{"meta":{"query_hash":"3f41ee49b2c1","filters":{"venue":"IEEE Transactions on Cognitive and Developmental Systems"},"cohort_total":14,"direct_labels_cover":0,"predictions_cover":14,"exported":14,"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/3f41ee49b2c1","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Cognitive+and+Developmental+Systems"},"results":[{"id":"W2511272914","doi":"10.1109/tcds.2016.2604375","title":"Selective Attention by Perceptual Filtering in a Robot Control Architecture","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Architecture; Perception; Robot; Human–computer interaction; Artificial intelligence; Cognitive architecture; Control (management); Robot control; Mobile robot; Cognition; Psychology; Neuroscience","score_opus":0.011008022543784343,"score_gpt":0.23499037348372906,"score_spread":0.22398235093994473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511272914","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13017645,0.000285264,0.8579077,0.0003473232,0.00007311311,0.00011037013,0.000029634133,0.004004853,0.007065271],"genre_scores_gemma":[0.8729488,0.000110694134,0.12348742,0.00015898554,0.000032479584,0.00008114845,0.000022634975,0.000094016505,0.0030636967],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997811,0.00002411964,0.000013662355,0.00006708499,0.00007320918,0.000040858413],"domain_scores_gemma":[0.99966943,0.000097330245,0.0000410229,0.000056751927,0.000101514444,0.000033986158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003348687,0.00036485866,0.00022420165,0.00034679356,0.00045074846,0.00082232285,0.0008252858,0.0004428464,0.0016594706],"category_scores_gemma":[0.00070920284,0.0001987034,0.0002584444,0.00014907525,0.0006290945,0.0008604853,0.00036671522,0.00045381772,0.00028521405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053460564,0.00034534626,0.0021491642,0.0001586897,0.00010684447,0.00038620832,0.00072210457,0.11590658,0.6098159,0.051847816,0.0031163637,0.21491037],"study_design_scores_gemma":[0.000081356025,0.0004096981,0.0030515217,0.000022173643,0.000102180624,0.00016759594,0.00006422646,0.86264104,0.10337551,0.022856815,0.0071656215,0.00006226775],"about_ca_topic_score_codex":0.0058463262,"about_ca_topic_score_gemma":0.0051710927,"teacher_disagreement_score":0.0058463262,"about_ca_system_score_codex":0.00068530254,"about_ca_system_score_gemma":0.0007945668,"threshold_uncertainty_score":0.011624575},"labels":[],"label_agreement":null},{"id":"W2755795939","doi":"10.1109/tcds.2017.2751963","title":"“To Approach Humans?”: A Unified Framework for Approaching Pose Prediction and Socially Aware Robot Navigation","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":92,"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 Prince Edward Island","funders":"","keywords":"Computer science; Mobile robot; Robot; Human–robot interaction; Social robot; Artificial intelligence; Human–computer interaction; Motion planning; Mobile robot navigation; Computer vision; Robot control","score_opus":0.08301663558085534,"score_gpt":0.3635278480550823,"score_spread":0.28051121247422695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2755795939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002485101,0.00011963426,0.9961702,0.00009459157,0.00002243659,0.00002825789,0.000029375598,0.0002497457,0.0008006249],"genre_scores_gemma":[0.34207746,0.00056784484,0.65296066,0.00017311344,0.00011876579,0.00031729782,0.00027677146,0.00011303179,0.00339495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938726,0.00013271646,0.000031586907,0.00019145741,0.00017903649,0.000077904224],"domain_scores_gemma":[0.9996705,0.00007975693,0.000057360594,0.000043809603,0.000105650084,0.000042938493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069526886,0.0010819332,0.0008845267,0.0008533738,0.0006601473,0.0010210298,0.002149968,0.0010665664,0.0015287334],"category_scores_gemma":[0.0013906539,0.00046028948,0.0012079898,0.00061783654,0.000996744,0.0016967416,0.00187184,0.001028425,0.0005192873],"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":[0.00010276891,0.00015870687,0.0040528863,0.00021100731,0.00014573903,0.000344491,0.00075309403,0.6513579,0.01059554,0.083759695,0.0043423604,0.24417579],"study_design_scores_gemma":[0.000005319421,0.00004762474,0.00040555687,0.000013255218,0.000022512711,0.000051526553,0.00006417319,0.98639476,0.00090390025,0.009987846,0.0020803956,0.000023021741],"about_ca_topic_score_codex":0.025308158,"about_ca_topic_score_gemma":0.023007745,"teacher_disagreement_score":0.025308158,"about_ca_system_score_codex":0.0007312935,"about_ca_system_score_gemma":0.0021976419,"threshold_uncertainty_score":0.0503217},"labels":[],"label_agreement":null},{"id":"W2912123949","doi":"10.1109/tcds.2019.2897618","title":"Combined Sensing, Cognition, Learning, and Control for Developing Future Neuro-Robotics Systems: A Survey","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","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 Toronto","funders":"National Natural Science Foundation of China","keywords":"Robotics; Artificial intelligence; Cognitive robotics; Computer science; Robot; Embodied cognition; Cognition; Perception; Developmental robotics; Cognitive neuroscience; Human–computer interaction; Cognitive science; Neuroscience; Psychology","score_opus":0.028009513216539446,"score_gpt":0.2596949502957785,"score_spread":0.23168543707923905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2912123949","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":[0.0032501156,0.8639516,0.105847344,0.002111198,0.00047406205,0.000083330524,0.000059135626,0.00020147061,0.024021747],"genre_scores_gemma":[0.044366516,0.87878984,0.067597635,0.0007997592,0.0013021807,0.00021805518,0.00019016863,0.00006045382,0.0066755153],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99948645,0.00010507422,0.00006901788,0.00012111697,0.00018529121,0.000033095755],"domain_scores_gemma":[0.9994373,0.00029861022,0.000053196156,0.0000439113,0.00013133169,0.00003571196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010133089,0.0013119327,0.0010416226,0.0025814173,0.00055911986,0.0022277175,0.001155768,0.0019050392,0.0039629573],"category_scores_gemma":[0.0009634436,0.0005524054,0.0006395991,0.0025484834,0.0013645536,0.00409788,0.0014172973,0.0013107437,0.0015745844],"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":[0.00003752209,0.00012613085,0.0010683216,0.007250193,0.00008678309,0.00016618276,0.00037810666,0.004451896,0.0034433522,0.1157111,0.0073582735,0.8599221],"study_design_scores_gemma":[0.000024241166,0.0004733452,0.0035388682,0.00468253,0.00024750974,0.0018284832,0.0009909779,0.026349023,0.006216451,0.15988617,0.79558897,0.00017347683],"about_ca_topic_score_codex":0.0012571077,"about_ca_topic_score_gemma":0.0013738908,"teacher_disagreement_score":0.0039629573,"about_ca_system_score_codex":0.00082055974,"about_ca_system_score_gemma":0.0013071004,"threshold_uncertainty_score":0.013257444},"labels":[],"label_agreement":null},{"id":"W2969776285","doi":"10.1109/tcds.2019.2932751","title":"Decentralized Energy-Aware Co-Planning of Motion and Communication Strategies for Networked Mobile Robots","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Distributed Control Multi-Agent Systems","field":"Computer Science","cited_by":9,"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; Robot; Software deployment; Mobile robot; Task (project management); Energy consumption; Motion planning; Distributed computing; Wireless; Motion (physics); Scheme (mathematics); Computer network; Real-time computing; Human–computer interaction; Artificial intelligence; Telecommunications; Engineering","score_opus":0.022997357876308012,"score_gpt":0.26915156412534486,"score_spread":0.24615420624903683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969776285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02099196,0.00012858055,0.9766663,0.000102565566,0.000016839194,0.000042135394,0.00001877047,0.00012340084,0.0019095315],"genre_scores_gemma":[0.89937294,0.00011285235,0.09837595,0.000034901397,0.000019550407,0.00016429521,0.00005007931,0.000025140185,0.001844319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996593,0.00008764935,0.00001550549,0.00009054115,0.00009123378,0.00005569053],"domain_scores_gemma":[0.9994772,0.00022817725,0.00012300661,0.000046292298,0.000080874066,0.000044525066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055224594,0.0007518926,0.0007973991,0.000412732,0.0006335368,0.0005474391,0.0010657503,0.000722553,0.00081950205],"category_scores_gemma":[0.0012934426,0.0005806551,0.0004474374,0.000420123,0.0008027687,0.00071396923,0.0009774757,0.000581584,0.00016133675],"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":[0.000028523837,0.000019777415,0.0002167552,0.000023515351,0.000013841362,0.00005614612,0.00004887084,0.9822035,0.0012867698,0.0053859483,0.00022288054,0.010493483],"study_design_scores_gemma":[0.000007169212,0.000018572466,0.00005760973,0.0000016739144,0.0000029309274,0.0000094128745,0.000009938374,0.9973455,0.00024224387,0.002120595,0.00018139266,0.0000029579346],"about_ca_topic_score_codex":0.005068588,"about_ca_topic_score_gemma":0.006267695,"teacher_disagreement_score":0.005068588,"about_ca_system_score_codex":0.00076356943,"about_ca_system_score_gemma":0.0014543821,"threshold_uncertainty_score":0.010078192},"labels":[],"label_agreement":null},{"id":"W2975485695","doi":"10.1109/tcds.2019.2920364","title":"Deep Residual Network With Adaptive Learning Framework for Fingerprint Liveness Detection","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":75,"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; Liveness; Artificial intelligence; Spoofing attack; Convolutional neural network; Fingerprint (computing); Pattern recognition (psychology); Feature extraction; Deep learning; Residual; Fingerprint recognition; Artificial neural network; Multilayer perceptron; Feature (linguistics); Machine learning; Algorithm; Computer security","score_opus":0.02163336013942574,"score_gpt":0.23581662265953193,"score_spread":0.2141832625201062,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975485695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03587867,0.002738592,0.95622826,0.00035920367,0.00009846813,0.00004422821,0.00016352341,0.0014860803,0.0030028766],"genre_scores_gemma":[0.82031345,0.0019391,0.16486678,0.00037860248,0.00011332031,0.00014826244,0.0007553445,0.00012787568,0.011357283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978,0.0000350128,0.000011933632,0.00006961599,0.000055602093,0.000047773283],"domain_scores_gemma":[0.9998373,0.000045468794,0.000023237808,0.000014892237,0.0000674845,0.000011664574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047397762,0.00077401276,0.00070554344,0.0004650625,0.00018344895,0.0005258371,0.0013823378,0.00074421655,0.001540446],"category_scores_gemma":[0.000879225,0.00035768686,0.0005748396,0.00042611422,0.00032876414,0.0007778017,0.00062515686,0.0011213137,0.0004291506],"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":[0.00022707343,0.00013492201,0.0019523897,0.00014710653,0.00012812653,0.0001914668,0.000078621604,0.62659544,0.016639043,0.0072770566,0.0038745787,0.34275413],"study_design_scores_gemma":[0.0000032355563,0.000023496214,0.00012065224,0.000003582155,0.000008939419,0.000013523606,0.000003490013,0.9977634,0.00094359403,0.00074404274,0.00036762972,0.000004419934],"about_ca_topic_score_codex":0.010537096,"about_ca_topic_score_gemma":0.008822281,"teacher_disagreement_score":0.010537096,"about_ca_system_score_codex":0.00060989463,"about_ca_system_score_gemma":0.0007517841,"threshold_uncertainty_score":0.02095151},"labels":[],"label_agreement":null},{"id":"W3078478003","doi":"10.1109/tcds.2020.3017100","title":"Accurate and Fast Deep Evolutionary Networks Structured Representation Through Activating and Freezing Dense Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Evolutionary Algorithms and Applications","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":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Variety (cybernetics); Convergence (economics); Construct (python library); Artificial neural network; Training (meteorology); Artificial intelligence; Deep learning; Representation (politics); Point (geometry); Evolutionary algorithm; Computer network","score_opus":0.027304680677245216,"score_gpt":0.2512561656906926,"score_spread":0.22395148501344736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3078478003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056761578,0.00020547041,0.93840504,0.00013025057,0.000047220306,0.0000646333,0.000091593094,0.0020253574,0.0022688229],"genre_scores_gemma":[0.66916263,0.00026673233,0.32557738,0.00016215685,0.000028189226,0.00017526081,0.00039125336,0.00026760527,0.0039688502],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975365,0.000046237674,0.000014589917,0.00006495483,0.00007284113,0.000047829002],"domain_scores_gemma":[0.9993907,0.00023153264,0.00006403783,0.0001551954,0.00012815539,0.000030243964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008298782,0.001015538,0.00058983645,0.00043053617,0.00037393,0.0006750572,0.0015318269,0.00075034896,0.0019084568],"category_scores_gemma":[0.002351174,0.0005947069,0.0005764598,0.00036692014,0.00065515697,0.0015161183,0.0013899674,0.0015805607,0.0005312078],"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":[0.0000850789,0.000054235312,0.0015925937,0.000058377427,0.00003843766,0.00012299993,0.00012197419,0.7590392,0.0172836,0.010076572,0.0020118414,0.20951506],"study_design_scores_gemma":[0.0000049981136,0.000021470958,0.00014189495,0.000007547675,0.000007926108,0.000026945796,0.000010468471,0.9913436,0.0045303027,0.0032575594,0.00064232684,0.000004879093],"about_ca_topic_score_codex":0.003933563,"about_ca_topic_score_gemma":0.0062581305,"teacher_disagreement_score":0.003933563,"about_ca_system_score_codex":0.00064146373,"about_ca_system_score_gemma":0.00062678946,"threshold_uncertainty_score":0.0078213215},"labels":[],"label_agreement":null},{"id":"W3136994693","doi":"10.1109/tcds.2021.3065200","title":"A Survey on Neuromarketing Using EEG Signals","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Army Research Office","keywords":"Neuromarketing; Computer science; Electroencephalography; Functional magnetic resonance imaging; Process (computing); Product (mathematics); Artificial intelligence; Human–computer interaction; Data science; Neuroscience; Psychology","score_opus":0.08177045781237519,"score_gpt":0.2942505910659309,"score_spread":0.2124801332535557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136994693","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":[0.014802559,0.9029177,0.039451584,0.0046325936,0.0014950682,0.00027600754,0.0023293432,0.0007810066,0.033314265],"genre_scores_gemma":[0.027188117,0.9435408,0.012413305,0.0023281053,0.0018570754,0.00021396762,0.0027902902,0.00011308653,0.009555281],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9986572,0.00027642708,0.00021542807,0.00023075101,0.0005550749,0.000064932006],"domain_scores_gemma":[0.99328715,0.0041696643,0.0004842152,0.00021142441,0.0017104128,0.00013720374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018008308,0.001086766,0.0009891541,0.0062517077,0.0003141431,0.0018898237,0.0010608586,0.0015376814,0.0075067612],"category_scores_gemma":[0.005622787,0.0005025798,0.00092633005,0.008014552,0.0003861996,0.0033797205,0.0006355597,0.00076261465,0.004980528],"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":[0.00019224743,0.00007696115,0.0049758423,0.006601001,0.00010569128,0.00018791441,0.00011991409,0.0004305578,0.0027750586,0.0012252941,0.02756817,0.95574147],"study_design_scores_gemma":[0.000052490013,0.00071355136,0.0540402,0.0108273,0.0004718601,0.008179374,0.0010445239,0.0030439184,0.0083848415,0.0041192635,0.90889794,0.00022476865],"about_ca_topic_score_codex":0.0011120675,"about_ca_topic_score_gemma":0.0011604191,"teacher_disagreement_score":0.0075067612,"about_ca_system_score_codex":0.00036218014,"about_ca_system_score_gemma":0.00060476875,"threshold_uncertainty_score":0.025112629},"labels":[],"label_agreement":null},{"id":"W3216889993","doi":"10.1109/tcds.2021.3131045","title":"Recurrent Network Knowledge Distillation for Image Rain Removal","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Image Enhancement Techniques","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":"Xiamen University; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Computer science; Block (permutation group theory); Streak; Residual; Artificial intelligence; Convolutional neural network; Image (mathematics); Artificial neural network; Channel (broadcasting); Computer vision; Deep learning; Pattern recognition (psychology); Machine learning; Algorithm; Mathematics; Telecommunications","score_opus":0.025689469394975406,"score_gpt":0.2803272227409553,"score_spread":0.2546377533459799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216889993","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05696161,0.0008678591,0.93717766,0.00024699038,0.00006485661,0.000048871807,0.00012904896,0.0026679542,0.001835167],"genre_scores_gemma":[0.7711621,0.0005522013,0.22084783,0.0003272313,0.00006578012,0.00009129303,0.00070977723,0.00016008895,0.0060836207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979514,0.000030586474,0.0000111611525,0.00006779721,0.000056187324,0.00003916235],"domain_scores_gemma":[0.9996222,0.0001488129,0.00004688978,0.00006390379,0.00009647841,0.000021730353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005194457,0.0007941384,0.00067884004,0.00045297298,0.00025426256,0.00040057534,0.0012700671,0.0007340568,0.001605009],"category_scores_gemma":[0.0014168209,0.00032815352,0.00058384944,0.0003953863,0.00035957515,0.0009760365,0.0007534419,0.0011456241,0.00040167515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002302869,0.00017320896,0.00094255677,0.00012274813,0.00012705843,0.00019723533,0.00011013979,0.38971445,0.032338932,0.004242934,0.0036158334,0.56818473],"study_design_scores_gemma":[0.000005234982,0.00003676064,0.00017968295,0.00000386996,0.000021542271,0.000023853217,0.0000059227414,0.99068016,0.007331309,0.0011484085,0.0005576911,0.0000055632213],"about_ca_topic_score_codex":0.009159703,"about_ca_topic_score_gemma":0.014020234,"teacher_disagreement_score":0.009159703,"about_ca_system_score_codex":0.00057705573,"about_ca_system_score_gemma":0.0008042484,"threshold_uncertainty_score":0.018212795},"labels":[],"label_agreement":null},{"id":"W4324292877","doi":"10.1109/tcds.2023.3257055","title":"Distilling Invariant Representations With Domain Adversarial Learning for Cross-Subject Children Seizure Prediction","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Adversarial system; Computer science; Invariant (physics); Artificial intelligence; Subject (documents); Theoretical computer science; World Wide Web; Mathematics","score_opus":0.028992475464924266,"score_gpt":0.27803536838431037,"score_spread":0.2490428929193861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324292877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04840257,0.0005930678,0.94874394,0.00024331007,0.000050441824,0.000055384662,0.00011143658,0.0007929645,0.0010069311],"genre_scores_gemma":[0.8873343,0.0004978227,0.10729293,0.00041198867,0.0000764482,0.00013090057,0.0006835166,0.000111231064,0.0034608215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994886,0.00017978955,0.000028433658,0.00013241133,0.00010710425,0.00006363947],"domain_scores_gemma":[0.9989089,0.0006671227,0.00011307497,0.00011579766,0.00014713383,0.000047855676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014338202,0.0010414965,0.00093997753,0.0006447149,0.00022586469,0.00050442154,0.00094455556,0.00080292363,0.0011891973],"category_scores_gemma":[0.0030626801,0.00031541465,0.0008239932,0.00048052784,0.0005776808,0.0007966024,0.0013265458,0.0015591126,0.00045586374],"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":[0.00023120189,0.00015292928,0.002252226,0.000052193882,0.00011135279,0.00017307143,0.00006679195,0.77721226,0.0063204137,0.0035317196,0.0027447215,0.20715111],"study_design_scores_gemma":[0.0000034342875,0.000027121256,0.0001776888,0.0000031533555,0.0000067826036,0.000021486909,0.000004801044,0.9973678,0.00091300596,0.0013125594,0.00015712656,0.000005170642],"about_ca_topic_score_codex":0.0023614708,"about_ca_topic_score_gemma":0.0019176553,"teacher_disagreement_score":0.0023614708,"about_ca_system_score_codex":0.00048231217,"about_ca_system_score_gemma":0.00061696157,"threshold_uncertainty_score":0.0075828433},"labels":[],"label_agreement":null},{"id":"W4385338508","doi":"10.1109/tcds.2023.3299755","title":"CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Domain Adaptation and Few-Shot Learning","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; University of Waterloo","funders":"Air Force Office of Scientific Research; California Institute of Technology; National Science Foundation","keywords":"Forgetting; Computer science; Artificial intelligence; Incremental learning; Robot; Class (philosophy); Machine learning; Object (grammar); Set (abstract data type)","score_opus":0.060527478791272384,"score_gpt":0.27734878666937335,"score_spread":0.21682130787810097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385338508","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016185127,0.00055781944,0.97840273,0.0004468682,0.00010061407,0.0001431256,0.00013977356,0.0013659783,0.0026579907],"genre_scores_gemma":[0.63976866,0.00059515407,0.35159534,0.0007778632,0.00015942093,0.0005789705,0.00046864554,0.00021432352,0.005841545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994517,0.00011283978,0.000021664802,0.00017528345,0.00017137984,0.000067180554],"domain_scores_gemma":[0.9985707,0.00062020903,0.00013345551,0.00026770882,0.00029180152,0.0001162031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010727149,0.0007269346,0.0010229493,0.0008610742,0.00058799563,0.0010785003,0.0051788725,0.0014245326,0.0025897042],"category_scores_gemma":[0.0044304677,0.00047757698,0.00091881055,0.0008925025,0.0013202148,0.002018332,0.0020251945,0.0024716565,0.00062258315],"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":[0.00026368667,0.00037219378,0.0022069826,0.00031227723,0.00016437755,0.00023920224,0.00030423337,0.629123,0.0062480774,0.04318047,0.00830975,0.30927578],"study_design_scores_gemma":[0.000012307382,0.00004772764,0.00015420739,0.000007298172,0.000011440812,0.000042381682,0.000007348366,0.98236185,0.00076650525,0.01540391,0.0011751422,0.000009816512],"about_ca_topic_score_codex":0.011968189,"about_ca_topic_score_gemma":0.013543452,"teacher_disagreement_score":0.011968189,"about_ca_system_score_codex":0.0012651314,"about_ca_system_score_gemma":0.0015522452,"threshold_uncertainty_score":0.023797095},"labels":[],"label_agreement":null},{"id":"W4389725445","doi":"10.1109/tcds.2023.3325984","title":"Guest Editorial Special Issue on Hybrid Brain–Computer Collaborative Intelligent System","year":2023,"lang":"en","type":"editorial","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","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":"Carleton University","funders":"","keywords":"Computer science; Human–computer interaction; Artificial intelligence; Field (mathematics); Cognition; Brain–computer interface; Data science","score_opus":0.01925475920281446,"score_gpt":0.27031137156131546,"score_spread":0.251056612358501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389725445","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00024731897,0.009338355,0.0006828048,0.023785224,0.9583936,0.000050129303,0.00019379475,0.00017827807,0.00713047],"genre_scores_gemma":[0.0015611354,0.009424306,0.00022792786,0.008269447,0.95742345,0.000038520608,0.00018024111,0.00009332898,0.022781618],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99838257,0.0001991834,0.00016289481,0.00037494456,0.0006836239,0.00019673802],"domain_scores_gemma":[0.9949746,0.0010899653,0.0002893433,0.00016726658,0.0023608995,0.0011179808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020343408,0.0016900911,0.0017513661,0.0016820751,0.0015003505,0.006099143,0.0020720086,0.0064873532,0.07292736],"category_scores_gemma":[0.0060666353,0.00049915095,0.0015673453,0.0006655808,0.0010657285,0.0033251543,0.0014625124,0.006982878,0.027525205],"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":[0.00009425418,0.000021889216,0.00008769082,0.0003368537,0.000021296193,0.00023717586,0.000017535855,0.00005520184,0.00028190805,0.0008263958,0.9830804,0.01493942],"study_design_scores_gemma":[0.000059523376,0.00007477291,0.00034271766,0.00027012095,0.000046163226,0.0005446069,0.000049122176,0.000290724,0.0004213024,0.0012662797,0.99661535,0.000019283378],"about_ca_topic_score_codex":0.00066738157,"about_ca_topic_score_gemma":0.0010132171,"teacher_disagreement_score":0.07292736,"about_ca_system_score_codex":0.0012772621,"about_ca_system_score_gemma":0.0013039663,"threshold_uncertainty_score":0.24396628},"labels":[],"label_agreement":null},{"id":"W4405710365","doi":"10.1109/tcds.2024.3520976","title":"Sensorimotor Integration: A Review of Neural and Computational Models and the Impact of Parkinson’s Disease","year":2024,"lang":"en","type":"review","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Motor Control and Adaptation","field":"Neuroscience","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":"Centre for Movement Disorders; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Computational model; Parkinson's disease; Artificial neural network; Disease; Neuroscience; Artificial intelligence; Medicine; Psychology","score_opus":0.0806558356020185,"score_gpt":0.33137130407353743,"score_spread":0.25071546847151893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405710365","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":[0.00011620997,0.9982926,0.0004459842,0.0002511359,0.0001176221,0.000004257559,0.00003089288,0.000009804893,0.00073137],"genre_scores_gemma":[0.00074759655,0.99835914,0.00038171082,0.00011782518,0.00011607327,0.0000071750137,0.000036037538,0.000002575947,0.00023187937],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978226,0.000041626907,0.000045134584,0.00004689344,0.00006862082,0.000015372885],"domain_scores_gemma":[0.99910945,0.0006049274,0.00008015721,0.000022423099,0.00015196578,0.00003109083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007058565,0.001334617,0.0017873232,0.0035325426,0.0003054249,0.0011919173,0.0012310299,0.0014221714,0.003690766],"category_scores_gemma":[0.0018070173,0.00047156034,0.001000381,0.0034078946,0.000638865,0.0018901612,0.0008132096,0.001513581,0.0014607997],"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":[0.0000774489,0.000061652434,0.00031485065,0.047213327,0.00027613482,0.00019858159,0.0001442553,0.001755006,0.001045311,0.010461951,0.02376646,0.914685],"study_design_scores_gemma":[0.000021181946,0.00018247055,0.0020272303,0.023239572,0.0006212989,0.0017722248,0.00014531708,0.0009515529,0.00065344037,0.014935397,0.9553652,0.00008510119],"about_ca_topic_score_codex":0.0028585913,"about_ca_topic_score_gemma":0.0033683716,"teacher_disagreement_score":0.003690766,"about_ca_system_score_codex":0.00092869805,"about_ca_system_score_gemma":0.0020557726,"threshold_uncertainty_score":0.012346864},"labels":[],"label_agreement":null},{"id":"W4408917650","doi":"10.1109/tcds.2025.3555517","title":"CLARE: Cognitive Load Assessment in Real-Time With Multimodal Data","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Queen's University","funders":"","keywords":"Computer science; Cognition; Cognitive load; Human–computer interaction; Psychology","score_opus":0.03893488905960187,"score_gpt":0.36704738077535776,"score_spread":0.3281124917157559,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408917650","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":[0.49768475,0.0034370073,0.0318074,0.0011590843,0.00073535,0.0016416463,0.44746473,0.008161191,0.007908793],"genre_scores_gemma":[0.46979126,0.00095591455,0.03667047,0.000664965,0.00038490948,0.002812652,0.48258793,0.00032206567,0.0058098566],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.99926895,0.00015700627,0.000081355654,0.0002281099,0.00018330319,0.0000812788],"domain_scores_gemma":[0.998546,0.00038373753,0.00016117394,0.0003261957,0.00039385832,0.0001889969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077695126,0.0017068613,0.00079734286,0.0016760702,0.00035361268,0.00079344737,0.0012311765,0.0016871089,0.004234333],"category_scores_gemma":[0.0036552884,0.00018990871,0.00069048186,0.0008422967,0.00026373824,0.000799488,0.0013679451,0.0008848314,0.0024380405],"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":[0.006897384,0.0043050065,0.11454867,0.0060583833,0.0016694941,0.002396995,0.0013442269,0.019929009,0.057458166,0.0016229843,0.45075402,0.3330157],"study_design_scores_gemma":[0.0011148528,0.0033755142,0.6553933,0.00086316053,0.0006352167,0.0045571444,0.0020199963,0.09931908,0.04212076,0.0055765905,0.18433388,0.0006904906],"about_ca_topic_score_codex":0.005509525,"about_ca_topic_score_gemma":0.0137507375,"teacher_disagreement_score":0.005509525,"about_ca_system_score_codex":0.0004811805,"about_ca_system_score_gemma":0.00042648608,"threshold_uncertainty_score":0.014165282},"labels":[],"label_agreement":null},{"id":"W4413318882","doi":"10.1109/tcds.2025.3600102","title":"Efficient 2-D/3-D Gaze Estimation Using TGGNet: A Transformer Graph Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Cognitive and Developmental Systems","topic":"Gaze Tracking and Assistive Technology","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":"Western University","funders":"","keywords":"Computer science; Gaze; Transformer; Artificial intelligence; Computer vision; Theoretical computer science; Voltage","score_opus":0.021644526259896057,"score_gpt":0.2545988458130528,"score_spread":0.23295431955315676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413318882","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044824693,0.00039444608,0.94638234,0.00026853607,0.0000862972,0.00007439227,0.0009738193,0.00416464,0.0028308085],"genre_scores_gemma":[0.6407072,0.00053523865,0.34775916,0.00027819278,0.000077758945,0.00017746081,0.0030918638,0.00055342494,0.0068198103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998419,0.000027202152,0.0000050636636,0.000059618953,0.000043371478,0.000022774899],"domain_scores_gemma":[0.9996809,0.00010726706,0.000031571573,0.00004599344,0.00010960032,0.000024637284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022894613,0.0011103187,0.0005402986,0.0013046731,0.00038367728,0.0005255335,0.001354659,0.00065445516,0.0034907034],"category_scores_gemma":[0.001621929,0.0004986074,0.0009432583,0.00084127486,0.00032393765,0.0009464393,0.0011225626,0.00089859706,0.0009433249],"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":[0.00023573988,0.00009708254,0.004351353,0.00014868008,0.000167651,0.0002445634,0.00017011646,0.6646824,0.012367617,0.00545592,0.008631045,0.30344787],"study_design_scores_gemma":[0.0000050982567,0.000011482921,0.00039972892,0.000005046738,0.000008139847,0.000029249324,0.00001560751,0.99624324,0.00084102195,0.0018888684,0.0005484612,0.00000409408],"about_ca_topic_score_codex":0.033613622,"about_ca_topic_score_gemma":0.054042265,"teacher_disagreement_score":0.033613622,"about_ca_system_score_codex":0.00080803584,"about_ca_system_score_gemma":0.00064164965,"threshold_uncertainty_score":0.06683594},"labels":[],"label_agreement":null}]}