type":"article","venue":"Uncertainty in Artificial Intelligence","topic":"Face and Expression Recognition","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 British Columbia","funders":"","keywords":"Coordinate descent; Greedy algorithm; Partition (number theory); Convergence (economics); Algorithm; Computer science; Descent (aeronautics); Mathematical optimization; Mathematics; Variable (mathematics); Rate of convergence; Key (lock); Combinatorics","score_opus":0.03679210610531011,"score_gpt":0.2756887765513355,"score_spread":0.2388966704460254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183593932","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004605682,0.00015706092,0.99365073,0.00006935608,0.00003850056,0.0000328546,0.000049551665,0.00056551234,0.0008307873],"genre_scores_gemma":[0.20166461,0.00028305285,0.79192424,0.0001862308,0.00010791069,0.00037953112,0.0008758756,0.0004185957,0.004159969],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905604,0.00030917404,0.000044965316,0.00021428504,0.00025560736,0.000119866345],"domain_scores_gemma":[0.9988488,0.0005591948,0.00007885478,0.00017277016,0.0002758662,0.000064504966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010313075,0.0013426833,0.0019162374,0.00082919124,0.0006041365,0.0011995089,0.0015736918,0.001020814,0.0027074327],"category_scores_gemma":[0.0048094355,0.00070937927,0.00086815015,0.0014229476,0.00085278193,0.0010041317,0.0016306466,0.0012609786,0.0013745879],"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":[0.00020936115,0.000065096494,0.0009879543,0.00015030973,0.00008011199,0.00012086045,0.00010492229,0.69858146,0.0047376603,0.03130374,0.012225329,0.2514332],"study_design_scores_gemma":[0.000016211825,0.000021952092,0.00007547929,0.0000034267468,0.000004293662,0.000020112106,0.000006630523,0.99406993,0.00057764404,0.0041964306,0.0010031962,0.0000046998794],"about_ca_topic_score_codex":0.005922697,"about_ca_topic_score_gemma":0.005265528,"teacher_disagreement_score":0.005922697,"about_ca_system_score_codex":0.0008068567,"about_ca_system_score_gemma":0.0016789584,"threshold_uncertainty_score":0.011776447},"labels":[],"label_agreement":null},{"id":"W3185277158","doi":"10.1109/tkde.2021.3100353","title":"On the Benefits of Two Dimensional Metric Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Université de Montréal; Western University; Université Laval; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Boosting (machine learning); Computer science; Metric (unit); Dimension (graph theory); Benchmark (surveying); Generalization; Intrinsic dimension; Algorithm; Rank (graph theory); Data structure; Artificial intelligence; Feature learning; External Data Representation; Learning to rank; Machine learning; Curse of dimensionality; Mathematics; Ranking (information retrieval)","score_opus":0.029957211545899712,"score_gpt":0.25831982516662566,"score_spread":0.22836261362072596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185277158","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01645768,0.002732859,0.97447145,0.0019622813,0.000104411985,0.00005935207,0.000086446074,0.00035996843,0.0037656003],"genre_scores_gemma":[0.46306038,0.002833615,0.527212,0.001729937,0.00073558296,0.000279028,0.00043516228,0.00030006503,0.0034140896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9911107,0.0049067806,0.0003232078,0.0010159855,0.0023716998,0.0002715398],"domain_scores_gemma":[0.95351386,0.033478543,0.002211625,0.0064626904,0.0034189406,0.00091429864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010482288,0.0017451182,0.0017655384,0.0018117264,0.0010515723,0.0030470106,0.0019981272,0.0025048675,0.0021841521],"category_scores_gemma":[0.052454256,0.00074782164,0.00094132184,0.0024636528,0.004129481,0.007815516,0.0057390584,0.0048199864,0.0011618093],"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":[0.00036223803,0.00025410496,0.004140109,0.00047001106,0.00015157985,0.0002575727,0.00041582953,0.2809484,0.005147925,0.42781904,0.0060746567,0.27395853],"study_design_scores_gemma":[0.000025636142,0.00018939257,0.00063937006,0.000047528152,0.000013857466,0.00019788956,0.000049047994,0.7693379,0.0021323639,0.22302051,0.0043064626,0.000040066257],"about_ca_topic_score_codex":0.0011667007,"about_ca_topic_score_gemma":0.00096721447,"teacher_disagreement_score":0.010482288,"about_ca_system_score_codex":0.0016014101,"about_ca_system_score_gemma":0.0012231027,"threshold_uncertainty_score":0.055436254},"labels":[],"label_agreement":null},{"id":"W3186415893","doi":"","title":"Facial emotion recognition and detection","year":2021,"lang":"en","type":"article","venue":"International journal of advance research, ideas and innovations in technology","topic":"Face and Expression Recognition","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":"Horizon College and Seminary","funders":"","keywords":"Facial recognition system; Computer science; Face (sociological concept); Biometrics; Field (mathematics); Face detection; Computer vision; Artificial intelligence; Three-dimensional face recognition; Emotion recognition; Computer security; Object-class detection; Image (mathematics); Security system; Pattern recognition (psychology)","score_opus":0.04242062643513598,"score_gpt":0.35770318733194106,"score_spread":0.31528256089680506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3186415893","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14836042,0.00434719,0.75327027,0.0014650067,0.001300859,0.0011870562,0.004583397,0.00603916,0.079446755],"genre_scores_gemma":[0.5967452,0.0036817766,0.331023,0.0011469247,0.00035557713,0.0013143958,0.005153339,0.0005652176,0.06001459],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990658,0.00008833404,0.00004865724,0.0002647926,0.00038730665,0.00014508619],"domain_scores_gemma":[0.99964666,0.000039765768,0.000031547923,0.00004658774,0.00021918215,0.000016291411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005693012,0.0006628758,0.00077067426,0.0008393744,0.00033137813,0.00091027503,0.0006190145,0.0008474709,0.0072323256],"category_scores_gemma":[0.0015672708,0.00019942733,0.0005993576,0.0005405124,0.0002498497,0.0006956397,0.0005487268,0.00052118127,0.0067968266],"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":[0.0003870505,0.00012920269,0.0063463044,0.0003356961,0.000053220883,0.0003459652,0.00023688165,0.0018971029,0.21629384,0.0039449264,0.019308746,0.7507211],"study_design_scores_gemma":[0.0000927505,0.0007238267,0.1331217,0.00030440473,0.00022318077,0.0056153545,0.0010441461,0.19272277,0.50059354,0.012329149,0.15295544,0.00027374137],"about_ca_topic_score_codex":0.001601954,"about_ca_topic_score_gemma":0.0011174978,"teacher_disagreement_score":0.0072323256,"about_ca_system_score_codex":0.0003227305,"about_ca_system_score_gemma":0.0003085389,"threshold_uncertainty_score":0.024194598},"labels":[],"label_agreement":null},{"id":"W3187067374","doi":"10.1371/journal.pone.0254965","title":"A face recognition software framework based on principal component analysis","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ontario Ministry of Research, Innovation and Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Facial recognition system; Biometrics; Principal component analysis; Identification (biology); Software; Face (sociological concept); Process (computing); Artificial intelligence; Fingerprint (computing); Machine learning; Component (thermodynamics); Implementation; Principal (computer security); Iris recognition; Data mining; Pattern recognition (psychology); Software engineering; Computer security; Operating system","score_opus":0.07484173792850403,"score_gpt":0.24739014624916258,"score_spread":0.17254840832065854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187067374","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012469525,0.00014427064,0.93887573,0.000050946273,0.000038585356,0.00017366168,0.00038002402,0.05726602,0.0018237438],"genre_scores_gemma":[0.040353693,0.0004916264,0.94113183,0.0002055887,0.000060614468,0.0009186025,0.0036923531,0.0061518825,0.0069938838],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991265,0.0000811361,0.00006351996,0.00019521947,0.00044192837,0.00009180765],"domain_scores_gemma":[0.9994549,0.00013509822,0.000039627845,0.00009892917,0.00022435123,0.000047000565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001065432,0.0013548726,0.0010587107,0.0020120572,0.0006791079,0.0012107833,0.0026460711,0.0009971282,0.012116196],"category_scores_gemma":[0.0022260235,0.00094180176,0.0018100783,0.0008444392,0.0005727919,0.0013891158,0.002114403,0.001954273,0.00792487],"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":[0.0004969833,0.00035697737,0.0025088617,0.0009113832,0.00036899815,0.0008358332,0.0006426953,0.033448745,0.06755497,0.046245247,0.08082466,0.7658047],"study_design_scores_gemma":[0.0002652597,0.00035729332,0.004338724,0.00028371645,0.00022902258,0.0025482848,0.00013523122,0.5600459,0.09171722,0.039258167,0.3003845,0.00043670996],"about_ca_topic_score_codex":0.0049420614,"about_ca_topic_score_gemma":0.0036444147,"teacher_disagreement_score":0.012116196,"about_ca_system_score_codex":0.00053303526,"about_ca_system_score_gemma":0.0016366355,"threshold_uncertainty_score":0.04053271},"labels":[],"label_agreement":null},{"id":"W3196099521","doi":"10.1007/978-3-030-61577-2_14","title":"Correction to: Dynamic Emotion Understanding Based on Two-Layer Fuzzy Support Vector Regression-Takagi-Sugeno Model","year":2021,"lang":"en","type":"book-chapter","venue":"Studies in computational intelligence","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"","keywords":"Computer science; Support vector machine; Layer (electronics); Artificial intelligence; Fuzzy logic; Machine learning; Data mining","score_opus":0.15577014685440277,"score_gpt":0.37427414986909757,"score_spread":0.2185040030146948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196099521","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002153061,0.0016228238,0.015302234,0.04529103,0.9241014,0.00009408458,0.0020420933,0.0029307709,0.0064624334],"genre_scores_gemma":[0.14061789,0.0053766603,0.07179225,0.041558936,0.14904642,0.00040347435,0.006679401,0.0058084354,0.5787165],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9975713,0.00029966416,0.00035697967,0.0004642108,0.0010751443,0.00023267155],"domain_scores_gemma":[0.9805381,0.003280024,0.0006765692,0.0019700697,0.012839033,0.00069631776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019797601,0.002027912,0.0023527348,0.001904905,0.002373315,0.0028206983,0.0034159708,0.00516504,0.13142048],"category_scores_gemma":[0.035639066,0.0006191142,0.0013052791,0.0021078093,0.0015500018,0.0029095737,0.0022373344,0.0054459423,0.04782864],"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":[0.00017596188,0.000020298616,0.00019928846,0.00030099726,0.000032289594,0.0007531221,0.00014505557,0.00041377338,0.0007487018,0.0059016934,0.9588028,0.032506038],"study_design_scores_gemma":[0.00009566424,0.00007305762,0.0019336607,0.00019196898,0.000049281032,0.0023179424,0.00034982065,0.010239904,0.0043111276,0.009051091,0.97126096,0.00012556635],"about_ca_topic_score_codex":0.009013045,"about_ca_topic_score_gemma":0.008343527,"teacher_disagreement_score":0.13142048,"about_ca_system_score_codex":0.0024304977,"about_ca_system_score_gemma":0.0023482365,"threshold_uncertainty_score":0.4396453},"labels":[],"label_agreement":null},{"id":"W3199268160","doi":"10.1109/mwscas47672.2021.9531690","title":"Performance Evaluation of Entropy Based LBP for Face Recognition","year":2021,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Local binary patterns; Histogram; Pattern recognition (psychology); Artificial intelligence; Facial recognition system; Computer science; Entropy (arrow of time); Classifier (UML); Binary number; Mathematics; Image (mathematics)","score_opus":0.06940697869200414,"score_gpt":0.2951276382788792,"score_spread":0.2257206595868751,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199268160","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7528322,0.0042532296,0.23133905,0.00031576076,0.0002813824,0.000116413576,0.00034985627,0.0021611045,0.008350994],"genre_scores_gemma":[0.96274436,0.0005942848,0.034211967,0.000040609473,0.000047698988,0.000037905585,0.00036673687,0.00004573669,0.0019106626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913776,0.00017362152,0.000042908607,0.00010501133,0.00045389557,0.00008674583],"domain_scores_gemma":[0.99916136,0.00032295147,0.00005071389,0.00006745734,0.00035810453,0.00003932201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010318736,0.00036549015,0.00046209543,0.0010128268,0.00022478022,0.00039534993,0.0004214511,0.00040829252,0.0017232652],"category_scores_gemma":[0.0024156338,0.000095719384,0.00024678657,0.00050305447,0.00019085866,0.00041961813,0.0003475666,0.00019283495,0.0005557516],"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":[0.0031752894,0.00040029184,0.009991286,0.00036079012,0.00013019863,0.0002469704,0.00012781877,0.04930432,0.19147739,0.0010990148,0.002850137,0.7408365],"study_design_scores_gemma":[0.000045859746,0.0015281539,0.024936153,0.00002892277,0.000087169195,0.00067455025,0.00009991025,0.8509718,0.11935098,0.00050994864,0.0017166263,0.000050009345],"about_ca_topic_score_codex":0.0016974962,"about_ca_topic_score_gemma":0.0008988561,"teacher_disagreement_score":0.0017232652,"about_ca_system_score_codex":0.00028981885,"about_ca_system_score_gemma":0.00021960551,"threshold_uncertainty_score":0.0057649016},"labels":[],"label_agreement":null},{"id":"W3199841460","doi":"","title":"Random Access in IoT Using Naïve Bayes Classification","year":2021,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Naive Bayes classifier; Computer science; Bayes' theorem; Internet of Things; Artificial intelligence; Bayesian probability; Computer security; Support vector machine","score_opus":0.096384096673225,"score_gpt":0.34306941506090266,"score_spread":0.24668531838767765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199841460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047224816,0.002070022,0.94107175,0.0007547922,0.00045314728,0.0001361271,0.00044536733,0.002486522,0.0053574997],"genre_scores_gemma":[0.83417124,0.0009451951,0.15544751,0.0004066474,0.00039914018,0.00012808462,0.0006521793,0.00012484139,0.0077251364],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975005,0.0008943671,0.00018290342,0.00049879914,0.0005837523,0.00033970733],"domain_scores_gemma":[0.9968753,0.0019665791,0.00015324204,0.00036513555,0.00054311677,0.000096564625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002722568,0.0007069969,0.0018076993,0.0011612047,0.00076217944,0.001953931,0.0014320463,0.0013027411,0.0039872075],"category_scores_gemma":[0.005371279,0.00046730955,0.0009950503,0.0011319994,0.0007001854,0.0033596586,0.0010229265,0.0012144175,0.0013205543],"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":[0.0011680799,0.0007756925,0.012258155,0.0003276286,0.00031308518,0.0005930251,0.00020047314,0.29527304,0.0054012113,0.050387323,0.017385093,0.61591727],"study_design_scores_gemma":[0.000013569345,0.000050468767,0.00043097165,0.000017739889,0.000022797529,0.00008070271,0.000029659834,0.9791366,0.00086279,0.018414486,0.00092566275,0.00001457391],"about_ca_topic_score_codex":0.0073642945,"about_ca_topic_score_gemma":0.006037773,"teacher_disagreement_score":0.0073642945,"about_ca_system_score_codex":0.0009613251,"about_ca_system_score_gemma":0.0010050505,"threshold_uncertainty_score":0.014642894},"labels":[],"label_agreement":null},{"id":"W3203828829","doi":"10.1088/1742-6596/2031/1/012023","title":"Two new methods for facial expression recognition using Convolutional Neural Networks","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Face and Expression Recognition","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":"Bishop's University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Sadness; Disgust; Facial expression; Normalization (sociology); Pattern recognition (psychology); Pooling; Image (mathematics); Facial expression recognition; Speech recognition; Facial recognition system; Anger","score_opus":0.10272346393823703,"score_gpt":0.3591246774530869,"score_spread":0.2564012135148499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203828829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019689955,0.0011353648,0.9716011,0.0003264155,0.00034814412,0.00010965105,0.00020716203,0.0030020862,0.0035799986],"genre_scores_gemma":[0.4028031,0.0017692319,0.57400006,0.0004915705,0.00029583188,0.00027120096,0.0010953222,0.00031905464,0.018954605],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993231,0.000079296005,0.00003898133,0.00019566726,0.0002728889,0.00009003737],"domain_scores_gemma":[0.99968505,0.000050752667,0.000030284798,0.00006230593,0.00014964343,0.00002194241],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007166171,0.0011118159,0.0006048166,0.0011571358,0.00025689302,0.00065694575,0.0012923847,0.0005847,0.0027922408],"category_scores_gemma":[0.00094971375,0.0003936433,0.0007994244,0.00068861956,0.00032735203,0.0011479171,0.0008334602,0.00090106885,0.0011984417],"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":[0.0002211853,0.00013900713,0.0016705627,0.00014965533,0.00013542685,0.00010197804,0.00006153216,0.02534037,0.078605704,0.0046480615,0.0066492106,0.88227725],"study_design_scores_gemma":[0.000020910813,0.000064739186,0.0022048436,0.000023139011,0.000054153552,0.00015491221,0.000019939163,0.93715966,0.051750608,0.001691471,0.006820696,0.00003488423],"about_ca_topic_score_codex":0.0052435105,"about_ca_topic_score_gemma":0.0054106168,"teacher_disagreement_score":0.0052435105,"about_ca_system_score_codex":0.0005959186,"about_ca_system_score_gemma":0.0005374595,"threshold_uncertainty_score":0.010425985},"labels":[],"label_agreement":null},{"id":"W3204838022","doi":"10.1002/ima.22656","title":"An efficient multiclass classifier for classification of Alzheimer's disease/mild cognitive impairment/Normal subjects","year":2021,"lang":"en","type":"article","venue":"International Journal of Imaging Systems and Technology","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; National Institute of Biomedical Imaging and Bioengineering; Northern California Institute for Research and Education; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense","keywords":"Overfitting; Artificial intelligence; Computer science; Multiclass classification; Pattern recognition (psychology); Binary classification; Classifier (UML); Machine learning; Artificial neural network; Support vector machine","score_opus":0.018800753397668776,"score_gpt":0.2966222467880779,"score_spread":0.2778214933904091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204838022","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24282686,0.0012577482,0.7521092,0.0004101306,0.00024413507,0.0001564749,0.00038592337,0.0011127729,0.001496762],"genre_scores_gemma":[0.8170394,0.00023738873,0.17990132,0.00012883001,0.00012478611,0.00013316478,0.00057787326,0.000032649496,0.0018247028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994493,0.00012940433,0.000043238626,0.00011739071,0.00018885442,0.00007180031],"domain_scores_gemma":[0.999371,0.00021652013,0.000056359488,0.00006421544,0.000253284,0.000038692295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011478788,0.0004014435,0.00077288103,0.0011307019,0.00030665795,0.000424483,0.00064258627,0.0007529021,0.001011168],"category_scores_gemma":[0.0019763072,0.00011478409,0.0005579936,0.0006157045,0.00016036957,0.00035847694,0.00036016217,0.0006031023,0.00041108674],"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":[0.00066297984,0.00032865582,0.008831023,0.00009729409,0.00010865986,0.00014430431,0.00006246103,0.051397305,0.023369493,0.0014199367,0.005289594,0.90828824],"study_design_scores_gemma":[0.000018272787,0.00008589398,0.0032516276,0.000008500966,0.00002701934,0.00009088657,0.000016159454,0.99006236,0.0053007556,0.00057257497,0.0005560293,0.000010082869],"about_ca_topic_score_codex":0.003107389,"about_ca_topic_score_gemma":0.0028739828,"teacher_disagreement_score":0.003107389,"about_ca_system_score_codex":0.00034069637,"about_ca_system_score_gemma":0.0006544717,"threshold_uncertainty_score":0.0061786175},"labels":[],"label_agreement":null},{"id":"W3205472581","doi":"10.1109/mipr51284.2021.00010","title":"A Manifold Semantic Canonical Correlation Framework for Effective Feature Fusion","year":2021,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Canonical correlation; Feature (linguistics); Computer science; Generality; Pattern recognition (psychology); Artificial intelligence; Manifold alignment; Correlation; Manifold (fluid mechanics); Representation (politics); Semantic feature; Nonlinear dimensionality reduction; Mathematics; Dimensionality reduction","score_opus":0.009878186679608124,"score_gpt":0.2616719620743502,"score_spread":0.25179377539474207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205472581","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001279105,0.0001435302,0.99781823,0.00004269864,0.000018020473,0.000016230595,0.000029688419,0.00017485133,0.000477618],"genre_scores_gemma":[0.19450493,0.0008306844,0.80067945,0.0001754126,0.0002239786,0.00025426468,0.00069791585,0.00026427448,0.0023691081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984604,0.00048679978,0.000073576215,0.00035685074,0.0005127428,0.00010966683],"domain_scores_gemma":[0.9991685,0.00015002869,0.000088994064,0.00017414888,0.0003701412,0.00004813601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018713038,0.0013309467,0.0012936082,0.0019175584,0.00064936024,0.0014503994,0.0015419422,0.00088994723,0.002854457],"category_scores_gemma":[0.0030067891,0.00045588418,0.0014207703,0.0029689865,0.0013228019,0.002494458,0.0024372751,0.0016613622,0.0010440004],"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":[0.00015779506,0.00009383957,0.00084462215,0.00023590836,0.00020955209,0.00026313387,0.00026929972,0.20462434,0.017560618,0.29277763,0.0086841155,0.4742791],"study_design_scores_gemma":[0.000008635782,0.000081372105,0.00030040953,0.000016659707,0.000028086712,0.00011398499,0.00004088374,0.9346529,0.003833825,0.054955896,0.0059290435,0.000038234553],"about_ca_topic_score_codex":0.003622284,"about_ca_topic_score_gemma":0.0026234416,"teacher_disagreement_score":0.003622284,"about_ca_system_score_codex":0.000880584,"about_ca_system_score_gemma":0.001448825,"threshold_uncertainty_score":0.009896517},"labels":[],"label_agreement":null},{"id":"W3206353477","doi":"10.1016/j.fss.2021.09.022","title":"Reinforced fuzzy clustering-based rule model constructed with the aid of exponentially weighted ℓ2 regularization strategy and augmented random vector functional link network","year":2021,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Ministry of Science and ICT, South Korea; Korea Electric Power Corporation; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning","keywords":"Mathematics; Regularization (linguistics); Fuzzy logic; Applied mathematics; Link (geometry); Exponential growth; Cluster analysis; Mathematical optimization; Algorithm; Artificial intelligence; Mathematical analysis; Computer science; Statistics; Combinatorics","score_opus":0.014987941960471358,"score_gpt":0.204604383980034,"score_spread":0.18961644201956265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206353477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028526137,0.00025978545,0.96758926,0.0001448401,0.00005844796,0.000033186858,0.00008161441,0.00024154743,0.0030651162],"genre_scores_gemma":[0.8675584,0.00032638863,0.12406635,0.00008363444,0.000049462535,0.00014499576,0.00029833437,0.000044126173,0.0074283225],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994941,0.00010979366,0.00002907794,0.00015853738,0.00016049815,0.000048010374],"domain_scores_gemma":[0.99930537,0.0002110781,0.00007401247,0.00005294803,0.00033174615,0.000024875004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008257818,0.00047745544,0.0011493837,0.0006648207,0.00044229836,0.00096161454,0.0017402373,0.0010292885,0.0018669723],"category_scores_gemma":[0.0020102034,0.00029617784,0.0006994522,0.00066198636,0.0005300844,0.0010590834,0.0004160925,0.0008765123,0.00041386057],"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":[0.000056901645,0.000035311365,0.0004996518,0.00005049969,0.00006243085,0.00008016509,0.00004881126,0.9432867,0.0021439812,0.016222436,0.0007554587,0.03675769],"study_design_scores_gemma":[0.0000017505693,0.0000065414215,0.00006721503,0.00000217961,0.0000064258775,0.000010714102,0.0000016281969,0.99838865,0.00017093652,0.001246185,0.00009365754,0.000004177918],"about_ca_topic_score_codex":0.014947169,"about_ca_topic_score_gemma":0.011540365,"teacher_disagreement_score":0.014947169,"about_ca_system_score_codex":0.0007843404,"about_ca_system_score_gemma":0.0009658907,"threshold_uncertainty_score":0.029720366},"labels":[],"label_agreement":null},{"id":"W3206956912","doi":"10.48550/arxiv.2110.09620","title":"Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Dimensionality reduction; Sliced inverse regression; Regression; Principal component regression; Mathematics; Sufficient dimension reduction; Regression analysis; Dimension (graph theory); Artificial intelligence; Statistics; Kernel regression; Variance reduction; Kernel (algebra); Principal component analysis; Pattern recognition (psychology); Polynomial regression; Computer science","score_opus":0.053852547856898775,"score_gpt":0.21044944383738393,"score_spread":0.15659689598048515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206956912","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014612797,0.05335739,0.93766123,0.00092541485,0.0004205761,0.000037483645,0.00027124488,0.00067077886,0.005194552],"genre_scores_gemma":[0.053864367,0.1359613,0.7895578,0.0011507706,0.0035683194,0.00051630504,0.0021917124,0.0011809642,0.012008483],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9983241,0.0004625166,0.00019458224,0.00043416317,0.000517479,0.000067195564],"domain_scores_gemma":[0.9973151,0.0015997463,0.00013445242,0.0004746095,0.0004001216,0.00007593556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023484386,0.0019500925,0.0019501472,0.0023485448,0.0004635703,0.0020845602,0.0013180382,0.0014102048,0.0052201035],"category_scores_gemma":[0.0073106564,0.0009558999,0.0016436349,0.0034717233,0.0017527846,0.0035815274,0.002122763,0.0034275348,0.004403891],"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":[0.000046685782,0.000112978785,0.0006618169,0.002308028,0.00015889923,0.00013840417,0.00027519235,0.02196755,0.0033035006,0.22396754,0.034631863,0.7124275],"study_design_scores_gemma":[0.00003096684,0.00018987803,0.002048395,0.0008285784,0.00012684795,0.0012987948,0.00016777274,0.24103129,0.005801689,0.5157335,0.23254819,0.00019421373],"about_ca_topic_score_codex":0.0011916022,"about_ca_topic_score_gemma":0.0010660025,"teacher_disagreement_score":0.0052201035,"about_ca_system_score_codex":0.00080427056,"about_ca_system_score_gemma":0.00095873384,"threshold_uncertainty_score":0.017463028},"labels":[],"label_agreement":null},{"id":"W3210345906","doi":"10.32920/ryerson.14668203.v1","title":"Protected multimodal emotion recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Sadness; Computer science; Disgust; Feature (linguistics); Mel-frequency cepstrum; Pattern recognition (psychology); Feature extraction; Artificial intelligence; Speech recognition; Emotion classification; Anger; Psychology","score_opus":0.034006758943689055,"score_gpt":0.2550391256393022,"score_spread":0.22103236669561316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210345906","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07195519,0.0018708368,0.9040507,0.0006620388,0.0005445964,0.00025654575,0.0006406232,0.0024351017,0.01758434],"genre_scores_gemma":[0.636061,0.002240342,0.33618298,0.0007387331,0.00047568142,0.00036839547,0.0016524012,0.00026367436,0.022016786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940586,0.00012308643,0.000031596683,0.0001797865,0.00018748901,0.000072153976],"domain_scores_gemma":[0.9996673,0.000066232365,0.00003393439,0.00007548063,0.00013925433,0.000017736733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005127853,0.00064653164,0.0005909072,0.0005584778,0.0002254114,0.0009670956,0.0006195362,0.0005836344,0.005981181],"category_scores_gemma":[0.001500292,0.00013520094,0.00071574893,0.0003309314,0.00030512107,0.0011911177,0.0009421796,0.0005910126,0.00294615],"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":[0.00051027176,0.00014421783,0.0016706062,0.0003041305,0.000079660305,0.0003677176,0.000358448,0.01122773,0.20028469,0.011334177,0.009319094,0.7643993],"study_design_scores_gemma":[0.000065905486,0.0008538802,0.016704278,0.00020405152,0.00024853856,0.0028578923,0.0007470111,0.6171206,0.25616655,0.03152783,0.07331192,0.00019156786],"about_ca_topic_score_codex":0.0003120911,"about_ca_topic_score_gemma":0.00027318954,"teacher_disagreement_score":0.005981181,"about_ca_system_score_codex":0.00027811262,"about_ca_system_score_gemma":0.00016066618,"threshold_uncertainty_score":0.02000904},"labels":[],"label_agreement":null},{"id":"W3212858387","doi":"","title":"Quantum-Assisted Support Vector Regression for Detecting Facial Landmarks.","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"University of Calgary","funders":"","keywords":"Support vector machine; Computer science; Landmark; Quantum annealing; Artificial intelligence; Simulated annealing; Machine learning; Python (programming language); Quantum; Regression; Solver; Algorithm; Pattern recognition (psychology); Quantum computer; Mathematics; Statistics","score_opus":0.0932493982859152,"score_gpt":0.21690168481483602,"score_spread":0.12365228652892081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212858387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009064702,0.00011417286,0.98887956,0.0001624436,0.000030032943,0.000019576217,0.000069434696,0.0007562016,0.00090390164],"genre_scores_gemma":[0.52294576,0.00017608562,0.47100845,0.00023214903,0.00004743689,0.00014140918,0.00046775243,0.00032279655,0.0046581333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995377,0.00017699366,0.000015895694,0.000101790916,0.00012984165,0.00003782588],"domain_scores_gemma":[0.99907434,0.0005227279,0.00009421483,0.00014413502,0.00013063119,0.000033893637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009530517,0.00047060678,0.0005043554,0.00035775267,0.000261278,0.00060968864,0.0010497245,0.00097313715,0.0026964315],"category_scores_gemma":[0.0037898638,0.00039776746,0.00059803063,0.00046481812,0.00062701775,0.00093338627,0.0007903313,0.001352988,0.0010190444],"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":[0.000118312586,0.000055280492,0.0012035609,0.00010495288,0.00006300557,0.00005370446,0.00007699444,0.8244135,0.008673336,0.026535878,0.004880653,0.13382083],"study_design_scores_gemma":[0.0000013761988,0.0000048976635,0.000045656798,0.0000011879993,0.0000010651329,0.000005771155,0.0000021190456,0.9972192,0.00055584405,0.0019765012,0.00018448535,0.0000018652564],"about_ca_topic_score_codex":0.0026653188,"about_ca_topic_score_gemma":0.0031868855,"teacher_disagreement_score":0.0026964315,"about_ca_system_score_codex":0.000605318,"about_ca_system_score_gemma":0.0007013926,"threshold_uncertainty_score":0.009020448},"labels":[],"label_agreement":null},{"id":"W3213957636","doi":"","title":"Fuzzy kernel K-medoids algorithm for multiclass multidimensional data classification","year":2015,"lang":"en","type":"article","venue":"Journal of Theoretical and Applied Information Technology","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Multiclass classification; Artificial intelligence; Pattern recognition (psychology); Medoid; k-medoids; Kernel (algebra); Data mining; Fuzzy logic; Machine learning; Algorithm; Support vector machine; Mathematics; Fuzzy clustering; Cluster analysis; Discrete mathematics","score_opus":0.024094567701051575,"score_gpt":0.26727277586075765,"score_spread":0.2431782081597061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213957636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003499805,0.00031261964,0.99567777,0.000050636845,0.000027424148,0.000023557663,0.000034349105,0.00018711002,0.00018685877],"genre_scores_gemma":[0.17589502,0.0004895838,0.82139796,0.00007575823,0.000055088425,0.00023983698,0.00036115214,0.00009421639,0.0013913866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807477,0.00062426494,0.0002042334,0.0004920429,0.0004987038,0.00010581635],"domain_scores_gemma":[0.99817455,0.0009206709,0.00012616735,0.00022016696,0.00050187553,0.000056534333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022114937,0.0007120995,0.0020559446,0.0014092636,0.00102869,0.0012557721,0.002124474,0.0015022419,0.0016727422],"category_scores_gemma":[0.00533196,0.0005653315,0.0019453418,0.0018671004,0.00075091625,0.0012872117,0.0015232865,0.0021400333,0.0010062673],"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":[0.00047756353,0.00010568345,0.0010013779,0.0003714446,0.00025871926,0.00009393292,0.00040351626,0.2989633,0.0058696745,0.013380427,0.0038099785,0.67526436],"study_design_scores_gemma":[0.000012573987,0.000046210356,0.00027684704,0.000025429988,0.000025168094,0.00006453239,0.000054690943,0.98811775,0.0018578303,0.008107174,0.0013890727,0.00002266102],"about_ca_topic_score_codex":0.006796849,"about_ca_topic_score_gemma":0.003517681,"teacher_disagreement_score":0.006796849,"about_ca_system_score_codex":0.00090467127,"about_ca_system_score_gemma":0.001546301,"threshold_uncertainty_score":0.013514578},"labels":[],"label_agreement":null},{"id":"W34050557","doi":"10.1007/978-3-642-16259-6_5","title":"Designing a Metric for the Difference between Gaussian Densities","year":2010,"lang":"en","type":"book-chapter","venue":"Advances in intelligent and soft computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Bhattacharyya distance; Kullback–Leibler divergence; Metric (unit); Mathematics; Divergence (linguistics); Mixture model; Gaussian; Pattern recognition (psychology); Cluster analysis; Artificial intelligence; Kernel (algebra); Statistics; Computer science; Combinatorics; Physics","score_opus":0.032675659008295874,"score_gpt":0.28189669281280666,"score_spread":0.24922103380451077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W34050557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033261613,0.00019491605,0.99573815,0.00007103355,0.00005006317,0.000027388774,0.000039823684,0.00014668987,0.00040584846],"genre_scores_gemma":[0.09970355,0.0004981293,0.8972655,0.0001798395,0.00015045523,0.00023467596,0.00044410734,0.0003382803,0.0011854722],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9935673,0.0021916148,0.0005854163,0.0012976755,0.0020868666,0.00027110995],"domain_scores_gemma":[0.98972857,0.005961513,0.00062730844,0.0008959192,0.0024425015,0.00034410154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068258196,0.0015666464,0.0018753685,0.0030655686,0.00068545283,0.0028093276,0.0037023495,0.0028426205,0.0017848817],"category_scores_gemma":[0.036613904,0.0008913254,0.0011301985,0.0022533766,0.0023573304,0.0060202763,0.0033777838,0.0029702869,0.0015231211],"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":[0.00045276433,0.00021119668,0.0050389953,0.0006801466,0.00019248117,0.00024970894,0.0005024522,0.13965717,0.04720443,0.20228752,0.00983055,0.59369266],"study_design_scores_gemma":[0.000025754065,0.00029620214,0.0018088524,0.00006538247,0.00004393709,0.0006567111,0.00014908359,0.8751997,0.015761256,0.098280795,0.0076069618,0.00010525857],"about_ca_topic_score_codex":0.0017770301,"about_ca_topic_score_gemma":0.0016914046,"teacher_disagreement_score":0.0068258196,"about_ca_system_score_codex":0.0017616106,"about_ca_system_score_gemma":0.0012562536,"threshold_uncertainty_score":0.036098838},"labels":[],"label_agreement":null},{"id":"W4200166130","doi":"10.1109/tcyb.2021.3131424","title":"Learning Performance of Weighted Distributed Learning With Support Vector Machines","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Independent and identically distributed random variables; Computer science; Markov chain; Ergodic theory; Generalization; Benchmark (surveying); Convergence (economics); Generalization error; Sampling (signal processing); Algorithm; Rate of convergence; Divide and conquer algorithms; Artificial intelligence; Machine learning; Mathematics; Artificial neural network; Statistics; Random variable","score_opus":0.008895109488162542,"score_gpt":0.21459868072992713,"score_spread":0.20570357124176458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200166130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25707003,0.0014830977,0.7375831,0.00062713114,0.0001822043,0.00007088755,0.00006559936,0.0009526385,0.0019652164],"genre_scores_gemma":[0.95900375,0.00021299363,0.039546963,0.000117086696,0.000052863918,0.00004956439,0.00017274119,0.000048750244,0.0007953384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972089,0.0010309989,0.00019005698,0.0006538872,0.0006690439,0.00024709437],"domain_scores_gemma":[0.9941777,0.0033185123,0.000483724,0.0005956163,0.0011922998,0.00023209242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005160847,0.0012682672,0.0013867471,0.00085478084,0.0005036245,0.001218484,0.001759023,0.0017255291,0.0009781274],"category_scores_gemma":[0.016100343,0.00040657545,0.00065237493,0.00084847596,0.0010873885,0.0029200797,0.001805194,0.0012576729,0.00024080146],"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":[0.00049000833,0.00013410047,0.002480756,0.00008862679,0.00009421048,0.00005832438,0.000045141165,0.8877595,0.001628148,0.004455086,0.0007671151,0.101999074],"study_design_scores_gemma":[0.000008575805,0.000032342563,0.000103563434,0.0000016738672,0.0000030919275,0.0000065209497,0.0000045798206,0.99817944,0.00031682584,0.0013096416,0.000031053507,0.000002734689],"about_ca_topic_score_codex":0.003963196,"about_ca_topic_score_gemma":0.0013093082,"teacher_disagreement_score":0.005160847,"about_ca_system_score_codex":0.001034688,"about_ca_system_score_gemma":0.0013217095,"threshold_uncertainty_score":0.027293503},"labels":[],"label_agreement":null},{"id":"W4205175500","doi":"10.1090/fic/026/03","title":"Extension of Fill’s perfect rejection sampling algorithm to general chains (Extended abstract)","year":2000,"lang":"en","type":"other","venue":"","topic":"Face and Expression Recognition","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":"University of Toronto; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Extension (predicate logic); Sampling (signal processing); Computer science; Mathematics; Algorithm; Combinatorics; Programming language; Telecommunications","score_opus":0.02294658661131061,"score_gpt":0.27563207622506936,"score_spread":0.25268548961375875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205175500","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053208345,0.00004025462,0.9916545,0.0001348471,0.000024669589,0.000043807595,0.000047904454,0.00026116997,0.0024719872],"genre_scores_gemma":[0.2883913,0.00017102144,0.7022256,0.00043727702,0.00013623646,0.00027350517,0.00037049723,0.00030209645,0.007692381],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974955,0.00079877244,0.00014188276,0.00048861635,0.00079748075,0.0002777315],"domain_scores_gemma":[0.99298614,0.0037577983,0.00035631788,0.0018123465,0.0008177585,0.000269727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004704133,0.0005269056,0.0012362843,0.0010840332,0.00084458117,0.0016733658,0.0027043908,0.0014773837,0.0077558965],"category_scores_gemma":[0.018362068,0.0005134074,0.0012370156,0.0012176035,0.0021428098,0.0032271773,0.004186748,0.0023932015,0.0013630353],"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":[0.000350885,0.0001079136,0.001968727,0.00012859669,0.000061713254,0.00020615302,0.00033010793,0.19948128,0.0033181012,0.5493668,0.0056355875,0.23904409],"study_design_scores_gemma":[0.000029309644,0.000043436397,0.00016715215,0.000017572618,0.000010076854,0.00008354671,0.000019565457,0.8270491,0.0015545237,0.16761683,0.0033885809,0.000020337719],"about_ca_topic_score_codex":0.0027576932,"about_ca_topic_score_gemma":0.0023898396,"teacher_disagreement_score":0.0077558965,"about_ca_system_score_codex":0.0010214146,"about_ca_system_score_gemma":0.0016043306,"threshold_uncertainty_score":0.02594608},"labels":[],"label_agreement":null},{"id":"W4205348060","doi":"10.1007/s13042-021-01502-6","title":"Global structure-guided neighborhood preserving embedding for dimensionality reduction","year":2022,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Dimensionality reduction; Computer science; Embedding; Principal component analysis; Artificial intelligence; Pattern recognition (psychology); Robustness (evolution); Graph; Computational intelligence; Graph embedding; Mathematics; Algorithm; Theoretical computer science","score_opus":0.01307863265498242,"score_gpt":0.303010164710925,"score_spread":0.2899315320559426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205348060","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03207721,0.00037562475,0.965678,0.00010946435,0.000041542236,0.000029315375,0.00014528274,0.0004801815,0.0010634265],"genre_scores_gemma":[0.47045967,0.00056521565,0.5207436,0.0001319943,0.000059478203,0.00014499611,0.0011521318,0.0002519316,0.006490877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997427,0.000055672816,0.000012750558,0.000068470115,0.000095442054,0.00002492301],"domain_scores_gemma":[0.999746,0.00006432635,0.000023613753,0.00007873406,0.000069670146,0.00001761448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023004368,0.00055162044,0.00084711,0.000548056,0.00032399045,0.0005191618,0.000725597,0.0004868393,0.0012470458],"category_scores_gemma":[0.00085060985,0.00022904413,0.00058551424,0.0007779566,0.00045622542,0.00095176144,0.0011141463,0.00092524645,0.00048457435],"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":[0.00022917807,0.00021961977,0.0010008487,0.00019446938,0.00009804578,0.00010946134,0.0002173337,0.22597624,0.054764092,0.040451873,0.011133754,0.665605],"study_design_scores_gemma":[0.000008280662,0.000094975265,0.0002968525,0.0000068102663,0.000012623084,0.00007878834,0.000033800006,0.9806604,0.0052629453,0.011422657,0.0021104338,0.0000115075645],"about_ca_topic_score_codex":0.0022612647,"about_ca_topic_score_gemma":0.0035135297,"teacher_disagreement_score":0.0022612647,"about_ca_system_score_codex":0.00024937515,"about_ca_system_score_gemma":0.0006241005,"threshold_uncertainty_score":0.004496217},"labels":[],"label_agreement":null},{"id":"W4205774037","doi":"10.18280/ts.380602","title":"Facial Expressions Recognition Based on Delaunay Triangulation of Landmark and Machine Learning","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"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":"Delaunay triangulation; Landmark; Artificial intelligence; Support vector machine; Discriminative model; Pattern recognition (psychology); Facial expression; Computer science; Classifier (UML); Facial recognition system; Feature vector; Computer vision; Algorithm","score_opus":0.032146590814332625,"score_gpt":0.23677960482249538,"score_spread":0.20463301400816275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205774037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020301072,0.00031273067,0.9760625,0.00008637748,0.00004487615,0.00009719779,0.0001293931,0.00068501127,0.0022808148],"genre_scores_gemma":[0.28741652,0.0007029404,0.7082062,0.00005212739,0.00004593895,0.00020877579,0.0005797619,0.00016899811,0.0026187687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986998,0.0002697093,0.000058132977,0.00039792788,0.0004776444,0.000096843454],"domain_scores_gemma":[0.9994332,0.00012625213,0.00008923473,0.00010813128,0.0002195594,0.000023546332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005405101,0.0006971509,0.0007671312,0.0021022002,0.00050109427,0.00064568094,0.0007802682,0.00053642923,0.0024924586],"category_scores_gemma":[0.0022598966,0.00044186536,0.0007892599,0.0018120031,0.000665091,0.0011634378,0.0009217247,0.0005231924,0.0014249954],"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":[0.00026316626,0.00005425757,0.0039798566,0.00031669374,0.000082969884,0.00019578876,0.0006606686,0.051486418,0.12302099,0.010180876,0.004748699,0.8050097],"study_design_scores_gemma":[0.000018963477,0.00019793176,0.009356522,0.000063771324,0.00004334421,0.0011542159,0.0006600155,0.8941247,0.06897609,0.011416717,0.01386189,0.00012600329],"about_ca_topic_score_codex":0.004749057,"about_ca_topic_score_gemma":0.0043810783,"teacher_disagreement_score":0.004749057,"about_ca_system_score_codex":0.00054681674,"about_ca_system_score_gemma":0.00062892423,"threshold_uncertainty_score":0.009442806},"labels":[],"label_agreement":null},{"id":"W4206442543","doi":"10.18280/ts.380629","title":"Euclidean Distance Versus Manhattan Distance for New Representative SFA Skin Samples for Human Skin Segmentation","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","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":"Euclidean distance; Segmentation; Face (sociological concept); Artificial intelligence; Computer science; Pattern recognition (psychology); Human skin; Measure (data warehouse); Computer vision; Skin color; Data mining","score_opus":0.05870582638371101,"score_gpt":0.3269719322831284,"score_spread":0.26826610589941735,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206442543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22031717,0.0015024693,0.77379644,0.00013887309,0.00019330828,0.00017726686,0.000404184,0.0012222067,0.0022480562],"genre_scores_gemma":[0.47621113,0.0005288142,0.5208306,0.000045478315,0.000040868912,0.00012929349,0.00078244694,0.00014043071,0.0012909482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988507,0.00035178754,0.000085216176,0.00024299779,0.0003837543,0.00008545919],"domain_scores_gemma":[0.99805355,0.00070419564,0.00015586615,0.00027486717,0.00072151446,0.00009003703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014416531,0.00088426046,0.00074848544,0.0021995544,0.00037534002,0.0011379443,0.00053661305,0.00054428744,0.0013070418],"category_scores_gemma":[0.0048729847,0.00017592145,0.0005002219,0.0014892543,0.0004187077,0.0010492556,0.00053191767,0.00050166965,0.000718662],"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":[0.0016482768,0.0003215184,0.008488515,0.00044011572,0.00023492098,0.00028951463,0.0003306626,0.10025814,0.10377302,0.006774279,0.004220307,0.7732207],"study_design_scores_gemma":[0.000028818744,0.00047868126,0.009774339,0.000030100164,0.00005353187,0.0007905833,0.00022029114,0.92218935,0.060382966,0.0024005098,0.003588393,0.00006238106],"about_ca_topic_score_codex":0.0016359977,"about_ca_topic_score_gemma":0.0019691105,"teacher_disagreement_score":0.0021995544,"about_ca_system_score_codex":0.00040547867,"about_ca_system_score_gemma":0.0003688021,"threshold_uncertainty_score":0.007624328},"labels":[],"label_agreement":null},{"id":"W4207013388","doi":"10.23952/jnva.6.2022.1.02","title":"Fast spectral clustering with self-weighted features","year":2022,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"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":"National Natural Science Foundation of China","keywords":"Cluster analysis; Spectral clustering; Computer science; Pattern recognition (psychology); Artificial intelligence; Mathematics","score_opus":0.0061863577833089585,"score_gpt":0.21940780877624008,"score_spread":0.21322145099293113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207013388","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008864002,0.00013494525,0.9899877,0.000053632844,0.000020321622,0.000030933625,0.00005525486,0.00042386225,0.0004293851],"genre_scores_gemma":[0.35446784,0.0003511254,0.64064276,0.00015171582,0.000086873915,0.0002313602,0.0011179876,0.00034361577,0.002606628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860114,0.0003024488,0.00007358596,0.0004142567,0.00048407982,0.00012456386],"domain_scores_gemma":[0.9980476,0.0005620941,0.00021112872,0.00039389718,0.0007139274,0.000071271264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001568884,0.0012887365,0.0011981546,0.0024981091,0.00096436794,0.0009653755,0.0018950719,0.0014420801,0.0014304795],"category_scores_gemma":[0.0051685306,0.00062652014,0.0012231558,0.0023228277,0.0010262169,0.0028184927,0.0016793398,0.0011555083,0.0011048906],"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":[0.00027990562,0.00013762708,0.0026819466,0.00023608885,0.00016208517,0.00014347219,0.0003419847,0.5191034,0.020599997,0.025450794,0.006287392,0.42457533],"study_design_scores_gemma":[0.000007095258,0.000017334176,0.00028755714,0.0000060780303,0.0000076779515,0.000038538063,0.00002378465,0.9889192,0.0023699417,0.0074623465,0.00084602536,0.000014455717],"about_ca_topic_score_codex":0.00510698,"about_ca_topic_score_gemma":0.0059135547,"teacher_disagreement_score":0.00510698,"about_ca_system_score_codex":0.000849807,"about_ca_system_score_gemma":0.001213247,"threshold_uncertainty_score":0.010154545},"labels":[],"label_agreement":null},{"id":"W4207024300","doi":"10.1088/1742-6596/2171/1/012004","title":"End-to-end Saliency Face Detection and Recognition","year":2022,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Face and Expression Recognition","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":"Nexen (Canada)","funders":"","keywords":"Facial recognition system; Computer science; Artificial intelligence; Face (sociological concept); Face detection; Pattern recognition (psychology); Feature (linguistics); Similarity (geometry); Computer vision; Task (project management); Feature extraction; Three-dimensional face recognition; Set (abstract data type); End-to-end principle; Object-class detection; Image (mathematics); Engineering","score_opus":0.028093996129640288,"score_gpt":0.23933062026669205,"score_spread":0.21123662413705177,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207024300","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041338045,0.00014780967,0.9413096,0.00014113124,0.000100884565,0.0002150585,0.00025616767,0.012637883,0.0038533807],"genre_scores_gemma":[0.67717195,0.00011189661,0.3082453,0.00027061877,0.000081359554,0.000191718,0.00083579856,0.00036565747,0.012725639],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997031,0.000018269391,0.000009858718,0.00009054563,0.000106493455,0.00007162565],"domain_scores_gemma":[0.9996842,0.00004642166,0.000020487774,0.00007999127,0.00013700177,0.00003182813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026957705,0.0006564088,0.0008285649,0.00046549365,0.00035785657,0.0005094378,0.0014849713,0.0005268586,0.008047457],"category_scores_gemma":[0.0007774627,0.00025746113,0.00032159532,0.00021894445,0.00025191926,0.0007977407,0.0009230553,0.0004889416,0.0049635945],"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":[0.0015094082,0.00045036312,0.0026284247,0.0001525873,0.00007831341,0.00039351635,0.00012070867,0.02493774,0.1971865,0.0037381393,0.02011001,0.74869436],"study_design_scores_gemma":[0.000041710588,0.0002831239,0.0030009034,0.000010395496,0.000025481904,0.00033472836,0.00003489763,0.84524596,0.1411446,0.0042752535,0.005571344,0.000031655876],"about_ca_topic_score_codex":0.0024104225,"about_ca_topic_score_gemma":0.0035539356,"teacher_disagreement_score":0.008047457,"about_ca_system_score_codex":0.00040843678,"about_ca_system_score_gemma":0.00075851503,"threshold_uncertainty_score":0.026921451},"labels":[],"label_agreement":null},{"id":"W4210647131","doi":"10.1109/tkde.2022.3144294","title":"Low-Rank Linear Embedding for Robust Clustering","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Face and Expression Recognition","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":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; Natural Science Foundation of Shenzhen City","keywords":"Cluster analysis; Dimensionality reduction; Computer science; Embedding; Robustness (evolution); Correlation clustering; Curse of dimensionality; Artificial intelligence; Pattern recognition (psychology); Algorithm","score_opus":0.03821421735241483,"score_gpt":0.27930823292496615,"score_spread":0.24109401557255133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210647131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020261768,0.00019989439,0.9963644,0.00008082219,0.000025688976,0.000019514813,0.00006981968,0.0007166779,0.0004969661],"genre_scores_gemma":[0.16756074,0.0006259572,0.8239546,0.00021253744,0.00016546623,0.0002577586,0.0014763739,0.0005253333,0.00522128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977748,0.0006568384,0.00010864818,0.000608703,0.00071478053,0.00013629666],"domain_scores_gemma":[0.9982862,0.0004424596,0.00020501263,0.0004487639,0.0005592691,0.000058200523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014522881,0.0018113547,0.0014170342,0.0014998235,0.00077363476,0.0013803783,0.0016466116,0.0014621873,0.0028020116],"category_scores_gemma":[0.0052942205,0.000559608,0.0009996712,0.0019839345,0.0010814576,0.001967546,0.0016158812,0.0022989037,0.0037798209],"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":[0.00016800818,0.000109708635,0.00048244966,0.00028801986,0.00012435137,0.00009480421,0.00020660846,0.48103037,0.018244844,0.03572703,0.013383576,0.45014024],"study_design_scores_gemma":[0.000004214278,0.000038767157,0.00014067307,0.000008775356,0.000007311457,0.000039319264,0.000023588103,0.98068917,0.0036695735,0.013125892,0.0022310184,0.000021683181],"about_ca_topic_score_codex":0.0034358762,"about_ca_topic_score_gemma":0.0041258545,"teacher_disagreement_score":0.0034358762,"about_ca_system_score_codex":0.00089631736,"about_ca_system_score_gemma":0.001269295,"threshold_uncertainty_score":0.009373665},"labels":[],"label_agreement":null},{"id":"W4213048988","doi":"10.1093/imaiai/iaac004","title":"An analysis of classical multidimensional scaling with applications to clustering","year":2022,"lang":"en","type":"article","venue":"Information and Inference A Journal of the IMA","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Institute of Dental and Craniofacial Research; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Cluster analysis; Scaling; Multidimensional scaling; Statistical physics; Scaling law; Econometrics; Computer science; Physics; Economics; Mathematics; Artificial intelligence; Machine learning; Geometry","score_opus":0.014548756245397169,"score_gpt":0.27289479637366815,"score_spread":0.258346040128271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213048988","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006713323,0.0018464166,0.9868832,0.0007863572,0.00013718498,0.00006286263,0.00010244671,0.000121234356,0.0033469377],"genre_scores_gemma":[0.39700186,0.004809596,0.58852196,0.0011193795,0.0013933446,0.0005957406,0.0005858814,0.0003671307,0.005605083],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99276966,0.0028849307,0.00031570406,0.0009931412,0.0027564429,0.00028007492],"domain_scores_gemma":[0.9714515,0.017988646,0.0018901032,0.0031468736,0.004883395,0.00063947676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012211969,0.0013914309,0.0014387792,0.005950406,0.0017276413,0.0030601942,0.0022333805,0.0016294911,0.0028036078],"category_scores_gemma":[0.054902043,0.0007011981,0.0019622021,0.0049784016,0.005150939,0.0042105247,0.0037883199,0.00309853,0.00062733603],"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":[0.00005413674,0.0000388832,0.0015588208,0.0003566856,0.00014686535,0.00017624277,0.00038156545,0.11537624,0.0018411468,0.82313013,0.003238483,0.053700827],"study_design_scores_gemma":[0.0000075292373,0.000046019624,0.0011114876,0.00007879862,0.000027171602,0.0001272186,0.000074639116,0.53684723,0.0007893124,0.45644066,0.004400677,0.00004934182],"about_ca_topic_score_codex":0.001982499,"about_ca_topic_score_gemma":0.0010281742,"teacher_disagreement_score":0.012211969,"about_ca_system_score_codex":0.00255715,"about_ca_system_score_gemma":0.0015421449,"threshold_uncertainty_score":0.06458378},"labels":[],"label_agreement":null},{"id":"W4220734064","doi":"10.1007/s13042-022-01529-3","title":"A novel feature selection method using generalized inverted Dirichlet-based HMMs for image categorization","year":2022,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"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":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Pattern recognition (psychology); Computational intelligence; Artificial intelligence; Categorization; Feature selection; Computer science; Selection (genetic algorithm); Latent Dirichlet allocation; Feature (linguistics); Hierarchical Dirichlet process; Dirichlet distribution; Image (mathematics); Mathematics; Topic model; Linguistics","score_opus":0.016426459178953794,"score_gpt":0.30391093679981235,"score_spread":0.28748447762085855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220734064","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01144099,0.0005724536,0.9850376,0.00012075229,0.00012348,0.000082257524,0.00021425245,0.0018166003,0.00059157115],"genre_scores_gemma":[0.33524638,0.00066427875,0.65221035,0.00043136778,0.0002956183,0.00043593306,0.002620302,0.00043152846,0.007664231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984316,0.0003491395,0.000120415214,0.0004932943,0.0003811343,0.00022433065],"domain_scores_gemma":[0.9990263,0.00040427386,0.000043341097,0.00014154152,0.00032081714,0.000063750136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011370422,0.0008652552,0.0022870416,0.0017345367,0.0010254786,0.0010361894,0.0020793115,0.0012711193,0.003114764],"category_scores_gemma":[0.0020530967,0.00052399415,0.0019160659,0.001840582,0.00049398554,0.0013264365,0.0012212994,0.0015737592,0.0025923625],"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":[0.00053747295,0.00034713544,0.0018298537,0.00014657137,0.00028168136,0.00014581322,0.00011328189,0.02530005,0.028351929,0.003225713,0.008849916,0.93087053],"study_design_scores_gemma":[0.000040074632,0.000092066366,0.0018676771,0.000014164257,0.00009882311,0.00016314539,0.000061513405,0.97897106,0.010870283,0.0051407903,0.00263285,0.000047569793],"about_ca_topic_score_codex":0.009473432,"about_ca_topic_score_gemma":0.011068157,"teacher_disagreement_score":0.009473432,"about_ca_system_score_codex":0.0005359445,"about_ca_system_score_gemma":0.0014149561,"threshold_uncertainty_score":0.018836617},"labels":[],"label_agreement":null},{"id":"W4220945643","doi":"10.36227/techrxiv.19357979","title":"Deep Multi-Representation Learning for Data Clustering","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Cluster analysis; Autoencoder; Computer science; Artificial intelligence; Clustering high-dimensional data; Pattern recognition (psychology); Representation (politics); Embedding; Benchmark (surveying); Correlation clustering; Cluster (spacecraft); AKA; Subspace topology; Data mining; Deep learning; Geography","score_opus":0.1619162002673303,"score_gpt":0.37482414671688086,"score_spread":0.21290794644955055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220945643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004410231,0.000479907,0.9935289,0.00017519413,0.000023339044,0.000023569899,0.00008982488,0.0008461637,0.000422912],"genre_scores_gemma":[0.36478725,0.0008877743,0.627941,0.00041079248,0.00010873441,0.00022787105,0.0014502715,0.000256374,0.0039299848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831945,0.0005442346,0.000103103135,0.0005032783,0.00035979084,0.00017022697],"domain_scores_gemma":[0.9985185,0.00043500398,0.00019351908,0.00047400405,0.00029333116,0.00008557477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020588797,0.001199481,0.0016316397,0.0020067818,0.00075316435,0.001497508,0.0022111982,0.001784247,0.0017366814],"category_scores_gemma":[0.0042203506,0.00068144023,0.0014728198,0.0027975633,0.0012603747,0.0031878655,0.0027794484,0.0028789202,0.0010660726],"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":[0.00016216027,0.00016878845,0.0014096102,0.00023471622,0.00021670418,0.00008259053,0.0001893318,0.5323889,0.006545123,0.03393874,0.0071193147,0.41754398],"study_design_scores_gemma":[0.0000042713787,0.000017434977,0.00015676852,0.000008868524,0.000007816718,0.000016987722,0.00001479006,0.97804606,0.0013876086,0.019459352,0.00087023515,0.000009860716],"about_ca_topic_score_codex":0.005295446,"about_ca_topic_score_gemma":0.0064943032,"teacher_disagreement_score":0.005295446,"about_ca_system_score_codex":0.002403563,"about_ca_system_score_gemma":0.0016311458,"threshold_uncertainty_score":0.017439127},"labels":[],"label_agreement":null},{"id":"W4221052463","doi":"10.36227/techrxiv.19357979.v1","title":"Deep Multi-Representation Learning for Data Clustering","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Compute Canada; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Cluster analysis; Autoencoder; Computer science; Artificial intelligence; Clustering high-dimensional data; Pattern recognition (psychology); Representation (politics); Embedding; Benchmark (surveying); Correlation clustering; Cluster (spacecraft); Subspace topology; AKA; Feature learning; Data mining; Deep learning; Geography","score_opus":0.1619162002673303,"score_gpt":0.37482414671688086,"score_spread":0.21290794644955055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221052463","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004531566,0.0004969541,0.9934081,0.00016712876,0.000023646866,0.000024130928,0.00009014459,0.00083239074,0.0004260717],"genre_scores_gemma":[0.35679746,0.00091997127,0.63624305,0.00038529502,0.00010479353,0.00023532606,0.0014661106,0.00025109993,0.00359694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99829406,0.000536358,0.000108484135,0.0005181281,0.00037440253,0.00016858947],"domain_scores_gemma":[0.9985525,0.00041809003,0.00019061804,0.0004648354,0.00029117882,0.00008276695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020813667,0.0012487521,0.0016529592,0.0020182752,0.00078438636,0.0015047925,0.002258884,0.001777695,0.001712639],"category_scores_gemma":[0.004248755,0.00068699295,0.0015284867,0.0028572145,0.0012695561,0.0030909479,0.0027564699,0.0028742563,0.0010848044],"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":[0.00015925708,0.00017262857,0.0015271126,0.0002572465,0.00022924138,0.000086818574,0.00020450572,0.5180365,0.0070557366,0.036604043,0.007519902,0.42814702],"study_design_scores_gemma":[0.000004355138,0.000017356295,0.00016334854,0.0000092966775,0.000008386618,0.000018505358,0.000015905203,0.97716695,0.0015379708,0.020127172,0.00092057575,0.000010200943],"about_ca_topic_score_codex":0.004954916,"about_ca_topic_score_gemma":0.006316833,"teacher_disagreement_score":0.004954916,"about_ca_system_score_codex":0.002380427,"about_ca_system_score_gemma":0.0016768888,"threshold_uncertainty_score":0.01727134},"labels":[],"label_agreement":null},{"id":"W4224930295","doi":"10.4108/eetiot.v7i28.685","title":"A facial expression recognizer using modified ResNet-152","year":2022,"lang":"en","type":"article","venue":"EAI Endorsed Transactions on Internet of Things","topic":"Face and Expression Recognition","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":"Carleton University","funders":"","keywords":"Facial expression recognition; Computer science; Facial expression; Speech recognition; Expression (computer science); Artificial intelligence; Residual neural network; Emotion recognition; Facial recognition system; Mainstream; Pattern recognition (psychology); Deep learning","score_opus":0.02971128683699219,"score_gpt":0.2529198750965703,"score_spread":0.22320858825957812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224930295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22606006,0.001965765,0.7118013,0.00073838123,0.0015287952,0.001215713,0.002912211,0.02531709,0.028460646],"genre_scores_gemma":[0.6550753,0.0009075986,0.31086957,0.00077119947,0.000236352,0.0008536673,0.0053583593,0.00047100056,0.025456939],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997037,0.0000341955,0.00002194125,0.00008737799,0.000101633006,0.0000511115],"domain_scores_gemma":[0.99990046,0.000013475784,0.000006881103,0.00001600437,0.00005322005,0.000009999912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052485045,0.0009826467,0.00068703975,0.0008419842,0.00029962405,0.0003817489,0.0009092656,0.00043754102,0.004626713],"category_scores_gemma":[0.0004692665,0.00027120425,0.0006208411,0.0004046568,0.00016180218,0.00070171186,0.0003582187,0.0005625056,0.0026700483],"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":[0.0007348029,0.00054040994,0.0020522312,0.00019443528,0.00021499865,0.00049133424,0.00008950229,0.018719506,0.18228896,0.0024360707,0.021833954,0.7704038],"study_design_scores_gemma":[0.00011240312,0.0007589436,0.007582965,0.000035932466,0.0001865362,0.0008408868,0.00006723505,0.83476293,0.13713142,0.0021743758,0.016241645,0.00010471819],"about_ca_topic_score_codex":0.0060166963,"about_ca_topic_score_gemma":0.00652726,"teacher_disagreement_score":0.0060166963,"about_ca_system_score_codex":0.00046643437,"about_ca_system_score_gemma":0.00046884752,"threshold_uncertainty_score":0.015477896},"labels":[],"label_agreement":null},{"id":"W4225550289","doi":"10.1016/j.eswa.2022.116928","title":"Optimizing feature selection methods by removing irrelevant features using sparse least squares","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Overfitting; Feature selection; Computer science; Artificial intelligence; Curse of dimensionality; Pattern recognition (psychology); Feature (linguistics); Selection (genetic algorithm); Singular value decomposition; Machine learning; Partial least squares regression; Data mining","score_opus":0.02191111192332507,"score_gpt":0.30416726898630836,"score_spread":0.2822561570629833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225550289","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010482616,0.00013691779,0.9884899,0.0000765883,0.00003643421,0.00002647275,0.000027143135,0.00043301826,0.00029095836],"genre_scores_gemma":[0.28641403,0.0003335777,0.7071792,0.00018660049,0.00013742415,0.00018606277,0.0005674504,0.00034017087,0.00465553],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993711,0.00015143867,0.00003453786,0.00011937019,0.00026036293,0.00006328648],"domain_scores_gemma":[0.99892765,0.000485476,0.00008382751,0.00011483338,0.00035528277,0.00003279553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008012837,0.0012793824,0.0012641323,0.000770874,0.00050365546,0.0006915553,0.0007493739,0.0008274348,0.0015008429],"category_scores_gemma":[0.0028486713,0.0005731797,0.00081478694,0.00075158855,0.00041312582,0.0008210935,0.00057344337,0.0009975453,0.000889656],"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":[0.00030715755,0.0002984558,0.0015036089,0.00019459508,0.00017557363,0.00012904423,0.000065109365,0.21464947,0.068250634,0.003567536,0.00826515,0.70259374],"study_design_scores_gemma":[0.000021992093,0.000053729593,0.00061221834,0.000005747346,0.000030865172,0.000044586664,0.00001312389,0.98746336,0.009118122,0.0015908944,0.0010357025,0.000009715195],"about_ca_topic_score_codex":0.0028473726,"about_ca_topic_score_gemma":0.00408363,"teacher_disagreement_score":0.0028473726,"about_ca_system_score_codex":0.00026580621,"about_ca_system_score_gemma":0.0008507364,"threshold_uncertainty_score":0.005661607},"labels":[],"label_agreement":null},{"id":"W4225751462","doi":"10.36227/techrxiv.14852652.v2","title":"Deep Clustering with Self-supervision using Pairwise Data Similarities","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Hypersphere; Cluster analysis; Autoencoder; Pairwise comparison; Embedding; Cluster (spacecraft); Computer science; Artificial intelligence; Benchmark (surveying); Pattern recognition (psychology); Set (abstract data type); Data mining; Mathematics; Deep learning; Geography","score_opus":0.07504930655030552,"score_gpt":0.28189275257814117,"score_spread":0.20684344602783566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225751462","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014696203,0.00008492007,0.9838256,0.00007334432,0.000010777652,0.000033608598,0.000052072188,0.00060344423,0.0006199929],"genre_scores_gemma":[0.5180601,0.00016637423,0.4776889,0.00013097179,0.00004341137,0.00015356857,0.00065381365,0.0002296433,0.0028731269],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986117,0.00032241445,0.00007990407,0.00048731672,0.0003906554,0.00010802055],"domain_scores_gemma":[0.99805033,0.0004252292,0.00031122405,0.0006106631,0.00048493585,0.00011757213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013943493,0.0009947238,0.0012663931,0.0012582156,0.0006932634,0.0012127224,0.002222456,0.0012467763,0.0016638759],"category_scores_gemma":[0.0036930528,0.0007163921,0.0010797513,0.0012733064,0.0015195372,0.0029139372,0.0029175375,0.0017331534,0.0008146181],"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":[0.00018251405,0.0001970668,0.003250683,0.00018604053,0.00018338853,0.000082838196,0.00031295963,0.5824462,0.016945183,0.029825335,0.0035369669,0.36285076],"study_design_scores_gemma":[0.0000057513653,0.00003508931,0.00029520996,0.0000070198967,0.000006349658,0.000030533025,0.000021784954,0.98599434,0.003385038,0.009673592,0.0005347953,0.000010481443],"about_ca_topic_score_codex":0.004404729,"about_ca_topic_score_gemma":0.0067278817,"teacher_disagreement_score":0.004404729,"about_ca_system_score_codex":0.0013646765,"about_ca_system_score_gemma":0.0015893189,"threshold_uncertainty_score":0.009901464},"labels":[],"label_agreement":null},{"id":"W4231251686","doi":"10.1007/978-981-32-9945-0_3","title":"Face Recognition","year":2019,"lang":"en","type":"book-chapter","venue":"Cognitive intelligence and robotics","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Biometrics; Computer security; Authentication (law); Identification (biology); Computer science; Phone; Facial recognition system; Access control; Face (sociological concept); Field (mathematics); Internet privacy; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.05963458351129151,"score_gpt":0.2661674054037441,"score_spread":0.20653282189245262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231251686","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032247568,0.008053858,0.20208114,0.0006864064,0.0013707008,0.00019112513,0.00091974926,0.005223872,0.7782483],"genre_scores_gemma":[0.023764718,0.005156786,0.06555464,0.00083035836,0.0003475016,0.00012449466,0.0020227735,0.00045459613,0.9017441],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996793,0.000016526536,0.000009319682,0.00009023221,0.00016839294,0.000036282025],"domain_scores_gemma":[0.99985814,0.000021165952,0.0000056371773,0.00004555429,0.000058290225,0.000011267222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024311992,0.0009933709,0.0005826048,0.0012969768,0.00068978703,0.0018877884,0.0014493902,0.0012751477,0.09062085],"category_scores_gemma":[0.00046910433,0.00037489674,0.00049960625,0.0010716864,0.00047454747,0.0015907352,0.0012023454,0.0010689574,0.100060396],"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":[0.000038693663,0.000055016917,0.00011506231,0.00016663242,0.000010509721,0.00006678516,0.0000541899,0.0006909113,0.024212323,0.019300595,0.10029527,0.854994],"study_design_scores_gemma":[0.000008019446,0.000062240404,0.0010044011,0.00013363053,0.000021063903,0.001112988,0.00007135367,0.007375741,0.047907304,0.014581367,0.9276857,0.000036312646],"about_ca_topic_score_codex":0.0013365167,"about_ca_topic_score_gemma":0.0023490866,"teacher_disagreement_score":0.09062085,"about_ca_system_score_codex":0.0005297589,"about_ca_system_score_gemma":0.00047620144,"threshold_uncertainty_score":0.3031569},"labels":[],"label_agreement":null},{"id":"W4231973565","doi":"10.1007/978-3-642-13022-9_30","title":"Analysis of the Inducing Factors Involved in Stem Cell Differentiation Using Feature Selection Techniques, Support Vector Machines and Decision Trees","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Health Research Foundation","funders":"","keywords":"Computer science; Feature selection; Decision tree; Stem cell; Support vector machine; Feature (linguistics); Selection (genetic algorithm); Process (computing); Cellular differentiation; Machine learning; Artificial intelligence; Data mining; Biology; Cell biology; Genetics; Gene","score_opus":0.016785735800871983,"score_gpt":0.2436260520724966,"score_spread":0.22684031627162463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231973565","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.46229273,0.0032523354,0.5308192,0.00012910798,0.000056311947,0.00010640652,0.0010855179,0.00083163084,0.0014268243],"genre_scores_gemma":[0.8008103,0.0016702999,0.19379456,0.000028422459,0.00003951449,0.00013164175,0.0017863114,0.000088723806,0.0016501996],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980265,0.000027586731,0.00001869225,0.000030100067,0.00009252024,0.000028491784],"domain_scores_gemma":[0.99928504,0.00044944923,0.00009014339,0.000030091142,0.00012340979,0.000021801912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006251739,0.00047812675,0.00085762603,0.00095024373,0.00021536442,0.00081510184,0.00029344333,0.0002504247,0.0007214208],"category_scores_gemma":[0.0011803984,0.00019992005,0.00076386094,0.0011014658,0.00020759272,0.00041341558,0.00015610541,0.0005331468,0.00028272677],"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":[0.0009278532,0.00033144353,0.009889133,0.00041088194,0.0001029919,0.00023821743,0.000074832016,0.08987496,0.40910923,0.00332035,0.0012268911,0.48449323],"study_design_scores_gemma":[0.000028718729,0.00042559515,0.018495498,0.000021307293,0.00017662859,0.00023695346,0.00006430498,0.76484174,0.20959963,0.004146363,0.001922268,0.00004107582],"about_ca_topic_score_codex":0.0011549208,"about_ca_topic_score_gemma":0.0011692179,"teacher_disagreement_score":0.0011549208,"about_ca_system_score_codex":0.00034485877,"about_ca_system_score_gemma":0.0004707548,"threshold_uncertainty_score":0.0033062696},"labels":[],"label_agreement":null},{"id":"W4236458069","doi":"10.32920/ryerson.14646240.v1","title":"Content-independent orientation detection with histogram of optimized local binary pattern","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Microsoft Research Asia; Microsoft Research","keywords":"Local binary patterns; Artificial intelligence; Histogram; Computer vision; Computer science; Orientation (vector space); Face detection; Pattern recognition (psychology); Luminance; Face (sociological concept); Object-class detection; Binary number; Grayscale; Feature extraction; Facial recognition system; Image (mathematics); Mathematics","score_opus":0.026448477490733743,"score_gpt":0.23988473721435988,"score_spread":0.21343625972362612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236458069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018388048,0.00041631344,0.9782608,0.000086505584,0.000097316886,0.00009097215,0.00011617819,0.00089350226,0.0016503222],"genre_scores_gemma":[0.2086343,0.00095067744,0.7840122,0.00013868262,0.00015330694,0.0001463761,0.00068355264,0.0002009938,0.005079923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995943,0.000055341407,0.000017619466,0.00009507233,0.00018887839,0.00004887007],"domain_scores_gemma":[0.9997228,0.000050699724,0.000042555886,0.00006852748,0.000096786986,0.000018654622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038469562,0.00043607183,0.0006435188,0.0013452555,0.00021974607,0.0008823247,0.000819827,0.00055738946,0.002120541],"category_scores_gemma":[0.0011154828,0.00035043622,0.0005400106,0.0012378802,0.00032715453,0.001035917,0.0007361875,0.00061131,0.0014724229],"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":[0.00020964934,0.00011480892,0.0013340601,0.00023439027,0.00008242945,0.000108818705,0.00008003566,0.016769733,0.17659934,0.00803249,0.0050531323,0.79138094],"study_design_scores_gemma":[0.000042733434,0.00028654587,0.0064865183,0.0000443394,0.00008575668,0.00065565103,0.0000832804,0.7607758,0.20838015,0.009250841,0.013827629,0.00008072752],"about_ca_topic_score_codex":0.0009226371,"about_ca_topic_score_gemma":0.0008826963,"teacher_disagreement_score":0.002120541,"about_ca_system_score_codex":0.00031586393,"about_ca_system_score_gemma":0.00040102183,"threshold_uncertainty_score":0.007093966},"labels":[],"label_agreement":null},{"id":"W4236592957","doi":"10.24124/2010/bpgub686","title":"Initial investigation into using a two-level regional voting approach for face verification.","year":2010,"lang":"en","type":"dissertation","venue":"","topic":"Face and Expression Recognition","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 Northern British Columbia; Library and Archives Canada","funders":"","keywords":"Face (sociological concept); Benchmark (surveying); Computer science; Voting; Identification (biology); Similarity (geometry); Embedding; Facial recognition system; Baseline (sea); Identity (music); A priori and a posteriori; Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology); Algorithm; Image (mathematics)","score_opus":0.11172522225787278,"score_gpt":0.3403756515861096,"score_spread":0.22865042932823681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236592957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122386515,0.002485112,0.85504925,0.00053690013,0.00022223932,0.00066172867,0.00011533243,0.0005971599,0.017945701],"genre_scores_gemma":[0.61429524,0.0011782673,0.37206373,0.00015055222,0.000049922364,0.00015365252,0.00018378544,0.000051615152,0.01187311],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990864,0.000339254,0.000033456257,0.00017497192,0.0003114019,0.00005447059],"domain_scores_gemma":[0.9992582,0.00023890803,0.00001854908,0.00013121351,0.00033526172,0.00001782994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018044997,0.00029011327,0.00043275006,0.00048850675,0.00048074152,0.0008161589,0.0006896888,0.00052341184,0.0030097954],"category_scores_gemma":[0.0026916002,0.00015404636,0.00049861264,0.00042984763,0.00044131916,0.0011867507,0.0004741955,0.00053494057,0.0009329537],"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":[0.0005848347,0.0002791273,0.004506861,0.00027609646,0.00013001675,0.00019264183,0.00024680397,0.023059687,0.11760745,0.025393851,0.0026834696,0.82503915],"study_design_scores_gemma":[0.0000884719,0.0016259094,0.008227522,0.00006750168,0.00016442523,0.001516503,0.00037793056,0.78882104,0.15963754,0.008503945,0.030866995,0.00010216905],"about_ca_topic_score_codex":0.0028649308,"about_ca_topic_score_gemma":0.0038202314,"teacher_disagreement_score":0.0030097954,"about_ca_system_score_codex":0.0005706833,"about_ca_system_score_gemma":0.0005596168,"threshold_uncertainty_score":0.010068774},"labels":[],"label_agreement":null},{"id":"W4236821994","doi":"10.22215/etd/2020-14357","title":"Multi-scale Deep Nearest Neighbors","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Face and Expression Recognition","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":"Carleton University","funders":"","keywords":"Embedding; Margin (machine learning); Cluster analysis; k-nearest neighbors algorithm; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Sample space; Differentiable function; Space (punctuation); Scale (ratio); Computer science; Feature vector; Sample (material); Mathematics; Algorithm; Machine learning; Geography; Physics; Pure mathematics; Cartography","score_opus":0.017658070328591197,"score_gpt":0.26137673561727554,"score_spread":0.24371866528868433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236821994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.084632754,0.00453147,0.8850631,0.0006071314,0.0006392333,0.00015225053,0.0014050865,0.0025883182,0.020380637],"genre_scores_gemma":[0.7571158,0.0019501704,0.2094105,0.00035160745,0.00024663974,0.00012708803,0.0024226857,0.00024404403,0.02813147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989441,0.00008522528,0.000058972942,0.00033114268,0.0004513201,0.00012925667],"domain_scores_gemma":[0.9990771,0.0001531971,0.0000823684,0.00022451485,0.00040715383,0.000055606473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070391945,0.0006120892,0.0011354435,0.0010530379,0.000622606,0.0013684174,0.0012661489,0.0010649561,0.0058484026],"category_scores_gemma":[0.0024017182,0.00039004887,0.0007671111,0.0013300412,0.0004207581,0.0021742047,0.0014443356,0.0009391271,0.00281396],"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":[0.0002633036,0.00021291358,0.0028245568,0.00022692431,0.00014738743,0.00013332472,0.00014877066,0.10604158,0.013732858,0.013926411,0.018124975,0.84421694],"study_design_scores_gemma":[0.000018607854,0.00010647522,0.00215655,0.000052935142,0.000039559756,0.00018514736,0.00008681714,0.9624458,0.00913095,0.017449178,0.008293869,0.000034171986],"about_ca_topic_score_codex":0.006413461,"about_ca_topic_score_gemma":0.011709486,"teacher_disagreement_score":0.006413461,"about_ca_system_score_codex":0.00071332004,"about_ca_system_score_gemma":0.00067612017,"threshold_uncertainty_score":0.019564867},"labels":[],"label_agreement":null},{"id":"W4237658792","doi":"10.24124/2014/bpgub981","title":"Scale-space and wavelet decomposition based scheme for face recognition using nearest linear combination.","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Northern British Columbia","keywords":"Facial recognition system; Artificial intelligence; Pattern recognition (psychology); Computer science; Decomposition; Scale (ratio); Face (sociological concept); Wavelet; Scale space; k-nearest neighbors algorithm; Wavelet transform; Computer vision; Image processing; Image (mathematics); Geography","score_opus":0.027562638701541093,"score_gpt":0.3031725685021738,"score_spread":0.2756099298006327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237658792","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014401959,0.00097030343,0.97832847,0.00017202664,0.00029792363,0.00018129808,0.0001324211,0.001277937,0.0042377175],"genre_scores_gemma":[0.19976582,0.0009232296,0.7856394,0.0001380728,0.000121685895,0.0002634667,0.00067962985,0.000090935886,0.01237773],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989002,0.00020328762,0.00006232693,0.00019540472,0.00058526645,0.000053531447],"domain_scores_gemma":[0.9996567,0.00004027423,0.0000277476,0.00008160538,0.00017107058,0.00002256066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086176035,0.0004823419,0.00069813756,0.0011855498,0.0004648855,0.00068028393,0.0008479608,0.00074701925,0.004396636],"category_scores_gemma":[0.0013541792,0.00028522575,0.00071620476,0.001490923,0.00036000405,0.00097048585,0.00059636147,0.00085628196,0.003887365],"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":[0.00018655026,0.00015938011,0.0008834818,0.00014053473,0.0000750401,0.00009080503,0.000082535065,0.0118517615,0.054940484,0.004386049,0.00546953,0.9217338],"study_design_scores_gemma":[0.000039067927,0.0003350172,0.0049838647,0.000032691918,0.000082807754,0.0010515562,0.00010504071,0.9064616,0.06188693,0.005320195,0.019607088,0.00009421267],"about_ca_topic_score_codex":0.0015095521,"about_ca_topic_score_gemma":0.0019325137,"teacher_disagreement_score":0.004396636,"about_ca_system_score_codex":0.0003489281,"about_ca_system_score_gemma":0.0004445804,"threshold_uncertainty_score":0.014708221},"labels":[],"label_agreement":null},{"id":"W4237822459","doi":"10.36227/techrxiv.14852652","title":"Deep Clustering with Self-supervision using Pairwise Data Similarities","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Cluster analysis; Hypersphere; Autoencoder; Pairwise comparison; Embedding; Computer science; Cluster (spacecraft); Benchmark (surveying); Set (abstract data type); Artificial intelligence; Pattern recognition (psychology); Data set; Data mining; Deep learning; Geography","score_opus":0.07504930655030552,"score_gpt":0.28189275257814117,"score_spread":0.20684344602783566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4237822459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015820723,0.00013050609,0.98168683,0.000107533146,0.00001857132,0.000040867988,0.00011644406,0.0012200818,0.00085844053],"genre_scores_gemma":[0.50034064,0.00020225102,0.49216858,0.00021787446,0.000057760837,0.00016911812,0.0013902982,0.00039910508,0.0050544436],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987399,0.00024583115,0.00006643745,0.00049060024,0.00034086802,0.000116391384],"domain_scores_gemma":[0.9984024,0.00029168505,0.00022080583,0.00055495807,0.00042407963,0.00010607179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011908959,0.0011989155,0.0013576277,0.0012465963,0.00070830114,0.0012327519,0.0025292085,0.0013716898,0.0022813147],"category_scores_gemma":[0.0030716443,0.0007711352,0.0011860563,0.0012975829,0.0013213041,0.0026488625,0.002783906,0.0018762622,0.0012601605],"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":[0.00021158677,0.00018640538,0.0027805648,0.00016763789,0.00019858607,0.00009023794,0.000245463,0.54011035,0.015801677,0.022315955,0.0065682344,0.4113233],"study_design_scores_gemma":[0.00000625647,0.00002613596,0.00023504674,0.000006341754,0.0000061623396,0.00002724192,0.000017034183,0.98852414,0.0029508988,0.0076065026,0.00058533315,0.000008892524],"about_ca_topic_score_codex":0.0070029898,"about_ca_topic_score_gemma":0.011053359,"teacher_disagreement_score":0.0070029898,"about_ca_system_score_codex":0.0014907448,"about_ca_system_score_gemma":0.0016818133,"threshold_uncertainty_score":0.01392442},"labels":[],"label_agreement":null},{"id":"W4238745157","doi":"10.22360/springsim.2018.cns.013","title":"Improving Support Vector Machine Classification Accuracy based on Kernel Parameters Optimization","year":2017,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Support vector machine; Artificial intelligence; Computer science; Hyperplane; Pattern recognition (psychology); Structured support vector machine; Machine learning; Kernel (algebra); Feature selection; Relevance vector machine; Decision boundary; Linear classifier; Statistical classification; Feature (linguistics); Mathematics","score_opus":0.03803200407509187,"score_gpt":0.2805750385439604,"score_spread":0.2425430344688685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238745157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09771075,0.0023441282,0.8938297,0.00032213784,0.00019865492,0.00007110427,0.00011943412,0.0030383016,0.0023657659],"genre_scores_gemma":[0.7867863,0.00080902904,0.20930749,0.0001024087,0.000106447595,0.0000937807,0.0005261963,0.00023146729,0.0020368842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997456,0.0004953709,0.0003036971,0.0003936307,0.0011234159,0.0002278757],"domain_scores_gemma":[0.99494344,0.0019112412,0.0004435737,0.0004978086,0.0021210753,0.00008285139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020118973,0.0009200567,0.0017053605,0.0015726823,0.00040464848,0.0014628244,0.0010453006,0.0012116025,0.0015890222],"category_scores_gemma":[0.013020441,0.0003045053,0.00068689865,0.0014896443,0.0003310614,0.0022017518,0.00075185177,0.0012726112,0.0013290966],"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":[0.000488641,0.00023303123,0.005757332,0.00025397717,0.00013172808,0.00017312224,0.000117625765,0.20108871,0.022556735,0.003618826,0.0046746107,0.7609056],"study_design_scores_gemma":[0.000016546483,0.00008197109,0.0018186636,0.000016666972,0.000028986746,0.00008157142,0.000028226623,0.985864,0.009300535,0.0014681856,0.0012733781,0.00002134043],"about_ca_topic_score_codex":0.0016097464,"about_ca_topic_score_gemma":0.0006815419,"teacher_disagreement_score":0.0020118973,"about_ca_system_score_codex":0.00048214258,"about_ca_system_score_gemma":0.0006297877,"threshold_uncertainty_score":0.010640025},"labels":[],"label_agreement":null},{"id":"W4240198870","doi":"10.32920/ryerson.14655924.v1","title":"Recognizing Human Emotional State from Audiovisual Signals","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University; ASTER","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology); Mahalanobis distance; Mel-frequency cepstrum; Speech recognition; Linear discriminant analysis; Classifier (UML); Formant; Feature selection; Feature extraction; Gaussian; Artificial neural network; Mixture model","score_opus":0.051547415127072116,"score_gpt":0.3003686274586947,"score_spread":0.24882121233162258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240198870","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6009175,0.0028645606,0.38415024,0.00025602014,0.00026217298,0.00011197872,0.0005383849,0.0012763377,0.0096227005],"genre_scores_gemma":[0.9167473,0.0011284976,0.07807079,0.00009810155,0.000113508664,0.00004789823,0.00051181635,0.000043113938,0.0032388614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998217,0.00003003532,0.000008488528,0.000040186867,0.0000743609,0.000025292662],"domain_scores_gemma":[0.9997931,0.000080644575,0.00002433267,0.000017286695,0.000072634095,0.000012095597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022291909,0.00027927698,0.00033814154,0.0005315066,0.00008673729,0.0005912399,0.00025331668,0.00041722847,0.0012779465],"category_scores_gemma":[0.0010451265,0.00006574497,0.00015074066,0.0002913898,0.0001495689,0.0004259469,0.00020504129,0.00014481404,0.00066780404],"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":[0.0004028366,0.00009693472,0.005010888,0.00030496175,0.00004446468,0.00023471173,0.00013690835,0.00723795,0.42289624,0.00083982747,0.001197198,0.5615971],"study_design_scores_gemma":[0.000048804246,0.00076617737,0.09918254,0.00014634078,0.00016877777,0.0019421277,0.00063511,0.47207305,0.407619,0.0037572386,0.013561246,0.000099627905],"about_ca_topic_score_codex":0.000519171,"about_ca_topic_score_gemma":0.00048390028,"teacher_disagreement_score":0.0012779465,"about_ca_system_score_codex":0.000110157926,"about_ca_system_score_gemma":0.000077990604,"threshold_uncertainty_score":0.0042752028},"labels":[],"label_agreement":null},{"id":"W4242301594","doi":"10.32920/ryerson.14655924","title":"Recognizing Human Emotional State from Audiovisual Signals","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University; ASTER","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Mahalanobis distance; Speech recognition; Mel-frequency cepstrum; Linear discriminant analysis; Classifier (UML); Formant; Feature selection; Gaussian; Feature extraction; Artificial neural network","score_opus":0.051547415127072116,"score_gpt":0.3003686274586947,"score_spread":0.24882121233162258,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242301594","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6009175,0.0028645606,0.38415024,0.00025602014,0.00026217298,0.00011197872,0.0005383849,0.0012763377,0.0096227005],"genre_scores_gemma":[0.9167473,0.0011284976,0.07807079,0.00009810155,0.000113508664,0.00004789823,0.00051181635,0.000043113938,0.0032388614],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998217,0.00003003532,0.000008488528,0.000040186867,0.0000743609,0.000025292662],"domain_scores_gemma":[0.9997931,0.000080644575,0.00002433267,0.000017286695,0.000072634095,0.000012095597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022291909,0.00027927698,0.00033814154,0.0005315066,0.00008673729,0.0005912399,0.00025331668,0.00041722847,0.0012779465],"category_scores_gemma":[0.0010451265,0.00006574497,0.00015074066,0.0002913898,0.0001495689,0.0004259469,0.00020504129,0.00014481404,0.00066780404],"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":[0.0004028366,0.00009693472,0.005010888,0.00030496175,0.00004446468,0.00023471173,0.00013690835,0.00723795,0.42289624,0.00083982747,0.001197198,0.5615971],"study_design_scores_gemma":[0.000048804246,0.00076617737,0.09918254,0.00014634078,0.00016877777,0.0019421277,0.00063511,0.47207305,0.407619,0.0037572386,0.013561246,0.000099627905],"about_ca_topic_score_codex":0.000519171,"about_ca_topic_score_gemma":0.00048390028,"teacher_disagreement_score":0.0012779465,"about_ca_system_score_codex":0.000110157926,"about_ca_system_score_gemma":0.000077990604,"threshold_uncertainty_score":0.0042752028},"labels":[],"label_agreement":null},{"id":"W4242556086","doi":"10.1007/978-1-4939-7131-2_100646","title":"Mean-Centered Partial Least Square Correlation","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"University of Calgary","funders":"","keywords":"Correlation; Square (algebra); Mathematics; Statistics; Geometry","score_opus":0.036799726914722296,"score_gpt":0.24179476137203917,"score_spread":0.20499503445731687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242556086","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0007938769,0.0036824483,0.9783781,0.00019507942,0.00055036304,0.000022915265,0.00014532356,0.001741331,0.014490556],"genre_scores_gemma":[0.038241494,0.008259256,0.8489747,0.0005325787,0.00081382215,0.0001462915,0.0015391978,0.0017083499,0.099784255],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992225,0.00009623963,0.000028444798,0.0001612856,0.00045923344,0.000032299355],"domain_scores_gemma":[0.9992005,0.0002457263,0.0000353681,0.0001509958,0.00034838152,0.000018961768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075784343,0.0011315779,0.0010924694,0.0008655244,0.00039104855,0.0010534151,0.0012183905,0.0010497051,0.014414865],"category_scores_gemma":[0.002149066,0.0005036312,0.0006253145,0.0019007382,0.0005928701,0.0013278718,0.0010544539,0.0012596834,0.020892046],"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":[0.000055127945,0.00002749861,0.00021205016,0.00023811511,0.000054684802,0.00007044909,0.000047999652,0.014708479,0.010359114,0.033753138,0.06383794,0.8766354],"study_design_scores_gemma":[0.000022912955,0.00013879708,0.0020931368,0.0002207441,0.00011380621,0.0014660521,0.000077080076,0.52555335,0.04231507,0.06837461,0.35945117,0.00017328816],"about_ca_topic_score_codex":0.0010210366,"about_ca_topic_score_gemma":0.0020410875,"teacher_disagreement_score":0.014414865,"about_ca_system_score_codex":0.00040831897,"about_ca_system_score_gemma":0.0007894908,"threshold_uncertainty_score":0.04822254},"labels":[],"label_agreement":null},{"id":"W4242949851","doi":"10.4018/9781615209910.ch012","title":"From Face to Facial Expression","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Face and Expression Recognition","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","funders":"","keywords":"Facial expression; Multidisciplinary approach; Expression (computer science); Face (sociological concept); Computer science; Facial recognition system; Everyday life; Human–computer interaction; Facial expression recognition; Artificial intelligence; Data science; Pattern recognition (psychology); Sociology; Epistemology; Social science","score_opus":0.028482697399824064,"score_gpt":0.24543779686188905,"score_spread":0.21695509946206498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242949851","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038308334,0.24574995,0.06665548,0.008440885,0.006163583,0.000087319335,0.00078731164,0.0011376913,0.6671469],"genre_scores_gemma":[0.04797428,0.21498096,0.044257812,0.005844922,0.004285193,0.00015867883,0.0013736327,0.00088275044,0.6802418],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998041,0.000029825223,0.0000067226315,0.000053693497,0.00009039292,0.000015358391],"domain_scores_gemma":[0.9998826,0.00006146007,0.000004377969,0.000016950511,0.000026358808,0.000008196694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001915215,0.00090057834,0.0004693452,0.0012661054,0.0005572495,0.0026395516,0.00074007,0.0011100713,0.03663238],"category_scores_gemma":[0.0007104689,0.00023999534,0.00038492127,0.0012331101,0.0010317232,0.0031435457,0.0012615853,0.0018200974,0.018352581],"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":[0.000024218105,0.000028786682,0.00016899554,0.00071893586,0.00001358295,0.00017713149,0.0010562253,0.0005449419,0.0029799405,0.10992517,0.18644775,0.69791436],"study_design_scores_gemma":[0.000002881245,0.000016310107,0.0005405938,0.00053201086,0.000009185362,0.00084926334,0.00033298126,0.00070442096,0.0009707997,0.04099416,0.95503336,0.000014096335],"about_ca_topic_score_codex":0.00093447324,"about_ca_topic_score_gemma":0.0015358113,"teacher_disagreement_score":0.03663238,"about_ca_system_score_codex":0.0007269599,"about_ca_system_score_gemma":0.00032205493,"threshold_uncertainty_score":0.12254757},"labels":[],"label_agreement":null},{"id":"W4243692630","doi":"10.1007/978-3-319-07491-7","title":"Pattern Recognition","year":2014,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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; Artificial intelligence; Library science","score_opus":0.01746567312772625,"score_gpt":0.23780883129458616,"score_spread":0.2203431581668599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243692630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005857097,0.010298621,0.6673768,0.0012824916,0.0031956746,0.00039269606,0.0045247474,0.021888647,0.2851832],"genre_scores_gemma":[0.042891074,0.008488991,0.27668208,0.0011610863,0.00064796855,0.00036879195,0.013693209,0.0015917809,0.6544751],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99935204,0.000036465302,0.000038862487,0.00016169326,0.00035661188,0.000054433756],"domain_scores_gemma":[0.99954,0.000035227225,0.000020761205,0.00017181083,0.00020930433,0.00002287353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004382632,0.0012529494,0.0010955997,0.0019374985,0.0005068883,0.002127459,0.0017688196,0.0011270054,0.09580987],"category_scores_gemma":[0.0008606595,0.0005207684,0.00064730295,0.0021622202,0.00045127558,0.0014508899,0.0012137752,0.0009707698,0.12082184],"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":[0.000051413546,0.000051577772,0.00018969543,0.00025041375,0.000025839909,0.00008167193,0.00002060139,0.0013382513,0.025131416,0.0077566323,0.13178681,0.8333156],"study_design_scores_gemma":[0.000026541566,0.00014325074,0.0019561604,0.0001594515,0.000053533484,0.0012023842,0.00007321334,0.03169562,0.075726844,0.019625109,0.86927754,0.000060338283],"about_ca_topic_score_codex":0.0012639319,"about_ca_topic_score_gemma":0.0017457813,"teacher_disagreement_score":0.09580987,"about_ca_system_score_codex":0.0004154938,"about_ca_system_score_gemma":0.00068368705,"threshold_uncertainty_score":0.320516},"labels":[],"label_agreement":null},{"id":"W4243794444","doi":"10.1613/jair.2251","title":"A Framework for Kernel-Based Multi-Category Classification","year":2007,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":18,"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 British Columbia","funders":"","keywords":"Benchmark (surveying); Computer science; Consistency (knowledge bases); Pairwise comparison; Kernel (algebra); Machine learning; Parallels; Binary classification; Binary number; Support vector machine; Focus (optics); Extension (predicate logic); Artificial intelligence; Algorithm; Data mining; Mathematics","score_opus":0.39400175163338985,"score_gpt":0.495229237634599,"score_spread":0.10122748600120912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243794444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00042317872,0.00014094001,0.9982639,0.00009130597,0.000023188455,0.00003613603,0.00004029451,0.00029519974,0.0006858228],"genre_scores_gemma":[0.070771284,0.0003774093,0.9245781,0.00018814372,0.000100404104,0.00042493385,0.00047986192,0.0002404319,0.002839466],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970151,0.0009708019,0.00020381354,0.0005938076,0.0009664382,0.00025002498],"domain_scores_gemma":[0.99776363,0.000690394,0.00019984797,0.0006409019,0.00058961567,0.00011567725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003440614,0.0008921008,0.0016214472,0.0028726535,0.0011537195,0.003384798,0.004978371,0.001994351,0.0072918455],"category_scores_gemma":[0.008465264,0.00058373535,0.0018148412,0.0030747894,0.0018825147,0.0038163392,0.004257129,0.0031871642,0.004350886],"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":[0.00007884762,0.00013796047,0.0009199058,0.00037923467,0.00016018919,0.00015347803,0.00032899296,0.12628159,0.00477062,0.4268898,0.010995149,0.42890427],"study_design_scores_gemma":[0.000010623329,0.0000661333,0.00047991402,0.0000626039,0.00002576615,0.00025173553,0.000097681186,0.5958493,0.0018569375,0.38209525,0.019154087,0.00004996352],"about_ca_topic_score_codex":0.0028023112,"about_ca_topic_score_gemma":0.002404207,"teacher_disagreement_score":0.0072918455,"about_ca_system_score_codex":0.0014974973,"about_ca_system_score_gemma":0.0015522194,"threshold_uncertainty_score":0.024393678},"labels":[],"label_agreement":null},{"id":"W4244199699","doi":"10.23952/jano.3.2021.1.02","title":"Augmented Lagrangian – fast projected gradient algorithm with working set selection for training support vector machines","year":2021,"lang":"en","type":"article","venue":"Journal of Applied and Numerical Optimization","topic":"Face and Expression Recognition","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":"","keywords":"Computer science; Training (meteorology); Set (abstract data type); Selection (genetic algorithm); Support vector machine; Lagrangian; Augmented Lagrangian method; Mathematical optimization; Algorithm; Artificial intelligence; Mathematics; Applied mathematics","score_opus":0.01678469710297636,"score_gpt":0.23597845643748847,"score_spread":0.2191937593345121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244199699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030956597,0.00008009721,0.9957468,0.000054580672,0.000030468986,0.000041657528,0.000021917918,0.0004745612,0.00045431117],"genre_scores_gemma":[0.0618465,0.00012916658,0.9351495,0.00007212552,0.000048949394,0.00046764157,0.00026603797,0.00022182436,0.0017983159],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9988518,0.00043007112,0.000060761216,0.00010648185,0.0004639701,0.00008686875],"domain_scores_gemma":[0.99867487,0.0005246801,0.00009407932,0.00017254136,0.00047018312,0.00006375835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020833723,0.0012632055,0.0015418372,0.0012596282,0.0007035127,0.001088695,0.0018976108,0.0011819567,0.003113793],"category_scores_gemma":[0.004363068,0.0007913162,0.0010157916,0.001135965,0.0008494622,0.0013702947,0.0014167507,0.0018131549,0.0020579463],"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":[0.00020230982,0.00017178984,0.0009162137,0.00021544006,0.00013562712,0.00020985054,0.00017205709,0.3915068,0.011486907,0.02534982,0.00829093,0.5613422],"study_design_scores_gemma":[0.00002044572,0.000054601267,0.000101389145,0.000009120072,0.000007468829,0.00003976769,0.0000069737816,0.99189925,0.0021068067,0.004093473,0.0016511058,0.000009505829],"about_ca_topic_score_codex":0.0015519438,"about_ca_topic_score_gemma":0.0013629106,"teacher_disagreement_score":0.003113793,"about_ca_system_score_codex":0.0004084395,"about_ca_system_score_gemma":0.001346631,"threshold_uncertainty_score":0.011018097},"labels":[],"label_agreement":null},{"id":"W4244753645","doi":"10.24124/2018/58868","title":"Face recognition using convolutional macropixel comparison approach","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Face and Expression Recognition","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 Northern British Columbia","funders":"","keywords":"Convolutional neural network; Artificial intelligence; Computer science; Deep learning; Facial recognition system; Face (sociological concept); Scope (computer science); Field (mathematics); Pattern recognition (psychology); Pixel; Machine learning; Mathematics","score_opus":0.06928080691826752,"score_gpt":0.3155530666562653,"score_spread":0.2462722597379978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244753645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.096303545,0.0011801383,0.8902543,0.0001961433,0.00014388179,0.00008567061,0.0001663074,0.001529495,0.010140432],"genre_scores_gemma":[0.6051033,0.0008944645,0.38084117,0.00018381789,0.00009075916,0.00007548542,0.00044164475,0.00012490684,0.01224448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964976,0.000025189673,0.000012529538,0.00011375926,0.00014861263,0.000050228256],"domain_scores_gemma":[0.9998375,0.000024483194,0.000017091355,0.000029876925,0.00007872798,0.0000122189285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003110135,0.00038537578,0.0005280227,0.00093856576,0.00021893172,0.00052272953,0.0007023916,0.0004165747,0.003116423],"category_scores_gemma":[0.00044025172,0.00018102153,0.00053744717,0.0004781054,0.00023014241,0.0007484394,0.00053667574,0.0003645084,0.00071173784],"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":[0.00022470688,0.00009461626,0.0021015967,0.00011254684,0.00009287184,0.00010934348,0.00005756883,0.035680775,0.12105147,0.008396907,0.003206452,0.8288712],"study_design_scores_gemma":[0.000011057888,0.00013846048,0.0050671385,0.000014775274,0.00006809069,0.00040894686,0.000032925316,0.8777976,0.1059226,0.004051689,0.0064607123,0.000025897061],"about_ca_topic_score_codex":0.0030050084,"about_ca_topic_score_gemma":0.0037945043,"teacher_disagreement_score":0.003116423,"about_ca_system_score_codex":0.00061008567,"about_ca_system_score_gemma":0.00046787784,"threshold_uncertainty_score":0.010425508},"labels":[],"label_agreement":null},{"id":"W4246149434","doi":"10.32920/ryerson.14661798","title":"A discriminative analysis framework for multi-modal information fusion","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Canonical correlation; Computer science; Modal; Artificial intelligence; Mutual information; Sensor fusion; Pattern recognition (psychology); Data mining; Machine learning","score_opus":0.05131854744278919,"score_gpt":0.3315410063576179,"score_spread":0.2802224589148287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246149434","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012805134,0.00014727448,0.9977083,0.00005935736,0.000017736398,0.000015092113,0.000022572676,0.00009284765,0.00065628625],"genre_scores_gemma":[0.29265022,0.0011778584,0.70149505,0.00024573892,0.00021266607,0.00024551732,0.0004372024,0.00018730469,0.0033484807],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984487,0.0005053624,0.000081588194,0.00030316785,0.0005390203,0.00012216097],"domain_scores_gemma":[0.9986318,0.00044814116,0.0001310945,0.00021367562,0.0005085464,0.00006676446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021452194,0.0011889146,0.0010040044,0.0018126752,0.00064701645,0.00182858,0.0011195153,0.0008536417,0.0019954084],"category_scores_gemma":[0.0039174277,0.00043093137,0.0013615666,0.0019987405,0.0015737016,0.0019831783,0.0021375625,0.0017039932,0.0006338632],"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":[0.00013846144,0.00008261195,0.00092160876,0.00026907757,0.00015556492,0.00025011756,0.00032998584,0.34634057,0.024878595,0.37126884,0.0038988646,0.25146568],"study_design_scores_gemma":[0.0000065226423,0.000048561717,0.00031937825,0.000016624634,0.000024494679,0.000088673885,0.00003334216,0.9379203,0.0030956634,0.05501684,0.0033935914,0.0000360045],"about_ca_topic_score_codex":0.0039435294,"about_ca_topic_score_gemma":0.0031164186,"teacher_disagreement_score":0.0039435294,"about_ca_system_score_codex":0.0010391185,"about_ca_system_score_gemma":0.0015400155,"threshold_uncertainty_score":0.011345148},"labels":[],"label_agreement":null},{"id":"W4247523917","doi":"10.32920/ryerson.14646240","title":"Content-independent orientation detection with histogram of optimized local binary pattern","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Microsoft Research Asia; Microsoft Research","keywords":"Local binary patterns; Artificial intelligence; Computer science; Histogram; Computer vision; Orientation (vector space); Face detection; Pattern recognition (psychology); Face (sociological concept); Luminance; Object-class detection; Grayscale; Binary number; Feature extraction; Feature (linguistics); Facial recognition system; Image (mathematics); Mathematics","score_opus":0.026448477490733743,"score_gpt":0.23988473721435988,"score_spread":0.21343625972362612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247523917","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018388048,0.00041631344,0.9782608,0.000086505584,0.000097316886,0.00009097215,0.00011617819,0.00089350226,0.0016503222],"genre_scores_gemma":[0.2086343,0.00095067744,0.7840122,0.00013868262,0.00015330694,0.0001463761,0.00068355264,0.0002009938,0.005079923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995943,0.000055341407,0.000017619466,0.00009507233,0.00018887839,0.00004887007],"domain_scores_gemma":[0.9997228,0.000050699724,0.000042555886,0.00006852748,0.000096786986,0.000018654622],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038469562,0.00043607183,0.0006435188,0.0013452555,0.00021974607,0.0008823247,0.000819827,0.00055738946,0.002120541],"category_scores_gemma":[0.0011154828,0.00035043622,0.0005400106,0.0012378802,0.00032715453,0.001035917,0.0007361875,0.00061131,0.0014724229],"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":[0.00020964934,0.00011480892,0.0013340601,0.00023439027,0.00008242945,0.000108818705,0.00008003566,0.016769733,0.17659934,0.00803249,0.0050531323,0.79138094],"study_design_scores_gemma":[0.000042733434,0.00028654587,0.0064865183,0.0000443394,0.00008575668,0.00065565103,0.0000832804,0.7607758,0.20838015,0.009250841,0.013827629,0.00008072752],"about_ca_topic_score_codex":0.0009226371,"about_ca_topic_score_gemma":0.0008826963,"teacher_disagreement_score":0.002120541,"about_ca_system_score_codex":0.00031586393,"about_ca_system_score_gemma":0.00040102183,"threshold_uncertainty_score":0.007093966},"labels":[],"label_agreement":null},{"id":"W4250355652","doi":"10.23952/jnva.5.2021.1.05","title":"Robust feature selection via nonconvex sparsity-based methods","year":2021,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":39,"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":"Đại học Huế; Strong; National Natural Science Foundation of China; National Foundation for Science and Technology Development","keywords":"Feature selection; Computer science; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Mathematical optimization; Mathematics; Algorithm","score_opus":0.026008784468796113,"score_gpt":0.2954556470453584,"score_spread":0.2694468625765623,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250355652","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026868214,0.000082281134,0.9968304,0.00006795756,0.000010478255,0.000014470683,0.000011852421,0.000099781646,0.00019590012],"genre_scores_gemma":[0.39100295,0.00048297647,0.60213226,0.00039311792,0.00023834691,0.00038067062,0.00041135788,0.00022775106,0.0047304817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998716,0.00047294085,0.000046134126,0.00024285662,0.00043454408,0.00008756881],"domain_scores_gemma":[0.998691,0.00066854735,0.00019752573,0.00014754373,0.00024300136,0.000052374213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001990347,0.0009525343,0.001866729,0.0010915202,0.00041511748,0.00087461143,0.0018190693,0.001312102,0.0011707062],"category_scores_gemma":[0.0037690573,0.00063125306,0.0009381567,0.0010083729,0.0011496235,0.0014989711,0.0016478655,0.0013058828,0.0005184989],"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":[0.000083660525,0.00006906507,0.00039891692,0.00009751251,0.00006754997,0.00010308395,0.000055632318,0.8528879,0.0063117077,0.017612966,0.002366616,0.11994535],"study_design_scores_gemma":[0.0000033952472,0.000010107175,0.000031838368,0.000001859665,0.0000017705424,0.000010032652,0.0000016116745,0.99732,0.00030457712,0.0021208099,0.00019129549,0.0000026302223],"about_ca_topic_score_codex":0.0017344888,"about_ca_topic_score_gemma":0.0016557052,"teacher_disagreement_score":0.001990347,"about_ca_system_score_codex":0.0005707439,"about_ca_system_score_gemma":0.0009109605,"threshold_uncertainty_score":0.010526121},"labels":[],"label_agreement":null},{"id":"W4250404798","doi":"10.32920/ryerson.14668203","title":"Protected multimodal emotion recognition","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Sadness; Computer science; Feature (linguistics); Disgust; Mel-frequency cepstrum; Pattern recognition (psychology); Feature extraction; Artificial intelligence; Emotion classification; Speech recognition; Anger; Psychology","score_opus":0.034006758943689055,"score_gpt":0.2550391256393022,"score_spread":0.22103236669561316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250404798","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07195519,0.0018708368,0.9040507,0.0006620388,0.0005445964,0.00025654575,0.0006406232,0.0024351017,0.01758434],"genre_scores_gemma":[0.636061,0.002240342,0.33618298,0.0007387331,0.00047568142,0.00036839547,0.0016524012,0.00026367436,0.022016786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940586,0.00012308643,0.000031596683,0.0001797865,0.00018748901,0.000072153976],"domain_scores_gemma":[0.9996673,0.000066232365,0.00003393439,0.00007548063,0.00013925433,0.000017736733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005127853,0.00064653164,0.0005909072,0.0005584778,0.0002254114,0.0009670956,0.0006195362,0.0005836344,0.005981181],"category_scores_gemma":[0.001500292,0.00013520094,0.00071574893,0.0003309314,0.00030512107,0.0011911177,0.0009421796,0.0005910126,0.00294615],"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":[0.00051027176,0.00014421783,0.0016706062,0.0003041305,0.000079660305,0.0003677176,0.000358448,0.01122773,0.20028469,0.011334177,0.009319094,0.7643993],"study_design_scores_gemma":[0.000065905486,0.0008538802,0.016704278,0.00020405152,0.00024853856,0.0028578923,0.0007470111,0.6171206,0.25616655,0.03152783,0.07331192,0.00019156786],"about_ca_topic_score_codex":0.0003120911,"about_ca_topic_score_gemma":0.00027318954,"teacher_disagreement_score":0.005981181,"about_ca_system_score_codex":0.00027811262,"about_ca_system_score_gemma":0.00016066618,"threshold_uncertainty_score":0.02000904},"labels":[],"label_agreement":null},{"id":"W4252338385","doi":"10.1109/ijcnn.2006.1716418","title":"A New Facial Expression Recognition Technique using 2-D DCT and Neural Networks Based Decision Tree","year":2006,"lang":"en","type":"article","venue":"The 2006 IEEE International Joint Conference on Neural Network Proceedings","topic":"Face and Expression Recognition","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":"Computer science; Decision tree; Pattern recognition (psychology); Artificial intelligence; Artificial neural network; Tree (set theory); Facial expression; Discrete cosine transform; Feedforward neural network; Support vector machine; Template matching; Facial recognition system; Image (mathematics); Mathematics","score_opus":0.049604429795636056,"score_gpt":0.26884666354888215,"score_spread":0.21924223375324609,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252338385","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015501647,0.0003007776,0.9822374,0.0000892867,0.00006600217,0.000071939634,0.00007428541,0.0005033826,0.0011552393],"genre_scores_gemma":[0.21292679,0.00041033432,0.78307796,0.00015254851,0.00006000225,0.00011988044,0.00034628896,0.00005039481,0.0028557482],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994708,0.00008586502,0.00003317759,0.00011174501,0.00025254276,0.00004594691],"domain_scores_gemma":[0.9996505,0.00012090349,0.00003781511,0.000037479986,0.00013636354,0.000017017725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066196424,0.00053818995,0.0006113045,0.00094503904,0.00022558942,0.000295886,0.0006800454,0.00049281307,0.0012061788],"category_scores_gemma":[0.0013514516,0.00023035138,0.00063210796,0.00074596074,0.00023676886,0.0008686574,0.00034559995,0.0006520719,0.00048663866],"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":[0.00022651076,0.00013405853,0.0011611428,0.00012091478,0.00006522776,0.00011691154,0.00006161515,0.038822237,0.0751378,0.0033548544,0.0027473688,0.8780513],"study_design_scores_gemma":[0.000026248948,0.00017828342,0.0024643068,0.000021370386,0.000048620197,0.00040139447,0.00002422693,0.95364755,0.037271746,0.0019678706,0.0039118542,0.00003661934],"about_ca_topic_score_codex":0.0020055654,"about_ca_topic_score_gemma":0.0027733976,"teacher_disagreement_score":0.0020055654,"about_ca_system_score_codex":0.0003315197,"about_ca_system_score_gemma":0.00039067573,"threshold_uncertainty_score":0.0040351152},"labels":[],"label_agreement":null},{"id":"W4252791518","doi":"10.24124/2015/bpgub1134","title":"Local binary pattern network: a deep learning approach for face recognition","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Face and Expression Recognition","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 Northern British Columbia","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Computer science; Deep learning; Convolutional neural network; Face (sociological concept); Artificial neural network; Facial recognition system; Kernel (algebra); Binary classification; Similarity (geometry); Machine learning; Image (mathematics); Support vector machine; Mathematics","score_opus":0.03573657183100495,"score_gpt":0.27570154075191194,"score_spread":0.239964968920907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252791518","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007537643,0.0024466862,0.9829338,0.00047118537,0.00017362795,0.000089237605,0.00035165882,0.0017811178,0.004215019],"genre_scores_gemma":[0.38772863,0.0062035075,0.57977587,0.0007947296,0.00031628215,0.00051425846,0.0022415556,0.00025655492,0.02216868],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997309,0.000044903692,0.000012730237,0.00006901014,0.00010928892,0.000033174132],"domain_scores_gemma":[0.9998776,0.00003108027,0.00001758147,0.000018593992,0.000044521075,0.000010462204],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045135408,0.00080881355,0.0006799022,0.0009054482,0.00027467665,0.0006564645,0.0012920235,0.00081827614,0.0031237167],"category_scores_gemma":[0.00070700195,0.00032429176,0.0005610475,0.001012986,0.00045192323,0.0012086671,0.0008768991,0.0014423061,0.0012210406],"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":[0.00017240742,0.00015900329,0.0012125075,0.0002714813,0.00015051286,0.00012272673,0.000052002855,0.12903105,0.01789116,0.020995256,0.017905246,0.8120367],"study_design_scores_gemma":[0.000013359721,0.00006984177,0.0005624704,0.000028408935,0.000025198005,0.00010065216,0.000013121253,0.9688603,0.0069126575,0.013735969,0.009661922,0.000016101843],"about_ca_topic_score_codex":0.0044299993,"about_ca_topic_score_gemma":0.0047446457,"teacher_disagreement_score":0.0044299993,"about_ca_system_score_codex":0.0006708401,"about_ca_system_score_gemma":0.00062052405,"threshold_uncertainty_score":0.010449946},"labels":[],"label_agreement":null},{"id":"W4255612787","doi":"10.1007/978-1-4471-7452-3_16","title":"Discriminant Analysis","year":2019,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"Linear discriminant analysis; Pattern recognition (psychology); Kernel Fisher discriminant analysis; Artificial intelligence; Discriminant; Optimal discriminant analysis; Computer science; Bayes' theorem; Principal component analysis; Naive Bayes classifier; Fisher kernel; Mathematics; Facial recognition system; Bayesian probability; Support vector machine","score_opus":0.02058069027488629,"score_gpt":0.22840428668218812,"score_spread":0.20782359640730183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255612787","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067621213,0.003998679,0.8143618,0.0004916581,0.0009130221,0.00016569781,0.001280047,0.00673534,0.16529164],"genre_scores_gemma":[0.07558551,0.004272755,0.3886702,0.00048772938,0.0007032019,0.0002508195,0.005876346,0.0017248794,0.52242863],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995883,0.000039674767,0.000013804561,0.000115965086,0.00020786449,0.000034396777],"domain_scores_gemma":[0.9996978,0.00004883436,0.000014297102,0.00007456904,0.00014729355,0.000017186621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004425837,0.0011085934,0.0008391092,0.0020515693,0.0007465588,0.0014675,0.0008761433,0.0006021475,0.058497343],"category_scores_gemma":[0.0008708253,0.00038331718,0.0005334876,0.0018023263,0.00042206445,0.0010046833,0.0010470424,0.0009527744,0.065205544],"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":[0.000052205105,0.00004906489,0.00023070554,0.0001055986,0.00001868224,0.00003067651,0.000026075817,0.0024896746,0.016361276,0.016141286,0.053348247,0.9111465],"study_design_scores_gemma":[0.00002681258,0.0001671206,0.004283903,0.00014103396,0.00008778922,0.0012499386,0.00013969123,0.132051,0.066009894,0.049789615,0.74594706,0.000106184256],"about_ca_topic_score_codex":0.0007267122,"about_ca_topic_score_gemma":0.0012763147,"teacher_disagreement_score":0.058497343,"about_ca_system_score_codex":0.00036437446,"about_ca_system_score_gemma":0.0004863991,"threshold_uncertainty_score":0.19569308},"labels":[],"label_agreement":null},{"id":"W4256196623","doi":"10.21611/qirt.2010.002","title":"A new fusion framework for multispectral IR face recognition in the texture space","year":2010,"lang":"en","type":"article","venue":"Proceedings of the 2010 International Conference on Quantitative InfraRed Thermography","topic":"Face and Expression Recognition","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":"Université Laval","funders":"","keywords":"Multispectral image; Artificial intelligence; Face (sociological concept); Computer vision; Computer science; Facial recognition system; Fusion; Texture (cosmology); Space (punctuation); Image texture; Pattern recognition (psychology); Image processing; Image (mathematics)","score_opus":0.046404671335819284,"score_gpt":0.30783509332656883,"score_spread":0.26143042199074956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256196623","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022915353,0.00017761454,0.99634844,0.000043589833,0.000047384365,0.000018820321,0.000048220474,0.00032491924,0.0006996057],"genre_scores_gemma":[0.1522844,0.00070480764,0.84082377,0.00020731546,0.00028179202,0.00014354243,0.0006013059,0.00019018496,0.0047628568],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989619,0.00012452061,0.000044078675,0.000190686,0.0005889292,0.000089977104],"domain_scores_gemma":[0.9995148,0.00006412469,0.00004294982,0.00009715162,0.00024417858,0.000036916466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012461025,0.0007846484,0.0009843005,0.0011583335,0.00042060108,0.0011055887,0.0012337203,0.00081088865,0.0024137443],"category_scores_gemma":[0.0013820549,0.00029619507,0.0014028844,0.0008586432,0.0005450498,0.0017612886,0.0015145591,0.0010923487,0.0014466598],"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":[0.00028165823,0.00019980312,0.0010422135,0.00025748665,0.00021014547,0.0001830802,0.0001642127,0.08320462,0.14535838,0.03610352,0.005482926,0.727512],"study_design_scores_gemma":[0.000020640226,0.00023147998,0.001827901,0.000033021508,0.000118035765,0.0004915723,0.000075244716,0.9127399,0.045736734,0.02395794,0.0146962,0.00007128117],"about_ca_topic_score_codex":0.0023519837,"about_ca_topic_score_gemma":0.0022808297,"teacher_disagreement_score":0.0024137443,"about_ca_system_score_codex":0.0004135788,"about_ca_system_score_gemma":0.00052979396,"threshold_uncertainty_score":0.00807482},"labels":[],"label_agreement":null},{"id":"W4283732568","doi":"10.1109/tnnls.2022.3185638","title":"Self-Supervised Self-Organizing Clustering Network: A Novel Unsupervised Representation Learning Method","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":51,"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":"Key Research and Development Projects of Shaanxi Province; National Key Research and Development Program of China; Education Department of Shaanxi Province; National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Unsupervised learning; Artificial intelligence; Representation (politics); Self representation; Feature learning; Conceptual clustering; Self-organizing map; Machine learning; Pattern recognition (psychology); Fuzzy clustering; Canopy clustering algorithm","score_opus":0.0217616238413056,"score_gpt":0.24989992996462373,"score_spread":0.22813830612331815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283732568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007836463,0.00032124977,0.98787713,0.00020968875,0.000057723057,0.000058072626,0.000120290046,0.0012403468,0.0022791224],"genre_scores_gemma":[0.40394658,0.00069821946,0.57805026,0.0006349772,0.00029371618,0.00043471676,0.0014863973,0.0006449437,0.01381033],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912435,0.0002129567,0.00003304838,0.00025746613,0.00027944247,0.00009277438],"domain_scores_gemma":[0.9991881,0.00017515101,0.00011229973,0.00016177686,0.00031019165,0.000052503474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010582297,0.0009078096,0.0010141032,0.001484265,0.0006930303,0.00090263755,0.0028137446,0.0013410054,0.0021941734],"category_scores_gemma":[0.0020877905,0.0005371594,0.0010611478,0.0015830044,0.0008996825,0.0019023519,0.0013167773,0.0014435126,0.0010661158],"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":[0.00018627958,0.00017806598,0.0021918025,0.00016974541,0.00024678552,0.00015419381,0.0002582598,0.42622748,0.007958016,0.039627526,0.022744566,0.50005734],"study_design_scores_gemma":[0.0000051737725,0.0000135874,0.000114132235,0.0000059649674,0.000008532704,0.000031307532,0.000009365105,0.99182147,0.0010555567,0.0055935606,0.0013324763,0.000008859798],"about_ca_topic_score_codex":0.0060412367,"about_ca_topic_score_gemma":0.0069859433,"teacher_disagreement_score":0.0060412367,"about_ca_system_score_codex":0.0014153098,"about_ca_system_score_gemma":0.0016122021,"threshold_uncertainty_score":0.012012124},"labels":[],"label_agreement":null},{"id":"W4285118772","doi":"10.2139/ssrn.4160311","title":"Employing New Automatic Contrast-Limited Adaptive Histogram Equalization with Adaptive Average Dual Gamma Correction in A Face Recognition System","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"","keywords":"Adaptive histogram equalization; Contrast (vision); Gamma correction; Histogram equalization; Dual (grammatical number); Artificial intelligence; Histogram; Pattern recognition (psychology); Face (sociological concept); Computer science; Equalization (audio); Computer vision; Algorithm; Image (mathematics)","score_opus":0.017818544333523607,"score_gpt":0.22034076356634635,"score_spread":0.20252221923282274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285118772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.105395764,0.00068866985,0.8855724,0.00016387369,0.00030867412,0.00013663604,0.000106419364,0.0033587539,0.0042688856],"genre_scores_gemma":[0.4949382,0.00053352775,0.49568775,0.00025453293,0.00014582582,0.00012430607,0.00025948088,0.0001397344,0.007916702],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997296,0.00003345571,0.000017740189,0.000067904264,0.00011822434,0.000033054403],"domain_scores_gemma":[0.99974626,0.00005612727,0.000013613133,0.000035691948,0.00013157516,0.000016809367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002767776,0.0003166714,0.0004356557,0.00044768804,0.00031417038,0.00052460557,0.00082747184,0.0005613602,0.0025072405],"category_scores_gemma":[0.0004894608,0.00025720673,0.00025976705,0.00041817373,0.00016500108,0.0005910934,0.00043229986,0.00042807354,0.0009877707],"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":[0.00039588546,0.00014099237,0.002019296,0.00013341036,0.000062126426,0.00014222426,0.00006280752,0.001735393,0.60735106,0.0010995046,0.0021567151,0.38470063],"study_design_scores_gemma":[0.00008590033,0.00053037563,0.013537566,0.00003699544,0.0002222599,0.0018719436,0.00006051,0.22581445,0.73988396,0.0008617278,0.01696999,0.00012428759],"about_ca_topic_score_codex":0.001040488,"about_ca_topic_score_gemma":0.0019294821,"teacher_disagreement_score":0.0025072405,"about_ca_system_score_codex":0.00016095508,"about_ca_system_score_gemma":0.0005289344,"threshold_uncertainty_score":0.008387566},"labels":[],"label_agreement":null},{"id":"W4285464816","doi":"10.32920/ryerson.14646201","title":"Human emotional state recognition using 3D facial expression features","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Computer science; Artificial intelligence; Isomap; Discriminative model; Feature extraction; Pattern recognition (psychology); Robustness (evolution); Computer vision; Facial expression; Support vector machine; Gesture recognition; Gesture; Dimensionality reduction; Nonlinear dimensionality reduction","score_opus":0.05594265068750391,"score_gpt":0.29896214741734306,"score_spread":0.24301949672983914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285464816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26038688,0.0008321851,0.72752476,0.00025950104,0.00015980704,0.00017003082,0.0011087307,0.002546507,0.0070115724],"genre_scores_gemma":[0.8483852,0.0008939824,0.14534977,0.00012989163,0.00007514268,0.0001475021,0.0012015711,0.00012356037,0.0036933813],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996972,0.000058543887,0.000014092199,0.00008133395,0.000115487885,0.00003344547],"domain_scores_gemma":[0.9998385,0.000032662774,0.00002551313,0.000024514198,0.0000685094,0.000010283856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525313,0.00048546042,0.00044203282,0.0008683847,0.000112878726,0.00050188205,0.00024944101,0.00036480304,0.0016363786],"category_scores_gemma":[0.0009020035,0.00016676914,0.00055774185,0.0004970055,0.00017633139,0.0004616798,0.00038762705,0.00024452407,0.0008835676],"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":[0.0003604487,0.00009362595,0.0072239186,0.0001349558,0.00008786442,0.0002956859,0.00022879339,0.0138101075,0.28711742,0.001695778,0.0044066254,0.68454474],"study_design_scores_gemma":[0.000036977428,0.00031942173,0.09582831,0.00006563684,0.00012222718,0.0015657825,0.0003171496,0.7286328,0.15849178,0.0045030415,0.009988773,0.00012818145],"about_ca_topic_score_codex":0.0012562595,"about_ca_topic_score_gemma":0.0011564961,"teacher_disagreement_score":0.0016363786,"about_ca_system_score_codex":0.00019415132,"about_ca_system_score_gemma":0.00012020729,"threshold_uncertainty_score":0.0054742694},"labels":[],"label_agreement":null},{"id":"W4286722913","doi":"10.2478/jaiscr-2022-0011","title":"Noise Robust Illumination Invariant Face Recognition Via Bivariate Wavelet Shrinkage in Logarithm Domain","year":2022,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence and Soft Computing Research","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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":"Wavelet; Complex wavelet transform; Artificial intelligence; Invariant (physics); Pattern recognition (psychology); Logarithm; Facial recognition system; Mathematics; Computer science; Bivariate analysis; Wavelet transform; White noise; Computer vision; Wavelet packet decomposition; Statistics; Mathematical analysis","score_opus":0.11715277142028645,"score_gpt":0.34002554662819334,"score_spread":0.22287277520790688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286722913","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11558752,0.00019653089,0.88206273,0.00008570574,0.000044638815,0.000025160247,0.000060032122,0.00078900997,0.00114868],"genre_scores_gemma":[0.6570743,0.0003311143,0.33989376,0.000072784904,0.000053728756,0.000042605046,0.00025468483,0.000085046224,0.0021919846],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996729,0.000054122946,0.000013777936,0.000073031675,0.00015783298,0.000028367309],"domain_scores_gemma":[0.9996948,0.00009265118,0.000049249484,0.000055673547,0.000091956375,0.000015710815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047724543,0.00031735023,0.00062119326,0.000681613,0.00014653926,0.00043974945,0.00037516293,0.0003046621,0.001023663],"category_scores_gemma":[0.0012747857,0.0001516051,0.00043699247,0.00064083224,0.0002753749,0.0005339839,0.00045229966,0.00043179572,0.0005859422],"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":[0.0003829474,0.00012712376,0.0021308968,0.000071879134,0.000055916244,0.000180619,0.000074191805,0.053866893,0.3134809,0.0028912243,0.0019891951,0.62474823],"study_design_scores_gemma":[0.000012259479,0.00008569322,0.0030502104,0.0000069172784,0.000027399883,0.00027890157,0.000023643795,0.90408903,0.09023905,0.0010965908,0.0010719603,0.00001826563],"about_ca_topic_score_codex":0.0007579319,"about_ca_topic_score_gemma":0.0005429296,"teacher_disagreement_score":0.001023663,"about_ca_system_score_codex":0.00017895442,"about_ca_system_score_gemma":0.00023838916,"threshold_uncertainty_score":0.0034244657},"labels":[],"label_agreement":null},{"id":"W4287169430","doi":"10.48550/arxiv.2105.12005","title":"Hierarchical Subspace Learning for Dimensionality Reduction to Improve\\n Classification Accuracy in Large Data Sets","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Dimensionality reduction; Linear discriminant analysis; Subspace topology; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Nonlinear dimensionality reduction; Projection (relational algebra); Mathematics; Computer science; Random subspace method; Curse of dimensionality; Machine learning; Algorithm","score_opus":0.16098918845615073,"score_gpt":0.26956047462806715,"score_spread":0.10857128617191641,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287169430","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026577672,0.0004498586,0.96924174,0.0002140421,0.000060876406,0.000093837705,0.00019241583,0.002091601,0.0010779466],"genre_scores_gemma":[0.27346733,0.00037419493,0.72275156,0.00013191366,0.00006789025,0.00023457245,0.0010941207,0.00019444215,0.0016840177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974458,0.00087095046,0.00016258296,0.00040213662,0.00093810563,0.00018037342],"domain_scores_gemma":[0.9978756,0.00071959454,0.00013655088,0.00064258405,0.0005579168,0.00006773814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021720137,0.0009262172,0.00095391937,0.0020049384,0.0007953463,0.00094068795,0.0008510583,0.00055073027,0.0019916734],"category_scores_gemma":[0.006582459,0.00021108182,0.0010270268,0.0025456003,0.000693425,0.0014089758,0.0014001975,0.0012067546,0.0011660741],"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":[0.00012778348,0.00027516342,0.00356942,0.00017298505,0.0001739233,0.000080926366,0.0002549342,0.07907375,0.024711126,0.013578912,0.009476928,0.86850405],"study_design_scores_gemma":[0.000014587843,0.00011464931,0.0023046008,0.000019242902,0.000028395967,0.00007322798,0.00008726568,0.9619573,0.018373966,0.013077614,0.0039201463,0.00002898255],"about_ca_topic_score_codex":0.0044455975,"about_ca_topic_score_gemma":0.007483081,"teacher_disagreement_score":0.0044455975,"about_ca_system_score_codex":0.00066860486,"about_ca_system_score_gemma":0.001323575,"threshold_uncertainty_score":0.011486828},"labels":[],"label_agreement":null},{"id":"W4287826022","doi":"10.5281/zenodo.3712472","title":"Short Percussive Samples Seperated by Category","year":2020,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"Psychology","score_opus":0.04989780057778736,"score_gpt":0.2386715837198796,"score_spread":0.18877378314209223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287826022","genre_codex":"empirical","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6943779,0.005171967,0.061311085,0.00036415874,0.0010484126,0.0010279737,0.20127085,0.011142342,0.024285274],"genre_scores_gemma":[0.5579123,0.0011187003,0.040645514,0.00025903358,0.00032292912,0.0005569863,0.36931115,0.0006722127,0.029201204],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992867,0.000030201363,0.000055352295,0.00021660865,0.00030145838,0.0001095779],"domain_scores_gemma":[0.99891734,0.00011369998,0.00010448992,0.0002702996,0.000513721,0.000080530684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026019264,0.0009806565,0.0007528856,0.0022895313,0.00058390753,0.00048670243,0.0006732454,0.0010086946,0.014519066],"category_scores_gemma":[0.0010790825,0.00024983397,0.00039669164,0.0015701215,0.00032675834,0.00042619425,0.00075612834,0.00042291405,0.012907495],"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":[0.004109825,0.0005628817,0.015443378,0.0012361269,0.00017174978,0.0015719719,0.00024045986,0.002087176,0.32647666,0.000512888,0.07649415,0.5710928],"study_design_scores_gemma":[0.00030604313,0.001846407,0.4051559,0.0002549428,0.00037148158,0.010046265,0.0010132721,0.029283801,0.2992268,0.0012279762,0.25102508,0.00024202431],"about_ca_topic_score_codex":0.004078874,"about_ca_topic_score_gemma":0.00952329,"teacher_disagreement_score":0.014519066,"about_ca_system_score_codex":0.00023435157,"about_ca_system_score_gemma":0.00042677033,"threshold_uncertainty_score":0.04857111},"labels":[],"label_agreement":null},{"id":"W4289277681","doi":"10.1155/2022/6446903","title":"An Orthogonal Matching Pursuit Variable Screening Algorithm for High-Dimensional Linear Regression Models","year":2022,"lang":"en","type":"article","venue":"Scientific Programming","topic":"Face and Expression Recognition","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":"Wilfrid Laurier University","funders":"","keywords":"Matching pursuit; Matching (statistics); Consistency (knowledge bases); Variable (mathematics); Feature selection; Dimension (graph theory); Algorithm; Mathematics; Computer science; Selection (genetic algorithm); Clustering high-dimensional data; Pattern recognition (psychology); Artificial intelligence; Statistics; Compressed sensing; Combinatorics","score_opus":0.0274718433282947,"score_gpt":0.27314169335685745,"score_spread":0.24566985002856276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289277681","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020022485,0.00011414497,0.9972485,0.00007396749,0.000012349295,0.00002667641,0.000020295212,0.00020504693,0.0002967001],"genre_scores_gemma":[0.16459411,0.00064443116,0.8305122,0.0002075335,0.00009385193,0.00056874723,0.0004953849,0.0001601153,0.002723526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980539,0.00088647037,0.00010914503,0.00030755062,0.00049243326,0.00015050212],"domain_scores_gemma":[0.9980611,0.0012345312,0.00019098156,0.00010975593,0.0003366795,0.00006686849],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036946726,0.0013561783,0.0018515836,0.0013846832,0.0009419422,0.0011122179,0.001789269,0.0017956116,0.0026872735],"category_scores_gemma":[0.008984754,0.0007574467,0.0015972947,0.0021799505,0.0009471808,0.0018330686,0.0022490013,0.0023106171,0.0011349083],"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":[0.00021208664,0.00016387145,0.0016538848,0.00025494437,0.0001875588,0.00015802766,0.00017835724,0.5256161,0.0040065204,0.045890067,0.0038177525,0.41786075],"study_design_scores_gemma":[0.000012670473,0.000039690218,0.000119405144,0.000008732781,0.000010799455,0.000017898827,0.000006961662,0.9922438,0.0004293773,0.0064470847,0.00065409,0.000009391413],"about_ca_topic_score_codex":0.0044588936,"about_ca_topic_score_gemma":0.002888824,"teacher_disagreement_score":0.0044588936,"about_ca_system_score_codex":0.00073307776,"about_ca_system_score_gemma":0.00250848,"threshold_uncertainty_score":0.019539535},"labels":[],"label_agreement":null},{"id":"W4291302383","doi":"10.1007/978-3-031-13870-6_27","title":"Illumination Invariant Face Recognition Using Directional Gradient Maps","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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; Artificial intelligence; Facial recognition system; Pixel; Invariant (physics); Computer vision; Face (sociological concept); Noise (video); Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.03352909154472673,"score_gpt":0.24568311200469914,"score_spread":0.2121540204599724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291302383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032151718,0.0015382202,0.94306993,0.00009006252,0.00019037093,0.00005930563,0.00039065248,0.004419388,0.018090278],"genre_scores_gemma":[0.26842886,0.0029673718,0.6761213,0.00021636991,0.000097273376,0.00011901271,0.0020477113,0.0007223844,0.04927965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999915,0.000005734582,0.000002981865,0.000020335028,0.000040001683,0.000015838401],"domain_scores_gemma":[0.999938,0.000011132172,0.0000048715397,0.000018005308,0.00002277461,0.000005212183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000822826,0.00041012754,0.00042352043,0.0005958215,0.00014031478,0.0005120316,0.0005168053,0.0002904141,0.00607185],"category_scores_gemma":[0.00019646119,0.00021382535,0.00035975414,0.00057474757,0.00016316977,0.00054489006,0.000395287,0.00035687606,0.0035609624],"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":[0.00007611481,0.000050013092,0.00031266932,0.000077680466,0.000022102098,0.000056198045,0.000017554772,0.0043106005,0.20166266,0.0027147767,0.0064616166,0.784238],"study_design_scores_gemma":[0.000023628963,0.00021124675,0.008887403,0.00006121667,0.000106786276,0.001685383,0.00010117362,0.41230112,0.52152044,0.009821352,0.045206226,0.00007400107],"about_ca_topic_score_codex":0.0010364688,"about_ca_topic_score_gemma":0.0016642618,"teacher_disagreement_score":0.00607185,"about_ca_system_score_codex":0.00014900164,"about_ca_system_score_gemma":0.00015930757,"threshold_uncertainty_score":0.02031231},"labels":[],"label_agreement":null},{"id":"W4292262008","doi":"10.1109/tip.2022.3194701","title":"Variational Bayesian Orthogonal Nonnegative Matrix Factorization Over the Stiefel Manifold","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Non-negative matrix factorization; Stiefel manifold; Orthogonality; Algorithm; Mathematics; Computer science; Matrix decomposition; Cluster analysis; Pattern recognition (psychology); Artificial intelligence; Mathematical optimization; Eigenvalues and eigenvectors","score_opus":0.011880336087971089,"score_gpt":0.25458785095518704,"score_spread":0.24270751486721595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292262008","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032412894,0.00012698217,0.9960324,0.00009515823,0.000013732462,0.000017990349,0.000046215708,0.000064946325,0.00036131704],"genre_scores_gemma":[0.27957112,0.0009797853,0.7145266,0.000250878,0.00015336844,0.0003062048,0.0008204686,0.0001769165,0.0032146918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879634,0.0005393296,0.00004692563,0.00024565004,0.0002681194,0.00010365552],"domain_scores_gemma":[0.9983537,0.0010379753,0.00017762459,0.00011501855,0.00024540542,0.00007037167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002324747,0.0014189956,0.0013361686,0.0007801507,0.00061242766,0.0009882838,0.0014099778,0.0014407996,0.0016979706],"category_scores_gemma":[0.005158566,0.00075159315,0.001420907,0.0009692375,0.0014035299,0.0016291892,0.0012468921,0.0019347784,0.00042445754],"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":[0.00007136824,0.00003750702,0.0006360366,0.00016333409,0.000075076554,0.00010583544,0.00009465058,0.8539772,0.0032033427,0.07772675,0.0024733245,0.061435577],"study_design_scores_gemma":[0.0000043092223,0.0000073698025,0.000069555674,0.000005102017,0.0000030926944,0.00001107282,0.0000047871276,0.9859833,0.00020092697,0.013331474,0.0003728516,0.000006203446],"about_ca_topic_score_codex":0.009406412,"about_ca_topic_score_gemma":0.010314203,"teacher_disagreement_score":0.009406412,"about_ca_system_score_codex":0.0011265739,"about_ca_system_score_gemma":0.0020972774,"threshold_uncertainty_score":0.018703341},"labels":[],"label_agreement":null},{"id":"W4292959221","doi":"10.5267/j.ijdns.2022.6.009","title":"Face recognition system based on the multi-resolution singular value decomposition fusion technique","year":2022,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Face and Expression Recognition","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":"Artificial intelligence; Pattern recognition (psychology); Computer science; Feature extraction; Local binary patterns; Facial recognition system; Principal component analysis; Face (sociological concept); Computer vision; Feature (linguistics); Singular value decomposition; Image (mathematics); Histogram","score_opus":0.040699096480186714,"score_gpt":0.3080308827334835,"score_spread":0.2673317862532968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292959221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049800117,0.00096832524,0.9439648,0.00011538241,0.00010424946,0.000088295834,0.00008818584,0.0018116154,0.0030590652],"genre_scores_gemma":[0.5494081,0.0010422526,0.44460955,0.0002084412,0.00011439032,0.000105785875,0.00047295602,0.000048384707,0.0039900416],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99922204,0.000072400544,0.000040351308,0.00016638862,0.0004462155,0.000052606403],"domain_scores_gemma":[0.9997745,0.00003383985,0.000024348723,0.00003862245,0.000118988435,0.000009806534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065370154,0.00041012286,0.0007901369,0.00083753123,0.00026358012,0.00044200415,0.0005844811,0.0005286091,0.0010983103],"category_scores_gemma":[0.0006810802,0.00019514908,0.00070643384,0.0004320825,0.00020533869,0.00095041207,0.00044745294,0.0003971493,0.0007450546],"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":[0.00022601949,0.000119260294,0.0022589331,0.0002032117,0.0001052607,0.00013113355,0.00014977064,0.0173629,0.21195619,0.0031001223,0.0023851776,0.762002],"study_design_scores_gemma":[0.00006119769,0.0010064321,0.014734714,0.00007437832,0.00022371547,0.0020084989,0.00010639783,0.78553927,0.17624612,0.0037516009,0.01608659,0.00016110901],"about_ca_topic_score_codex":0.0012210237,"about_ca_topic_score_gemma":0.0008541437,"teacher_disagreement_score":0.0012210237,"about_ca_system_score_codex":0.00028352768,"about_ca_system_score_gemma":0.0002785903,"threshold_uncertainty_score":0.0036742091},"labels":[],"label_agreement":null},{"id":"W4293795333","doi":"10.1109/tsipn.2022.3202035","title":"Kernel Regression for Matrix-Variate Gaussian Distributed Signals Over Sample Graphs","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Signal and Information Processing over Networks","topic":"Face and Expression Recognition","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":"University of Windsor","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Kernel regression; Hyperparameter; Kernel (algebra); Mathematics; Graph kernel; Polynomial kernel; Kernel method; Kernel embedding of distributions; Covariance matrix; Variable kernel density estimation; Artificial intelligence; Pattern recognition (psychology); Estimation of covariance matrices; Covariance; Regression; Algorithm; Computer science; Statistics; Support vector machine; Combinatorics","score_opus":0.01188241520242316,"score_gpt":0.24654624668990757,"score_spread":0.2346638314874844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293795333","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019036924,0.00014682744,0.9973348,0.000053989774,0.00001124655,0.000011193572,0.000025968044,0.0003286993,0.0001835984],"genre_scores_gemma":[0.35503465,0.0015126432,0.6363197,0.00025736864,0.00014373417,0.00019897024,0.0008292483,0.0006826335,0.005021035],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987381,0.0005391567,0.000047607853,0.00034214297,0.00024223028,0.00009070365],"domain_scores_gemma":[0.9970884,0.0016910818,0.00028846422,0.00045126854,0.00041413278,0.00006675543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002139003,0.0013735507,0.0011863105,0.0011312206,0.00036395492,0.00116195,0.001748077,0.0012001507,0.0019357107],"category_scores_gemma":[0.009443945,0.0005485684,0.0010340377,0.0018116883,0.0012378267,0.0021554274,0.0012429791,0.002326764,0.0012543555],"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":[0.00010185859,0.00005484702,0.0010875852,0.0001386305,0.00010127924,0.00008970842,0.00008161722,0.7943211,0.0045318045,0.040129237,0.002977788,0.1563844],"study_design_scores_gemma":[0.0000028839258,0.000008455588,0.00011738376,0.0000039925903,0.00000397317,0.000015683185,0.0000051655006,0.99110126,0.0005054271,0.0076811938,0.00054906064,0.000005620295],"about_ca_topic_score_codex":0.005321549,"about_ca_topic_score_gemma":0.004399319,"teacher_disagreement_score":0.005321549,"about_ca_system_score_codex":0.0010437752,"about_ca_system_score_gemma":0.00095754024,"threshold_uncertainty_score":0.011312246},"labels":[],"label_agreement":null},{"id":"W4297794681","doi":"10.5121/ijaia.2022.13406","title":"New Local Binary Pattern Feature Extractor with Adaptive Threshold for Face Recognition Applications","year":2022,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Local binary patterns; Pattern recognition (psychology); Thresholding; Artificial intelligence; Computer science; Pixel; Feature extraction; Facial recognition system; Binary number; Support vector machine; Histogram; Computer vision; Mathematics; Image (mathematics)","score_opus":0.04913380572319091,"score_gpt":0.3072572551650916,"score_spread":0.2581234494419007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297794681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020325622,0.0012520652,0.9725045,0.0001439905,0.0001883633,0.00011278041,0.00030906036,0.0033651283,0.0017984512],"genre_scores_gemma":[0.2186846,0.001456026,0.7658394,0.00032580245,0.00016307377,0.00032983205,0.0014765648,0.00026082882,0.011463873],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995802,0.000034954726,0.000027772056,0.00006771029,0.0002556951,0.00003369957],"domain_scores_gemma":[0.9997876,0.000038666363,0.00002491812,0.000027230262,0.000108921355,0.000012669094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004368917,0.0004465984,0.0006250869,0.0010025946,0.00019780568,0.00041861046,0.00077581895,0.00056006585,0.0031394986],"category_scores_gemma":[0.0007533015,0.0002531926,0.0005214494,0.0009429914,0.0001806558,0.0009778086,0.00041522947,0.00059999345,0.001583079],"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":[0.00030471195,0.00010014134,0.000805587,0.00017470706,0.000056164932,0.00015789158,0.000044870583,0.0044912603,0.16278586,0.0018497258,0.007724683,0.8215045],"study_design_scores_gemma":[0.00010517541,0.0005742426,0.009502034,0.00008167847,0.0001611728,0.002133676,0.00009376565,0.64576226,0.2909448,0.0028145676,0.04769586,0.00013076229],"about_ca_topic_score_codex":0.001014553,"about_ca_topic_score_gemma":0.0012291125,"teacher_disagreement_score":0.0031394986,"about_ca_system_score_codex":0.00025184386,"about_ca_system_score_gemma":0.00030978778,"threshold_uncertainty_score":0.010502636},"labels":[],"label_agreement":null},{"id":"W4297900902","doi":"10.7717/peerj-cs.1081","title":"Minimizing features while maintaining performance in data classification problems","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Feature (linguistics); Computer science; Artificial intelligence; Principal component analysis; Pattern recognition (psychology); Machine learning; Selection (genetic algorithm); Data mining; Reduction (mathematics); Dimensionality reduction; Mathematics","score_opus":0.07281756164724634,"score_gpt":0.2739164239555219,"score_spread":0.20109886230827556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297900902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16957057,0.0012195549,0.82515454,0.00066379353,0.0000857686,0.00022625139,0.000118758784,0.0015727396,0.0013880132],"genre_scores_gemma":[0.5806359,0.0005700391,0.4155135,0.00021336648,0.00020088763,0.00038990055,0.00044869832,0.00023534415,0.0017924081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970668,0.0008693024,0.0002475089,0.00057197554,0.0009635782,0.00028082696],"domain_scores_gemma":[0.99434376,0.0034101736,0.0003964389,0.0009805026,0.0007676429,0.00010158581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031840298,0.0016892031,0.0018764087,0.00094738073,0.00088234025,0.0012655684,0.0018437306,0.0012249425,0.0007304303],"category_scores_gemma":[0.015372903,0.0004223784,0.0007549868,0.002090402,0.00093077315,0.0021245233,0.0010844485,0.0011363496,0.0006177751],"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":[0.00057106186,0.00049503526,0.006472515,0.00043389478,0.00012940259,0.000491734,0.0003117248,0.3012036,0.029335946,0.00670158,0.0040785004,0.64977497],"study_design_scores_gemma":[0.000072563314,0.00034552318,0.0024360097,0.000021076226,0.000051722785,0.00024515894,0.00008000408,0.9695907,0.015078461,0.009814212,0.0022413179,0.000023209797],"about_ca_topic_score_codex":0.0017850888,"about_ca_topic_score_gemma":0.0016953947,"teacher_disagreement_score":0.0031840298,"about_ca_system_score_codex":0.00050601794,"about_ca_system_score_gemma":0.0012814507,"threshold_uncertainty_score":0.016838968},"labels":[],"label_agreement":null},{"id":"W4299784441","doi":"10.1007/978-1-4614-7163-9_110192-2","title":"Barycentric Discriminant Analysis","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"British Columbia Children's Hospital","funders":"","keywords":"Barycentric coordinate system; Linear discriminant analysis; Mathematics; Artificial intelligence; Computer science; Geometry","score_opus":0.027612171972222235,"score_gpt":0.24890708293371766,"score_spread":0.22129491096149542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299784441","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027324357,0.01575989,0.7842972,0.0007614979,0.0017151162,0.000047705133,0.00047831348,0.0026487312,0.19155903],"genre_scores_gemma":[0.089565724,0.02162991,0.4674291,0.00081029115,0.002627133,0.00014878767,0.0032502434,0.0028723315,0.41166636],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963605,0.00004588256,0.000013856304,0.0000860303,0.00018931153,0.000028842356],"domain_scores_gemma":[0.999741,0.000044812175,0.000014613377,0.00005998425,0.0001178948,0.00002162743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004187733,0.00094832166,0.00088307494,0.0019783953,0.0005751854,0.0016798035,0.00077638106,0.0005297849,0.024315858],"category_scores_gemma":[0.00086321455,0.00043735947,0.00044028633,0.0018791684,0.00085663906,0.0011969033,0.0013045607,0.0015832606,0.027810182],"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":[0.000031012816,0.000019145476,0.00011940332,0.00014045167,0.00001907864,0.000024465311,0.00003413278,0.004513828,0.00477711,0.12433601,0.09302764,0.7729578],"study_design_scores_gemma":[0.000009165585,0.00003549974,0.0011239157,0.00014013759,0.00003551779,0.00052810565,0.00005389601,0.058851004,0.009606798,0.24997179,0.6795756,0.000068657304],"about_ca_topic_score_codex":0.00092255586,"about_ca_topic_score_gemma":0.0016243074,"teacher_disagreement_score":0.024315858,"about_ca_system_score_codex":0.00069497013,"about_ca_system_score_gemma":0.0006632651,"threshold_uncertainty_score":0.081344664},"labels":[],"label_agreement":null},{"id":"W4299909536","doi":"10.1007/978-1-4614-7163-9_110192-1","title":"Barycentric Discriminant Analysis","year":2017,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"British Columbia Children's Hospital","funders":"","keywords":"Barycentric coordinate system; Linear discriminant analysis; Computer science; Mathematics; Artificial intelligence; Geometry","score_opus":0.027612171972222235,"score_gpt":0.24890708293371766,"score_spread":0.22129491096149542,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4299909536","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027324357,0.01575989,0.7842972,0.0007614979,0.0017151162,0.000047705133,0.00047831348,0.0026487312,0.19155903],"genre_scores_gemma":[0.089565724,0.02162991,0.4674291,0.00081029115,0.002627133,0.00014878767,0.0032502434,0.0028723315,0.41166636],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99963605,0.00004588256,0.000013856304,0.0000860303,0.00018931153,0.000028842356],"domain_scores_gemma":[0.999741,0.000044812175,0.000014613377,0.00005998425,0.0001178948,0.00002162743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004187733,0.00094832166,0.00088307494,0.0019783953,0.0005751854,0.0016798035,0.00077638106,0.0005297849,0.024315858],"category_scores_gemma":[0.00086321455,0.00043735947,0.00044028633,0.0018791684,0.00085663906,0.0011969033,0.0013045607,0.0015832606,0.027810182],"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":[0.000031012816,0.000019145476,0.00011940332,0.00014045167,0.00001907864,0.000024465311,0.00003413278,0.004513828,0.00477711,0.12433601,0.09302764,0.7729578],"study_design_scores_gemma":[0.000009165585,0.00003549974,0.0011239157,0.00014013759,0.00003551779,0.00052810565,0.00005389601,0.058851004,0.009606798,0.24997179,0.6795756,0.000068657304],"about_ca_topic_score_codex":0.00092255586,"about_ca_topic_score_gemma":0.0016243074,"teacher_disagreement_score":0.024315858,"about_ca_system_score_codex":0.00069497013,"about_ca_system_score_gemma":0.0006632651,"threshold_uncertainty_score":0.081344664},"labels":[],"label_agreement":null},{"id":"W4300788249","doi":"10.1007/978-3-031-01656-1_6","title":"Feature Selection and Pattern Classification","year":2013,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on biomedical engineering","topic":"Face and Expression Recognition","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 Calgary","funders":"","keywords":"Feature selection; Pattern recognition (psychology); Selection (genetic algorithm); Artificial intelligence; Computer science; Feature (linguistics); Linguistics","score_opus":0.010425933212130646,"score_gpt":0.19494573481124197,"score_spread":0.18451980159911133,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300788249","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033100953,0.02666885,0.9201127,0.0009773847,0.001684542,0.0000701936,0.0005626244,0.0022217473,0.04439188],"genre_scores_gemma":[0.07630468,0.03465741,0.5491229,0.000835712,0.003384834,0.0004268157,0.0036891687,0.0011250046,0.3304535],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9995735,0.000041922314,0.000027713822,0.00013393487,0.00019336714,0.000029501069],"domain_scores_gemma":[0.99975616,0.00008293367,0.000014268678,0.00006128732,0.000072331,0.000012996885],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003719307,0.0011231027,0.0013318869,0.0013943366,0.0003915788,0.0017080366,0.0010096855,0.00064358104,0.01714809],"category_scores_gemma":[0.0008671973,0.000474747,0.00062499486,0.0026527226,0.0007378081,0.0015767532,0.00088610925,0.0010924006,0.012069876],"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":[0.00002271752,0.000022052789,0.0001271997,0.00024175516,0.000030833333,0.000041280815,0.00003683446,0.0035770829,0.0049837665,0.018692398,0.053573184,0.91865087],"study_design_scores_gemma":[0.00002392924,0.00015614454,0.003102058,0.00027126673,0.00009209875,0.00092124095,0.000088037945,0.11499335,0.023707464,0.24321583,0.6133317,0.00009690068],"about_ca_topic_score_codex":0.0007701794,"about_ca_topic_score_gemma":0.0010062174,"teacher_disagreement_score":0.01714809,"about_ca_system_score_codex":0.0004264999,"about_ca_system_score_gemma":0.00039589583,"threshold_uncertainty_score":0.057366014},"labels":[],"label_agreement":null},{"id":"W4306246352","doi":"10.48550/arxiv.2210.06300","title":"Generalised Mutual Information for Discriminative Clustering","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"Centre hospitalier de l'Université Laval","funders":"","keywords":"Mutual information; Cluster analysis; Computer science; Divergence (linguistics); Context (archaeology); Artificial intelligence; Discriminative model; A priori and a posteriori; Artificial neural network; Set (abstract data type); Information theory; Relevance (law); Kullback–Leibler divergence; Property (philosophy); Machine learning; Data mining; Mathematics; Geography","score_opus":0.09679593961579883,"score_gpt":0.20243927713316143,"score_spread":0.1056433375173626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4306246352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011108759,0.0014463655,0.9825068,0.00045085614,0.000049502105,0.000060270577,0.00031702576,0.00053484505,0.0035255367],"genre_scores_gemma":[0.6078752,0.0021858818,0.3770489,0.0008029923,0.00045114008,0.0005158667,0.0023562743,0.00086099416,0.007902664],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99511755,0.0020056167,0.00031639062,0.001124444,0.0011814691,0.0002545157],"domain_scores_gemma":[0.99328303,0.0037196814,0.0007276073,0.0011996467,0.00081117294,0.0002587608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043705185,0.0014934081,0.0022236905,0.0030764062,0.0010095229,0.0024995496,0.0024562166,0.0025245755,0.003303198],"category_scores_gemma":[0.018053407,0.00075351074,0.0014493122,0.0028008844,0.0035772063,0.0036446326,0.0043145283,0.002485112,0.0013822595],"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":[0.00024486132,0.00007797451,0.0028134463,0.0006218716,0.00034647126,0.00019635908,0.000466879,0.46107268,0.003982395,0.36152002,0.007903677,0.16075331],"study_design_scores_gemma":[0.0000111575555,0.00005188595,0.00086706405,0.00006003002,0.000025483334,0.00012743454,0.000041693518,0.7141569,0.0015025958,0.27901158,0.004088545,0.000055659075],"about_ca_topic_score_codex":0.0024427895,"about_ca_topic_score_gemma":0.0026966943,"teacher_disagreement_score":0.0043705185,"about_ca_system_score_codex":0.0026037993,"about_ca_system_score_gemma":0.0012621832,"threshold_uncertainty_score":0.023113847},"labels":[],"label_agreement":null},{"id":"W4307993103","doi":"10.32920/21428721.v1","title":"Knowledge-Based Green’s Kernel for Support Vector Regression","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Support vector machine; Kernel (algebra); Radial basis function kernel; Polynomial kernel; Kernel method; Regularization (linguistics); Regularization perspectives on support vector machines; Kernel embedding of distributions; Mathematics; Artificial intelligence; Computer science; Benchmark (surveying); Pattern recognition (psychology); Machine learning; Inverse problem; Pure mathematics; Mathematical analysis; Tikhonov regularization","score_opus":0.048142085556225726,"score_gpt":0.31493147637885316,"score_spread":0.2667893908226274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307993103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025380608,0.0001421508,0.9966208,0.00006055045,0.000015975504,0.000009273773,0.00002033772,0.00017198871,0.00042089625],"genre_scores_gemma":[0.3848558,0.0010623586,0.6061679,0.00028519178,0.00016007417,0.00015414941,0.0006090492,0.0003140591,0.0063913995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849916,0.00043532482,0.00008757469,0.00028854772,0.0005834675,0.0001058783],"domain_scores_gemma":[0.9973846,0.0012799606,0.00020965324,0.00041611202,0.00063436106,0.00007534772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017369889,0.00085814024,0.0013596857,0.0011627261,0.0003543485,0.0013093587,0.0014852074,0.0017264616,0.0020746992],"category_scores_gemma":[0.0077267704,0.0004005009,0.0011614785,0.0013007221,0.0012104189,0.002584899,0.0012117784,0.0019415738,0.0012810511],"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":[0.0001948169,0.00010836504,0.0007615825,0.00023754987,0.00012224918,0.0001900794,0.00011721335,0.57523334,0.014060045,0.09193603,0.0035824878,0.3134563],"study_design_scores_gemma":[0.0000035495184,0.000023329081,0.00011200982,0.000009747971,0.00000638786,0.000036547903,0.0000051894963,0.9855409,0.0025768843,0.010551873,0.0011230145,0.00001059646],"about_ca_topic_score_codex":0.0019088872,"about_ca_topic_score_gemma":0.001047178,"teacher_disagreement_score":0.0020746992,"about_ca_system_score_codex":0.0010167419,"about_ca_system_score_gemma":0.00093584537,"threshold_uncertainty_score":0.009186149},"labels":[],"label_agreement":null},{"id":"W4307993127","doi":"10.32920/21428721","title":"Knowledge-Based Green’s Kernel for Support Vector Regression","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Support vector machine; Kernel (algebra); Radial basis function kernel; Polynomial kernel; Kernel method; Regularization (linguistics); Kernel embedding of distributions; Regularization perspectives on support vector machines; Mathematics; Artificial intelligence; Computer science; Benchmark (surveying); Pattern recognition (psychology); Machine learning; Inverse problem; Pure mathematics; Mathematical analysis; Tikhonov regularization; Geography","score_opus":0.048142085556225726,"score_gpt":0.31493147637885316,"score_spread":0.2667893908226274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307993127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025380608,0.0001421508,0.9966208,0.00006055045,0.000015975504,0.000009273773,0.00002033772,0.00017198871,0.00042089625],"genre_scores_gemma":[0.3848558,0.0010623586,0.6061679,0.00028519178,0.00016007417,0.00015414941,0.0006090492,0.0003140591,0.0063913995],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849916,0.00043532482,0.00008757469,0.00028854772,0.0005834675,0.0001058783],"domain_scores_gemma":[0.9973846,0.0012799606,0.00020965324,0.00041611202,0.00063436106,0.00007534772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017369889,0.00085814024,0.0013596857,0.0011627261,0.0003543485,0.0013093587,0.0014852074,0.0017264616,0.0020746992],"category_scores_gemma":[0.0077267704,0.0004005009,0.0011614785,0.0013007221,0.0012104189,0.002584899,0.0012117784,0.0019415738,0.0012810511],"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":[0.0001948169,0.00010836504,0.0007615825,0.00023754987,0.00012224918,0.0001900794,0.00011721335,0.57523334,0.014060045,0.09193603,0.0035824878,0.3134563],"study_design_scores_gemma":[0.0000035495184,0.000023329081,0.00011200982,0.000009747971,0.00000638786,0.000036547903,0.0000051894963,0.9855409,0.0025768843,0.010551873,0.0011230145,0.00001059646],"about_ca_topic_score_codex":0.0019088872,"about_ca_topic_score_gemma":0.001047178,"teacher_disagreement_score":0.0020746992,"about_ca_system_score_codex":0.0010167419,"about_ca_system_score_gemma":0.00093584537,"threshold_uncertainty_score":0.009186149},"labels":[],"label_agreement":null},{"id":"W4310153826","doi":"10.1007/s11749-022-00839-6","title":"Sparse overlapped linear discriminant analysis","year":2022,"lang":"en","type":"article","venue":"Test","topic":"Face and Expression Recognition","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":"Université du Québec à Montréal","funders":"","keywords":"Linear discriminant analysis; Optimal discriminant analysis; Discriminant; Pattern recognition (psychology); Consistency (knowledge bases); Binary number; Generalization; Artificial intelligence; Class (philosophy); Mathematics; Binary classification; Multiple discriminant analysis; Computer science; Bayes' theorem; Algorithm; Bayesian probability; Support vector machine","score_opus":0.021851081366755436,"score_gpt":0.2508689964683555,"score_spread":0.22901791510160005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310153826","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15626168,0.00044946512,0.8239822,0.00039465344,0.00025797109,0.0002597959,0.00098184,0.0039516413,0.0134608075],"genre_scores_gemma":[0.7502107,0.00024103087,0.22803046,0.0003123827,0.00020125558,0.00028593358,0.0042748037,0.00040122744,0.016042156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998645,0.0003563691,0.000064168,0.00022103332,0.00051754445,0.00019590292],"domain_scores_gemma":[0.9984372,0.0004276157,0.00009504918,0.00033970937,0.00058859383,0.00011181911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013961496,0.0010046844,0.0010923696,0.0016682128,0.00091645337,0.0009979232,0.0008699997,0.0007647208,0.010662874],"category_scores_gemma":[0.0041424255,0.00026807105,0.0006920904,0.0011241966,0.00065320794,0.000908965,0.0015149306,0.00088572304,0.004921018],"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":[0.0009721958,0.0003998378,0.0040455465,0.0000999768,0.00012052491,0.00016383165,0.00008355988,0.015525781,0.048409894,0.0055124676,0.011551846,0.91311467],"study_design_scores_gemma":[0.000099500634,0.00041653463,0.008540108,0.00002802495,0.00010024248,0.0004827344,0.00018122183,0.9113468,0.06419585,0.0049899668,0.009576323,0.000042804688],"about_ca_topic_score_codex":0.0018774531,"about_ca_topic_score_gemma":0.0030216617,"teacher_disagreement_score":0.010662874,"about_ca_system_score_codex":0.0002581624,"about_ca_system_score_gemma":0.0011026773,"threshold_uncertainty_score":0.035670877},"labels":[],"label_agreement":null},{"id":"W4311472972","doi":"10.1007/s42044-022-00130-9","title":"A new trigonometric kernel function for support vector machine","year":2022,"lang":"en","type":"article","venue":"Iran Journal of Computer Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Lethbridge","funders":"","keywords":"Radial basis function kernel; Polynomial kernel; Kernel (algebra); Kernel embedding of distributions; Kernel method; Gaussian function; Variable kernel density estimation; Kernel smoother; Support vector machine; Kernel principal component analysis; Mathematics; Artificial intelligence; Trigonometric functions; Pattern recognition (psychology); Computer science; Algorithm; Gaussian; Pure mathematics","score_opus":0.026996792846209716,"score_gpt":0.25506418308090284,"score_spread":0.22806739023469313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311472972","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0054804175,0.0005565036,0.9924304,0.00010254558,0.00021591251,0.000030232677,0.00004791204,0.0006273476,0.0005087747],"genre_scores_gemma":[0.23283614,0.0013381694,0.7576636,0.00021700935,0.00038540087,0.00021144202,0.00060076575,0.00034497134,0.0064025573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984578,0.00036642945,0.00013463241,0.0002144226,0.0007094423,0.0001173011],"domain_scores_gemma":[0.99805945,0.00046045292,0.00010944385,0.00022185885,0.0010568255,0.00009188696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012509277,0.00078080856,0.0014877659,0.0011988585,0.00047600106,0.001291409,0.00146502,0.0014135662,0.0023531714],"category_scores_gemma":[0.0040704375,0.00033099198,0.0010932122,0.001612523,0.0005023274,0.002189848,0.000967709,0.0020001363,0.0025640994],"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":[0.00053945807,0.000277177,0.0012019817,0.0004341947,0.00015593735,0.00020468178,0.00009141417,0.07389607,0.03734301,0.022003783,0.009885374,0.85396695],"study_design_scores_gemma":[0.000018602563,0.000115294366,0.00063091295,0.000018500603,0.000031541887,0.00019361591,0.00002064102,0.98382336,0.0065778955,0.003504108,0.005029362,0.000036195488],"about_ca_topic_score_codex":0.0020701962,"about_ca_topic_score_gemma":0.0011012334,"teacher_disagreement_score":0.0023531714,"about_ca_system_score_codex":0.0005386521,"about_ca_system_score_gemma":0.0011531404,"threshold_uncertainty_score":0.007872105},"labels":[],"label_agreement":null},{"id":"W4312177448","doi":"10.18280/ria.360520","title":"Deep Neural Networks for Automatic Facial Expression Recognition","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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; Artificial intelligence; Facial expression; Local binary patterns; Histogram; Classifier (UML); Deep learning; Pattern recognition (psychology); Facial recognition system; Facial expression recognition; Artificial neural network; Expression (computer science); Speech recognition; Image (mathematics)","score_opus":0.04658577910050946,"score_gpt":0.2696359850984988,"score_spread":0.22305020599798933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312177448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03502577,0.0108364215,0.93500704,0.0014521756,0.0004914242,0.000076999706,0.0010096561,0.0043441774,0.011756415],"genre_scores_gemma":[0.73411787,0.008053269,0.2179016,0.00082576514,0.0003052963,0.00023617157,0.0031432237,0.00027394787,0.035142742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976224,0.000042505566,0.000013617323,0.00006686338,0.000077503646,0.000037319704],"domain_scores_gemma":[0.9998808,0.000034352805,0.000016377568,0.0000209045,0.00004162998,0.0000059539702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035844947,0.00059477973,0.00036143168,0.00039456753,0.00017235795,0.0004497178,0.0006000243,0.0005779612,0.003203377],"category_scores_gemma":[0.00078308384,0.0002444821,0.00044151166,0.00048750965,0.00025893547,0.0005463635,0.00047808004,0.001240364,0.0014688029],"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":[0.00014151716,0.000106407715,0.0008653803,0.00017061472,0.00008379818,0.00008525202,0.000044177832,0.10630376,0.034012914,0.010663894,0.021344744,0.8261776],"study_design_scores_gemma":[0.000006898529,0.000036489146,0.001088471,0.00003120436,0.000018340756,0.000050746326,0.000014092876,0.97299767,0.008692747,0.008845162,0.008204549,0.000013570084],"about_ca_topic_score_codex":0.0060868617,"about_ca_topic_score_gemma":0.0071813413,"teacher_disagreement_score":0.0060868617,"about_ca_system_score_codex":0.0005695655,"about_ca_system_score_gemma":0.00043414457,"threshold_uncertainty_score":0.012102842},"labels":[],"label_agreement":null},{"id":"W4312442702","doi":"10.1109/ijcnn55064.2022.9891956","title":"Evaluation of Self-taught Learning-based Representations for Facial Emotion Recognition","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Artificial intelligence; Initialization; Pattern recognition (psychology); Unsupervised learning; Random forest; Ensemble learning; Feature learning; Emotion recognition; Machine learning; Support vector machine; Feature (linguistics); Feature selection","score_opus":0.083390703633033,"score_gpt":0.31248590296305234,"score_spread":0.22909519933001934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312442702","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7088359,0.0026935386,0.27824688,0.00036118607,0.0003420512,0.00044516491,0.0006592584,0.00229993,0.006116056],"genre_scores_gemma":[0.91102153,0.00058978726,0.08393477,0.00010491723,0.00004866794,0.00017715465,0.0017710408,0.00011164728,0.0022405107],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985593,0.0005237325,0.000087273635,0.0002410153,0.00047462436,0.00011405175],"domain_scores_gemma":[0.9979752,0.000877788,0.00012075371,0.00032105533,0.0006300969,0.00007521208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036391837,0.0008414756,0.00058664125,0.0008352196,0.0002418615,0.0005892116,0.0008050132,0.00075227354,0.0011309794],"category_scores_gemma":[0.006125843,0.00015798306,0.00055044633,0.00043915148,0.00041881582,0.0010215548,0.0007334383,0.00070585916,0.00038729238],"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":[0.0015166408,0.0012157849,0.0065356437,0.00041290783,0.0004782461,0.00011090565,0.00017625449,0.23304345,0.033221137,0.0022690403,0.004602629,0.7164173],"study_design_scores_gemma":[0.00004322101,0.0009395505,0.004420371,0.000028059,0.00006528276,0.00014423432,0.00006814252,0.97041816,0.02235309,0.00067571393,0.00082015275,0.00002401694],"about_ca_topic_score_codex":0.001463282,"about_ca_topic_score_gemma":0.0016534029,"teacher_disagreement_score":0.0036391837,"about_ca_system_score_codex":0.0005871458,"about_ca_system_score_gemma":0.00042049383,"threshold_uncertainty_score":0.019246042},"labels":[],"label_agreement":null},{"id":"W4312700750","doi":"10.1007/978-3-031-16990-8_13","title":"Support Vector Machine","year":2022,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Public Health Ontario; York University","funders":"","keywords":"Computer science; Vector (molecular biology); Support vector machine; Artificial intelligence; Biology","score_opus":0.061538180471741984,"score_gpt":0.3960111665633341,"score_spread":0.33447298609159215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312700750","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014427109,0.0054400004,0.9226815,0.0009786283,0.0013283672,0.0003118561,0.004507137,0.014798431,0.03552693],"genre_scores_gemma":[0.2580487,0.0044641336,0.61991817,0.0007607795,0.00090161467,0.00057529757,0.022503365,0.0010249785,0.09180299],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991924,0.00012337741,0.00006407827,0.00020867717,0.00035466984,0.0000567774],"domain_scores_gemma":[0.99901545,0.00027861138,0.00006375151,0.00016799667,0.00043994375,0.00003425323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006625967,0.001295976,0.0010811522,0.0014049863,0.0003717184,0.0014976804,0.0010209886,0.0008820241,0.019501911],"category_scores_gemma":[0.003093116,0.00033816282,0.000663996,0.0017161856,0.00024411455,0.0014108194,0.00080143876,0.0012695907,0.017403025],"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":[0.00008396803,0.0000941589,0.00040597527,0.00013507696,0.000051105697,0.000032543365,0.00001109655,0.0145571465,0.002488969,0.0042901505,0.03678844,0.9410614],"study_design_scores_gemma":[0.00004810201,0.00027296462,0.002064689,0.00012458414,0.00006968119,0.00023060336,0.00006281521,0.8589231,0.016334701,0.023788454,0.09801805,0.00006231914],"about_ca_topic_score_codex":0.0013290757,"about_ca_topic_score_gemma":0.0012618166,"teacher_disagreement_score":0.019501911,"about_ca_system_score_codex":0.00027756614,"about_ca_system_score_gemma":0.0006358293,"threshold_uncertainty_score":0.06524044},"labels":[],"label_agreement":null},{"id":"W4313555673","doi":"10.1109/tcbb.2022.3233380","title":"An Edge-Cloud-Aided Private High-Order Fuzzy C-Means Clustering Algorithm in Smart Healthcare","year":2023,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"Key Research and Development Project of Hainan Province; National Natural Science Foundation of China","keywords":"Cloud computing; Cluster analysis; Computer science; Data mining; Enhanced Data Rates for GSM Evolution; Health care; Fuzzy logic; Fuzzy clustering; Modal; Artificial intelligence","score_opus":0.02298494899177393,"score_gpt":0.2885581679173901,"score_spread":0.26557321892561614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313555673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019773934,0.00016365672,0.97821075,0.0001780405,0.000034911518,0.000033014087,0.000057598678,0.0004063698,0.0011417756],"genre_scores_gemma":[0.511895,0.00023210149,0.4834211,0.00025508154,0.000047095404,0.00007586101,0.0003216291,0.00009404016,0.0036580514],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944025,0.00010378289,0.000027956612,0.00014700236,0.0001899471,0.00009108853],"domain_scores_gemma":[0.9996445,0.00006967231,0.000033223027,0.000069882255,0.00014709958,0.000035651607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006319366,0.0005057467,0.0007800463,0.00058665045,0.00080659846,0.00081119896,0.0013369675,0.0011455471,0.0015377253],"category_scores_gemma":[0.0013091442,0.00026534993,0.00064847025,0.00088365236,0.00051513384,0.001299498,0.0012492482,0.0009664057,0.0005024021],"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":[0.000643459,0.0001682103,0.0017762397,0.00007731562,0.00008841846,0.00017607724,0.00020426862,0.5731972,0.018785363,0.01603464,0.006882958,0.3819659],"study_design_scores_gemma":[0.0000073380893,0.00001313663,0.000151891,0.0000023592986,0.000004085236,0.000026405272,0.000016531689,0.9947226,0.0023984872,0.0021612628,0.00048815366,0.000007669861],"about_ca_topic_score_codex":0.012142753,"about_ca_topic_score_gemma":0.010466552,"teacher_disagreement_score":0.012142753,"about_ca_system_score_codex":0.0011873455,"about_ca_system_score_gemma":0.0016031602,"threshold_uncertainty_score":0.024144113},"labels":[],"label_agreement":null},{"id":"W4313598197","doi":"10.48550/arxiv.2301.01383","title":"How to get the most out of Twinned Regression Methods","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs; Government of Canada; Compute Canada; Ministero dello Sviluppo Economico; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Regression; Regression analysis; Segmented regression; Computer science; Linear regression; Regression diagnostic; Robust regression; Polynomial regression; Artificial intelligence; Data mining; Statistics; Machine learning; Mathematics","score_opus":0.1551694741957048,"score_gpt":0.261697879543998,"score_spread":0.10652840534829319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313598197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016839422,0.00053625845,0.9959656,0.0005710562,0.00011769155,0.000016975024,0.00002845936,0.0005964934,0.000483522],"genre_scores_gemma":[0.048315216,0.0009562399,0.94478637,0.00063821103,0.0002651149,0.00009293938,0.0002193767,0.0013871308,0.003339423],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9957308,0.0017267104,0.00024552166,0.0011124308,0.0010234353,0.00016110748],"domain_scores_gemma":[0.993509,0.0025359336,0.00032067308,0.001732478,0.0016616029,0.00024023902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0066151386,0.0021830124,0.0026059463,0.0011637427,0.001004545,0.0024707925,0.0025942815,0.002791398,0.0045082173],"category_scores_gemma":[0.029515848,0.001700555,0.0016965307,0.0011775705,0.0020190494,0.0077340486,0.0031405378,0.005530954,0.004972575],"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":[0.00038128815,0.00012278887,0.0015457142,0.0005070534,0.00052713,0.00014406863,0.00043021984,0.1894364,0.017095739,0.06493316,0.025797324,0.69907916],"study_design_scores_gemma":[0.000046588128,0.000073654905,0.00035623438,0.00014775786,0.00008541886,0.00018054861,0.000101190155,0.8578289,0.008651161,0.119422644,0.013019593,0.00008618426],"about_ca_topic_score_codex":0.0031099883,"about_ca_topic_score_gemma":0.0031748416,"teacher_disagreement_score":0.0066151386,"about_ca_system_score_codex":0.00063235685,"about_ca_system_score_gemma":0.0012854567,"threshold_uncertainty_score":0.03498459},"labels":[],"label_agreement":null},{"id":"W4315648476","doi":"10.1007/s10463-022-00861-3","title":"Correction to: Group least squares regression for linear models with strongly correlated predictor variables","year":2023,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Face and Expression Recognition","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":"University of Victoria","funders":"","keywords":"Mathematics; Statistics; Linear regression; Generalized least squares; Regression; Group (periodic table); Total least squares; Regression analysis; Chemistry","score_opus":0.06068453922395285,"score_gpt":0.3102164468924399,"score_spread":0.24953190766848704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4315648476","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019704686,0.0018059029,0.058891185,0.015945474,0.89523804,0.0002048364,0.01211938,0.009700059,0.004124668],"genre_scores_gemma":[0.12750652,0.005245574,0.24322686,0.031655498,0.17897363,0.0024028057,0.033730604,0.045674745,0.33158377],"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98319155,0.0050285324,0.0030398068,0.0034538289,0.0038277681,0.00145845],"domain_scores_gemma":[0.8768363,0.037234757,0.0059120157,0.027701559,0.049459793,0.0028555682],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013903007,0.00559364,0.00609104,0.0069354777,0.0041099354,0.006617834,0.006328064,0.0071898825,0.2605898],"category_scores_gemma":[0.19602418,0.0030109675,0.003794598,0.008515682,0.002392194,0.0050179968,0.004377556,0.010861258,0.1283271],"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":[0.00021960078,0.00003640338,0.00053859514,0.000609539,0.00015227194,0.00039969338,0.00009863327,0.00049405365,0.00038479562,0.0034830226,0.9745787,0.019004736],"study_design_scores_gemma":[0.00038320967,0.00011881498,0.005475485,0.0008904939,0.0003122653,0.0021393623,0.00027238837,0.009733138,0.0029537294,0.02819291,0.94925904,0.0002691423],"about_ca_topic_score_codex":0.006042826,"about_ca_topic_score_gemma":0.010129484,"teacher_disagreement_score":0.2605898,"about_ca_system_score_codex":0.002689256,"about_ca_system_score_gemma":0.006671494,"threshold_uncertainty_score":0.8717598},"labels":[],"label_agreement":null},{"id":"W4318976644","doi":"10.1007/978-3-031-10602-6_6","title":"Fisher Discriminant Analysis","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Linear discriminant analysis; Kernel Fisher discriminant analysis; Discriminant; Optimal discriminant analysis; Statistics; Mathematics; Artificial intelligence; Pattern recognition (psychology); Computer science","score_opus":0.039440306542219875,"score_gpt":0.24147663493237811,"score_spread":0.20203632839015823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318976644","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032010444,0.0047810185,0.7722406,0.00046370807,0.00073888426,0.00010160071,0.000953868,0.0066631436,0.21085607],"genre_scores_gemma":[0.038232107,0.0052491343,0.35619736,0.00043301273,0.000415252,0.00016546434,0.00427504,0.0016572336,0.5933755],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9997116,0.000023563316,0.000009357575,0.000065489556,0.00016690459,0.000022927385],"domain_scores_gemma":[0.9997861,0.000038128823,0.000009501448,0.00004968471,0.000105434105,0.000011075441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032036853,0.0011237074,0.000803809,0.0018102109,0.0006356924,0.0012657022,0.0007960261,0.0006533583,0.06401889],"category_scores_gemma":[0.00075360376,0.00041843354,0.00045017267,0.001668725,0.00037626646,0.0011696785,0.0010425434,0.0009105704,0.072547235],"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":[0.00002562913,0.000029211158,0.00014004318,0.0000904958,0.000012516027,0.00002989929,0.000021916176,0.002187793,0.009369934,0.018834077,0.07957436,0.8896842],"study_design_scores_gemma":[0.0000116028,0.00007188472,0.0022657402,0.00014362892,0.000047393427,0.0008825237,0.00008256973,0.09504518,0.036613464,0.06121295,0.80353373,0.000089163186],"about_ca_topic_score_codex":0.0012143274,"about_ca_topic_score_gemma":0.002634593,"teacher_disagreement_score":0.06401889,"about_ca_system_score_codex":0.00035261657,"about_ca_system_score_gemma":0.0005426686,"threshold_uncertainty_score":0.2141645},"labels":[],"label_agreement":null},{"id":"W4318977545","doi":"10.1007/978-3-031-10602-6_5","title":"Principal Component Analysis","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":45,"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 Waterloo","funders":"","keywords":"Principal component analysis; Dimensionality reduction; Subspace topology; Pattern recognition (psychology); Statistical analysis; Curse of dimensionality; Artificial intelligence; Mathematics; Statistics; Computer science","score_opus":0.03969583219601328,"score_gpt":0.2502735352190259,"score_spread":0.21057770302301262,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318977545","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014845628,0.0042366036,0.8592897,0.000365916,0.00093579,0.0001612453,0.0012881866,0.0077579203,0.12448005],"genre_scores_gemma":[0.022182874,0.0073211375,0.51508236,0.00041409006,0.00060615025,0.00036354415,0.0065033375,0.003185507,0.44434103],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99943274,0.000054135755,0.000020838932,0.00013048876,0.00032998438,0.000031758187],"domain_scores_gemma":[0.99956125,0.00008161121,0.000017844006,0.00010082975,0.00021989892,0.000018635892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044649004,0.0016701373,0.0009373535,0.0020349447,0.00073429605,0.002289992,0.0010801696,0.000933908,0.07921291],"category_scores_gemma":[0.0011703171,0.00061099255,0.00070867053,0.0027609197,0.00048715237,0.0013845752,0.0013063636,0.0011265342,0.09964602],"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":[0.000023135337,0.000028542241,0.00014116592,0.00016517934,0.000029430073,0.00004222955,0.000037540973,0.0028649636,0.00624569,0.013413704,0.10787578,0.8691327],"study_design_scores_gemma":[0.000013261857,0.00006372465,0.0026437868,0.00017779914,0.00007195157,0.0006424273,0.00011226105,0.067798786,0.020984747,0.044315334,0.86308146,0.00009449849],"about_ca_topic_score_codex":0.001305838,"about_ca_topic_score_gemma":0.0022603278,"teacher_disagreement_score":0.07921291,"about_ca_system_score_codex":0.0003377641,"about_ca_system_score_gemma":0.00070489146,"threshold_uncertainty_score":0.26499355},"labels":[],"label_agreement":null},{"id":"W4318977940","doi":"10.1007/978-3-031-10602-6_13","title":"Probabilistic Metric Learning","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Metric (unit); Probabilistic logic; Artificial intelligence; Computer science; Machine learning; Engineering","score_opus":0.03709726476585862,"score_gpt":0.23942023834939827,"score_spread":0.20232297358353965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318977940","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015406662,0.010482546,0.86972934,0.0011046373,0.00071146834,0.00004034534,0.0008472852,0.002185893,0.113357835],"genre_scores_gemma":[0.09845064,0.018079,0.53912175,0.0009941616,0.0015404845,0.00027212387,0.0061786673,0.0023582869,0.33300495],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99944216,0.000100368576,0.000019704896,0.00014874482,0.00026278704,0.000026259602],"domain_scores_gemma":[0.9994622,0.00016659137,0.000023144024,0.00017661323,0.00014276152,0.000028715245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006149928,0.0012091179,0.0010524598,0.0011283697,0.00047261265,0.0014015597,0.0013691442,0.00086179934,0.024948336],"category_scores_gemma":[0.0020804994,0.0005016728,0.00056434947,0.0021333161,0.0008600893,0.0021863605,0.0015862392,0.0019984324,0.015512711],"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":[0.000019819072,0.000026980992,0.00017462911,0.00013391091,0.000030370224,0.00002306209,0.000031114396,0.02071439,0.0009953916,0.15265729,0.13167629,0.69351673],"study_design_scores_gemma":[0.000005740339,0.000042549465,0.00067798275,0.00010804093,0.00002340762,0.00032897352,0.000026507189,0.15941334,0.0024105017,0.50326574,0.33365452,0.00004275549],"about_ca_topic_score_codex":0.0023097533,"about_ca_topic_score_gemma":0.0035066996,"teacher_disagreement_score":0.024948336,"about_ca_system_score_codex":0.0008911018,"about_ca_system_score_gemma":0.0007208315,"threshold_uncertainty_score":0.08346045},"labels":[],"label_agreement":null},{"id":"W4318977949","doi":"10.1007/978-3-031-10602-6_15","title":"Sufficient Dimension Reduction and Kernel Dimension Reduction","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Dimensionality reduction; Sufficient dimension reduction; Dimension (graph theory); Reduction (mathematics); Sliced inverse regression; Mathematics; Kernel (algebra); Transformation (genetics); Dimensional reduction; Data reduction; Artificial intelligence; Pattern recognition (psychology); Computer science; Statistics; Pure mathematics; Geometry; Biology","score_opus":0.025982309766162143,"score_gpt":0.2344712784167201,"score_spread":0.20848896865055797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318977949","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007171094,0.0034137215,0.93388957,0.001026234,0.0006965286,0.00004419803,0.00037602556,0.0009172611,0.052465346],"genre_scores_gemma":[0.2734333,0.0067454535,0.58068067,0.0011770393,0.0015727577,0.00031019715,0.0021848276,0.0018019491,0.13209386],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99934095,0.00014263712,0.00003631453,0.00014302654,0.00026900598,0.000068093635],"domain_scores_gemma":[0.99930656,0.00023726566,0.000029570387,0.00023196466,0.00016676029,0.000027878596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065200386,0.0008718474,0.0008452749,0.00112826,0.000546837,0.0016691007,0.0008013387,0.0006001839,0.009478328],"category_scores_gemma":[0.0023836042,0.0004329805,0.0008917842,0.0010287454,0.0014102618,0.0025909215,0.0018156723,0.0027862843,0.004306643],"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":[0.000069207585,0.000052042516,0.00017745177,0.00027656142,0.000032537635,0.000061413826,0.000114824485,0.008020886,0.0050892862,0.739744,0.043947723,0.20241418],"study_design_scores_gemma":[0.0000095161695,0.000028637827,0.000378445,0.000048769063,0.000022968912,0.00022640271,0.000052931606,0.05389652,0.005696467,0.88083804,0.058773946,0.000027302858],"about_ca_topic_score_codex":0.0005570451,"about_ca_topic_score_gemma":0.0005391306,"teacher_disagreement_score":0.009478328,"about_ca_system_score_codex":0.00057166105,"about_ca_system_score_gemma":0.0005340269,"threshold_uncertainty_score":0.03170818},"labels":[],"label_agreement":null},{"id":"W4319779660","doi":"10.1109/icdmw58026.2022.00116","title":"Data-driven Kernel Subspace Clustering with Local Manifold Preservation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Kernel (algebra); Cluster analysis; Kernel embedding of distributions; Kernel method; Kernel principal component analysis; Computer science; Nonlinear dimensionality reduction; Subspace topology; Manifold alignment; Variable kernel density estimation; String kernel; Tree kernel; Artificial intelligence; Manifold (fluid mechanics); Pattern recognition (psychology); Weighting; Mathematics; Dimensionality reduction; Support vector machine; Discrete mathematics","score_opus":0.17319036239941235,"score_gpt":0.3311012339830176,"score_spread":0.15791087158360526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319779660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007762725,0.000060172817,0.9915091,0.000045064695,0.000007690187,0.000018873803,0.000024726736,0.00034820856,0.00022348706],"genre_scores_gemma":[0.4259155,0.00021113364,0.5699688,0.00013994277,0.000050523646,0.00019221664,0.00068454456,0.0002585306,0.0025787489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862885,0.00039363114,0.000076233664,0.0003441676,0.00045504945,0.000102199854],"domain_scores_gemma":[0.9983444,0.00030903865,0.0001899697,0.00051547354,0.00056094746,0.000080246755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013725498,0.00072406017,0.0012526345,0.0011682555,0.0006680851,0.0011017659,0.0021302437,0.0010812387,0.0008720123],"category_scores_gemma":[0.0039033212,0.00043892252,0.0010270023,0.0019243585,0.0010351909,0.0024221153,0.0024046255,0.0014024669,0.0008806927],"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":[0.00019383602,0.00018376633,0.002480365,0.0001782149,0.00016322093,0.00009968907,0.0003943157,0.52397865,0.024993382,0.032712623,0.0045367675,0.41008517],"study_design_scores_gemma":[0.000004311212,0.000021102067,0.00013250076,0.000002520975,0.0000046435484,0.00002356363,0.000016794458,0.9924948,0.002676035,0.0040784725,0.00053319643,0.000012084775],"about_ca_topic_score_codex":0.003086181,"about_ca_topic_score_gemma":0.0031901044,"teacher_disagreement_score":0.003086181,"about_ca_system_score_codex":0.00073929987,"about_ca_system_score_gemma":0.0014419147,"threshold_uncertainty_score":0.0072588325},"labels":[],"label_agreement":null},{"id":"W4319989602","doi":"10.1007/978-3-031-10602-6_8","title":"Locally Linear Embedding","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Embedding; Nonlinear dimensionality reduction; Dimensionality reduction; Mathematics; Pattern recognition (psychology); Manifold (fluid mechanics); Curse of dimensionality; Artificial intelligence; Computer science; Topology (electrical circuits); Combinatorics; Engineering","score_opus":0.03715705856816767,"score_gpt":0.26900973066345835,"score_spread":0.23185267209529067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319989602","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00562446,0.0050103045,0.83921903,0.00062541926,0.0006369523,0.000058254976,0.0008030171,0.0060276506,0.1419949],"genre_scores_gemma":[0.13732903,0.005968639,0.33612934,0.00057403895,0.00063774805,0.00020579477,0.00477769,0.003286768,0.51109093],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99978095,0.000034900488,0.000008087607,0.00007155434,0.0000853569,0.0000191256],"domain_scores_gemma":[0.9998202,0.000033048287,0.000009089526,0.00008397252,0.000041834563,0.0000119969645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019914325,0.0010041211,0.0006261118,0.00063645805,0.0003359059,0.0010048834,0.0007394366,0.00057180953,0.037883785],"category_scores_gemma":[0.00063844223,0.000385158,0.0003675722,0.00078915013,0.00059238804,0.001985405,0.0016084764,0.0013689675,0.02999766],"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":[0.0000590324,0.000043224914,0.00011475673,0.00020034192,0.00002437719,0.000070975475,0.00008321609,0.017531104,0.011255496,0.107279345,0.08887248,0.7744656],"study_design_scores_gemma":[0.000017070948,0.00013328619,0.00059479085,0.00013124205,0.000041620053,0.0007289623,0.00011546614,0.24629992,0.025030566,0.25527057,0.47156534,0.00007121774],"about_ca_topic_score_codex":0.0009076409,"about_ca_topic_score_gemma":0.001555784,"teacher_disagreement_score":0.037883785,"about_ca_system_score_codex":0.00041184964,"about_ca_system_score_gemma":0.0002969537,"threshold_uncertainty_score":0.1267339},"labels":[],"label_agreement":null},{"id":"W4320068442","doi":"10.1007/978-3-031-10602-6_11","title":"Spectral Metric Learning","year":2022,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":13,"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 Waterloo","funders":"","keywords":"Metric (unit); Dimensionality reduction; Embedding; Metric space; Artificial intelligence; Equivalence of metrics; Mathematics; Computer science; Pattern recognition (psychology); Convex metric space; Discrete mathematics; Engineering","score_opus":0.021937973035117568,"score_gpt":0.2264010646041617,"score_spread":0.20446309156904413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320068442","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023266654,0.01087404,0.7935546,0.0010863825,0.0011685962,0.000048032965,0.00065286906,0.00284655,0.18744232],"genre_scores_gemma":[0.07422363,0.014873628,0.45380798,0.001102502,0.0013902513,0.00017228523,0.0042032953,0.002359933,0.44786644],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99968624,0.000045638768,0.000010374504,0.0000853716,0.00015538262,0.000016945938],"domain_scores_gemma":[0.99971026,0.0000573887,0.000012341763,0.00009140443,0.00010799469,0.00002050199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035958955,0.0011365229,0.0007574871,0.0009766165,0.00043787816,0.0010825992,0.0009336451,0.00071239274,0.027174199],"category_scores_gemma":[0.0010545704,0.00032004615,0.00036855592,0.0016298882,0.0006500209,0.001590402,0.0012302323,0.001601045,0.02309878],"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":[0.000017178447,0.000030490193,0.000090579706,0.000105592044,0.00001628984,0.000015443382,0.00002398277,0.008729354,0.0023458712,0.07400536,0.13343555,0.78118426],"study_design_scores_gemma":[0.0000047309954,0.000058386384,0.0007553645,0.0001208824,0.00001900216,0.00041247794,0.000049507005,0.12619232,0.0073095514,0.30076426,0.56426316,0.000050431092],"about_ca_topic_score_codex":0.0013869704,"about_ca_topic_score_gemma":0.00268672,"teacher_disagreement_score":0.027174199,"about_ca_system_score_codex":0.00058842765,"about_ca_system_score_gemma":0.00048279358,"threshold_uncertainty_score":0.0909068},"labels":[],"label_agreement":null},{"id":"W4321019977","doi":"10.1016/j.patcog.2023.109417","title":"Timid semi–supervised learning for face expression analysis","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ontario Ministry of Research and Innovation; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Nvidia","keywords":"Computer science; Artificial intelligence; Machine learning; Face (sociological concept); Supervised learning; Domain (mathematical analysis); Expression (computer science); Semi-supervised learning; Action (physics); Labeled data; Pattern recognition (psychology); Mathematics","score_opus":0.04501966001723489,"score_gpt":0.27698631947015706,"score_spread":0.23196665945292216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321019977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011841892,0.0004309059,0.9827795,0.00017772852,0.000115181705,0.00009850134,0.00036574365,0.003071906,0.0011186649],"genre_scores_gemma":[0.3035887,0.00045126892,0.6671111,0.0006109347,0.00030165567,0.0008274969,0.0049555292,0.0010868007,0.021066554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998338,0.0005926817,0.000087310735,0.00041654852,0.00037939762,0.00018596131],"domain_scores_gemma":[0.9980185,0.0008446042,0.000117027404,0.0003991742,0.00053454394,0.000086059496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024500985,0.0011185224,0.0013963259,0.00074954354,0.0008427242,0.0009810041,0.0025103805,0.0014295719,0.0042938516],"category_scores_gemma":[0.004318883,0.00062169554,0.0014742196,0.00067799306,0.0006907024,0.0013338869,0.001859248,0.0026590088,0.003350167],"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":[0.0006086,0.0004205148,0.00092222623,0.00020333703,0.0002230549,0.00008579929,0.00013155647,0.10003696,0.015384084,0.0049464344,0.021079324,0.85595816],"study_design_scores_gemma":[0.000010074025,0.00004964675,0.0002822267,0.000006990309,0.000013670955,0.000035416095,0.00001641294,0.99160904,0.0040841172,0.0025154618,0.0013661109,0.00001089212],"about_ca_topic_score_codex":0.0038552026,"about_ca_topic_score_gemma":0.007103484,"teacher_disagreement_score":0.0042938516,"about_ca_system_score_codex":0.00066675217,"about_ca_system_score_gemma":0.0015395295,"threshold_uncertainty_score":0.014364362},"labels":[],"label_agreement":null},{"id":"W4321439807","doi":"10.1007/978-3-031-25271-6_8","title":"Deep Matrix Factorization for Multi-view Clustering Using Density-Based Preprocessing","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University; Memorial University of Newfoundland","funders":"","keywords":"Cluster analysis; Computer science; Non-negative matrix factorization; Data mining; Artificial intelligence; Matrix decomposition; Pattern recognition (psychology); Theoretical computer science","score_opus":0.06628412759752729,"score_gpt":0.2972755181536737,"score_spread":0.23099139055614643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321439807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014570146,0.00018446367,0.9953956,0.000060725502,0.00003673296,0.000034465884,0.0002688401,0.001983242,0.0005788564],"genre_scores_gemma":[0.055857748,0.00042321516,0.9346152,0.00015489021,0.00008280688,0.00019194784,0.0030394378,0.0007213125,0.0049134893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904245,0.00013768165,0.000046137196,0.00028802713,0.0003121946,0.00017361091],"domain_scores_gemma":[0.99879056,0.00031893144,0.000076451775,0.000368859,0.00036546637,0.00007977075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072086725,0.0019254411,0.0021886826,0.0015517251,0.0010294381,0.0016831686,0.0031161206,0.0018427839,0.011930672],"category_scores_gemma":[0.002485145,0.0012754216,0.0028435343,0.0024374814,0.0007187688,0.0023679514,0.0026480502,0.0036261277,0.010699587],"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":[0.00026759884,0.00023784851,0.0005438332,0.000329536,0.00017415879,0.00012901652,0.00017240806,0.088242404,0.047444724,0.021343775,0.031187782,0.80992687],"study_design_scores_gemma":[0.000014305568,0.000053948042,0.00039207467,0.000021628448,0.000021903315,0.00013031984,0.0000530329,0.96368086,0.010855417,0.018404752,0.0063404557,0.00003126084],"about_ca_topic_score_codex":0.014943891,"about_ca_topic_score_gemma":0.027802937,"teacher_disagreement_score":0.014943891,"about_ca_system_score_codex":0.0012190522,"about_ca_system_score_gemma":0.0017338439,"threshold_uncertainty_score":0.039912105},"labels":[],"label_agreement":null},{"id":"W4322755793","doi":"10.1016/j.engappai.2023.106014","title":"<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" id=\"d1e4378\" altimg=\"si182.svg\"><mml:mi>β</mml:mi></mml:math>-divergence NMF with biorthogonal regularization for data representation","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Northwest University; Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Non-negative matrix factorization; Euclidean distance; Cluster analysis; Computer science; Orthogonality; Similarity measure; Similarity (geometry); Pattern recognition (psychology); Mathematics; Algorithm; Artificial intelligence; Matrix decomposition; Eigenvalues and eigenvectors; Image (mathematics)","score_opus":0.03807137198866075,"score_gpt":0.2798129788570344,"score_spread":0.24174160686837362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322755793","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010103786,0.0003031922,0.59543794,0.0031692153,0.0012867672,0.0004076447,0.10781245,0.16274376,0.12782866],"genre_scores_gemma":[0.021437846,0.0010488324,0.42074507,0.0021996202,0.00060827576,0.0014051079,0.20022915,0.15546864,0.19685745],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908507,0.00013040454,0.000100157195,0.00013096552,0.00048970623,0.000063596126],"domain_scores_gemma":[0.997658,0.0005346633,0.00011708693,0.0007631103,0.00081790966,0.00010921948],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011847035,0.0018041347,0.0010481577,0.0018133234,0.0006991666,0.003739733,0.0037045574,0.0023990795,0.49451125],"category_scores_gemma":[0.0069263345,0.0011099767,0.0010165258,0.0030439268,0.00058596727,0.0032234539,0.0022372783,0.0025027655,0.40222684],"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":[0.000060083272,0.000032841166,0.00010621477,0.00028383508,0.000019405783,0.000059113536,0.000048843813,0.0014364555,0.0029428527,0.019593248,0.8923794,0.08303777],"study_design_scores_gemma":[0.0000699942,0.000020915139,0.0004160218,0.00008334639,0.0000095642035,0.00018577624,0.000032191056,0.024346014,0.011293783,0.02649288,0.9369924,0.000057069665],"about_ca_topic_score_codex":0.006785464,"about_ca_topic_score_gemma":0.0115124835,"teacher_disagreement_score":0.49451125,"about_ca_system_score_codex":0.0012800168,"about_ca_system_score_gemma":0.0014317094,"threshold_uncertainty_score":0.7210183},"labels":[],"label_agreement":null},{"id":"W4323654749","doi":"10.18280/isi.280118","title":"Facial Emotion Recognition Using HOG and Convolution Neural Network","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"Convolution (computer science); Computer science; Artificial intelligence; Pattern recognition (psychology); Artificial neural network; Speech recognition; Psychology","score_opus":0.03425552885147001,"score_gpt":0.24408696514397313,"score_spread":0.20983143629250312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323654749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17992683,0.0019286796,0.80411035,0.00037528374,0.00046655152,0.00025561612,0.0007768803,0.003432284,0.008727472],"genre_scores_gemma":[0.7390891,0.0017470192,0.24844062,0.00030599246,0.00011482659,0.00015896972,0.0013098379,0.000115348805,0.008718328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997509,0.00002899054,0.000012530017,0.00006646419,0.000097383854,0.00004369304],"domain_scores_gemma":[0.99987257,0.00001596767,0.000012311449,0.000015264468,0.000075236385,0.000008756991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036911393,0.00048339224,0.00044342998,0.00087572384,0.00015295461,0.00038481687,0.00032568013,0.0003574932,0.0015392371],"category_scores_gemma":[0.00045893434,0.00019091126,0.00044055,0.000591372,0.00017657332,0.0005758903,0.0004133238,0.00029400564,0.00051532144],"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":[0.00027133865,0.00014136947,0.0055821445,0.00013309238,0.00010672716,0.00017318835,0.000048265338,0.0133551145,0.14236271,0.0011423262,0.0059148706,0.8307689],"study_design_scores_gemma":[0.000024483401,0.0002353229,0.024067886,0.000038145005,0.00010125409,0.00066342624,0.00010003275,0.8605692,0.10385576,0.0025820446,0.00769966,0.00006281256],"about_ca_topic_score_codex":0.003642808,"about_ca_topic_score_gemma":0.004309584,"teacher_disagreement_score":0.003642808,"about_ca_system_score_codex":0.00031567848,"about_ca_system_score_gemma":0.00029955464,"threshold_uncertainty_score":0.007243216},"labels":[],"label_agreement":null},{"id":"W4323654856","doi":"10.18280/isi.280116","title":"Hybrid Learning Predictions on Learning Quality Using Multiple Linear Regression","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"Direktorat Jenderal Pendidikan Tinggi","keywords":"Quality (philosophy); Linear regression; Computer science; Machine learning; Regression; Artificial intelligence; Statistics; Mathematics","score_opus":0.04472399046861803,"score_gpt":0.29386675928667716,"score_spread":0.24914276881805913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323654856","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79741144,0.0006321897,0.1956008,0.0008270218,0.00010635765,0.00013855021,0.0007255404,0.0010960735,0.003461959],"genre_scores_gemma":[0.9874319,0.00009596582,0.0110972775,0.000028844022,0.000016512218,0.00004801557,0.0003404697,0.000025703137,0.0009152818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977022,0.0011956792,0.00013995686,0.00049296237,0.00030143105,0.00016777214],"domain_scores_gemma":[0.9858543,0.011237424,0.0008467338,0.00054358767,0.0012953081,0.00022258471],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006193368,0.0012432898,0.00080413424,0.0017208224,0.0003148294,0.0017394024,0.00094217557,0.0009251513,0.0022000147],"category_scores_gemma":[0.017611748,0.00028405176,0.0010890269,0.0012407458,0.0004073457,0.0016722669,0.00091991515,0.0014642439,0.0008917477],"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":[0.0006677813,0.0010337814,0.18023746,0.00015358618,0.0005130798,0.00017411447,0.00047268797,0.6271572,0.0015903144,0.001654791,0.0020012255,0.1843439],"study_design_scores_gemma":[0.000005533181,0.00009588324,0.0094599025,0.0000151823515,0.000024366786,0.000012475472,0.00005937649,0.9891928,0.00043783025,0.00055660977,0.00012816947,0.000011984233],"about_ca_topic_score_codex":0.008832782,"about_ca_topic_score_gemma":0.0051449165,"teacher_disagreement_score":0.008832782,"about_ca_system_score_codex":0.0009325303,"about_ca_system_score_gemma":0.0004996241,"threshold_uncertainty_score":0.032754064},"labels":[],"label_agreement":null},{"id":"W4324122995","doi":"10.2139/ssrn.4387135","title":"Cyclic Style Generative Adversarial Network for Near Infrared and Visible Light Face Recognition","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Adversarial system; Generative grammar; Style (visual arts); Face (sociological concept); Artificial intelligence; Generative adversarial network; Infrared; Computer vision; Facial recognition system; Computer science; Pattern recognition (psychology); Optics; Art; Linguistics; Image (mathematics); Physics; Visual arts; Philosophy","score_opus":0.013900292991116293,"score_gpt":0.24561691971169913,"score_spread":0.23171662672058285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324122995","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03317783,0.0005399687,0.960696,0.00030981752,0.00014106723,0.000045428118,0.00019103203,0.00090149534,0.00399736],"genre_scores_gemma":[0.8129509,0.00050729146,0.16256733,0.0005502741,0.00017194336,0.00012789448,0.00085517514,0.00018888156,0.022080392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997236,0.0000735299,0.0000075326575,0.00007644683,0.00006980559,0.000049016242],"domain_scores_gemma":[0.9996013,0.00019737138,0.000032076754,0.00007193419,0.000068916095,0.000028428052],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005706814,0.00065802794,0.0005980007,0.0003543943,0.0002896056,0.00042741036,0.0012513716,0.0010522853,0.0027991615],"category_scores_gemma":[0.001140556,0.00034611876,0.0007177524,0.00040831938,0.0005458655,0.00050375506,0.0011522966,0.0014841824,0.001081893],"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":[0.0002723174,0.00009169391,0.00082289305,0.00005411943,0.00006969946,0.00013248323,0.0000580138,0.7808309,0.008776623,0.009856014,0.005164797,0.19387048],"study_design_scores_gemma":[0.0000016662146,0.000012180635,0.000087728265,0.000002010402,0.000003751805,0.000016046532,0.0000023097914,0.9977617,0.0006446304,0.0012480278,0.00021682456,0.0000030782967],"about_ca_topic_score_codex":0.0048395614,"about_ca_topic_score_gemma":0.00699872,"teacher_disagreement_score":0.0048395614,"about_ca_system_score_codex":0.0005519586,"about_ca_system_score_gemma":0.0005376934,"threshold_uncertainty_score":0.009622812},"labels":[],"label_agreement":null},{"id":"W4327718153","doi":"10.1016/j.knosys.2023.110465","title":"Robust dual-graph discriminative NMF for data classification","year":2023,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Northwest University; Shanxi Provincial Key Research and Development Project; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Non-negative matrix factorization; Discriminative model; Outlier; Matrix decomposition; Robustness (evolution); Computer science; Pattern recognition (psychology); Artificial intelligence; Graph; Feature vector; Dual graph; k-nearest neighbors algorithm; Algorithm; Theoretical computer science","score_opus":0.2440555756760268,"score_gpt":0.3397255605977658,"score_spread":0.09566998492173903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4327718153","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071718707,0.00048017778,0.9896423,0.0002331165,0.00011936907,0.000036432702,0.00019091649,0.0014258765,0.00070002413],"genre_scores_gemma":[0.348109,0.00064264575,0.63699776,0.0006768761,0.00030296858,0.00031000157,0.0032351462,0.00062884344,0.0090967445],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984956,0.00038302768,0.000072575815,0.00043712504,0.0003999558,0.00021168447],"domain_scores_gemma":[0.99847656,0.0005220232,0.00011404459,0.00038717606,0.0004210443,0.00007916999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001280315,0.0013102875,0.0020616818,0.001395507,0.0008145494,0.0010100617,0.0023438125,0.0023317712,0.0030350175],"category_scores_gemma":[0.0042712977,0.0005501219,0.0015620237,0.001768324,0.00085681985,0.0013448001,0.0016383382,0.0024478831,0.0025413577],"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":[0.00040054368,0.00032471187,0.0008803507,0.00019659827,0.00014561176,0.00010211014,0.000054159325,0.12166309,0.025748232,0.008282628,0.014437282,0.8277647],"study_design_scores_gemma":[0.000011024338,0.000030579653,0.00038732725,0.000009313982,0.000014504132,0.00005491402,0.000010711744,0.989776,0.0036288132,0.0046566497,0.0014099268,0.000010253357],"about_ca_topic_score_codex":0.008465497,"about_ca_topic_score_gemma":0.011270696,"teacher_disagreement_score":0.008465497,"about_ca_system_score_codex":0.00079470803,"about_ca_system_score_gemma":0.002004471,"threshold_uncertainty_score":0.01683247},"labels":[],"label_agreement":null},{"id":"W4362695238","doi":"10.31763/ijrcs.v3i2.939","title":"Improving the Recognition Percentage of the Identity Check System by Applying the SVM Method on the Face Image Using Special Faces","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Control Systems","topic":"Face and Expression Recognition","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 Victoria","funders":"","keywords":"Facial recognition system; Artificial intelligence; Support vector machine; Face (sociological concept); Pattern recognition (psychology); Computer science; Identity (music); Image (mathematics); Field (mathematics); Standard test image; Sample (material); Computer vision; Mathematics; Image processing","score_opus":0.026476884889627777,"score_gpt":0.279577055202665,"score_spread":0.2531001703130372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362695238","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5376416,0.0021693965,0.44553798,0.00032838454,0.0004705354,0.00013341215,0.00020834997,0.0058655767,0.0076447576],"genre_scores_gemma":[0.86952573,0.00049092434,0.12640093,0.000083076964,0.000051093084,0.00004317237,0.00031638323,0.00010345842,0.00298516],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885046,0.00021698579,0.00007942701,0.00021058481,0.00051915215,0.00012339755],"domain_scores_gemma":[0.9989612,0.0002862597,0.00007457645,0.0001619318,0.00048196435,0.000034158467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014961119,0.00059374917,0.0009042627,0.0009860342,0.00029575374,0.00052657595,0.0004797431,0.0005532812,0.0017445815],"category_scores_gemma":[0.0030427983,0.00014326136,0.0003701118,0.00035331174,0.00020110923,0.000869659,0.00048923906,0.00039076325,0.0012421795],"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":[0.00073956477,0.00018129675,0.008704365,0.00018815901,0.000086146385,0.00020681649,0.00013152276,0.008795302,0.13054658,0.0005041956,0.00334003,0.8465761],"study_design_scores_gemma":[0.00005991131,0.00129033,0.05976139,0.00006288419,0.00022297527,0.0020622413,0.00027669032,0.595437,0.3321086,0.0005984441,0.0079891,0.00013049295],"about_ca_topic_score_codex":0.0013566769,"about_ca_topic_score_gemma":0.0007993124,"teacher_disagreement_score":0.0017445815,"about_ca_system_score_codex":0.00022833071,"about_ca_system_score_gemma":0.00030011265,"threshold_uncertainty_score":0.007912278},"labels":[],"label_agreement":null},{"id":"W4365794469","doi":"10.1007/978-981-99-1642-9_3","title":"Binary Orthogonal Non-negative Matrix Factorization","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Face and Expression Recognition","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":"University of Lethbridge","funders":"","keywords":"Factorization; Matrix decomposition; Binary number; Computer science; Matrix (chemical analysis); Cluster analysis; Pattern recognition (psychology); Logical matrix; Space (punctuation); Non-negative matrix factorization; Artificial intelligence; Algorithm; Mathematics; Arithmetic; Physics; Eigenvalues and eigenvectors; Chemistry","score_opus":0.041931324097486435,"score_gpt":0.3055638094217586,"score_spread":0.26363248532427214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4365794469","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028748845,0.0073199803,0.9394266,0.00077005854,0.0022093467,0.00006704999,0.0007501859,0.0014831842,0.04509867],"genre_scores_gemma":[0.07187521,0.014455966,0.74345315,0.0010020089,0.001986244,0.00027608091,0.004673424,0.00090194267,0.16137594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996439,0.000057328663,0.000016548896,0.000075982185,0.00017183836,0.000034388915],"domain_scores_gemma":[0.9995921,0.00012460211,0.000035449477,0.000088060195,0.0001366215,0.000023130902],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043975384,0.0011491746,0.00067916483,0.00061862107,0.000437777,0.0011406759,0.00060394645,0.00056170195,0.018423248],"category_scores_gemma":[0.0013752693,0.00029959634,0.00039365047,0.001198891,0.0005789983,0.0012630863,0.00095502776,0.0011119202,0.01688826],"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":[0.00008528469,0.00007022182,0.00012804836,0.0004114397,0.000023236491,0.000091397786,0.00008181679,0.011212285,0.015533208,0.07620771,0.13442929,0.7617261],"study_design_scores_gemma":[0.000038454044,0.00015084326,0.0009665183,0.0002462396,0.000040505744,0.0010053685,0.0001339296,0.28841636,0.02795371,0.24721491,0.43374476,0.00008846391],"about_ca_topic_score_codex":0.00060072355,"about_ca_topic_score_gemma":0.0012052808,"teacher_disagreement_score":0.018423248,"about_ca_system_score_codex":0.00023238284,"about_ca_system_score_gemma":0.0004606994,"threshold_uncertainty_score":0.061631918},"labels":[],"label_agreement":null},{"id":"W4366412507","doi":"10.21203/rs.3.rs-2822747/v1","title":"Facial expression detection using Viola-Jones algorithm in the learning environment","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Face and Expression Recognition","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":"Artificial intelligence; Computer science; Support vector machine; Histogram of oriented gradients; Face detection; Face (sociological concept); Histogram; Pattern recognition (psychology); Feature extraction; Facial recognition system; Facial expression; Computer vision; Three-dimensional face recognition; Viola–Jones object detection framework; Object-class detection; Feature (linguistics); Facial expression recognition; Image (mathematics)","score_opus":0.13718556972614138,"score_gpt":0.390473095858029,"score_spread":0.2532875261318876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366412507","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27401817,0.0003045686,0.7129728,0.00024851988,0.00011630798,0.00024899436,0.0003669084,0.0056860684,0.0060377037],"genre_scores_gemma":[0.74405706,0.00016584549,0.24990055,0.0001053104,0.00004032794,0.00015208348,0.0007335962,0.00021254424,0.0046326476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986008,0.00024335856,0.00006954808,0.00040775278,0.00048740575,0.00019126224],"domain_scores_gemma":[0.9992925,0.00013337366,0.000051973984,0.00014151241,0.00030938085,0.000071350165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013256574,0.0005062748,0.00094114424,0.0010580901,0.0004683783,0.00089733634,0.0010289527,0.00056726055,0.002519674],"category_scores_gemma":[0.0021403427,0.00018827434,0.00038763313,0.0012202198,0.00031676894,0.0007569686,0.0007276804,0.0007579106,0.0015355927],"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":[0.0005140109,0.00065669237,0.0070877923,0.00005865395,0.00008948651,0.00009655888,0.00006846618,0.029221768,0.07784276,0.0012073618,0.004827486,0.878329],"study_design_scores_gemma":[0.000021577245,0.00012502652,0.007546377,0.0000040645114,0.000014884431,0.000080693324,0.000040447656,0.9485972,0.041530572,0.0008032983,0.0012174925,0.000018284118],"about_ca_topic_score_codex":0.0041697216,"about_ca_topic_score_gemma":0.0031048968,"teacher_disagreement_score":0.0041697216,"about_ca_system_score_codex":0.00036593506,"about_ca_system_score_gemma":0.0005909405,"threshold_uncertainty_score":0.00842911},"labels":[],"label_agreement":null},{"id":"W4376869352","doi":"10.18280/isi.280221","title":"Single Imputation Using Statistics-Based and K Nearest Neighbor Methods for Numerical Datasets","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"k-nearest neighbors algorithm; Imputation (statistics); Computer science; Statistics; Data mining; Pattern recognition (psychology); Mathematics; Artificial intelligence; Missing data","score_opus":0.04364174731652539,"score_gpt":0.33478802202128694,"score_spread":0.29114627470476157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376869352","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008767563,0.0014964469,0.9849551,0.00035797415,0.00025991662,0.0002515885,0.001460084,0.0012380416,0.0012133553],"genre_scores_gemma":[0.120673455,0.0013964043,0.87030184,0.00020722095,0.00020148446,0.0010419142,0.004372437,0.00032210085,0.0014831781],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9753724,0.015298993,0.0020151625,0.0037692327,0.0031462663,0.00039801197],"domain_scores_gemma":[0.95459276,0.029877475,0.0036695162,0.007067513,0.0044532823,0.00033948917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02364046,0.0012364237,0.0031206494,0.0042718127,0.0014935597,0.0033462162,0.0033525801,0.0017623177,0.0046051065],"category_scores_gemma":[0.08449956,0.00092882145,0.0038887132,0.007755937,0.0010223088,0.0030832158,0.0021793232,0.0026364818,0.0024432088],"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":[0.0007067411,0.00041166015,0.04354196,0.0033944608,0.0038951882,0.00053291576,0.0010461754,0.19246764,0.0015514549,0.03887364,0.023897232,0.68968093],"study_design_scores_gemma":[0.00012561103,0.00034143738,0.018036325,0.00069683813,0.00042624556,0.000727779,0.0007141699,0.8327386,0.0035420994,0.11494283,0.027419468,0.00028853625],"about_ca_topic_score_codex":0.00451115,"about_ca_topic_score_gemma":0.0056156837,"teacher_disagreement_score":0.02364046,"about_ca_system_score_codex":0.001138161,"about_ca_system_score_gemma":0.0030958108,"threshold_uncertainty_score":0.12502414},"labels":[],"label_agreement":null},{"id":"W4376875412","doi":"10.1007/s10489-023-04666-6","title":"Robust generalized canonical correlation analysis","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Outlier; Robustness (evolution); Matrix norm; Canonical correlation; Mathematical optimization; Algorithm; Property (philosophy); Norm (philosophy); Artificial intelligence; Mathematics","score_opus":0.051369488536145376,"score_gpt":0.2699172873648217,"score_spread":0.2185477988286763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376875412","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006409375,0.00056713884,0.9871021,0.00014901212,0.00019000757,0.000052385003,0.00027595967,0.001231827,0.004022096],"genre_scores_gemma":[0.30381134,0.0014286981,0.67235595,0.00037294975,0.0004809824,0.00027335816,0.0029790746,0.0016374498,0.0166602],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974726,0.0007837225,0.00010190275,0.0006140003,0.0008110101,0.0002167555],"domain_scores_gemma":[0.9977424,0.00033820415,0.00020681252,0.00080699864,0.00083111133,0.00007446265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017938807,0.0017313714,0.0015226201,0.0022425116,0.000867544,0.0019693372,0.0010622052,0.0010152071,0.0068555297],"category_scores_gemma":[0.005689917,0.00056834833,0.0017148785,0.0028492052,0.001142091,0.0013340063,0.0017901312,0.0015528883,0.005137127],"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":[0.00038941365,0.00013417818,0.001734479,0.0002778496,0.0004217526,0.00023572685,0.00014736224,0.10990427,0.024962116,0.14822526,0.030847358,0.68272024],"study_design_scores_gemma":[0.000029546036,0.000103166705,0.0030689032,0.00007040057,0.00014759034,0.00028347602,0.00006999321,0.90362525,0.019915104,0.04115504,0.03141305,0.00011848805],"about_ca_topic_score_codex":0.0031189006,"about_ca_topic_score_gemma":0.0047818213,"teacher_disagreement_score":0.0068555297,"about_ca_system_score_codex":0.0004275654,"about_ca_system_score_gemma":0.002302853,"threshold_uncertainty_score":0.02293402},"labels":[],"label_agreement":null},{"id":"W4377249777","doi":"10.1007/978-3-031-33271-5_17","title":"Scalable and Near-Optimal $$\\varepsilon $$-Tube Clusterwise Regression","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"Cluster analysis; Computer science; Benchmark (surveying); Disjoint sets; Data point; Scalability; Regression; Mathematical optimization; Constraint (computer-aided design); Data mining; Algorithm; Mathematics; Machine learning; Statistics; Database; Combinatorics","score_opus":0.020123878138711162,"score_gpt":0.25060303279962626,"score_spread":0.2304791546609151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377249777","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013346061,0.00060679554,0.9698732,0.0006451145,0.0003039115,0.00010241566,0.00045735438,0.009423117,0.0052420488],"genre_scores_gemma":[0.15728582,0.0002939714,0.81067514,0.0006929447,0.00031460932,0.00037275584,0.003513922,0.002968989,0.023881754],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9982768,0.00051879504,0.00006526349,0.0004713976,0.0004132863,0.0002544501],"domain_scores_gemma":[0.99708396,0.0015088294,0.000103005594,0.0006161533,0.0005343399,0.00015376006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014808895,0.0026729337,0.00306842,0.0009257614,0.0010062783,0.0018825703,0.0055167796,0.002985702,0.01855898],"category_scores_gemma":[0.0073197386,0.0012110093,0.001307652,0.0014552535,0.0013572172,0.0028023457,0.0039575137,0.0042919437,0.0109552415],"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":[0.001055941,0.00040754201,0.0009613838,0.00031164198,0.00016674939,0.00022754174,0.0001521204,0.3021678,0.011225312,0.031499833,0.08342254,0.56840163],"study_design_scores_gemma":[0.00002412393,0.00003162581,0.000088053705,0.000009214691,0.000008569466,0.000025336114,0.000014151558,0.99162006,0.0013470302,0.0056724697,0.001150162,0.000009204487],"about_ca_topic_score_codex":0.01059117,"about_ca_topic_score_gemma":0.020067943,"teacher_disagreement_score":0.01855898,"about_ca_system_score_codex":0.0012358109,"about_ca_system_score_gemma":0.0030947968,"threshold_uncertainty_score":0.062085986},"labels":[],"label_agreement":null},{"id":"W4378418057","doi":"10.1016/j.patcog.2023.109720","title":"Generalization capacity of multi-class SVM based on Markovian resampling","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"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","funders":"","keywords":"Generalization; Resampling; Support vector machine; Computer science; Class (philosophy); Artificial intelligence; Algorithm; Mathematics; Mathematical optimization","score_opus":0.08778870370375463,"score_gpt":0.2774680876862693,"score_spread":0.18967938398251466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378418057","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16035272,0.0015689537,0.8323244,0.00073358585,0.00015509712,0.000051132,0.00020278138,0.00086246914,0.0037488486],"genre_scores_gemma":[0.9592951,0.00042281716,0.03764608,0.00012799501,0.000110295034,0.00004605619,0.00026723734,0.000073038216,0.002011455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988294,0.00040284847,0.00008462664,0.00022577826,0.00028824317,0.00016904608],"domain_scores_gemma":[0.99365616,0.004242915,0.0003194562,0.00068624306,0.000939941,0.00015534702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032127653,0.0004893869,0.0014283798,0.0008387787,0.00047986658,0.0009771655,0.0011429666,0.0009789468,0.002375296],"category_scores_gemma":[0.008252594,0.00035086664,0.00077932596,0.0005263456,0.0006725365,0.0022965807,0.0012892798,0.0012286052,0.00046333746],"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":[0.0009952226,0.00022715851,0.0037113002,0.0003114052,0.00024535027,0.00015262765,0.00020642503,0.56622213,0.02417181,0.06397895,0.005087207,0.33469042],"study_design_scores_gemma":[0.000002273158,0.00001685667,0.00029222216,0.0000038563503,0.0000055323017,0.000019558385,0.0000043528094,0.9950375,0.00074800977,0.0037752448,0.00008857433,0.0000059602507],"about_ca_topic_score_codex":0.0046398635,"about_ca_topic_score_gemma":0.0023072744,"teacher_disagreement_score":0.0046398635,"about_ca_system_score_codex":0.00088257686,"about_ca_system_score_gemma":0.0009791254,"threshold_uncertainty_score":0.0169909},"labels":[],"label_agreement":null},{"id":"W4379259741","doi":"10.5267/j.dsl.2023.5.002","title":"Fuzzy support vector machine for classification of time series data: A simulation study","year":2023,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Face and Expression Recognition","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":"Support vector machine; Computer science; Data mining; Time series; Fuzzy logic; Series (stratigraphy); Machine learning; Artificial intelligence; Data classification; Multiclass classification; Relevance vector machine; Structured support vector machine; Constraint (computer-aided design); Mathematics","score_opus":0.0853780376327949,"score_gpt":0.3656848115221568,"score_spread":0.2803067738893619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379259741","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.90358233,0.00096245966,0.08786818,0.00089020736,0.00009464097,0.00010840029,0.00033732792,0.00020488765,0.0059516123],"genre_scores_gemma":[0.9884674,0.00023245701,0.0104504805,0.000025547784,0.000007875214,0.000049764483,0.00015290114,0.0000067071624,0.00060683454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995701,0.0002021543,0.00003070088,0.000048303227,0.0000915214,0.000057180012],"domain_scores_gemma":[0.9946371,0.0041431584,0.00024466886,0.00019283874,0.00067082664,0.00011138704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021408775,0.00040460084,0.0005622003,0.00081908755,0.00040741908,0.0006347047,0.0004931427,0.0010607925,0.0013442514],"category_scores_gemma":[0.006713958,0.00016877777,0.00076323084,0.00076930545,0.00036681103,0.000818612,0.00036130578,0.0008857045,0.00013884701],"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":[0.00012255489,0.000114744915,0.0055443645,0.00005700098,0.00003747567,0.00011351067,0.000049849597,0.9820638,0.00052750105,0.002549064,0.00044121608,0.008378985],"study_design_scores_gemma":[0.0000046657683,0.00003303539,0.00052308873,0.0000040940718,0.000004343917,0.000014144194,0.000014060192,0.9988116,0.00019453732,0.00031809436,0.00007501041,0.0000033061658],"about_ca_topic_score_codex":0.014317946,"about_ca_topic_score_gemma":0.006830454,"teacher_disagreement_score":0.014317946,"about_ca_system_score_codex":0.00087667856,"about_ca_system_score_gemma":0.0005865669,"threshold_uncertainty_score":0.028469265},"labels":[],"label_agreement":null},{"id":"W4380343408","doi":"10.1007/978-981-99-2295-6_6","title":"Multidimensional Space","year":2023,"lang":"en","type":"book-chapter","venue":"Behaviormetrics","topic":"Face and Expression Recognition","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 Toronto","funders":"","keywords":"Multidimensional scaling; Principal component analysis; Multidimensional data; Multidimensional analysis; Space (punctuation); Computer science; Factor (programming language); Mathematics; Data mining; Artificial intelligence; Statistics; Machine learning","score_opus":0.06084697132315647,"score_gpt":0.26962475831668914,"score_spread":0.20877778699353267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380343408","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034039428,0.005681067,0.86184347,0.0011467017,0.0010092554,0.00005689074,0.0015104443,0.0015375927,0.123810574],"genre_scores_gemma":[0.11611199,0.010313807,0.652931,0.00069937145,0.0013106676,0.00043589371,0.0048554707,0.001279504,0.21206228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99934644,0.00012798983,0.00003814515,0.0001685994,0.00029046947,0.000028432512],"domain_scores_gemma":[0.9994711,0.00011040116,0.000035770332,0.00016594812,0.00018129969,0.000035514684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004821637,0.0009162932,0.00067625614,0.0015862777,0.0006907604,0.0033867063,0.00066711,0.0004979776,0.02804729],"category_scores_gemma":[0.0017440958,0.00026263518,0.00045348713,0.0028632558,0.0010321985,0.0023215208,0.0021127458,0.0013650949,0.013265153],"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":[0.000021832397,0.00001634385,0.00025908716,0.00017954585,0.000019049348,0.00003686461,0.00018979814,0.0028506033,0.0023769974,0.48558015,0.06833327,0.4401365],"study_design_scores_gemma":[0.0000047456015,0.000036809866,0.0009200721,0.000102494385,0.000015120229,0.00043447947,0.00019075119,0.038259137,0.0027609298,0.40412566,0.55311996,0.000029893947],"about_ca_topic_score_codex":0.0005997573,"about_ca_topic_score_gemma":0.0006211918,"teacher_disagreement_score":0.02804729,"about_ca_system_score_codex":0.0005210566,"about_ca_system_score_gemma":0.00042807986,"threshold_uncertainty_score":0.093827546},"labels":[],"label_agreement":null},{"id":"W4381193492","doi":"10.36227/techrxiv.14852652.v3","title":"Deep Clustering with Self-supervision using Pairwise Data Similarities","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Hypersphere; Cluster analysis; Autoencoder; Pairwise comparison; Embedding; Cluster (spacecraft); Computer science; Artificial intelligence; Benchmark (surveying); Set (abstract data type); Pattern recognition (psychology); Mathematics; Data mining; Deep learning; Geography","score_opus":0.13680797846551576,"score_gpt":0.3048059021646434,"score_spread":0.16799792369912767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381193492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014784376,0.000084574174,0.98373485,0.00007339346,0.0000107845435,0.000033579643,0.000052027128,0.00060540135,0.0006209066],"genre_scores_gemma":[0.5196005,0.00016487468,0.47615728,0.00013067263,0.000043297805,0.0001527179,0.0006514773,0.00022920052,0.002869985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986125,0.00032228435,0.00007983924,0.0004866604,0.00039048734,0.000108117514],"domain_scores_gemma":[0.9980484,0.00042563473,0.00031086217,0.00061227725,0.0004851338,0.000117607786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001396961,0.0009914543,0.0012649454,0.0012549972,0.00069182826,0.0012128911,0.0022202812,0.0012449364,0.0016626219],"category_scores_gemma":[0.003696258,0.00071515783,0.001077985,0.0012670181,0.0015149977,0.0029074869,0.0029194702,0.0017296819,0.0008131795],"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":[0.00018285941,0.00019684048,0.0032418696,0.00018490008,0.00018245092,0.00008246137,0.0003117567,0.58277303,0.01693334,0.029691475,0.0035190128,0.36270002],"study_design_scores_gemma":[0.000005759545,0.000035118577,0.0002950746,0.000006985978,0.0000063242774,0.00003045913,0.000021717211,0.9860124,0.0033803948,0.009663216,0.0005321587,0.000010439118],"about_ca_topic_score_codex":0.0043942407,"about_ca_topic_score_gemma":0.0067130807,"teacher_disagreement_score":0.0043942407,"about_ca_system_score_codex":0.0013637078,"about_ca_system_score_gemma":0.00158952,"threshold_uncertainty_score":0.009894431},"labels":[],"label_agreement":null},{"id":"W4382317605","doi":"10.1609/aaai.v37i9.26334","title":"Optimal Sparse Regression Trees","year":2023,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Regression; Statistics; Mathematics; Computer science","score_opus":0.10977304209321331,"score_gpt":0.31343853793222004,"score_spread":0.20366549583900673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382317605","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010566682,0.00082518475,0.9802075,0.000904142,0.0000911904,0.000057355042,0.00037065434,0.0007890528,0.0061882543],"genre_scores_gemma":[0.43127385,0.0022088445,0.5496313,0.0011245427,0.00057366194,0.00050046877,0.0034914901,0.0010632785,0.010132588],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968118,0.0011624256,0.00014417416,0.00068483304,0.00085193664,0.00034492725],"domain_scores_gemma":[0.98997444,0.0075562773,0.000461763,0.00087208714,0.0008982342,0.00023720859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022574994,0.0011871662,0.0019292176,0.0010209797,0.0009108325,0.0018194218,0.0015580857,0.0017658451,0.0062769596],"category_scores_gemma":[0.02513642,0.0008753856,0.0011029883,0.0018427076,0.0015094178,0.0039171297,0.002460877,0.0035311666,0.0022363276],"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":[0.000201003,0.00013925102,0.0014216789,0.00040468728,0.0000797471,0.00017972589,0.00023080363,0.52848345,0.0032256653,0.24168509,0.028732732,0.19521612],"study_design_scores_gemma":[0.000029505474,0.00003254541,0.0001649303,0.000041322408,0.000013235061,0.00005119189,0.000032775464,0.81286883,0.00071690854,0.18265937,0.0033789887,0.000010443468],"about_ca_topic_score_codex":0.0017708787,"about_ca_topic_score_gemma":0.0033393917,"teacher_disagreement_score":0.0062769596,"about_ca_system_score_codex":0.0011118544,"about_ca_system_score_gemma":0.0017387642,"threshold_uncertainty_score":0.020998538},"labels":[],"label_agreement":null},{"id":"W4382394488","doi":"10.18280/ts.400319","title":"Deep Learning-Based Micro Facial Expression Recognition Using an Adaptive Tiefes FCNN Model","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":21,"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":"Facial expression recognition; Deep learning; Artificial intelligence; Computer science; Expression (computer science); Facial expression; Pattern recognition (psychology); Speech recognition; Facial recognition system; Programming language","score_opus":0.07496411765075692,"score_gpt":0.2735325855066259,"score_spread":0.19856846785586899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4382394488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12661162,0.0008726427,0.86116487,0.00049725897,0.00018348207,0.000083664505,0.0002347631,0.0014200805,0.008931635],"genre_scores_gemma":[0.88404703,0.000521053,0.101409756,0.00026305718,0.00004540795,0.00011761393,0.00051549845,0.000049136717,0.013031419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998877,0.000010979966,0.0000042434162,0.00003389675,0.000035219764,0.000027965822],"domain_scores_gemma":[0.99990773,0.000020572403,0.000008640774,0.000008178456,0.000048920556,0.000006064106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026839046,0.0005312326,0.00034404552,0.00026916846,0.00021288349,0.00034795134,0.00092999166,0.00057840534,0.0015000621],"category_scores_gemma":[0.0004979415,0.00018062568,0.00049325597,0.00021108906,0.00022622268,0.00037023306,0.0003466891,0.00064433133,0.0005093698],"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":[0.0001896758,0.00013781073,0.0024974167,0.000065823326,0.00008410008,0.00017134032,0.00007002034,0.5911355,0.030906485,0.0030373393,0.0036695194,0.368035],"study_design_scores_gemma":[0.0000011645286,0.00001596775,0.0002772549,0.000003398116,0.0000053702533,0.000013665732,0.000003976496,0.9977125,0.0014694373,0.00023260863,0.00026198546,0.000002710607],"about_ca_topic_score_codex":0.01579971,"about_ca_topic_score_gemma":0.01577891,"teacher_disagreement_score":0.01579971,"about_ca_system_score_codex":0.0006660634,"about_ca_system_score_gemma":0.0005439755,"threshold_uncertainty_score":0.031415462},"labels":[],"label_agreement":null},{"id":"W4383503423","doi":"10.1109/tnnls.2023.3289158","title":"Deep Multirepresentation Learning for Data Clustering","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Clustering high-dimensional data; Artificial intelligence; Embedding; AKA; Pattern recognition (psychology); Benchmark (surveying); Correlation clustering; Subspace topology; Cluster (spacecraft); Data mining; Data point; Geography","score_opus":0.052003446006355376,"score_gpt":0.2925107670119717,"score_spread":0.24050732100561634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383503423","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005242698,0.00049848726,0.9925547,0.0001902087,0.000025676212,0.00002564418,0.00010472014,0.00084239943,0.0005154461],"genre_scores_gemma":[0.36681184,0.0010442173,0.6254064,0.0004617774,0.00011275407,0.00024981846,0.0017746498,0.0002551206,0.003883388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831307,0.0005476037,0.000115660296,0.0004695511,0.00039564626,0.00015843526],"domain_scores_gemma":[0.9985947,0.00039857326,0.00018731863,0.00044824637,0.00028085042,0.00009024871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002106058,0.0012186621,0.0016671863,0.0021674712,0.0008022235,0.0016354471,0.0022502171,0.001649034,0.0017168083],"category_scores_gemma":[0.0043240925,0.00061611953,0.0016029525,0.00300917,0.0013019113,0.0032771435,0.0029338342,0.0027328325,0.0010584313],"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":[0.0001532649,0.00019439927,0.001839922,0.00026526794,0.0002407828,0.00009221349,0.00024536118,0.48787996,0.006032498,0.041759335,0.0076493043,0.4536477],"study_design_scores_gemma":[0.000004975487,0.00002279614,0.00017917885,0.0000125435645,0.000010731105,0.000021790827,0.000023877941,0.9691665,0.0015336325,0.027877029,0.0011351837,0.000011824655],"about_ca_topic_score_codex":0.004457008,"about_ca_topic_score_gemma":0.0061453567,"teacher_disagreement_score":0.004457008,"about_ca_system_score_codex":0.002332103,"about_ca_system_score_gemma":0.0018900665,"threshold_uncertainty_score":0.016920686},"labels":[],"label_agreement":null},{"id":"W4383560525","doi":"10.54254/2755-2721/6/20230836","title":"Comparing the effect of CNN and linear regression on facial expression recognition","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Face and Expression Recognition","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":"Kamloops Art Gallery","funders":"","keywords":"Convolutional neural network; Computer science; Facial expression; Facial expression recognition; Artificial intelligence; Expression (computer science); Pattern recognition (psychology); Linear regression; Regression; Facial recognition system; Linear model; Machine learning; Mathematics; Statistics","score_opus":0.014842560057323937,"score_gpt":0.22474121916694875,"score_spread":0.20989865910962482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383560525","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.74843615,0.025048938,0.18355441,0.0020482715,0.0022624005,0.00029834066,0.0020204256,0.0072912388,0.029039888],"genre_scores_gemma":[0.9260758,0.005029045,0.05593533,0.00038674314,0.00025053046,0.00009370705,0.002634565,0.0004010204,0.009193167],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99826825,0.0004337013,0.00008285417,0.0004398256,0.0005063342,0.00026904827],"domain_scores_gemma":[0.9983936,0.00087217346,0.00008805666,0.00018541385,0.00040813367,0.000052571813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002492019,0.0015789504,0.00092309486,0.0010009526,0.00023220568,0.0007908692,0.0007075557,0.0007865248,0.0029841098],"category_scores_gemma":[0.007899201,0.00030733316,0.00077925617,0.00070274976,0.00030109304,0.0017891848,0.00065431395,0.0008040859,0.0011480739],"study_design_candidate":"observational","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":[0.0038073203,0.00053455285,0.01161496,0.00077541877,0.00071404374,0.00018490803,0.00008133723,0.09371167,0.027871525,0.0012810471,0.009098785,0.85032445],"study_design_scores_gemma":[0.00008019607,0.0013551026,0.015196532,0.00007113738,0.0003685552,0.00020030547,0.00014905282,0.93801564,0.03902536,0.0012170345,0.0042525753,0.00006851145],"about_ca_topic_score_codex":0.010289593,"about_ca_topic_score_gemma":0.009557427,"teacher_disagreement_score":0.010289593,"about_ca_system_score_codex":0.0008104839,"about_ca_system_score_gemma":0.0005709896,"threshold_uncertainty_score":0.020459354},"labels":[],"label_agreement":null},{"id":"W4384786991","doi":"10.2139/ssrn.4496382","title":"Incremental Semi-Supervised Graph Learning Nmf with Block-Diagonal","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"","keywords":"Diagonal; Graph; Artificial intelligence; Semi-supervised learning; Computer science; Block (permutation group theory); Non-negative matrix factorization; Mathematics; Pattern recognition (psychology); Machine learning; Theoretical computer science; Combinatorics; Matrix decomposition; Physics","score_opus":0.015419656137443533,"score_gpt":0.23756636013751384,"score_spread":0.2221467040000703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384786991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073551694,0.0003146523,0.9867324,0.000245676,0.00016772097,0.00013297296,0.0002826689,0.0038267826,0.0009419039],"genre_scores_gemma":[0.17329714,0.00022681062,0.81612366,0.000576616,0.0002766155,0.0006297447,0.0025775642,0.000895988,0.005395868],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980767,0.0006947959,0.00009336478,0.0005835214,0.00038655236,0.0001650101],"domain_scores_gemma":[0.99448377,0.0028517225,0.00026794654,0.0010116759,0.0011540574,0.0002307569],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001946218,0.0020673561,0.0030137713,0.0015512893,0.0010497591,0.0012983703,0.0041305046,0.0035560436,0.006283807],"category_scores_gemma":[0.00900171,0.0012412741,0.0020057764,0.0013540095,0.0012729628,0.0021500213,0.0023110854,0.0037255648,0.0043001063],"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":[0.00060038123,0.0005248565,0.0006962048,0.0004871824,0.00031100825,0.00014618525,0.00014114316,0.28977177,0.011665042,0.0077057285,0.023873342,0.6640772],"study_design_scores_gemma":[0.000026853533,0.000050561473,0.000116331816,0.0000103536795,0.000015258366,0.000028287724,0.000008943246,0.99458545,0.0011390841,0.003318373,0.00069134554,0.000009264822],"about_ca_topic_score_codex":0.011892924,"about_ca_topic_score_gemma":0.023142612,"teacher_disagreement_score":0.011892924,"about_ca_system_score_codex":0.00092659047,"about_ca_system_score_gemma":0.0026323586,"threshold_uncertainty_score":0.023647368},"labels":[],"label_agreement":null},{"id":"W4385602719","doi":"10.3390/electronics12153355","title":"Supervised Dimensionality Reduction of Proportional Data Using Exponential Family Distributions","year":2023,"lang":"en","type":"article","venue":"Electronics","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dimensionality reduction; Exponential family; Curse of dimensionality; Modal; Algorithm; Heuristic; Projection (relational algebra); Computer science; Reduction (mathematics); Exponential function; Measure (data warehouse); Mathematical optimization; Dimension (graph theory); Mathematics; Pattern recognition (psychology); Artificial intelligence; Data mining; Machine learning","score_opus":0.07840668563455257,"score_gpt":0.3108969895135942,"score_spread":0.23249030387904163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385602719","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021358602,0.00011041349,0.9778374,0.000064938526,0.000014996593,0.000030313684,0.000047185113,0.00025360505,0.00028248364],"genre_scores_gemma":[0.44433963,0.0003264527,0.551594,0.00011621583,0.00006601834,0.00026686836,0.00081419025,0.00015863612,0.0023180277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984554,0.00049964344,0.00008716574,0.00038508986,0.00046655908,0.000106130436],"domain_scores_gemma":[0.99757975,0.0011707965,0.00022477737,0.00038956082,0.0005686881,0.000066490014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020561365,0.00080367463,0.0012678185,0.0011704252,0.0005892108,0.00090537407,0.0011533975,0.00078995066,0.0008523604],"category_scores_gemma":[0.005216962,0.00047253587,0.0014176673,0.0009908099,0.0008331275,0.0017853532,0.001332388,0.0014801325,0.00041739477],"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":[0.0002807454,0.00030383113,0.0035945056,0.00018451018,0.00019667996,0.00017789868,0.00038024285,0.45583412,0.016857143,0.015079729,0.0039504697,0.50316024],"study_design_scores_gemma":[0.0000042929314,0.000022995386,0.00048616182,0.0000043335554,0.0000048107263,0.00004253733,0.0000266505,0.9931617,0.0019201093,0.0038630343,0.00045282763,0.000010620264],"about_ca_topic_score_codex":0.0023315162,"about_ca_topic_score_gemma":0.0024776538,"teacher_disagreement_score":0.0023315162,"about_ca_system_score_codex":0.00060318544,"about_ca_system_score_gemma":0.0010967833,"threshold_uncertainty_score":0.010874033},"labels":[],"label_agreement":null},{"id":"W4385610037","doi":"10.31763/ijrcs.v3i3.1057","title":"Comparison of Feature Extraction with PCA and LTP Methods and Investigating the Effect of Dimensionality Reduction in the Bat Algorithm for Face Recognition","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Control Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"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 Victoria","funders":"","keywords":"Dimensionality reduction; Facial recognition system; Artificial intelligence; Computer science; Pattern recognition (psychology); Feature extraction; Face (sociological concept); Feature (linguistics); Curse of dimensionality; Reduction (mathematics); Dimension (graph theory); Feature vector; Principal component analysis; Computer vision; Mathematics","score_opus":0.03415752056300397,"score_gpt":0.36347143393319126,"score_spread":0.3293139133701873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385610037","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23792784,0.0051087756,0.74653935,0.00045132684,0.00038240213,0.0002911077,0.0004167735,0.0021367476,0.0067456807],"genre_scores_gemma":[0.626598,0.003248237,0.36550567,0.00010870714,0.000095711206,0.00025789117,0.0010989006,0.0001960399,0.0028908828],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988814,0.00020357168,0.00008763579,0.000146856,0.00058634544,0.00009425784],"domain_scores_gemma":[0.9981652,0.0007383308,0.000109460976,0.00020797057,0.0007431862,0.000035830904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013069198,0.0007149224,0.00078295445,0.0015443247,0.00039105114,0.0008153151,0.000536519,0.00054078014,0.0022969192],"category_scores_gemma":[0.0045630285,0.00019558385,0.00096533366,0.0015505664,0.00038604095,0.0018082304,0.00041835837,0.00055051665,0.00059991964],"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":[0.00088479207,0.00032699818,0.0040647117,0.00063206773,0.00021888007,0.00017765812,0.00020531705,0.033623047,0.043008424,0.0034949747,0.0033923986,0.90997064],"study_design_scores_gemma":[0.000119640354,0.0016435542,0.039009817,0.00009779085,0.00026326315,0.0013166886,0.00038844594,0.8455777,0.10031248,0.0028321235,0.008319352,0.00011915643],"about_ca_topic_score_codex":0.0036359816,"about_ca_topic_score_gemma":0.0020829649,"teacher_disagreement_score":0.0036359816,"about_ca_system_score_codex":0.00036446672,"about_ca_system_score_gemma":0.00062276924,"threshold_uncertainty_score":0.0076839924},"labels":[],"label_agreement":null},{"id":"W4385841878","doi":"10.5121/csit.2023.131316","title":"Classifying Galaxy Images Using Improved Residual Networks","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Galaxy; Residual neural network; Task (project management); Class (philosophy); Residual; Contextual image classification; Field (mathematics); Pattern recognition (psychology); Artificial neural network; Image (mathematics); Astrophysics; Mathematics; Physics; Algorithm; Engineering","score_opus":0.042358560257840044,"score_gpt":0.28264003532273685,"score_spread":0.2402814750648968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385841878","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.519311,0.0027109603,0.45534685,0.0010638434,0.00033040732,0.0002025582,0.0011628054,0.008587504,0.011284026],"genre_scores_gemma":[0.9131224,0.00045450192,0.075939044,0.00030962197,0.00012399173,0.00005569415,0.0028875922,0.000108348184,0.0069988384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996146,0.00007624418,0.000016874992,0.00012990045,0.00009304147,0.00006921063],"domain_scores_gemma":[0.99954116,0.00009685389,0.000053839794,0.00007865377,0.00019907841,0.000030423113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007007552,0.0010258836,0.000537521,0.0013163585,0.0002270332,0.00068325945,0.0011817259,0.00063983357,0.0015142917],"category_scores_gemma":[0.0013316403,0.0002432289,0.0007272457,0.00053035177,0.00031649935,0.0009765807,0.0005584052,0.00083933317,0.0011090271],"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":[0.00063889794,0.0004159119,0.009889572,0.000114222195,0.0002026328,0.00020513107,0.00012297182,0.24229889,0.032290712,0.0030814752,0.011868369,0.69887125],"study_design_scores_gemma":[0.0000064394194,0.000045526074,0.0011716732,0.0000040624996,0.000014602317,0.000021950316,0.000016665352,0.9946485,0.0027099117,0.0006844811,0.0006695576,0.000006631611],"about_ca_topic_score_codex":0.013306592,"about_ca_topic_score_gemma":0.014736782,"teacher_disagreement_score":0.013306592,"about_ca_system_score_codex":0.00070195063,"about_ca_system_score_gemma":0.00041460415,"threshold_uncertainty_score":0.026458263},"labels":[],"label_agreement":null},{"id":"W4386103310","doi":"10.1109/isitia59021.2023.10221117","title":"Emotion Recognition from Video Frame Sequence using Face Mesh and Pre-Trained Models of Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Computer science; Convolutional neural network; Frame (networking); Artificial intelligence; Face (sociological concept); Sequence (biology); Facial recognition system; Pattern recognition (psychology); Computer vision; Speech recognition; Emotion recognition; Artificial neural network; Computer network","score_opus":0.08600541284812725,"score_gpt":0.2843323920870014,"score_spread":0.19832697923887416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386103310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28162217,0.0014144813,0.6988122,0.00026058056,0.0003915372,0.00026924076,0.0034455715,0.00822246,0.0055617252],"genre_scores_gemma":[0.7302398,0.000829213,0.25380474,0.00015215874,0.00008975233,0.00020397568,0.007451048,0.000177083,0.007052183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998379,0.000009598171,0.000007227482,0.00007319868,0.000041863637,0.000030166566],"domain_scores_gemma":[0.99989545,0.00002218427,0.000010484964,0.000017459124,0.000046812736,0.0000075298294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017232688,0.00090876024,0.00043530719,0.0007859864,0.00016588054,0.00035619372,0.0005766244,0.00043483046,0.0026321434],"category_scores_gemma":[0.0007136012,0.00023155657,0.00057554024,0.0004273217,0.00013815222,0.00056797336,0.00033362053,0.0005605558,0.0010146967],"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":[0.00074372557,0.00022538351,0.0038236736,0.00016476221,0.00014188932,0.00034053388,0.00008264973,0.078076705,0.15735121,0.0015386628,0.009564035,0.7479468],"study_design_scores_gemma":[0.000008015223,0.000097355565,0.0045507303,0.000012191127,0.000028027709,0.00011310754,0.000027013788,0.959547,0.033142813,0.00074131205,0.0017198945,0.00001252878],"about_ca_topic_score_codex":0.009202006,"about_ca_topic_score_gemma":0.010800018,"teacher_disagreement_score":0.009202006,"about_ca_system_score_codex":0.00063164515,"about_ca_system_score_gemma":0.00029841048,"threshold_uncertainty_score":0.018296897},"labels":[],"label_agreement":null},{"id":"W4386322208","doi":"10.1109/tpami.2023.3310908","title":"Handling Multi-Class Problem by Intuitionistic Fuzzy Twin Support Vector Machines Based on Relative Density Information","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"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","funders":"","keywords":"Artificial intelligence; Support vector machine; Class (philosophy); Computer science; Pattern recognition (psychology); Machine learning; Mathematics; Data mining","score_opus":0.019658464185623982,"score_gpt":0.26215250644663934,"score_spread":0.24249404226101537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386322208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0131527865,0.00016174962,0.9860081,0.000092883754,0.000028694958,0.000029355557,0.0000149797725,0.00017382334,0.00033768808],"genre_scores_gemma":[0.7091926,0.00030424522,0.2886904,0.00018681453,0.000111883826,0.00016408885,0.00022719882,0.000067790854,0.0010550428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974571,0.00069570384,0.00021017977,0.00044933124,0.00096486503,0.00022274713],"domain_scores_gemma":[0.9968815,0.001487546,0.00035726512,0.00024629085,0.0008840967,0.00014332327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003910302,0.0012479816,0.0023989675,0.0014592049,0.0006635917,0.0019638136,0.0026000747,0.0015683918,0.0010056228],"category_scores_gemma":[0.010921415,0.00060580974,0.001416137,0.0015049113,0.001045886,0.002891659,0.0018962928,0.0022563105,0.0002664624],"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":[0.00029152795,0.0001430375,0.0023887982,0.00023103418,0.00019674393,0.00027641945,0.00028588777,0.6417125,0.0054482985,0.023737043,0.0022213594,0.32306743],"study_design_scores_gemma":[0.0000033489546,0.000024792098,0.00009152049,0.000004850186,0.0000071027844,0.00002846223,0.0000072546673,0.996332,0.0004095873,0.0029872942,0.00009664674,0.0000071211985],"about_ca_topic_score_codex":0.0028268115,"about_ca_topic_score_gemma":0.0013137402,"teacher_disagreement_score":0.003910302,"about_ca_system_score_codex":0.00088202715,"about_ca_system_score_gemma":0.0014062237,"threshold_uncertainty_score":0.020679891},"labels":[],"label_agreement":null},{"id":"W4386698882","doi":"10.1007/s11760-023-02769-8","title":"Face recognition via selective denoising, filter faces and hog features","year":2023,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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":"Artificial intelligence; Facial recognition system; Pattern recognition (psychology); Computer science; Hyperspectral imaging; Histogram; Invariant (physics); Face (sociological concept); Three-dimensional face recognition; Computer vision; Noise reduction; Filter (signal processing); Feature (linguistics); Face detection; Image (mathematics); Mathematics","score_opus":0.01817666361677486,"score_gpt":0.2631844331137689,"score_spread":0.24500776949699402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386698882","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13117911,0.0007503188,0.8625825,0.00018611018,0.00012605269,0.000071948045,0.00015744855,0.00071581244,0.0042306767],"genre_scores_gemma":[0.53224224,0.001179375,0.44889364,0.00024304495,0.00012312902,0.00010212594,0.0006667767,0.0001322957,0.016417332],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998196,0.000021549899,0.0000062442723,0.00003845365,0.0000811389,0.000033044456],"domain_scores_gemma":[0.999861,0.00003379021,0.0000110180845,0.000026222648,0.000056841134,0.000011127898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004320176,0.00045847218,0.0004909753,0.00048719652,0.00019360063,0.0004325072,0.00034540184,0.00051665824,0.0015079],"category_scores_gemma":[0.0006440538,0.00022353993,0.00049963885,0.00030950553,0.00026504602,0.0005105839,0.0004134357,0.00040329984,0.0010309524],"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":[0.00032221165,0.00013995127,0.0024933792,0.00009910243,0.00009944486,0.00009627328,0.000055492048,0.007773196,0.40818256,0.0023637353,0.002053537,0.5763211],"study_design_scores_gemma":[0.000035992856,0.00048757097,0.020333977,0.000038526163,0.00020013955,0.0013282137,0.00014938186,0.5073262,0.4564356,0.005549683,0.00807202,0.00004272342],"about_ca_topic_score_codex":0.00149453,"about_ca_topic_score_gemma":0.0036167419,"teacher_disagreement_score":0.0015079,"about_ca_system_score_codex":0.0001744119,"about_ca_system_score_gemma":0.00039573567,"threshold_uncertainty_score":0.0050444603},"labels":[],"label_agreement":null},{"id":"W4386754305","doi":"10.18178/wcse.2023.06.018","title":"Learning to Cluster Faces via Hypergraph Convolution with Transformer on Large Graph","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China; National Science Foundation","keywords":"Hypergraph; Computer science; Graph; Transformer; Cluster (spacecraft); Theoretical computer science; Artificial intelligence; Mathematics; Discrete mathematics; Computer network; Engineering; Voltage; Electrical engineering","score_opus":0.00977431414038727,"score_gpt":0.22909624992671695,"score_spread":0.2193219357863297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386754305","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03854789,0.00020126403,0.95307535,0.00025976144,0.00004488444,0.000093925075,0.00031039782,0.00527075,0.0021957594],"genre_scores_gemma":[0.5056529,0.0003492034,0.48020792,0.0005418992,0.00006450452,0.00019040181,0.0027223362,0.0006485399,0.0096222805],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996275,0.000062031526,0.000012887492,0.00014243287,0.000088449466,0.000066808854],"domain_scores_gemma":[0.9995447,0.000120094126,0.000048497615,0.00014771069,0.000097377604,0.00004164139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047761088,0.0014420582,0.0011097515,0.00144212,0.00056103047,0.0007003693,0.0017612659,0.0009837961,0.0040410436],"category_scores_gemma":[0.0014207197,0.00054439506,0.0012075577,0.0012785905,0.00083106756,0.0018848771,0.0013974474,0.0013181964,0.0018059355],"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":[0.00028314584,0.00022942545,0.0022288004,0.00015380849,0.00014037584,0.00015241794,0.00013876898,0.3633995,0.021914266,0.019099563,0.0146280285,0.5776319],"study_design_scores_gemma":[0.000009163967,0.000023582275,0.00020485227,0.0000032453518,0.000009403185,0.0000357912,0.000018475534,0.98561424,0.0031543844,0.010218675,0.0007019396,0.0000062131994],"about_ca_topic_score_codex":0.013360641,"about_ca_topic_score_gemma":0.02637866,"teacher_disagreement_score":0.013360641,"about_ca_system_score_codex":0.0014485947,"about_ca_system_score_gemma":0.0011534074,"threshold_uncertainty_score":0.02656579},"labels":[],"label_agreement":null},{"id":"W4386911956","doi":"10.1016/j.engappai.2023.107136","title":"A tutorial-based survey on feature selection: Recent advancements on feature selection","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Computer science; Artificial intelligence; Feature selection; Machine learning; Markov blanket; Curse of dimensionality; Feature learning; Dimensionality reduction; Data mining; Markov chain; Markov model","score_opus":0.03226190195504664,"score_gpt":0.2942325593279676,"score_spread":0.26197065737292097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386911956","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004425914,0.7719908,0.21033692,0.0025766185,0.0025686226,0.00008863966,0.00044151256,0.0006698915,0.0069011585],"genre_scores_gemma":[0.038643237,0.80570334,0.12940153,0.002522572,0.012251576,0.00020994333,0.0024661277,0.00028372157,0.008517838],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99915314,0.00019592336,0.00012348885,0.00020053974,0.00027452465,0.000052388204],"domain_scores_gemma":[0.99706835,0.0021070594,0.00010473647,0.00010052429,0.0005475273,0.000071891365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016815411,0.001210612,0.0015609807,0.003614694,0.0003368934,0.0016205929,0.0011742467,0.000984023,0.005552265],"category_scores_gemma":[0.0048541864,0.0004262589,0.0009847765,0.0071689663,0.0004284716,0.0023407436,0.00075530517,0.0011825834,0.0023914592],"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":[0.00009646123,0.0001231918,0.0013431213,0.0032891852,0.00013299975,0.00010724832,0.00006169928,0.0025178588,0.0027193485,0.005482825,0.046356313,0.9377697],"study_design_scores_gemma":[0.00010870117,0.0010092456,0.008851268,0.0029008384,0.0008032718,0.003443952,0.00028072402,0.076927654,0.010145398,0.048493747,0.84679586,0.00023931883],"about_ca_topic_score_codex":0.0012230963,"about_ca_topic_score_gemma":0.0013748392,"teacher_disagreement_score":0.005552265,"about_ca_system_score_codex":0.00040597413,"about_ca_system_score_gemma":0.000748075,"threshold_uncertainty_score":0.018574178},"labels":[],"label_agreement":null},{"id":"W4387390352","doi":"10.48550/arxiv.2310.03010","title":"Spectral alignment of stochastic gradient descent for high-dimensional classification tasks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; National Science Foundation","keywords":"Hessian matrix; Outlier; Rank (graph theory); Stochastic gradient descent; Eigenvalues and eigenvectors; Gradient descent; Artificial intelligence; Pattern recognition (psychology); Computer science; Mathematics; Artificial neural network; Algorithm; Applied mathematics; Combinatorics; Physics","score_opus":0.12243653760447859,"score_gpt":0.21117632131449743,"score_spread":0.08873978371001884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387390352","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07552068,0.00025064664,0.9201126,0.0005291489,0.000039756815,0.000035093733,0.000055165456,0.00032392284,0.0031330353],"genre_scores_gemma":[0.8955081,0.00030810034,0.09939162,0.00021232573,0.000075670185,0.00013533041,0.0002028383,0.00025423066,0.003911789],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987826,0.00057634036,0.000054432996,0.00019048566,0.00029048393,0.000105716324],"domain_scores_gemma":[0.99320215,0.004025636,0.0009280723,0.00070152216,0.00068930833,0.00045329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032328686,0.0008441234,0.00089241954,0.00074259564,0.00066437514,0.001388196,0.001334411,0.0013774375,0.0026838484],"category_scores_gemma":[0.02162535,0.0006614673,0.0005271741,0.00065224536,0.0023473497,0.0029394622,0.0022495997,0.0019840298,0.0006615108],"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":[0.00010925713,0.00009600214,0.0030473971,0.00008869235,0.000054421584,0.00012544659,0.00027744618,0.80410266,0.0041972823,0.16404141,0.0013324652,0.02252749],"study_design_scores_gemma":[0.000002751638,0.000014744852,0.00024032027,0.000004955703,0.0000012329087,0.000009502159,0.000009050212,0.9722996,0.00025143856,0.027030556,0.00013116763,0.0000046112127],"about_ca_topic_score_codex":0.0029825496,"about_ca_topic_score_gemma":0.0026037337,"teacher_disagreement_score":0.0032328686,"about_ca_system_score_codex":0.0016157465,"about_ca_system_score_gemma":0.0012245155,"threshold_uncertainty_score":0.017097235},"labels":[],"label_agreement":null},{"id":"W4387537438","doi":"10.1016/j.eswa.2023.121923","title":"Unified embedding and clustering","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"European Commission","keywords":"Cluster analysis; Embedding; Computer science; Correlation clustering; Artificial intelligence; Clustering high-dimensional data; CURE data clustering algorithm; Manifold (fluid mechanics); Pattern recognition (psychology); Data mining","score_opus":0.022415870151486193,"score_gpt":0.279926667443693,"score_spread":0.2575107972922068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387537438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034777948,0.0005635954,0.9927874,0.00015431664,0.000082849205,0.000027549178,0.00022215112,0.0007096196,0.0019746344],"genre_scores_gemma":[0.19749388,0.0013308945,0.77061296,0.00024529197,0.0002941001,0.00023111804,0.0033283236,0.00092730834,0.025536153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985481,0.00039749293,0.000088887085,0.0005450267,0.00032293366,0.00009751663],"domain_scores_gemma":[0.9986204,0.00027430578,0.00007789257,0.00065650477,0.00032564308,0.000045354504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010069751,0.0011919161,0.0016120785,0.0023670685,0.0009004248,0.0018346083,0.001913414,0.0016275506,0.005712301],"category_scores_gemma":[0.00396371,0.0008387947,0.0013163358,0.002805858,0.00094599614,0.003051304,0.0027127992,0.001698504,0.004282454],"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":[0.00017385934,0.00007979508,0.0005077473,0.00021490868,0.00016508481,0.00009671766,0.00019440208,0.11646108,0.008283883,0.18695462,0.019231088,0.6676369],"study_design_scores_gemma":[0.000013326492,0.00005578836,0.0005959689,0.00003457361,0.000046270645,0.00015319613,0.000075215634,0.80587095,0.0052371696,0.1704637,0.017416446,0.000037332822],"about_ca_topic_score_codex":0.0034420223,"about_ca_topic_score_gemma":0.004408573,"teacher_disagreement_score":0.005712301,"about_ca_system_score_codex":0.0007806203,"about_ca_system_score_gemma":0.0006950301,"threshold_uncertainty_score":0.019109547},"labels":[],"label_agreement":null},{"id":"W4387623800","doi":"10.1109/tsmc.2023.3319694","title":"A New Oversampling Method Based on Triangulation of Sample Space","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Face and Expression Recognition","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 Alberta","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Oversampling; Sample (material); Triangulation; Space (punctuation); Computer science; Artificial intelligence; Geography; Cartography; Physics; Telecommunications; Bandwidth (computing)","score_opus":0.0305026542586941,"score_gpt":0.27370385566475763,"score_spread":0.24320120140606352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387623800","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038162027,0.000073512,0.99515164,0.000033324402,0.00003717978,0.000032937383,0.000031596348,0.0002697178,0.00055394677],"genre_scores_gemma":[0.09695388,0.00018001582,0.8999286,0.00010743526,0.00007136598,0.00020627324,0.00042665206,0.00022034848,0.0019054476],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99865735,0.0003098303,0.00008108965,0.00029691082,0.000574241,0.00008052032],"domain_scores_gemma":[0.9984145,0.00054757815,0.00014682808,0.0003057287,0.0005098859,0.00007537893],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013968193,0.0008096132,0.0010812655,0.0016510552,0.0007047021,0.0009594625,0.001186102,0.0009793316,0.0028505737],"category_scores_gemma":[0.0050293417,0.0005206656,0.0014459388,0.0010616973,0.00072004506,0.0013864362,0.0015842107,0.0012388651,0.000873751],"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":[0.0003051827,0.00009253562,0.0034437617,0.00030950064,0.0001306026,0.00022003833,0.0005566588,0.26571178,0.056587048,0.036192045,0.005280744,0.6311701],"study_design_scores_gemma":[0.000019759393,0.0000874304,0.00043822642,0.000021340662,0.00002093221,0.00017911124,0.000055763885,0.971621,0.0104179075,0.008186918,0.008924745,0.000026980984],"about_ca_topic_score_codex":0.0029425484,"about_ca_topic_score_gemma":0.0032390053,"teacher_disagreement_score":0.0029425484,"about_ca_system_score_codex":0.0006086949,"about_ca_system_score_gemma":0.0009083599,"threshold_uncertainty_score":0.0095360875},"labels":[],"label_agreement":null},{"id":"W4387654814","doi":"10.5121/ijaia.2023.14501","title":"Performance Evaluation of Block-Sized Algorithms for Majority Vote in Facial Recognition","year":2023,"lang":"en","type":"article","venue":"International Journal of Artificial Intelligence & Applications","topic":"Face and Expression Recognition","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 Windsor","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Computer science; Feature extraction; Support vector machine; Facial recognition system; Preprocessor; Histogram equalization; Principal component analysis; Histogram; Linear discriminant analysis; Block (permutation group theory); Local binary patterns; Mathematics; Image (mathematics)","score_opus":0.12684627077144237,"score_gpt":0.38374476809900987,"score_spread":0.25689849732756753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387654814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3731759,0.011824728,0.59358746,0.00078309485,0.0013017063,0.000666877,0.0007797883,0.005998214,0.011882265],"genre_scores_gemma":[0.6772778,0.001179799,0.31263998,0.00022507625,0.0001449734,0.0002590083,0.0018336172,0.00027487983,0.0061647934],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.996029,0.0011643596,0.00032960216,0.00073031976,0.0013957226,0.00035102802],"domain_scores_gemma":[0.9948949,0.0024498308,0.00023059727,0.0006861174,0.0015492847,0.00018928965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00447642,0.0011428725,0.00220067,0.0012216158,0.00083014986,0.0011711675,0.0022384971,0.001291933,0.003904825],"category_scores_gemma":[0.009421592,0.00036579586,0.0008875689,0.0009825475,0.00038893014,0.0017818384,0.0014283042,0.0010258792,0.0018227379],"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":[0.003168197,0.00052031217,0.0039162682,0.0002570069,0.00029520356,0.000056600544,0.0001229477,0.12021962,0.012476571,0.0019217975,0.0044158385,0.8526296],"study_design_scores_gemma":[0.00006119705,0.00051070267,0.0013432268,0.000016951853,0.000039483166,0.00009840737,0.000067738525,0.98650485,0.0093216235,0.00082385197,0.0011921856,0.000019723679],"about_ca_topic_score_codex":0.0057778955,"about_ca_topic_score_gemma":0.0048045926,"teacher_disagreement_score":0.0057778955,"about_ca_system_score_codex":0.00092195795,"about_ca_system_score_gemma":0.00153572,"threshold_uncertainty_score":0.023673832},"labels":[],"label_agreement":null},{"id":"W4388021485","doi":"10.18280/ts.400519","title":"Support Vector Machines Optimisation for Face Recognition Using Sparrow Search Algorithm","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","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":"Division of Graduate Education","keywords":"Sparrow; Computer science; Support vector machine; Pattern recognition (psychology); Face (sociological concept); Artificial intelligence; Facial recognition system; Algorithm; Machine learning; Biology","score_opus":0.08361589975467128,"score_gpt":0.3070604881117004,"score_spread":0.22344458835702913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388021485","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033841368,0.00049047096,0.96314126,0.00008706915,0.000028543473,0.00006201065,0.00002232771,0.00035550428,0.0019714285],"genre_scores_gemma":[0.5395701,0.00032752755,0.4551266,0.00013534601,0.000028176846,0.00031563884,0.00015958492,0.00008722676,0.004249824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963355,0.00013195563,0.000024427267,0.000074842166,0.000096589065,0.00003855209],"domain_scores_gemma":[0.9994254,0.0003314438,0.00006403798,0.000038881357,0.00012408831,0.000016204003],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010733048,0.0006706838,0.0010477958,0.00064828363,0.00023351336,0.0007002832,0.0006435819,0.0009646657,0.0018394986],"category_scores_gemma":[0.002273729,0.00037410008,0.000639824,0.0005198747,0.00045001024,0.00060585333,0.00053444545,0.0006924117,0.0006002706],"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":[0.000082626466,0.00004864118,0.0007953999,0.00008967906,0.00006114864,0.000062656065,0.00008004499,0.87625647,0.0043961,0.0057240915,0.000715066,0.11168795],"study_design_scores_gemma":[0.000004233053,0.000027316379,0.00007782766,0.0000047874278,0.0000027214385,0.000011987479,0.0000057554203,0.9984812,0.0004979764,0.0006141203,0.00026930773,0.0000027082392],"about_ca_topic_score_codex":0.0028316947,"about_ca_topic_score_gemma":0.0026253962,"teacher_disagreement_score":0.0028316947,"about_ca_system_score_codex":0.00038038517,"about_ca_system_score_gemma":0.000794688,"threshold_uncertainty_score":0.0061537027},"labels":[],"label_agreement":null},{"id":"W4388197681","doi":"10.1007/978-3-031-23636-5_3","title":"Robot Identification using Modern Pattern Recognition Techniques","year":2023,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Face and Expression Recognition","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":"University of New Brunswick; Dalhousie University","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Computer vision; Robot; Robustness (evolution); Hough transform; Classifier (UML); Pattern recognition (psychology); Standard test image; Image processing; Image (mathematics)","score_opus":0.05831645537751028,"score_gpt":0.30004862052083625,"score_spread":0.24173216514332596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388197681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00344284,0.0064471825,0.9400097,0.00029498216,0.00051333325,0.0000866649,0.00017433068,0.002105387,0.04692546],"genre_scores_gemma":[0.039079744,0.0110932235,0.7741657,0.00035985323,0.00034023562,0.00014292632,0.0006437726,0.0003370701,0.17383744],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966717,0.000022180135,0.000019781035,0.00007436826,0.00020187935,0.000014694551],"domain_scores_gemma":[0.99982786,0.00005128377,0.00001391359,0.00005289413,0.00004966731,0.0000042905926],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024886377,0.00080130284,0.0007521169,0.0013169992,0.00037858737,0.0012829835,0.0010663982,0.0008906828,0.016312597],"category_scores_gemma":[0.0004951408,0.00042954492,0.0005505221,0.0016599527,0.0005554879,0.0020638441,0.00075409125,0.0007932119,0.017900404],"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":[0.000015977883,0.000029199558,0.00022808329,0.00029307092,0.000021251499,0.00007411867,0.000041786807,0.0025910893,0.036386143,0.013110074,0.0107356245,0.93647355],"study_design_scores_gemma":[0.000012461525,0.00025688822,0.0043020174,0.00033225742,0.000100037774,0.0035630045,0.0001863454,0.09830709,0.101811804,0.05969689,0.73130953,0.0001216766],"about_ca_topic_score_codex":0.0006637233,"about_ca_topic_score_gemma":0.0011355322,"teacher_disagreement_score":0.016312597,"about_ca_system_score_codex":0.00024496182,"about_ca_system_score_gemma":0.00032927917,"threshold_uncertainty_score":0.054571092},"labels":[],"label_agreement":null},{"id":"W4388413877","doi":"10.18280/isi.280505","title":"Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"","keywords":"Noise (video); Psychology; Facial expression; Computer science; Speech recognition; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.05101750661093455,"score_gpt":0.2873030949020892,"score_spread":0.23628558829115467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388413877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8213476,0.016191285,0.13516708,0.0012771694,0.0011661223,0.00021189525,0.0014359042,0.004177024,0.01902596],"genre_scores_gemma":[0.9637844,0.0024140147,0.025855139,0.00026857117,0.00007552545,0.00008355585,0.0024035429,0.00012516751,0.004990155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994696,0.000087671,0.000032154483,0.00012170706,0.00016910378,0.00011983388],"domain_scores_gemma":[0.99954456,0.00013396342,0.000034392513,0.00004461431,0.0002155621,0.000026805898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015955954,0.001674877,0.0008436852,0.00083081657,0.0002720236,0.0005886085,0.0010368574,0.001001345,0.0010941478],"category_scores_gemma":[0.0026805345,0.00017559348,0.0006796,0.00040021967,0.00029747392,0.0007535122,0.00048181228,0.0006977418,0.00063415716],"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":[0.0019184325,0.00043257413,0.012438422,0.00061346235,0.0005015747,0.0004143253,0.00012927702,0.44833896,0.023663187,0.0017034577,0.013015765,0.49683046],"study_design_scores_gemma":[0.000015036147,0.00016742527,0.0025560476,0.000031483818,0.00007428346,0.000076311735,0.000027864373,0.98818713,0.0077894516,0.00025571187,0.0008017229,0.000017483937],"about_ca_topic_score_codex":0.021960566,"about_ca_topic_score_gemma":0.017050248,"teacher_disagreement_score":0.021960566,"about_ca_system_score_codex":0.001046805,"about_ca_system_score_gemma":0.00079065294,"threshold_uncertainty_score":0.04366547},"labels":[],"label_agreement":null},{"id":"W4388695206","doi":"10.3390/a16110522","title":"Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence","year":2023,"lang":"en","type":"article","venue":"Algorithms","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure","funders":"National Key Research and Development Program of China","keywords":"Dimensionality reduction; Computer science; Convergence (economics); Relational database; Kernel (algebra); Curse of dimensionality; Discriminative model; Reduction (mathematics); Representation (politics); Statistical relational learning; Generalization; Grey relational analysis; Artificial intelligence; Machine learning; Data mining; Mathematics","score_opus":0.07379055914236558,"score_gpt":0.29603289971799596,"score_spread":0.22224234057563036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388695206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023932508,0.00023377665,0.99665797,0.000117249445,0.000022012728,0.000027827891,0.00004511876,0.00018031056,0.0003224663],"genre_scores_gemma":[0.1522181,0.0012183331,0.8424911,0.00026169323,0.00016287487,0.00029663174,0.00067167665,0.00023838077,0.0024411646],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99687374,0.00109397,0.00021818824,0.0007170736,0.0009345173,0.00016244898],"domain_scores_gemma":[0.9960841,0.001690257,0.0003839852,0.00094272575,0.00076494314,0.00013399836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004952439,0.001419466,0.0018860087,0.0020793106,0.00091630843,0.001891501,0.002104446,0.001162431,0.0025851843],"category_scores_gemma":[0.015267951,0.0005817519,0.0017575694,0.0025083132,0.0019048813,0.004774257,0.003731768,0.0025903338,0.0013144597],"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":[0.00016438404,0.00012771,0.0034481883,0.00041691618,0.0002660275,0.00019536741,0.0005319421,0.20321804,0.009787908,0.22065969,0.009626382,0.55155736],"study_design_scores_gemma":[0.000009722121,0.00004919693,0.0005126145,0.000028145654,0.000026530466,0.000122005,0.000054035765,0.91367,0.0024151036,0.07985053,0.0032287147,0.00003335807],"about_ca_topic_score_codex":0.0029020677,"about_ca_topic_score_gemma":0.0024340055,"teacher_disagreement_score":0.004952439,"about_ca_system_score_codex":0.0008750653,"about_ca_system_score_gemma":0.0020749508,"threshold_uncertainty_score":0.026191354},"labels":[],"label_agreement":null},{"id":"W4388900178","doi":"10.36227/techrxiv.14852652.v4","title":"Deep Clustering with Self-supervision using Pairwise Data Similarities","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Face and Expression Recognition","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":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Cluster analysis; Hypersphere; Autoencoder; Pairwise comparison; Embedding; Computer science; Cluster (spacecraft); Benchmark (surveying); Artificial intelligence; Set (abstract data type); Pattern recognition (psychology); Data set; Code (set theory); Data mining; Deep learning; Geography","score_opus":0.13680797846551576,"score_gpt":0.3048059021646434,"score_spread":0.16799792369912767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388900178","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014282012,0.00009434172,0.9838621,0.000082927974,0.00001361224,0.000036433383,0.00007472274,0.0008398024,0.0007140948],"genre_scores_gemma":[0.48619005,0.00017872179,0.50817573,0.00016859765,0.000050769362,0.00017297268,0.0010097729,0.00032589977,0.0037274817],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99858725,0.0002876851,0.00007960139,0.0005399595,0.00038455497,0.00012102071],"domain_scores_gemma":[0.9981116,0.0003648265,0.00027775948,0.0006343163,0.00049312494,0.00011842653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013324122,0.0011277082,0.00140789,0.0014218593,0.0007666324,0.0013217485,0.002500292,0.0013290561,0.0019913777],"category_scores_gemma":[0.0035903158,0.00077749044,0.0012375012,0.0013840748,0.0015462082,0.0029255785,0.003068461,0.0018653815,0.001067347],"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":[0.00019756751,0.00019471832,0.0032403897,0.00018746482,0.00019377921,0.00008339019,0.0003161843,0.55911475,0.017017521,0.028813487,0.005045937,0.38559482],"study_design_scores_gemma":[0.0000063436096,0.000030058525,0.00029747558,0.000007586233,0.000006562515,0.000031432042,0.00002279738,0.9850269,0.003350979,0.010571988,0.00063685013,0.000011078476],"about_ca_topic_score_codex":0.005599693,"about_ca_topic_score_gemma":0.008504658,"teacher_disagreement_score":0.005599693,"about_ca_system_score_codex":0.0015047322,"about_ca_system_score_gemma":0.0017010942,"threshold_uncertainty_score":0.011134207},"labels":[],"label_agreement":null},{"id":"W4389009914","doi":"10.1007/s00180-023-01438-1","title":"Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of Alberta; Alberta Machine Intelligence Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Kernel embedding of distributions; Reproducing kernel Hilbert space; Wavelet; Kernel method; Radial basis function kernel; Mathematics; Kernel (algebra); Variable kernel density estimation; Kernel principal component analysis; Pattern recognition (psychology); Artificial intelligence; Polynomial kernel; Algorithm; Computer science; Support vector machine; Hilbert space; Mathematical analysis; Discrete mathematics","score_opus":0.0497231692914875,"score_gpt":0.3388928568962744,"score_spread":0.2891696876047869,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389009914","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010015056,0.000080164704,0.9986999,0.00002463135,0.0000075989055,0.00000587152,0.00001916719,0.000096760836,0.00006435564],"genre_scores_gemma":[0.13930893,0.0009934063,0.85470873,0.0001183858,0.00009894635,0.00021827206,0.00068360346,0.00032708287,0.0035427157],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9984687,0.0005165717,0.000103900275,0.00023333126,0.00057566434,0.000101822465],"domain_scores_gemma":[0.99673504,0.0015858026,0.00023479552,0.00045632344,0.0008814045,0.0001065899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025617809,0.0005997593,0.0015910161,0.0011384799,0.00048739833,0.0012597488,0.0018200254,0.0011162115,0.002432634],"category_scores_gemma":[0.008767469,0.0006198377,0.0012176161,0.0020580275,0.00081507396,0.001908382,0.0017739116,0.0022818367,0.0015724907],"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":[0.00039716167,0.0002076,0.0014609116,0.00041743112,0.0002771068,0.00009962115,0.00017837486,0.3391225,0.018395549,0.072044194,0.0056853113,0.56171423],"study_design_scores_gemma":[0.0000050774047,0.000016560536,0.00022633703,0.000006857154,0.000012491266,0.00002972165,0.0000074247387,0.99127287,0.0010842623,0.0065054153,0.0008220024,0.000011001878],"about_ca_topic_score_codex":0.0054995846,"about_ca_topic_score_gemma":0.0049465867,"teacher_disagreement_score":0.0054995846,"about_ca_system_score_codex":0.00076455675,"about_ca_system_score_gemma":0.0021708792,"threshold_uncertainty_score":0.013548136},"labels":[],"label_agreement":null},{"id":"W4389560506","doi":"10.1016/j.engappai.2023.107668","title":"Incremental semi-supervised graph learning NMF with block-diagonal","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":10,"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":"Non-negative matrix factorization; Computer science; Matrix decomposition; Dimensionality reduction; Pattern recognition (psychology); Diagonal; Graph; Artificial intelligence; Curse of dimensionality; Coefficient matrix; Block matrix; Regularization (linguistics); Mathematics; Theoretical computer science","score_opus":0.01909905820611718,"score_gpt":0.24567712595426633,"score_spread":0.22657806774814915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389560506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0084221,0.00030769076,0.9862867,0.000217471,0.00017587571,0.00012931325,0.00022753498,0.0032378654,0.0009955553],"genre_scores_gemma":[0.1771526,0.00020780497,0.81407267,0.00049140526,0.0002139149,0.00053351506,0.0020432617,0.00063093536,0.0046538385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985374,0.00047573802,0.0000807307,0.00044843977,0.0003270442,0.00013076447],"domain_scores_gemma":[0.996062,0.0018934415,0.00019117427,0.0006611556,0.0010281395,0.00016404563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016202242,0.0016551355,0.0025193251,0.0014037404,0.0009528923,0.00105647,0.0034327607,0.0027293868,0.0048280903],"category_scores_gemma":[0.006721324,0.0009892404,0.0016631759,0.0011419436,0.0010430157,0.0018109254,0.0017789478,0.0028334078,0.0027931451],"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":[0.00049657346,0.0004881145,0.0007526083,0.00038439943,0.00027297207,0.00012862649,0.00012675702,0.28015277,0.0111294305,0.007170441,0.020142639,0.6787547],"study_design_scores_gemma":[0.000021872891,0.000039094928,0.00010956552,0.000007364553,0.000013403529,0.000023017863,0.0000076016413,0.9958851,0.0010696361,0.0021629056,0.0006526596,0.000007812657],"about_ca_topic_score_codex":0.013128812,"about_ca_topic_score_gemma":0.025841156,"teacher_disagreement_score":0.013128812,"about_ca_system_score_codex":0.00081621524,"about_ca_system_score_gemma":0.0026493615,"threshold_uncertainty_score":0.026104808},"labels":[],"label_agreement":null},{"id":"W4390244753","doi":"10.18280/ria.370612","title":"Intelligent Vehicle Driver Face and Conscious Recognition","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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":"Facial recognition system; Computer science; Face (sociological concept); Artificial intelligence; Human–computer interaction; Computer vision; Pattern recognition (psychology); Sociology","score_opus":0.054338581961657306,"score_gpt":0.2745980259288356,"score_spread":0.22025944396717828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390244753","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61903554,0.0010652938,0.36377487,0.000515659,0.00016985505,0.00008142501,0.00023977076,0.0007045501,0.014413052],"genre_scores_gemma":[0.98168683,0.00020408095,0.014792256,0.000062210485,0.000019967983,0.00001654102,0.00012052482,0.000014406463,0.0030831604],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996257,0.000082874394,0.0000112416155,0.00010223184,0.00012745302,0.000050515653],"domain_scores_gemma":[0.99978966,0.00005893264,0.000029718352,0.000044847187,0.000067423,0.000009490326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045705552,0.0003223658,0.000267028,0.00039562533,0.00017182023,0.00061856717,0.00038351896,0.0005713067,0.0007419194],"category_scores_gemma":[0.0011976343,0.00014625852,0.00039838793,0.00015291013,0.00028316106,0.00072948594,0.00047458976,0.0003446404,0.00039598192],"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":[0.0005133675,0.00034232286,0.028263215,0.00017036483,0.00014128751,0.00040308072,0.00084164966,0.10415058,0.13807277,0.016022392,0.0042068283,0.7068721],"study_design_scores_gemma":[0.000007549148,0.00024900766,0.029488962,0.000020828009,0.00006235542,0.00067561097,0.00023895162,0.9123507,0.044501375,0.0071671307,0.0051880414,0.00004944834],"about_ca_topic_score_codex":0.002397221,"about_ca_topic_score_gemma":0.001834018,"teacher_disagreement_score":0.002397221,"about_ca_system_score_codex":0.00032061632,"about_ca_system_score_gemma":0.0002848575,"threshold_uncertainty_score":0.0047665834},"labels":[],"label_agreement":null},{"id":"W4390584655","doi":"10.2174/0126662558277567231201063458","title":"Supervised Rank Aggregation (SRA): A Novel Rank AggregationApproach for Ensemble-based Feature Selection","year":2024,"lang":"en","type":"article","venue":"Recent Advances in Computer Science and Communications","topic":"Face and Expression Recognition","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":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Feature selection; Categorical variable; Computer science; Feature (linguistics); Rank (graph theory); Machine learning; Artificial intelligence; Data mining; Ensemble learning; Selection (genetic algorithm); Pattern recognition (psychology); Mathematics","score_opus":0.03293109741034664,"score_gpt":0.3043938891665709,"score_spread":0.27146279175622423,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390584655","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008154362,0.00050347694,0.9898443,0.00012390775,0.00006153965,0.00006263312,0.00012398223,0.0005557383,0.00057006156],"genre_scores_gemma":[0.35243496,0.0007903726,0.6416485,0.00026584617,0.0004848643,0.00042174515,0.0012789264,0.00019520083,0.002479642],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963749,0.0012204322,0.00025936388,0.00064203097,0.0012997846,0.00020341555],"domain_scores_gemma":[0.9957416,0.0016691894,0.00052882644,0.0007563842,0.0011648576,0.00013919699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039036477,0.0017244682,0.0022973348,0.0025592467,0.0009012046,0.0013413676,0.0018887224,0.0010835784,0.0019117639],"category_scores_gemma":[0.0063933777,0.00047341388,0.001976738,0.0024379292,0.0006257637,0.0013594215,0.0015190048,0.0017137709,0.00090888573],"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":[0.0002041273,0.00027389635,0.0066503645,0.00023025056,0.0005797575,0.0001712943,0.00015044257,0.22817732,0.0077203903,0.007898689,0.010656807,0.73728657],"study_design_scores_gemma":[0.00001798884,0.000124233,0.0012699926,0.00001740737,0.00006439391,0.00010508505,0.00001927423,0.9881493,0.002039045,0.0056720935,0.0024941887,0.000027048212],"about_ca_topic_score_codex":0.0030950266,"about_ca_topic_score_gemma":0.0038155268,"teacher_disagreement_score":0.0039036477,"about_ca_system_score_codex":0.00056808704,"about_ca_system_score_gemma":0.0013188958,"threshold_uncertainty_score":0.020644665},"labels":[],"label_agreement":null},{"id":"W4390781314","doi":"10.23977/jeis.2023.080613","title":"A Functional Data Classification Model Utilizing Functional Mahalanobis Distance and Regenerative Kernel Methods","year":2023,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Face and Expression Recognition","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":"Mahalanobis distance; Pattern recognition (psychology); Computer science; Kernel principal component analysis; Artificial intelligence; Kernel (algebra); Functional data analysis; Kernel method; Euclidean distance; Categorical variable; Data mining; Radial basis function kernel; Mathematics; Support vector machine; Machine learning","score_opus":0.10601128903675296,"score_gpt":0.3464381953583081,"score_spread":0.24042690632155517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390781314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0063060047,0.00016252529,0.9925476,0.00016049952,0.00003784916,0.000038502072,0.000038082042,0.00014904747,0.00055998017],"genre_scores_gemma":[0.5826796,0.0009072766,0.40843996,0.00026478386,0.0002759794,0.00048945955,0.00060482137,0.00012500612,0.0062130853],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972234,0.0007890348,0.00021557653,0.00066932617,0.00090487226,0.00019786734],"domain_scores_gemma":[0.99681205,0.0012345829,0.0003959506,0.0003350637,0.0011031475,0.00011921677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0043990645,0.0009871366,0.0013736691,0.0029494155,0.0007281271,0.0019875956,0.0028553663,0.001784969,0.0015961211],"category_scores_gemma":[0.008537232,0.0004062821,0.0017853089,0.002582637,0.0013209343,0.0040156604,0.0015986124,0.0018202473,0.0009840924],"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":[0.000283652,0.00036024,0.0073189815,0.00041426806,0.00029163607,0.0003688069,0.00043860334,0.4337278,0.0076917703,0.17351697,0.004454046,0.37113318],"study_design_scores_gemma":[0.0000034511113,0.000043173153,0.00034198494,0.000010207582,0.000014664586,0.00006309004,0.000018963672,0.9846265,0.0005044651,0.0135033205,0.0008528738,0.000017409478],"about_ca_topic_score_codex":0.003645093,"about_ca_topic_score_gemma":0.001978177,"teacher_disagreement_score":0.0043990645,"about_ca_system_score_codex":0.0012435616,"about_ca_system_score_gemma":0.0014064882,"threshold_uncertainty_score":0.023264766},"labels":[],"label_agreement":null},{"id":"W4390974568","doi":"10.5267/j.ijdns.2023.11.022","title":"Hybrid feature selection based ScC and forward selection methods","year":2024,"lang":"en","type":"article","venue":"International Journal of Data and Network Science","topic":"Face and Expression Recognition","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; Feature selection; Selection (genetic algorithm); Data mining; Preprocessor; Reduct; Process (computing); Data pre-processing; Artificial intelligence; Feature (linguistics); Stability (learning theory); Information gain ratio; Random forest; Machine learning; Pattern recognition (psychology); Rough set","score_opus":0.026543963472370112,"score_gpt":0.36339972960290456,"score_spread":0.3368557661305345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390974568","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035518173,0.0010113757,0.95812875,0.00025631167,0.00018595783,0.00043256205,0.00039995264,0.0016534687,0.0024134393],"genre_scores_gemma":[0.41538447,0.0008918221,0.57082635,0.00041256216,0.00028266278,0.0009778622,0.0022097565,0.00022386272,0.008790593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997891,0.00039029974,0.00015974074,0.00037549977,0.0010119937,0.00017144937],"domain_scores_gemma":[0.9971193,0.0009174839,0.00013458957,0.00024318809,0.0015209218,0.00006454877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027133268,0.0015661519,0.0018082897,0.0035458345,0.0009177642,0.0010828881,0.0015178272,0.0009015262,0.0021718019],"category_scores_gemma":[0.0038276603,0.00046659537,0.0024315014,0.0034420649,0.0005304013,0.0011837916,0.0008536474,0.0008652696,0.000886276],"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":[0.000292505,0.00026547554,0.007696202,0.000285155,0.0004749815,0.00032713581,0.00019462228,0.11493074,0.013265418,0.0048438343,0.0075661605,0.8498577],"study_design_scores_gemma":[0.000088709676,0.00031985913,0.0047476212,0.000050478196,0.00022023529,0.00046311837,0.00007916457,0.97096103,0.010213645,0.0052411216,0.0075591253,0.000055860415],"about_ca_topic_score_codex":0.007946433,"about_ca_topic_score_gemma":0.00825065,"teacher_disagreement_score":0.007946433,"about_ca_system_score_codex":0.0006334417,"about_ca_system_score_gemma":0.0017264476,"threshold_uncertainty_score":0.015800357},"labels":[],"label_agreement":null},{"id":"W4391418209","doi":"10.1016/j.engappai.2024.107978","title":"Bayesian non-negative matrix factorization with Student’s t-distribution for outlier removal and data clustering","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"","keywords":"Computer science; Outlier; Bayesian probability; Cluster analysis; Matrix decomposition; Non-negative matrix factorization; Artificial intelligence; Data mining; Pattern recognition (psychology)","score_opus":0.027460207100336045,"score_gpt":0.32119362984227307,"score_spread":0.29373342274193703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391418209","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017125094,0.00019621802,0.99747914,0.0000814837,0.000045051405,0.000037495356,0.000037478836,0.00026087894,0.00014972262],"genre_scores_gemma":[0.10133538,0.000542672,0.89486396,0.00031607447,0.00021881916,0.00047559745,0.0007858106,0.00023681419,0.0012249566],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9925736,0.002923138,0.00042749185,0.001806027,0.0018668962,0.00040277088],"domain_scores_gemma":[0.99177223,0.004639954,0.00082343165,0.0007215078,0.0018329954,0.00020986324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007976818,0.0019776772,0.0023997356,0.0021896358,0.0016888791,0.0015800123,0.002313958,0.002901807,0.0015542227],"category_scores_gemma":[0.025480952,0.001039422,0.00286701,0.002669123,0.0019216129,0.0027979426,0.0016199079,0.0038003654,0.0012483151],"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":[0.00047197018,0.00033414437,0.0040101316,0.0007241798,0.0005110042,0.00028644296,0.0006099738,0.46266317,0.011209561,0.034260206,0.011369866,0.47354937],"study_design_scores_gemma":[0.000028410765,0.00006364198,0.00062323397,0.00003374573,0.000024913932,0.000077685334,0.00004659669,0.9763014,0.0021922493,0.017621873,0.0029455563,0.00004063015],"about_ca_topic_score_codex":0.008759516,"about_ca_topic_score_gemma":0.008619299,"teacher_disagreement_score":0.008759516,"about_ca_system_score_codex":0.0015268015,"about_ca_system_score_gemma":0.003271785,"threshold_uncertainty_score":0.042185962},"labels":[],"label_agreement":null},{"id":"W4392200119","doi":"10.18280/isi.290135","title":"Nesterov Accelerated Gradient Descent for Optimizing Fast Harmonic Mean Linear Discriminant Analysis","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"","keywords":"Linear discriminant analysis; Gradient descent; Mathematics; Descent (aeronautics); Statistics; Harmonic mean; Pattern recognition (psychology); Artificial intelligence; Computer science; Applied mathematics; Engineering; Artificial neural network","score_opus":0.03570105133167164,"score_gpt":0.26894417377068547,"score_spread":0.23324312243901382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392200119","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006706252,0.0003357411,0.9913384,0.00012927293,0.000042794494,0.000033986184,0.00004352686,0.0005796675,0.0007903814],"genre_scores_gemma":[0.21512118,0.00047698064,0.7764118,0.0002437143,0.00010565523,0.0003222496,0.00055699016,0.00035601083,0.006405381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992212,0.0002743829,0.00003394783,0.00015411286,0.0002492217,0.00006713957],"domain_scores_gemma":[0.99923515,0.00036590447,0.00006228413,0.000075457,0.0002238779,0.000037415466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016283675,0.0012749339,0.0014285416,0.0007151268,0.00050580094,0.00073767244,0.0012183604,0.0011396446,0.0019765105],"category_scores_gemma":[0.004431126,0.0005549052,0.00082244753,0.0007039017,0.0007342971,0.00086118665,0.0010229961,0.00177114,0.0011402571],"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":[0.0001340599,0.00008834037,0.0010816583,0.00019968997,0.00008208557,0.00010265679,0.00012449069,0.7229935,0.0068490296,0.022227636,0.0075499075,0.23856696],"study_design_scores_gemma":[0.0000059446384,0.00001245846,0.00006710911,0.000003650272,0.0000023471969,0.000007805821,0.0000038581334,0.996591,0.00038005633,0.0022534437,0.0006685503,0.0000038552716],"about_ca_topic_score_codex":0.006815529,"about_ca_topic_score_gemma":0.007409797,"teacher_disagreement_score":0.006815529,"about_ca_system_score_codex":0.0009817488,"about_ca_system_score_gemma":0.0018193782,"threshold_uncertainty_score":0.013551712},"labels":[],"label_agreement":null},{"id":"W4392354383","doi":"10.18280/ria.380104","title":"The Histogram of Enhanced Gradients (HEG) - A Fast Descriptor for Noisy Face Recognition","year":2024,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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":"","keywords":"Histogram; Artificial intelligence; Pattern recognition (psychology); Computer science; Face (sociological concept); Facial recognition system; Histogram of oriented gradients; Histogram matching; Computer vision; Image (mathematics); Linguistics","score_opus":0.07303848420812503,"score_gpt":0.2930971518628626,"score_spread":0.22005866765473758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392354383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043985818,0.0034947642,0.94112813,0.00030581251,0.0005188461,0.00035449883,0.0018023119,0.0043026996,0.004107075],"genre_scores_gemma":[0.39796463,0.0033601513,0.57778865,0.00040384178,0.00033960154,0.00054039975,0.007929893,0.00053014135,0.011142717],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991346,0.00010429738,0.000046305962,0.00017705293,0.00046506998,0.00007269107],"domain_scores_gemma":[0.9993086,0.00016741811,0.00009585924,0.00014558819,0.0002491781,0.000033299242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088433415,0.000735336,0.0012566743,0.0018771947,0.00032367095,0.0010058202,0.0010686305,0.0006707752,0.0026162434],"category_scores_gemma":[0.002274995,0.00023990589,0.0006623546,0.0015530147,0.00052936893,0.0014780156,0.0008933286,0.000750442,0.0020287402],"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":[0.0003772051,0.0001364005,0.0027175092,0.00035510346,0.00011551632,0.00016830239,0.00008332955,0.01820297,0.06042677,0.007842569,0.022611901,0.8869625],"study_design_scores_gemma":[0.00009125785,0.0007354807,0.02027179,0.00017496718,0.00016020636,0.003240762,0.00036849623,0.72413635,0.16400975,0.019121332,0.0674085,0.0002810425],"about_ca_topic_score_codex":0.0031788687,"about_ca_topic_score_gemma":0.0038984222,"teacher_disagreement_score":0.0031788687,"about_ca_system_score_codex":0.00062662736,"about_ca_system_score_gemma":0.0007843135,"threshold_uncertainty_score":0.008752227},"labels":[],"label_agreement":null},{"id":"W4392366334","doi":"10.18280/ria.380105","title":"A Novel Approach for Sequential Three-Way Decision Using Chi-Square Statistic as the Assessment Metric","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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":"Statistic; Metric (unit); Chi-square test; Statistics; Square (algebra); Mathematics; Pearson's chi-squared test; Computer science; Test statistic; Statistical hypothesis testing; Engineering; Operations management","score_opus":0.10498576257661435,"score_gpt":0.35535925890308,"score_spread":0.25037349632646566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392366334","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031493625,0.00012026568,0.9953029,0.00013417705,0.0000473142,0.00014066261,0.00004859256,0.00018018651,0.0008764822],"genre_scores_gemma":[0.11056371,0.0001951509,0.8871593,0.000142511,0.00011489399,0.0005516242,0.00017483607,0.000110085326,0.0009878711],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.97363216,0.012660187,0.0018141505,0.0036951976,0.007449812,0.0007485431],"domain_scores_gemma":[0.9711683,0.019454516,0.001834935,0.0014773548,0.0053288955,0.00073593133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016684266,0.001848119,0.002485157,0.0064894003,0.0016591224,0.004463108,0.0033497978,0.002063091,0.005053207],"category_scores_gemma":[0.035697777,0.0007306276,0.0025242153,0.0046680816,0.0035297123,0.004427798,0.0034657335,0.0036636319,0.0009758375],"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":[0.00072574907,0.0005118784,0.012122842,0.0010200808,0.0006503184,0.00060976984,0.0021113728,0.17524074,0.009951935,0.26310682,0.0066105826,0.52733785],"study_design_scores_gemma":[0.000075508164,0.00064300117,0.0016924612,0.00012706213,0.00015892569,0.0004589051,0.00045596223,0.80565757,0.0049218847,0.1762928,0.009322156,0.00019382367],"about_ca_topic_score_codex":0.0036309992,"about_ca_topic_score_gemma":0.0040082578,"teacher_disagreement_score":0.016684266,"about_ca_system_score_codex":0.0030163385,"about_ca_system_score_gemma":0.005249822,"threshold_uncertainty_score":0.088235855},"labels":[],"label_agreement":null},{"id":"W4392620321","doi":"10.1007/s00180-024-01476-3","title":"A subspace aggregating algorithm for accurate classification","year":2024,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Face and Expression Recognition","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":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Subspace topology; Computer science; Pattern recognition (psychology); Artificial intelligence; Algorithm","score_opus":0.042770400028653954,"score_gpt":0.3232887550374884,"score_spread":0.28051835500883443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392620321","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003083874,0.00016651815,0.9953511,0.000058385343,0.000047882913,0.000021214484,0.00008082928,0.00071432383,0.00047600403],"genre_scores_gemma":[0.07213735,0.000331915,0.9219072,0.00015120175,0.00017541532,0.00015954635,0.0009994416,0.0002626008,0.0038753152],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805474,0.0004575549,0.00013533728,0.00038192738,0.0007885836,0.00018179086],"domain_scores_gemma":[0.9973719,0.0007854878,0.000119788056,0.00082858803,0.0008006937,0.000093452385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001959842,0.0011602703,0.0021502841,0.001594716,0.0012343427,0.0014272227,0.0018258794,0.0011050175,0.004812143],"category_scores_gemma":[0.004481725,0.0006416191,0.0012465988,0.0032063578,0.000682127,0.0022163326,0.0022979076,0.0025287503,0.0038317025],"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":[0.00019334255,0.00011798297,0.0006390161,0.00005446029,0.000077137585,0.00003931759,0.000068748544,0.056763373,0.0120198345,0.014154043,0.009419364,0.9064534],"study_design_scores_gemma":[0.00000922362,0.000056119537,0.0004407991,0.0000074567106,0.000021946043,0.00006079884,0.000019303368,0.97643024,0.005757782,0.013233974,0.0039409227,0.000021448463],"about_ca_topic_score_codex":0.0042965882,"about_ca_topic_score_gemma":0.0049529606,"teacher_disagreement_score":0.004812143,"about_ca_system_score_codex":0.00053525966,"about_ca_system_score_gemma":0.0014732665,"threshold_uncertainty_score":0.01609826},"labels":[],"label_agreement":null},{"id":"W4392981053","doi":"10.1109/icaiccit60255.2023.10466005","title":"Facial Expression Recognition in the Wild using Artificial Rabbits Optimizer based Residual Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Horizon College and Seminary","funders":"","keywords":"Residual; Artificial neural network; Artificial intelligence; Computer science; Expression (computer science); Facial expression recognition; Speech recognition; Pattern recognition (psychology); Facial recognition system; Algorithm","score_opus":0.08541286141994292,"score_gpt":0.28688904065048976,"score_spread":0.20147617923054684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392981053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22493125,0.0011701332,0.7630963,0.00043045517,0.00027785994,0.0001497953,0.00032088108,0.0032860509,0.0063373824],"genre_scores_gemma":[0.8196846,0.00041730472,0.17023547,0.00029313215,0.000057608733,0.00014815106,0.0010716297,0.00015785283,0.007934149],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997677,0.000051694667,0.000014041838,0.00008519847,0.00005157043,0.000029683399],"domain_scores_gemma":[0.99986875,0.00003525357,0.000016185453,0.000018856763,0.000051680265,0.000009194698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006399665,0.00088138354,0.00055462,0.0002855356,0.00019384426,0.00042441502,0.00080127805,0.00043469225,0.0013709161],"category_scores_gemma":[0.00078129466,0.00025362312,0.0006727986,0.00024210261,0.00026253,0.0005240216,0.00045018617,0.0007251111,0.0005148516],"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":[0.0006363568,0.00030422892,0.003049134,0.00009248236,0.00024035777,0.00018619058,0.000099569435,0.43221506,0.053392623,0.002170143,0.0077177165,0.49989614],"study_design_scores_gemma":[0.0000044839308,0.00003596203,0.00034679435,0.0000017420351,0.000008264454,0.000011456782,0.000006194396,0.99696594,0.0021487554,0.00024810986,0.0002185892,0.0000035751675],"about_ca_topic_score_codex":0.005502616,"about_ca_topic_score_gemma":0.006439887,"teacher_disagreement_score":0.005502616,"about_ca_system_score_codex":0.00048651177,"about_ca_system_score_gemma":0.0003341655,"threshold_uncertainty_score":0.010941148},"labels":[],"label_agreement":null},{"id":"W4393152877","doi":"10.1609/aaai.v38i15.29584","title":"Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; Sichuan University; National Science Foundation","keywords":"Outlier; Cluster analysis; Center (category theory); Computer science; Artificial intelligence; Data mining; Machine learning; Chemistry","score_opus":0.055863891624737975,"score_gpt":0.29119884103318433,"score_spread":0.23533494940844635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393152877","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036461543,0.00022282627,0.9610916,0.0002795614,0.00005865013,0.00009564209,0.000055135704,0.0010171562,0.00071789057],"genre_scores_gemma":[0.6968206,0.000167852,0.3005176,0.00022421786,0.000097211545,0.00019819658,0.00020885516,0.00024175101,0.0015236697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99515176,0.0011755395,0.00039075996,0.0016033109,0.0011107612,0.0005678837],"domain_scores_gemma":[0.9857465,0.0057060737,0.001882807,0.0035570422,0.002454318,0.00065322866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006409683,0.0011715993,0.0023944553,0.0013548932,0.0016657694,0.0021315608,0.0037269918,0.0018518956,0.0011077219],"category_scores_gemma":[0.023576077,0.00061494793,0.0011129853,0.0013373011,0.0022075924,0.0034696897,0.0033692745,0.0025106596,0.0004892456],"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":[0.00046305015,0.00017991006,0.0027034266,0.00009617152,0.000120895944,0.00011675628,0.00035609351,0.84052956,0.0049467604,0.033212703,0.0032933613,0.11398133],"study_design_scores_gemma":[0.000014336558,0.000047321406,0.00016295,0.0000062120203,0.000009048018,0.000029527167,0.000030624757,0.9816996,0.0017243242,0.015926745,0.00033720245,0.000012181501],"about_ca_topic_score_codex":0.0033147878,"about_ca_topic_score_gemma":0.0029740073,"teacher_disagreement_score":0.006409683,"about_ca_system_score_codex":0.0021464813,"about_ca_system_score_gemma":0.0020775008,"threshold_uncertainty_score":0.033898056},"labels":[],"label_agreement":null},{"id":"W4393619291","doi":"10.23952/jnva.8.2024.3.06","title":"A fast and effective algorithm for sparse linear regression with $\\ell_p$-norm data fidelity and elastic net regularization","year":2024,"lang":"en","type":"article","venue":"Journal of Nonlinear and Variational Analysis","topic":"Face and Expression Recognition","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":"Natural Science Foundation of Henan Province; National Natural Science Foundation of China","keywords":"Elastic net regularization; Fidelity; Regularization (linguistics); Norm (philosophy); Mathematics; Linear regression; Regression; Algorithm; Lasso (programming language); Applied mathematics; Computer science; Mathematical optimization; Statistics; Artificial intelligence","score_opus":0.016150785880876123,"score_gpt":0.2794741153414174,"score_spread":0.26332332946054127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393619291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00041242398,0.00006176424,0.9989519,0.000062161336,0.000021928136,0.000020826563,0.000015409389,0.00016489669,0.0002887642],"genre_scores_gemma":[0.035801165,0.00040810867,0.9586551,0.00018866424,0.0001210846,0.0003861127,0.00038967095,0.00021543051,0.0038345926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99895656,0.000251272,0.00007068424,0.00019992326,0.00044152336,0.00007993594],"domain_scores_gemma":[0.99902534,0.00044887353,0.000115751536,0.000106327214,0.00024525032,0.000058522564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001796769,0.0015130918,0.0015211188,0.0011225353,0.0006940624,0.0011529091,0.0019527077,0.002276139,0.0042079287],"category_scores_gemma":[0.0035990397,0.000833832,0.0017396112,0.0017082538,0.0010449198,0.0018590771,0.0029815868,0.0042271614,0.0019966862],"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":[0.00011656979,0.00011060367,0.00066225074,0.00037639958,0.00013015703,0.00020384805,0.00019429885,0.50473267,0.009464833,0.058796864,0.013189135,0.41202238],"study_design_scores_gemma":[0.000015387437,0.000030368148,0.00006780708,0.000012765395,0.000007275452,0.00007789118,0.00001191068,0.9880967,0.0011938548,0.007254463,0.0032190902,0.000012468627],"about_ca_topic_score_codex":0.0033592037,"about_ca_topic_score_gemma":0.0029553052,"teacher_disagreement_score":0.0042079287,"about_ca_system_score_codex":0.0007466154,"about_ca_system_score_gemma":0.0022211073,"threshold_uncertainty_score":0.014076948},"labels":[],"label_agreement":null},{"id":"W4393904469","doi":"10.48550/arxiv.2404.00408","title":"Deep Learning with Parametric Lenses","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Parametric statistics; Computer science; Optometry; Mathematics; Medicine; Statistics","score_opus":0.053537496107846964,"score_gpt":0.17713031715052255,"score_spread":0.12359282104267558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393904469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053689918,0.0003052564,0.9876478,0.0004532883,0.000051477888,0.000017992834,0.000116404626,0.00057064986,0.0054681413],"genre_scores_gemma":[0.54266685,0.0012715545,0.44040278,0.0007730394,0.0002683969,0.00023044263,0.00053771626,0.0006482448,0.013201027],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851435,0.0003900762,0.00009683194,0.0002900367,0.00058140233,0.00012742844],"domain_scores_gemma":[0.9983235,0.0006382223,0.0001569939,0.00043635254,0.00030608763,0.00013884107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001638151,0.0007221069,0.0005533643,0.0018338417,0.00055985845,0.0037800793,0.0016310007,0.0010188092,0.0055014263],"category_scores_gemma":[0.007092321,0.0004053017,0.0009908793,0.0014332877,0.0031336679,0.006766017,0.004513817,0.0030797601,0.0014156549],"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":[0.000032766064,0.000015746988,0.00041014393,0.00006893565,0.000018054563,0.000053256543,0.000117553944,0.018534372,0.0013954293,0.91968364,0.0018178597,0.05785223],"study_design_scores_gemma":[0.000007457526,0.000035629037,0.00016571768,0.000037327267,0.000009473358,0.000090589034,0.0000444505,0.15992242,0.0021585748,0.82471025,0.012800749,0.000017386974],"about_ca_topic_score_codex":0.00171331,"about_ca_topic_score_gemma":0.0018734549,"teacher_disagreement_score":0.0055014263,"about_ca_system_score_codex":0.001674401,"about_ca_system_score_gemma":0.00092140335,"threshold_uncertainty_score":0.018404067},"labels":[],"label_agreement":null},{"id":"W4394629167","doi":"10.1109/csce60160.2023.00083","title":"Modified K-Means Clustering Algorithms for Feature Selection","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Cluster analysis; Computer science; Feature selection; Selection (genetic algorithm); Algorithm; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Data mining","score_opus":0.04251639642722542,"score_gpt":0.2872932192864803,"score_spread":0.24477682285925484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394629167","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016569704,0.0004393787,0.9953165,0.000090522306,0.000072968076,0.00019901837,0.00020465732,0.0014456708,0.0005742439],"genre_scores_gemma":[0.030590286,0.0005919916,0.9640251,0.000111100046,0.000104935716,0.00083976967,0.0012096771,0.0003473705,0.0021797926],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99619544,0.0009212654,0.00039408403,0.0008686438,0.0014230075,0.000197575],"domain_scores_gemma":[0.99702257,0.0009071333,0.0002259343,0.0004094629,0.0013814596,0.00005346732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028060658,0.0026194418,0.002780773,0.0046377764,0.0017903587,0.0017120251,0.0034104558,0.0018983056,0.0051729996],"category_scores_gemma":[0.008120779,0.0010823167,0.0028953666,0.005915921,0.00070174626,0.0017594292,0.001444868,0.0021515295,0.005610627],"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":[0.00020289602,0.00014848833,0.0013468162,0.0005002294,0.00049661077,0.00010829524,0.00021554709,0.14273101,0.008042226,0.00921622,0.024127478,0.8128642],"study_design_scores_gemma":[0.00006467501,0.00008473024,0.0016988105,0.00006427436,0.00006759306,0.00019984576,0.00008440844,0.9561923,0.006609543,0.013944494,0.020869471,0.00011984579],"about_ca_topic_score_codex":0.011139474,"about_ca_topic_score_gemma":0.012965723,"teacher_disagreement_score":0.011139474,"about_ca_system_score_codex":0.0013287592,"about_ca_system_score_gemma":0.0025467582,"threshold_uncertainty_score":0.022149265},"labels":[],"label_agreement":null},{"id":"W4394862804","doi":"10.1109/tkde.2024.3388526","title":"Feature Selection With Discernibility and Independence Criteria","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Feature selection; Selection (genetic algorithm); Independence (probability theory); Artificial intelligence; Feature (linguistics); Data mining; Pattern recognition (psychology); Mathematics; Statistics","score_opus":0.01885862692112788,"score_gpt":0.2740728741851766,"score_spread":0.25521424726404873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394862804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011485111,0.00040158897,0.9853092,0.00016436145,0.000044355063,0.00027197832,0.000246213,0.0006377157,0.0014395622],"genre_scores_gemma":[0.3506616,0.00069490244,0.63976806,0.00029970697,0.00037369918,0.0012608668,0.0025722736,0.00025929857,0.004109529],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9950917,0.0012096573,0.00050211226,0.00077110494,0.002079631,0.0003458195],"domain_scores_gemma":[0.9940672,0.003403342,0.00045587137,0.0005112236,0.0013704557,0.00019183522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052129747,0.002041048,0.0027535458,0.0048643188,0.00079440785,0.0024998183,0.0020421748,0.0015156204,0.0025874746],"category_scores_gemma":[0.015965495,0.00059684593,0.0025009268,0.003954451,0.0011130531,0.0017658822,0.0020569104,0.0017128624,0.001121756],"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":[0.00089050893,0.0002420198,0.004036619,0.0004606986,0.00036574807,0.0004690473,0.00016485363,0.23819059,0.01132775,0.025997873,0.008701055,0.7091532],"study_design_scores_gemma":[0.00013115721,0.0002559493,0.0019506857,0.000048831458,0.000105542465,0.00024609594,0.000039667226,0.96332884,0.0067104986,0.022265563,0.0048629516,0.000054221044],"about_ca_topic_score_codex":0.0023014983,"about_ca_topic_score_gemma":0.0013103948,"teacher_disagreement_score":0.0052129747,"about_ca_system_score_codex":0.0010088105,"about_ca_system_score_gemma":0.0019520806,"threshold_uncertainty_score":0.027569175},"labels":[],"label_agreement":null},{"id":"W4395463077","doi":"10.18280/isi.290204","title":"Enhancing K-Means Clustering with Post-Redistribution","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"Cluster analysis; Redistribution (election); Computer science; Artificial intelligence; Political science","score_opus":0.008982281386227477,"score_gpt":0.22014954205808698,"score_spread":0.2111672606718595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395463077","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028227482,0.0002606814,0.9675496,0.00028293132,0.00008074223,0.00011026875,0.00008385677,0.001811518,0.0015928842],"genre_scores_gemma":[0.27117497,0.00029801324,0.7230599,0.00025822496,0.00011121051,0.00026479267,0.00063491205,0.00069646415,0.0035015692],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981844,0.000390654,0.00016451813,0.00044499908,0.0006534068,0.00016194064],"domain_scores_gemma":[0.9959858,0.0011455681,0.00035668092,0.000856331,0.0015386244,0.00011695825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023614785,0.0015458107,0.0015313487,0.0019564843,0.001597856,0.0016018205,0.0023868005,0.0017313979,0.0021046863],"category_scores_gemma":[0.0091402745,0.00069252454,0.0014785018,0.0020116437,0.0011837065,0.002418324,0.002289624,0.0015015118,0.0021337376],"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":[0.00054015603,0.0003503557,0.0048932824,0.00044750512,0.00027379414,0.0001771567,0.000985587,0.45649597,0.03496159,0.011936225,0.008912044,0.48002633],"study_design_scores_gemma":[0.00005777098,0.00010112654,0.0013571676,0.000030776035,0.00005204024,0.000089941226,0.00019593761,0.9631424,0.019537644,0.011217086,0.0041665244,0.00005152596],"about_ca_topic_score_codex":0.007604441,"about_ca_topic_score_gemma":0.011731508,"teacher_disagreement_score":0.007604441,"about_ca_system_score_codex":0.00120408,"about_ca_system_score_gemma":0.002491822,"threshold_uncertainty_score":0.015120387},"labels":[],"label_agreement":null},{"id":"W4396717279","doi":"10.3934/electreng.2024010","title":"Robust CNN for facial emotion recognition and real-time GUI","year":2024,"lang":"en","type":"article","venue":"AIMS Electronics and Electrical Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"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":"Computer science; Facial expression; Emotion recognition; Speech recognition; Pattern recognition (psychology); Artificial intelligence","score_opus":0.015159267609821359,"score_gpt":0.20647216493505285,"score_spread":0.1913128973252315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396717279","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042947955,0.0012078607,0.92791444,0.0004336971,0.0003829746,0.00014417832,0.0012011877,0.014442468,0.0113252],"genre_scores_gemma":[0.5792458,0.00091609696,0.3932167,0.00059064885,0.0001201566,0.0003188286,0.003917829,0.0006312644,0.021042623],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954706,0.000041237992,0.0000199663,0.00014677108,0.00016303846,0.00008181967],"domain_scores_gemma":[0.99969447,0.00004064672,0.000026828717,0.00009424886,0.00013126704,0.00001262876],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048643458,0.0009084589,0.00043866737,0.0004837105,0.0001815683,0.0005860469,0.0012968965,0.0006432824,0.005958069],"category_scores_gemma":[0.0012998922,0.0003391362,0.00064896053,0.00044512696,0.00024558994,0.0009937505,0.0006099406,0.00079149386,0.002534082],"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":[0.0003688432,0.00014841846,0.0015557823,0.00015797932,0.00013006506,0.00024073191,0.000050084564,0.09906613,0.16836643,0.0051422305,0.02597961,0.6987937],"study_design_scores_gemma":[0.000009235169,0.000059253936,0.0015270648,0.0000147031815,0.000026627758,0.00008684144,0.0000131435845,0.9496744,0.039820675,0.0015775687,0.007169721,0.00002081372],"about_ca_topic_score_codex":0.012131432,"about_ca_topic_score_gemma":0.01618533,"teacher_disagreement_score":0.012131432,"about_ca_system_score_codex":0.0010407348,"about_ca_system_score_gemma":0.0007079772,"threshold_uncertainty_score":0.024121642},"labels":[],"label_agreement":null},{"id":"W4398169052","doi":"10.1007/s11760-024-03282-2","title":"Illumination invariant face recognition via multiscale filter faces and voting technique","year":2024,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Face and Expression Recognition","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":"Voting; Invariant (physics); Computer vision; Artificial intelligence; Facial recognition system; Computer science; Face (sociological concept); Pattern recognition (psychology); Filter (signal processing); Mathematics; Political science","score_opus":0.017947594381897268,"score_gpt":0.26056700683728495,"score_spread":0.24261941245538768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398169052","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06690321,0.00021474286,0.9304591,0.000061011793,0.00006551636,0.000046956,0.00007941972,0.0005391534,0.0016308912],"genre_scores_gemma":[0.5249782,0.00022522092,0.46982855,0.00006306374,0.00005468152,0.000073006064,0.00034800422,0.0000976522,0.0043315636],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967396,0.00005022899,0.000016182517,0.00008362104,0.00012742479,0.000048576938],"domain_scores_gemma":[0.9997458,0.00005506029,0.000021534668,0.000059325903,0.00010366622,0.000014618311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005176698,0.0002994233,0.00071555324,0.00066196016,0.00028696322,0.00041342902,0.00064552313,0.00044839465,0.0016956974],"category_scores_gemma":[0.0007273146,0.00022699987,0.0007553984,0.00047375335,0.00023364167,0.0005622563,0.00046802458,0.00037583162,0.0005225833],"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":[0.00028715187,0.00009822025,0.0022437526,0.000063546286,0.00010377846,0.00007471091,0.000053088326,0.010179777,0.3986865,0.0055825063,0.0018309929,0.58079594],"study_design_scores_gemma":[0.000026295313,0.00035697527,0.012000289,0.000012591446,0.00016548912,0.000640389,0.000057621048,0.7693641,0.20907377,0.0040493812,0.0042052628,0.000047762835],"about_ca_topic_score_codex":0.0014135473,"about_ca_topic_score_gemma":0.0027973242,"teacher_disagreement_score":0.0016956974,"about_ca_system_score_codex":0.00023156783,"about_ca_system_score_gemma":0.0003350689,"threshold_uncertainty_score":0.0056726336},"labels":[],"label_agreement":null},{"id":"W4398190802","doi":"10.2139/ssrn.4836407","title":"Multivariate Kernel Regression in Vector and Product Metric Spaces","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Face and Expression Recognition","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":"McGill University","funders":"","keywords":"Multivariate statistics; Mathematics; Kernel (algebra); Metric (unit); Product (mathematics); Product metric; Inner product space; Regression; Metric space; Pure mathematics; Statistics; Business; Geometry","score_opus":0.014026579413382806,"score_gpt":0.2727096635120494,"score_spread":0.2586830840986666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398190802","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018388541,0.0011149321,0.97842044,0.00029189626,0.00008210494,0.0000149681155,0.000091811926,0.00020805294,0.0013871982],"genre_scores_gemma":[0.60369134,0.0047192485,0.36461318,0.00019104748,0.00063309836,0.00013658445,0.0007008937,0.00054120284,0.024773497],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99856,0.0006957432,0.00007605384,0.00021578954,0.00037388987,0.00007855745],"domain_scores_gemma":[0.99618775,0.0019448247,0.00048851006,0.0005189833,0.0007045637,0.00015530319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021253433,0.00082684285,0.0012295891,0.0010891784,0.00028802973,0.0018213764,0.0010403765,0.001027059,0.0029737076],"category_scores_gemma":[0.009659262,0.0004766665,0.000674481,0.002010745,0.0010596097,0.0037059337,0.001646879,0.0015113056,0.00094075873],"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":[0.00022556282,0.00011651388,0.0017884955,0.000324371,0.00017086219,0.00014540725,0.00019657341,0.16379611,0.005673245,0.60971797,0.0066721244,0.21117273],"study_design_scores_gemma":[0.000009575986,0.000035991638,0.00078576495,0.000012562644,0.000017929615,0.00006458612,0.00002531135,0.83584493,0.00076536473,0.1594935,0.00292338,0.000021104905],"about_ca_topic_score_codex":0.0028821894,"about_ca_topic_score_gemma":0.0016475972,"teacher_disagreement_score":0.0029737076,"about_ca_system_score_codex":0.0005973584,"about_ca_system_score_gemma":0.0007252061,"threshold_uncertainty_score":0.0112400055},"labels":[],"label_agreement":null},{"id":"W4398349452","doi":"10.3390/make6020052","title":"Locally-Scaled Kernels and Confidence Voting","year":2024,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Face and Expression Recognition","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":"McMaster University","funders":"Mitacs","keywords":"Voting; Computer science; Political science; Law; Politics","score_opus":0.011671512419377554,"score_gpt":0.29089840322773763,"score_spread":0.2792268908083601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398349452","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03668299,0.0007836402,0.95883185,0.0002051597,0.000060477774,0.00005739205,0.00008195864,0.00077165436,0.0025248397],"genre_scores_gemma":[0.8676642,0.00024327975,0.12903999,0.0001021479,0.00009607436,0.00007027563,0.00039004188,0.00014211847,0.002251858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.994193,0.0019827294,0.0003548677,0.0012303806,0.0018578687,0.0003811227],"domain_scores_gemma":[0.9869221,0.0061151553,0.0014342418,0.002696259,0.0025747558,0.000257446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061482526,0.00063378253,0.0017414627,0.0021745625,0.0005660213,0.0022284354,0.0024105476,0.0015196169,0.0016980012],"category_scores_gemma":[0.034458276,0.00037388082,0.0007355091,0.001920078,0.0014826447,0.0033660287,0.0018968162,0.0013082961,0.00086140406],"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":[0.00059167854,0.00017461176,0.0059298603,0.00020483424,0.00019249656,0.00011355223,0.00019682408,0.4678968,0.0065094726,0.078358285,0.0034006042,0.43643096],"study_design_scores_gemma":[0.000012589295,0.000060710612,0.0009259572,0.000013479731,0.000014442123,0.000080993006,0.000019900825,0.97118706,0.0023453531,0.024300655,0.0010125183,0.000026393893],"about_ca_topic_score_codex":0.0032083094,"about_ca_topic_score_gemma":0.0016697159,"teacher_disagreement_score":0.0061482526,"about_ca_system_score_codex":0.0013727909,"about_ca_system_score_gemma":0.0008844736,"threshold_uncertainty_score":0.032515466},"labels":[],"label_agreement":null},{"id":"W4399311157","doi":"10.4310/22-sii773","title":"$L_1$-regularized functional support vector machine","year":2024,"lang":"en","type":"article","venue":"Statistics and Its Interface","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Support vector machine; Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Machine learning","score_opus":0.016227792026938293,"score_gpt":0.2649567671262607,"score_spread":0.2487289750993224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399311157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009142364,0.00020208846,0.98973477,0.0002079974,0.000028618346,0.000024117156,0.00006749712,0.00027930396,0.00031327098],"genre_scores_gemma":[0.49991235,0.0005790907,0.49367556,0.00034965947,0.0002364536,0.0004414549,0.0008644219,0.00020241427,0.003738682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99884856,0.0005856438,0.00006862739,0.00023246677,0.0001818966,0.0000828335],"domain_scores_gemma":[0.9969543,0.0018853458,0.00023614302,0.00025709125,0.0005829099,0.00008410967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029092154,0.0009065921,0.0015631417,0.00064374943,0.0003406722,0.0007706011,0.0014939565,0.0015209357,0.0018443062],"category_scores_gemma":[0.01041708,0.00035319693,0.000835405,0.00076317193,0.00089624565,0.001204244,0.0009316702,0.0015809641,0.0010018613],"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":[0.00036987106,0.00015900939,0.0028723453,0.0003318506,0.00010527204,0.00017027183,0.0001287183,0.5429934,0.00739387,0.05412116,0.0063720183,0.38498214],"study_design_scores_gemma":[0.0000040237983,0.000029817515,0.00016290597,0.0000067385304,0.0000033238234,0.00002023541,0.000003928882,0.9936918,0.00044652904,0.005282406,0.00034108624,0.0000072048156],"about_ca_topic_score_codex":0.0015811282,"about_ca_topic_score_gemma":0.0010368668,"teacher_disagreement_score":0.0029092154,"about_ca_system_score_codex":0.00046535544,"about_ca_system_score_gemma":0.000919587,"threshold_uncertainty_score":0.015385568},"labels":[],"label_agreement":null},{"id":"W4399695341","doi":"10.48550/arxiv.2406.08880","title":"Jackknife inference with two-way clustering","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada; Danmarks Grundforskningsfond; National Research Foundation","keywords":"Jackknife resampling; Inference; Cluster analysis; Computer science; Artificial intelligence; Mathematics; Data mining; Statistics","score_opus":0.06961469638243965,"score_gpt":0.19934719196584927,"score_spread":0.1297324955834096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399695341","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002721049,0.000077020246,0.9954614,0.000078168996,0.000065175074,0.00010835483,0.00017031642,0.00051701773,0.0008014703],"genre_scores_gemma":[0.10757058,0.00013354434,0.88630563,0.0003911335,0.00009105057,0.0011202925,0.0010169448,0.00083709415,0.0025337718],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.94633824,0.038156617,0.002120635,0.008135061,0.004156423,0.0010930145],"domain_scores_gemma":[0.8891326,0.07100545,0.004473867,0.02718319,0.00724402,0.0009608594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.039678175,0.0017988726,0.0035710826,0.0035839004,0.0034755971,0.0043868907,0.005168518,0.0033822048,0.009446872],"category_scores_gemma":[0.21781364,0.0018959335,0.003701989,0.0049772155,0.0031214992,0.004986829,0.0043623033,0.0066854116,0.0029309455],"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":[0.0009799163,0.00034931986,0.014598554,0.0010814511,0.0020528913,0.00069163844,0.0024648656,0.18003374,0.0021838956,0.4879811,0.027146123,0.2804365],"study_design_scores_gemma":[0.0001589436,0.00008810961,0.001760939,0.00017344145,0.00019435084,0.00022272256,0.0002747141,0.5347535,0.0029014458,0.44594058,0.013395113,0.00013613797],"about_ca_topic_score_codex":0.008398709,"about_ca_topic_score_gemma":0.010695574,"teacher_disagreement_score":0.039678175,"about_ca_system_score_codex":0.0017806158,"about_ca_system_score_gemma":0.0038451636,"threshold_uncertainty_score":0.20984071},"labels":[],"label_agreement":null},{"id":"W4399801303","doi":"10.1109/tnnls.2024.3408208","title":"Bi-Level Spectral Feature Selection","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Cluster analysis; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Linear classifier; Data mining; Feature (linguistics); Machine learning","score_opus":0.017814326819717213,"score_gpt":0.23515123502782148,"score_spread":0.21733690820810428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399801303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019427892,0.00020627168,0.97787184,0.00008369868,0.000030454625,0.00009185177,0.00017218298,0.0012590198,0.0008567815],"genre_scores_gemma":[0.44626507,0.00025820185,0.5457004,0.00028250372,0.000099052544,0.0004922587,0.0023007672,0.00034882274,0.004252888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855095,0.00034783248,0.00009009101,0.00035266674,0.00049834274,0.00016012101],"domain_scores_gemma":[0.9987538,0.0002877314,0.00010803675,0.00021088636,0.00059150805,0.00004813448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013998909,0.001237585,0.0015233053,0.001976646,0.00063361524,0.000867499,0.0012960586,0.00086920965,0.0020663962],"category_scores_gemma":[0.0036369283,0.00028335393,0.0012775596,0.0016236787,0.0005823,0.0012507573,0.0010889895,0.0007471681,0.0014367426],"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":[0.00038162735,0.00032699865,0.004086427,0.00018582144,0.00015353071,0.0001156334,0.00011498129,0.06729437,0.046393126,0.0057251197,0.009635441,0.86558694],"study_design_scores_gemma":[0.000043239048,0.00017520468,0.0030207161,0.000020735126,0.000053950705,0.00019493612,0.00006758828,0.9595877,0.024103804,0.0076535963,0.0050351224,0.00004349182],"about_ca_topic_score_codex":0.0017808082,"about_ca_topic_score_gemma":0.0026012806,"teacher_disagreement_score":0.0020663962,"about_ca_system_score_codex":0.00041747713,"about_ca_system_score_gemma":0.0010652954,"threshold_uncertainty_score":0.0074034333},"labels":[],"label_agreement":null},{"id":"W4399847064","doi":"10.1007/s41060-024-00589-8","title":"Twin neural network improved k-nearest neighbor regression","year":2024,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Regional Municipality of Waterloo; Perimeter Institute; University of Waterloo","funders":"Mitacs","keywords":"Artificial neural network; Regression; k-nearest neighbors algorithm; Artificial intelligence; Pattern recognition (psychology); Computer science; Statistics; Mathematics","score_opus":0.04646352333519553,"score_gpt":0.34085379085422907,"score_spread":0.2943902675190335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399847064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039582685,0.0011217631,0.9554034,0.00016944481,0.00049102306,0.00003932122,0.00013365141,0.0007362973,0.0023224473],"genre_scores_gemma":[0.5965802,0.0006446919,0.38702708,0.00018797314,0.00019975776,0.000099083794,0.0009032664,0.00035428663,0.014003598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843377,0.000498161,0.00011769517,0.0003374499,0.0004711481,0.00014179212],"domain_scores_gemma":[0.9978927,0.0005975317,0.000098104625,0.00031372978,0.0010229807,0.00007489509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020980882,0.0006021248,0.0018912841,0.0008346225,0.0006656893,0.0011689228,0.0023206396,0.0013521893,0.0033447538],"category_scores_gemma":[0.0050862636,0.00049105956,0.0012162101,0.0014912793,0.00046704995,0.0017932585,0.0017230969,0.0019044634,0.001425962],"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":[0.00069785496,0.00030993955,0.0039542965,0.00019504143,0.0003438599,0.00016858868,0.000106075255,0.42540303,0.0060119946,0.013529714,0.009802698,0.53947693],"study_design_scores_gemma":[0.0000059093227,0.000020267447,0.00015103524,0.0000035345554,0.000016412843,0.000025593836,0.0000068965064,0.99787295,0.00071629346,0.0007689105,0.00040721337,0.0000050335284],"about_ca_topic_score_codex":0.0075265504,"about_ca_topic_score_gemma":0.006759384,"teacher_disagreement_score":0.0075265504,"about_ca_system_score_codex":0.00055504567,"about_ca_system_score_gemma":0.0012695779,"threshold_uncertainty_score":0.014965475},"labels":[],"label_agreement":null},{"id":"W4399855192","doi":"10.18280/isi.290338","title":"Facial Expression Recognition Using Data Augmentation and Transfer Learning","year":2024,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"Facial expression recognition; Transfer of learning; Computer science; Facial expression; Artificial intelligence; Psychology; Pattern recognition (psychology); Speech recognition; Facial recognition system","score_opus":0.050829658475946504,"score_gpt":0.27670687965115653,"score_spread":0.22587722117521003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399855192","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113599345,0.0010810504,0.86937654,0.0005566517,0.00038455785,0.00020213432,0.0005845588,0.004719218,0.009496035],"genre_scores_gemma":[0.77310526,0.00091650034,0.21150064,0.0003339389,0.00011889709,0.00020905554,0.0016064487,0.00018799798,0.012021279],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996339,0.000054554363,0.000016580074,0.00010961796,0.00012669992,0.00005872252],"domain_scores_gemma":[0.99981576,0.000035594487,0.000015285337,0.00005561623,0.00006865114,0.000009184492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004854262,0.00062356296,0.0004863071,0.0006535278,0.00019858616,0.00044812117,0.0007686225,0.00039307371,0.0024529512],"category_scores_gemma":[0.0008708971,0.00019756272,0.0008990669,0.0006128696,0.00033237477,0.00075726764,0.00066976144,0.0007545843,0.0011813916],"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":[0.00018242109,0.00017112591,0.0014698592,0.00006487549,0.00006723613,0.00010694161,0.00007513483,0.036578085,0.05385075,0.0016789736,0.0051556085,0.90059894],"study_design_scores_gemma":[0.000012650098,0.00017123281,0.003263087,0.000023760322,0.000039505234,0.00022515365,0.000061473205,0.93594515,0.050812982,0.0032579252,0.0061613517,0.000025683146],"about_ca_topic_score_codex":0.0026754858,"about_ca_topic_score_gemma":0.002557363,"teacher_disagreement_score":0.0026754858,"about_ca_system_score_codex":0.00037923973,"about_ca_system_score_gemma":0.00043088023,"threshold_uncertainty_score":0.008205891},"labels":[],"label_agreement":null},{"id":"W4400221954","doi":"10.4230/lipics.esa.2024.100","title":"Fully Dynamic k-Means Coreset in Near-Optimal Update Time","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":"Agence Nationale de la Recherche; Austrian Science Fund; European Commission; Institute of Science and Technology Austria","keywords":"Computer science; Economics","score_opus":0.028275985350301937,"score_gpt":0.1837184589021733,"score_spread":0.15544247355187138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400221954","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075438194,0.0007180683,0.9079907,0.0013224186,0.00019062278,0.0002414943,0.0007203908,0.00886899,0.004509161],"genre_scores_gemma":[0.35147014,0.00024458717,0.63766736,0.00054497673,0.00014289451,0.00032141793,0.0027526794,0.0011911665,0.00566473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971334,0.00044521104,0.00018635794,0.0008402254,0.0010752628,0.0003195558],"domain_scores_gemma":[0.99379325,0.0024696463,0.00036850508,0.002357062,0.00076588965,0.00024553327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017112609,0.0012625247,0.0022586673,0.0009383963,0.0013817074,0.0023664662,0.0038874997,0.0018565349,0.00566139],"category_scores_gemma":[0.01245041,0.001078226,0.0007600178,0.0026401018,0.0013236885,0.0066640466,0.0036954768,0.0022708317,0.002473064],"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":[0.0027682877,0.0006089637,0.0033503005,0.0004615509,0.00017537286,0.00043926644,0.0006986786,0.3533032,0.023382993,0.036156107,0.03688,0.5417753],"study_design_scores_gemma":[0.00011774809,0.00011749625,0.00035172535,0.000012118177,0.00001762102,0.00022213165,0.000161432,0.96374696,0.00615883,0.024737868,0.0043362365,0.000019677493],"about_ca_topic_score_codex":0.006263159,"about_ca_topic_score_gemma":0.009499468,"teacher_disagreement_score":0.006263159,"about_ca_system_score_codex":0.0014246948,"about_ca_system_score_gemma":0.0027316124,"threshold_uncertainty_score":0.018939257},"labels":[],"label_agreement":null},{"id":"W4400447955","doi":"10.1109/taffc.2024.3424882","title":"Exploring the Boundaries of Semi-Supervised Facial Expression Recognition Using In-Distribution, Out-of-Distribution, and Unconstrained Data","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Affective Computing","topic":"Face and Expression Recognition","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":"Queen's University","funders":"","keywords":"Facial expression; Facial expression recognition; Distribution (mathematics); Pattern recognition (psychology); Emotion recognition; Artificial intelligence; Expression (computer science); Computer science; Speech recognition; Facial recognition system; Mathematics","score_opus":0.11736365231658848,"score_gpt":0.3011579387551304,"score_spread":0.18379428643854195,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400447955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17712954,0.0029489403,0.8067197,0.0017545931,0.00018278712,0.00027712467,0.00044440254,0.0020557942,0.008487139],"genre_scores_gemma":[0.8582748,0.0009501001,0.13351305,0.0009359866,0.0001703858,0.00025233312,0.0016794902,0.00041416066,0.0038097606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99452543,0.002457164,0.00022544924,0.0015455297,0.00087119715,0.00037527658],"domain_scores_gemma":[0.98869413,0.0072552813,0.00070409646,0.0017739293,0.0011706271,0.00040193935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009125909,0.0019064095,0.00139583,0.0012864494,0.000857142,0.0021377099,0.0026019067,0.0022016387,0.001288877],"category_scores_gemma":[0.020467091,0.00063239597,0.0012029902,0.00058339746,0.003199982,0.00416025,0.002845789,0.0038914757,0.0012063064],"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":[0.0013632396,0.0009723856,0.01718326,0.000679973,0.00024300184,0.0004228574,0.0009525332,0.23502098,0.022924012,0.016738737,0.013035551,0.6904635],"study_design_scores_gemma":[0.000032058986,0.00018784913,0.0033039085,0.00011403529,0.000032581112,0.0002642,0.00025647666,0.9541374,0.01240698,0.026241591,0.0029834525,0.000039370625],"about_ca_topic_score_codex":0.0030730255,"about_ca_topic_score_gemma":0.004577504,"teacher_disagreement_score":0.009125909,"about_ca_system_score_codex":0.0013982994,"about_ca_system_score_gemma":0.0014083822,"threshold_uncertainty_score":0.048262954},"labels":[],"label_agreement":null},{"id":"W4400733833","doi":"10.1007/s11222-024-10467-9","title":"Sparse and geometry-aware generalisation of the mutual information for joint discriminative clustering and feature selection","year":2024,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Face and Expression Recognition","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":"Université Laval; Centre hospitalier de l'Université Laval","funders":"Horizon 2020; Agence Nationale de la Recherche","keywords":"Discriminative model; Mutual information; Cluster analysis; Feature selection; Pattern recognition (psychology); Artificial intelligence; Joint (building); Feature (linguistics); Mathematics; Selection (genetic algorithm); Computer science; Data mining; Engineering","score_opus":0.018794132973712088,"score_gpt":0.25028350275152933,"score_spread":0.23148936977781726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400733833","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033806285,0.00010373531,0.9956806,0.000072249626,0.00001405169,0.00001515349,0.00008239686,0.00023409461,0.00041706694],"genre_scores_gemma":[0.3675449,0.00066822197,0.6234395,0.00024726856,0.00024499986,0.00024752773,0.0019302036,0.00050537585,0.005171931],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884295,0.00040807092,0.00005281341,0.00022530144,0.00037463766,0.0000961973],"domain_scores_gemma":[0.99827754,0.00082732405,0.00014161857,0.00036615515,0.00031865854,0.00006869113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012828283,0.00082312763,0.0013312731,0.0011865145,0.00036451692,0.0008417608,0.0022071765,0.0009799728,0.0022357171],"category_scores_gemma":[0.005809281,0.0006157843,0.001162705,0.0017356623,0.00095866574,0.0016922398,0.002108929,0.0014230215,0.0009610957],"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":[0.00022422039,0.000117809956,0.0009530516,0.00019705424,0.00014185949,0.000098909484,0.00016460872,0.6605139,0.018025186,0.07402129,0.006431516,0.2391106],"study_design_scores_gemma":[0.0000050090825,0.000023847684,0.00031963582,0.000004781031,0.000007729564,0.000036317488,0.000008341423,0.98206043,0.0011063039,0.015517556,0.0008999648,0.00001013655],"about_ca_topic_score_codex":0.0053114644,"about_ca_topic_score_gemma":0.009549071,"teacher_disagreement_score":0.0053114644,"about_ca_system_score_codex":0.0008316114,"about_ca_system_score_gemma":0.0010402189,"threshold_uncertainty_score":0.010561109},"labels":[],"label_agreement":null},{"id":"W4400771743","doi":"10.1109/eiceeai60672.2023.10590608","title":"Deep Feature Extraction Framework Based on DNN for Enhancing Mirai Attachment Classification in Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Computer science; Feature extraction; Artificial intelligence; Machine learning; Artificial neural network; Deep learning; Pattern recognition (psychology)","score_opus":0.0315724810496687,"score_gpt":0.32149157876066,"score_spread":0.2899190977109913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400771743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09540041,0.0012779087,0.89433604,0.0004023437,0.00025150916,0.00015680403,0.00044229117,0.0034251567,0.0043075634],"genre_scores_gemma":[0.78333557,0.00077549653,0.20665789,0.000375136,0.00010905489,0.00018713124,0.0012471515,0.00010360253,0.00720903],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977916,0.000030037994,0.000016702934,0.000064824606,0.00006288124,0.000046360456],"domain_scores_gemma":[0.99977726,0.00006176459,0.000026241214,0.000018311162,0.00010221653,0.0000142079825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063879526,0.0009780057,0.0006178725,0.0009709982,0.000305939,0.0005265817,0.00092904235,0.00072707067,0.0012282109],"category_scores_gemma":[0.0009463423,0.00028213815,0.00064251246,0.0006296166,0.0002469669,0.00096033554,0.0005301618,0.0010825811,0.00068077457],"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":[0.00026748786,0.00047090327,0.0063301153,0.0001382871,0.00012658909,0.00036721022,0.00011435389,0.30979848,0.029427037,0.004917796,0.0070048654,0.6410368],"study_design_scores_gemma":[0.0000033098001,0.00003008948,0.0004591092,0.000006758106,0.000012590177,0.000023849683,0.0000067331066,0.9945695,0.0033767147,0.00091243436,0.0005926623,0.000006163051],"about_ca_topic_score_codex":0.009125586,"about_ca_topic_score_gemma":0.010411689,"teacher_disagreement_score":0.009125586,"about_ca_system_score_codex":0.000792242,"about_ca_system_score_gemma":0.0006683841,"threshold_uncertainty_score":0.018144965},"labels":[],"label_agreement":null},{"id":"W4401122007","doi":"10.1027/1618-3169/a000611","title":"A Feature-Space Theory of the Production Effect in Recognition","year":2024,"lang":"en","type":"article","venue":"Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"","keywords":"Feature (linguistics); Space (punctuation); Production (economics); Computer science; Artificial intelligence; Pattern recognition (psychology); Linguistics; Philosophy; Economics; Microeconomics","score_opus":0.028378057409833093,"score_gpt":0.3517948978036049,"score_spread":0.32341684039377183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401122007","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2738277,0.0005922664,0.6947782,0.0027740982,0.00013121439,0.00009367235,0.00025782423,0.00056844717,0.026976563],"genre_scores_gemma":[0.9654655,0.00015621212,0.032280184,0.00023504152,0.00006124085,0.00007423107,0.00007213602,0.00007491802,0.001580581],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99931085,0.00023560598,0.00004240299,0.00017441041,0.00019356832,0.000043172193],"domain_scores_gemma":[0.9945363,0.0038351982,0.00037151563,0.00085216743,0.0002959564,0.00010895044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016677434,0.00040399423,0.00043658278,0.0007062588,0.00029196986,0.0014082937,0.00083924475,0.00072803965,0.00881785],"category_scores_gemma":[0.006789675,0.00037803393,0.00077658123,0.00031103604,0.0024530164,0.0036643005,0.001369373,0.0011177538,0.0005947698],"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":[0.00073060713,0.00021671073,0.0050257565,0.0003191438,0.00009645716,0.00039047794,0.00058484456,0.021008026,0.085373394,0.77312475,0.0015311511,0.11159865],"study_design_scores_gemma":[0.00012286272,0.00037480966,0.010973201,0.000026159132,0.000058699523,0.0007195285,0.00010025679,0.1395109,0.01797178,0.8284248,0.001671946,0.00004510454],"about_ca_topic_score_codex":0.00031918898,"about_ca_topic_score_gemma":0.00014738468,"teacher_disagreement_score":0.00881785,"about_ca_system_score_codex":0.00062358147,"about_ca_system_score_gemma":0.00025150934,"threshold_uncertainty_score":0.029498637},"labels":[],"label_agreement":null},{"id":"W4401262713","doi":"10.1016/j.physa.2024.129997","title":"Dual-dual subspace learning with low-rank consideration for feature selection","year":2024,"lang":"en","type":"article","venue":"Physica A Statistical Mechanics and its Applications","topic":"Face and Expression Recognition","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":"University of Regina; Seneca Polytechnic","funders":"","keywords":"Feature selection; Dimensionality reduction; Artificial intelligence; Computer science; Pattern recognition (psychology); Matrix decomposition; Feature learning; Non-negative matrix factorization; Curse of dimensionality; Subspace topology; Regularization (linguistics); Feature (linguistics); Machine learning; Benchmark (surveying)","score_opus":0.010812504389142623,"score_gpt":0.26323279728654997,"score_spread":0.25242029289740736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401262713","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004984919,0.00037524177,0.99359244,0.00012551525,0.000035450063,0.000014150217,0.000049710394,0.00011944162,0.00070297846],"genre_scores_gemma":[0.4034437,0.0012342317,0.5851034,0.00036925855,0.000370778,0.00026005358,0.0009789945,0.00020155187,0.008038085],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989396,0.00039715477,0.000055609846,0.00017980582,0.00032900256,0.00009888335],"domain_scores_gemma":[0.99875045,0.00049317,0.00008234344,0.00021174901,0.00036929772,0.00009286144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013437184,0.00084034435,0.0015557426,0.00085154816,0.0005324734,0.0012527884,0.0012855522,0.001067882,0.0031294893],"category_scores_gemma":[0.0039686887,0.00045028704,0.0009430804,0.0012670832,0.00084573164,0.0016632883,0.0019967535,0.001583357,0.0012463145],"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":[0.0007031702,0.0004192419,0.0016947073,0.0005073546,0.00024447613,0.00018911352,0.00016267863,0.2116525,0.029783929,0.098274134,0.01476031,0.6416085],"study_design_scores_gemma":[0.000012641823,0.000058473262,0.00016241179,0.000007526565,0.000014806677,0.000059888862,0.0000133107105,0.98133904,0.001813622,0.015090373,0.001413254,0.000014678427],"about_ca_topic_score_codex":0.0014115329,"about_ca_topic_score_gemma":0.0012597896,"teacher_disagreement_score":0.0031294893,"about_ca_system_score_codex":0.00033417344,"about_ca_system_score_gemma":0.0009945086,"threshold_uncertainty_score":0.010469139},"labels":[],"label_agreement":null},{"id":"W4401543053","doi":"10.1007/978-981-97-5594-3_2","title":"Hyperspectral Face Recognition via Existing 2D Face Recognition Methods","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Hyperspectral imaging; Facial recognition system; Computer science; Face (sociological concept); Three-dimensional face recognition; Artificial intelligence; Pattern recognition (psychology); Face Recognition Grand Challenge; Face detection; Computer vision; Speech recognition","score_opus":0.06131487703917009,"score_gpt":0.32125570511951734,"score_spread":0.25994082808034724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401543053","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044015422,0.00044641143,0.9856775,0.00008282286,0.00012232442,0.00006971757,0.0002572886,0.0017817137,0.0071606534],"genre_scores_gemma":[0.049937513,0.0014702766,0.92599297,0.00024811263,0.00015211494,0.00018675071,0.0011434524,0.00032790212,0.020540923],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995933,0.000031324606,0.00001289925,0.00009409043,0.00023714891,0.00003128099],"domain_scores_gemma":[0.9997899,0.000042338965,0.000013112661,0.00007688291,0.00006950259,0.000008264287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027362382,0.00093410106,0.0007410043,0.0011972522,0.00033517275,0.001068176,0.0009926358,0.0007387745,0.0141294],"category_scores_gemma":[0.00048233932,0.00050008437,0.00092153414,0.0010163084,0.00031302113,0.0013523999,0.0009833295,0.0006751045,0.010631922],"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":[0.00005410879,0.00008260446,0.00033176108,0.0001408095,0.000048090118,0.00004350866,0.00002720844,0.006378153,0.10988938,0.0032603424,0.007145389,0.8725987],"study_design_scores_gemma":[0.000018974753,0.00015194727,0.0047593242,0.000082255625,0.00009366344,0.0014946464,0.000088172965,0.6878769,0.22804677,0.010858255,0.06643687,0.000092174225],"about_ca_topic_score_codex":0.0010836197,"about_ca_topic_score_gemma":0.0029185528,"teacher_disagreement_score":0.0141294,"about_ca_system_score_codex":0.0002214164,"about_ca_system_score_gemma":0.00033014454,"threshold_uncertainty_score":0.047267556},"labels":[],"label_agreement":null},{"id":"W4401598201","doi":"10.17615/w2q1-1632","title":"Robust multicategory support vector machines using difference convex algorithm","year":2024,"lang":"en","type":"article","venue":"UNC Libraries","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Support vector machine; Regular polygon; Algorithm; Computer science; Mathematics; Artificial intelligence; Pattern recognition (psychology); Mathematical optimization; Geometry","score_opus":0.04644104226973954,"score_gpt":0.2459782912603636,"score_spread":0.19953724899062408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401598201","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008455446,0.00019978339,0.9898414,0.00009903152,0.00004988658,0.000024967458,0.00006329862,0.0007010553,0.00056514004],"genre_scores_gemma":[0.36499673,0.00023052307,0.6276349,0.0001736926,0.0001008157,0.00017267423,0.00096894865,0.0004256249,0.005296101],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976947,0.00054574525,0.00016016215,0.00053293194,0.0008663843,0.00020000708],"domain_scores_gemma":[0.9964239,0.0013628853,0.00025533416,0.00065261405,0.0011920234,0.00011322057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002229882,0.0010674252,0.0024597207,0.0010518834,0.0005299596,0.0017318223,0.0026293509,0.0014002742,0.0032267391],"category_scores_gemma":[0.007003169,0.000839046,0.0012314046,0.0013830272,0.0007907915,0.0019319648,0.0022950198,0.0024682425,0.0016070841],"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":[0.00059808744,0.00015675137,0.0011118852,0.00020829531,0.00017966972,0.00007961079,0.00006356624,0.21649289,0.014498904,0.014879524,0.0069312793,0.74479955],"study_design_scores_gemma":[0.000005535043,0.000033880973,0.00017942974,0.0000043831874,0.000007657927,0.000023866993,0.0000065295167,0.9936487,0.002333725,0.0032028202,0.00054378505,0.00000971194],"about_ca_topic_score_codex":0.003495942,"about_ca_topic_score_gemma":0.003132067,"teacher_disagreement_score":0.003495942,"about_ca_system_score_codex":0.00080843054,"about_ca_system_score_gemma":0.0014915875,"threshold_uncertainty_score":0.011792898},"labels":[],"label_agreement":null},{"id":"W4401831252","doi":"10.18280/ria.380414","title":"Facial Expression Recognition Using Deep Learning and Neural Embeddings","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Face and Expression Recognition","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":"","keywords":"Facial expression recognition; Artificial intelligence; Facial expression; Deep learning; Computer science; Pattern recognition (psychology); Facial recognition system","score_opus":0.04945404289006257,"score_gpt":0.2972744789225931,"score_spread":0.24782043603253057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401831252","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21162342,0.002139618,0.77125305,0.0006897931,0.0003767764,0.00013493326,0.00073708274,0.0027043188,0.010340991],"genre_scores_gemma":[0.8723848,0.0010877589,0.116579734,0.00021105619,0.00007514597,0.00010785475,0.0014106819,0.000094786716,0.008048051],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996057,0.000098514465,0.000018257162,0.00010470113,0.000105769046,0.00006708768],"domain_scores_gemma":[0.99977094,0.000069368165,0.000030144443,0.000036800702,0.00007854733,0.00001423385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005850181,0.00076927495,0.0004180936,0.00064486056,0.00015870186,0.00063360506,0.000490515,0.00038456108,0.002148565],"category_scores_gemma":[0.0014361385,0.00019001594,0.0005410345,0.00047317657,0.00023689734,0.0010448393,0.0006793999,0.0008547091,0.0010755393],"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":[0.0003447268,0.00023554038,0.0055018254,0.00011219609,0.00011047077,0.000112196736,0.000118505624,0.07759462,0.03533866,0.004661564,0.0063831545,0.8694866],"study_design_scores_gemma":[0.000006156164,0.000083199026,0.0030118485,0.000026773465,0.000027814203,0.00007474553,0.000074862946,0.9790658,0.011982225,0.0035123401,0.002120053,0.000014186352],"about_ca_topic_score_codex":0.002293039,"about_ca_topic_score_gemma":0.0028467556,"teacher_disagreement_score":0.002293039,"about_ca_system_score_codex":0.00045440602,"about_ca_system_score_gemma":0.0002791481,"threshold_uncertainty_score":0.0071876645},"labels":[],"label_agreement":null},{"id":"W4402041383","doi":"10.1063/5.0229433","title":"Study and analysis on an EEG signal based facial emotion recognition","year":2024,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Face and Expression Recognition","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Electroencephalography; Computer science; Emotion recognition; Speech recognition; SIGNAL (programming language); Pattern recognition (psychology); Facial recognition system; Artificial intelligence; Psychology; Neuroscience","score_opus":0.042232469600302136,"score_gpt":0.28047287342657745,"score_spread":0.23824040382627532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402041383","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9139558,0.0016183928,0.07542124,0.00022242495,0.0001386648,0.00012752014,0.00028168465,0.00014021399,0.008094037],"genre_scores_gemma":[0.98270595,0.0008771433,0.0111557,0.000048692575,0.000078510806,0.000043146967,0.00023009312,0.000023144827,0.004837626],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999232,0.000012637456,0.0000049246855,0.000020846535,0.000028007653,0.00001041587],"domain_scores_gemma":[0.99983895,0.000055390807,0.000008521117,0.000012733217,0.00007349212,0.000010953839],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012582261,0.00018725815,0.00010861618,0.00024840364,0.00013762576,0.0002042228,0.00013652781,0.00017206762,0.002017071],"category_scores_gemma":[0.00041251982,0.000039199003,0.00020151192,0.00027136592,0.00009762311,0.00023600887,0.000078148434,0.00014094234,0.00037997944],"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":[0.00056072604,0.00018399574,0.011365287,0.00025788983,0.00007863035,0.00078421267,0.0004351648,0.0007652531,0.7417785,0.0009591529,0.0010307411,0.24180052],"study_design_scores_gemma":[0.00007389846,0.0022760963,0.4787626,0.00006441114,0.0004934151,0.0047016824,0.0014939501,0.059697583,0.43611714,0.001374261,0.014872939,0.00007204583],"about_ca_topic_score_codex":0.0009915786,"about_ca_topic_score_gemma":0.0007571977,"teacher_disagreement_score":0.002017071,"about_ca_system_score_codex":0.00006763228,"about_ca_system_score_gemma":0.00009385556,"threshold_uncertainty_score":0.006747782},"labels":[],"label_agreement":null},{"id":"W4402508706","doi":"10.1109/tetci.2024.3451562","title":"Dual Completion Learning for Incomplete Multi-View Clustering","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Face and Expression Recognition","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":"University of Ottawa","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Cluster analysis; Dual (grammatical number); Computer science; Artificial intelligence; Philosophy","score_opus":0.07737740757536209,"score_gpt":0.34703392899844604,"score_spread":0.26965652142308394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402508706","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004111734,0.0002450412,0.99475527,0.00009244907,0.000024284658,0.000021875137,0.00008459043,0.00033166315,0.0003332338],"genre_scores_gemma":[0.31099594,0.00082937843,0.68111944,0.00038425464,0.00020175453,0.00024705354,0.0026472085,0.0003886881,0.0031862713],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978726,0.00066561095,0.00011227644,0.00062321394,0.00051302236,0.00021324742],"domain_scores_gemma":[0.99619126,0.001278311,0.00035555993,0.00090158364,0.0009870136,0.00028629528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003205133,0.0018182669,0.002575798,0.001960927,0.0008975763,0.0016871796,0.0030425903,0.0019185169,0.001975757],"category_scores_gemma":[0.0083806515,0.0008001728,0.0020176328,0.0022902263,0.0015903496,0.0028544432,0.0026354538,0.003825352,0.0011377176],"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":[0.00028540025,0.00018598289,0.0023335058,0.0003728094,0.0002942322,0.00019663124,0.00037950746,0.6441364,0.0075131343,0.033490527,0.010850872,0.29996094],"study_design_scores_gemma":[0.0000065792287,0.000021404969,0.0001298384,0.000009057178,0.000009580356,0.000026059846,0.000025072553,0.98664504,0.00092851574,0.01144574,0.0007384598,0.000014721314],"about_ca_topic_score_codex":0.009359794,"about_ca_topic_score_gemma":0.009091925,"teacher_disagreement_score":0.009359794,"about_ca_system_score_codex":0.0015307099,"about_ca_system_score_gemma":0.0025582595,"threshold_uncertainty_score":0.018610656},"labels":[],"label_agreement":null},{"id":"W4402968148","doi":"10.1109/ap-s/inc-usnc-ursi52054.2024.10686804","title":"A CNN-based Material Classification Approach Using Heatmap of a Dual-band Microwave Sensor","year":2024,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Polytechnique Montréal; Canada Research Chairs; University of Toronto; University of Alberta","funders":"","keywords":"Microwave; Computer science; Dual (grammatical number); Multi-band device; Electronic engineering; Artificial intelligence; Telecommunications; Engineering","score_opus":0.05050341233649263,"score_gpt":0.27278101593827603,"score_spread":0.2222776036017834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402968148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12536585,0.00053972565,0.86237437,0.00027695158,0.00016190688,0.000119087905,0.00038869304,0.004998565,0.0057748314],"genre_scores_gemma":[0.7539749,0.00037070323,0.23575208,0.00016874041,0.00005495551,0.00012012811,0.0007434002,0.00019328149,0.008621858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99990654,0.0000070110104,0.0000031441389,0.000031609106,0.000027988066,0.000023669068],"domain_scores_gemma":[0.9999275,0.000011311976,0.000008907454,0.000011172087,0.000034880577,0.000006155966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020001327,0.00071846135,0.0004408485,0.0007997448,0.00019956828,0.0006010374,0.00074070535,0.0005202886,0.002228729],"category_scores_gemma":[0.00032909654,0.00029200493,0.0006390577,0.00053883775,0.00020882713,0.00047897868,0.00036293798,0.00040126045,0.00065620115],"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":[0.0003554304,0.00022820359,0.0028108743,0.00016508304,0.00015427897,0.0002540738,0.000068649504,0.22662608,0.14880046,0.0026146772,0.0049727093,0.61294955],"study_design_scores_gemma":[0.0000023241912,0.00002430178,0.0008622638,0.000004003395,0.000011673373,0.00003278227,0.000009232267,0.9847387,0.013112772,0.0005136125,0.00068087847,0.0000074091863],"about_ca_topic_score_codex":0.0051064612,"about_ca_topic_score_gemma":0.0054792874,"teacher_disagreement_score":0.0051064612,"about_ca_system_score_codex":0.00051463715,"about_ca_system_score_gemma":0.00047234472,"threshold_uncertainty_score":0.010153472},"labels":[],"label_agreement":null},{"id":"W4403219460","doi":"10.11834/jig.210248","title":"Video sequence-based human facial expression recognition using Transformer networks","year":2022,"lang":"en","type":"article","venue":"Journal of Image and Graphics","topic":"Face and Expression Recognition","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":"Computer science; Transformer; Facial expression; Artificial intelligence; Sequence (biology); Facial expression recognition; Pattern recognition (psychology); Computer vision; Facial recognition system; Engineering; Biology; Electrical engineering; Genetics; Voltage","score_opus":0.04652806180588515,"score_gpt":0.28327815208428153,"score_spread":0.23675009027839639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403219460","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1270071,0.0017418285,0.8348532,0.000833854,0.00030691252,0.00018709265,0.00053484045,0.0015212672,0.033013977],"genre_scores_gemma":[0.8936236,0.0012404373,0.09475349,0.00017408765,0.000060760736,0.00010241232,0.0003859461,0.00007731705,0.009581965],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995852,0.000105276995,0.000023174858,0.00013371027,0.00011080458,0.00004187917],"domain_scores_gemma":[0.999798,0.00007609833,0.000023315875,0.00001754411,0.00007323485,0.000011792337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006727579,0.0003791009,0.00034332505,0.000577772,0.0002770202,0.0009152906,0.00042872186,0.00038111035,0.002552194],"category_scores_gemma":[0.0016797483,0.0002161851,0.0004197621,0.0007454281,0.00052010117,0.0019948531,0.0005677003,0.0006332226,0.00058074313],"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":[0.00042429322,0.00012218526,0.007980172,0.00037095853,0.00015768052,0.00037787986,0.0017667778,0.07459263,0.049531553,0.04417996,0.008986825,0.81150895],"study_design_scores_gemma":[0.000053815686,0.00014025338,0.011020191,0.000059695692,0.00011752376,0.00031690253,0.000892113,0.91077703,0.028022885,0.03521492,0.013302576,0.0000821476],"about_ca_topic_score_codex":0.008046129,"about_ca_topic_score_gemma":0.0048478222,"teacher_disagreement_score":0.008046129,"about_ca_system_score_codex":0.0005016362,"about_ca_system_score_gemma":0.00061841216,"threshold_uncertainty_score":0.015998602},"labels":[],"label_agreement":null},{"id":"W4403407057","doi":"","title":"Regularization Functions in Subspace Learning-based Feature Selection: Tutorial","year":2024,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Face and Expression Recognition","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; Toronto Zoo","funders":"","keywords":"Subspace topology; Regularization (linguistics); Feature selection; Computer science; Artificial intelligence; Machine learning; Selection (genetic algorithm); Pattern recognition (psychology); Feature (linguistics)","score_opus":0.008750393884452583,"score_gpt":0.21602310569831126,"score_spread":0.20727271181385867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403407057","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012178087,0.110270694,0.85055405,0.0025523067,0.0042628497,0.00016756731,0.0012144847,0.0025003375,0.027259873],"genre_scores_gemma":[0.019618813,0.15695107,0.71764565,0.0044933893,0.01068645,0.0012042113,0.004666772,0.0036716198,0.08106207],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9994411,0.00015206916,0.000054459226,0.00011419526,0.00020775717,0.000030398547],"domain_scores_gemma":[0.99918765,0.00050752994,0.00003715277,0.000053002514,0.00018334105,0.000031364285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014454962,0.0024174592,0.0014084667,0.0015807991,0.00035871507,0.0015289307,0.001061936,0.0018474329,0.022907978],"category_scores_gemma":[0.0022607462,0.0007642366,0.001512031,0.00271516,0.0007623469,0.0027420307,0.0011319871,0.0039692316,0.020673918],"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":[0.000078885096,0.00015223898,0.00023343587,0.0020571921,0.0001587457,0.00024360245,0.00020949656,0.012038152,0.005548635,0.094475955,0.3080184,0.57678527],"study_design_scores_gemma":[0.000025569263,0.00017768722,0.0012661301,0.0008248685,0.000065493834,0.0011143613,0.000071424925,0.03731392,0.0034232785,0.14126767,0.81434447,0.00010514305],"about_ca_topic_score_codex":0.0012452655,"about_ca_topic_score_gemma":0.0010154016,"teacher_disagreement_score":0.022907978,"about_ca_system_score_codex":0.0006573012,"about_ca_system_score_gemma":0.0005705177,"threshold_uncertainty_score":0.076634824},"labels":[],"label_agreement":null},{"id":"W4403483116","doi":"10.1016/j.ipm.2024.103923","title":"Unsupervised feature selection using sparse manifold learning: Auto-encoder approach","year":2024,"lang":"en","type":"article","venue":"Information Processing & Management","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Seneca Polytechnic; Sunnybrook Health Science Centre","funders":"Graduate University of Advanced Technology","keywords":"Autoencoder; Artificial intelligence; Feature selection; Computer science; Nonlinear dimensionality reduction; Selection (genetic algorithm); Pattern recognition (psychology); Feature learning; Unsupervised learning; Feature (linguistics); Machine learning; Manifold (fluid mechanics); Encoder; Dimensionality reduction; Deep learning; Engineering","score_opus":0.01827452959897996,"score_gpt":0.2442923255483582,"score_spread":0.22601779594937824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403483116","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010285075,0.00014996427,0.98844796,0.000089506844,0.000024396413,0.00002041443,0.00005811716,0.00054298725,0.00038153114],"genre_scores_gemma":[0.50827444,0.00039174702,0.4862122,0.00017186125,0.00013635414,0.00016907195,0.00095830363,0.00021820606,0.00346777],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994149,0.00018990679,0.000028606417,0.00012905468,0.00016786536,0.00006966574],"domain_scores_gemma":[0.9988939,0.00044019427,0.000082749306,0.00019775625,0.00034029968,0.000045082532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008038404,0.00065336644,0.0012205278,0.00075243413,0.0004556617,0.00052971067,0.0011922142,0.00072593143,0.0013432456],"category_scores_gemma":[0.0023119363,0.00045115707,0.0008268873,0.0010534368,0.00048364824,0.0011215765,0.0010636137,0.001238313,0.0006793704],"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":[0.00028091526,0.00031173776,0.0018720246,0.00013178877,0.00016720017,0.0001165947,0.000119737946,0.282731,0.021249749,0.015066461,0.0074382094,0.6705146],"study_design_scores_gemma":[0.0000059815784,0.000033863915,0.00024205985,0.000003221101,0.000009315721,0.00002472019,0.0000071449617,0.9946518,0.0019348774,0.002685325,0.00039524137,0.000006544741],"about_ca_topic_score_codex":0.0037741312,"about_ca_topic_score_gemma":0.0043853023,"teacher_disagreement_score":0.0037741312,"about_ca_system_score_codex":0.0003458033,"about_ca_system_score_gemma":0.0010127549,"threshold_uncertainty_score":0.007504344},"labels":[],"label_agreement":null},{"id":"W4403484636","doi":"10.2139/ssrn.4990872","title":"A Novel Hypergraph Neural Network Combining Multi-View Learning with Density Awareness for Semi-Supervised Node Classification","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Face and Expression Recognition","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":"Hypergraph; Artificial neural network; Node (physics); Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.037110618101863876,"score_gpt":0.27675163603459485,"score_spread":0.23964101793273096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403484636","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016226282,0.00054063986,0.98024344,0.0002334406,0.000104144936,0.000066638444,0.00019643988,0.0014442472,0.00094469724],"genre_scores_gemma":[0.52088004,0.0006227482,0.46917388,0.00061667396,0.00026723676,0.00029723262,0.0013111866,0.00028858997,0.0065423734],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993092,0.00015116193,0.00003073335,0.00026494754,0.00016657922,0.000077469194],"domain_scores_gemma":[0.99902403,0.00039551518,0.00006957404,0.00017023814,0.00026084503,0.00007985859],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007803537,0.0010945445,0.0017043297,0.00121176,0.0005845822,0.001084246,0.003362118,0.0022686902,0.0019226804],"category_scores_gemma":[0.0021409895,0.0007437391,0.0009775865,0.001524247,0.00070398353,0.002225241,0.0021447663,0.0016473267,0.0009491484],"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":[0.0002642454,0.00031608518,0.002060509,0.00015163011,0.00027254247,0.00012744738,0.00011132018,0.34860912,0.012528644,0.007178131,0.00826811,0.62011224],"study_design_scores_gemma":[0.0000038048922,0.000016014794,0.00010654605,0.000003806914,0.000010848334,0.00001308505,0.0000051701795,0.9974783,0.0006463082,0.0015035545,0.00020802874,0.0000045463094],"about_ca_topic_score_codex":0.012045616,"about_ca_topic_score_gemma":0.016285243,"teacher_disagreement_score":0.012045616,"about_ca_system_score_codex":0.0009412433,"about_ca_system_score_gemma":0.0011478836,"threshold_uncertainty_score":0.023950994},"labels":[],"label_agreement":null},{"id":"W4403637162","doi":"10.1109/cosera60846.2024.10720377","title":"OMP-Net: Neural network unrolling of weighted Orthogonal Matching Pursuit","year":2024,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Concordia University","funders":"","keywords":"Matching pursuit; Computer science; Artificial neural network; Matching (statistics); Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.014125494875469376,"score_gpt":0.24461185915883707,"score_spread":0.2304863642833677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403637162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009454069,0.0001559701,0.9878127,0.00013988592,0.000065072236,0.000029725006,0.00003328157,0.00087091426,0.0014383879],"genre_scores_gemma":[0.41819572,0.0003145043,0.5744415,0.0003932123,0.00011267091,0.00019115588,0.0003114351,0.00029649594,0.0057433215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995382,0.000118359865,0.00002289756,0.00009167029,0.0001686615,0.00006027827],"domain_scores_gemma":[0.9992304,0.00036876762,0.00007704733,0.00011007687,0.00017008958,0.00004364814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001162828,0.001005948,0.0008272309,0.00043451865,0.00035637934,0.00066474185,0.0013199225,0.0011516468,0.0026906827],"category_scores_gemma":[0.003923107,0.0003987008,0.0004886236,0.0005379044,0.0009203505,0.001447324,0.0016536423,0.0017938962,0.00085383863],"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":[0.00038792455,0.00015216188,0.0007710663,0.00016010093,0.000079899415,0.0001815469,0.00008827391,0.6656851,0.014704949,0.022189654,0.004810585,0.29078874],"study_design_scores_gemma":[0.0000060969096,0.000031321782,0.00003597837,0.00000412004,0.0000024164747,0.000020265279,0.0000027356066,0.9950229,0.0019208835,0.0024769302,0.00047256635,0.0000038372314],"about_ca_topic_score_codex":0.002052528,"about_ca_topic_score_gemma":0.0026435116,"teacher_disagreement_score":0.0026906827,"about_ca_system_score_codex":0.00046178122,"about_ca_system_score_gemma":0.00083908206,"threshold_uncertainty_score":0.009001195},"labels":[],"label_agreement":null},{"id":"W4403723340","doi":"10.1109/iacis61494.2024.10721631","title":"Support Vector Machine with Tunicate Swarm Optimization Algorithm for Emotion Recognition in Human-Robot Interaction","year":2024,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"CGI (Canada)","funders":"","keywords":"Tunicate; Computer science; Artificial intelligence; Support vector machine; Robot; Human–robot interaction; Swarm behaviour; Algorithm; Computer vision","score_opus":0.03449356307421798,"score_gpt":0.2938799892784036,"score_spread":0.2593864262041856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403723340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03203509,0.0006410954,0.964985,0.00016421771,0.000081315375,0.000054017546,0.000030358397,0.0005465239,0.0014623534],"genre_scores_gemma":[0.75072885,0.0004931061,0.24448557,0.00011600753,0.000056119887,0.0002386876,0.00020250352,0.00004901184,0.0036301245],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996978,0.00008413017,0.000029919282,0.000067032975,0.000086046115,0.00003516381],"domain_scores_gemma":[0.99975485,0.00010387181,0.000028460416,0.000014459753,0.00008874262,0.0000095349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060172897,0.0005509471,0.00061268016,0.00042697502,0.0002475539,0.00048292315,0.0005383011,0.00051851274,0.00089203997],"category_scores_gemma":[0.0012446844,0.00022992703,0.0005367847,0.0004423049,0.0002461305,0.00044643728,0.0003767905,0.0006555176,0.00023988714],"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":[0.00016516272,0.00009704574,0.0022774008,0.000118887736,0.00010940467,0.000098727716,0.00011787393,0.5844217,0.008857267,0.002893436,0.0023564582,0.39848658],"study_design_scores_gemma":[0.0000032657986,0.000023565135,0.00019023837,0.0000026361677,0.0000033502058,0.0000068006498,0.0000080379905,0.9987205,0.0005285254,0.00028498986,0.00022564278,0.0000024346596],"about_ca_topic_score_codex":0.0044371174,"about_ca_topic_score_gemma":0.0022928133,"teacher_disagreement_score":0.0044371174,"about_ca_system_score_codex":0.0003063269,"about_ca_system_score_gemma":0.00047002803,"threshold_uncertainty_score":0.00882256},"labels":[],"label_agreement":null},{"id":"W4403918287","doi":"10.1109/tcyb.2024.3483068","title":"Fast Transfer Learning Method Using Random Layer Freezing and Feature Refinement Strategy","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; University of Windsor; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Feature (linguistics); Layer (electronics); Transfer of learning; Computer science; Transfer (computing); Materials science; Biological system; Artificial intelligence; Nanotechnology; Parallel computing; Biology","score_opus":0.03172700890505439,"score_gpt":0.2925876304386554,"score_spread":0.260860621533601,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403918287","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009176894,0.00013564776,0.9874451,0.00010149398,0.00004062356,0.000067346045,0.000028075536,0.002071198,0.0009335131],"genre_scores_gemma":[0.42634332,0.00020285783,0.56507194,0.00031441957,0.0000701693,0.0004822944,0.00039600048,0.00048849866,0.006630474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999589,0.00007213253,0.000025028477,0.000090465306,0.00016090745,0.000062521656],"domain_scores_gemma":[0.9994159,0.00020121783,0.00005291986,0.00011715148,0.00017909627,0.00003376692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095542875,0.0010937857,0.0009877984,0.0007191114,0.0005308168,0.000498233,0.002318727,0.0010900535,0.003032035],"category_scores_gemma":[0.00248765,0.0005034332,0.00075824396,0.0006187435,0.0007090532,0.0011418838,0.0013052288,0.0016278109,0.0013235095],"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":[0.00017721126,0.00011091025,0.00095430925,0.00012274082,0.00008094327,0.00024518344,0.00015728491,0.5903926,0.014999093,0.009503141,0.005872809,0.37738377],"study_design_scores_gemma":[0.000010827529,0.000022913966,0.000058432233,0.0000028068152,0.0000047715225,0.000025012054,0.000004307863,0.9963348,0.0019278786,0.001186084,0.00041687157,0.000005231918],"about_ca_topic_score_codex":0.008390342,"about_ca_topic_score_gemma":0.0074832262,"teacher_disagreement_score":0.008390342,"about_ca_system_score_codex":0.0007968077,"about_ca_system_score_gemma":0.0016082145,"threshold_uncertainty_score":0.016682982},"labels":[],"label_agreement":null},{"id":"W4403965030","doi":"10.48550/arxiv.2408.03407","title":"Deep Clustering via Distribution Learning","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Cluster analysis; Artificial intelligence; Distribution (mathematics); Computer science; Deep learning; Mathematics","score_opus":0.04261388414991503,"score_gpt":0.18041496868706614,"score_spread":0.13780108453715112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403965030","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007819924,0.00025015234,0.9890309,0.00026034762,0.00002515798,0.00003044127,0.00013749507,0.0011665128,0.0012790418],"genre_scores_gemma":[0.47384924,0.00084106036,0.5127702,0.0006505377,0.00013286299,0.00029405233,0.0019394985,0.00071327906,0.008809376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986613,0.00033061666,0.000057937912,0.00045435675,0.00033658784,0.00015920312],"domain_scores_gemma":[0.99821836,0.000568773,0.00018479282,0.00047184044,0.0004457057,0.000110480665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001727522,0.0011187227,0.0014020744,0.0020454729,0.0009501935,0.0017551249,0.0026961057,0.0017756105,0.0032864301],"category_scores_gemma":[0.0049187,0.00071020896,0.0014071246,0.0019399639,0.0017225868,0.0033880943,0.0026944024,0.0023857835,0.0015048776],"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":[0.00015812539,0.00011659125,0.0023237665,0.00020098455,0.00013664855,0.00007618145,0.00018721959,0.6113874,0.0058428096,0.09190431,0.008700585,0.27896538],"study_design_scores_gemma":[0.000007198513,0.000012646283,0.00019765482,0.000009120719,0.000007423512,0.00002823365,0.000018613118,0.9594514,0.0017124558,0.03724906,0.0012953222,0.000010895624],"about_ca_topic_score_codex":0.0068100565,"about_ca_topic_score_gemma":0.00858202,"teacher_disagreement_score":0.0068100565,"about_ca_system_score_codex":0.002948294,"about_ca_system_score_gemma":0.0020354406,"threshold_uncertainty_score":0.021391511},"labels":[],"label_agreement":null},{"id":"W4404388632","doi":"10.18280/ts.410545","title":"Biometric Face Identification: Utilizing Soft Computing Methods for Feature-Based Recognition","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","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":"Biometrics; Computer science; Facial recognition system; Identification (biology); Artificial intelligence; Face (sociological concept); Feature (linguistics); Pattern recognition (psychology); Soft computing; Three-dimensional face recognition; Computer vision; Speech recognition; Face detection; Artificial neural network","score_opus":0.066541554457275,"score_gpt":0.35587576135347637,"score_spread":0.2893342068962014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404388632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014988865,0.0009460235,0.9804819,0.00020642577,0.00012600035,0.00006735436,0.00011290636,0.00070050114,0.0023700837],"genre_scores_gemma":[0.29749367,0.0019346892,0.68948495,0.00025123754,0.00023113278,0.000187941,0.000306375,0.00015226997,0.009957763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992537,0.00011485882,0.00003978457,0.00012229665,0.0004316977,0.0000376984],"domain_scores_gemma":[0.99936837,0.00021113672,0.000092630464,0.00008687432,0.00021560738,0.000025259069],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007181054,0.00046588428,0.000778544,0.0012384162,0.0003104974,0.001081655,0.0006009349,0.00060735596,0.0024601128],"category_scores_gemma":[0.0015268887,0.00021868345,0.0005020172,0.0012943157,0.00057622575,0.0011373217,0.00068918784,0.00063691224,0.0014636291],"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":[0.000113881164,0.00011561177,0.0012557125,0.00031105513,0.00005971321,0.000064313535,0.000075188065,0.009033093,0.098597094,0.008965106,0.002112428,0.8792968],"study_design_scores_gemma":[0.000021469543,0.00034739752,0.010568769,0.00014690237,0.00009191197,0.00093740906,0.00014152625,0.7618914,0.19025122,0.020104477,0.015377325,0.00012019862],"about_ca_topic_score_codex":0.00093128235,"about_ca_topic_score_gemma":0.0010442397,"teacher_disagreement_score":0.0024601128,"about_ca_system_score_codex":0.00028783016,"about_ca_system_score_gemma":0.00046639683,"threshold_uncertainty_score":0.008229911},"labels":[],"label_agreement":null},{"id":"W4404445448","doi":"10.1609/aiide.v20i1.31872","title":"Generalized Entropy and Solution Information for Measuring Puzzle Difficulty","year":2024,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Canadian Institute for Advanced Research","keywords":"Entropy (arrow of time); Mathematics; Statistical physics; Computer science; Physics; Thermodynamics","score_opus":0.04732470678743708,"score_gpt":0.272570042256081,"score_spread":0.22524533546864392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404445448","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33455566,0.0015631136,0.6472347,0.0008557203,0.00015934979,0.00032840768,0.0013534906,0.0008113299,0.013138234],"genre_scores_gemma":[0.8748606,0.00048140594,0.12142268,0.00012322552,0.000079929676,0.00034436406,0.001083133,0.00017203062,0.0014327036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99513525,0.0018088722,0.00037911325,0.0006512226,0.0017340813,0.0002913884],"domain_scores_gemma":[0.9644018,0.026362974,0.0029729821,0.0038046876,0.001410139,0.0010473244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003938801,0.001292271,0.0010788897,0.003959922,0.00052547763,0.0018081194,0.0013143515,0.0018438069,0.0037681407],"category_scores_gemma":[0.04694069,0.0004064291,0.0009870201,0.0024398654,0.0030259916,0.006716341,0.0026259618,0.0022302584,0.00037179797],"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":[0.0009355592,0.0006685369,0.041911185,0.0007933653,0.0005040126,0.0002143674,0.0009946603,0.56057465,0.011313355,0.207568,0.0041019903,0.1704203],"study_design_scores_gemma":[0.00006967125,0.0003881724,0.015555376,0.00008828707,0.00005804908,0.00015933877,0.00017747813,0.7272743,0.006427415,0.24786718,0.0018277945,0.00010686477],"about_ca_topic_score_codex":0.001061935,"about_ca_topic_score_gemma":0.0012002346,"teacher_disagreement_score":0.003959922,"about_ca_system_score_codex":0.0015473393,"about_ca_system_score_gemma":0.00095399655,"threshold_uncertainty_score":0.020830572},"labels":[],"label_agreement":null},{"id":"W4404459465","doi":"10.1007/s00521-024-10556-w","title":"Representation ensemble learning applied to facial expression recognition","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computational Science and Engineering; Computer science; Facial expression recognition; Representation (politics); Artificial intelligence; Ensemble learning; Expression (computer science); Pattern recognition (psychology); Facial expression; Machine learning; Speech recognition; Facial recognition system","score_opus":0.03135945182487265,"score_gpt":0.30134607785220013,"score_spread":0.2699866260273275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404459465","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028663656,0.0010586571,0.96735704,0.00019476307,0.0002323691,0.000036033627,0.00010839898,0.00089225045,0.0014568429],"genre_scores_gemma":[0.6741258,0.0014830596,0.31406742,0.00018354386,0.00024119897,0.00014347098,0.0007926014,0.00023355278,0.008729474],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993949,0.00020952478,0.000038459904,0.00012416975,0.00015824767,0.000074662705],"domain_scores_gemma":[0.99907684,0.00038191237,0.000043599834,0.00017004546,0.00029665674,0.000030861203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015442175,0.00061941997,0.0010911018,0.0006820344,0.0004793377,0.0006868663,0.00074727554,0.0006426965,0.001940856],"category_scores_gemma":[0.0027077908,0.00029720206,0.00092253654,0.0010860959,0.00028041087,0.00096162606,0.00087902683,0.0015910706,0.00062344],"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":[0.000105231025,0.00011535927,0.0014328661,0.000066964414,0.00024706524,0.000051623505,0.00007913645,0.24523288,0.013118627,0.0055901157,0.0043114857,0.7296486],"study_design_scores_gemma":[0.0000015246787,0.000022581748,0.00041433013,0.0000044988146,0.000018383273,0.000018497845,0.0000079238325,0.994697,0.0023879316,0.001781903,0.0006405607,0.000004903732],"about_ca_topic_score_codex":0.0050653717,"about_ca_topic_score_gemma":0.005431988,"teacher_disagreement_score":0.0050653717,"about_ca_system_score_codex":0.0003820975,"about_ca_system_score_gemma":0.00061279256,"threshold_uncertainty_score":0.010071814},"labels":[],"label_agreement":null},{"id":"W4404565353","doi":"10.1109/eexpolytech62224.2024.10755588","title":"Search, Estimate, and Predict: Efficient Weakly-Supervised Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Supervised learning; Artificial neural network","score_opus":0.013502742404776003,"score_gpt":0.2590627280212287,"score_spread":0.2455599856164527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404565353","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027160617,0.00019345082,0.96832263,0.00015578223,0.000033173586,0.00009646614,0.00014685586,0.0028192424,0.001071781],"genre_scores_gemma":[0.67862016,0.00022772551,0.31107116,0.0004879396,0.00010683117,0.00034537032,0.0015886438,0.00041801092,0.0071340697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985115,0.00039995433,0.00007661622,0.0005068577,0.00036602272,0.00013904399],"domain_scores_gemma":[0.99758184,0.00085844303,0.00022545153,0.00068318506,0.0005098884,0.00014107832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019515435,0.0014333056,0.0016920521,0.0007110813,0.000504422,0.0011571419,0.0038875956,0.0015021211,0.0020251647],"category_scores_gemma":[0.0051566036,0.0007343573,0.0010681959,0.00058900105,0.0012026632,0.0023829206,0.0027292052,0.0017571611,0.0018298554],"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":[0.0007721427,0.0005219713,0.005565454,0.00020156073,0.00019717307,0.0001997322,0.0002090008,0.28253737,0.016747316,0.00826546,0.009279433,0.67550343],"study_design_scores_gemma":[0.000010899653,0.00004399226,0.00021961208,0.000005039792,0.000010610893,0.00003119628,0.000012028305,0.99282104,0.0027182428,0.0036706326,0.00044990837,0.00000694066],"about_ca_topic_score_codex":0.0032364032,"about_ca_topic_score_gemma":0.0044568493,"teacher_disagreement_score":0.0038875956,"about_ca_system_score_codex":0.00065341406,"about_ca_system_score_gemma":0.0015222122,"threshold_uncertainty_score":0.010320902},"labels":[],"label_agreement":null},{"id":"W4404835218","doi":"10.1016/j.procs.2024.09.368","title":"A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition","year":2024,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Face and Expression Recognition","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":"Royal Military College of Canada","funders":"","keywords":"Computer science; Pattern recognition (psychology); Feature selection; Artificial intelligence; Feature (linguistics); Facial expression; Facial expression recognition; Selection (genetic algorithm); Expression (computer science); Morphology (biology); Facial recognition system; Programming language; Biology","score_opus":0.026430010025742516,"score_gpt":0.3020272920240673,"score_spread":0.2755972819983248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404835218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03586994,0.00014819582,0.96137935,0.00007752471,0.000046847443,0.00014281581,0.00013699352,0.0013836765,0.00081472116],"genre_scores_gemma":[0.33636224,0.00023953016,0.6554577,0.00015440705,0.00005641744,0.00048435447,0.0012822122,0.00030251103,0.005660683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964106,0.000046631347,0.000019823312,0.00009948836,0.00015046755,0.000042547785],"domain_scores_gemma":[0.9996991,0.00006272228,0.00002686064,0.000034012977,0.0001586749,0.000018777337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062274636,0.0006909213,0.00062518276,0.00091089495,0.0002754319,0.00035640347,0.00066994526,0.0003683348,0.0017460214],"category_scores_gemma":[0.0010591692,0.00016889913,0.0006976268,0.0005988822,0.00023800919,0.00035585312,0.00043484787,0.0004986324,0.00087116973],"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":[0.00020862649,0.00018136989,0.0015693061,0.00005401102,0.00006257129,0.00014533785,0.000071764756,0.017625192,0.20970705,0.0011194113,0.0043566404,0.7648987],"study_design_scores_gemma":[0.000041317344,0.0002777731,0.009719981,0.000014160072,0.000068208734,0.0006217875,0.00007784083,0.8744231,0.10626233,0.0014691921,0.006977461,0.000046966168],"about_ca_topic_score_codex":0.0017598582,"about_ca_topic_score_gemma":0.0023957333,"teacher_disagreement_score":0.0017598582,"about_ca_system_score_codex":0.0002481073,"about_ca_system_score_gemma":0.0005301916,"threshold_uncertainty_score":0.0058410764},"labels":[],"label_agreement":null},{"id":"W4405424698","doi":"10.3390/math12243935","title":"Exploring Kernel Machines and Support Vector Machines: Principles, Techniques, and Future Directions","year":2024,"lang":"en","type":"article","venue":"Mathematics","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":97,"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":"Support vector machine; Kernel (algebra); Computer science; Kernel method; Machine learning; Artificial intelligence; Data science; Management science; Engineering; Mathematics","score_opus":0.05510280073983388,"score_gpt":0.27053692924313,"score_spread":0.21543412850329613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405424698","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005595815,0.5036525,0.460659,0.013442713,0.00096823764,0.00007487492,0.00015413939,0.0005955586,0.014857125],"genre_scores_gemma":[0.10554827,0.55756,0.31995717,0.0016952909,0.0058288635,0.00022463666,0.0005178714,0.00029438589,0.008373392],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980471,0.00080985914,0.00012562827,0.00029885536,0.00060352223,0.000114959315],"domain_scores_gemma":[0.99462646,0.0035492578,0.00024966703,0.00034570598,0.0010488015,0.00017998592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004080895,0.0015139241,0.0016164813,0.0035316332,0.0005545037,0.0039543984,0.0018851317,0.002459903,0.004003526],"category_scores_gemma":[0.008264259,0.0009351599,0.0010557163,0.005915579,0.0023714225,0.010481541,0.0017491282,0.003448453,0.0021953052],"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":[0.00007550398,0.00019176751,0.0023625572,0.0023530894,0.00013189996,0.00015051688,0.0005417174,0.018509991,0.0009845853,0.32990858,0.019885901,0.6249039],"study_design_scores_gemma":[0.000023206421,0.00016669583,0.0018503786,0.0014481873,0.000047192796,0.00039984132,0.0006613858,0.09384292,0.0015118401,0.70249844,0.19740798,0.00014193103],"about_ca_topic_score_codex":0.0017095633,"about_ca_topic_score_gemma":0.0011374397,"teacher_disagreement_score":0.004080895,"about_ca_system_score_codex":0.0013390363,"about_ca_system_score_gemma":0.0013993299,"threshold_uncertainty_score":0.021582067},"labels":[],"label_agreement":null},{"id":"W4405730226","doi":"10.54254/3029-0880/3/2024019","title":"Integrating advanced principal component analysis into naive bayes for enhanced classification performance","year":2024,"lang":"en","type":"article","venue":"Advances in Operation Research and Production Management","topic":"Face and Expression Recognition","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 Ottawa","funders":"","keywords":"Naive Bayes classifier; Principal component analysis; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Support vector machine","score_opus":0.040683369092858634,"score_gpt":0.38395892012115557,"score_spread":0.34327555102829693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405730226","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0040869503,0.0005942407,0.9924952,0.00023496085,0.00013348997,0.00011632448,0.00006574722,0.0012894415,0.0009836688],"genre_scores_gemma":[0.09703122,0.00063317944,0.89943755,0.0003217245,0.00031174076,0.0003071422,0.00040667036,0.00024613098,0.0013045847],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98888373,0.004371078,0.00082442764,0.0014537192,0.0040114373,0.00045552023],"domain_scores_gemma":[0.98055905,0.011102015,0.0007552938,0.001606677,0.0057402677,0.00023675828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010979344,0.0019518371,0.0032326074,0.0035545353,0.0013020575,0.0030494742,0.0018735897,0.0019292369,0.004027264],"category_scores_gemma":[0.03514046,0.0009835974,0.0018007881,0.0038244203,0.0012436865,0.0037195242,0.0015404541,0.0034402627,0.0033672908],"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":[0.0003233555,0.00022598439,0.0042779963,0.00039476476,0.00029355544,0.00015227219,0.00030789102,0.101001926,0.007619199,0.017871704,0.007940656,0.8595909],"study_design_scores_gemma":[0.000046236706,0.0000880557,0.0012074957,0.00006913259,0.00009575636,0.00013963648,0.0000438122,0.9554247,0.003483913,0.034202762,0.0051195333,0.00007889213],"about_ca_topic_score_codex":0.0069500813,"about_ca_topic_score_gemma":0.006723534,"teacher_disagreement_score":0.010979344,"about_ca_system_score_codex":0.0010146628,"about_ca_system_score_gemma":0.0030338506,"threshold_uncertainty_score":0.058064997},"labels":[],"label_agreement":null},{"id":"W4406098331","doi":"10.1016/j.ins.2024.121859","title":"Multi-view data representation via adaptive label propagation nonnegative matrix factorization","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"","keywords":"Representation (politics); Non-negative matrix factorization; Computer science; Matrix decomposition; Matrix (chemical analysis); External Data Representation; Nonnegative matrix; Artificial intelligence; Mathematics; Theoretical computer science; Algebra over a field; Symmetric matrix; Pure mathematics","score_opus":0.1040003659572495,"score_gpt":0.37818157387121376,"score_spread":0.2741812079139643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406098331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003056877,0.00013058861,0.9956436,0.000115714836,0.000052492145,0.000030778832,0.0001428942,0.00060418434,0.0002228121],"genre_scores_gemma":[0.16645682,0.0005284715,0.8266323,0.00030625658,0.00019785897,0.00030121292,0.0024245323,0.0003488587,0.0028035822],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982672,0.00034685296,0.00008218602,0.0005815639,0.0005488139,0.00017327645],"domain_scores_gemma":[0.99770087,0.00067039044,0.00025092313,0.0005585207,0.0006951453,0.00012403922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014949185,0.0016052466,0.0018673242,0.001424264,0.00078089297,0.0018025031,0.0029021488,0.001740595,0.0020378763],"category_scores_gemma":[0.0048743123,0.0008202337,0.0020011475,0.002172363,0.0008329485,0.002830373,0.0024658216,0.0034828566,0.0018672345],"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":[0.0004604417,0.00047817652,0.001528825,0.0002945859,0.00023157566,0.00018767791,0.00025225023,0.108185895,0.04701411,0.017146152,0.017935243,0.80628496],"study_design_scores_gemma":[0.000009644507,0.000033801774,0.00023012249,0.000009298822,0.000020845073,0.000057368226,0.000025370788,0.9889663,0.003186439,0.006347611,0.0010950016,0.000018115627],"about_ca_topic_score_codex":0.007060334,"about_ca_topic_score_gemma":0.0098529635,"teacher_disagreement_score":0.007060334,"about_ca_system_score_codex":0.00075492705,"about_ca_system_score_gemma":0.0013931454,"threshold_uncertainty_score":0.014038503},"labels":[],"label_agreement":null},{"id":"W4406204531","doi":"10.1016/j.sigpro.2025.110054","title":"Aggregated f -average Neural Network applied to Few-Shot Class Incremental Learning","year":2025,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Face and Expression Recognition","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":"École de Technologie Supérieure","funders":"","keywords":"Shot (pellet); Class (philosophy); Artificial neural network; Artificial intelligence; Computer science; Incremental learning; One shot; Machine learning; Engineering; Chemistry","score_opus":0.016225224856142403,"score_gpt":0.23405971964511543,"score_spread":0.21783449478897304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406204531","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06833461,0.0023016185,0.9231497,0.00031900502,0.00042852375,0.00012637781,0.0003207787,0.0030155457,0.0020037394],"genre_scores_gemma":[0.74488974,0.0006441624,0.24494384,0.00031728702,0.00028901958,0.00020528398,0.001405714,0.00024681343,0.0070580477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992768,0.00012562051,0.00004791021,0.00028334215,0.00015162061,0.00011467857],"domain_scores_gemma":[0.99828064,0.00084189203,0.000068011075,0.00023846247,0.00049583195,0.000075043776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001791941,0.0010697127,0.002062296,0.001120832,0.0007724418,0.0011263791,0.0024804838,0.0020470843,0.0031195797],"category_scores_gemma":[0.0044179964,0.000491738,0.0009874174,0.0011623617,0.0005581452,0.0017668591,0.001415562,0.0018970988,0.00078572356],"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":[0.00043529013,0.00026399706,0.0013317452,0.0001597723,0.00021321459,0.00012574132,0.00010374139,0.24543801,0.0074041965,0.0026417538,0.005078919,0.7368037],"study_design_scores_gemma":[0.000003882821,0.000027325743,0.00022431662,0.0000043561286,0.000015617832,0.0000152150715,0.0000050286167,0.9970552,0.0013238128,0.0010341307,0.000286546,0.0000046631812],"about_ca_topic_score_codex":0.021154981,"about_ca_topic_score_gemma":0.019245597,"teacher_disagreement_score":0.021154981,"about_ca_system_score_codex":0.001041643,"about_ca_system_score_gemma":0.0014483326,"threshold_uncertainty_score":0.042063653},"labels":[],"label_agreement":null},{"id":"W4406321275","doi":"10.1080/10618600.2025.2451680","title":"Variable Selection and Basis Learning for Ordinal Classification","year":2025,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Face and Expression Recognition","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":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Ordinal regression; Ordinal data; Linear discriminant analysis; Mathematics; Ordinal optimization; Basis (linear algebra); Dimension (graph theory); Feature selection; Variable (mathematics); Variables; Statistics; Artificial intelligence; Pattern recognition (psychology); Computer science; Combinatorics","score_opus":0.01272680945528477,"score_gpt":0.2700614872194298,"score_spread":0.25733467776414504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406321275","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026218,0.000060471077,0.9968143,0.000073179704,0.000019009114,0.000021755517,0.000038469403,0.00015978841,0.00019129223],"genre_scores_gemma":[0.19042991,0.00026075408,0.80518746,0.00020909043,0.00021894858,0.0005830975,0.00069013797,0.00015508496,0.00226561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99637896,0.0019942594,0.0001491649,0.00045372557,0.0007967925,0.00022710186],"domain_scores_gemma":[0.9936737,0.0036499714,0.00040998202,0.00094956293,0.0011033916,0.0002134127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004613365,0.0008698401,0.0014001003,0.0017940779,0.00072593044,0.0011945792,0.0023207413,0.0011908975,0.0035415308],"category_scores_gemma":[0.016730962,0.00042863848,0.0011225952,0.0021192974,0.0013139666,0.0014304358,0.0020837192,0.0028628665,0.0012715431],"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":[0.00028435793,0.00023309208,0.0037798644,0.00023824588,0.00015944142,0.00011870567,0.00017410272,0.2543751,0.00607817,0.09575152,0.0065243277,0.63228303],"study_design_scores_gemma":[0.00001760276,0.000040740608,0.0003201772,0.0000136812,0.000009933692,0.00003135668,0.000011926923,0.96165735,0.00095039594,0.035803046,0.0011309979,0.000012853376],"about_ca_topic_score_codex":0.0014669305,"about_ca_topic_score_gemma":0.0014755039,"teacher_disagreement_score":0.004613365,"about_ca_system_score_codex":0.0006299819,"about_ca_system_score_gemma":0.0013671337,"threshold_uncertainty_score":0.024398148},"labels":[],"label_agreement":null},{"id":"W4406805586","doi":"10.7717/peerj-cs.2497","title":"Random k conditional nearest neighbor for high-dimensional data","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"k-nearest neighbors algorithm; Pattern recognition (psychology); Computer science; Large margin nearest neighbor; Nearest-neighbor chain algorithm; Feature (linguistics); Best bin first; Metric (unit); Artificial intelligence; Data mining; Nearest neighbor search; Cluster analysis","score_opus":0.0267616871584268,"score_gpt":0.2954529372423534,"score_spread":0.2686912500839266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406805586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0078667905,0.0005599377,0.9902396,0.00014604145,0.000051656116,0.000058741014,0.00012154254,0.00048741713,0.00046827085],"genre_scores_gemma":[0.36141393,0.0008823875,0.6332721,0.0001926785,0.00014550712,0.00043266136,0.0013440631,0.00017552111,0.0021411888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99469286,0.0018988244,0.0003180146,0.0011125394,0.0017628827,0.00021487738],"domain_scores_gemma":[0.9903257,0.00551874,0.000844554,0.001547408,0.0015591534,0.00020439962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0057043247,0.0007156334,0.0017277045,0.0022474474,0.0012609306,0.0014454018,0.002494635,0.0015279527,0.0015052238],"category_scores_gemma":[0.0188017,0.0005658878,0.0012308711,0.0026703419,0.0012085916,0.002274653,0.0016108097,0.0020810806,0.00091092964],"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":[0.0001922903,0.00012521581,0.005713346,0.00032201537,0.00016183141,0.00025399379,0.00018578532,0.7170073,0.0023975563,0.030370101,0.0053361505,0.2379344],"study_design_scores_gemma":[0.000003833807,0.000015876254,0.00040441903,0.000009744067,0.000004669369,0.000049146216,0.00001412792,0.99003536,0.00043417816,0.008431795,0.00058493967,0.000011992439],"about_ca_topic_score_codex":0.010922455,"about_ca_topic_score_gemma":0.0108774165,"teacher_disagreement_score":0.010922455,"about_ca_system_score_codex":0.0018352048,"about_ca_system_score_gemma":0.0018424051,"threshold_uncertainty_score":0.030167758},"labels":[],"label_agreement":null},{"id":"W4406848163","doi":"10.1007/s10044-025-01411-2","title":"Separability and scatteredness (S&amp;S) ratio-based efficient SVM regularization parameter, kernel, and kernel parameter selection","year":2025,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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 Toronto; McGill University","funders":"","keywords":"Kernel (algebra); Pattern recognition (psychology); Mathematics; Artificial intelligence; Regularization (linguistics); Support vector machine; Radial basis function kernel; Kernel method; Selection (genetic algorithm); Applied mathematics; Computer science; Combinatorics","score_opus":0.013679103178572296,"score_gpt":0.2721746611905991,"score_spread":0.2584955580120268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406848163","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015876329,0.00018869976,0.9828419,0.00007114316,0.000018423862,0.000030341738,0.000034446086,0.0003486519,0.00058997277],"genre_scores_gemma":[0.38394365,0.00031585526,0.61129475,0.000088878056,0.000082427985,0.00014102971,0.0004232124,0.00033529103,0.0033749721],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979791,0.0005840864,0.00018831997,0.00036087623,0.0007938856,0.00009370121],"domain_scores_gemma":[0.9961436,0.0013670181,0.00037423198,0.0005843005,0.0013848486,0.0001460901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003232162,0.0011102775,0.0014611509,0.0015253947,0.0005310352,0.0016470867,0.0015470941,0.0012043747,0.0020917172],"category_scores_gemma":[0.010545304,0.00048354708,0.0011305523,0.0013483114,0.00086015003,0.0025369448,0.0019012993,0.0014272655,0.0008452329],"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":[0.00092616945,0.00040323945,0.0034796272,0.00032678345,0.0002149914,0.00016291662,0.00024909573,0.27672434,0.059253793,0.042657617,0.0039108214,0.6116907],"study_design_scores_gemma":[0.000011602177,0.00006372107,0.0005104672,0.000007587945,0.0000219731,0.00007516336,0.000013128272,0.987437,0.008046646,0.0032027937,0.0005953484,0.0000145373],"about_ca_topic_score_codex":0.0012769556,"about_ca_topic_score_gemma":0.0012048865,"teacher_disagreement_score":0.003232162,"about_ca_system_score_codex":0.0006650546,"about_ca_system_score_gemma":0.0012661513,"threshold_uncertainty_score":0.01709354},"labels":[],"label_agreement":null},{"id":"W4406894623","doi":"10.1109/icecs61496.2024.10848620","title":"A Shift-Invariant Robust Deep Framework with Improved Classification Performance","year":2024,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Invariant (physics); Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.02033842065158687,"score_gpt":0.22850939963298383,"score_spread":0.20817097898139697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406894623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025682598,0.0005793317,0.9685305,0.000230598,0.000095945645,0.000041905158,0.00018163321,0.0017953715,0.0028621252],"genre_scores_gemma":[0.59662646,0.0006929867,0.38689867,0.00038724008,0.0001834408,0.0001008467,0.0012277903,0.00026316734,0.013619422],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973375,0.000037219404,0.000011453582,0.000086968765,0.00008931214,0.0000413339],"domain_scores_gemma":[0.99980146,0.000027708142,0.000024332456,0.00004875315,0.0000827764,0.000014917858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056722,0.0009829561,0.0005567977,0.00060876715,0.00024404773,0.00062406715,0.0011734337,0.00078224816,0.0024805712],"category_scores_gemma":[0.00068828394,0.00026321723,0.0005123409,0.00057581294,0.0003815939,0.0010789803,0.000867425,0.0010704543,0.0016303104],"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":[0.00017617825,0.000168194,0.00082935754,0.00008921316,0.000111219924,0.00009463016,0.000044756212,0.19827162,0.087400995,0.014860296,0.0065363124,0.6914173],"study_design_scores_gemma":[0.0000043940463,0.00005129575,0.0002527959,0.0000047803205,0.0000143595535,0.000034806326,0.0000058136534,0.98604345,0.008744557,0.003293557,0.0015417157,0.000008498729],"about_ca_topic_score_codex":0.0033739626,"about_ca_topic_score_gemma":0.0057062777,"teacher_disagreement_score":0.0033739626,"about_ca_system_score_codex":0.0005836594,"about_ca_system_score_gemma":0.00078313047,"threshold_uncertainty_score":0.008298337},"labels":[],"label_agreement":null},{"id":"W4407052408","doi":"10.1002/cjs.11837","title":"The quantile‐based classifier with variable‐wise parameters","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Face and Expression Recognition","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":"European Commission","keywords":"Quantile; Classifier (UML); Artificial intelligence; Variable (mathematics); Computer science; Pattern recognition (psychology); Statistics; Econometrics; Machine learning; Mathematics","score_opus":0.018186386285053428,"score_gpt":0.22632469541555303,"score_spread":0.2081383091304996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407052408","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02595545,0.0004069039,0.9722188,0.00018970943,0.000058486607,0.000040252304,0.00015981398,0.00038332594,0.0005871712],"genre_scores_gemma":[0.6699875,0.00032808134,0.32681984,0.00023145833,0.00016474418,0.00015514219,0.00059730135,0.000112086906,0.0016038821],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998538,0.0005063952,0.00010363523,0.000357112,0.0003650024,0.00012989773],"domain_scores_gemma":[0.9968773,0.0017873406,0.0002590938,0.00038959773,0.0005956118,0.00009111603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003365928,0.00050319644,0.0013053312,0.0010165946,0.0003912044,0.0012151732,0.0015980577,0.0015347435,0.0016884812],"category_scores_gemma":[0.008988231,0.00029338346,0.0006335431,0.0011480029,0.00060433056,0.0013559562,0.0009800933,0.001732157,0.0006951801],"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":[0.00058709615,0.00016243261,0.008681247,0.00015356226,0.00013650832,0.00012301981,0.000090785274,0.45633942,0.014199997,0.025028966,0.0060149324,0.4884821],"study_design_scores_gemma":[0.00001419523,0.000034721266,0.000942737,0.000012086715,0.000013500072,0.000040901465,0.000009389634,0.99053204,0.0017482426,0.005939381,0.00070029276,0.000012467702],"about_ca_topic_score_codex":0.0020361315,"about_ca_topic_score_gemma":0.0011105796,"teacher_disagreement_score":0.003365928,"about_ca_system_score_codex":0.00096186146,"about_ca_system_score_gemma":0.0008846936,"threshold_uncertainty_score":0.017800987},"labels":[],"label_agreement":null},{"id":"W4407056673","doi":"10.1145/3700706.3700707","title":"A Novel K-Means Clustering under Bi-Partition of Feature Set","year":2024,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University; University of Guelph","funders":"","keywords":"Partition (number theory); Cluster analysis; Computer science; Pattern recognition (psychology); Set (abstract data type); Data mining; Artificial intelligence; Mathematics; Combinatorics","score_opus":0.032555921726330234,"score_gpt":0.2738393187199487,"score_spread":0.24128339699361848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407056673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047036842,0.00026860647,0.99329656,0.000098255114,0.000129342,0.000073118805,0.00009628065,0.0007679461,0.0005662682],"genre_scores_gemma":[0.07191369,0.0003427059,0.9221177,0.00014595634,0.00013595365,0.00027710618,0.00092421286,0.00027763098,0.003864951],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99666923,0.0004940954,0.0002306088,0.0010599802,0.0012924274,0.0002536426],"domain_scores_gemma":[0.99872667,0.00016678883,0.00008393758,0.00021691773,0.0007298193,0.00007585675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011824183,0.0012851176,0.0024393268,0.0020894888,0.0019642913,0.0019561984,0.0038957812,0.0019756057,0.00235743],"category_scores_gemma":[0.0029406813,0.00076194096,0.0019506856,0.0035904604,0.0009655742,0.0021999634,0.0026107025,0.0013897602,0.0024945699],"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":[0.00052157417,0.00022371224,0.0015174934,0.00035397723,0.00038498282,0.00010584474,0.000298185,0.07865009,0.03199825,0.013130369,0.0143168485,0.8584987],"study_design_scores_gemma":[0.000050252213,0.00009163612,0.0010916558,0.000023362163,0.00006718675,0.00020153576,0.00009602925,0.9771469,0.008109612,0.0075989086,0.0054544467,0.000068449066],"about_ca_topic_score_codex":0.017022418,"about_ca_topic_score_gemma":0.014410867,"teacher_disagreement_score":0.017022418,"about_ca_system_score_codex":0.0012366687,"about_ca_system_score_gemma":0.0029509303,"threshold_uncertainty_score":0.033846676},"labels":[],"label_agreement":null},{"id":"W4407920608","doi":"10.18280/jesa.580119","title":"Complex Face Emotion Recognition Using Convolutional Neural Networks","year":2025,"lang":"fr","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"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":"Convolutional neural network; Computer science; Facial recognition system; Face (sociological concept); Artificial intelligence; Pattern recognition (psychology); Emotion recognition; Psychology; Speech recognition; Sociology","score_opus":0.0690255854139264,"score_gpt":0.2952028281834314,"score_spread":0.22617724276950502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407920608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32156843,0.0015508834,0.66477996,0.00062218116,0.00033816503,0.00007499811,0.00047249207,0.0018320882,0.00876088],"genre_scores_gemma":[0.9064617,0.0006756388,0.079864785,0.0001756625,0.00008460923,0.000043528435,0.0006923009,0.00011888266,0.011882883],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998369,0.000021420694,0.0000065702748,0.000055462846,0.000041898707,0.00003764755],"domain_scores_gemma":[0.9998299,0.00005310303,0.000020787,0.00003063489,0.000052657982,0.000012881414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032917966,0.00049066043,0.00034218887,0.00029382657,0.00017118605,0.0007618516,0.00034984626,0.0004204618,0.002487591],"category_scores_gemma":[0.00065666385,0.00019051085,0.00042868487,0.00023115399,0.0002140465,0.0005950757,0.0004338647,0.0006294291,0.00078310963],"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":[0.00043697696,0.00021349695,0.00565551,0.00010514517,0.00018761843,0.00014952708,0.00010691908,0.06307829,0.21684198,0.005664138,0.006866466,0.7006939],"study_design_scores_gemma":[0.000008503759,0.0000531108,0.0076677185,0.000014146621,0.00003886503,0.00007308539,0.00003026956,0.9591489,0.027117997,0.0034173287,0.0024141965,0.000015949812],"about_ca_topic_score_codex":0.0034310482,"about_ca_topic_score_gemma":0.0047075013,"teacher_disagreement_score":0.0034310482,"about_ca_system_score_codex":0.00041723344,"about_ca_system_score_gemma":0.0002384669,"threshold_uncertainty_score":0.008321822},"labels":[],"label_agreement":null},{"id":"W4407934590","doi":"10.1007/978-3-031-81010-7_20","title":"Machine Learning and Optimization Algorithms for Feature Selection","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"","keywords":"Computer science; Feature selection; Selection (genetic algorithm); Artificial intelligence; Machine learning; Feature (linguistics); Algorithm; Pattern recognition (psychology)","score_opus":0.01164240956668274,"score_gpt":0.24994856431448711,"score_spread":0.23830615474780437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407934590","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009097343,0.0035399878,0.9933096,0.00011777968,0.00013563005,0.000020560905,0.000099400495,0.0004907909,0.0013764942],"genre_scores_gemma":[0.04316908,0.0046437015,0.93711025,0.00016952518,0.0005072287,0.00029947882,0.0008741097,0.00052364747,0.012702942],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99916315,0.00025744297,0.00005714305,0.0001899832,0.0002824305,0.00004984267],"domain_scores_gemma":[0.99875975,0.0008378808,0.00005215456,0.00015323027,0.00017885618,0.000018212595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010408042,0.0013979592,0.0023546114,0.0010607783,0.00045853885,0.0013015573,0.0020298692,0.0011299939,0.009206573],"category_scores_gemma":[0.004215182,0.00065072783,0.0012689262,0.0034021824,0.0006873024,0.0015601343,0.0010136126,0.0020882126,0.004844874],"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":[0.00008344089,0.00006134319,0.0002429611,0.00028703455,0.00008279147,0.000058965776,0.000031360807,0.11426317,0.0024507495,0.022034938,0.020543743,0.8398595],"study_design_scores_gemma":[0.00002269535,0.00004061041,0.0004929779,0.00003909743,0.000027720687,0.00012898703,0.000015330528,0.9304368,0.0018122065,0.05435982,0.012595904,0.00002777274],"about_ca_topic_score_codex":0.0023326844,"about_ca_topic_score_gemma":0.0022608559,"teacher_disagreement_score":0.009206573,"about_ca_system_score_codex":0.00052445557,"about_ca_system_score_gemma":0.0005495774,"threshold_uncertainty_score":0.030799031},"labels":[],"label_agreement":null},{"id":"W4408010925","doi":"10.18280/isi.300215","title":"HWKPA: Optimized Ensemble Clustering with Hybrid Weighted K-Means Pollination with Major Voting Consensus Function for Enhancing Cluster Quality","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Face and Expression Recognition","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":"Cluster analysis; Voting; Cluster (spacecraft); Pollination; Computer science; Function (biology); Mathematics; Biology; Artificial intelligence; Botany; Pollen; Evolutionary biology","score_opus":0.012714420179379292,"score_gpt":0.24334001051207063,"score_spread":0.23062559033269134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408010925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017257156,0.00011602697,0.9799436,0.00005723612,0.00008354471,0.00005384004,0.00004814348,0.0016743181,0.0007661293],"genre_scores_gemma":[0.4378211,0.000073775656,0.5576076,0.00012557325,0.00006516245,0.0002477674,0.00037644303,0.00047140362,0.0032112112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987525,0.00032247312,0.000055611734,0.0002755748,0.00045613624,0.0001377918],"domain_scores_gemma":[0.9985929,0.0003893994,0.00008647063,0.0002720417,0.00057812996,0.00008100443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015887856,0.00094522454,0.0013754691,0.00088999857,0.0011232701,0.0009100006,0.003039381,0.0012649298,0.0023149664],"category_scores_gemma":[0.003095334,0.0004926412,0.0009124733,0.0011710464,0.00046273434,0.0013369031,0.001797036,0.0010065889,0.0007288512],"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":[0.0005552373,0.00024407188,0.0015840712,0.00012344732,0.00028450208,0.00011457477,0.00021097795,0.4966241,0.027339999,0.004781314,0.008321893,0.45981577],"study_design_scores_gemma":[0.0000127074245,0.000023678933,0.00020197371,0.0000016147776,0.000009357767,0.000017787701,0.00001005565,0.9960757,0.0025855766,0.00068264885,0.0003712409,0.00000760016],"about_ca_topic_score_codex":0.007593175,"about_ca_topic_score_gemma":0.0094941445,"teacher_disagreement_score":0.007593175,"about_ca_system_score_codex":0.00082914584,"about_ca_system_score_gemma":0.0014130037,"threshold_uncertainty_score":0.015097976},"labels":[],"label_agreement":null},{"id":"W4408041000","doi":"10.18280/ts.420113","title":"Enhanced Face Identification Performance Using Online Mining Strategy in Multi-Task Cascaded Mask Convolutional Networks","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":2,"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":"Computer science; Task (project management); Identification (biology); Face (sociological concept); Artificial intelligence; Convolutional neural network; Pattern recognition (psychology); Data mining; Machine learning; Engineering","score_opus":0.04331497330206996,"score_gpt":0.2917285100891658,"score_spread":0.24841353678709582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408041000","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.387983,0.0012631752,0.60214305,0.00041252022,0.0002963063,0.000088726214,0.00022771723,0.0029697604,0.0046158303],"genre_scores_gemma":[0.892607,0.0002124905,0.101716064,0.000118756805,0.000050903403,0.000037053152,0.0003115076,0.00006148384,0.0048848125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968016,0.00004431385,0.000015771322,0.000095883006,0.00009581664,0.000068168476],"domain_scores_gemma":[0.99955004,0.00016250683,0.000028177632,0.000077184224,0.00015429346,0.000027861253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006906942,0.00082424254,0.0006422526,0.0003715223,0.00035436792,0.00044202714,0.0011239332,0.00088033034,0.002383772],"category_scores_gemma":[0.0014242837,0.00028152068,0.00040130274,0.00024910423,0.00019341169,0.0011154094,0.0007559803,0.0006866833,0.0007205197],"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":[0.0015804173,0.0007135046,0.0037567765,0.0001476885,0.00014263888,0.00025754923,0.000081161576,0.07884557,0.13604364,0.0022004724,0.0040026675,0.77222794],"study_design_scores_gemma":[0.0000128823485,0.00015080458,0.0015833037,0.000004783856,0.000034713183,0.000108636006,0.000010060712,0.96975625,0.02743358,0.00049295643,0.0004033174,0.000008794362],"about_ca_topic_score_codex":0.0044466145,"about_ca_topic_score_gemma":0.0072762608,"teacher_disagreement_score":0.0044466145,"about_ca_system_score_codex":0.0003511466,"about_ca_system_score_gemma":0.0008316189,"threshold_uncertainty_score":0.008841515},"labels":[],"label_agreement":null},{"id":"W4408045313","doi":"10.1016/b978-0-44-332818-3.00016-2","title":"Multi-dimensional scaling","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"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 Victoria; University of Saskatchewan","funders":"","keywords":"Scaling; Computer science; Mathematics; Geometry","score_opus":0.020141621575346057,"score_gpt":0.24897125064987302,"score_spread":0.22882962907452697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408045313","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032497845,0.008968978,0.75475144,0.0006606501,0.002252792,0.00010882025,0.0010284082,0.0065614404,0.22241774],"genre_scores_gemma":[0.06408552,0.010813625,0.5616516,0.0005067391,0.0011428912,0.0003557268,0.003352508,0.0034079957,0.35468346],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995053,0.00005173381,0.000031263502,0.00012080156,0.000260344,0.00003056962],"domain_scores_gemma":[0.99951315,0.00007478891,0.000021432246,0.00023463313,0.00013234021,0.000023618792],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046504504,0.0013117696,0.00091191364,0.0015531693,0.00069157546,0.0021475174,0.0008709739,0.0007118118,0.0839376],"category_scores_gemma":[0.0014712856,0.0005165952,0.00074828573,0.00265953,0.00062444614,0.0017475387,0.0019386054,0.0011725014,0.039142113],"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":[0.000024443874,0.000019794801,0.00012209227,0.00017919074,0.000017875745,0.000066073095,0.00007809674,0.0037757629,0.009947222,0.0568219,0.07577558,0.853172],"study_design_scores_gemma":[0.000013178988,0.00005459508,0.0015926653,0.00015251423,0.000036037407,0.0009081724,0.00012753326,0.06312352,0.0125886435,0.07433906,0.8470042,0.00005983552],"about_ca_topic_score_codex":0.00070025347,"about_ca_topic_score_gemma":0.000849051,"teacher_disagreement_score":0.0839376,"about_ca_system_score_codex":0.0003418522,"about_ca_system_score_gemma":0.00037412933,"threshold_uncertainty_score":0.28079927},"labels":[],"label_agreement":null},{"id":"W4408062911","doi":"10.18280/ts.420138","title":"C-URFMN: Cervical Cancer Diagnosis by Using Unbounded Recurrent Fuzzy Min-Max Neural Network Diagnosis","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Face and Expression Recognition","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":"Fuzzy logic; Artificial neural network; Computer science; Artificial intelligence; Mathematics; Medicine","score_opus":0.02720996837393484,"score_gpt":0.28323280288851777,"score_spread":0.2560228345145829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408062911","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12767935,0.0018541506,0.85856986,0.00067482353,0.00039598183,0.0001820036,0.00056981156,0.0033714355,0.0067026173],"genre_scores_gemma":[0.8538237,0.00026880446,0.13837092,0.00022715186,0.000094015224,0.000105056555,0.0005230707,0.00006368514,0.006523629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997334,0.000037017122,0.000019228697,0.000097126904,0.00006367958,0.00004959546],"domain_scores_gemma":[0.999757,0.00007176934,0.00001964287,0.000019584808,0.000115420364,0.000016598602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067300606,0.0006969639,0.00066162145,0.00055133476,0.00048124016,0.0005271193,0.0011470076,0.0012427707,0.0020452917],"category_scores_gemma":[0.001199645,0.0002230524,0.00062291825,0.00029259935,0.00021767162,0.00060052436,0.0006304645,0.0005725433,0.0004775351],"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":[0.00075293204,0.0002548215,0.00720354,0.0002232686,0.00017775978,0.000573942,0.00012132631,0.23205002,0.022482533,0.0036964517,0.008974008,0.7234894],"study_design_scores_gemma":[0.000008138298,0.000041208663,0.0006399287,0.000007814718,0.000020210715,0.000060990045,0.000008661918,0.9946859,0.0035802675,0.0005182157,0.0004204264,0.00000834006],"about_ca_topic_score_codex":0.012778049,"about_ca_topic_score_gemma":0.011565321,"teacher_disagreement_score":0.012778049,"about_ca_system_score_codex":0.0006351416,"about_ca_system_score_gemma":0.0008527389,"threshold_uncertainty_score":0.025407314},"labels":[],"label_agreement":null},{"id":"W4408076558","doi":"10.1038/s41598-025-92564-x","title":"Robust self supervised symmetric nonnegative matrix factorization to the graph clustering","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"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":"Non-negative matrix factorization; Cluster analysis; Computer science; Biclustering; Graph; Matrix decomposition; Combinatorics; Mathematics; Artificial intelligence; Pattern recognition (psychology); Theoretical computer science; Correlation clustering; CURE data clustering algorithm; Physics","score_opus":0.019068471146696152,"score_gpt":0.24987760191269462,"score_spread":0.23080913076599846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408076558","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036298425,0.00022078436,0.99526155,0.00009132735,0.000029154253,0.000026424617,0.0000495766,0.00033473078,0.00035656826],"genre_scores_gemma":[0.23503457,0.0006000748,0.76002747,0.00029225348,0.00017167888,0.00022291434,0.0008130612,0.00032132008,0.0025166105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99839824,0.000708315,0.00004980955,0.0004388107,0.0003066412,0.00009821182],"domain_scores_gemma":[0.99766266,0.0010345432,0.0002950155,0.0003955933,0.0005041389,0.000108032094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020864268,0.001474661,0.0016046368,0.0015502084,0.00070701615,0.0008447917,0.0019086711,0.00151706,0.0015609798],"category_scores_gemma":[0.0070195952,0.00058238604,0.0013535692,0.0015849846,0.0012883389,0.001526106,0.0014838475,0.0018028197,0.00085055514],"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":[0.00012891204,0.00010388418,0.00067469943,0.00031088653,0.00014427416,0.0001256675,0.00020599988,0.7486039,0.0073041795,0.036084723,0.008890509,0.19742236],"study_design_scores_gemma":[0.000003861525,0.000010184529,0.00007655907,0.000006167334,0.0000040820737,0.000018160094,0.000008930596,0.9885492,0.00057059,0.010139606,0.0006055311,0.000007154867],"about_ca_topic_score_codex":0.0056870827,"about_ca_topic_score_gemma":0.0076663205,"teacher_disagreement_score":0.0056870827,"about_ca_system_score_codex":0.0011350807,"about_ca_system_score_gemma":0.0016376692,"threshold_uncertainty_score":0.011307955},"labels":[],"label_agreement":null},{"id":"W4408091964","doi":"10.1117/12.3060400","title":"A comparative study on facial expression recognition using MobileNetV2, VGG-16, ResNet and Swin Transformer","year":2025,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Semtech (Canada)","funders":"","keywords":"Residual neural network; Transformer; Artificial intelligence; Computer science; Facial expression recognition; Pattern recognition (psychology); Facial expression; Facial recognition system; Speech recognition; Computer vision; Deep learning; Engineering; Electrical engineering; Voltage","score_opus":0.06661826404277414,"score_gpt":0.33282777087381943,"score_spread":0.2662095068310453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408091964","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8739987,0.020067314,0.057550754,0.0013196009,0.0017490992,0.00036864573,0.004752872,0.011568842,0.0286242],"genre_scores_gemma":[0.9427041,0.0054723327,0.030193299,0.00033814914,0.0001623677,0.00014492385,0.010938169,0.00048423817,0.009562416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872226,0.00023978166,0.00009727234,0.00028413418,0.00044687878,0.0002097619],"domain_scores_gemma":[0.99939096,0.00022492277,0.000045487825,0.00009329606,0.00019585901,0.000049401722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020036432,0.001963382,0.00092740665,0.0020916734,0.0003143127,0.0009808143,0.0013379201,0.00084834226,0.0020868618],"category_scores_gemma":[0.0031704938,0.0002937123,0.00076948246,0.0012047092,0.00035259622,0.0020933263,0.0006111256,0.00073613523,0.0013009958],"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":[0.003186395,0.0008948255,0.01506352,0.0012088292,0.00078710035,0.0005764005,0.0001992859,0.09671229,0.021822402,0.0020749774,0.036726847,0.82074714],"study_design_scores_gemma":[0.00012330602,0.0019326477,0.01948676,0.00013907938,0.0003055578,0.0007076471,0.0004037446,0.9342694,0.03106409,0.0013876659,0.01009235,0.000087833454],"about_ca_topic_score_codex":0.016565377,"about_ca_topic_score_gemma":0.015862256,"teacher_disagreement_score":0.016565377,"about_ca_system_score_codex":0.001137412,"about_ca_system_score_gemma":0.00063292147,"threshold_uncertainty_score":0.032937944},"labels":[],"label_agreement":null},{"id":"W4408338108","doi":"10.1101/2025.03.05.641673","title":"k-Nearest Neighbour Adaptive Sampling (kNN-AS), a Simple Tool to Efficiently Explore Conformational Space","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Face and Expression Recognition","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":"Vector Institute; Structural Genomics Consortium; University of Toronto","funders":"","keywords":"Simple (philosophy); Nearest neighbour; k-nearest neighbors algorithm; Sampling (signal processing); Space (punctuation); Computer science; Adaptive sampling; Pattern recognition (psychology); Artificial intelligence; Algorithm; Mathematics; Data mining; Statistics; Computer vision","score_opus":0.029754779557825104,"score_gpt":0.25556900872382704,"score_spread":0.22581422916600194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408338108","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041958317,0.0004757808,0.95092946,0.00018294243,0.00008800665,0.00012220285,0.00024537643,0.0028641976,0.0031337438],"genre_scores_gemma":[0.39958817,0.0002954362,0.59637433,0.00017334761,0.000040016606,0.00021818186,0.0005535793,0.00044043944,0.0023164498],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993131,0.00021743377,0.000034519722,0.00010534403,0.00028856297,0.000040999912],"domain_scores_gemma":[0.9990392,0.00044597712,0.00009499408,0.00019653709,0.00015710465,0.000066234774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010217441,0.00047878377,0.00082261284,0.0006401489,0.00062400504,0.0006411325,0.0014787912,0.00091354153,0.002530329],"category_scores_gemma":[0.003915657,0.0003728724,0.0005393711,0.0007432859,0.00056629296,0.0009796362,0.0009746626,0.00089318777,0.0006274709],"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":[0.00058496976,0.00020477912,0.0035641335,0.0003788786,0.00023214385,0.00020325917,0.00015187272,0.702961,0.018614553,0.034536935,0.009179888,0.22938758],"study_design_scores_gemma":[0.000025316996,0.00003226281,0.00021256186,0.0000075593084,0.0000074828768,0.000044172746,0.00001089369,0.9871133,0.0028096323,0.007903332,0.0018195544,0.0000139488775],"about_ca_topic_score_codex":0.004466037,"about_ca_topic_score_gemma":0.007419989,"teacher_disagreement_score":0.004466037,"about_ca_system_score_codex":0.0004978744,"about_ca_system_score_gemma":0.0008246181,"threshold_uncertainty_score":0.008880079},"labels":[],"label_agreement":null},{"id":"W4408703518","doi":"10.1109/ipas63548.2025.10924517","title":"Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Dictionary learning; Recursive least squares filter; Artificial intelligence; Pattern recognition (psychology); Algorithm; Adaptive filter; Sparse approximation","score_opus":0.01749953403633325,"score_gpt":0.2537596552933232,"score_spread":0.23626012125698997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408703518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037519224,0.00006826023,0.9954822,0.00003912136,0.000018475639,0.000017740927,0.000019888537,0.00020147084,0.0004009163],"genre_scores_gemma":[0.28057244,0.00029777415,0.7135746,0.0002497765,0.00008767177,0.0001780339,0.0004925054,0.00024784642,0.0042994176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889815,0.0003500561,0.000051855463,0.0002405702,0.00038780193,0.000071519666],"domain_scores_gemma":[0.9985092,0.00051111664,0.00016366546,0.0003277903,0.00044865743,0.000039529234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010070106,0.00076013943,0.0011271467,0.0004670742,0.00030106294,0.00084265997,0.0016068744,0.0009307997,0.0013847892],"category_scores_gemma":[0.0046281605,0.00037629856,0.0005950987,0.00076933106,0.0007110043,0.0012383336,0.0010985264,0.0013168474,0.0009590563],"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":[0.00021943604,0.00010586438,0.0011723854,0.00023405961,0.000118826814,0.00013117502,0.00019346796,0.46725148,0.025037192,0.028561411,0.005864206,0.47111058],"study_design_scores_gemma":[0.000009333529,0.000033743236,0.00008770293,0.0000059908816,0.0000061664155,0.000037470123,0.000008816067,0.99302685,0.0027492824,0.0031288958,0.0008974423,0.000008365723],"about_ca_topic_score_codex":0.0022862942,"about_ca_topic_score_gemma":0.0033075192,"teacher_disagreement_score":0.0022862942,"about_ca_system_score_codex":0.0003497547,"about_ca_system_score_gemma":0.0008324974,"threshold_uncertainty_score":0.0053256154},"labels":[],"label_agreement":null},{"id":"W4408703526","doi":"10.1109/ipas63548.2025.10924540","title":"Semi-Supervised Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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","funders":"","keywords":"Recursive least squares filter; Dictionary learning; Computer science; Artificial intelligence; Least-squares function approximation; Pattern recognition (psychology); Algorithm; Mathematics; Mathematical optimization; Adaptive filter; Statistics; Sparse approximation","score_opus":0.012109418864032563,"score_gpt":0.23893631268288273,"score_spread":0.22682689381885018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408703526","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037671742,0.00008595986,0.99552155,0.000023357947,0.000016453327,0.000017402854,0.000018107587,0.0002443028,0.00030574744],"genre_scores_gemma":[0.28937218,0.00039157434,0.7050837,0.00017661914,0.00012635022,0.0002186862,0.00052196794,0.00023575229,0.0038731275],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988506,0.0003962786,0.000058461294,0.0002598799,0.00036402282,0.00007080262],"domain_scores_gemma":[0.9982779,0.0006156085,0.00021477565,0.00040398684,0.00044044273,0.000047325142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009399309,0.00071738375,0.0014844065,0.00050723535,0.00028436826,0.00069546903,0.0019214053,0.00075085723,0.0012249803],"category_scores_gemma":[0.003334342,0.00044972447,0.00070605567,0.00065118755,0.00069145253,0.0011571248,0.0010971967,0.0011248677,0.000871653],"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":[0.00024824962,0.00016117793,0.0010773459,0.0003812648,0.00019457795,0.00013752794,0.00024797677,0.42053032,0.031412765,0.016357215,0.004607222,0.5246444],"study_design_scores_gemma":[0.000007905644,0.000042604235,0.000111313915,0.0000061019223,0.0000072511343,0.000048410766,0.000009463848,0.99438715,0.002460215,0.0017970211,0.0011131854,0.000009378512],"about_ca_topic_score_codex":0.001602851,"about_ca_topic_score_gemma":0.0024325773,"teacher_disagreement_score":0.0019214053,"about_ca_system_score_codex":0.00024264405,"about_ca_system_score_gemma":0.00075322285,"threshold_uncertainty_score":0.004970908},"labels":[],"label_agreement":null},{"id":"W4408912893","doi":"10.1016/j.csda.2025.108179","title":"Sparse factor analysis for categorical data with the group-sparse generalized singular value decomposition","year":2025,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre for Addiction and Mental Health","funders":"Professional Staff Congress and City University of New York; Campbell Family Mental Health Research Institute; Centre for Addiction and Mental Health; National Science Foundation","keywords":"Singular value decomposition; Mathematics; Categorical variable; Group (periodic table); Sparse approximation; Applied mathematics; Value (mathematics); Combinatorics; Statistics; Algorithm; Physics","score_opus":0.05499552134827709,"score_gpt":0.3428226146050209,"score_spread":0.2878270932567438,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408912893","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0029198166,0.00016616705,0.9962179,0.00007750882,0.00003241301,0.00006658784,0.0001860358,0.0002020721,0.00013140647],"genre_scores_gemma":[0.09134153,0.00052221667,0.9052061,0.00012615127,0.00015423408,0.00051154813,0.0013270037,0.0001224309,0.00068878493],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9966774,0.0017855879,0.00020292462,0.0005490192,0.00061954214,0.0001655643],"domain_scores_gemma":[0.9942931,0.0031131362,0.00041799663,0.0012089965,0.0008141379,0.00015259476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046667927,0.001201141,0.0015512389,0.001538413,0.0005394755,0.0013210129,0.0012291805,0.00084182696,0.0027608804],"category_scores_gemma":[0.01976894,0.000422673,0.00210802,0.0028996596,0.0010193076,0.0014049481,0.0015121419,0.002348376,0.0011785603],"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":[0.0004563597,0.00032569704,0.005247718,0.00065477635,0.00071538135,0.00026278748,0.00063876784,0.112343326,0.014373512,0.07977935,0.014612394,0.7705898],"study_design_scores_gemma":[0.000055412544,0.0002418567,0.0029917797,0.00007403783,0.00011148035,0.00023726675,0.00014359645,0.8798536,0.0024554254,0.10833977,0.0054232595,0.00007249441],"about_ca_topic_score_codex":0.003507328,"about_ca_topic_score_gemma":0.0041031,"teacher_disagreement_score":0.0046667927,"about_ca_system_score_codex":0.00041218745,"about_ca_system_score_gemma":0.0020215644,"threshold_uncertainty_score":0.024680614},"labels":[],"label_agreement":null},{"id":"W4409120969","doi":"10.1007/s10898-025-01483-8","title":"Support vector machines with the hard-margin loss: optimal training via combinatorial Benders’ cuts","year":2025,"lang":"en","type":"article","venue":"Journal of Global Optimization","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Mathematics; Margin (machine learning); Mathematical optimization; Benders' decomposition; Training (meteorology); Combinatorial optimization; Support vector machine; Combinatorics; Applied mathematics; Algorithm; Artificial intelligence; Machine learning; Computer science","score_opus":0.010075420650805862,"score_gpt":0.24275097177985316,"score_spread":0.2326755511290473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409120969","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009884452,0.0005194134,0.9854833,0.000677105,0.00006436365,0.000081147315,0.00011225307,0.0005968232,0.0025811202],"genre_scores_gemma":[0.33352798,0.0007514528,0.65236145,0.00088311255,0.00039908238,0.0007984419,0.0011965537,0.00077466865,0.009307242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99784446,0.001155299,0.00009856022,0.0003476056,0.00036949848,0.00018450282],"domain_scores_gemma":[0.9935482,0.0053130104,0.0002652303,0.00033361817,0.00034927076,0.00019073201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036367623,0.0023428781,0.0035510408,0.0012247622,0.0006425168,0.0022536863,0.0029778457,0.0045758337,0.0078043155],"category_scores_gemma":[0.016929088,0.0021813263,0.0011499418,0.0016562057,0.0019070448,0.0045676203,0.002624888,0.005897358,0.0022104008],"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":[0.0005387354,0.0002901304,0.00041271525,0.00025152852,0.00007266431,0.00007519126,0.00008488452,0.78045005,0.0015152368,0.046071324,0.011159849,0.15907766],"study_design_scores_gemma":[0.000030213743,0.000042564385,0.00004058026,0.000016347505,0.00000455681,0.000011534508,0.000007768697,0.97736394,0.00018573955,0.021998614,0.0002930991,0.0000050690237],"about_ca_topic_score_codex":0.002002443,"about_ca_topic_score_gemma":0.0019184493,"teacher_disagreement_score":0.0078043155,"about_ca_system_score_codex":0.0012627972,"about_ca_system_score_gemma":0.0016774222,"threshold_uncertainty_score":0.026108027},"labels":[],"label_agreement":null},{"id":"W4409187835","doi":"10.1002/9781394294404.ch7","title":"Quantile Regression Using Log‐Cosh","year":2025,"lang":"en","type":"other","venue":"","topic":"Face and Expression Recognition","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":"Carleton University; University of British Columbia","funders":"","keywords":"Quantile regression; Statistics; Quantile; Regression; Computer science; Regression analysis; Econometrics; Mathematics","score_opus":0.030423866810356106,"score_gpt":0.29898040217784194,"score_spread":0.26855653536748586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409187835","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006563095,0.0011107071,0.9856269,0.00061071006,0.00012142321,0.00003761907,0.00039222257,0.0013501657,0.00418724],"genre_scores_gemma":[0.5700515,0.0061713452,0.3878168,0.0011096594,0.0006584397,0.00039438868,0.0029220243,0.0018540407,0.029021783],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9980895,0.000872174,0.00007720525,0.00030526592,0.0005193343,0.00013642601],"domain_scores_gemma":[0.99582136,0.0026581949,0.00037923176,0.0005767552,0.00046444064,0.00009996729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041545564,0.00088854297,0.0008603049,0.0012051723,0.00040723523,0.0020359056,0.0015029816,0.0010333472,0.008808844],"category_scores_gemma":[0.017599162,0.0003481753,0.0007789603,0.0021754992,0.0011745732,0.0021052854,0.0018802519,0.0025162199,0.0035451625],"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":[0.00023092618,0.00009922071,0.01001691,0.00031419168,0.00016959105,0.00023290611,0.0001956954,0.34030813,0.002443861,0.2272578,0.025606466,0.39312437],"study_design_scores_gemma":[0.00002105037,0.000070242815,0.0031430086,0.00007669908,0.000031160667,0.00015057102,0.000067367735,0.88280535,0.0018167894,0.090314865,0.021462236,0.000040681887],"about_ca_topic_score_codex":0.004120482,"about_ca_topic_score_gemma":0.002486274,"teacher_disagreement_score":0.008808844,"about_ca_system_score_codex":0.0009414128,"about_ca_system_score_gemma":0.0010897211,"threshold_uncertainty_score":0.029468536},"labels":[],"label_agreement":null},{"id":"W4409255056","doi":"10.1016/j.engappai.2025.110715","title":"Orthogonal Diversity Nonnegative Matrix Factorization for multi-view clustering","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Non-negative matrix factorization; Diversity (politics); Cluster analysis; Matrix (chemical analysis); Nonnegative matrix; Matrix decomposition; Artificial intelligence; Biclustering; Pattern recognition (psychology); Symmetric matrix; Fuzzy clustering; Eigenvalues and eigenvectors; CURE data clustering algorithm","score_opus":0.04363394103741704,"score_gpt":0.31799586231385335,"score_spread":0.2743619212764363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409255056","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002961846,0.00041311226,0.9956886,0.00009631419,0.000052271214,0.00003325033,0.00013079318,0.00020578656,0.00041794943],"genre_scores_gemma":[0.19481577,0.0009263085,0.7980107,0.0002501015,0.00025624697,0.00035078393,0.0020851076,0.00022129426,0.0030837194],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99775,0.0008252257,0.00011429792,0.00050806825,0.00059091696,0.00021154094],"domain_scores_gemma":[0.99703336,0.0011660213,0.00023837881,0.00054963585,0.0008565411,0.00015599933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002098781,0.0014510342,0.0019918073,0.0013852306,0.0011101675,0.0015511033,0.0022035816,0.0016520582,0.0024291005],"category_scores_gemma":[0.006593203,0.00070148817,0.0019834945,0.002345895,0.0009740735,0.0019925258,0.0022038596,0.002225174,0.0017212091],"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":[0.00049853197,0.00034758577,0.0014382607,0.0005679268,0.0003848743,0.00023591454,0.00039332293,0.2440191,0.029205848,0.051703226,0.025431082,0.6457744],"study_design_scores_gemma":[0.000014462257,0.00005220994,0.0003541716,0.0000191022,0.000024162142,0.00008065738,0.00005796064,0.9731478,0.0018346851,0.022239344,0.0021426969,0.00003273327],"about_ca_topic_score_codex":0.0050304136,"about_ca_topic_score_gemma":0.00635449,"teacher_disagreement_score":0.0050304136,"about_ca_system_score_codex":0.0007287341,"about_ca_system_score_gemma":0.0013736403,"threshold_uncertainty_score":0.011099577},"labels":[],"label_agreement":null},{"id":"W4409603440","doi":"10.1007/978-3-031-88226-5_16","title":"Functional Sparse Data Clustering Using Conditional Expectation PACE Method","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Face and Expression Recognition","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":"Université de Montréal; Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Cluster analysis; Pace; Data mining; Pattern recognition (psychology); Artificial intelligence; Physics","score_opus":0.15505149710650282,"score_gpt":0.3661923080572737,"score_spread":0.2111408109507709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409603440","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0008364995,0.000050420054,0.99853253,0.000026403675,0.000015487421,0.00001024411,0.000036015965,0.00024072123,0.00025155774],"genre_scores_gemma":[0.075191446,0.00029011862,0.91859996,0.00011255542,0.000094107985,0.0001450241,0.0010903374,0.0004265871,0.0040498553],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990964,0.00025671825,0.000051108418,0.00022683735,0.00030598126,0.000062944455],"domain_scores_gemma":[0.99858946,0.00053496787,0.000080748156,0.0002769553,0.00045629608,0.00006159374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014155963,0.000804892,0.0013992924,0.0011306023,0.00065783696,0.001005162,0.002327386,0.0010837229,0.0042883544],"category_scores_gemma":[0.003199518,0.00067402894,0.0015180954,0.0017147852,0.0007311601,0.0013735023,0.0020068868,0.0017198495,0.0024000383],"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":[0.00026138083,0.00011401515,0.0008810888,0.0002982523,0.00020854823,0.0001323919,0.00015006089,0.3748235,0.017212654,0.053943798,0.013860088,0.5381142],"study_design_scores_gemma":[0.0000059143827,0.000020195645,0.0001432943,0.0000054111765,0.0000107628875,0.000053186974,0.000010117897,0.9883108,0.0021345546,0.007684488,0.0016081339,0.00001317995],"about_ca_topic_score_codex":0.0032421027,"about_ca_topic_score_gemma":0.00289886,"teacher_disagreement_score":0.0042883544,"about_ca_system_score_codex":0.00053508877,"about_ca_system_score_gemma":0.0011136269,"threshold_uncertainty_score":0.014345944},"labels":[],"label_agreement":null},{"id":"W4409618148","doi":"10.1007/978-3-031-86302-8_2","title":"Comparative Analysis of Improved K-Means Clustering for Human Freedom Index","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Face and Expression Recognition","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":"Horizon College and Seminary","funders":"","keywords":"Index (typography); Cluster analysis; Computer science; Information retrieval; Artificial intelligence; World Wide Web","score_opus":0.051644602779820775,"score_gpt":0.3331275381133378,"score_spread":0.281482935333517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409618148","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.348834,0.0063430057,0.6139381,0.00036025507,0.00036598937,0.00023433864,0.0018279986,0.0033355395,0.024760827],"genre_scores_gemma":[0.74412435,0.001070737,0.24510986,0.000049589973,0.00008153704,0.00010462347,0.0027301658,0.0004983696,0.006230823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985886,0.00035993452,0.00007203999,0.00021800987,0.00062615913,0.000135241],"domain_scores_gemma":[0.9966979,0.0012978751,0.00009445818,0.00033096888,0.0015222456,0.0000565176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001678312,0.0005492323,0.0008624306,0.0030293034,0.001081299,0.001146771,0.0011696261,0.0005677944,0.0062591922],"category_scores_gemma":[0.0051617804,0.00014052111,0.0008456748,0.003306017,0.00040391085,0.0010954661,0.00056854583,0.00030755947,0.001123959],"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":[0.002536617,0.0002492508,0.008025289,0.00073615357,0.00034544803,0.00012801023,0.00041764072,0.21269457,0.014751861,0.011233169,0.012615608,0.73626643],"study_design_scores_gemma":[0.00003418441,0.00023539919,0.019525144,0.00003309124,0.0001707261,0.00013775346,0.00047554777,0.9600854,0.010257637,0.004294668,0.0046874853,0.00006307198],"about_ca_topic_score_codex":0.021271314,"about_ca_topic_score_gemma":0.018814033,"teacher_disagreement_score":0.021271314,"about_ca_system_score_codex":0.0014959088,"about_ca_system_score_gemma":0.0010111912,"threshold_uncertainty_score":0.04229504},"labels":[],"label_agreement":null},{"id":"W4410286698","doi":"10.1016/j.patcog.2025.111775","title":"A novel hypergraph neural network combining multi-view learning with density awareness","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"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":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Hypergraph; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Pattern recognition (psychology); Mathematics; Discrete mathematics","score_opus":0.03532779691959618,"score_gpt":0.2615745250765521,"score_spread":0.22624672815695593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410286698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017361503,0.0009207352,0.9771633,0.00036798915,0.0001967725,0.00006409896,0.00019756507,0.0015909271,0.0021370468],"genre_scores_gemma":[0.5789689,0.0011814742,0.40652487,0.00096811465,0.00028772428,0.00023757693,0.0009812239,0.0002465356,0.0106036635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996648,0.00005314969,0.000015933612,0.00013132626,0.00008345042,0.000051386138],"domain_scores_gemma":[0.99957293,0.00014478323,0.000029552752,0.00006888864,0.00013063432,0.000053222862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046017053,0.000995193,0.0013179027,0.000920186,0.0005189748,0.00091365475,0.0025718352,0.0019230401,0.0026773964],"category_scores_gemma":[0.0011685141,0.00066398364,0.00080009806,0.0012401869,0.0005476541,0.0020674565,0.0017925064,0.0013304144,0.00076530944],"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":[0.0002031221,0.0002950458,0.001365454,0.00013378152,0.00026965217,0.00013771422,0.00005427018,0.36086774,0.013538745,0.009894802,0.009368931,0.6038708],"study_design_scores_gemma":[0.000005742352,0.000022227732,0.000103278144,0.0000039816646,0.0000147900955,0.00001998365,0.000004094164,0.9964479,0.0009342099,0.0020676563,0.00036936984,0.000006719883],"about_ca_topic_score_codex":0.017911984,"about_ca_topic_score_gemma":0.018225638,"teacher_disagreement_score":0.017911984,"about_ca_system_score_codex":0.00089385017,"about_ca_system_score_gemma":0.001052572,"threshold_uncertainty_score":0.035615444},"labels":[],"label_agreement":null},{"id":"W4410420444","doi":"10.1101/2025.05.16.25327749","title":"Automatic detection of n-degree family members","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Face and Expression Recognition","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":"Institute for Biological Sciences","funders":"Novo Nordisk","keywords":"Degree (music); Computer science; Mathematics; Physics; Acoustics","score_opus":0.03627537906486184,"score_gpt":0.26908425514984036,"score_spread":0.23280887608497852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410420444","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42239162,0.0053611733,0.515854,0.00093406445,0.00043027097,0.00036187074,0.020792972,0.011817872,0.022056067],"genre_scores_gemma":[0.78601974,0.0010197589,0.18488134,0.00022496688,0.00016332371,0.00016206414,0.016962875,0.0006961241,0.009869746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988003,0.00021122246,0.00005955224,0.0005665834,0.00025718438,0.00010516945],"domain_scores_gemma":[0.9984389,0.0006392753,0.00022559894,0.00025477016,0.00033378872,0.000107773754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096752186,0.00061270146,0.00045856132,0.00351266,0.0007118169,0.00073996,0.00084376754,0.00062136765,0.005899659],"category_scores_gemma":[0.003635097,0.00027456277,0.00045378334,0.0014629526,0.00027367965,0.00051478756,0.0009961795,0.00040364024,0.0034571425],"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":[0.0008206481,0.00015544875,0.16350813,0.0006964285,0.00044809878,0.0017214266,0.0009791364,0.013597504,0.050932523,0.008469341,0.10597673,0.6526946],"study_design_scores_gemma":[0.00011061575,0.0001470075,0.20956963,0.00032918682,0.00045019438,0.007243512,0.0010589507,0.5350958,0.0543075,0.03723916,0.15430136,0.0001471166],"about_ca_topic_score_codex":0.0052613267,"about_ca_topic_score_gemma":0.010633286,"teacher_disagreement_score":0.005899659,"about_ca_system_score_codex":0.00034526043,"about_ca_system_score_gemma":0.000493521,"threshold_uncertainty_score":0.01973635},"labels":[],"label_agreement":null},{"id":"W4410454723","doi":"10.20944/preprints202505.1095.v1","title":"Efficient Dynamic Emotion Recognition from Facial Expressions Using Statistical Spatio-Temporal Geometric Features","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Face and Expression Recognition","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":"Centre intégré de santé et de services sociaux de Chaudière-Appalaches","funders":"","keywords":"Computer science; Pattern recognition (psychology); Artificial intelligence; Facial expression; Emotion recognition; Speech recognition","score_opus":0.09988299566617437,"score_gpt":0.35025747713135635,"score_spread":0.250374481465182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410454723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19657011,0.0010258037,0.79411405,0.00030892526,0.00015333596,0.0001350479,0.0011309903,0.0022035812,0.0043581882],"genre_scores_gemma":[0.8082016,0.00075374666,0.18379171,0.000101731835,0.00008733709,0.00015067257,0.0022501324,0.00015777223,0.004505168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996996,0.00004534056,0.00002097527,0.00008405028,0.000112954556,0.000037052887],"domain_scores_gemma":[0.9998373,0.00003284829,0.000035743054,0.000025555351,0.00006043332,0.00000806571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027197358,0.00057761255,0.00045790483,0.0006743814,0.00015390996,0.0004485999,0.00045000875,0.00027502282,0.001608719],"category_scores_gemma":[0.0007169385,0.00012423164,0.0004950589,0.00059117423,0.0001837201,0.00050944585,0.00044558782,0.000319983,0.0011495994],"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":[0.00034644344,0.00012456725,0.0033215587,0.00009083512,0.00005765402,0.00013180583,0.000063656465,0.023108514,0.14414592,0.0016901438,0.006742508,0.8201764],"study_design_scores_gemma":[0.000018296116,0.00021282026,0.016420983,0.00002183796,0.000056910627,0.00043114915,0.00012930762,0.92027235,0.052998606,0.0029461714,0.006453304,0.000038235034],"about_ca_topic_score_codex":0.0014725955,"about_ca_topic_score_gemma":0.0018072132,"teacher_disagreement_score":0.001608719,"about_ca_system_score_codex":0.00022990552,"about_ca_system_score_gemma":0.00024213077,"threshold_uncertainty_score":0.005381763},"labels":[],"label_agreement":null},{"id":"W4410491745","doi":"10.1109/tnnls.2025.3563889","title":"A Robust Three-Way Classifier With Shadowed Granular Balls Based on Justifiable Granularity","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Granularity; Classifier (UML); Granular computing; Computer science; Artificial intelligence; Pattern recognition (psychology); Rough set","score_opus":0.018126707080082613,"score_gpt":0.21291551288774427,"score_spread":0.19478880580766167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410491745","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040009756,0.0006257531,0.95596355,0.00044811747,0.00007888439,0.00013050031,0.00019473216,0.0009617682,0.0015868943],"genre_scores_gemma":[0.7569348,0.00045790337,0.23775397,0.0004052244,0.00013039974,0.0003554185,0.0008039371,0.00015202852,0.0030063852],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997482,0.00035392682,0.000250333,0.0006419118,0.0009533168,0.0003185343],"domain_scores_gemma":[0.9969797,0.001223105,0.00033845828,0.00046642256,0.00081708236,0.00017520812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020777609,0.0009839335,0.0023906846,0.0022006396,0.0009902955,0.002563197,0.0023454633,0.0016527143,0.0013799452],"category_scores_gemma":[0.0072957436,0.00047602557,0.001583927,0.0017909493,0.0015157011,0.0041547837,0.00266404,0.002027558,0.0005879074],"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":[0.00081778556,0.00015875198,0.009688379,0.00022741254,0.00015776778,0.00036193535,0.00044673873,0.45835605,0.010574621,0.03511792,0.008065948,0.47602674],"study_design_scores_gemma":[0.000017230632,0.00004631712,0.00044787006,0.000014559353,0.000019175215,0.00007451613,0.000034074415,0.98566586,0.0019622848,0.010581468,0.0011185344,0.000018117404],"about_ca_topic_score_codex":0.007798817,"about_ca_topic_score_gemma":0.0036221999,"teacher_disagreement_score":0.007798817,"about_ca_system_score_codex":0.0017175358,"about_ca_system_score_gemma":0.001938942,"threshold_uncertainty_score":0.015506864},"labels":[],"label_agreement":null},{"id":"W4410512047","doi":"10.33423/jabe.v27i3.7649","title":"Exploring the Geometric Mean of Grouped Data","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Face and Expression Recognition","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":"Mathematics; Statistics","score_opus":0.08495088506240413,"score_gpt":0.23587450839804003,"score_spread":0.15092362333563591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410512047","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008092887,0.00024802153,0.9906176,0.0001311559,0.00004768871,0.00003323625,0.000103441045,0.00021730087,0.0005087198],"genre_scores_gemma":[0.28134733,0.00086420303,0.71384394,0.00034957906,0.0004121901,0.00042862073,0.0011832414,0.0002938412,0.0012770685],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9922536,0.0033124832,0.000387643,0.0017398073,0.0019569434,0.00034957545],"domain_scores_gemma":[0.96148425,0.026280982,0.0028770838,0.005145018,0.0038298978,0.00038274183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015315938,0.0009016147,0.0016888398,0.0047501386,0.00084479136,0.0029290107,0.0025651911,0.0016934469,0.0022063304],"category_scores_gemma":[0.07291236,0.00060480676,0.0014393637,0.0036936793,0.002170561,0.0046017244,0.0032848483,0.0023560277,0.0010166067],"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":[0.00034959833,0.00021348477,0.03784484,0.000650055,0.0006682855,0.00048886635,0.0018716836,0.22716299,0.007887684,0.2549935,0.006873169,0.4609959],"study_design_scores_gemma":[0.000048859532,0.00025128783,0.0072331703,0.000173914,0.00010105111,0.00032985205,0.0003986607,0.7083292,0.004383437,0.26654196,0.012103068,0.00010559609],"about_ca_topic_score_codex":0.00142164,"about_ca_topic_score_gemma":0.0010184738,"teacher_disagreement_score":0.015315938,"about_ca_system_score_codex":0.00096839375,"about_ca_system_score_gemma":0.00145736,"threshold_uncertainty_score":0.080999374},"labels":[],"label_agreement":null},{"id":"W4410932871","doi":"10.1002/cjs.70013","title":"A deep support vector clustering algorithm for unsupervised and semi‐supervised learning","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Face and Expression Recognition","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":"National Natural Science Foundation of China","keywords":"Cluster analysis; Artificial intelligence; Computer science; Unsupervised learning; Pattern recognition (psychology); Semi-supervised learning; Machine learning; Algorithm","score_opus":0.013627152955640858,"score_gpt":0.23192099257655302,"score_spread":0.21829383962091217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410932871","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005877203,0.000102722224,0.9926393,0.00009466537,0.00002418166,0.000058113157,0.00005322211,0.00072570995,0.0004248863],"genre_scores_gemma":[0.22413203,0.00012340621,0.7707378,0.00019559087,0.00007699567,0.00032065884,0.0007947737,0.00030793363,0.003310878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99795437,0.0005917452,0.00015035112,0.0005691323,0.0005672996,0.0001669742],"domain_scores_gemma":[0.996609,0.0012045306,0.0003213085,0.0005426057,0.0011541481,0.00016831483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029347984,0.0011007745,0.001576155,0.0021946712,0.0009951418,0.0013066261,0.0032989976,0.001995718,0.0031948297],"category_scores_gemma":[0.0064076637,0.0007913965,0.0014379758,0.0019246422,0.0012301104,0.0018287557,0.0023268822,0.002947024,0.0016381058],"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":[0.00023126253,0.0001612131,0.0012125035,0.0001079612,0.00014734946,0.00007104097,0.00015858904,0.50681156,0.0040299455,0.025400622,0.006651874,0.45501608],"study_design_scores_gemma":[0.0000043598584,0.000011404169,0.000043405984,0.0000035939058,0.0000020874688,0.000009029203,0.000005879276,0.9954786,0.00052535546,0.0036414145,0.0002706948,0.0000042745364],"about_ca_topic_score_codex":0.0050179088,"about_ca_topic_score_gemma":0.004666398,"teacher_disagreement_score":0.0050179088,"about_ca_system_score_codex":0.0015249576,"about_ca_system_score_gemma":0.0020390411,"threshold_uncertainty_score":0.015520871},"labels":[],"label_agreement":null},{"id":"W4411162948","doi":"10.1007/s10791-025-09622-1","title":"A unified and scalable machine learning framework for feature fusion in object classification using weighted PCA with adaptive concatenation and dynamic scaling","year":2025,"lang":"en","type":"article","venue":"Discover Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; California Institute of Technology","keywords":"Concatenation (mathematics); Computer science; Feature (linguistics); Artificial intelligence; Fusion; Pattern recognition (psychology); Scaling; Scalability; Object (grammar); Mathematics; Arithmetic","score_opus":0.016727920794609743,"score_gpt":0.27278855435937444,"score_spread":0.2560606335647647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411162948","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0019976727,0.00017538601,0.9963791,0.000051316198,0.000030874122,0.00003803598,0.000048811184,0.0008907856,0.0003880612],"genre_scores_gemma":[0.16510846,0.00046503753,0.8310801,0.00016959204,0.00020121066,0.000384492,0.0007469341,0.0002648845,0.0015793657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974922,0.00050437346,0.00019582725,0.0006148468,0.0010060448,0.00018670024],"domain_scores_gemma":[0.99839264,0.0002947919,0.00016636676,0.0004808001,0.0005879956,0.00007735755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029035222,0.0021606837,0.002148018,0.0024482731,0.001037177,0.0018305681,0.0028435115,0.0011119092,0.0021430107],"category_scores_gemma":[0.0047411076,0.00074564415,0.002103588,0.0040970137,0.0013402279,0.004554341,0.0038795327,0.002594063,0.0018908053],"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":[0.00015694421,0.00017167914,0.0012272042,0.00014689734,0.0002164048,0.00013687064,0.0001821967,0.20835544,0.02152332,0.026507419,0.007898581,0.7334771],"study_design_scores_gemma":[0.000014826799,0.00006238079,0.0003360459,0.000012391658,0.00003320477,0.00006526835,0.000025964704,0.97010684,0.005862139,0.020037882,0.0034129475,0.000030135556],"about_ca_topic_score_codex":0.003698626,"about_ca_topic_score_gemma":0.0033478292,"teacher_disagreement_score":0.003698626,"about_ca_system_score_codex":0.0009120269,"about_ca_system_score_gemma":0.0016736652,"threshold_uncertainty_score":0.015355468},"labels":[],"label_agreement":null},{"id":"W4411171623","doi":"10.1109/tai.2025.3578585","title":"Toward Robust Nonlinear Subspace Clustering: A Kernel Learning Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Face and Expression Recognition","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":"Université de Sherbrooke","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Kernel (algebra); Cluster analysis; Subspace topology; Nonlinear system; Artificial intelligence; Computer science; Kernel method; Pattern recognition (psychology); Mathematics; Support vector machine; Physics; Combinatorics","score_opus":0.07639253927367157,"score_gpt":0.2930379507840904,"score_spread":0.21664541151041883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411171623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00165372,0.000088431436,0.9976267,0.000055208304,0.000009024652,0.00001199645,0.00001908814,0.00021144925,0.00032427273],"genre_scores_gemma":[0.22749902,0.0006242223,0.76578647,0.00020585665,0.0001303852,0.00020494754,0.0006969629,0.0003949695,0.0044570244],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99840254,0.0005050683,0.00007502825,0.00035540495,0.0005473422,0.0001146996],"domain_scores_gemma":[0.99822444,0.0004879998,0.00018020152,0.00042334746,0.0006078665,0.00007622299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016672132,0.0012213244,0.0014266927,0.0016716182,0.0007315682,0.0013711707,0.002334997,0.001393144,0.0015056395],"category_scores_gemma":[0.0053344844,0.00055719115,0.001097122,0.0019955556,0.0011168246,0.0018091808,0.0028734405,0.0019493771,0.0016570574],"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":[0.00010485407,0.000103102924,0.0008036152,0.00019142596,0.00017462694,0.000072436334,0.00019059368,0.6196294,0.009913827,0.056892794,0.0051888814,0.30673444],"study_design_scores_gemma":[0.0000029900339,0.00001431363,0.00007135226,0.0000041969693,0.0000047700155,0.000019032994,0.000013531095,0.9877104,0.0009747541,0.0102629345,0.00091224554,0.000009415467],"about_ca_topic_score_codex":0.0038796936,"about_ca_topic_score_gemma":0.0033445216,"teacher_disagreement_score":0.0038796936,"about_ca_system_score_codex":0.00089083257,"about_ca_system_score_gemma":0.0016622595,"threshold_uncertainty_score":0.008817196},"labels":[],"label_agreement":null},{"id":"W4411510004","doi":"10.1007/s40747-025-01989-4","title":"Improving SVM performance through data reduction and misclassification analysis with linear programming","year":2025,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Escuela Superior Politécnica del Litoral","keywords":"Support vector machine; Computational intelligence; Reduction (mathematics); Computer science; Data mining; Dimensionality reduction; Data reduction; Linear programming; Artificial intelligence; Machine learning; Pattern recognition (psychology); Mathematics; Algorithm","score_opus":0.09051585609405494,"score_gpt":0.31009623369091033,"score_spread":0.2195803775968554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411510004","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011702296,0.00013193706,0.98695964,0.00022040248,0.000026774293,0.000031911422,0.00002542881,0.00044195578,0.00045973007],"genre_scores_gemma":[0.2476555,0.00031373758,0.7492355,0.0001716794,0.000109070716,0.00018362142,0.00027257786,0.0003096938,0.001748564],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968297,0.0010110784,0.0002760312,0.00038462091,0.0013195026,0.00017902165],"domain_scores_gemma":[0.99236166,0.0040278146,0.0006569805,0.0012719233,0.0015724921,0.000108992805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041264817,0.0016840085,0.0021366917,0.0016356199,0.00055975973,0.0027042301,0.0014145849,0.001061488,0.0013954911],"category_scores_gemma":[0.0155085465,0.0006195862,0.0011434485,0.0015103868,0.00089747796,0.0029175736,0.0018763838,0.0029161493,0.001125848],"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":[0.00029012733,0.0003606278,0.0033057379,0.00018839074,0.0001443065,0.00010986796,0.00022772489,0.41022736,0.017544026,0.024335636,0.003987437,0.53927886],"study_design_scores_gemma":[0.0000047567837,0.00002807441,0.00024755232,0.000007622792,0.000008268649,0.000016500946,0.000010726808,0.989151,0.003934739,0.006157559,0.00042451496,0.000008622258],"about_ca_topic_score_codex":0.0016865516,"about_ca_topic_score_gemma":0.0014603469,"teacher_disagreement_score":0.0041264817,"about_ca_system_score_codex":0.0008488973,"about_ca_system_score_gemma":0.0013314849,"threshold_uncertainty_score":0.021823168},"labels":[],"label_agreement":null},{"id":"W4411618004","doi":"10.51847/zj8cx3zdkr","title":"10.51847/ZJ8cX3zdKR","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Face and Expression Recognition","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":"Cluster analysis; Fuzzy clustering; Computer science; Artificial intelligence; Fuzzy logic; Machine learning; Mathematics","score_opus":0.006991137310934684,"score_gpt":0.18207593174394485,"score_spread":0.17508479443301017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411618004","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007820936,0.0021238471,0.032416347,0.0016855838,0.0018259415,0.00053822726,0.0020948262,0.008333661,0.9431607],"genre_scores_gemma":[0.011454322,0.00082614104,0.010339905,0.00046668557,0.00015196732,0.0001688567,0.002071253,0.00067871594,0.97384214],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992441,0.00009126296,0.000070285205,0.0002446438,0.00023913693,0.00011055327],"domain_scores_gemma":[0.9988023,0.00028971667,0.000073150055,0.00026053435,0.00042147297,0.00015279259],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00091085635,0.0018596682,0.0009627444,0.0022272237,0.0011222177,0.0030222738,0.0016551906,0.0040485696,0.9053642],"category_scores_gemma":[0.001574035,0.000571411,0.0007621065,0.0030133228,0.0009432223,0.002047401,0.0020170384,0.0013765967,0.92548895],"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":[0.0004959852,0.00027677158,0.001428171,0.00070490077,0.000050878512,0.00046152333,0.00015320598,0.0018394479,0.014226729,0.008661242,0.15332034,0.81838083],"study_design_scores_gemma":[0.00011741748,0.0001523811,0.0018276449,0.0002308876,0.000047062924,0.00079053594,0.00020671402,0.0060272785,0.0044760546,0.0026951542,0.98337656,0.000052274165],"about_ca_topic_score_codex":0.0047466657,"about_ca_topic_score_gemma":0.0034308934,"teacher_disagreement_score":0.094635785,"about_ca_system_score_codex":0.0010565324,"about_ca_system_score_gemma":0.0005772177,"threshold_uncertainty_score":0.13498646},"labels":[],"label_agreement":null},{"id":"W4411771749","doi":"10.5539/ijsp.v14n2p13","title":"Resampling-based Inference Procedure for Median Regression Estimator with Censored Data","year":2025,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Face and Expression Recognition","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":"Illinois Wesleyan University","keywords":"Resampling; Mathematics; Statistics; Estimator; Inference; Regression; Computer science; Artificial intelligence","score_opus":0.03627330618705634,"score_gpt":0.34518981674063065,"score_spread":0.30891651055357433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411771749","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0010094048,0.00010417402,0.9982784,0.00005454007,0.000034710516,0.000047891423,0.000044930188,0.00020828794,0.00021770468],"genre_scores_gemma":[0.08588373,0.0005980886,0.90951884,0.00026472402,0.0002822437,0.00068758975,0.0006122628,0.00031714817,0.0018353083],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948085,0.003118913,0.00024560463,0.00075720914,0.00086451584,0.00020533365],"domain_scores_gemma":[0.9876601,0.009119795,0.00067717937,0.0010725089,0.0013268292,0.00014362845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010734327,0.0009829397,0.0016044574,0.0020151648,0.000910672,0.00093104364,0.0025813668,0.0015545423,0.0069212266],"category_scores_gemma":[0.042588297,0.0004977973,0.0018276584,0.0019321938,0.0010570931,0.0016984264,0.0016922969,0.00294725,0.001507536],"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":[0.00038398037,0.00024071953,0.0068281502,0.0007424328,0.0005407204,0.0007110897,0.00051397155,0.24141788,0.008121712,0.2270497,0.013608816,0.49984086],"study_design_scores_gemma":[0.00008736705,0.00009046737,0.0012711487,0.000094494695,0.00008507593,0.00024150785,0.00006542935,0.9149627,0.004207848,0.072427884,0.00640652,0.0000595658],"about_ca_topic_score_codex":0.0047015552,"about_ca_topic_score_gemma":0.004539684,"teacher_disagreement_score":0.010734327,"about_ca_system_score_codex":0.00080507185,"about_ca_system_score_gemma":0.0019404477,"threshold_uncertainty_score":0.05676925},"labels":[],"label_agreement":null},{"id":"W4411991677","doi":"10.1016/j.asoc.2025.113508","title":"Multi-view nonnegative matrix factorization via orthogonal and adversarial graph regularization","year":2025,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Adversarial system; Matrix decomposition; Factorization; Mathematics; Regularization (linguistics); Computer science; Graph; Artificial intelligence; Algorithm; Combinatorics; Eigenvalues and eigenvectors; Physics","score_opus":0.0087458857584294,"score_gpt":0.25124766849080526,"score_spread":0.24250178273237585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411991677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0023796633,0.00007220876,0.9967044,0.00009595856,0.000037697297,0.00001850067,0.000040283856,0.0001988412,0.00045235487],"genre_scores_gemma":[0.28261238,0.00051617634,0.7071236,0.0004005493,0.00019513117,0.00025543504,0.00084937277,0.00039145479,0.0076559256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988514,0.00045151584,0.000036920614,0.00024996191,0.00028796188,0.00012224358],"domain_scores_gemma":[0.9984004,0.0006505813,0.00015854434,0.00033291877,0.0003510684,0.000106420935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016571,0.0014880848,0.0013417036,0.0008149127,0.0005432436,0.0011429247,0.002148983,0.0017132432,0.0024869742],"category_scores_gemma":[0.0047775027,0.00086696603,0.0017231872,0.00096809084,0.0012715879,0.0019126722,0.002351956,0.0030885625,0.0013154298],"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":[0.00023713545,0.00021056854,0.0006734002,0.00019497712,0.0001832911,0.0001666602,0.00013093615,0.67568916,0.012380393,0.08225473,0.014006887,0.21387175],"study_design_scores_gemma":[0.000003675967,0.00001095518,0.0000391565,0.0000036496845,0.000005412765,0.000017594515,0.0000053676013,0.9916414,0.00049803994,0.007401888,0.00036738117,0.000005627671],"about_ca_topic_score_codex":0.0049910825,"about_ca_topic_score_gemma":0.007298762,"teacher_disagreement_score":0.0049910825,"about_ca_system_score_codex":0.00067667296,"about_ca_system_score_gemma":0.0012065679,"threshold_uncertainty_score":0.009924054},"labels":[],"label_agreement":null},{"id":"W4412049285","doi":"10.1016/j.patcog.2025.112045","title":"Adaptive kernel subspace clustering with discrete group structure constraint","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","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":"University of Calgary","funders":"Natural Science Foundation of Jiangxi Province; National Natural Science Foundation of China","keywords":"Subspace topology; Kernel (algebra); Constraint (computer-aided design); Cluster analysis; Mathematics; Computer science; Group (periodic table); Artificial intelligence; Pattern recognition (psychology); Combinatorics; Physics","score_opus":0.014354029705217767,"score_gpt":0.23182256168407392,"score_spread":0.21746853197885616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412049285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006604393,0.0000770714,0.992258,0.000065026084,0.000023607781,0.000022175658,0.00004753655,0.00028574586,0.0006164117],"genre_scores_gemma":[0.29653227,0.00021693719,0.696039,0.000112890164,0.00006920041,0.00020183335,0.00082810165,0.00028065406,0.0057190834],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860674,0.00039150045,0.000063515916,0.00027221284,0.0005556432,0.00011034145],"domain_scores_gemma":[0.9985857,0.00033114257,0.000115727526,0.00039154597,0.00050155533,0.0000744165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008809646,0.0006669934,0.0014219831,0.00068122114,0.00056011946,0.0009522675,0.0020656795,0.000976676,0.0023983333],"category_scores_gemma":[0.00292067,0.00048107453,0.0009910818,0.0016428166,0.0007335928,0.0014026599,0.0017255332,0.0012689099,0.0013050155],"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":[0.00048311002,0.00024346792,0.0009833401,0.00024183682,0.00023027448,0.00008435233,0.00012400254,0.4744272,0.025118655,0.037194554,0.010393065,0.4504762],"study_design_scores_gemma":[0.000010765629,0.000029771438,0.00014268693,0.0000025096438,0.0000076012034,0.000021647804,0.000008740692,0.99340606,0.0019892803,0.003478424,0.00089378306,0.000008788201],"about_ca_topic_score_codex":0.0055223103,"about_ca_topic_score_gemma":0.0062227105,"teacher_disagreement_score":0.0055223103,"about_ca_system_score_codex":0.00054393534,"about_ca_system_score_gemma":0.0017553922,"threshold_uncertainty_score":0.010980308},"labels":[],"label_agreement":null},{"id":"W4412487375","doi":"10.1016/j.neunet.2025.107802","title":"Disentangled representation learning for multi-view clustering via von Mises–Fisher hyperspherical embedding","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Face and Expression Recognition","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":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Computer science; Cluster analysis; Discriminative model; Embedding; Artificial intelligence; Representation (politics); Gaussian; Similarity (geometry); Feature learning; Consistency (knowledge bases); Benchmark (surveying); Data mining; Machine learning; Pattern recognition (psychology)","score_opus":0.035195628978821954,"score_gpt":0.32027135453665245,"score_spread":0.2850757255578305,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412487375","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011661716,0.00021417216,0.9870839,0.00016399365,0.000024588364,0.000020975458,0.0000753106,0.00022029657,0.00053505244],"genre_scores_gemma":[0.58289737,0.00057611865,0.4087659,0.0002560879,0.00012742008,0.00019315915,0.0012325534,0.00030608082,0.0056452504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991228,0.00030091466,0.000043163614,0.00023356605,0.00020809428,0.00009158555],"domain_scores_gemma":[0.99848086,0.00062716124,0.00014352695,0.00034219676,0.00031649324,0.00008971106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015080551,0.0012584153,0.001495233,0.0011033193,0.0006218896,0.0013754057,0.001786074,0.00190616,0.0021085818],"category_scores_gemma":[0.0052746995,0.0007254522,0.0015215381,0.0011124823,0.0010123275,0.0028954053,0.0027347147,0.0025863785,0.0010824709],"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":[0.00032037174,0.00020958018,0.0013731424,0.00024898755,0.0003163168,0.00016169243,0.00034158007,0.51453996,0.016506042,0.09034185,0.007822662,0.3678178],"study_design_scores_gemma":[0.000003158205,0.000017385823,0.00015933852,0.0000062341974,0.000007700366,0.000020035095,0.000012585452,0.9834245,0.0006915504,0.015296848,0.0003510283,0.000009721909],"about_ca_topic_score_codex":0.0052031125,"about_ca_topic_score_gemma":0.0055272174,"teacher_disagreement_score":0.0052031125,"about_ca_system_score_codex":0.0007539622,"about_ca_system_score_gemma":0.0010324622,"threshold_uncertainty_score":0.010345638},"labels":[],"label_agreement":null},{"id":"W4413018418","doi":"10.1109/fg61629.2025.11099202","title":"Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data","year":2025,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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; École de Technologie Supérieure","funders":"","keywords":"Personalization; Computer science; Facial expression recognition; Facial expression; Expression (computer science); Speech recognition; Human–computer interaction; Pattern recognition (psychology); Artificial intelligence; Facial recognition system; World Wide Web","score_opus":0.034644447026207864,"score_gpt":0.2697093111204252,"score_spread":0.2350648640942173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413018418","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037653197,0.00058339915,0.9573014,0.00017763069,0.00014513177,0.00009788522,0.00041378627,0.0017325289,0.0018951212],"genre_scores_gemma":[0.6612743,0.0011970167,0.32101154,0.00058466365,0.00015104804,0.0003696799,0.003296298,0.0004442717,0.011671177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947494,0.000110151705,0.000026554562,0.00020869756,0.00012547788,0.000054237884],"domain_scores_gemma":[0.9995616,0.00012382433,0.000031069947,0.00013830815,0.00011864754,0.000026487862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007299592,0.00095004274,0.0005765296,0.00036601583,0.0001953358,0.0004362456,0.00075742684,0.00044810004,0.001673205],"category_scores_gemma":[0.0024820098,0.00027408727,0.0007821103,0.0003398063,0.00035442927,0.00078969816,0.00094976206,0.0012672251,0.0013395337],"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":[0.00057643396,0.00029379842,0.002918204,0.0001336814,0.00014545201,0.0003162641,0.0002445464,0.100959376,0.10049248,0.0027476915,0.009510517,0.7816614],"study_design_scores_gemma":[0.000018282735,0.00014176586,0.0029481158,0.00002167909,0.00003869403,0.0003003849,0.0000728771,0.95746017,0.02998911,0.0038624313,0.0051131323,0.000033298427],"about_ca_topic_score_codex":0.0024378852,"about_ca_topic_score_gemma":0.0038932224,"teacher_disagreement_score":0.0024378852,"about_ca_system_score_codex":0.00029394985,"about_ca_system_score_gemma":0.00043753372,"threshold_uncertainty_score":0.0055974126},"labels":[],"label_agreement":null},{"id":"W4413146150","doi":"10.1109/cvpr52734.2025.01432","title":"A Unified Framework for Heterogeneous Semi-supervised Learning","year":2025,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence","score_opus":0.018935250005797863,"score_gpt":0.278192111045143,"score_spread":0.25925686103934514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00096014986,0.00010393771,0.9983619,0.00007148966,0.000015174488,0.000030663246,0.000025232235,0.00020096537,0.00023056247],"genre_scores_gemma":[0.23280291,0.000398699,0.7620983,0.00045910876,0.00036729942,0.00066906115,0.0007516035,0.0002724591,0.002180571],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99438393,0.0026721396,0.0002872648,0.0012925849,0.0010965832,0.00026745323],"domain_scores_gemma":[0.99475276,0.0023708013,0.0003735062,0.001079227,0.0011660004,0.00025755996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0063854456,0.0014289934,0.0019099914,0.0014200703,0.0007954438,0.001752034,0.0045707626,0.0016727861,0.0016981645],"category_scores_gemma":[0.008831789,0.0007457525,0.0014540858,0.0013083634,0.002035858,0.003076019,0.004405273,0.0029771437,0.0007497197],"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":[0.00022831984,0.00032988202,0.0019011927,0.0004494502,0.0003956924,0.000261332,0.0005018539,0.5206959,0.0066593755,0.08216744,0.00846331,0.3779463],"study_design_scores_gemma":[0.000010497097,0.00003736541,0.00009250127,0.000010658255,0.00001151349,0.00002795363,0.000021361679,0.9731112,0.0007198619,0.024758957,0.0011871132,0.000011001494],"about_ca_topic_score_codex":0.0022679586,"about_ca_topic_score_gemma":0.0031700116,"teacher_disagreement_score":0.0063854456,"about_ca_system_score_codex":0.0011402685,"about_ca_system_score_gemma":0.0022103766,"threshold_uncertainty_score":0.033769846},"labels":[],"label_agreement":null},{"id":"W4413146532","doi":"10.1109/cvpr52734.2025.01885","title":"Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Potts model; Segmentation; Artificial intelligence; Computer science; Image segmentation; Pattern recognition (psychology); Statistical physics; Physics","score_opus":0.012243033839658744,"score_gpt":0.2646022047552896,"score_spread":0.25235917091563087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146532","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01747184,0.000147235,0.97727865,0.00033206353,0.00004904018,0.00005833131,0.00013224127,0.001476508,0.0030541176],"genre_scores_gemma":[0.4595084,0.0002305247,0.5253631,0.00064350566,0.00017355167,0.0003655586,0.0012321019,0.0015712768,0.010912034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987847,0.0003455259,0.00006201902,0.00041102362,0.0002617728,0.00013486568],"domain_scores_gemma":[0.99703574,0.0016006165,0.00023482626,0.00058745715,0.0003560718,0.0001852818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002464228,0.0015690589,0.0013616371,0.00073917455,0.00075504865,0.0016046612,0.002879331,0.002499079,0.006944588],"category_scores_gemma":[0.008002608,0.0009060015,0.0012402537,0.00073560997,0.0023407738,0.0032223158,0.003200966,0.0037675074,0.0016585483],"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":[0.00020299623,0.00009565718,0.00073154754,0.00017623621,0.000048216843,0.00011511552,0.00019946862,0.882534,0.005905601,0.032890067,0.003917184,0.07318387],"study_design_scores_gemma":[0.0000066652606,0.000019174882,0.000059643146,0.000009165914,0.000003410674,0.000015751784,0.000009087172,0.9869769,0.0011770895,0.011311245,0.00040737513,0.0000045242987],"about_ca_topic_score_codex":0.0038288569,"about_ca_topic_score_gemma":0.0059598396,"teacher_disagreement_score":0.006944588,"about_ca_system_score_codex":0.0017051747,"about_ca_system_score_gemma":0.0017749344,"threshold_uncertainty_score":0.023231983},"labels":[],"label_agreement":null},{"id":"W4413165906","doi":"10.1080/10618600.2025.2541012","title":"Boosting Prediction with Data Missing Not at Random","year":2025,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Boosting (machine learning); Missing data; Computer science; Artificial intelligence; Random forest; Machine learning; Econometrics; Statistics; Data mining; Mathematics","score_opus":0.02070957838485018,"score_gpt":0.2705212398444851,"score_spread":0.24981166145963493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413165906","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064505474,0.00023990266,0.9922126,0.00018194421,0.000072351904,0.000037581132,0.00005874074,0.00027347778,0.00047277968],"genre_scores_gemma":[0.5189665,0.00071838684,0.4742151,0.00064241205,0.00044240215,0.00040199282,0.0008299971,0.000240699,0.0035424894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99601597,0.0023492314,0.00016237267,0.0005716469,0.0006219769,0.00027881813],"domain_scores_gemma":[0.9818981,0.012764297,0.00084885105,0.0026057486,0.0015124731,0.0003705734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0179755,0.0011388492,0.003330378,0.00077737676,0.00073190924,0.001408236,0.0031505644,0.0017866427,0.003102151],"category_scores_gemma":[0.03825467,0.00081482384,0.0013295467,0.001057559,0.0014148011,0.0018611487,0.0020786724,0.0029227312,0.0014082801],"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":[0.00066832674,0.0001870145,0.007671131,0.00049571146,0.00026923668,0.0005305674,0.00028505837,0.6248094,0.0027396537,0.13688654,0.009799494,0.2156579],"study_design_scores_gemma":[0.000020279445,0.000035768713,0.0002950641,0.000023185114,0.000020740987,0.0000534172,0.000007931713,0.96959275,0.00052763114,0.028206361,0.0012062817,0.000010520968],"about_ca_topic_score_codex":0.0011986643,"about_ca_topic_score_gemma":0.0011652312,"teacher_disagreement_score":0.0179755,"about_ca_system_score_codex":0.000588503,"about_ca_system_score_gemma":0.0015831739,"threshold_uncertainty_score":0.09506464},"labels":[],"label_agreement":null},{"id":"W4413325339","doi":"10.1016/j.eswa.2025.129306","title":"Corrigendum to: “Multi-view neutrosophic <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si1.svg\"><mml:mi>c</mml:mi></mml:math>-means clustering algorithms” [Expert Syst. Appl. 260 (2025) 126763]","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"","keywords":"Algorithm; Cluster analysis; Computer science; Mathematics; Algebra over a field; Artificial intelligence; Pure mathematics","score_opus":0.02688683925754727,"score_gpt":0.2685451494034896,"score_spread":0.24165831014594236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413325339","genre_codex":"editorial","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00040153624,0.0027853167,0.004051812,0.042619675,0.88336897,0.00012340386,0.0027278461,0.0014564858,0.062464844],"genre_scores_gemma":[0.010294841,0.0047483775,0.004743519,0.015708571,0.101016924,0.00016908148,0.0053656283,0.0017046313,0.8562484],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844724,0.00022643953,0.00015480186,0.00030351657,0.0007186954,0.00014931938],"domain_scores_gemma":[0.98751605,0.0020247435,0.00022613755,0.00060441816,0.009080351,0.0005484193],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0013334557,0.0020304227,0.002110255,0.002999214,0.002525993,0.0031819753,0.0024591836,0.003071384,0.475174],"category_scores_gemma":[0.017508367,0.00077645906,0.0018944326,0.0021622097,0.0010587056,0.0023154456,0.0020574117,0.0029198576,0.25997522],"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":[0.000016178692,0.000007901575,0.00002753984,0.000055786604,0.0000064087853,0.000058867805,0.000014577587,0.000052869003,0.00008439512,0.0006691023,0.99388015,0.005126224],"study_design_scores_gemma":[0.000019734129,0.0000251515,0.00061952864,0.00009264794,0.000022061331,0.0001971089,0.000061980616,0.0005680427,0.0004865592,0.0015668008,0.9963098,0.000030469415],"about_ca_topic_score_codex":0.018259156,"about_ca_topic_score_gemma":0.026474753,"teacher_disagreement_score":0.475174,"about_ca_system_score_codex":0.0032395981,"about_ca_system_score_gemma":0.0017407874,"threshold_uncertainty_score":0.7486006},"labels":[],"label_agreement":null},{"id":"W4413325899","doi":"10.3390/bdcc9080213","title":"Efficient Dynamic Emotion Recognition from Facial Expressions Using Statistical Spatio-Temporal Geometric Features","year":2025,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Face and Expression Recognition","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":"Université du Québec à Rimouski","funders":"","keywords":"Emotion recognition; Facial expression; Computer science; Artificial intelligence; Pattern recognition (psychology); Speech recognition","score_opus":0.06730876091431709,"score_gpt":0.31953913844771636,"score_spread":0.2522303775333993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413325899","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15889181,0.001224033,0.8320044,0.00030762164,0.00016779512,0.00013026563,0.00095030875,0.0017174947,0.0046062283],"genre_scores_gemma":[0.7815043,0.0012663597,0.21072304,0.00013844574,0.00011866797,0.00018574295,0.001793622,0.0001520178,0.0041177818],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971193,0.000043151438,0.000017090138,0.00007946974,0.000111143236,0.000037134356],"domain_scores_gemma":[0.9997985,0.000048741054,0.000038748884,0.000028399892,0.00007547888,0.000010220373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027875637,0.0006479451,0.0005432397,0.00075693685,0.00015517786,0.00042415535,0.0004266961,0.00023959068,0.0012125057],"category_scores_gemma":[0.0009171658,0.00013046777,0.00052957714,0.0007046393,0.00019607642,0.0006699865,0.00044046287,0.00039412844,0.0008195275],"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":[0.00042055236,0.00013060858,0.003978715,0.000117449854,0.00006668481,0.00018711635,0.0001277903,0.026024528,0.13343744,0.002259992,0.006673252,0.8265759],"study_design_scores_gemma":[0.000022344246,0.00023028093,0.01992497,0.00003108831,0.00008176662,0.0006560406,0.00027249305,0.9217958,0.04643033,0.0045689805,0.005931125,0.000054743225],"about_ca_topic_score_codex":0.001348031,"about_ca_topic_score_gemma":0.0018408779,"teacher_disagreement_score":0.001348031,"about_ca_system_score_codex":0.00022142252,"about_ca_system_score_gemma":0.00024339919,"threshold_uncertainty_score":0.0040562153},"labels":[],"label_agreement":null},{"id":"W4413785340","doi":"10.1109/tkde.2025.3603594","title":"Multi-View Clustering via High-Order Bipartite Graph Learning and Tensor Low-Rank Representation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Face and Expression Recognition","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 British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Cluster analysis; Bipartite graph; Representation (politics); Graph; Tensor (intrinsic definition); Rank (graph theory); Artificial intelligence; Theoretical computer science; Pattern recognition (psychology); Combinatorics; Mathematics","score_opus":0.021527688451080735,"score_gpt":0.28638668068049006,"score_spread":0.26485899222940934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413785340","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034536584,0.00012972933,0.9950917,0.00007192051,0.000018420089,0.000022777163,0.000078741636,0.0006837856,0.0004492727],"genre_scores_gemma":[0.21201931,0.0005873595,0.78107107,0.00029149922,0.00012213363,0.00020664674,0.0020254038,0.0005611177,0.0031153786],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978241,0.0006333624,0.000092747534,0.0006699836,0.0005905963,0.00018927896],"domain_scores_gemma":[0.99796367,0.00045263834,0.00032272033,0.0005249875,0.0005925911,0.00014343113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015032872,0.0021687534,0.0019235811,0.0029140483,0.0010241688,0.0017756603,0.002492557,0.0016272848,0.0018821732],"category_scores_gemma":[0.0047267247,0.0007998496,0.0018654703,0.003384142,0.0012049938,0.0027025987,0.0020184857,0.0022563557,0.0017388196],"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":[0.00026625994,0.00019975126,0.0021048007,0.00043648205,0.00034981788,0.00024079555,0.00040857823,0.4458855,0.03397177,0.04488394,0.01464568,0.4566067],"study_design_scores_gemma":[0.000007557191,0.000028490393,0.000258704,0.000008365502,0.000020231902,0.00007234042,0.000038277805,0.98247874,0.0030975072,0.012744142,0.0012169826,0.000028681248],"about_ca_topic_score_codex":0.00889748,"about_ca_topic_score_gemma":0.01159869,"teacher_disagreement_score":0.00889748,"about_ca_system_score_codex":0.0011106414,"about_ca_system_score_gemma":0.0018312521,"threshold_uncertainty_score":0.017691374},"labels":[],"label_agreement":null},{"id":"W4413801157","doi":"10.1007/s42081-025-00314-0","title":"Applying non-negative matrix factorization with covariates to multivariate time series data as a vector autoregression model","year":2025,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"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":"Japan Society for the Promotion of Science","keywords":"Covariate; Vector autoregression; Multivariate statistics; Autoregressive model; Series (stratigraphy); Time series; Econometrics; Statistics; Mathematics; Computer science; Biology","score_opus":0.025026571645971923,"score_gpt":0.3298188312038354,"score_spread":0.3047922595578635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413801157","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006119713,0.00017481663,0.9929389,0.00020104488,0.000041116757,0.000024703355,0.00013440239,0.00014859677,0.00021679627],"genre_scores_gemma":[0.3694457,0.00095516746,0.6245132,0.0003050029,0.00037427357,0.00037943549,0.0012079303,0.00012991019,0.0026894165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981737,0.00096817414,0.000081725026,0.00044491197,0.00023033858,0.00010113411],"domain_scores_gemma":[0.9963231,0.0025089094,0.00042609317,0.00029321562,0.00035972567,0.00008882174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038783925,0.0010356707,0.0011147902,0.0011421421,0.0004893967,0.0010965294,0.0011326494,0.0010143496,0.0018255883],"category_scores_gemma":[0.009332703,0.0005630213,0.0017697135,0.0016036966,0.0008063291,0.0014133744,0.0008114533,0.001689931,0.00044744337],"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":[0.00011133008,0.00015053738,0.008633998,0.00025003453,0.00038480936,0.00031882306,0.00027759382,0.74098206,0.0039446442,0.09777294,0.0057850196,0.14138818],"study_design_scores_gemma":[0.0000065414392,0.000017070412,0.00063588016,0.000010278653,0.000012983166,0.000017289018,0.00000982273,0.981871,0.00016163352,0.016307937,0.00093772914,0.000011814375],"about_ca_topic_score_codex":0.010376267,"about_ca_topic_score_gemma":0.011734808,"teacher_disagreement_score":0.010376267,"about_ca_system_score_codex":0.0007248016,"about_ca_system_score_gemma":0.0016949776,"threshold_uncertainty_score":0.02063173},"labels":[],"label_agreement":null},{"id":"W4413887432","doi":"10.1109/les.2025.3604285","title":"Low-Power Face Recognition Using Joint Optical and Electronic Deep Neural Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Embedded Systems Letters","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Joint (building); Facial recognition system; Artificial intelligence; Face (sociological concept); Artificial neural network; Power (physics); Pattern recognition (psychology); Speech recognition; Computer vision","score_opus":0.015543077065319174,"score_gpt":0.23728345646558893,"score_spread":0.22174037940026975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413887432","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1449881,0.0011934282,0.8385298,0.0007507336,0.00019354862,0.0000816399,0.00023893433,0.003537269,0.010486511],"genre_scores_gemma":[0.8540841,0.0003516197,0.13690975,0.0004158729,0.000050231843,0.000060063834,0.00020393472,0.000057941525,0.007866497],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997609,0.000030428582,0.000008433434,0.00005251023,0.00010384937,0.00004389876],"domain_scores_gemma":[0.99981505,0.0000613837,0.00002369163,0.00003415859,0.00005584238,0.000009832515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003347956,0.00050690304,0.00027140163,0.0002896042,0.00021975006,0.00057729497,0.0010522382,0.0004932812,0.0030425698],"category_scores_gemma":[0.0006554136,0.0002570454,0.00020776459,0.00025052618,0.0002843295,0.0012669415,0.00072408805,0.0005039525,0.000662758],"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":[0.00049283804,0.00029301338,0.0037693533,0.00017573661,0.00013730176,0.00016419272,0.000060550774,0.048766185,0.13497901,0.005997438,0.007480147,0.79768425],"study_design_scores_gemma":[0.000032006457,0.00020645531,0.0028288816,0.000028604703,0.00005498389,0.00023978764,0.00004398967,0.872964,0.112224944,0.0056196004,0.005723934,0.000032833068],"about_ca_topic_score_codex":0.0023628834,"about_ca_topic_score_gemma":0.0076458734,"teacher_disagreement_score":0.0030425698,"about_ca_system_score_codex":0.000425897,"about_ca_system_score_gemma":0.00038748074,"threshold_uncertainty_score":0.010178447},"labels":[],"label_agreement":null},{"id":"W4413944941","doi":"10.1016/j.neucom.2025.131441","title":"Anchor-aware representation learning for multi-view clustering","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Key Laboratory of Synthetic and Natural Functional Molecular Chemistry; Sichuan Province Science and Technology Support Program; Ministry of Natural Resources","keywords":"Computer science; Cluster analysis; Artificial intelligence; Representation (politics); Feature learning; Machine learning","score_opus":0.05526431671058828,"score_gpt":0.342538638952467,"score_spread":0.2872743222418787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413944941","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0043744654,0.00034775445,0.99318284,0.000100306315,0.000058430534,0.00003434993,0.0001528882,0.0013014161,0.00044751907],"genre_scores_gemma":[0.29055962,0.0007161138,0.69904006,0.0003501787,0.00020016343,0.00025628752,0.0035231637,0.0006650284,0.0046892464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978896,0.0005240647,0.00011675246,0.00067424856,0.00050529186,0.00029004767],"domain_scores_gemma":[0.99757725,0.00075247156,0.00017025053,0.00073101773,0.0006314202,0.00013753348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016708473,0.0017776841,0.0030628722,0.0020301917,0.0010205557,0.00173876,0.004198401,0.003012472,0.004392839],"category_scores_gemma":[0.006367051,0.0009758696,0.0023865087,0.0035148638,0.00091200956,0.0027931759,0.0036115765,0.0031533032,0.003932627],"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":[0.00047969134,0.00035973298,0.0011347187,0.00024780596,0.00030853073,0.00012852107,0.00019653869,0.19766574,0.017747523,0.010545428,0.019333549,0.7518521],"study_design_scores_gemma":[0.000012528426,0.00005188225,0.00020746172,0.000012919612,0.000024377554,0.00005502323,0.000032746135,0.98780453,0.002658624,0.007963806,0.0011587044,0.000017295411],"about_ca_topic_score_codex":0.008103003,"about_ca_topic_score_gemma":0.009939135,"teacher_disagreement_score":0.008103003,"about_ca_system_score_codex":0.0009501355,"about_ca_system_score_gemma":0.0014427053,"threshold_uncertainty_score":0.016111672},"labels":[],"label_agreement":null},{"id":"W4413982673","doi":"10.1016/j.knosys.2025.114401","title":"FDGC: Fuzzy deep clustering with dual-granularity contrastive learning","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"Graduate Research and Innovation Projects of Jiangsu Province; Natural Science Research of Jiangsu Higher Education Institutions of China; National Natural Science Foundation of China; Qinglan Project of Jiangsu Province of China; Natural Science Foundation of Nantong City","keywords":"Granularity; Dual (grammatical number); Computer science; Artificial intelligence; Cluster analysis; Fuzzy logic; Fuzzy clustering; Linguistics; Philosophy","score_opus":0.010090904584725415,"score_gpt":0.24144892036281707,"score_spread":0.23135801577809165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413982673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028193679,0.00035137776,0.96763456,0.00023229924,0.000058129113,0.000100566824,0.0001388202,0.0016210593,0.0016695609],"genre_scores_gemma":[0.47600693,0.0002315028,0.5177583,0.00064460543,0.000071188704,0.00020621167,0.0007852958,0.00024626806,0.004049682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994111,0.00008857533,0.000026433256,0.00020025796,0.00018708207,0.00008656244],"domain_scores_gemma":[0.9991652,0.0002282209,0.00008545577,0.00018976087,0.00025994758,0.00007157072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001155439,0.0010421035,0.000969043,0.0013105089,0.0007310842,0.0010360245,0.0029876542,0.0017674725,0.0016752955],"category_scores_gemma":[0.0029515673,0.00047853737,0.0008444593,0.0009117081,0.0010491472,0.0016034623,0.0019284501,0.0018745959,0.0005971065],"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":[0.00029657004,0.00026535796,0.0030693274,0.00012299986,0.0001490787,0.00014219113,0.00017518019,0.3895056,0.018066933,0.015033238,0.0072697443,0.56590384],"study_design_scores_gemma":[0.000009394951,0.000033093955,0.00020641551,0.00000708509,0.00000745698,0.000029351344,0.000009577591,0.9921113,0.002761077,0.004200947,0.0006135381,0.000010798198],"about_ca_topic_score_codex":0.010637505,"about_ca_topic_score_gemma":0.014568633,"teacher_disagreement_score":0.010637505,"about_ca_system_score_codex":0.0018210167,"about_ca_system_score_gemma":0.0014842185,"threshold_uncertainty_score":0.021151125},"labels":[],"label_agreement":null},{"id":"W4414015058","doi":"10.1016/j.engappai.2025.112189","title":"A robust fuzzy twin support vector machine with kernel-target alignment for binary classification","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Computer science; Support vector machine; Kernel (algebra); Artificial intelligence; Pattern recognition (psychology); Fuzzy logic; Relevance vector machine; Kernel method; Binary number; Binary classification; Machine learning; Data mining; Mathematics","score_opus":0.029119790643440294,"score_gpt":0.2650865367776494,"score_spread":0.2359667461342091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015058","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0132163325,0.00021627374,0.98492116,0.00007785082,0.00008660843,0.000031081418,0.000039998722,0.00092871883,0.0004818534],"genre_scores_gemma":[0.3606974,0.00020441921,0.63428885,0.00013390972,0.00009882145,0.00014110704,0.00040169756,0.00017804121,0.003855697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986864,0.00026350952,0.00009297262,0.00031150263,0.00051437947,0.00013123138],"domain_scores_gemma":[0.9988709,0.00026300878,0.000085591586,0.0001781089,0.0005268211,0.00007550039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015037118,0.00064963626,0.0018854853,0.00084063417,0.00062220055,0.001210687,0.0023272464,0.0015909434,0.0027472824],"category_scores_gemma":[0.0032254492,0.0005502924,0.0010204887,0.001045886,0.00042561107,0.0018045969,0.0016086969,0.0015163133,0.001629747],"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":[0.0006601807,0.00022527245,0.00097075076,0.00013758002,0.00017976617,0.000092138114,0.00006584588,0.07972156,0.02737652,0.0068813404,0.004120162,0.87956893],"study_design_scores_gemma":[0.000012714203,0.000058398444,0.00020571158,0.000004103289,0.000018760436,0.00004691682,0.000009022516,0.9931658,0.0047728745,0.001001435,0.00068992365,0.000014416632],"about_ca_topic_score_codex":0.0032767977,"about_ca_topic_score_gemma":0.0026315977,"teacher_disagreement_score":0.0032767977,"about_ca_system_score_codex":0.00046939915,"about_ca_system_score_gemma":0.0013097859,"threshold_uncertainty_score":0.0091905},"labels":[],"label_agreement":null},{"id":"W4414126261","doi":"10.1007/s10586-025-05417-7","title":"Deep fuzzy clustering neural network (DFC-NN): fast convergence and enhanced clustering performance","year":2025,"lang":"en","type":"article","venue":"Cluster Computing","topic":"Face and Expression Recognition","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Cluster analysis; Adaptability; Robustness (evolution); Artificial neural network; Fuzzy clustering; Benchmark (surveying); Convergence (economics)","score_opus":0.01054450954194845,"score_gpt":0.23587270439286945,"score_spread":0.225328194850921,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414126261","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1428695,0.0012596279,0.8479234,0.00048152605,0.00028207235,0.00007787922,0.00027495832,0.0017210891,0.0051099067],"genre_scores_gemma":[0.7068576,0.0002723969,0.28575578,0.00018301471,0.000047852867,0.0000583437,0.00036843275,0.00011820849,0.0063383514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965656,0.000051039926,0.000019275412,0.00008625996,0.00013293087,0.000054006363],"domain_scores_gemma":[0.9990853,0.00018292619,0.000043332086,0.00010833409,0.00053592253,0.00004411053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009305018,0.00044389284,0.00063260546,0.00043570448,0.00050578575,0.0005776431,0.0010921203,0.0011144956,0.0016572564],"category_scores_gemma":[0.002135782,0.00026890016,0.00031013647,0.00063936715,0.00036505767,0.00086741545,0.0007400577,0.00095264363,0.0005903769],"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":[0.00050525775,0.00019888698,0.0023629146,0.00013122942,0.00008510684,0.00006890157,0.000083569284,0.3589712,0.027485432,0.007844774,0.008279733,0.593983],"study_design_scores_gemma":[0.000006318087,0.00002968009,0.00034070262,0.000003868893,0.000005836664,0.00002330139,0.00000823056,0.9930894,0.005025165,0.0010076206,0.00045120358,0.000008665493],"about_ca_topic_score_codex":0.018327313,"about_ca_topic_score_gemma":0.025381807,"teacher_disagreement_score":0.018327313,"about_ca_system_score_codex":0.001261476,"about_ca_system_score_gemma":0.0013973162,"threshold_uncertainty_score":0.036441326},"labels":[],"label_agreement":null},{"id":"W4414693646","doi":"10.1016/j.knosys.2025.114547","title":"Exploring non-negativity for improved manifold embedding: Application to t-SNE","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec en Outaouais; Bishop's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Embedding; Leverage (statistics); Multiplicative function; Focus (optics); Nonlinear dimensionality reduction; Gradient descent; Curse of dimensionality; Dimensionality reduction","score_opus":0.053126058535404724,"score_gpt":0.306361562401843,"score_spread":0.2532355038664382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414693646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0333449,0.000199947,0.9635495,0.00018229723,0.000047790396,0.000039457715,0.00006855582,0.00045624166,0.002111352],"genre_scores_gemma":[0.5062325,0.00030108873,0.48625264,0.000149056,0.00006698992,0.0000927358,0.0005047098,0.00032201593,0.0060782745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993292,0.0002798147,0.000040256426,0.00016521433,0.00013857569,0.000046993755],"domain_scores_gemma":[0.99791163,0.0011524386,0.00009284771,0.00036761243,0.00041011794,0.00006537015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001837696,0.00092600717,0.00089711574,0.0009008753,0.000673652,0.000978295,0.0012261665,0.0012145928,0.0045551537],"category_scores_gemma":[0.006432468,0.000304507,0.0008512682,0.000821959,0.0011998306,0.0026167762,0.0020992185,0.0012327313,0.0009147527],"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":[0.0004675069,0.00025568058,0.0016051015,0.00033529697,0.00016219362,0.000263476,0.00040193181,0.42615595,0.024579098,0.13601846,0.0054625412,0.4042928],"study_design_scores_gemma":[0.000005105375,0.000037654856,0.00010789681,0.00000549817,0.0000062457048,0.00002914574,0.000023665665,0.9719382,0.0019957817,0.025098762,0.0007452846,0.000006735394],"about_ca_topic_score_codex":0.0026089088,"about_ca_topic_score_gemma":0.0038113047,"teacher_disagreement_score":0.0045551537,"about_ca_system_score_codex":0.00043424597,"about_ca_system_score_gemma":0.0005577304,"threshold_uncertainty_score":0.0152385235},"labels":[],"label_agreement":null},{"id":"W4414700058","doi":"10.1016/j.fss.2025.109614","title":"Dual Weight Vector-driven Iterative Reinforced Fuzzy Clustering-based Network Architecture by Autoencoder-based Interval Weighting Strategy and Residual-based Tournament Selection Mechanism","year":2025,"lang":"en","type":"article","venue":"Fuzzy Sets and Systems","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"Ministry of Science and ICT, South Korea; Natural Science Foundation of Shandong Province; National Research Foundation of Korea; National Natural Science Foundation of China","keywords":"Initialization; Weighting; Fuzzy logic; Generalization; Weight; Iterative method; Artificial neural network; Selection (genetic algorithm); Neuro-fuzzy","score_opus":0.012067010858812855,"score_gpt":0.24108346894324928,"score_spread":0.22901645808443644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414700058","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023484489,0.00021224545,0.97295105,0.000095055206,0.00005989011,0.000037065663,0.000017998022,0.00033875802,0.0028033922],"genre_scores_gemma":[0.81811315,0.00020431294,0.1754212,0.00012092887,0.000054842178,0.00017036781,0.00011623074,0.000056325007,0.0057427296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995963,0.000084331914,0.000023256813,0.000117496915,0.00011997556,0.00005870183],"domain_scores_gemma":[0.999603,0.00007906128,0.000037866394,0.00003215647,0.00022020191,0.000027706657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008642338,0.00066360907,0.00090755965,0.00045209602,0.0006021182,0.0008881925,0.0023704998,0.001135755,0.0017271215],"category_scores_gemma":[0.0010784864,0.00036845473,0.00054319884,0.0004965729,0.0005144292,0.00083380094,0.0009670569,0.0007541562,0.0004973054],"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":[0.00013275103,0.00012009263,0.00083679415,0.00006954194,0.000109304194,0.00008124006,0.00012395253,0.7911189,0.013373742,0.008776576,0.0018510901,0.18340603],"study_design_scores_gemma":[0.0000038411117,0.000021543945,0.000060967785,0.000002400124,0.000007563112,0.000012543076,0.0000035999867,0.9986438,0.00064075313,0.0004539142,0.00014430309,0.0000047966537],"about_ca_topic_score_codex":0.008029468,"about_ca_topic_score_gemma":0.008981324,"teacher_disagreement_score":0.008029468,"about_ca_system_score_codex":0.0007308839,"about_ca_system_score_gemma":0.00087656616,"threshold_uncertainty_score":0.015965462},"labels":[],"label_agreement":null},{"id":"W4415366566","doi":"10.1109/tsmc.2025.3616371","title":"Contrastive Multiview Low-Rank Latent Subspace Self-Representation and Classification Network","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Face and Expression Recognition","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":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Subspace topology; Discriminative model; Robustness (evolution); Pattern recognition (psychology); Consistency (knowledge bases); Linear discriminant analysis; Training set; Contextual image classification","score_opus":0.021919641145622396,"score_gpt":0.25629655322825173,"score_spread":0.23437691208262934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415366566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014202662,0.00032797066,0.9827456,0.00017747004,0.000055724842,0.000054186243,0.00028474836,0.0011844261,0.0009672391],"genre_scores_gemma":[0.44712391,0.00058374414,0.5363228,0.0005174543,0.00019406757,0.0003082087,0.0031454111,0.0003571494,0.011447253],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991493,0.00019687993,0.000031472202,0.0002862464,0.00023862378,0.00009758028],"domain_scores_gemma":[0.999074,0.00023539813,0.00010856868,0.00019821998,0.00030844333,0.00007526424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013767948,0.0011456023,0.001382723,0.0010017806,0.0005379925,0.0012491406,0.0021317564,0.0011799713,0.003207402],"category_scores_gemma":[0.0032388652,0.00042189713,0.0010682391,0.0010756186,0.0008562881,0.0017458817,0.0021352048,0.0019154132,0.0017057355],"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":[0.00035242076,0.0002449524,0.002609021,0.0001530473,0.00013895078,0.00012111774,0.00017726948,0.20081112,0.016351476,0.016706904,0.014593111,0.7477406],"study_design_scores_gemma":[0.0000073360256,0.000035919595,0.00022926547,0.000007743438,0.000009497979,0.000039392173,0.00001565489,0.9905772,0.0025929543,0.005465396,0.0010087084,0.000010876668],"about_ca_topic_score_codex":0.0034975787,"about_ca_topic_score_gemma":0.005474423,"teacher_disagreement_score":0.0034975787,"about_ca_system_score_codex":0.00094140565,"about_ca_system_score_gemma":0.0010126126,"threshold_uncertainty_score":0.010729849},"labels":[],"label_agreement":null},{"id":"W4415437011","doi":"10.1016/j.knosys.2025.114736","title":"A probabilistic mixture-of-experts regression framework with structure-aware feature representation adaptation in GMM-guided posterior-weighted RVFL networks","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"Young Scientists Fund; Natural Science Foundation of Shandong Province; Taishan Scholar Foundation of Shandong Province; National Natural Science Foundation of China","keywords":"Probabilistic logic; Cluster analysis; Representation (politics); Feature (linguistics); Feature vector; Feature learning; Stability (learning theory); Pattern recognition (psychology)","score_opus":0.01782415144996411,"score_gpt":0.2823262253102243,"score_spread":0.26450207386026015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415437011","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034894561,0.00026742762,0.9952147,0.00011092006,0.00002799592,0.000013994778,0.000033088523,0.00036888014,0.00047350608],"genre_scores_gemma":[0.4526704,0.0008493304,0.5340262,0.0005591653,0.00023755473,0.00027252152,0.0006854955,0.00051178574,0.010187553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992161,0.00029681128,0.000033530305,0.0002173345,0.00013316546,0.00010305786],"domain_scores_gemma":[0.9987036,0.0007723438,0.000096376694,0.00008360299,0.00028774026,0.000056272565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020201853,0.0013570343,0.0016223707,0.00084739696,0.00046402303,0.0009489892,0.0033652019,0.0025732059,0.0026931802],"category_scores_gemma":[0.0047527296,0.0010418915,0.0012458344,0.0008830438,0.00089941814,0.0018443958,0.001868167,0.002006117,0.0012268119],"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":[0.00012000709,0.00006695069,0.00033326625,0.000087065055,0.00008284466,0.00006576956,0.000085634594,0.83047813,0.0036156792,0.010249432,0.0026686867,0.15214661],"study_design_scores_gemma":[0.0000020016469,0.000007693526,0.000022809973,0.000003373509,0.0000055325727,0.000006574123,0.000001983089,0.9982705,0.0002509079,0.0012824115,0.00014305099,0.0000031814097],"about_ca_topic_score_codex":0.0146039985,"about_ca_topic_score_gemma":0.014191386,"teacher_disagreement_score":0.0146039985,"about_ca_system_score_codex":0.00096578716,"about_ca_system_score_gemma":0.0011343365,"threshold_uncertainty_score":0.029038012},"labels":[],"label_agreement":null},{"id":"W4415748259","doi":"10.1109/tnnls.2025.3622100","title":"Spectral Embedding Representation Based on Random Anchor Graph Aggregation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Face and Expression Recognition","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":"National Natural Science Foundation of China; National Research Foundation","keywords":"Embedding; Cluster analysis; Graph; Random walk; Spectral clustering; Representation (politics); Graph embedding; Sampling (signal processing)","score_opus":0.0125876371732386,"score_gpt":0.2585460769527925,"score_spread":0.24595843977955392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415748259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064081624,0.00016165254,0.99214244,0.00006606111,0.000023957145,0.00002362289,0.000072712086,0.0005365102,0.0005648659],"genre_scores_gemma":[0.45496365,0.0008405439,0.5367749,0.00025588743,0.00012618714,0.00032313855,0.001479097,0.0005005904,0.0047360295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987966,0.00034246885,0.00005667432,0.0003504163,0.00035294227,0.00010087866],"domain_scores_gemma":[0.9979972,0.0005955759,0.00025035103,0.00048708747,0.0005783485,0.00009145899],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001166289,0.0013202538,0.0012199507,0.0020114456,0.00054722966,0.00128811,0.0018313053,0.001198319,0.001902792],"category_scores_gemma":[0.0058723297,0.00044189062,0.0011281327,0.0023213837,0.0009956766,0.0032396612,0.0018206864,0.0015228377,0.0012913506],"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":[0.00014858668,0.00009693907,0.001297795,0.000221886,0.000100798665,0.00016719071,0.00035594887,0.65295124,0.013492321,0.052518573,0.0063401842,0.27230856],"study_design_scores_gemma":[0.0000046536115,0.00001903183,0.00013340783,0.000006905899,0.0000095067935,0.00003750655,0.000023507515,0.98534125,0.0013385342,0.011915637,0.0011569402,0.000013221881],"about_ca_topic_score_codex":0.0034003335,"about_ca_topic_score_gemma":0.003702068,"teacher_disagreement_score":0.0034003335,"about_ca_system_score_codex":0.00080313405,"about_ca_system_score_gemma":0.00077292736,"threshold_uncertainty_score":0.006761074},"labels":[],"label_agreement":null},{"id":"W4416040596","doi":"10.1016/j.patcog.2025.112717","title":"REC-GCN: Robust ensemble clustering with graph convolutional networks","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Agriculture","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Cluster analysis; Robustness (evolution); Convolutional neural network; Graph; Ensemble learning; Pattern recognition (psychology); Leverage (statistics); Feature learning; External Data Representation","score_opus":0.02193299151676963,"score_gpt":0.22321495898231258,"score_spread":0.20128196746554294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416040596","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015807854,0.00069042214,0.95026505,0.0002585158,0.00026469099,0.00016509765,0.0015133586,0.028587785,0.0024473716],"genre_scores_gemma":[0.17934464,0.00040373582,0.79393786,0.0005107253,0.00012322397,0.0003161925,0.009008625,0.0025303205,0.01382461],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991241,0.0001361091,0.000028496217,0.0003437161,0.00023556197,0.00013201297],"domain_scores_gemma":[0.99911386,0.0001412959,0.000049255,0.00034973258,0.00028771523,0.000058064532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010085953,0.0021570318,0.0018821689,0.0017784999,0.0010464416,0.0011357355,0.0042398716,0.002043842,0.005336504],"category_scores_gemma":[0.0020041782,0.00091820274,0.0015208386,0.0019783794,0.00058513856,0.0017670661,0.0022374142,0.002438545,0.0041457615],"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":[0.00036110292,0.0002519359,0.0011935964,0.0001454736,0.0004150383,0.00012930302,0.00007019847,0.28016037,0.011016425,0.005514107,0.042867906,0.6578745],"study_design_scores_gemma":[0.000012106789,0.000025262958,0.00022484828,0.000006949799,0.000021106944,0.000031767304,0.000013326947,0.9911652,0.003532979,0.0027441152,0.002208153,0.00001422541],"about_ca_topic_score_codex":0.04146782,"about_ca_topic_score_gemma":0.08193618,"teacher_disagreement_score":0.04146782,"about_ca_system_score_codex":0.001477805,"about_ca_system_score_gemma":0.0022717728,"threshold_uncertainty_score":0.08245289},"labels":[],"label_agreement":null},{"id":"W4416051783","doi":"10.1016/j.patcog.2025.112693","title":"Efficient spectral embedding representation approximation for large-scale data clustering","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Face and Expression Recognition","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":"University of Alberta","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Science and Technology Planning Project of Shenzhen Municipality; National Natural Science Foundation of China","keywords":"Spectral clustering; Embedding; Cluster analysis; Representation (politics); Spectral space; Similarity (geometry); Eigenvalues and eigenvectors; Matrix (chemical analysis); Time complexity","score_opus":0.05550411338020398,"score_gpt":0.3306349493512409,"score_spread":0.2751308359710369,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416051783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034658737,0.00015334327,0.99541926,0.00006616404,0.000017279124,0.000019976695,0.000052806045,0.0004929333,0.00031235223],"genre_scores_gemma":[0.2502426,0.0005906906,0.74375325,0.00016432728,0.00010944971,0.00032957215,0.0013883448,0.00036424756,0.0030574857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984787,0.00049409067,0.000072374976,0.0003109983,0.0005272881,0.000116555864],"domain_scores_gemma":[0.9975924,0.001152182,0.00019888041,0.0004704647,0.0004890123,0.00009705727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014088557,0.0014173216,0.0016317298,0.0017616827,0.0006797315,0.00152141,0.0019094115,0.0014654786,0.0018970907],"category_scores_gemma":[0.0068479404,0.00056534546,0.001288183,0.002800963,0.0008293454,0.0026364757,0.002012944,0.002370387,0.001731453],"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":[0.00013929901,0.00011875499,0.0008517496,0.00019859555,0.00008259217,0.00013549163,0.00020185577,0.74863577,0.0067212656,0.025786532,0.00646167,0.21066643],"study_design_scores_gemma":[0.0000019072841,0.0000060526313,0.000040997977,0.0000025032286,0.0000025189888,0.000014766141,0.000011672995,0.99399006,0.00032493199,0.0052789752,0.00032221247,0.0000034729733],"about_ca_topic_score_codex":0.0037188404,"about_ca_topic_score_gemma":0.004061223,"teacher_disagreement_score":0.0037188404,"about_ca_system_score_codex":0.0010149252,"about_ca_system_score_gemma":0.0011021218,"threshold_uncertainty_score":0.0074508786},"labels":[],"label_agreement":null},{"id":"W4416677236","doi":"10.1109/dsaa65442.2025.11247986","title":"Scalable Deep Subspace Clustering Network","year":2025,"lang":"","type":"article","venue":"","topic":"Face and Expression Recognition","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 du Québec à Montréal","funders":"","keywords":"Cluster analysis; Pattern recognition (psychology); Subspace topology; Spectral clustering; Pairwise comparison; Computational complexity theory; Correlation clustering; Feature (linguistics); Bottleneck","score_opus":0.012530134011213235,"score_gpt":0.24619783470782142,"score_spread":0.2336677006966082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416677236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020332512,0.0004961923,0.9677114,0.00061556505,0.0000983227,0.00008554547,0.0010759414,0.0037625097,0.005821992],"genre_scores_gemma":[0.5088149,0.0007361838,0.45419508,0.0007592102,0.00018017094,0.00047829255,0.008103546,0.00061972975,0.026112927],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99905735,0.0001810809,0.000033406188,0.00032638453,0.00024653436,0.00015528011],"domain_scores_gemma":[0.9991879,0.00019117164,0.000059088794,0.00018993845,0.0002918483,0.00008013171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007165236,0.0012702238,0.0016467504,0.0010035938,0.0009372664,0.0011910774,0.0025980475,0.0015385462,0.007375636],"category_scores_gemma":[0.002375799,0.00055948,0.0010689003,0.0018594655,0.00090401724,0.0024955869,0.0025034547,0.001672419,0.0025033418],"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":[0.00022075145,0.000120561395,0.001019296,0.00011117518,0.0001126927,0.00013469195,0.00008519651,0.69032365,0.005067911,0.036173075,0.029667739,0.23696321],"study_design_scores_gemma":[0.0000049384025,0.000009622919,0.00006779237,0.0000028291627,0.000004101876,0.000013085709,0.000009753068,0.9889419,0.00074673304,0.008991425,0.0012016732,0.000006008079],"about_ca_topic_score_codex":0.014641548,"about_ca_topic_score_gemma":0.026265526,"teacher_disagreement_score":0.014641548,"about_ca_system_score_codex":0.0020644178,"about_ca_system_score_gemma":0.0023930112,"threshold_uncertainty_score":0.029112637},"labels":[],"label_agreement":null},{"id":"W4416957741","doi":"10.1016/j.jmva.2025.105563","title":"A robust mixed functional classifier with adaptive large margin loss","year":2025,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Face and Expression Recognition","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":"University of Waterloo","funders":"Shanghai University of Finance and Economics; National Natural Science Foundation of China; Shanghai Science and Technology Development Foundation","keywords":"Classifier (UML); Covariate; Functional data analysis; Margin classifier; Pattern recognition (psychology); Scalar (mathematics); Margin (machine learning)","score_opus":0.026615108790705506,"score_gpt":0.2468906778824764,"score_spread":0.2202755690917709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416957741","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008112891,0.00035263514,0.9898599,0.00019255621,0.00009340734,0.000033391887,0.00006467718,0.0007268565,0.0005637101],"genre_scores_gemma":[0.3006872,0.00043134543,0.6833833,0.0005878456,0.00037180755,0.00025379084,0.00087309483,0.0004042601,0.013007443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870145,0.00036102484,0.00009066201,0.00031392925,0.00041891116,0.00011386507],"domain_scores_gemma":[0.9989717,0.0003463688,0.00006321306,0.00016505305,0.0003830377,0.00007061739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024957813,0.0011916369,0.0022246197,0.0009937822,0.0006083573,0.0013106981,0.002444018,0.0026216493,0.0036749772],"category_scores_gemma":[0.0027048846,0.0007467068,0.0015742757,0.00056878186,0.00048114563,0.0021560406,0.0021481642,0.0018743942,0.0029120632],"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":[0.0006819695,0.00030081972,0.0008973267,0.00011461719,0.00021323134,0.00010348699,0.000040202965,0.070770554,0.037006877,0.007353021,0.009670739,0.872847],"study_design_scores_gemma":[0.000010864671,0.000080033285,0.00020519026,0.0000055789096,0.00002332738,0.00006656364,0.000004428631,0.99326926,0.00405321,0.0013140908,0.0009543571,0.000013147331],"about_ca_topic_score_codex":0.0017270559,"about_ca_topic_score_gemma":0.002310363,"teacher_disagreement_score":0.0036749772,"about_ca_system_score_codex":0.0004968802,"about_ca_system_score_gemma":0.0009704026,"threshold_uncertainty_score":0.013199091},"labels":[],"label_agreement":null},{"id":"W4417284306","doi":"10.1109/tfuzz.2025.3643471","title":"Low-Rank Matrix Factorization Induced Adaptive Divergent Graph Learning for Fuzzy Clustering","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Face and Expression Recognition","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":"Cluster analysis; Matrix decomposition; Fuzzy clustering; Graph; Pattern recognition (psychology); Robustness (evolution); Outlier; Fuzzy logic; Graph embedding","score_opus":0.030039987168480765,"score_gpt":0.27295744939915695,"score_spread":0.24291746223067617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417284306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033650286,0.00009541569,0.99585116,0.000055271103,0.00001156663,0.00001686548,0.000023260494,0.00020941994,0.0003720597],"genre_scores_gemma":[0.28647605,0.000304655,0.70949996,0.000217886,0.00006768849,0.00017397499,0.0005510942,0.00021864347,0.0024900562],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988438,0.00036127382,0.000043794917,0.00028955,0.00038556202,0.00007597815],"domain_scores_gemma":[0.99823254,0.00067975343,0.0001726672,0.00028090816,0.000552131,0.00008203361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001585116,0.0012640529,0.0012104858,0.0013181054,0.00081077975,0.0011056367,0.0021955068,0.0013967083,0.0012913898],"category_scores_gemma":[0.0051516597,0.00039574396,0.0008688362,0.0013800603,0.0012719587,0.0017517162,0.0014459732,0.0019063725,0.0007119763],"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":[0.000104180064,0.00007805555,0.000811163,0.00016514435,0.00009480268,0.00008704728,0.00020370059,0.73530126,0.008331561,0.04491073,0.003919411,0.2059929],"study_design_scores_gemma":[0.0000031680963,0.000013601683,0.00004748496,0.0000031240909,0.0000032582584,0.00001175353,0.000008360716,0.98971796,0.00078947336,0.009001546,0.0003933412,0.000006880723],"about_ca_topic_score_codex":0.0049272776,"about_ca_topic_score_gemma":0.0061019296,"teacher_disagreement_score":0.0049272776,"about_ca_system_score_codex":0.0013441131,"about_ca_system_score_gemma":0.001455205,"threshold_uncertainty_score":0.009797215},"labels":[],"label_agreement":null},{"id":"W561402864","doi":"10.1016/j.neucom.2015.05.102","title":"Granular fuzzy modeling with evolving hyperboxes in multi-dimensional space of numerical data","year":2015,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Construct (python library); Computer science; Cluster analysis; Algorithm; Granular computing; Partition (number theory); Basis (linear algebra); Parametric statistics; Artificial neural network; Set (abstract data type); Context (archaeology); Fuzzy logic; Data mining; Mathematics; Artificial intelligence; Rough set","score_opus":0.10066457485325496,"score_gpt":0.2894161789787335,"score_spread":0.18875160412547853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W561402864","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053402696,0.00019943918,0.94511414,0.00013161563,0.00004443953,0.000025751435,0.00004755523,0.0001044478,0.00092999433],"genre_scores_gemma":[0.8875633,0.00026528916,0.11023802,0.000052641317,0.00003911579,0.000081817314,0.00007917243,0.000035835663,0.0016447166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926347,0.00027567713,0.00005974686,0.00014140792,0.00019071683,0.00006895097],"domain_scores_gemma":[0.997703,0.0012945943,0.00029837192,0.00024203231,0.00030776547,0.00015419514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023285588,0.00049544976,0.0011190253,0.0010493646,0.00055979175,0.0022706534,0.001398298,0.0013642586,0.0012716831],"category_scores_gemma":[0.0069537847,0.0005600562,0.0010913927,0.0010440741,0.0012881474,0.0024226499,0.0015195648,0.001092098,0.00015670538],"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":[0.000069465576,0.000029338411,0.00076371006,0.000047994203,0.00003649786,0.00008028783,0.00007969479,0.9517448,0.0012858004,0.035290595,0.00017380866,0.010397987],"study_design_scores_gemma":[0.0000010504972,0.000003674896,0.000036926744,0.0000025645172,0.0000019518043,0.0000032817197,0.0000031576185,0.9971554,0.00007937545,0.0026765044,0.000034123103,0.0000020048851],"about_ca_topic_score_codex":0.006091153,"about_ca_topic_score_gemma":0.0039120708,"teacher_disagreement_score":0.006091153,"about_ca_system_score_codex":0.0011588664,"about_ca_system_score_gemma":0.0006885162,"threshold_uncertainty_score":0.012314737},"labels":[],"label_agreement":null},{"id":"W566797584","doi":"10.1016/j.neunet.2015.05.001","title":"Optimized face recognition algorithm using radial basis function neural networks and its practical applications","year":2015,"lang":"en","type":"article","venue":"Neural Networks","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":39,"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":"Radial basis function; Artificial intelligence; Computer science; Pattern recognition (psychology); Principal component analysis; Artificial neural network; Fuzzy logic; Facial recognition system; Gradient descent; Histogram equalization; Cluster analysis; Basis (linear algebra); Algorithm; Histogram; Mathematics; Image (mathematics)","score_opus":0.06442352418217857,"score_gpt":0.29025595312425867,"score_spread":0.2258324289420801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W566797584","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015422549,0.00054636505,0.9811251,0.0000873384,0.000054224784,0.000022986662,0.000026279899,0.0005282195,0.0021869238],"genre_scores_gemma":[0.29443908,0.00073777785,0.6951207,0.00010092727,0.0000828488,0.00012188104,0.00015769241,0.00015850402,0.009080619],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996511,0.000066215696,0.000019290717,0.00007993561,0.00015821095,0.000025164758],"domain_scores_gemma":[0.9996388,0.000089504014,0.000027187496,0.00003677963,0.00019995889,0.000007797821],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005206125,0.00042625723,0.00062560063,0.00044365553,0.000308779,0.0004794599,0.000700402,0.0006935314,0.0024127157],"category_scores_gemma":[0.0010306068,0.00025236353,0.00041561996,0.00054146635,0.0002272491,0.0006913481,0.00028084297,0.00047382247,0.00077106216],"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":[0.00016383397,0.00009867999,0.0011092991,0.00013675846,0.000073459436,0.00009367039,0.00006177533,0.29579583,0.027442722,0.0112759145,0.0050135995,0.6587344],"study_design_scores_gemma":[0.0000052928285,0.0000144453625,0.00039456305,0.0000036542656,0.000010692195,0.00005197859,0.000006462147,0.9935766,0.0040443423,0.0010644854,0.0008181293,0.000009328531],"about_ca_topic_score_codex":0.004758629,"about_ca_topic_score_gemma":0.0033771468,"teacher_disagreement_score":0.004758629,"about_ca_system_score_codex":0.00039248492,"about_ca_system_score_gemma":0.0007131766,"threshold_uncertainty_score":0.00946188},"labels":[],"label_agreement":null},{"id":"W5960048","doi":"10.1007/978-3-642-24466-7_10","title":"Reduced Versus Complete Space Configurations in Total Information Analysis","year":2012,"lang":"en","type":"book-chapter","venue":"Studies in classification, data analysis, and knowledge organization","topic":"Face and Expression Recognition","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":"University of Toronto","funders":"","keywords":"GRASP; Dimension (graph theory); Point (geometry); Space (punctuation); Dimensionality reduction; Computer science; Key (lock); Reduction (mathematics); Information space; Data mining; Algorithm; Theoretical computer science; Mathematics; Artificial intelligence; Pure mathematics; Geometry","score_opus":0.13295365760318625,"score_gpt":0.3403312368409699,"score_spread":0.20737757923778363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W5960048","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05228052,0.0066092075,0.90841705,0.0008603696,0.0001603652,0.00007339639,0.0005000574,0.00059204875,0.030507073],"genre_scores_gemma":[0.5436875,0.004205636,0.43901208,0.00028073497,0.00039179254,0.00029714263,0.001243878,0.0007116558,0.010169466],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99798054,0.0010686053,0.00012509125,0.00025636278,0.0004614799,0.000107952175],"domain_scores_gemma":[0.99679047,0.0018869264,0.00009499926,0.0008468839,0.00028760653,0.00009320855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018683315,0.000714623,0.0011705362,0.0021707728,0.00087924843,0.0030092087,0.001161103,0.0006909381,0.0056341747],"category_scores_gemma":[0.0062705707,0.00043535721,0.0010640596,0.0035432545,0.0028664758,0.006420031,0.0021399853,0.0016274158,0.0010089119],"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":[0.00012193198,0.000027684948,0.00028435493,0.00018368528,0.00005021609,0.00005852286,0.00034516913,0.019525688,0.0008813262,0.8657818,0.0045047686,0.10823486],"study_design_scores_gemma":[0.0000072247444,0.000027722996,0.00012216897,0.000028861961,0.000015659924,0.000047010355,0.000061925275,0.03383981,0.00048261066,0.9617656,0.0035878704,0.000013552533],"about_ca_topic_score_codex":0.0007768384,"about_ca_topic_score_gemma":0.0006810342,"teacher_disagreement_score":0.0056341747,"about_ca_system_score_codex":0.00068648113,"about_ca_system_score_gemma":0.00054149184,"threshold_uncertainty_score":0.01884824},"labels":[],"label_agreement":null},{"id":"W6893608142","doi":"10.5281/zenodo.3866898","title":"Cylindroiulus limitaneus","year":2017,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"BC Research (Canada)","funders":"","keywords":"Distribution (mathematics); Habitat; Natural (archaeology); Vegetation (pathology)","score_opus":0.05188510660751094,"score_gpt":0.25897376296138336,"score_spread":0.2070886563538724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893608142","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18576762,0.015227682,0.006040668,0.00069489283,0.0007885169,0.00054055656,0.0061039394,0.0010319705,0.7838041],"genre_scores_gemma":[0.9429321,0.0034909754,0.004802747,0.00055154925,0.0003013471,0.00030125,0.0057993713,0.00005671072,0.041763887],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997577,0.000025633373,0.00002636527,0.00006992571,0.00008859321,0.000031838757],"domain_scores_gemma":[0.9996238,0.000065493296,0.00016670937,0.00003168206,0.000073509735,0.000038804235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014524662,0.0005527157,0.00029119843,0.0024823593,0.0011432192,0.0003559381,0.0006348726,0.00064619124,0.022342376],"category_scores_gemma":[0.0007703145,0.00025839734,0.00021176136,0.0009930255,0.00069328526,0.00109196,0.0010753804,0.00042245025,0.009214475],"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":[0.00057554164,0.00016666869,0.052299466,0.0011703738,0.00006999747,0.0023437026,0.0008505848,0.0015157575,0.035462007,0.006404159,0.048999246,0.8501426],"study_design_scores_gemma":[0.00009551183,0.0005225764,0.586441,0.0011018699,0.00011652719,0.0110640265,0.0011284464,0.0017350782,0.005888521,0.0017475187,0.39008203,0.000076880264],"about_ca_topic_score_codex":0.0098437425,"about_ca_topic_score_gemma":0.016314259,"teacher_disagreement_score":0.022342376,"about_ca_system_score_codex":0.0007254152,"about_ca_system_score_gemma":0.00024408825,"threshold_uncertainty_score":0.074742734},"labels":[],"label_agreement":null},{"id":"W6894212559","doi":"10.5281/zenodo.8366844","title":"PBrockmann/PANGAEA_Scraping: 20231110","year":2023,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"Environment and Climate Change Canada","funders":"","keywords":"","score_opus":0.04109732702410594,"score_gpt":0.24828391876390105,"score_spread":0.2071865917397951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6894212559","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002061658,0.00061324897,0.023259962,0.0004694861,0.0007341288,0.0002680874,0.02666354,0.15635008,0.7895799],"genre_scores_gemma":[0.00971721,0.00036551798,0.010658382,0.00043124933,0.00023207605,0.0001710683,0.036275342,0.046036094,0.89611304],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996151,0.000029228577,0.000012729431,0.000072686715,0.00019338625,0.000076837656],"domain_scores_gemma":[0.9992118,0.000085389205,0.000022017357,0.00024702723,0.00023694243,0.00019684286],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00043166193,0.0015860246,0.00093258586,0.0024855281,0.0011303665,0.0027697403,0.0014250748,0.0014176982,0.71662563],"category_scores_gemma":[0.001071885,0.00055017904,0.0008030671,0.0016618677,0.00034003882,0.0011910916,0.0021534243,0.0009962337,0.6846524],"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":[0.00012475585,0.000042666336,0.00011391826,0.00011842281,0.000010008886,0.00009491739,0.000029476252,0.00026557205,0.0029099926,0.0020999776,0.8718406,0.122349694],"study_design_scores_gemma":[0.000038252412,0.00002602917,0.00065491046,0.000053417814,0.00000650189,0.00015530556,0.000025013298,0.0016948122,0.0040774546,0.0024174391,0.9908269,0.000023851315],"about_ca_topic_score_codex":0.0048237983,"about_ca_topic_score_gemma":0.0069624935,"teacher_disagreement_score":0.28337437,"about_ca_system_score_codex":0.00052880036,"about_ca_system_score_gemma":0.0005232398,"threshold_uncertainty_score":0.40419912},"labels":[],"label_agreement":null},{"id":"W6901715557","doi":"10.60692/68c3r-s6f27","title":"Unified Embedding and Clustering","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Face and Expression Recognition","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 du Québec à Trois-Rivières","funders":"","keywords":"Cluster analysis; Embedding; Correlation clustering; CURE data clustering algorithm; Fuzzy clustering; Clustering high-dimensional data; Canopy clustering algorithm; Data stream clustering","score_opus":0.03215399639491679,"score_gpt":0.22165329539705386,"score_spread":0.18949929900213708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6901715557","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025669916,0.0005406853,0.99450535,0.00014740226,0.00006370614,0.00004041476,0.00010948887,0.0005417398,0.0014841518],"genre_scores_gemma":[0.12088892,0.0011820461,0.8691121,0.00022265693,0.00023690521,0.00023051354,0.0017298099,0.00061455567,0.005782462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968811,0.00075977226,0.0001636584,0.0011030558,0.0008882112,0.00020422389],"domain_scores_gemma":[0.99794465,0.00049660896,0.00018891552,0.00072713045,0.0005535485,0.00008914703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022418918,0.0019095804,0.002054726,0.0036230073,0.0013272061,0.0029305695,0.0028786026,0.0022657618,0.003780099],"category_scores_gemma":[0.00734331,0.0007572716,0.0015514998,0.004327652,0.0018763515,0.0050167018,0.004051861,0.0024961757,0.0027517958],"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":[0.0001476981,0.00009277028,0.0013649653,0.00036716208,0.0002342257,0.00011832255,0.00038346343,0.2325574,0.005907313,0.19655313,0.012027799,0.5502458],"study_design_scores_gemma":[0.000014804328,0.00007680219,0.00064530014,0.000057827892,0.000038576163,0.00019910737,0.00013153217,0.8209328,0.0035105245,0.15803875,0.01630272,0.00005129853],"about_ca_topic_score_codex":0.0029254812,"about_ca_topic_score_gemma":0.0029586607,"teacher_disagreement_score":0.003780099,"about_ca_system_score_codex":0.0013981049,"about_ca_system_score_gemma":0.0014400218,"threshold_uncertainty_score":0.012645662},"labels":[],"label_agreement":null},{"id":"W6930227897","doi":"10.5281/zenodo.10940162","title":"Data and code for \"Species interactions affect dispersal: a meta-analysis\"","year":2024,"lang":"fr","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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","funders":"","keywords":"Biological dispersal; Affect (linguistics); Habitat; Code (set theory); Empirical research","score_opus":0.24745005793415625,"score_gpt":0.3484352734807999,"score_spread":0.10098521554664364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930227897","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00020373992,0.0001935147,0.0012661031,0.00039491875,0.0002124083,0.0003088357,0.9934731,0.0029375437,0.0010097899],"genre_scores_gemma":[0.0039545456,0.0005137412,0.021298848,0.0015255685,0.00012789067,0.012856729,0.9484092,0.0059985104,0.0053149755],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9961733,0.0010817766,0.00059427315,0.0010032373,0.00082094356,0.00032647373],"domain_scores_gemma":[0.9777895,0.0155168,0.001342767,0.0029802737,0.0017768851,0.0005937703],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0069559193,0.0023402616,0.0026984978,0.004536235,0.0011439552,0.0045442586,0.0054224464,0.0036009036,0.39570248],"category_scores_gemma":[0.056615267,0.00159692,0.004798832,0.007133087,0.00097385916,0.0031959491,0.003922056,0.0040658433,0.12869217],"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":[0.00021971576,0.000038873004,0.0010304104,0.0058247573,0.00037084747,0.000046645382,0.0000671639,0.00074504537,0.0002699791,0.0014123167,0.98622465,0.0037496546],"study_design_scores_gemma":[0.0030471317,0.000076443335,0.0058397055,0.0033858372,0.00042227292,0.00015147695,0.00012886195,0.0012870174,0.00052343163,0.009682037,0.97533935,0.000116422736],"about_ca_topic_score_codex":0.0136356875,"about_ca_topic_score_gemma":0.014978986,"teacher_disagreement_score":0.39570248,"about_ca_system_score_codex":0.0020345303,"about_ca_system_score_gemma":0.006376319,"threshold_uncertainty_score":0.8619571},"labels":[],"label_agreement":null},{"id":"W6930417139","doi":"10.5281/zenodo.14231987","title":"Human mitochondrial calcium uniporter dominant negative beta (MCUB) amino terminal domain (NTD) M119R molecular dynamics at 310 K.","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"Western University","funders":"","keywords":"Terminal (telecommunication); Domain (mathematical analysis); Molecular dynamics; Amino terminal; BETA (programming language); Calcium; Mitochondrion","score_opus":0.021617040985559763,"score_gpt":0.2625184310967043,"score_spread":0.24090139011114453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930417139","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002821507,0.00033764716,0.00071495445,0.00016746146,0.00007081392,0.000039837047,0.9906109,0.0026982706,0.0025386217],"genre_scores_gemma":[0.0020989242,0.00010793065,0.001017505,0.000053447482,0.000005478829,0.000106708525,0.99534374,0.0002352508,0.0010310715],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996432,0.000052892537,0.000018969828,0.00013079622,0.0000885323,0.00006561448],"domain_scores_gemma":[0.9997328,0.000058789636,0.000026806241,0.000077140554,0.000058039474,0.000046364454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006622987,0.0032879438,0.0020625943,0.0009834601,0.001538495,0.0013953495,0.0048492877,0.0023214943,0.029288292],"category_scores_gemma":[0.000983551,0.00073850807,0.0011469256,0.0018890612,0.0004068792,0.000950121,0.0010025683,0.0024051664,0.04248412],"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":[0.00027149447,0.00008919241,0.0005495679,0.00074525963,0.00006887925,0.000056370984,0.000022002936,0.0024694111,0.001827319,0.0010374037,0.99030465,0.0025585825],"study_design_scores_gemma":[0.0017443195,0.00017196788,0.006758809,0.0003045767,0.00018571514,0.0003334069,0.00011189641,0.021916293,0.012265917,0.007643637,0.94843715,0.00012633536],"about_ca_topic_score_codex":0.024577202,"about_ca_topic_score_gemma":0.046753075,"teacher_disagreement_score":0.029288292,"about_ca_system_score_codex":0.0017106786,"about_ca_system_score_gemma":0.001869559,"threshold_uncertainty_score":0.09797907},"labels":[],"label_agreement":null},{"id":"W6930805981","doi":"10.5281/zenodo.14019481","title":"Limnesia (Limnesia) iberica Lundblad 1954","year":2024,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"Bin; Population; Portuguese; Holotype","score_opus":0.030149546167372344,"score_gpt":0.24492287297048965,"score_spread":0.2147733268031173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6930805981","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14863284,0.071644954,0.0036616272,0.0013931117,0.0018945262,0.00071197475,0.0130133405,0.0011514549,0.7578962],"genre_scores_gemma":[0.8021434,0.030044824,0.010523491,0.0015049592,0.0009349549,0.00065871683,0.011349215,0.0002444387,0.14259598],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997093,0.00003091619,0.000038044396,0.00009757093,0.00006882449,0.000055272238],"domain_scores_gemma":[0.9997738,0.000018181654,0.00009598567,0.000018414265,0.00006926722,0.000024463336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035021568,0.0012196836,0.00065736356,0.0039470955,0.0028443956,0.0009533042,0.0008122742,0.0009462347,0.02660291],"category_scores_gemma":[0.00076631544,0.00032000832,0.0002638514,0.0021663618,0.0009853584,0.0010059658,0.0010374795,0.0005045345,0.013192767],"study_design_candidate":"observational","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":[0.0017661001,0.0001461607,0.041571412,0.001999733,0.000096503936,0.0023505636,0.0020555186,0.0007274243,0.018638063,0.00687602,0.06708894,0.8566835],"study_design_scores_gemma":[0.00010305106,0.00013290497,0.121138975,0.0012658741,0.000078033554,0.002617078,0.0009999173,0.00019186176,0.0011390455,0.0010935233,0.8712067,0.000033041735],"about_ca_topic_score_codex":0.02425641,"about_ca_topic_score_gemma":0.030523714,"teacher_disagreement_score":0.02660291,"about_ca_system_score_codex":0.0018834673,"about_ca_system_score_gemma":0.00083964353,"threshold_uncertainty_score":0.088995636},"labels":[],"label_agreement":null},{"id":"W6931102804","doi":"10.5281/zenodo.5601784","title":"Cadre de gestion - Banque de données sur la santé durable","year":2019,"lang":"fr","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"Université Laval","funders":"","keywords":"Context (archaeology); Monetary system; Third party; Electrocution","score_opus":0.03282403686309962,"score_gpt":0.24115506242581503,"score_spread":0.2083310255627154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931102804","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01802526,0.00750997,0.11574685,0.045596886,0.0029755046,0.0010936738,0.03652438,0.011662677,0.76086485],"genre_scores_gemma":[0.17138676,0.011526539,0.16466421,0.006721979,0.0016359034,0.0015083804,0.031096436,0.0031741073,0.60828567],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9945563,0.001655037,0.00036857143,0.00076272135,0.002251708,0.00040569238],"domain_scores_gemma":[0.98813975,0.004232555,0.0005594511,0.0021709735,0.003610745,0.0012865377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0068556503,0.00074103434,0.0007708512,0.0023924843,0.002585246,0.00805451,0.0017645732,0.0024667718,0.13843395],"category_scores_gemma":[0.014178351,0.0005742991,0.00089643145,0.0030537725,0.0013594455,0.0042737196,0.0050766976,0.0023831339,0.044772703],"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":[0.00025293714,0.00008958386,0.0048665116,0.0010029678,0.000056581524,0.0003145027,0.0038378122,0.0021040468,0.0041889804,0.09882578,0.4211585,0.46330178],"study_design_scores_gemma":[0.000013001806,0.00002788739,0.002914923,0.00024526482,0.000011332852,0.00011752703,0.00063885027,0.00095016725,0.00084634474,0.0048511396,0.9893524,0.000031225336],"about_ca_topic_score_codex":0.08157949,"about_ca_topic_score_gemma":0.06858626,"teacher_disagreement_score":0.13843395,"about_ca_system_score_codex":0.005822449,"about_ca_system_score_gemma":0.008897463,"threshold_uncertainty_score":0.4631077},"labels":[],"label_agreement":null},{"id":"W6931397993","doi":"10.5281/zenodo.4683477","title":"Replication package for The UK as a Technological Follower: Higher Education Expansion, Technological Adoption, and the Labour Market","year":2021,"lang":"en","type":"other","venue":"Figshare","topic":"Face and Expression Recognition","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 British Columbia","funders":"","keywords":"Higher education; Replication (statistics); Technological change; Replicate; Syntax","score_opus":0.027413203829706446,"score_gpt":0.273798368120813,"score_spread":0.24638516429110655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931397993","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012555962,0.0010930047,0.04085818,0.0070771542,0.013900331,0.004053151,0.8373485,0.03083413,0.06357989],"genre_scores_gemma":[0.024379872,0.003020389,0.13663946,0.003981708,0.0035675585,0.05180068,0.51430595,0.044988334,0.21731603],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9942008,0.0020767867,0.0007721167,0.0006093988,0.0018491265,0.0004917587],"domain_scores_gemma":[0.8868625,0.05142174,0.0029043928,0.023397341,0.033651937,0.0017621326],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.011220046,0.0019477691,0.0017136412,0.0050902152,0.0015877783,0.003429262,0.0034518633,0.0022320175,0.6713914],"category_scores_gemma":[0.14595652,0.001979716,0.0054007825,0.009006995,0.001069023,0.0038192784,0.0031609791,0.0033794811,0.31894937],"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":[0.00021377619,0.000031453088,0.000203544,0.000945333,0.000066324894,0.00002829635,0.00010167134,0.00018719994,0.00008018067,0.0030990154,0.9766848,0.018358562],"study_design_scores_gemma":[0.0011567402,0.00014515422,0.007222626,0.0014535517,0.00020362122,0.00012483622,0.00024570755,0.0006955161,0.0006585655,0.01345128,0.9744862,0.00015631222],"about_ca_topic_score_codex":0.017828647,"about_ca_topic_score_gemma":0.01796329,"teacher_disagreement_score":0.6713914,"about_ca_system_score_codex":0.001674908,"about_ca_system_score_gemma":0.0058716787,"threshold_uncertainty_score":0.4687202},"labels":[],"label_agreement":null},{"id":"W6931862651","doi":"10.5281/zenodo.8224240","title":"Variability-aware Neo4j for Analyzing a Graphical Model of a Software Product Line","year":2023,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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 Waterloo","funders":"","keywords":"Software; Product (mathematics); Software product line; Product line; Graphical model; Software system; Line (geometry)","score_opus":0.05364457145924168,"score_gpt":0.2659641035786877,"score_spread":0.21231953211944604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931862651","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056375116,0.000039159317,0.96661156,0.00007267421,0.00004242325,0.00007335465,0.0007169479,0.025298817,0.0015075882],"genre_scores_gemma":[0.16403042,0.00015116435,0.8048181,0.00022338728,0.000047048507,0.00039162787,0.005846811,0.017463155,0.0070282714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99822134,0.00025151653,0.00014441244,0.00030126626,0.00093006296,0.00015135045],"domain_scores_gemma":[0.99843603,0.00045362805,0.00015957715,0.0005598979,0.00034359106,0.000047199213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013897901,0.0015114069,0.0006532565,0.0014893684,0.00061958644,0.0023643652,0.0022697581,0.0010115742,0.008060527],"category_scores_gemma":[0.003942189,0.0011976149,0.0037809666,0.0007360437,0.0007797572,0.001688167,0.0019513643,0.0021019122,0.00215723],"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":[0.0009916704,0.00036520648,0.011608368,0.0016451549,0.0008027553,0.0022023583,0.0013750123,0.27191454,0.14858691,0.15417,0.065438814,0.34089917],"study_design_scores_gemma":[0.000088395966,0.00009125181,0.001869435,0.000111515255,0.0002008757,0.00050979195,0.00007518664,0.82699645,0.06266196,0.044450387,0.06282761,0.00011710429],"about_ca_topic_score_codex":0.0035270012,"about_ca_topic_score_gemma":0.005667744,"teacher_disagreement_score":0.008060527,"about_ca_system_score_codex":0.0010425185,"about_ca_system_score_gemma":0.0012604473,"threshold_uncertainty_score":0.026965082},"labels":[],"label_agreement":null},{"id":"W6931983689","doi":"10.5281/zenodo.7740607","title":"Power Dissipation of an Inductively Coupled Plasma Torch under E Mode Dominated Regime","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"Torch; Plasma torch; Dissipation; Electromagnetic coil; Inductively coupled plasma; Nozzle; Plasma; Coolant","score_opus":0.030515124448407836,"score_gpt":0.26341131731574235,"score_spread":0.2328961928673345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6931983689","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9657231,0.0004903078,0.029136067,0.00011380701,0.000035604804,0.00006029414,0.00017551977,0.00044758414,0.00381774],"genre_scores_gemma":[0.9954027,0.00012562383,0.0025787537,0.000023944402,0.000008675619,0.000025223078,0.00006935278,0.00004663031,0.0017190634],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996289,0.000042124466,0.000012852947,0.0000937063,0.00014312904,0.00007930922],"domain_scores_gemma":[0.99952114,0.00021494327,0.00006441415,0.00005479723,0.00012582364,0.000018820585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034819258,0.0003747699,0.00036519588,0.0003057113,0.00043353045,0.00051490456,0.00044587287,0.00039479256,0.0014497491],"category_scores_gemma":[0.0011347056,0.00020303206,0.00016663228,0.00037732717,0.00073721743,0.00075768813,0.00032912978,0.00026971305,0.00038789038],"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":[0.00057999266,0.0000337208,0.001682967,0.00015004077,0.000019158897,0.00028023953,0.0004188617,0.003745016,0.9754332,0.0006456799,0.0004410347,0.016570156],"study_design_scores_gemma":[0.000022830727,0.0006508715,0.0072014616,0.000008840754,0.000017590122,0.00027889723,0.00015922033,0.013014725,0.9752308,0.0002557319,0.0031412137,0.000017796196],"about_ca_topic_score_codex":0.0006366935,"about_ca_topic_score_gemma":0.00039942056,"teacher_disagreement_score":0.0014497491,"about_ca_system_score_codex":0.00055694714,"about_ca_system_score_gemma":0.00022785747,"threshold_uncertainty_score":0.0048499703},"labels":[],"label_agreement":null},{"id":"W6976938555","doi":"10.60692/e0qpc-62853","title":"Unified Embedding and Clustering","year":2021,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Face and Expression Recognition","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 du Québec à Trois-Rivières","funders":"","keywords":"Cluster analysis; Embedding; Correlation clustering; CURE data clustering algorithm; Fuzzy clustering; Clustering high-dimensional data; Canopy clustering algorithm; Data stream clustering","score_opus":0.03215399639491679,"score_gpt":0.22165329539705386,"score_spread":0.18949929900213708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6976938555","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0025669916,0.0005406853,0.99450535,0.00014740226,0.00006370614,0.00004041476,0.00010948887,0.0005417398,0.0014841518],"genre_scores_gemma":[0.12088892,0.0011820461,0.8691121,0.00022265693,0.00023690521,0.00023051354,0.0017298099,0.00061455567,0.005782462],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968811,0.00075977226,0.0001636584,0.0011030558,0.0008882112,0.00020422389],"domain_scores_gemma":[0.99794465,0.00049660896,0.00018891552,0.00072713045,0.0005535485,0.00008914703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022418918,0.0019095804,0.002054726,0.0036230073,0.0013272061,0.0029305695,0.0028786026,0.0022657618,0.003780099],"category_scores_gemma":[0.00734331,0.0007572716,0.0015514998,0.004327652,0.0018763515,0.0050167018,0.004051861,0.0024961757,0.0027517958],"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":[0.0001476981,0.00009277028,0.0013649653,0.00036716208,0.0002342257,0.00011832255,0.00038346343,0.2325574,0.005907313,0.19655313,0.012027799,0.5502458],"study_design_scores_gemma":[0.000014804328,0.00007680219,0.00064530014,0.000057827892,0.000038576163,0.00019910737,0.00013153217,0.8209328,0.0035105245,0.15803875,0.01630272,0.00005129853],"about_ca_topic_score_codex":0.0029254812,"about_ca_topic_score_gemma":0.0029586607,"teacher_disagreement_score":0.003780099,"about_ca_system_score_codex":0.0013981049,"about_ca_system_score_gemma":0.0014400218,"threshold_uncertainty_score":0.012645662},"labels":[],"label_agreement":null},{"id":"W6979334484","doi":"","title":"Towards Robust Nonlinear Subspace Clustering: A Kernel Learning Approach","year":2025,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","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":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Subspace topology; Nonlinear system; Kernel (algebra); Robustness (evolution); Linear subspace; Weighting; Cluster analysis; Kernel method","score_opus":0.05597119882640911,"score_gpt":0.1868977882898067,"score_spread":0.13092658946339758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979334484","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001333667,0.00009863189,0.9979227,0.00005503604,0.0000097201555,0.000011475981,0.000018980643,0.00021553317,0.00033426617],"genre_scores_gemma":[0.1703119,0.0006380351,0.8230433,0.00020718272,0.0001465151,0.00017654143,0.00067866105,0.00043881318,0.0043590707],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812096,0.0006142108,0.00008865456,0.00042472553,0.0006293307,0.00012207605],"domain_scores_gemma":[0.9978264,0.0006091758,0.00020526616,0.0005451275,0.00072095235,0.00009295247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019063951,0.0013030631,0.0015522105,0.0020133934,0.00080827577,0.0015895496,0.0025326214,0.0014986878,0.0017172955],"category_scores_gemma":[0.0062547415,0.0006096567,0.0011696417,0.0024054605,0.0013170494,0.0019986322,0.0033302421,0.002276138,0.0020803805],"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":[0.000111426685,0.0001086012,0.00083708967,0.00024018796,0.00019613594,0.00008002537,0.00023390105,0.53360885,0.010799716,0.06908952,0.006567608,0.37812704],"study_design_scores_gemma":[0.000003700928,0.000016630867,0.000093461385,0.000006376939,0.0000059489903,0.000024797866,0.000020401048,0.97985953,0.0012288163,0.01732778,0.0014002479,0.000012273527],"about_ca_topic_score_codex":0.0038545136,"about_ca_topic_score_gemma":0.003357295,"teacher_disagreement_score":0.0038545136,"about_ca_system_score_codex":0.001011669,"about_ca_system_score_gemma":0.0016302158,"threshold_uncertainty_score":0.010082126},"labels":[],"label_agreement":null},{"id":"W6990833783","doi":"","title":"Elite-driven support vector machines for classification","year":2024,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Face and Expression Recognition","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 Manitoba","funders":"","keywords":"Support vector machine; Decision boundary; Structured support vector machine; Classifier (UML); Soundness; Linear classifier; Set (abstract data type); Decision support system; Boundary (topology)","score_opus":0.022029523324377402,"score_gpt":0.23973474209442208,"score_spread":0.21770521877004467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990833783","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035045883,0.00072622293,0.9943351,0.00017446128,0.000046159355,0.000027424123,0.000039278788,0.00018357833,0.00096311216],"genre_scores_gemma":[0.4078788,0.0030302787,0.5820958,0.00040839415,0.00047675884,0.00044181145,0.0006555388,0.0001858476,0.004826803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807775,0.00069124036,0.00012380049,0.0002991668,0.000699225,0.00010875302],"domain_scores_gemma":[0.9965669,0.0019941812,0.000298153,0.00031719124,0.0007340031,0.00008959683],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034176088,0.0011033656,0.0010449128,0.0012749139,0.0004336121,0.0015657352,0.001713028,0.0013451697,0.0018684542],"category_scores_gemma":[0.010475094,0.00046226967,0.00083306717,0.0014491755,0.0010158854,0.0024242753,0.0014963569,0.0029750664,0.00091453356],"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":[0.000098421035,0.000096307485,0.0017122646,0.0003130939,0.00011719944,0.00008245342,0.00017635028,0.56337535,0.0038675375,0.17453656,0.0047168564,0.25090754],"study_design_scores_gemma":[0.000003780128,0.000034581135,0.00012855534,0.000023156466,0.0000044903145,0.00001742044,0.000009224596,0.95603156,0.00078068196,0.041190647,0.0017674349,0.000008441674],"about_ca_topic_score_codex":0.0008691396,"about_ca_topic_score_gemma":0.00072380656,"teacher_disagreement_score":0.0034176088,"about_ca_system_score_codex":0.0011420774,"about_ca_system_score_gemma":0.00079667603,"threshold_uncertainty_score":0.018074274},"labels":[],"label_agreement":null},{"id":"W6999720541","doi":"","title":"Deep discriminant analysis based neural network pruning and compact architecture search","year":2021,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Face and Expression Recognition","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":"Pattern recognition (psychology); Artificial neural network; Feature (linguistics); Pruning; Linear discriminant analysis; Feature extraction","score_opus":0.018635919169674425,"score_gpt":0.25437638535004337,"score_spread":0.23574046618036895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6999720541","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.075921126,0.0006624025,0.91176003,0.0006623222,0.00015732629,0.000094520576,0.00038086687,0.0026778965,0.0076835128],"genre_scores_gemma":[0.47576413,0.00029281664,0.50131166,0.00033308053,0.00009725169,0.000201368,0.0014051595,0.00039064215,0.020203877],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976057,0.00003723532,0.000015835652,0.00006562444,0.000065009255,0.000055799577],"domain_scores_gemma":[0.9994605,0.00017764574,0.00003949353,0.00010124421,0.00018162173,0.00003958365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045729434,0.0008141149,0.001113058,0.0007603941,0.00050658546,0.0010173827,0.0014378793,0.00090332504,0.007933835],"category_scores_gemma":[0.0019566026,0.00047413286,0.00063695654,0.00077614246,0.0004334858,0.0009764113,0.0012232809,0.001640429,0.0019089862],"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":[0.00030258246,0.0002020188,0.0016439368,0.00012419197,0.00010366706,0.00016839876,0.00009580607,0.25294507,0.02705758,0.023547094,0.019953959,0.6738557],"study_design_scores_gemma":[0.00002073955,0.000030620944,0.0002088178,0.0000101350915,0.000018239913,0.000027009157,0.000011350927,0.9893618,0.0032069155,0.0060269176,0.0010723432,0.0000050252934],"about_ca_topic_score_codex":0.0058110217,"about_ca_topic_score_gemma":0.015328814,"teacher_disagreement_score":0.007933835,"about_ca_system_score_codex":0.0009221055,"about_ca_system_score_gemma":0.0016159258,"threshold_uncertainty_score":0.026541293},"labels":[],"label_agreement":null},{"id":"W7008712025","doi":"","title":"Classification of biomedical spectra using stochastic feature selection","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":9,"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":"Feature selection; Pattern recognition (psychology); Feature (linguistics); Selection (genetic algorithm); Noise (video)","score_opus":0.02455159168801881,"score_gpt":0.27202948433329177,"score_spread":0.24747789264527295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008712025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21481405,0.0007648665,0.77894396,0.0006650116,0.00022170265,0.00012875108,0.0010124247,0.001479581,0.0019696334],"genre_scores_gemma":[0.8325605,0.00044093435,0.16229415,0.00014088789,0.00017415312,0.00011088132,0.0019625868,0.00010523164,0.0022106154],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996674,0.00008496884,0.000026525906,0.00006894431,0.0001088003,0.000043291224],"domain_scores_gemma":[0.9991374,0.00038798025,0.00009456232,0.00008703949,0.00023947949,0.000053458796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009856048,0.0003870396,0.0005915169,0.0014674936,0.00022637953,0.0006562687,0.00036700044,0.0005582572,0.0014532275],"category_scores_gemma":[0.0020598117,0.0001416798,0.0009907783,0.0007674339,0.00027013564,0.00038318563,0.00040455456,0.0003803877,0.00068707217],"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":[0.0013025376,0.00043236045,0.0115802875,0.00023254247,0.00020156508,0.00029755288,0.00007112661,0.0429047,0.17865948,0.0027270664,0.007532265,0.7540585],"study_design_scores_gemma":[0.000062332845,0.00028191044,0.016996462,0.000032290518,0.000105606276,0.00066966395,0.000046242167,0.9343734,0.039867863,0.00485181,0.002674768,0.00003764129],"about_ca_topic_score_codex":0.00065816246,"about_ca_topic_score_gemma":0.0007803311,"teacher_disagreement_score":0.0014674936,"about_ca_system_score_codex":0.000212622,"about_ca_system_score_gemma":0.00042453664,"threshold_uncertainty_score":0.005212426},"labels":[],"label_agreement":null},{"id":"W7009826153","doi":"","title":"Face recognition using histograms of fuzzy oriented gradients","year":2013,"lang":"en","type":"article","venue":"NPARC","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Pattern recognition (psychology); Facial recognition system; Feature (linguistics); Face (sociological concept); Histogram of oriented gradients; Fuzzy logic","score_opus":0.03034177376071553,"score_gpt":0.24231995984497376,"score_spread":0.21197818608425822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7009826153","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091756344,0.0010271912,0.901281,0.00016539602,0.00012342285,0.00007995101,0.00025112648,0.001465299,0.003850365],"genre_scores_gemma":[0.6412368,0.00059502776,0.3550237,0.00012954943,0.00005999872,0.00005631668,0.00035032962,0.000055753168,0.0024924495],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998368,0.000025346892,0.000007714472,0.00003080405,0.00007651238,0.000022755023],"domain_scores_gemma":[0.9998416,0.000037224276,0.000014719657,0.000023279987,0.00007139563,0.000011792851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031220363,0.00016695258,0.00029831176,0.00077638996,0.00014465617,0.00047397966,0.00027877124,0.00025660513,0.0012518312],"category_scores_gemma":[0.0007555827,0.00013822284,0.00022117438,0.00042854718,0.00019315699,0.0006646532,0.0002851257,0.00022133894,0.0004690713],"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":[0.00020146121,0.00006388758,0.0015360814,0.00008969608,0.00004403568,0.000055282235,0.000040006486,0.01850805,0.123764694,0.0046294625,0.0027993321,0.84826803],"study_design_scores_gemma":[0.0000476691,0.00025357987,0.014767187,0.000039487128,0.000046649213,0.0005396939,0.00009069035,0.81763476,0.14575806,0.0133194225,0.007412803,0.00008993998],"about_ca_topic_score_codex":0.0021838425,"about_ca_topic_score_gemma":0.0024619973,"teacher_disagreement_score":0.0021838425,"about_ca_system_score_codex":0.00024506252,"about_ca_system_score_gemma":0.00022259733,"threshold_uncertainty_score":0.004342258},"labels":[],"label_agreement":null},{"id":"W7018737232","doi":"","title":"Discriminative manifold learning for automatic speech recognition","year":2016,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Face and Expression Recognition","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":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Discriminative model; Pattern recognition (psychology); Nonlinear dimensionality reduction; Manifold alignment; Feature vector; Feature (linguistics); Manifold (fluid mechanics); Linear discriminant analysis; Curse of dimensionality","score_opus":0.026411355750665833,"score_gpt":0.26136606156403136,"score_spread":0.23495470581336553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018737232","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002799271,0.0034292743,0.9900448,0.00046226644,0.00013113847,0.000046056495,0.00014927829,0.0010006183,0.0019372759],"genre_scores_gemma":[0.26019126,0.0091167055,0.71586525,0.00040469243,0.0008296977,0.00041581018,0.0017361598,0.00044767695,0.010992669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998941,0.0003615639,0.00005566033,0.00027080943,0.0003077949,0.00006314406],"domain_scores_gemma":[0.9987494,0.0006293529,0.00011366439,0.00023784862,0.00023921167,0.000030481167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011991863,0.00092481374,0.0011100591,0.0011709019,0.00042803315,0.0010835547,0.0011721943,0.0010933403,0.004611557],"category_scores_gemma":[0.0040159784,0.00040466659,0.0011183001,0.0016631895,0.0010909525,0.0015135615,0.0013261947,0.0023450034,0.002807454],"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":[0.00009636994,0.000083439256,0.0006812931,0.00040581578,0.000113441354,0.00010155947,0.00020847155,0.15766315,0.010227624,0.12834091,0.017611625,0.6844663],"study_design_scores_gemma":[0.000008480697,0.000056069628,0.0006580255,0.00003876355,0.000011456571,0.00007577327,0.000036268622,0.88822865,0.0025382647,0.09260752,0.015710112,0.000030637482],"about_ca_topic_score_codex":0.0029675753,"about_ca_topic_score_gemma":0.002344815,"teacher_disagreement_score":0.004611557,"about_ca_system_score_codex":0.0011255537,"about_ca_system_score_gemma":0.00078579923,"threshold_uncertainty_score":0.015427172},"labels":[],"label_agreement":null},{"id":"W7019346173","doi":"","title":"Face Recognition based on Logarithmic Fusion of SVD and KT","year":2012,"lang":"en","type":"article","venue":"ePrints@Bangalore University (Bangalore University)","topic":"Face and Expression Recognition","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":"Alpha Technologies (Canada)","funders":"","keywords":"Nucleofection; Articular cartilage damage; Fusible alloy; Gestational period; Dysgeusia; Hyporeflexia","score_opus":0.017180254516128944,"score_gpt":0.1813641407134988,"score_spread":0.16418388619736984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7019346173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14369503,0.0008248315,0.8480288,0.00017176241,0.00019846616,0.00009704178,0.00021342545,0.0017366972,0.00503402],"genre_scores_gemma":[0.6921305,0.0007206668,0.30142233,0.00008041964,0.00009448552,0.00006664149,0.00063144165,0.00008790429,0.0047656423],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99890375,0.000098217046,0.00007346981,0.00021732625,0.0006158173,0.00009135386],"domain_scores_gemma":[0.99940634,0.0001174019,0.00007219283,0.00010598138,0.00027400744,0.00002406406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074254966,0.0005153094,0.00078728626,0.0013391707,0.0003129337,0.0008465871,0.0004975283,0.00041649985,0.0022373623],"category_scores_gemma":[0.001965972,0.00020292871,0.00085835246,0.0011279307,0.00041305085,0.0014066999,0.0007684467,0.0004544567,0.0013253689],"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":[0.00059902354,0.00012807272,0.0033306214,0.00018936225,0.00009379306,0.00021412621,0.00013017283,0.021510955,0.14038652,0.0031071627,0.0013861221,0.8289242],"study_design_scores_gemma":[0.000046115936,0.0010103424,0.017291507,0.000061575316,0.00012775138,0.003598332,0.00032364842,0.7399441,0.22294922,0.0063133333,0.008191831,0.00014222642],"about_ca_topic_score_codex":0.0013150314,"about_ca_topic_score_gemma":0.0011475787,"teacher_disagreement_score":0.0022373623,"about_ca_system_score_codex":0.00031938378,"about_ca_system_score_gemma":0.00044455877,"threshold_uncertainty_score":0.0074846745},"labels":[],"label_agreement":null},{"id":"W7023651830","doi":"","title":"Pattern recognition using robust discrimination and fuzzy set theoretic preprocessing","year":2007,"lang":"en","type":"other","venue":"Library and Archives Canada (Government of Canada)","topic":"Face and Expression Recognition","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":"Pattern recognition (psychology); Outlier; Preprocessor; Fuzzy logic; Fuzzy set; Classifier (UML); Iterative and incremental development; Test set; Interpretability","score_opus":0.011897134955461606,"score_gpt":0.17707388012711597,"score_spread":0.16517674517165437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023651830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0075618513,0.00024263548,0.9897537,0.00013238165,0.00003771186,0.000061013703,0.00006002872,0.00067221705,0.0014783649],"genre_scores_gemma":[0.19698775,0.00046369806,0.7995865,0.00017470334,0.000078434525,0.00020130796,0.00036083616,0.00009744035,0.0020493432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984189,0.00022785067,0.00013759243,0.00040698846,0.00068796024,0.000120673954],"domain_scores_gemma":[0.9983621,0.00061508967,0.00017368073,0.00037301984,0.00044612327,0.000030004947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021322547,0.00073665346,0.0010343253,0.0018127287,0.0004243323,0.0019810288,0.0012897871,0.0008276106,0.0025050272],"category_scores_gemma":[0.0066105346,0.00034317386,0.0011387266,0.0016453039,0.0013376544,0.0014981676,0.0011103648,0.0015365236,0.0015985651],"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":[0.00022889984,0.00014732362,0.0009254844,0.00029982277,0.00008422569,0.00016333262,0.00019501132,0.08472016,0.073351756,0.04876186,0.0021402491,0.7889819],"study_design_scores_gemma":[0.000040946237,0.0003571008,0.002332322,0.00009945814,0.000050301223,0.00041659226,0.00010860361,0.84728664,0.058006357,0.080765665,0.010451333,0.00008470618],"about_ca_topic_score_codex":0.0012857977,"about_ca_topic_score_gemma":0.00077966653,"teacher_disagreement_score":0.0025050272,"about_ca_system_score_codex":0.00096753525,"about_ca_system_score_gemma":0.0010276287,"threshold_uncertainty_score":0.011276543},"labels":[],"label_agreement":null},{"id":"W7023884085","doi":"","title":"#195 Propaganda, Politics & The Reality of Censorship with Sarah Swain & Karla Joy Treadway","year":2024,"lang":"en","type":"other","venue":"Internet Archive (Internet Archive)","topic":"Face and Expression Recognition","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; Gestational period; TSG101; Hyporeflexia; Pretext; Fusible alloy; Demotion; Diafiltration","score_opus":0.020752369658316613,"score_gpt":0.2528945317555048,"score_spread":0.2321421620971882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023884085","genre_codex":"other","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00998896,0.006616362,0.00092345296,0.22026168,0.0056549176,0.000030205494,0.00021393131,0.00019181416,0.7561187],"genre_scores_gemma":[0.08786858,0.002359825,0.00037184858,0.029631367,0.001057761,0.000032976033,0.00008054108,0.0002334669,0.8783637],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948716,0.00017283624,0.000010982279,0.00005534355,0.00014793001,0.00012568457],"domain_scores_gemma":[0.9993874,0.00024154503,0.00004247667,0.00005760394,0.00009430576,0.0001765857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006028799,0.0002619165,0.00016537675,0.00035286203,0.010908577,0.006466172,0.000398416,0.00233192,0.05507738],"category_scores_gemma":[0.0018847102,0.00021627788,0.00020100168,0.00047483435,0.004719576,0.0037946934,0.0025069057,0.004072125,0.009615985],"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":[0.000007367655,0.00001020454,0.00025775077,0.000018662358,0.0000010957812,0.00015526958,0.009794205,0.000012210323,0.00010835697,0.04347966,0.9344165,0.011738765],"study_design_scores_gemma":[7.248887e-7,0.000001617693,0.00018939552,0.000029264644,3.7267483e-7,0.00007298429,0.0046193944,0.00001003499,0.000061385945,0.0009887147,0.99402446,0.0000017998664],"about_ca_topic_score_codex":0.04631301,"about_ca_topic_score_gemma":0.11211961,"teacher_disagreement_score":0.05507738,"about_ca_system_score_codex":0.0034649477,"about_ca_system_score_gemma":0.001792759,"threshold_uncertainty_score":0.18425214},"labels":[],"label_agreement":null},{"id":"W7024426873","doi":"","title":"Review of Diagnostic Methods","year":2002,"lang":"en","type":"report","venue":"ScholarWorks - UA (University of Alaska System)","topic":"Face and Expression Recognition","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":"Receipt; Plan (archaeology); Principal (computer security); Quarter (Canadian coin); Program evaluation; Human services; Evaluation methods","score_opus":0.04771782113496845,"score_gpt":0.299544255444826,"score_spread":0.2518264343098575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024426873","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033953562,0.6115712,0.13453224,0.04522656,0.043944195,0.022631759,0.015952485,0.0015091551,0.12123697],"genre_scores_gemma":[0.04159253,0.5782799,0.25068688,0.025495226,0.011553745,0.03436408,0.0141520165,0.0015106796,0.04236497],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.8712254,0.06475885,0.028637612,0.006470748,0.027507603,0.0013997913],"domain_scores_gemma":[0.5824352,0.15004444,0.023599694,0.035660174,0.20460245,0.0036579866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.10853823,0.0018714571,0.0031022306,0.036254127,0.0037111277,0.009439852,0.007822944,0.0033473822,0.041015513],"category_scores_gemma":[0.41838768,0.0015356088,0.0031015244,0.0215266,0.0049579972,0.007191803,0.0055260486,0.0047975956,0.014343966],"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":[0.00024037488,0.00013049415,0.0021108163,0.034381352,0.00034997443,0.00021644976,0.0017855549,0.00015672432,0.00027864677,0.02684297,0.18984902,0.7436575],"study_design_scores_gemma":[0.00008794251,0.00013136768,0.0030351863,0.08500352,0.0004939114,0.00071904954,0.0014101889,0.00032470337,0.00078003644,0.013421468,0.89450186,0.00009066474],"about_ca_topic_score_codex":0.013349682,"about_ca_topic_score_gemma":0.018640516,"teacher_disagreement_score":0.10853823,"about_ca_system_score_codex":0.013578624,"about_ca_system_score_gemma":0.055163983,"threshold_uncertainty_score":0.5740119},"labels":[],"label_agreement":null},{"id":"W7024485852","doi":"","title":"Real-time automatic face tracking using adaptive random forests","year":2010,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Random forest; AdaBoost; Pattern recognition (psychology); Classifier (UML); Pixel; Tracking (education); Boosting (machine learning); Ensemble learning; Feature (linguistics)","score_opus":0.02274446171657799,"score_gpt":0.2615197620310392,"score_spread":0.23877530031446123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024485852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021241061,0.00020985698,0.9754708,0.000053910688,0.000041467025,0.00003436056,0.00006713069,0.0018811413,0.0010002364],"genre_scores_gemma":[0.35839903,0.00030292617,0.6372367,0.00006092349,0.0000609268,0.00009653244,0.0004635471,0.00025144665,0.00312785],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994516,0.000103761515,0.000023736244,0.0001767868,0.00017557052,0.000068501766],"domain_scores_gemma":[0.999464,0.000229523,0.000056748704,0.00008464948,0.00014145343,0.000023667732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010486256,0.0004943954,0.00072155526,0.0007342107,0.00043475875,0.00063970155,0.00091942266,0.0006089273,0.001692582],"category_scores_gemma":[0.0015001663,0.0004045729,0.0008235917,0.0006208725,0.00023583813,0.0007149748,0.00041311936,0.00058708456,0.001352874],"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":[0.00022254203,0.00010951693,0.0016255674,0.00005169238,0.00006270289,0.00007723157,0.000052311698,0.18854804,0.04995053,0.0020642898,0.0034367922,0.7537988],"study_design_scores_gemma":[0.000008633119,0.000024409368,0.0008230643,0.0000052996074,0.000007806651,0.000055563756,0.0000057653015,0.9883958,0.008148159,0.0015271169,0.0009880059,0.000010380492],"about_ca_topic_score_codex":0.0032749728,"about_ca_topic_score_gemma":0.004139704,"teacher_disagreement_score":0.0032749728,"about_ca_system_score_codex":0.00041339826,"about_ca_system_score_gemma":0.00049350574,"threshold_uncertainty_score":0.0065118074},"labels":[],"label_agreement":null},{"id":"W7025076788","doi":"","title":"Thyroid hormone quantifications in serum and tissue","year":2023,"lang":"en","type":"article","venue":"Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU)","topic":"Face and Expression Recognition","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":"University of Victoria","funders":"","keywords":"Thyroid; Hormone; Thyroid hormones; Endocrine gland; Thyroid function tests","score_opus":0.017784994211475308,"score_gpt":0.22466848986513013,"score_spread":0.20688349565365483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7025076788","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.85744864,0.032460447,0.072848365,0.0008265953,0.0013283619,0.00048923196,0.0053700707,0.0030177163,0.026210465],"genre_scores_gemma":[0.9184658,0.0074051,0.043951955,0.00085631193,0.00024982344,0.0005655225,0.006103049,0.00031685855,0.022085613],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985475,0.00022133594,0.00013397205,0.00044296854,0.00044634784,0.00020786836],"domain_scores_gemma":[0.99945134,0.00012378894,0.00005493106,0.00009480254,0.00019072904,0.00008436255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017581802,0.0010545071,0.0005372802,0.0023197366,0.00065588416,0.0010048494,0.0006120416,0.0009168722,0.0022643285],"category_scores_gemma":[0.0016600973,0.00043548626,0.00055407075,0.0011152825,0.0009775865,0.00045275842,0.00055755844,0.0008718627,0.0015461089],"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":[0.0027643426,0.00018877813,0.023264794,0.0003872807,0.00016745755,0.0005377855,0.00064485386,0.0003746383,0.93110543,0.0007704884,0.001146755,0.03864731],"study_design_scores_gemma":[0.000110400266,0.0012674067,0.054841284,0.00012516289,0.00042157684,0.0026667446,0.0005471992,0.003202131,0.9233848,0.0009438007,0.012421334,0.000068156856],"about_ca_topic_score_codex":0.004804488,"about_ca_topic_score_gemma":0.0037921725,"teacher_disagreement_score":0.004804488,"about_ca_system_score_codex":0.0006716211,"about_ca_system_score_gemma":0.0011961813,"threshold_uncertainty_score":0.009553075},"labels":[],"label_agreement":null},{"id":"W7067506583","doi":"","title":"Metric learning revisited: new approaches for supervised and unsupervised metric learning with analysis and algorithms","year":2012,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Metric (unit); Metric space; Equivalence of metrics; Set (abstract data type); Unsupervised learning; Semi-supervised learning; Supervised learning; Euclidean distance","score_opus":0.03169242187199419,"score_gpt":0.24183821753617168,"score_spread":0.2101457956641775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7067506583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000554831,0.01994242,0.9716281,0.0033591103,0.0009824682,0.00007057932,0.0000809304,0.00015403186,0.0032274995],"genre_scores_gemma":[0.036926616,0.022129973,0.9250341,0.0031876683,0.006556805,0.0006794933,0.00030563274,0.0003569035,0.004822696],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9779017,0.010841362,0.0016875245,0.0043246816,0.004891226,0.0003534839],"domain_scores_gemma":[0.97307193,0.018990122,0.0012481115,0.0032325496,0.002756114,0.00070107816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019125875,0.00433208,0.004499426,0.00768652,0.0018695635,0.010586634,0.006304131,0.007228345,0.0028537984],"category_scores_gemma":[0.03832347,0.001745378,0.0038708048,0.011326333,0.018190455,0.020645024,0.009922042,0.017214099,0.0022755414],"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":[0.000041624928,0.000039565857,0.00045259835,0.000494766,0.00013985585,0.000091669164,0.00042418388,0.008471841,0.00033850624,0.8941818,0.00770552,0.0876181],"study_design_scores_gemma":[0.000018016426,0.00006643432,0.00022882501,0.00017545692,0.000028698556,0.00021939543,0.00008994307,0.053614594,0.00042773102,0.8925881,0.052472927,0.000069908114],"about_ca_topic_score_codex":0.0023686432,"about_ca_topic_score_gemma":0.0013730302,"teacher_disagreement_score":0.019125875,"about_ca_system_score_codex":0.004595569,"about_ca_system_score_gemma":0.0028225633,"threshold_uncertainty_score":0.101148546},"labels":[],"label_agreement":null},{"id":"W7084409976","doi":"10.57745/djoz1m","title":"06.0001.2024.02.26.18.11.15.000.N.miniseed","year":2025,"lang":"fr","type":"dataset","venue":"Recherche Data Gouv France","topic":"Face and Expression Recognition","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 du Québec à Rimouski","funders":"","keywords":"","score_opus":0.26018900940210565,"score_gpt":0.3937587607027559,"score_spread":0.13356975130065024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084409976","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018885717,0.00012207894,0.0002887863,0.0001349453,0.00012317189,0.000029548459,0.992814,0.0036763046,0.0026222863],"genre_scores_gemma":[0.00053878746,0.00006432944,0.0007244693,0.00006928738,0.000023740888,0.00007571978,0.9959,0.0003520731,0.0022516353],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990089,0.00014790476,0.00006522271,0.00037199428,0.0001992177,0.00020666291],"domain_scores_gemma":[0.9985879,0.00019844047,0.00006539748,0.0005154125,0.0003710292,0.00026183354],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0011997317,0.0037966573,0.0021336917,0.002333972,0.0013233513,0.003626753,0.0045305192,0.0029963497,0.17891444],"category_scores_gemma":[0.0051240255,0.0009900668,0.0017181075,0.00355166,0.0006115545,0.0020790652,0.002468584,0.0019641893,0.43053454],"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":[0.000038193855,0.0000136226745,0.00013986582,0.00010026492,0.000008337632,0.000005499058,0.000007773603,0.00011000812,0.00008002291,0.00018251862,0.9973652,0.0019488111],"study_design_scores_gemma":[0.00026477926,0.000026921442,0.0014452639,0.0001345841,0.000013462305,0.000057260186,0.00006259303,0.0010916595,0.00054699177,0.0017968693,0.99453074,0.000028947125],"about_ca_topic_score_codex":0.02600571,"about_ca_topic_score_gemma":0.03961668,"teacher_disagreement_score":0.8210856,"about_ca_system_score_codex":0.001836921,"about_ca_system_score_gemma":0.0014648401,"threshold_uncertainty_score":0.59852844},"labels":[],"label_agreement":null},{"id":"W7084584214","doi":"10.5281/zenodo.17260005","title":"Plastic Surgery Demand Trends: A Cross-City Analysis of 12 Major North American Cities (2023–2025)","year":2025,"lang":"en","type":"report","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face and Expression Recognition","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":"Observational study; Contouring; Rejuvenation; Consumption (sociology); Planner; Plastic surgery","score_opus":0.0533327225882537,"score_gpt":0.2893564071134783,"score_spread":0.23602368452522463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084584214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.93244624,0.0003901451,0.00014648576,0.00036492487,0.000016203947,0.000041635838,0.06410655,0.000035070087,0.0024527775],"genre_scores_gemma":[0.9488669,0.0005211943,0.00030007353,0.0002782737,0.000024490855,0.00014558944,0.04786548,0.000029548626,0.0019683572],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995766,0.000054838696,0.00004860066,0.00010040208,0.00011179255,0.00010773452],"domain_scores_gemma":[0.99838865,0.00014263118,0.0006912335,0.00008672826,0.00041695122,0.00027390185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033591344,0.00026006319,0.00023220634,0.0015932317,0.0005094548,0.000911013,0.00042001088,0.00033959677,0.0036469952],"category_scores_gemma":[0.001008334,0.00024468685,0.000617278,0.0050743427,0.00019879531,0.0008746602,0.00095276255,0.0005898966,0.0012425978],"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":[0.00006603376,0.00004482019,0.9891266,0.000051780942,0.00007132201,0.00008520795,0.00041100837,0.00009480529,0.00011385079,0.000049049482,0.007200857,0.0026846083],"study_design_scores_gemma":[0.0000033905178,0.00001757295,0.9956155,0.000017620088,0.000019044955,0.00008762919,0.0014302848,0.00025496428,0.0000459795,0.000012225278,0.0024898122,0.0000059988392],"about_ca_topic_score_codex":0.2420511,"about_ca_topic_score_gemma":0.34091997,"teacher_disagreement_score":0.2420511,"about_ca_system_score_codex":0.0012743658,"about_ca_system_score_gemma":0.0012430465,"threshold_uncertainty_score":0.48128438},"labels":[],"label_agreement":null},{"id":"W7091187876","doi":"","title":"DUAL: Learning Diverse Kernels for Aggregated Two-sample and Independence Testing","year":2025,"lang":"en","type":"article","venue":"ArXiv.org","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; University of Melbourne; Commonwealth Scientific and Industrial Research Organisation; UK Research and Innovation; National Science Foundation","keywords":"Consistency (knowledge bases); Kernel (algebra); Independence (probability theory); Test statistic; Statistical hypothesis testing; Kernel method; Statistic; Selection (genetic algorithm)","score_opus":0.05716577108503421,"score_gpt":0.2970092325681098,"score_spread":0.2398434614830756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7091187876","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027028024,0.00023230797,0.96987534,0.00037790663,0.000049595412,0.00010076943,0.00022155484,0.0009122393,0.0012022282],"genre_scores_gemma":[0.6643307,0.0002521523,0.32924464,0.0007278998,0.00029521115,0.0005200128,0.0015620766,0.00047121238,0.0025959613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99112797,0.0048349565,0.0004135601,0.0016577061,0.0014475127,0.0005182881],"domain_scores_gemma":[0.9650261,0.021757493,0.0018911046,0.0065146955,0.003575781,0.0012347467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013676254,0.0016118069,0.0024377436,0.0022397488,0.00088634476,0.0025083702,0.0038929102,0.0023589344,0.0034289162],"category_scores_gemma":[0.05724705,0.0006016011,0.0015282532,0.0020849402,0.0028158566,0.003981563,0.0053120973,0.0039553884,0.0012986931],"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":[0.0018888167,0.0009918934,0.03171828,0.00057285634,0.00079876307,0.00054586376,0.00060071767,0.31913882,0.009596013,0.12709098,0.011152689,0.49590424],"study_design_scores_gemma":[0.00010935583,0.0002492068,0.0021296963,0.000040742307,0.000068806854,0.00018688133,0.00006062221,0.92097837,0.0030702595,0.07153276,0.0015364714,0.000036763548],"about_ca_topic_score_codex":0.0014933761,"about_ca_topic_score_gemma":0.0015125977,"teacher_disagreement_score":0.013676254,"about_ca_system_score_codex":0.0010980091,"about_ca_system_score_gemma":0.0022077658,"threshold_uncertainty_score":0.07232779},"labels":[],"label_agreement":null},{"id":"W7097377549","doi":"","title":"Author manuscript, published in &amp;quot;ICDM, Vancouver,BC: Canada (2011)&amp;quot; DOI: 10.1109/ICDM.2011.42 Constraint Selection based Semi-supervised Feature Selection","year":2013,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Pairwise comparison; Feature selection; Dimensionality reduction; Relevance (law); Feature (linguistics); Selection (genetic algorithm); Task (project management); Constraint (computer-aided design)","score_opus":0.01727555107672935,"score_gpt":0.21863420610042594,"score_spread":0.2013586550236966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097377549","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013493371,0.018229093,0.17233561,0.03958191,0.099202715,0.0012215469,0.037217114,0.013292387,0.6054263],"genre_scores_gemma":[0.021410553,0.0066470825,0.054489024,0.0014855951,0.0026487096,0.00013570765,0.014840355,0.002118998,0.89622396],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99866486,0.00015666075,0.00011995045,0.00028011424,0.0006051131,0.00017324995],"domain_scores_gemma":[0.99595624,0.00052963855,0.000090958085,0.0006934368,0.0022852614,0.00044445693],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0021498022,0.00097601226,0.0015615269,0.0020993873,0.0020867903,0.0066165053,0.0019334181,0.0015158779,0.37886673],"category_scores_gemma":[0.0069599347,0.000720995,0.00066888495,0.002891795,0.0011734393,0.0021665958,0.00181495,0.0018474804,0.1843181],"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":[0.000111653804,0.00003612162,0.00059088296,0.00017600604,0.000031917403,0.00012549432,0.000048021066,0.0013241861,0.0014547263,0.0030715733,0.7787352,0.21429427],"study_design_scores_gemma":[0.000033741853,0.000041571402,0.0011855382,0.00012049728,0.000017372264,0.00019696404,0.00013920314,0.008194396,0.0023271781,0.0032329655,0.98448193,0.000028695607],"about_ca_topic_score_codex":0.033750523,"about_ca_topic_score_gemma":0.08038623,"teacher_disagreement_score":0.37886673,"about_ca_system_score_codex":0.0031014788,"about_ca_system_score_gemma":0.0033526472,"threshold_uncertainty_score":0.8859712},"labels":[],"label_agreement":null},{"id":"W7099286530","doi":"","title":"Short and long run causality measures: theory and inference ∗ Jean-Marie Dufour † Université","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Causality (physics); Inference; Foundation (evidence); Estimation; Causal inference","score_opus":0.01896284128663352,"score_gpt":0.24242010015881862,"score_spread":0.2234572588721851,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7099286530","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23711796,0.01532606,0.7141573,0.019845964,0.0007796294,0.00016091707,0.0018191974,0.0005554756,0.010237437],"genre_scores_gemma":[0.93640894,0.0064674346,0.045088805,0.0010865128,0.0015078867,0.0003453373,0.0010097862,0.00016988318,0.007915377],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9879546,0.007691915,0.00061051064,0.0025124876,0.00069075223,0.0005397102],"domain_scores_gemma":[0.5345186,0.4327437,0.017356047,0.009071724,0.0039528827,0.0023569549],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.028124679,0.0012335295,0.0032281883,0.0055196504,0.0020344588,0.0059946016,0.0026047556,0.0034215543,0.015138233],"category_scores_gemma":[0.18069942,0.001490974,0.0023191727,0.006082268,0.007015272,0.011344186,0.0029023767,0.0056276573,0.000977309],"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":[0.00044495802,0.000313329,0.07488743,0.0005006274,0.0015447898,0.0003917483,0.00084835873,0.0262768,0.00017914808,0.8076856,0.0061382507,0.08078889],"study_design_scores_gemma":[0.00006119784,0.000047796155,0.007549916,0.00009100538,0.00021834346,0.00010327742,0.00017278237,0.05040919,0.00011582053,0.9395271,0.0016640819,0.000039480088],"about_ca_topic_score_codex":0.007130997,"about_ca_topic_score_gemma":0.005662128,"teacher_disagreement_score":0.028124679,"about_ca_system_score_codex":0.0023618753,"about_ca_system_score_gemma":0.0016969759,"threshold_uncertainty_score":0.14873928},"labels":[],"label_agreement":null},{"id":"W7100456879","doi":"","title":"Face recognition with weighted locally linear embedding, in: The Second Canadian Conference on Computer and Robot Vision","year":2005,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Facial recognition system; Nonlinear dimensionality reduction; Face (sociological concept); Dimensionality reduction; Embedding; Principal component analysis; Robot; Pattern recognition (psychology); Software","score_opus":0.019528588746474576,"score_gpt":0.25026611197331483,"score_spread":0.23073752322684027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100456879","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018763417,0.012313392,0.9573781,0.0011580971,0.0013342557,0.000116581956,0.00031839698,0.0035027848,0.005115042],"genre_scores_gemma":[0.20892802,0.010174363,0.7358686,0.00052668044,0.0010754679,0.00017702248,0.0019506784,0.00062694773,0.040672243],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960333,0.00007122977,0.000023057908,0.000100866215,0.00015503386,0.000046411224],"domain_scores_gemma":[0.99962366,0.00007949456,0.000027410717,0.00007063761,0.00016903761,0.000029706986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090337754,0.0007617758,0.0007123757,0.0012787556,0.00036720868,0.001143589,0.0011287685,0.00073497515,0.006836318],"category_scores_gemma":[0.0014747867,0.0003223873,0.00037946377,0.0010575756,0.00071381475,0.0020705657,0.00074055605,0.0005545526,0.0021822627],"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":[0.00017811447,0.00008398052,0.0007136948,0.00015740102,0.00007400809,0.00012554693,0.00008971742,0.012319575,0.01869396,0.0037440306,0.055125087,0.9086949],"study_design_scores_gemma":[0.00006305233,0.00029249454,0.0066015,0.000101149955,0.00016891325,0.0013313058,0.00028595634,0.7998203,0.047001187,0.026547052,0.11765495,0.00013214212],"about_ca_topic_score_codex":0.010930102,"about_ca_topic_score_gemma":0.018645583,"teacher_disagreement_score":0.010930102,"about_ca_system_score_codex":0.00055199256,"about_ca_system_score_gemma":0.0008025767,"threshold_uncertainty_score":0.022869766},"labels":[],"label_agreement":null},{"id":"W7125777947","doi":"10.21428/594757db.06794ffe","title":"Unsupervised Feature Selection Using Orthogonally Constrained Matrix Factorization with Hessian Regularization and Non-Convex Sparsity","year":2025,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","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":"Sunnybrook Health Science Centre; Sunnybrook Hospital; Seneca Polytechnic","funders":"","keywords":"Hessian matrix; Regularization (linguistics); Feature selection; Pattern recognition (psychology); Cluster analysis; Feature (linguistics); Matrix decomposition; Non-negative matrix factorization; Noise (video)","score_opus":0.007474369218693743,"score_gpt":0.23328023572085574,"score_spread":0.225805866502162,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7125777947","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005668489,0.000056302204,0.99369603,0.00004692848,0.000011436544,0.000038048413,0.00003973148,0.00020497147,0.00023808442],"genre_scores_gemma":[0.27865326,0.00023117672,0.717252,0.00017347542,0.000120846926,0.00041672593,0.001084301,0.00015570167,0.0019125831],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983736,0.0006042398,0.000075856115,0.00036814355,0.00045724717,0.00012090024],"domain_scores_gemma":[0.99844736,0.00066968263,0.00020201894,0.0001930701,0.00042757334,0.000060367558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015154724,0.0013487347,0.001584897,0.0013281482,0.0007281212,0.0008899159,0.0012731968,0.0007804054,0.0008838094],"category_scores_gemma":[0.004015502,0.00044598556,0.0013904892,0.0014016478,0.0008111332,0.0012319525,0.0010980017,0.0011460109,0.00052689953],"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":[0.00027019717,0.00035079906,0.0034682907,0.00024237072,0.0003251659,0.00031682418,0.00027181715,0.3093741,0.044598192,0.023019899,0.0106183505,0.60714394],"study_design_scores_gemma":[0.000014585885,0.000054424756,0.000564141,0.0000073086467,0.000013910212,0.000074803786,0.000025410738,0.9875129,0.003878679,0.006876366,0.00095867814,0.000018826584],"about_ca_topic_score_codex":0.002952304,"about_ca_topic_score_gemma":0.004388249,"teacher_disagreement_score":0.002952304,"about_ca_system_score_codex":0.00050223473,"about_ca_system_score_gemma":0.0012702582,"threshold_uncertainty_score":0.008014739},"labels":[],"label_agreement":null},{"id":"W7133391277","doi":"","title":"Divide-and-Conquer for Debiased <i>l</i><sub>1</sub>-norm Support Vector Machine in Ultra-high Dimensions","year":2018,"lang":"en","type":"article","venue":"CityU Scholars","topic":"Face and Expression Recognition","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","funders":"City University of Hong Kong","keywords":"Support vector machine; Hessian matrix; Estimator; Extension (predicate logic); Convergence (economics); Set (abstract data type); Rate of convergence; Relevance vector machine; Matrix (chemical analysis)","score_opus":0.015898885792821647,"score_gpt":0.24637667933165472,"score_spread":0.23047779353883308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133391277","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0104019875,0.00044397367,0.9876222,0.00023243816,0.000034438122,0.000049520102,0.00002134058,0.0005705317,0.000623626],"genre_scores_gemma":[0.2532213,0.00036476497,0.74241275,0.00034421685,0.00018407774,0.00031818295,0.00019346307,0.00022395073,0.0027372956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980444,0.0006325942,0.00015031951,0.00048784408,0.0005207753,0.00016419099],"domain_scores_gemma":[0.99367803,0.0035525074,0.00061517046,0.00098075,0.0009937162,0.00017986455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0053397473,0.0011106578,0.001753323,0.0012641534,0.0009159855,0.0014648639,0.0020986763,0.0016006998,0.0031547453],"category_scores_gemma":[0.018826813,0.0005948492,0.0007196026,0.0013062654,0.002101289,0.0023158323,0.0020778438,0.0022266156,0.0010235328],"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":[0.0006014979,0.00024486487,0.0036173433,0.00025727705,0.00013564604,0.0002273822,0.00042676568,0.23310025,0.00952219,0.057443686,0.005007103,0.689416],"study_design_scores_gemma":[0.000016711167,0.000058872236,0.00027852127,0.000011827636,0.000011922572,0.000049880524,0.000030996194,0.976905,0.0030404807,0.018571155,0.0010129167,0.0000116757665],"about_ca_topic_score_codex":0.002535643,"about_ca_topic_score_gemma":0.0033820036,"teacher_disagreement_score":0.0053397473,"about_ca_system_score_codex":0.0010917666,"about_ca_system_score_gemma":0.0015052416,"threshold_uncertainty_score":0.028239608},"labels":[],"label_agreement":null},{"id":"W7143812471","doi":"10.71465/ajsip983","title":"Signal Processing Approaches for Real-Time Face Detection in Images","year":2022,"lang":"","type":"article","venue":"American Journal of Signal and Image Processing","topic":"Face and Expression Recognition","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","funders":"","keywords":"Signal processing; Face detection; Pattern recognition (psychology); Face (sociological concept); Discrete wavelet transform; Image processing; Wavelet transform; Haar wavelet; Biometrics","score_opus":0.01972877818920778,"score_gpt":0.24634729011288098,"score_spread":0.2266185119236732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7143812471","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018750556,0.0024835512,0.9934457,0.0001401,0.000105697276,0.00003846788,0.00005610644,0.00045223715,0.0014030351],"genre_scores_gemma":[0.082197584,0.010162207,0.89998704,0.00033081684,0.0005769982,0.00025338313,0.0004152862,0.00016346123,0.0059131742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947375,0.00006785562,0.00003432337,0.00009901316,0.0002945465,0.000030571216],"domain_scores_gemma":[0.9996673,0.000119051256,0.000038287748,0.000055516568,0.00011027213,0.000009696292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005267436,0.0012869156,0.000705003,0.0013781879,0.00029402834,0.0010951161,0.0010161694,0.0011719151,0.0037759442],"category_scores_gemma":[0.0013237321,0.0003241233,0.00074700505,0.0014926126,0.00039553313,0.0012011422,0.0005986097,0.0013655701,0.0027376672],"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":[0.0000935095,0.000107510095,0.000456451,0.00056314125,0.0000990674,0.00012662711,0.00008218155,0.02833405,0.08306178,0.02160291,0.004430066,0.86104274],"study_design_scores_gemma":[0.000032962475,0.00037704164,0.00227643,0.0002259377,0.00013706612,0.0011849268,0.00009577357,0.8282856,0.086306915,0.03320935,0.04778402,0.000083867424],"about_ca_topic_score_codex":0.00083634566,"about_ca_topic_score_gemma":0.000996227,"teacher_disagreement_score":0.0037759442,"about_ca_system_score_codex":0.00040577102,"about_ca_system_score_gemma":0.00039446657,"threshold_uncertainty_score":0.012631834},"labels":[],"label_agreement":null},{"id":"W744717813","doi":"","title":"A PAC-Bayes Sample-compression Approach to Kernel Methods","year":2011,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":15,"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":"Computer science; Support vector machine; Artificial intelligence; Naive Bayes classifier; Kernel (algebra); Machine learning; Pattern recognition (psychology); Bayes' theorem; Sample (material); Polynomial kernel; Kernel method; Bayesian probability; Mathematics","score_opus":0.09764408497220832,"score_gpt":0.32488529819215783,"score_spread":0.2272412132199495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W744717813","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016056704,0.00026262456,0.99682486,0.00015518717,0.000034226596,0.000026386235,0.0000144964315,0.00014820723,0.00092835096],"genre_scores_gemma":[0.19196172,0.0011200926,0.7976708,0.0005245439,0.0006244545,0.00040632443,0.0002531779,0.00045020538,0.006988758],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9938758,0.001980825,0.00025088058,0.00052528357,0.0031475022,0.0002195772],"domain_scores_gemma":[0.98951656,0.0052039726,0.0005605722,0.0021636544,0.002368782,0.00018655934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004521395,0.001168182,0.0018172684,0.0012424623,0.00097574317,0.0024227328,0.0034027258,0.0019920156,0.003983093],"category_scores_gemma":[0.02737833,0.0008691815,0.0009760081,0.0015720273,0.0022933872,0.0050889766,0.003313396,0.004729518,0.0018541083],"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":[0.00026687986,0.00016154694,0.0010655645,0.00023253977,0.0000998519,0.00019396683,0.00025100433,0.24364583,0.0061179777,0.40369263,0.006175701,0.3380964],"study_design_scores_gemma":[0.0000113548795,0.00004297614,0.0001517954,0.000020408826,0.00001399897,0.00012325295,0.000011607227,0.9083832,0.0032593485,0.08499591,0.0029651274,0.000021036862],"about_ca_topic_score_codex":0.001187699,"about_ca_topic_score_gemma":0.001079048,"teacher_disagreement_score":0.004521395,"about_ca_system_score_codex":0.0015169308,"about_ca_system_score_gemma":0.0014195099,"threshold_uncertainty_score":0.023911715},"labels":[],"label_agreement":null},{"id":"W76477875","doi":"","title":"Sorted Kernel Matrices as Cluster Validity Indexes.","year":2009,"lang":"en","type":"article","venue":"European Society for Fuzzy Logic and Technology Conference","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":5,"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":"Kernel (algebra); Cluster analysis; String kernel; Sorting; Computer science; Kernel embedding of distributions; Variable kernel density estimation; Kernel method; Metric (unit); Mathematics; Pattern recognition (psychology); Polynomial kernel; Fuzzy clustering; Kernel principal component analysis; Radial basis function kernel; Similarity (geometry); Artificial intelligence; Data mining; Algorithm; Support vector machine; Combinatorics; Image (mathematics)","score_opus":0.032777952666215575,"score_gpt":0.2588354751814697,"score_spread":0.22605752251525413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W76477875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030695679,0.0008672935,0.9631102,0.00032116377,0.0000907494,0.00025271304,0.0005216782,0.0005796538,0.0035608474],"genre_scores_gemma":[0.4610225,0.00051316246,0.534247,0.00014454023,0.00012685863,0.0004776629,0.0010794862,0.00018935143,0.0021995115],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99455535,0.002405426,0.0004449502,0.0008155285,0.0015077138,0.00027104357],"domain_scores_gemma":[0.9859315,0.007606135,0.0014797569,0.002024844,0.002694745,0.00026298975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005996017,0.0009028793,0.0009951926,0.0047206692,0.0009821657,0.0039953757,0.0015880618,0.0011340092,0.0034502987],"category_scores_gemma":[0.035888467,0.00041027105,0.00069761416,0.0044648917,0.0018577036,0.004973918,0.0019913788,0.0013987249,0.0013349641],"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":[0.00084526115,0.0002451249,0.008346186,0.00092986843,0.0003909706,0.00017450504,0.00110778,0.12080192,0.015415948,0.39099872,0.007869535,0.45287415],"study_design_scores_gemma":[0.000046167843,0.0002439539,0.0070459717,0.00017554875,0.00010902762,0.00036073048,0.00073579507,0.62022334,0.017913394,0.33985716,0.013176332,0.00011255528],"about_ca_topic_score_codex":0.0012842207,"about_ca_topic_score_gemma":0.0014373336,"teacher_disagreement_score":0.005996017,"about_ca_system_score_codex":0.0018808936,"about_ca_system_score_gemma":0.0015176083,"threshold_uncertainty_score":0.031710386},"labels":[],"label_agreement":null},{"id":"W80338197","doi":"10.1007/978-3-642-21596-4_24","title":"Individual Feature–Appearance for Facial Action Recognition","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","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":"Université de Montréal","funders":"","keywords":"Computer science; Facial expression; Gabor wavelet; Artificial intelligence; Pattern recognition (psychology); Face hallucination; Feature (linguistics); Expression (computer science); Facial recognition system; Computer vision; Face (sociological concept); Facial expression recognition; Active appearance model; Wavelet; Face detection; Image (mathematics); Wavelet transform; Discrete wavelet transform","score_opus":0.0604620392069759,"score_gpt":0.27226861098626487,"score_spread":0.21180657177928897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W80338197","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011103264,0.001743667,0.9674849,0.00012179428,0.00030253959,0.000059873444,0.0006680217,0.004984677,0.013531344],"genre_scores_gemma":[0.25375214,0.0033384576,0.65934026,0.00023996958,0.00025179758,0.00016441985,0.0034512426,0.0008937455,0.078567974],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998349,0.000015989406,0.000007712295,0.000044727145,0.00007841111,0.00001818377],"domain_scores_gemma":[0.9999027,0.000014996099,0.00000766343,0.000032071894,0.0000359534,0.000006548968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019732345,0.00041249485,0.0003950346,0.0003824636,0.00011659436,0.00040354,0.0007102662,0.00047184498,0.013014627],"category_scores_gemma":[0.000342086,0.00015748416,0.00036464725,0.00065702345,0.00021145004,0.0006930646,0.00041130706,0.00044212004,0.009675862],"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":[0.000064657186,0.000034211986,0.00019951022,0.000081667495,0.0000147481505,0.000042501153,0.000015888263,0.0031849083,0.077152796,0.0032547214,0.0100227045,0.9059317],"study_design_scores_gemma":[0.000019371964,0.0004054637,0.010600617,0.000099581244,0.000084500614,0.0020484421,0.0000786136,0.5598632,0.22994278,0.023235207,0.17353344,0.00008879352],"about_ca_topic_score_codex":0.00096774223,"about_ca_topic_score_gemma":0.0014711412,"teacher_disagreement_score":0.013014627,"about_ca_system_score_codex":0.00017833897,"about_ca_system_score_gemma":0.00017666478,"threshold_uncertainty_score":0.043538213},"labels":[],"label_agreement":null},{"id":"W815473107","doi":"10.1016/j.eswa.2015.06.044","title":"An enhanced noise resilient K-associated graph classifier","year":2015,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Face and Expression Recognition","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":"University of Ottawa","funders":"","keywords":"Computer science; Classifier (UML); Graph; Algorithm; Pattern recognition (psychology); Parametric statistics; Training set; Artificial intelligence; Support vector machine; Decision tree; Mathematics; Theoretical computer science","score_opus":0.025092311759180325,"score_gpt":0.27529213597713986,"score_spread":0.25019982421795955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W815473107","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038234424,0.0009974354,0.953479,0.00033510578,0.00045692277,0.00011085162,0.00041274907,0.003084448,0.0028892139],"genre_scores_gemma":[0.40016288,0.0008118093,0.57955605,0.0005519281,0.0003204083,0.00013215467,0.0020608811,0.0003825416,0.016021345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990835,0.00013413398,0.000045653334,0.00022339904,0.00041673915,0.00009658637],"domain_scores_gemma":[0.99897003,0.00024362693,0.000049352286,0.0002258081,0.0004447123,0.00006655233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059355877,0.0007834545,0.0014264956,0.0013713242,0.00075210945,0.0012223258,0.0020340637,0.0016640115,0.003251927],"category_scores_gemma":[0.002242755,0.0003476108,0.0008572731,0.0011935727,0.00039157135,0.0015722908,0.0014520551,0.001219712,0.0031883523],"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":[0.0007336678,0.0003265141,0.0013862696,0.00014882951,0.00013023177,0.0001912657,0.000053845444,0.05601699,0.04689824,0.006171068,0.010905805,0.8770373],"study_design_scores_gemma":[0.000018085126,0.00007751949,0.0005848837,0.0000091964575,0.000044899636,0.00015294066,0.000016016344,0.9832691,0.009681854,0.0028255668,0.0033017434,0.000018212251],"about_ca_topic_score_codex":0.0067242123,"about_ca_topic_score_gemma":0.010352229,"teacher_disagreement_score":0.0067242123,"about_ca_system_score_codex":0.00047076494,"about_ca_system_score_gemma":0.0013502326,"threshold_uncertainty_score":0.013370156},"labels":[],"label_agreement":null},{"id":"W862919699","doi":"10.1609/aaai.v29i1.9544","title":"On the Equivalence of Linear Discriminant Analysis and Least Squares","year":2015,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Face and Expression Recognition","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":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Linear discriminant analysis; Mathematics; Eigenvalues and eigenvectors; Equivalence (formal languages); Equivalence class (music); Dimensionality reduction; Matrix (chemical analysis); Discriminant; Principal component analysis; Cluster analysis; Eigendecomposition of a matrix; Range (aeronautics); Pattern recognition (psychology); Algorithm; Artificial intelligence; Computer science; Statistics; Discrete mathematics; Physics","score_opus":0.142375336487628,"score_gpt":0.31384443950322466,"score_spread":0.17146910301559665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W862919699","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005017951,0.0011091598,0.98804575,0.00072450074,0.00016354701,0.000047424397,0.000040080715,0.00014148632,0.004710186],"genre_scores_gemma":[0.2850113,0.0026884587,0.70491594,0.0013426541,0.0011192696,0.00043509857,0.00037767136,0.00041358866,0.0036960896],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9834458,0.008294892,0.00076986017,0.0023742102,0.0047207624,0.0003944912],"domain_scores_gemma":[0.96659005,0.024062052,0.0012935821,0.0032577128,0.00446976,0.00032682027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013582074,0.0014806617,0.0016143343,0.002349718,0.0012987865,0.0030459156,0.0015168396,0.0017819343,0.002695548],"category_scores_gemma":[0.061409116,0.0006318035,0.0011464412,0.0024793467,0.0072906925,0.0050958134,0.00542551,0.00426837,0.0012940518],"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":[0.00024661527,0.00009390972,0.0018477307,0.0002281805,0.00012976781,0.00023091745,0.0008416829,0.047136955,0.0036244981,0.68025494,0.0027041717,0.26266065],"study_design_scores_gemma":[0.00008156527,0.00017977186,0.0017921665,0.00012609399,0.000034272485,0.00026749194,0.0002143557,0.26652148,0.0033759805,0.7139173,0.013384793,0.00010469173],"about_ca_topic_score_codex":0.003021518,"about_ca_topic_score_gemma":0.0012825504,"teacher_disagreement_score":0.013582074,"about_ca_system_score_codex":0.0012766151,"about_ca_system_score_gemma":0.001324616,"threshold_uncertainty_score":0.071829796},"labels":[],"label_agreement":null},{"id":"W924202900","doi":"10.1007/s10514-015-9439-y","title":"An optimal data association method based on the minimum weighted bipartite perfect matching","year":2015,"lang":"en","type":"article","venue":"Autonomous Robots","topic":"Face and Expression Recognition","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":"Simon Fraser University","funders":"Universidad de Zaragoza; Jinan University; Else Kröner-Fresenius-Stiftung; National Natural Science Foundation of China","keywords":"Bipartite graph; Computer science; Matching (statistics); Association (psychology); Integer programming; 3-dimensional matching; Data association; Blossom algorithm; Association scheme; Graph; Algorithm; Hungarian algorithm; Assignment problem; Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics; Statistics","score_opus":0.062010858358229226,"score_gpt":0.31784306764162623,"score_spread":0.255832209283397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W924202900","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0044786655,0.00014764298,0.9942086,0.00008823676,0.000054689466,0.00004046282,0.00006626191,0.00042771793,0.0004877988],"genre_scores_gemma":[0.16307929,0.00031113045,0.8310331,0.00021716417,0.00014594376,0.00022554598,0.0008895842,0.00020686923,0.0038912063],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955527,0.001113962,0.00031206777,0.0013479252,0.0013465335,0.00032669815],"domain_scores_gemma":[0.99685824,0.001100812,0.00026485883,0.000856425,0.0007483718,0.00017124122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002635004,0.000783188,0.0027215367,0.0022817543,0.0014280655,0.0017511126,0.0037355605,0.0017048831,0.0039524967],"category_scores_gemma":[0.008306889,0.00097405264,0.0015973633,0.00441447,0.00093459693,0.0037872419,0.0037929004,0.0017317439,0.0020329421],"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":[0.00064120826,0.00035348578,0.0023786149,0.0003335328,0.00026978363,0.00015542225,0.00016631272,0.119842336,0.011163243,0.03204305,0.008544708,0.8241083],"study_design_scores_gemma":[0.00004278186,0.00008106718,0.00051241164,0.000016896856,0.000057429726,0.00020820458,0.000049928796,0.9673222,0.003729755,0.025065055,0.0028851249,0.000029272014],"about_ca_topic_score_codex":0.003477616,"about_ca_topic_score_gemma":0.002861821,"teacher_disagreement_score":0.0039524967,"about_ca_system_score_codex":0.0007001578,"about_ca_system_score_gemma":0.0028909568,"threshold_uncertainty_score":0.013935387},"labels":[],"label_agreement":null},{"id":"W967827082","doi":"10.1007/978-3-642-11568-4_5","title":"A Bayesian Method for Infrared Face Recognition","year":2011,"lang":"en","type":"book-chapter","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":21,"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":"Facial recognition system; Artificial intelligence; Computer science; Biometrics; Infrared; Gaussian; Pattern recognition (psychology); Bayesian probability; Face (sociological concept); Context (archaeology); Computer vision; Mixture model; Gaussian network model; Noise (video); Image (mathematics); Optics; Physics","score_opus":0.05751277762135145,"score_gpt":0.2762907931052757,"score_spread":0.21877801548392425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W967827082","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00021520768,0.00052015093,0.9970963,0.000051647825,0.00004716956,0.000010231795,0.000039557333,0.0002779674,0.0017418072],"genre_scores_gemma":[0.016008953,0.0018757996,0.9601957,0.0001985729,0.00024416106,0.00013006867,0.00036853823,0.00035513093,0.020623013],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916565,0.00017981754,0.000033976674,0.00016702931,0.00040421268,0.00004926474],"domain_scores_gemma":[0.9994524,0.00024402831,0.000026301575,0.00008351755,0.00017482147,0.000018891598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095761,0.000941294,0.0010387929,0.00097616384,0.00053068175,0.0011149177,0.0023949188,0.0012798285,0.011243627],"category_scores_gemma":[0.0022030512,0.00089157774,0.0010193537,0.0012040323,0.0007740816,0.001724914,0.0012274571,0.0022334526,0.007327716],"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":[0.000059841735,0.000075223674,0.00024857346,0.0001911015,0.00007243061,0.000052019226,0.00008558023,0.050799526,0.009300346,0.068828434,0.021961117,0.84832585],"study_design_scores_gemma":[0.00001582857,0.0000418939,0.00080701266,0.00009078767,0.000065589455,0.00041592884,0.000031062587,0.82353735,0.007776412,0.12248768,0.044647865,0.000082731094],"about_ca_topic_score_codex":0.0045517213,"about_ca_topic_score_gemma":0.0072738733,"teacher_disagreement_score":0.011243627,"about_ca_system_score_codex":0.00068898615,"about_ca_system_score_gemma":0.00080282567,"threshold_uncertainty_score":0.03761375},"labels":[],"label_agreement":null},{"id":"W987718657","doi":"10.1007/978-3-642-38067-9_29","title":"A Novel Pattern Rejection Criterion Based on Multiple Classifiers","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":4,"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; MNIST database; Pattern recognition (psychology); Classifier (UML); Artificial intelligence; Machine learning; Weighted voting; Heuristic; Support vector machine; Data mining; Artificial neural network; Voting","score_opus":0.02598161920033348,"score_gpt":0.24050233432079937,"score_spread":0.2145207151204659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W987718657","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038136896,0.0002903723,0.99453473,0.00006116483,0.00010100449,0.00004743892,0.000025507607,0.00036411185,0.00076204684],"genre_scores_gemma":[0.13262583,0.00046974435,0.85788727,0.00035388098,0.0004997623,0.00025829498,0.00057092716,0.00047398108,0.0068602436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9931265,0.0013150532,0.00047243503,0.0009347821,0.0037920608,0.0003591513],"domain_scores_gemma":[0.9919561,0.0026624764,0.00045157198,0.00080799026,0.003838186,0.0002836498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003815293,0.0015233562,0.003670358,0.0022818802,0.0010217158,0.0026622075,0.0042801914,0.003036039,0.0043302393],"category_scores_gemma":[0.009530999,0.00075934303,0.0017452992,0.002173761,0.0009653947,0.0031671727,0.0027114258,0.002267993,0.0032493595],"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":[0.0009778099,0.000321118,0.0015066157,0.00034063353,0.00028202086,0.00022181633,0.00008204497,0.04506192,0.06259709,0.01317395,0.007480246,0.8679548],"study_design_scores_gemma":[0.00004428732,0.00019721517,0.00092659413,0.000027059388,0.00009714768,0.0003758686,0.000020899668,0.97186434,0.016264118,0.005680632,0.004458326,0.000043451026],"about_ca_topic_score_codex":0.0015284364,"about_ca_topic_score_gemma":0.0018683332,"teacher_disagreement_score":0.0043302393,"about_ca_system_score_codex":0.000664892,"about_ca_system_score_gemma":0.0014712961,"threshold_uncertainty_score":0.020177424},"labels":[],"label_agreement":null}]}