{"meta":{"query_hash":"6834cd656661","filters":{"venue":"IEEE Transactions on Artificial Intelligence"},"cohort_total":64,"direct_labels_cover":1,"predictions_cover":64,"exported":64,"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/6834cd656661","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Artificial+Intelligence"},"results":[{"id":"W3137165991","doi":"10.1109/tai.2020.3041816","title":"Deep Learning-Based Fault Localization in Video Networks Using Only Client-Side QoE","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Image and Video Quality Assessment","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":"Ciena (Canada); University of Ottawa","funders":"Mitacs","keywords":"Computer science; Quality of experience; Testbed; Service provider; Client-side; Computer network; The Internet; Service (business); Server-side; Artificial intelligence; Quality of service; World Wide Web","score_opus":0.07229948565725056,"score_gpt":0.32711646841202846,"score_spread":0.25481698275477793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137165991","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":[0.6306423,0.0016663695,0.3558808,0.0009818382,0.00023515258,0.00013305267,0.0010595181,0.005433894,0.0039670896],"genre_scores_gemma":[0.98102975,0.00014992076,0.016443826,0.00011856248,0.00003265497,0.000026515181,0.00072021113,0.000023237006,0.0014552682],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995566,0.00006601852,0.00002392903,0.00014377272,0.000066364446,0.00014330362],"domain_scores_gemma":[0.99922395,0.00029864218,0.00012226422,0.00005587877,0.00023025616,0.000069016336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007409881,0.0012090005,0.00063580176,0.0008572678,0.00028282247,0.0006673631,0.001202075,0.0009432682,0.00065815804],"category_scores_gemma":[0.0026848542,0.0003042067,0.00037508766,0.00054274,0.00042559195,0.0011974814,0.0006852853,0.0010260346,0.00026582344],"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":[0.00045737348,0.0004679856,0.015231538,0.00012527044,0.0000931461,0.0002949037,0.000095137824,0.7459255,0.0068895784,0.0010788102,0.0052380427,0.22410269],"study_design_scores_gemma":[0.0000037954017,0.000022231761,0.000807885,0.000004608239,0.0000046198643,0.0000101465275,0.000012025685,0.9970401,0.0014746548,0.00051591767,0.00010102888,0.0000029978949],"about_ca_topic_score_codex":0.025610588,"about_ca_topic_score_gemma":0.018917555,"teacher_disagreement_score":0.025610588,"about_ca_system_score_codex":0.0016821697,"about_ca_system_score_gemma":0.0008537548,"threshold_uncertainty_score":0.05092305},"labels":[],"label_agreement":null},{"id":"W3175406278","doi":"10.1109/tai.2021.3074122","title":"Optimal Policy for Bernoulli Bandits: Computation and Algorithm Gauge","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Bernoulli's principle; Algorithm; Mathematical optimization; Computation; Variety (cybernetics); Approximate Bayesian computation; Time horizon; Thompson sampling; Bayesian probability; Artificial intelligence; Mathematics; Inference","score_opus":0.16748091965585976,"score_gpt":0.45981303194073087,"score_spread":0.2923321122848711,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175406278","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031341102,0.00035835803,0.96078783,0.00044877964,0.000071426504,0.00010610636,0.00007046616,0.00094735634,0.0058685527],"genre_scores_gemma":[0.48941353,0.00038150095,0.506654,0.00029648744,0.00007218124,0.00032597434,0.00026305698,0.0003925274,0.0022006836],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807954,0.00080619886,0.0001044212,0.00029883458,0.0004703081,0.00024059597],"domain_scores_gemma":[0.99107504,0.006815203,0.00045771975,0.0007097325,0.0006583996,0.00028391482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034073049,0.0009415882,0.0013252225,0.00087487523,0.0007460592,0.0021417742,0.0013074285,0.0015225484,0.004419297],"category_scores_gemma":[0.023098033,0.00048500183,0.00057607476,0.00097373716,0.0015863162,0.0022237264,0.0014524873,0.0021681325,0.00071259635],"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":[0.00022322469,0.00011643446,0.0012745314,0.00009208212,0.000028400684,0.00004485106,0.000080866754,0.8605194,0.0011473557,0.06359945,0.0022708382,0.070602626],"study_design_scores_gemma":[0.000015759852,0.000017565715,0.000061269624,0.0000125312345,0.000003886993,0.000010392651,0.000009218609,0.98608404,0.0005238388,0.012922401,0.00033457775,0.000004491913],"about_ca_topic_score_codex":0.0073711546,"about_ca_topic_score_gemma":0.005767425,"teacher_disagreement_score":0.0073711546,"about_ca_system_score_codex":0.0025644714,"about_ca_system_score_gemma":0.0049948096,"threshold_uncertainty_score":0.018606663},"labels":[],"label_agreement":null},{"id":"W3177177219","doi":"10.1109/tai.2021.3081057","title":"Robust Vehicle Detection in High-Resolution Aerial Images With Imbalanced Data","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","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":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Context (archaeology); Class (philosophy); Computer vision; Detector; Intersection (aeronautics); Feature (linguistics); Aerial image; Flexibility (engineering); Image (mathematics); Mathematics; Geography","score_opus":0.06558423746214823,"score_gpt":0.28720486041413296,"score_spread":0.22162062295198473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177177219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30017096,0.000993418,0.6920332,0.0006486263,0.0002241907,0.00014759415,0.00062865583,0.0031519784,0.0020013882],"genre_scores_gemma":[0.7788816,0.00041688472,0.21661116,0.0002692306,0.00012550427,0.00006891655,0.0018283635,0.00016342888,0.0016348015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987306,0.00019544645,0.000059892714,0.00032589777,0.00047554928,0.00021262991],"domain_scores_gemma":[0.9981982,0.00069999875,0.00021349304,0.0002888558,0.0005019703,0.00009750617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018844223,0.0010366051,0.0012109153,0.0020588357,0.00048540795,0.0012544021,0.0012140579,0.0010116835,0.0006438863],"category_scores_gemma":[0.0042841705,0.0004951707,0.00082380674,0.0013202087,0.00064003724,0.0016291747,0.0012658812,0.0010691233,0.0005837565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014291573,0.0003361407,0.017280178,0.000288792,0.00026697182,0.00080714864,0.0002676489,0.24878906,0.1038041,0.002063565,0.008471407,0.6161958],"study_design_scores_gemma":[0.000011889812,0.000055096974,0.005749538,0.000009177698,0.000022680519,0.0001362236,0.000095377334,0.9743025,0.017387941,0.0012006642,0.0010144289,0.000014504073],"about_ca_topic_score_codex":0.004917323,"about_ca_topic_score_gemma":0.004785758,"teacher_disagreement_score":0.004917323,"about_ca_system_score_codex":0.0007921926,"about_ca_system_score_gemma":0.0005592667,"threshold_uncertainty_score":0.009965837},"labels":[],"label_agreement":null},{"id":"W3196269934","doi":"10.1109/tai.2021.3104791","title":"CapsCovNet: A Modified Capsule Network to Diagnose COVID-19 From Multimodal Medical Imaging","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":31,"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":"Concatenation (mathematics); Computer science; Discriminative model; Artificial intelligence; Convolutional neural network; Benchmark (surveying); Context (archaeology); Deep learning; Pattern recognition (psychology); Cartography; Geography","score_opus":0.06715238529970749,"score_gpt":0.3703806134277315,"score_spread":0.30322822812802397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196269934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16500092,0.005253959,0.7967441,0.0022382785,0.0008808473,0.00054900936,0.004842549,0.011878981,0.012611358],"genre_scores_gemma":[0.7951162,0.0019933106,0.17237677,0.0015179629,0.0003752853,0.00034367936,0.011360228,0.0003412296,0.016575387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981755,0.000025997113,0.000010731237,0.0000677352,0.000038869464,0.000039164686],"domain_scores_gemma":[0.999798,0.000054441898,0.000026109903,0.000028699154,0.00006282973,0.000029965915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041679415,0.0014093361,0.0006579107,0.00084554474,0.00033335536,0.0005925597,0.0015824633,0.0010945362,0.002319099],"category_scores_gemma":[0.0013571312,0.000357482,0.0007875016,0.000569239,0.00034981305,0.0009309439,0.00094242196,0.0010537966,0.0009807242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00096888404,0.000531729,0.014432771,0.0003819516,0.00056986714,0.0010005047,0.00010416822,0.34102106,0.02003083,0.0047828145,0.04510509,0.5710704],"study_design_scores_gemma":[0.000017784872,0.00010655478,0.0009112536,0.0000186311,0.00005042713,0.0001298766,0.000014492852,0.9908433,0.0034896126,0.0016986501,0.0027065212,0.000012929262],"about_ca_topic_score_codex":0.014479179,"about_ca_topic_score_gemma":0.021713529,"teacher_disagreement_score":0.014479179,"about_ca_system_score_codex":0.0008551458,"about_ca_system_score_gemma":0.0012680069,"threshold_uncertainty_score":0.028789818},"labels":[],"label_agreement":null},{"id":"W3204597830","doi":"10.1109/tai.2022.3195968","title":"Multiview Video-Based 3-D Hand Pose Estimation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pose; Artificial intelligence; Computer vision; Computer science; Estimation; Engineering","score_opus":0.058803631670398704,"score_gpt":0.29923958715074195,"score_spread":0.24043595548034324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204597830","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07585555,0.0029419698,0.90291,0.00016434517,0.00019485006,0.00019292501,0.00617726,0.0067581176,0.0048050047],"genre_scores_gemma":[0.6382054,0.0024836469,0.3368374,0.0003010952,0.0002455448,0.0002282937,0.013964421,0.00039432338,0.00733991],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996132,0.00002565109,0.000014740637,0.00017575387,0.00011848643,0.000052201016],"domain_scores_gemma":[0.99977237,0.000054704262,0.000039262766,0.0000487694,0.00006524896,0.000019809737],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026322604,0.0013325018,0.001183968,0.0013704462,0.00015749977,0.0005261187,0.00073264854,0.00070445065,0.004657026],"category_scores_gemma":[0.001065331,0.00044801377,0.0006418102,0.0009406359,0.0002842078,0.00071954366,0.0008252729,0.0007002839,0.002016503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005687989,0.00017631309,0.004863541,0.00048123123,0.00016053658,0.00034385812,0.00007647412,0.075151846,0.100495204,0.0011700311,0.013599724,0.8029124],"study_design_scores_gemma":[0.00004385557,0.00024139941,0.015190376,0.0001289571,0.000069510694,0.0011443159,0.00010828386,0.9060322,0.063934766,0.0033794122,0.009667987,0.000058830705],"about_ca_topic_score_codex":0.0039959126,"about_ca_topic_score_gemma":0.010321185,"teacher_disagreement_score":0.004657026,"about_ca_system_score_codex":0.00030137968,"about_ca_system_score_gemma":0.00039284292,"threshold_uncertainty_score":0.015579343},"labels":[],"label_agreement":null},{"id":"W3210819377","doi":"10.1109/tai.2021.3120043","title":"Smoothed Generalized Dirichlet: A Novel Count-Data Model for Detecting Emotional States","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Text and Document Classification Technologies","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dirichlet distribution; Burstiness; Count data; Generalized Dirichlet distribution; Mathematics; Computer science; Hierarchical Dirichlet process; Applied mathematics; Multinomial distribution; Cluster analysis; Algorithm; Artificial intelligence; Statistics; Dirichlet series; Mathematical analysis","score_opus":0.16196338292156895,"score_gpt":0.3418206002684397,"score_spread":0.17985721734687074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210819377","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008622046,0.0003847737,0.9889685,0.00030189127,0.00009010302,0.00007364529,0.00041172642,0.00048429234,0.0006630292],"genre_scores_gemma":[0.4321586,0.0013622631,0.55374795,0.0006518534,0.00064568367,0.00092099066,0.0034380658,0.0005105662,0.0065640546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99566996,0.0019288755,0.00027161685,0.0010891616,0.0007581327,0.0002823542],"domain_scores_gemma":[0.99135876,0.005843722,0.00063451694,0.0010508883,0.0009008669,0.00021121469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052046697,0.0015173625,0.002534986,0.0034574217,0.0011377882,0.0029981493,0.0049808514,0.0022632042,0.00394162],"category_scores_gemma":[0.022871777,0.0011134375,0.0023180787,0.0041290848,0.001803363,0.0060586645,0.0025316929,0.003166581,0.0015993227],"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":[0.0010123852,0.0003338887,0.012305203,0.000710016,0.0005588939,0.00056824874,0.0015768048,0.42379618,0.0071453047,0.19832465,0.014170895,0.33949748],"study_design_scores_gemma":[0.000015671263,0.000025252213,0.00057359686,0.000024943263,0.000023978142,0.00008712392,0.0000611729,0.93144566,0.0006646499,0.064960435,0.0020872625,0.000030294535],"about_ca_topic_score_codex":0.005260356,"about_ca_topic_score_gemma":0.0063238824,"teacher_disagreement_score":0.005260356,"about_ca_system_score_codex":0.0016656116,"about_ca_system_score_gemma":0.00125074,"threshold_uncertainty_score":0.027525187},"labels":[],"label_agreement":null},{"id":"W3213490051","doi":"10.1109/tai.2021.3125918","title":"Multiadvisor Reinforcement Learning for Multiagent Multiobjective Smart Home Energy Control","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Smart Grid Energy Management","field":"Engineering","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":"Lakehead University","funders":"","keywords":"Reinforcement learning; Computer science; Curse of dimensionality; Scalability; Key (lock); Smart grid; Control (management); Demand response; Artificial intelligence; Machine learning; Engineering; Electricity","score_opus":0.024276005802833612,"score_gpt":0.24571070988491348,"score_spread":0.22143470408207985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3213490051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023761788,0.00013818193,0.97289103,0.0002182147,0.000042301963,0.0000659066,0.000016256063,0.0002968167,0.0025694363],"genre_scores_gemma":[0.91992867,0.000066495886,0.077159055,0.00012297084,0.00003097739,0.00013138153,0.000034047258,0.000029988314,0.0024965112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943143,0.00024119129,0.000024050896,0.00010420626,0.00013039839,0.00006869291],"domain_scores_gemma":[0.9986749,0.0007922187,0.00016025362,0.0000829674,0.00020641007,0.000083223924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001820131,0.000737404,0.00087525503,0.0002850232,0.00044317538,0.000595824,0.0013894679,0.000797164,0.0020231167],"category_scores_gemma":[0.0029098284,0.00031102594,0.00033138887,0.00024746658,0.00080093526,0.0006306358,0.00093016843,0.0014257942,0.0002858167],"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":[0.000043592605,0.00005920373,0.0003289389,0.000024334566,0.000022582442,0.00002894642,0.000023480588,0.98014003,0.00056705263,0.0039531705,0.00033586138,0.0144727975],"study_design_scores_gemma":[0.0000059268705,0.000015772208,0.000019628174,0.0000012326223,0.0000014570954,0.0000031033308,0.0000017343299,0.99878997,0.0001032409,0.00096707244,0.00008963234,0.0000013157947],"about_ca_topic_score_codex":0.00393669,"about_ca_topic_score_gemma":0.004281515,"teacher_disagreement_score":0.00393669,"about_ca_system_score_codex":0.0008558338,"about_ca_system_score_gemma":0.0008420183,"threshold_uncertainty_score":0.009625912},"labels":[],"label_agreement":null},{"id":"W3215887789","doi":"10.1109/tai.2021.3117743","title":"Delayed Reward Bernoulli Bandits: Optimal Policy and Predictive Meta-Algorithm PARDI","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Bandit Algorithms Research","field":"Decision Sciences","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":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bernoulli's principle; Computer science; Outcome (game theory); Reinforcement learning; Mathematical optimization; Index (typography); Algorithm; Range (aeronautics); Artificial intelligence; Mathematics; Mathematical economics; Engineering","score_opus":0.19533306039765136,"score_gpt":0.4366686007048083,"score_spread":0.24133554030715693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215887789","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040659983,0.0011767066,0.95148647,0.0005966365,0.00011683429,0.00011848284,0.00010157005,0.0009253374,0.0048180968],"genre_scores_gemma":[0.74748266,0.00042379886,0.24811344,0.0003889251,0.00008330234,0.0002854527,0.00022655932,0.00013519033,0.0028606902],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988053,0.00048818666,0.00005472134,0.00022258455,0.0002648162,0.00016441087],"domain_scores_gemma":[0.99561274,0.003176387,0.00034372078,0.0002950482,0.00037413227,0.00019796815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029678238,0.0009722789,0.0016520622,0.00087640167,0.000477044,0.0015412229,0.0022648005,0.0017040896,0.0020643098],"category_scores_gemma":[0.010287621,0.0005288722,0.0005558888,0.00089742703,0.00093373185,0.0015530525,0.0011345828,0.002242036,0.00045657632],"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":[0.00013834202,0.00006923902,0.00064138207,0.00004789095,0.00004404215,0.000023921768,0.000034229804,0.9525262,0.00039985907,0.009745201,0.00084796816,0.035481773],"study_design_scores_gemma":[0.000013456639,0.000024292669,0.000036827067,0.0000058787473,0.000006101474,0.000007579884,0.000003244247,0.9967616,0.00020344135,0.0027403636,0.00019412138,0.0000030800422],"about_ca_topic_score_codex":0.0042584147,"about_ca_topic_score_gemma":0.0035692877,"teacher_disagreement_score":0.0042584147,"about_ca_system_score_codex":0.0017858496,"about_ca_system_score_gemma":0.0029537797,"threshold_uncertainty_score":0.015695512},"labels":[],"label_agreement":null},{"id":"W4285134718","doi":"10.1109/tai.2022.3178065","title":"Toward Personalization of User Preferences in Partially Observable Smart Home Environments","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Personalization; Reinforcement learning; Computer science; Preference; Home automation; Baseline (sea); Human–computer interaction; Machine learning; Artificial intelligence; World Wide Web; Telecommunications","score_opus":0.11718370842104574,"score_gpt":0.2788703902975181,"score_spread":0.16168668187647234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285134718","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19928184,0.00026141596,0.79857427,0.00020214016,0.000016033273,0.000059024336,0.00007690791,0.000527518,0.001000864],"genre_scores_gemma":[0.9544015,0.00008705234,0.044618223,0.0000599014,0.00001124174,0.000037609196,0.00007896314,0.000021204578,0.000684255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990689,0.00042144035,0.000036683512,0.00022219171,0.00016597834,0.00008473344],"domain_scores_gemma":[0.9976299,0.0014097118,0.00026735512,0.00030069036,0.00027631051,0.000116039824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018665928,0.00058660936,0.0008005656,0.00035233263,0.00020483007,0.00053802074,0.0006589756,0.00053657114,0.0005614201],"category_scores_gemma":[0.006741515,0.00037578147,0.0004331981,0.00023562438,0.00047367095,0.0014069524,0.0007213022,0.0010296332,0.00015936364],"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":[0.0005028967,0.00036792044,0.022652943,0.00010319829,0.00013471408,0.00013075504,0.0007321111,0.79798573,0.0070083807,0.008256242,0.00092026393,0.16120487],"study_design_scores_gemma":[0.000008794843,0.00005080169,0.0011878854,0.0000034383734,0.000006897939,0.000015400788,0.000024572257,0.9945058,0.0007223724,0.003310269,0.00015766887,0.0000061844407],"about_ca_topic_score_codex":0.0058956896,"about_ca_topic_score_gemma":0.0067975186,"teacher_disagreement_score":0.0058956896,"about_ca_system_score_codex":0.0006366834,"about_ca_system_score_gemma":0.0006534038,"threshold_uncertainty_score":0.011722803},"labels":[],"label_agreement":null},{"id":"W4285239211","doi":"10.1109/tai.2022.3187676","title":"Retracted: Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":33,"is_retracted":true,"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":"Intrusion detection system; Computer science; Health care; Quantum; Artificial intelligence; Physics; Political science","score_opus":0.055353061718252876,"score_gpt":0.2847184341270269,"score_spread":0.22936537240877403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285239211","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057514943,0.00056689576,0.9361131,0.00070879003,0.00013038602,0.000091185284,0.00009476192,0.0021796136,0.0026004117],"genre_scores_gemma":[0.8654161,0.0002122431,0.12947482,0.000376567,0.000066551336,0.00014713993,0.0002064282,0.00012316098,0.003977056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994481,0.0002396769,0.000030225747,0.00011283346,0.00011309914,0.000056021134],"domain_scores_gemma":[0.9990044,0.0005171903,0.000056112753,0.00015200881,0.00022710302,0.00004315406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014544217,0.000558411,0.00058988476,0.00033286438,0.00039913206,0.00061264075,0.0016548273,0.0010251802,0.002879128],"category_scores_gemma":[0.003390884,0.00029636652,0.0004287983,0.00039496794,0.00067518494,0.0014254154,0.001227339,0.001357276,0.00048517413],"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":[0.00024776257,0.000201766,0.001449018,0.00011560554,0.000073722564,0.00012861466,0.00014535543,0.7316673,0.005336718,0.016715422,0.0034522295,0.24046649],"study_design_scores_gemma":[0.0000035233782,0.000018109551,0.000053929234,0.0000021973526,0.0000022131699,0.000007427503,0.0000040222776,0.9962773,0.000510723,0.002902203,0.00021623907,0.0000022030304],"about_ca_topic_score_codex":0.003231816,"about_ca_topic_score_gemma":0.0034303488,"teacher_disagreement_score":0.003231816,"about_ca_system_score_codex":0.0007526506,"about_ca_system_score_gemma":0.0010290374,"threshold_uncertainty_score":0.009631693},"labels":[],"label_agreement":null},{"id":"W4285241469","doi":"10.1109/tai.2022.3177394","title":"Nonoverlapping Feature Projection Convolutional Neural Network With Differentiable Loss Function","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","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, Okanagan Campus; University of British Columbia","funders":"","keywords":"Feature (linguistics); Differentiable function; Convolution (computer science); Computer science; Pattern recognition (psychology); Pixel; Artificial intelligence; Feature vector; Convolutional neural network; Projection (relational algebra); Separable space; Algorithm; Function (biology); Mathematics; Artificial neural network; Mathematical analysis","score_opus":0.030979467884886477,"score_gpt":0.25520140654106543,"score_spread":0.22422193865617895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285241469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019239007,0.0004975866,0.97409284,0.00024331667,0.00008006183,0.000049874252,0.00031401482,0.0029755798,0.0025076845],"genre_scores_gemma":[0.46224976,0.000694145,0.51605153,0.0005970895,0.000108536835,0.00029239204,0.0025141705,0.00033031686,0.017162029],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995515,0.000055343946,0.000020113604,0.00015787102,0.00014591009,0.00006917637],"domain_scores_gemma":[0.9997106,0.000059369097,0.00003425158,0.00007664863,0.00009310748,0.000026019432],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062916917,0.0012615913,0.0009115448,0.0005449854,0.0002625876,0.0007958368,0.0022144555,0.001255283,0.002856102],"category_scores_gemma":[0.0010713837,0.00050861586,0.0006313984,0.00094342645,0.0007376879,0.0020860278,0.0014778142,0.0015716988,0.0012534687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028916763,0.0002286769,0.0011358052,0.00013122884,0.00015181319,0.00020750203,0.000062609026,0.38310778,0.021274846,0.017541345,0.014902264,0.56096697],"study_design_scores_gemma":[0.000012659643,0.00003631536,0.00016764515,0.000004474828,0.000008283953,0.00003938553,0.0000037255256,0.99170786,0.002821207,0.0038449676,0.0013461837,0.000007250746],"about_ca_topic_score_codex":0.0067094504,"about_ca_topic_score_gemma":0.011275692,"teacher_disagreement_score":0.0067094504,"about_ca_system_score_codex":0.0009794496,"about_ca_system_score_gemma":0.0013818344,"threshold_uncertainty_score":0.013340831},"labels":[],"label_agreement":null},{"id":"W4293428470","doi":"10.1109/tai.2022.3201807","title":"Optimizing Multidocument Summarization by Blending Reinforcement Learning Policies","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Topic Modeling","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","funders":"National Science Foundation","keywords":"Automatic summarization; Computer science; Reinforcement learning; Relevance (law); Redundancy (engineering); Multi-document summarization; Sentence; Artificial intelligence; Quality (philosophy); Machine learning; Information retrieval; Natural language processing","score_opus":0.04336013915883275,"score_gpt":0.2878445156778042,"score_spread":0.24448437651897148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293428470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03227767,0.0006708348,0.96387655,0.00027726684,0.000047911595,0.0001356326,0.00006570057,0.0016906196,0.00095777237],"genre_scores_gemma":[0.5039702,0.00055490475,0.4901277,0.00033246772,0.00015041335,0.00043962654,0.00048589066,0.00034899803,0.0035896965],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998868,0.00039373664,0.00009743156,0.00033017236,0.0002224067,0.00008828404],"domain_scores_gemma":[0.9965205,0.0023017188,0.0003909412,0.0001988337,0.00043422362,0.0001538462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027194498,0.0013986988,0.0016371656,0.0010393724,0.00045985638,0.0011173945,0.001197242,0.0016984983,0.0015315848],"category_scores_gemma":[0.0073111304,0.00059979706,0.0007485653,0.000982467,0.00065049686,0.002248365,0.0011130071,0.001570469,0.00058190664],"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":[0.00021691134,0.00029189585,0.0009648538,0.00026145144,0.00010941952,0.00012892154,0.00019338084,0.7688207,0.013352959,0.0035206943,0.0015333316,0.21060543],"study_design_scores_gemma":[0.000026342523,0.00007798693,0.000103951286,0.000007370599,0.000017192486,0.000014675615,0.000014649397,0.99377936,0.003180128,0.0022056536,0.0005644914,0.000008260806],"about_ca_topic_score_codex":0.0025921885,"about_ca_topic_score_gemma":0.0032374754,"teacher_disagreement_score":0.0027194498,"about_ca_system_score_codex":0.0012866404,"about_ca_system_score_gemma":0.0013634157,"threshold_uncertainty_score":0.014382005},"labels":[],"label_agreement":null},{"id":"W4295308551","doi":"10.1109/tai.2022.3205567","title":"DReD–A Descriptive Relation Dataset for Expanding Relation Extraction","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Topic Modeling","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 Saskatchewan","funders":"","keywords":"Relationship extraction; Computer science; Relation (database); Benchmark (surveying); Sentence; Natural language processing; Task (project management); Artificial intelligence; Code (set theory); Set (abstract data type); Information retrieval; Data mining","score_opus":0.12257939294696753,"score_gpt":0.3446536909603573,"score_spread":0.2220742980133898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295308551","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":[0.033243768,0.0016866183,0.026762504,0.0010968134,0.00028823185,0.00072384265,0.90022415,0.024045974,0.011928151],"genre_scores_gemma":[0.014108355,0.00018128491,0.03365224,0.0001979682,0.000034302153,0.00044931477,0.94937027,0.00034835326,0.0016579579],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9948571,0.0008882221,0.00089281827,0.0013973794,0.0017106388,0.0002539225],"domain_scores_gemma":[0.9899603,0.003286449,0.0009416822,0.0032909026,0.0019818528,0.00053870934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026126252,0.0021714277,0.0010549497,0.0076232087,0.0020793353,0.0017058118,0.0039262515,0.0028929203,0.007899724],"category_scores_gemma":[0.01163561,0.0006885159,0.0017722541,0.0069567035,0.00082777586,0.004588405,0.0024953256,0.0026596123,0.010741408],"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":[0.000539166,0.00076579105,0.012468941,0.003047232,0.00019773708,0.001017595,0.00072736334,0.005772845,0.0135699455,0.011673534,0.85334176,0.09687813],"study_design_scores_gemma":[0.0003868384,0.0003037743,0.028846888,0.0004218681,0.00013666676,0.0017712469,0.00096688996,0.04777291,0.024488121,0.010538053,0.8841552,0.00021151704],"about_ca_topic_score_codex":0.015619209,"about_ca_topic_score_gemma":0.031090748,"teacher_disagreement_score":0.015619209,"about_ca_system_score_codex":0.0022306787,"about_ca_system_score_gemma":0.0029496413,"threshold_uncertainty_score":0.031056583},"labels":[],"label_agreement":null},{"id":"W4296211812","doi":"10.1109/tai.2022.3207450","title":"FaceTopoNet: Facial Expression Recognition Using Face Topology Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Face recognition and analysis","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Facial expression recognition; Computer science; Facial recognition system; Facial expression; Topology (electrical circuits); Pattern recognition (psychology); Artificial intelligence; Face (sociological concept); Speech recognition; Mathematics; Combinatorics; Sociology","score_opus":0.09058653516303239,"score_gpt":0.3079096537704102,"score_spread":0.2173231186073778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296211812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06758572,0.0008371326,0.88978016,0.00033285323,0.00027200143,0.0004199407,0.0029330947,0.02931074,0.008528383],"genre_scores_gemma":[0.51544565,0.0006589607,0.45169544,0.0005852235,0.00009716913,0.00061827374,0.013127232,0.001098318,0.016673686],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968183,0.000037090766,0.000008408034,0.0001237338,0.000106074505,0.000042784424],"domain_scores_gemma":[0.99982387,0.000037566188,0.000014225458,0.000057404035,0.000049663515,0.000017324955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046025476,0.0012321826,0.0006571906,0.00069100363,0.0002637106,0.00062698364,0.0017565356,0.0007010762,0.005216042],"category_scores_gemma":[0.001301822,0.00034842896,0.00060642645,0.00041296775,0.00034278134,0.0012966259,0.0012150824,0.0009701914,0.0019631379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037392756,0.0002609208,0.0030156616,0.00015353173,0.00013777282,0.00017658103,0.000099881465,0.0919144,0.04359957,0.004383761,0.0417371,0.8141469],"study_design_scores_gemma":[0.00002174187,0.000088416746,0.0011944466,0.000011277091,0.000018513267,0.00015883976,0.000029110903,0.97610056,0.013783297,0.004065104,0.00450931,0.000019439185],"about_ca_topic_score_codex":0.006824453,"about_ca_topic_score_gemma":0.01244623,"teacher_disagreement_score":0.006824453,"about_ca_system_score_codex":0.0007234714,"about_ca_system_score_gemma":0.00058130245,"threshold_uncertainty_score":0.017449379},"labels":[],"label_agreement":null},{"id":"W4312287303","doi":"10.1109/tai.2022.3224417","title":"Ultralight-Weight Three-Prior Convolutional Neural Network for Single Image Super Resolution","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Image Processing Techniques","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":"Concordia University","funders":"","keywords":"Convolutional neural network; Benchmark (surveying); Computer science; Task (project management); Image (mathematics); Artificial intelligence; Convolution (computer science); Deep learning; Resolution (logic); Artificial neural network; Focus (optics); Pattern recognition (psychology); Image resolution; Superresolution; Optimization problem; Machine learning; Algorithm; Engineering; Geography","score_opus":0.054053651219946056,"score_gpt":0.2977028328842106,"score_spread":0.24364918166426458,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312287303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006255401,0.00042398513,0.9913344,0.00019668817,0.000018624982,0.000013333314,0.000028852384,0.00018130343,0.0015474656],"genre_scores_gemma":[0.41443866,0.0017245128,0.57397765,0.00042192623,0.000086459564,0.00011200219,0.00023880326,0.00014570761,0.008854241],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998074,0.000037790793,0.00000955829,0.000039429357,0.0000782697,0.000027497084],"domain_scores_gemma":[0.99984133,0.000049657327,0.000029763312,0.000026750744,0.000038709546,0.000013730212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049322617,0.0006304723,0.00052296283,0.00030135122,0.00024618825,0.0005443728,0.0012418317,0.0010858641,0.0015731197],"category_scores_gemma":[0.00092971534,0.00033424032,0.00052146625,0.00045766679,0.0005832354,0.0014361911,0.0010175364,0.0017725553,0.0003668785],"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":[0.00016744422,0.00009700674,0.00064356235,0.00018285796,0.00007569503,0.0002340989,0.00010262392,0.6643895,0.03627494,0.06752441,0.005010767,0.22529715],"study_design_scores_gemma":[0.0000022360227,0.000009785715,0.00006666932,0.0000050855224,0.0000056952276,0.000033282362,0.0000028108236,0.9928872,0.0024551444,0.003686777,0.000841142,0.000004208181],"about_ca_topic_score_codex":0.003969771,"about_ca_topic_score_gemma":0.005982051,"teacher_disagreement_score":0.003969771,"about_ca_system_score_codex":0.00080283487,"about_ca_system_score_gemma":0.00092025223,"threshold_uncertainty_score":0.007893324},"labels":[],"label_agreement":null},{"id":"W4312570070","doi":"10.1109/tai.2022.3225132","title":"The Different Faces of AI Ethics Across the World: A Principle-to-Practice Gap Analysis","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":35,"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; Mitacs; Canadian Institute for Advanced Research","keywords":"Operationalization; Engineering ethics; Context (archaeology); Corporate governance; Software deployment; Diversity (politics); Political science; Management science; Computer science; Business; Epistemology; Law; Engineering","score_opus":0.1485754638317311,"score_gpt":0.47691068808556514,"score_spread":0.32833522425383405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312570070","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24166515,0.0241056,0.12099974,0.45805347,0.001216387,0.0013488248,0.00037732508,0.00017037017,0.15206313],"genre_scores_gemma":[0.9302125,0.0056873993,0.052659564,0.009109633,0.00012507018,0.00074348703,0.0001387782,0.000061396386,0.00126212],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.8703401,0.08803719,0.00742723,0.00503323,0.02363679,0.0055254106],"domain_scores_gemma":[0.7291356,0.21354307,0.012370502,0.00812691,0.030926568,0.005897364],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.12689032,0.0006908663,0.0012530144,0.011367221,0.011573898,0.02526445,0.003222769,0.006514075,0.0034240184],"category_scores_gemma":[0.15236968,0.0009138619,0.0009633908,0.012102375,0.031736627,0.03450749,0.017396176,0.010337402,0.0003854877],"study_design_candidate":"qualitative","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004308137,0.00013906929,0.008070035,0.0012857276,0.000056866542,0.00041491428,0.15963165,0.0007529277,0.00018722513,0.7228382,0.006650494,0.09992969],"study_design_scores_gemma":[0.00003479272,0.00012325519,0.008267131,0.0054815854,0.00006875826,0.00060179556,0.39033985,0.0031383543,0.000393443,0.50525886,0.08620362,0.00008858557],"about_ca_topic_score_codex":0.0043008947,"about_ca_topic_score_gemma":0.004423417,"teacher_disagreement_score":0.12689032,"about_ca_system_score_codex":0.018967502,"about_ca_system_score_gemma":0.045063835,"threshold_uncertainty_score":0.6710682},"labels":[],"label_agreement":null},{"id":"W4316876965","doi":"10.1109/tai.2023.3237787","title":"Accelerating Point-Voxel Representation of 3-D Object Detection for Automatic Driving","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","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":"McMaster University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Voxel; Computer science; Artificial intelligence; Feature (linguistics); Representation (politics); Computer vision; Benchmark (surveying); Matching (statistics); Object (grammar); Point (geometry); Pattern recognition (psychology); Set (abstract data type); Mathematics","score_opus":0.09089608000098295,"score_gpt":0.34780675142971024,"score_spread":0.25691067142872726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316876965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012074671,0.00025487816,0.9835864,0.000052772135,0.0000338836,0.00005710972,0.00020865934,0.0030886976,0.000642846],"genre_scores_gemma":[0.2848503,0.00045929622,0.710916,0.000088530775,0.000041505216,0.0001875872,0.0015184751,0.00039686583,0.0015413853],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938905,0.0000623509,0.000024585384,0.00012283411,0.00032499986,0.00007617683],"domain_scores_gemma":[0.99958247,0.00008354674,0.000045988127,0.000091875045,0.00016958786,0.000026588588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042731932,0.00091297156,0.0009240769,0.00165362,0.0003090373,0.0010782141,0.0016348097,0.0006913273,0.0023696225],"category_scores_gemma":[0.0014139102,0.00046621053,0.0009875402,0.0017230657,0.00035319306,0.0011756725,0.0016079741,0.0008741969,0.0014141331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002955551,0.00011408736,0.0027593242,0.00022213308,0.00011568764,0.00021268375,0.0001883526,0.10961156,0.06229134,0.009601976,0.008532322,0.80605495],"study_design_scores_gemma":[0.000014099999,0.00007449807,0.0010737573,0.00001239174,0.000026230358,0.0002975924,0.00005158795,0.9582562,0.028569464,0.004847402,0.0067409514,0.000035773555],"about_ca_topic_score_codex":0.0049568396,"about_ca_topic_score_gemma":0.0055153565,"teacher_disagreement_score":0.0049568396,"about_ca_system_score_codex":0.00045043707,"about_ca_system_score_gemma":0.001202776,"threshold_uncertainty_score":0.009855986},"labels":[],"label_agreement":null},{"id":"W4318148717","doi":"10.1109/tai.2023.3240113","title":"Fine-Grained Early Frequency Attention for Deep Speaker Representation Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Speech Recognition and Synthesis","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Speech recognition; Deep learning; Convolutional neural network; Artificial intelligence; Speaker recognition; Feature learning; Speech processing; Transfer of learning","score_opus":0.09106019514806173,"score_gpt":0.32258037748894486,"score_spread":0.23152018234088312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318148717","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07080113,0.0033630934,0.9149412,0.00062283454,0.0003072948,0.00008242283,0.00035334373,0.005443691,0.004085009],"genre_scores_gemma":[0.8431839,0.0013126369,0.14525206,0.0005732206,0.00021042506,0.00012591839,0.0008717959,0.00022370425,0.008246378],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997335,0.000050863087,0.0000118540875,0.00008975715,0.000056943536,0.00005715092],"domain_scores_gemma":[0.9996619,0.00013628215,0.000029657027,0.0000715921,0.000072650124,0.000027819022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069370726,0.00110021,0.00051999313,0.0005688542,0.00031497082,0.0005015126,0.0013280793,0.00089607656,0.0033956005],"category_scores_gemma":[0.0013684012,0.0002767024,0.0007235273,0.0004738103,0.0004250345,0.0013397972,0.0011804427,0.0018124499,0.0013281975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040382924,0.00036242438,0.0024498985,0.00018750096,0.00015721655,0.0001725723,0.00015925844,0.20429796,0.07347737,0.008133707,0.009022817,0.7011755],"study_design_scores_gemma":[0.00001618688,0.000118079355,0.00110158,0.00001811735,0.000050491584,0.00007828639,0.000017091292,0.97153723,0.016814029,0.0071026688,0.003127646,0.000018592273],"about_ca_topic_score_codex":0.0062646666,"about_ca_topic_score_gemma":0.010818847,"teacher_disagreement_score":0.0062646666,"about_ca_system_score_codex":0.0007994445,"about_ca_system_score_gemma":0.0006822057,"threshold_uncertainty_score":0.0124563575},"labels":[],"label_agreement":null},{"id":"W4319777521","doi":"10.1109/tai.2023.3243596","title":"Audio Representation Learning by Distilling Video as Privileged Information","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Music and Audio Processing","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Representation (politics); Multimedia; Artificial intelligence; Political science","score_opus":0.041341582093932634,"score_gpt":0.3015254047184407,"score_spread":0.26018382262450807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319777521","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050350193,0.0006738062,0.94196403,0.00053301925,0.00009017139,0.000050892442,0.0005178302,0.0028156303,0.0030043472],"genre_scores_gemma":[0.81772226,0.0006105534,0.17080368,0.00036317008,0.00018397765,0.0001262125,0.0019845043,0.00017268471,0.008032997],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994838,0.00010985192,0.000018555535,0.00020409386,0.00010495947,0.000078672434],"domain_scores_gemma":[0.99905556,0.0004088058,0.000099332094,0.00024166642,0.00013596175,0.000058656427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006985039,0.0010807903,0.00056461256,0.0008778145,0.0002845429,0.0010036086,0.0014404352,0.0010510309,0.0027632602],"category_scores_gemma":[0.0038740733,0.00039538648,0.00070267584,0.00084386003,0.0006364253,0.003102365,0.0018684223,0.0021205815,0.0010348881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041173495,0.00024890434,0.0015000836,0.00013613986,0.00008354395,0.00013203612,0.00016518388,0.13623549,0.025679126,0.01272943,0.005529204,0.81714904],"study_design_scores_gemma":[0.000019530347,0.00010366857,0.00044001781,0.000018206942,0.000024174233,0.00004287407,0.000039237046,0.97001755,0.011573904,0.0153726535,0.0023331402,0.000015124401],"about_ca_topic_score_codex":0.0046878257,"about_ca_topic_score_gemma":0.006118233,"teacher_disagreement_score":0.0046878257,"about_ca_system_score_codex":0.0007967311,"about_ca_system_score_gemma":0.0009410972,"threshold_uncertainty_score":0.009321034},"labels":[],"label_agreement":null},{"id":"W4321608625","doi":"10.1109/tai.2023.3244177","title":"A Long Short-Term Memory-Based Interconnected Architecture for Classification of Grasp Types Using Surface-Electromyography Signals","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Muscle activation and electromyography studies","field":"Engineering","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 Saskatchewan","funders":"","keywords":"Computer science; GRASP; Artificial intelligence; Overfitting; Deep learning; Pattern recognition (psychology); Convolutional neural network; SIGNAL (programming language); Artificial neural network; Machine learning; Feature extraction","score_opus":0.064663463141726,"score_gpt":0.29781789876205755,"score_spread":0.23315443562033156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321608625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23242934,0.0013396023,0.75478977,0.0004553627,0.0003064002,0.00013305845,0.0003756608,0.003942518,0.0062283897],"genre_scores_gemma":[0.8980878,0.0004173774,0.091319576,0.00022750747,0.000045001285,0.0001681379,0.0006597039,0.000041953455,0.009032998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998826,0.000011229416,0.0000080922255,0.000041599807,0.000026478938,0.000030000849],"domain_scores_gemma":[0.99987006,0.000026366373,0.000015542504,0.00001638473,0.000059518487,0.000012139344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030558772,0.00068854145,0.00040143816,0.00030874726,0.00026396892,0.0005459233,0.0009946451,0.000810258,0.0018559862],"category_scores_gemma":[0.0004498321,0.0002181108,0.0004980094,0.00030079382,0.00025032443,0.00067848625,0.00047094424,0.0008944746,0.0006728432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004662008,0.00052093813,0.0042672744,0.00019052072,0.0002565648,0.00022141612,0.0001429983,0.25773123,0.074676655,0.002624839,0.0043946044,0.6545068],"study_design_scores_gemma":[0.000012650836,0.0003261225,0.0015724106,0.000024979547,0.00006124541,0.00005670187,0.000019783829,0.9814975,0.01433039,0.0012023997,0.0008796542,0.000016106642],"about_ca_topic_score_codex":0.0046095285,"about_ca_topic_score_gemma":0.006948334,"teacher_disagreement_score":0.0046095285,"about_ca_system_score_codex":0.00047622254,"about_ca_system_score_gemma":0.00067580485,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4361766013","doi":"10.1109/tai.2023.3262503","title":"Pseudo-shot Learning for Soil Classification With Laser-Induced Breakdown Spectroscopy","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","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; University of Regina","funders":"","keywords":"Laser-induced breakdown spectroscopy; Artificial intelligence; Computer science; Spectral line; Similarity (geometry); Transfer of learning; Pattern recognition (psychology); Machine learning; Biological system; Mathematics; Laser; Optics; Physics","score_opus":0.06929833732698092,"score_gpt":0.36931854454565044,"score_spread":0.3000202072186695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4361766013","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20307843,0.0018140778,0.7840279,0.0005081379,0.00019742831,0.0001851345,0.0007556009,0.0066748606,0.0027585106],"genre_scores_gemma":[0.7243075,0.0004906876,0.26586565,0.00054001564,0.00010405783,0.00018868424,0.003551768,0.00023076861,0.0047209114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925643,0.00017353319,0.000036454447,0.0002532318,0.00019137029,0.00008890773],"domain_scores_gemma":[0.9990859,0.00028384972,0.00008720016,0.00015285333,0.00032137588,0.00006893615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012070995,0.001238389,0.0009279688,0.0013619453,0.00043260714,0.00088888715,0.0019433775,0.0013822042,0.0009744197],"category_scores_gemma":[0.0028158752,0.0003091375,0.0009445903,0.00085038535,0.00059095176,0.0015836096,0.0011735036,0.0016561806,0.00083425717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057879713,0.00095520576,0.0111379875,0.0002523788,0.00022315938,0.00030688074,0.0001761374,0.179248,0.044186488,0.002810076,0.00909605,0.7510289],"study_design_scores_gemma":[0.00001352209,0.000086734835,0.0010714517,0.000009628642,0.000014328271,0.000071096045,0.000036272435,0.98524183,0.009434005,0.00286444,0.0011402238,0.000016564389],"about_ca_topic_score_codex":0.0034528868,"about_ca_topic_score_gemma":0.004496832,"teacher_disagreement_score":0.0034528868,"about_ca_system_score_codex":0.00060248387,"about_ca_system_score_gemma":0.00083469885,"threshold_uncertainty_score":0.0068656206},"labels":[],"label_agreement":null},{"id":"W4364322545","doi":"10.1109/tai.2023.3266183","title":"Visual Relationship Detection for Workplace Safety Applications","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Multimodal Machine Learning Applications","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":"Computer science; Bayesian network; Context (archaeology); Object detection; Visualization; Artificial neural network; Artificial intelligence; Object (grammar); Graphical user interface; Machine learning; Data mining; Pattern recognition (psychology); Programming language","score_opus":0.05401277960001292,"score_gpt":0.34603996480937,"score_spread":0.29202718520935705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364322545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0724835,0.0013878185,0.86038715,0.0017477156,0.0003561691,0.00051325397,0.003219207,0.04571884,0.014186263],"genre_scores_gemma":[0.55805737,0.00055405434,0.4257096,0.00086823176,0.0001129293,0.0003220178,0.0049750297,0.0007196627,0.008681071],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988009,0.00020397891,0.000049423383,0.00037557288,0.00047213965,0.00009803091],"domain_scores_gemma":[0.9981281,0.00058188284,0.00014667152,0.0002845835,0.00074786594,0.00011091669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013575482,0.0010540261,0.00047132664,0.0014058072,0.0004419376,0.0012672795,0.002095411,0.0018508743,0.012549068],"category_scores_gemma":[0.0079668965,0.0003876494,0.00062846794,0.0006326187,0.00030353022,0.0018305794,0.0018137402,0.0010988713,0.0048738746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094212336,0.00059652166,0.008517876,0.00048258778,0.00009922912,0.00031935197,0.00020481765,0.03259439,0.039283436,0.002977534,0.036288988,0.8776932],"study_design_scores_gemma":[0.0000762973,0.00043867517,0.008535148,0.00015092191,0.00006830465,0.0004598184,0.00021998122,0.8804022,0.06550353,0.020654175,0.023418047,0.00007296047],"about_ca_topic_score_codex":0.0054332986,"about_ca_topic_score_gemma":0.008523021,"teacher_disagreement_score":0.012549068,"about_ca_system_score_codex":0.0009069036,"about_ca_system_score_gemma":0.00080432306,"threshold_uncertainty_score":0.041980803},"labels":[],"label_agreement":null},{"id":"W4376464555","doi":"10.1109/tai.2023.3275133","title":"Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced Data Classification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Imbalanced Data Classification Techniques","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":"Overfitting; Computer science; Softmax function; Margin (machine learning); Artificial intelligence; Machine learning; Discriminative model; MNIST database; Leverage (statistics); Robustness (evolution); Class (philosophy); Pattern recognition (psychology); Deep learning; Artificial neural network","score_opus":0.14057980307582246,"score_gpt":0.34483369803144504,"score_spread":0.20425389495562257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376464555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0272381,0.00069264654,0.96871525,0.0004540263,0.0000953742,0.000093879804,0.00017450296,0.001503143,0.0010331536],"genre_scores_gemma":[0.7455297,0.00051962066,0.24650261,0.00072490016,0.00021821285,0.00052086025,0.001159786,0.00035815104,0.0044661933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823356,0.00053899386,0.00012275646,0.00035511376,0.0005436436,0.00020589781],"domain_scores_gemma":[0.99772507,0.00090213,0.00028721805,0.00048147838,0.0004654608,0.00013870782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048631625,0.0015882857,0.0014759038,0.0010559382,0.00058026426,0.0010781767,0.0028908798,0.0015809874,0.001709342],"category_scores_gemma":[0.007446568,0.00044636577,0.0006878033,0.0011427047,0.0013722138,0.0029057385,0.002808549,0.003022531,0.00081399194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007610339,0.000546905,0.0040211705,0.00021705407,0.00015115435,0.00014020174,0.00021422694,0.45859888,0.008886822,0.018593974,0.012444573,0.49542403],"study_design_scores_gemma":[0.000015922013,0.000074547905,0.00030098812,0.000015987944,0.000009383368,0.000026235319,0.000013280623,0.98246,0.0024160054,0.013902872,0.00075608573,0.000008770568],"about_ca_topic_score_codex":0.0012927874,"about_ca_topic_score_gemma":0.0014960513,"teacher_disagreement_score":0.0048631625,"about_ca_system_score_codex":0.0015759725,"about_ca_system_score_gemma":0.0013368306,"threshold_uncertainty_score":0.025719166},"labels":[],"label_agreement":null},{"id":"W4377971347","doi":"10.1109/tai.2023.3279057","title":"Interpreting Tangled Program Graphs Under Partially Observable Dota 2 Invoker Tasks","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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":"Computer science; Interpretability; Artificial intelligence; Graph; Context (archaeology); Machine learning; Task (project management); Theoretical computer science","score_opus":0.08019492026766367,"score_gpt":0.32383461407993724,"score_spread":0.24363969381227357,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377971347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12139292,0.00009171293,0.86868936,0.00037000465,0.000029165478,0.00013753845,0.0004036624,0.002562775,0.0063228933],"genre_scores_gemma":[0.78062534,0.00014103437,0.21339317,0.00014416728,0.000016272566,0.00023383461,0.0006097201,0.00046231964,0.0043741316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991906,0.0002948894,0.000057649417,0.00019782461,0.0001646011,0.0000944065],"domain_scores_gemma":[0.9972862,0.0014640259,0.00033384754,0.00053209,0.00024687775,0.00013694138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088795344,0.0006710338,0.00032064994,0.0006684771,0.00042133304,0.001586246,0.0010198045,0.0008848854,0.003179056],"category_scores_gemma":[0.006252816,0.00040023337,0.0007991205,0.0002604088,0.001992469,0.002800152,0.002137968,0.0013536093,0.00031113243],"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":[0.00018665609,0.00012537785,0.003395136,0.00017973014,0.000044512217,0.0010084112,0.001745818,0.67276293,0.010479013,0.266059,0.0010542208,0.04295916],"study_design_scores_gemma":[0.00002087235,0.00005182957,0.0002903024,0.000025095836,0.000018455461,0.00006734954,0.00018392621,0.7853457,0.0048692743,0.20606935,0.003039346,0.000018408213],"about_ca_topic_score_codex":0.005632783,"about_ca_topic_score_gemma":0.007143462,"teacher_disagreement_score":0.005632783,"about_ca_system_score_codex":0.0013765483,"about_ca_system_score_gemma":0.0010254399,"threshold_uncertainty_score":0.011200011},"labels":[],"label_agreement":null},{"id":"W4379117001","doi":"10.1109/tai.2023.3282199","title":"Optimization of Patient Specific Stimulus for Deep Brain Stimulation Using Spatially Distributed Neural Sources","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Neurological disorders and treatments","field":"Medicine","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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Deep brain stimulation; Subthalamic nucleus; Stimulus (psychology); Local field potential; Computer science; Electroencephalography; Neuroscience; Stimulation; Artificial intelligence; Pattern recognition (psychology); Psychology; Parkinson's disease; Medicine","score_opus":0.07310910454365126,"score_gpt":0.3204073445427288,"score_spread":0.24729823999907752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379117001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08312754,0.00025777644,0.90988576,0.00041089643,0.00003045457,0.0001225954,0.00010291198,0.00044336185,0.0056185923],"genre_scores_gemma":[0.93319577,0.00015108465,0.0651192,0.0000816124,0.000007752537,0.00016474906,0.000072604256,0.000069141184,0.0011379881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999125,0.000026354595,0.0000056152153,0.000013409423,0.000029413548,0.000012732405],"domain_scores_gemma":[0.99985266,0.00008296012,0.000021039812,0.000008771661,0.000023627266,0.000010832857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024817543,0.0004131152,0.00024404564,0.00013421728,0.000108042455,0.0003419386,0.0003942669,0.00042013545,0.0015410468],"category_scores_gemma":[0.00080444873,0.00015942793,0.0002459358,0.00011908784,0.00026094116,0.00035208717,0.0004317959,0.0003182616,0.00018400901],"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":[0.000108157736,0.00006168422,0.0005783955,0.000084686326,0.00002059747,0.00010008528,0.0000353457,0.95294815,0.023460668,0.0033173214,0.00041301228,0.018871907],"study_design_scores_gemma":[0.000018876506,0.00008370597,0.00018393261,0.000005545419,0.0000067625833,0.00004081605,0.000009864587,0.9923814,0.004800904,0.001900297,0.0005626479,0.0000052245236],"about_ca_topic_score_codex":0.0005937811,"about_ca_topic_score_gemma":0.0007020747,"teacher_disagreement_score":0.0015410468,"about_ca_system_score_codex":0.0003383771,"about_ca_system_score_gemma":0.00049191195,"threshold_uncertainty_score":0.005155325},"labels":[],"label_agreement":null},{"id":"W4384789574","doi":"10.1109/tai.2023.3296714","title":"Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Mineral Processing and Grinding","field":"Engineering","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":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Proximate; Coal; Spectroscopy; Infrared; Infrared spectroscopy; Environmental science; Chemistry; Engineering; Physics; Optics; Food science; Waste management; Astronomy; Organic chemistry","score_opus":0.03147731671089384,"score_gpt":0.2822681783860683,"score_spread":0.2507908616751745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384789574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07835116,0.00095150684,0.9155324,0.00039326487,0.000103692946,0.000054536245,0.00013301036,0.002011852,0.0024685224],"genre_scores_gemma":[0.83616334,0.0004976323,0.15702358,0.00034280642,0.00007750374,0.00009523518,0.00047549116,0.000077317185,0.0052469894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997367,0.0000329813,0.000012916266,0.00010102563,0.0000750375,0.000041294938],"domain_scores_gemma":[0.99974567,0.00008029443,0.000037197213,0.000032106822,0.0000836063,0.000021092143],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050101423,0.0009739047,0.00063780765,0.00079849135,0.00030925276,0.00063321594,0.0014413286,0.00088834437,0.0010556092],"category_scores_gemma":[0.0007769701,0.0004109964,0.0010148899,0.00063340634,0.00039273078,0.0013457931,0.0010298162,0.0011035984,0.00040264142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024680424,0.00036254234,0.006557514,0.00016457916,0.00023382761,0.00026092993,0.00012006537,0.37543583,0.04695579,0.0031712682,0.0028345212,0.56365633],"study_design_scores_gemma":[0.0000029819498,0.00002229805,0.00054880755,0.0000034794368,0.000012237706,0.000021601914,0.0000055314476,0.9946715,0.0037819666,0.0006792763,0.00024429892,0.000005993975],"about_ca_topic_score_codex":0.0055986308,"about_ca_topic_score_gemma":0.008215806,"teacher_disagreement_score":0.0055986308,"about_ca_system_score_codex":0.00069062045,"about_ca_system_score_gemma":0.00066997146,"threshold_uncertainty_score":0.011132121},"labels":[],"label_agreement":null},{"id":"W4385194783","doi":"10.1109/tai.2023.3298588","title":"A Visual and Textual Information Fusion-Based Zero-Shot Framework for Hazardous Material Placard Detection and Recognition","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Mitacs","keywords":"Computer science; Artificial intelligence; Margin (machine learning); Fuse (electrical); Logarithm; Shot (pellet); Machine learning; Object (grammar); Object detection; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.029027541238847442,"score_gpt":0.27193001374162457,"score_spread":0.24290247250277713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385194783","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028124625,0.0006825529,0.96752065,0.00018232378,0.000084708416,0.00011358039,0.00027033532,0.0013811057,0.0016400662],"genre_scores_gemma":[0.5656934,0.0008688062,0.42240235,0.00045396225,0.00022726845,0.00023379388,0.0024170543,0.00020703016,0.0074963025],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990839,0.00008114418,0.000041987143,0.00034491683,0.00029341414,0.0001545062],"domain_scores_gemma":[0.99957937,0.00008275327,0.00006360683,0.00006975313,0.00015254035,0.000051975567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008536433,0.0011494145,0.0014739311,0.0024631957,0.00052402064,0.0010101075,0.0022781205,0.0013307888,0.0016196881],"category_scores_gemma":[0.0011895315,0.00044237278,0.0014230073,0.0010608409,0.00092503533,0.0019262846,0.0019934648,0.0014556493,0.00078330736],"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":[0.0005208964,0.0006299737,0.0029714915,0.00027900378,0.0001694524,0.00035184828,0.0002991439,0.085818835,0.061519574,0.0073374812,0.0073401206,0.83276224],"study_design_scores_gemma":[0.000011390054,0.00014277319,0.0016671744,0.000022037239,0.00005772146,0.00019755041,0.000059609192,0.9731015,0.017069604,0.005535456,0.002101481,0.000033595054],"about_ca_topic_score_codex":0.006975852,"about_ca_topic_score_gemma":0.008639055,"teacher_disagreement_score":0.006975852,"about_ca_system_score_codex":0.0009343179,"about_ca_system_score_gemma":0.0014169829,"threshold_uncertainty_score":0.013870537},"labels":[],"label_agreement":null},{"id":"W4385213925","doi":"10.1109/tai.2023.3298297","title":"Contrastive-Enhanced Domain Generalization With Federated Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Normalization (sociology); Classifier (UML); Artificial intelligence; Generalization; Embedding; Machine learning","score_opus":0.04008017325056603,"score_gpt":0.28246220511734005,"score_spread":0.242382031866774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385213925","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029012118,0.00032407427,0.9676212,0.0001801675,0.000030527473,0.000057715075,0.00009649973,0.0019198102,0.0007578676],"genre_scores_gemma":[0.7588495,0.0001943453,0.23716229,0.0004774936,0.00006524755,0.00015249159,0.0006584826,0.00017512299,0.0022650347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985827,0.0004328314,0.00006388265,0.00056762784,0.00024575638,0.000107207285],"domain_scores_gemma":[0.99758446,0.000786969,0.00018928114,0.0010951593,0.00024665502,0.000097462485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025220287,0.0011840183,0.0015108581,0.00086931273,0.00052132166,0.00096483296,0.0029255634,0.0015341955,0.0010369295],"category_scores_gemma":[0.0050715655,0.00040789455,0.00122616,0.0009896363,0.0013153383,0.003727424,0.003279083,0.0022027525,0.00051886257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051534444,0.00051850756,0.0039824382,0.00015989637,0.00024623412,0.00030659063,0.00031575537,0.43452707,0.015500659,0.014727791,0.0055470173,0.52365273],"study_design_scores_gemma":[0.00001786677,0.000086834654,0.0003198833,0.000007979343,0.00001986944,0.00010911353,0.000030043273,0.9790796,0.004061892,0.015317986,0.0009352019,0.000013732295],"about_ca_topic_score_codex":0.002226999,"about_ca_topic_score_gemma":0.0023860761,"teacher_disagreement_score":0.0029255634,"about_ca_system_score_codex":0.00107574,"about_ca_system_score_gemma":0.0009767872,"threshold_uncertainty_score":0.01333791},"labels":[],"label_agreement":null},{"id":"W4385299175","doi":"10.1109/tai.2023.3299252","title":"Facilitating Sim-to-Real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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 British Columbia, Okanagan Campus; University of British Columbia; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Computer science; Robot; Artificial intelligence; Heuristic; Robotics; Generalizability theory; Task (project management); Simulation; Engineering","score_opus":0.06739551366683552,"score_gpt":0.3288240012675382,"score_spread":0.2614284876007027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385299175","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029296996,0.00009833808,0.9681777,0.00013332871,0.000026469004,0.000051171595,0.000013616217,0.0004532132,0.0017491857],"genre_scores_gemma":[0.8814804,0.00014236379,0.11734407,0.000121254634,0.000019591542,0.00016455878,0.00003822399,0.00011411129,0.0005754204],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985329,0.0008056133,0.000078667246,0.00016714925,0.00033965442,0.0000760148],"domain_scores_gemma":[0.99399734,0.0038629642,0.00080095243,0.0009074512,0.00028956265,0.00014173484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023794079,0.00052060164,0.0004918299,0.0002696169,0.0002605474,0.00076823693,0.00088048587,0.00059297157,0.0014753515],"category_scores_gemma":[0.01007528,0.0003472243,0.00047706056,0.00022387439,0.0016367236,0.0013086705,0.0013484232,0.001383628,0.0002616155],"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":[0.00021346791,0.00012201851,0.0014574069,0.000116227406,0.000044027067,0.00008725602,0.00013015264,0.92082995,0.011828962,0.036216605,0.00028660806,0.028667258],"study_design_scores_gemma":[0.000015810621,0.00007387058,0.00014917502,0.000009677457,0.000006376127,0.000027437261,0.000008260975,0.9880784,0.0036907424,0.007197446,0.0007344907,0.000008332073],"about_ca_topic_score_codex":0.000772948,"about_ca_topic_score_gemma":0.00074485363,"teacher_disagreement_score":0.0023794079,"about_ca_system_score_codex":0.0005800649,"about_ca_system_score_gemma":0.0010203191,"threshold_uncertainty_score":0.012583613},"labels":[],"label_agreement":null},{"id":"W4387068273","doi":"10.1109/tai.2023.3319301","title":"Double-Quantitative Feature Selection Approach for Multigranularity Ordered Decision Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Rough Sets and Fuzzy Logic","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":"Natural Science Foundation of Chongqing; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Viewpoints; Granular computing; Feature selection; Data mining; Feature (linguistics); Completeness (order theory); Focus (optics); Perspective (graphical); Artificial intelligence; Selection (genetic algorithm); Greedy algorithm; Rough set; Machine learning; Algorithm; Mathematics","score_opus":0.1139263091861794,"score_gpt":0.339383797119346,"score_spread":0.2254574879331666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387068273","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019007614,0.00038317582,0.97938,0.00014575358,0.000031576783,0.000084086794,0.00008157786,0.00022050909,0.00066573],"genre_scores_gemma":[0.6274413,0.0002729926,0.37098625,0.00010176782,0.00006221987,0.00026384284,0.0002741664,0.000036001926,0.0005614014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99743134,0.00068898796,0.00026716222,0.0004054761,0.0010181732,0.0001888623],"domain_scores_gemma":[0.9982066,0.0009962401,0.00019298839,0.000118108204,0.00041374267,0.00007221745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025130431,0.0009932242,0.0014369559,0.0024429685,0.00060562964,0.0017684855,0.0008784353,0.0005802786,0.0011269099],"category_scores_gemma":[0.004306387,0.00032562733,0.0012634642,0.0017139537,0.0005666955,0.0014225933,0.0010146338,0.0007837356,0.0001509639],"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":[0.00037639227,0.0001991153,0.0054620397,0.00067608285,0.0003244407,0.000452835,0.0004028637,0.5249363,0.01716063,0.03704929,0.0025885003,0.41037148],"study_design_scores_gemma":[0.00002632656,0.00010202807,0.000988552,0.000028164524,0.00005291627,0.00009579989,0.00005866745,0.97656703,0.0029046638,0.017508496,0.0016410396,0.000026422871],"about_ca_topic_score_codex":0.0017554598,"about_ca_topic_score_gemma":0.0013327637,"teacher_disagreement_score":0.0025130431,"about_ca_system_score_codex":0.001120532,"about_ca_system_score_gemma":0.0012199414,"threshold_uncertainty_score":0.013290346},"labels":[],"label_agreement":null},{"id":"W4389584550","doi":"10.1109/tai.2023.3340982","title":"Manipulation Attacks on Learned Image Compression","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Adversarial Robustness in Machine Learning","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 Calgary; University of Waterloo","funders":"Army Research Office; Science and Technology Program of Hubei Province; National Natural Science Foundation of China","keywords":"Computer science; Image compression; Lossy compression; Lossless compression; Artificial intelligence; JPEG; Deep learning; Robustness (evolution); Image quality; Computer vision; Data compression; Computer engineering; Image processing; Image (mathematics)","score_opus":0.10140778479716481,"score_gpt":0.357783726830343,"score_spread":0.25637594203317815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389584550","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":[0.66130257,0.0008493774,0.322655,0.00086001435,0.00009556042,0.00009159935,0.00026518994,0.0024693492,0.011411362],"genre_scores_gemma":[0.9866599,0.0001337864,0.011806608,0.000082334554,0.000014651016,0.000019238425,0.00008562313,0.000054051186,0.0011437503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994431,0.000111564405,0.0000248473,0.00007508504,0.00024448935,0.00010078975],"domain_scores_gemma":[0.9986929,0.0006599102,0.00017493553,0.00031810903,0.000117673866,0.000036504032],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004968513,0.00052591285,0.00033272067,0.00044642494,0.00021467701,0.00046820642,0.0004754152,0.0005448815,0.0013298773],"category_scores_gemma":[0.0032330852,0.00014850462,0.0003373699,0.00023641335,0.0009763309,0.0010757872,0.0009668169,0.00086992804,0.00022251882],"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":[0.0005120718,0.000097782446,0.0024663978,0.00012282717,0.00007521403,0.0003904329,0.00011451841,0.82358843,0.0709614,0.025570363,0.0015907838,0.07450983],"study_design_scores_gemma":[0.0000072290773,0.00008182104,0.00044927993,0.000010534418,0.0000073548963,0.00008386057,0.000008866717,0.9642404,0.030708458,0.0037486942,0.00064478203,0.000008651371],"about_ca_topic_score_codex":0.0010232257,"about_ca_topic_score_gemma":0.00065775303,"teacher_disagreement_score":0.0013298773,"about_ca_system_score_codex":0.00064727303,"about_ca_system_score_gemma":0.00029986125,"threshold_uncertainty_score":0.004696369},"labels":[],"label_agreement":null},{"id":"W4389664544","doi":"10.1109/tai.2023.3342104","title":"3-D Dynamic Multitarget Detection Algorithm Based on Cross-View Feature Fusion","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Infrared Target Detection Methodologies","field":"Engineering","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":"McMaster University","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Feature (linguistics); Artificial intelligence; Point cloud; Fusion; Feature extraction; Software portability; Pattern recognition (psychology); Image fusion; Computer vision; Algorithm; Image (mathematics)","score_opus":0.046118044650793,"score_gpt":0.3202741740307436,"score_spread":0.2741561293799506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389664544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022591399,0.00025696668,0.9742833,0.00007350902,0.000051484087,0.00006765416,0.000070836606,0.0014394434,0.0011653961],"genre_scores_gemma":[0.44752043,0.00044517993,0.5473706,0.00019307274,0.00005599984,0.00019617596,0.00077280926,0.00022454864,0.0032211277],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999131,0.000051199368,0.00004737422,0.00030281686,0.00034709516,0.000120507146],"domain_scores_gemma":[0.9995334,0.00006217539,0.000046804795,0.00006535773,0.0002625583,0.000029674575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008014273,0.0010655243,0.0012535701,0.0019695857,0.00063120434,0.001216866,0.0015190127,0.0010294287,0.0016945436],"category_scores_gemma":[0.0012086072,0.00056032214,0.0015414446,0.001287915,0.00039313178,0.0018827934,0.0017739437,0.0010427972,0.0007849266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026451994,0.00016497853,0.0040478786,0.00010173439,0.00019937898,0.0001855151,0.00017889302,0.10042506,0.07973437,0.0033168062,0.0030710918,0.80830985],"study_design_scores_gemma":[0.0000142928075,0.0000958878,0.0023613968,0.000009710506,0.000042747663,0.00025266997,0.00004860335,0.97085404,0.022405356,0.001480544,0.0024027363,0.00003211597],"about_ca_topic_score_codex":0.005194565,"about_ca_topic_score_gemma":0.0039916886,"teacher_disagreement_score":0.005194565,"about_ca_system_score_codex":0.0006344788,"about_ca_system_score_gemma":0.0008824234,"threshold_uncertainty_score":0.0103286505},"labels":[],"label_agreement":null},{"id":"W4391697002","doi":"10.1109/tai.2024.3364127","title":"Alternating Direction Method of Multipliers-Based Parallel Optimization for Multi-Agent Collision-Free Model Predictive Control","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Control Systems Optimization","field":"Engineering","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 British Columbia","funders":"National Natural Science Foundation of China","keywords":"Mathematical optimization; Initialization; Quadratic programming; Computer science; Convergence (economics); Convex optimization; Model predictive control; Integer programming; Quadratically constrained quadratic program; Quadratic equation; Optimization problem; Regular polygon; Mathematics; Control (management); Artificial intelligence","score_opus":0.04486114021600532,"score_gpt":0.3181066622173674,"score_spread":0.2732455220013621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391697002","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027589982,0.00031348874,0.99508274,0.000099728466,0.000044293807,0.00002419956,0.000014138004,0.00014977885,0.001512733],"genre_scores_gemma":[0.5686073,0.0010866225,0.4238718,0.00020838513,0.00014435187,0.0004900188,0.00018944302,0.00016706334,0.0052350312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995759,0.00013908274,0.0000173889,0.000077356584,0.00014278933,0.000047451256],"domain_scores_gemma":[0.999468,0.00026556666,0.00008430887,0.000044671262,0.00011169878,0.000025732917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011071692,0.0011542443,0.0012394028,0.00046413802,0.0005563699,0.0010345485,0.001141818,0.0009114158,0.0015451368],"category_scores_gemma":[0.0017961287,0.0006060682,0.000742342,0.0007590124,0.0008209763,0.00085956743,0.0011269726,0.0018300896,0.0003437744],"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":[0.00002691937,0.000017815131,0.00011046913,0.000060719532,0.000024504989,0.000039525537,0.000026248179,0.9723335,0.000587437,0.008341559,0.00057071116,0.01786055],"study_design_scores_gemma":[0.0000037222235,0.000009714516,0.000011821318,0.0000018709484,0.0000020468356,0.000003976854,0.0000019487095,0.9978308,0.000098877266,0.0017071341,0.00032631477,0.0000017423829],"about_ca_topic_score_codex":0.0046937945,"about_ca_topic_score_gemma":0.0032918623,"teacher_disagreement_score":0.0046937945,"about_ca_system_score_codex":0.0007216182,"about_ca_system_score_gemma":0.0017589863,"threshold_uncertainty_score":0.009332955},"labels":[],"label_agreement":null},{"id":"W4391853794","doi":"10.1109/tai.2024.3366174","title":"Multistream Gaze Estimation With Anatomical Eye Region Isolation by Synthetic to Real Transfer Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Gaze Tracking and Assistive Technology","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":"Queen's University","funders":"Innovation for Defence Excellence and Security","keywords":"Gaze; Isolation (microbiology); Transfer of learning; Computer science; Artificial intelligence; Estimation; Computer vision; Optometry; Engineering; Medicine; Biology","score_opus":0.024896728066047768,"score_gpt":0.28726717062696483,"score_spread":0.26237044256091707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391853794","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17121823,0.001096196,0.81118065,0.00042562486,0.00015740642,0.000116630334,0.0008363696,0.012096361,0.00287251],"genre_scores_gemma":[0.8048064,0.00029001318,0.18541723,0.00028546114,0.00006816701,0.00012238974,0.0018802509,0.00032429438,0.006805818],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997762,0.000039641793,0.0000075276384,0.00010526307,0.00003781124,0.000033652603],"domain_scores_gemma":[0.99959844,0.00013679499,0.00004083704,0.00010502859,0.0000928509,0.000026080803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056151435,0.0012492804,0.0006217619,0.00056233676,0.00020792235,0.0004452853,0.0015541457,0.0009979355,0.0037892319],"category_scores_gemma":[0.0022061183,0.0004892824,0.0007944302,0.00039475417,0.00043009172,0.0012771976,0.001302037,0.0013742888,0.0010372828],"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":[0.0006470441,0.00038275818,0.0055669188,0.0001912987,0.00027729617,0.00026654958,0.00020110935,0.40613538,0.05889626,0.0029350573,0.008333283,0.5161671],"study_design_scores_gemma":[0.000014891191,0.0000824731,0.0007305663,0.0000073994042,0.000013075474,0.00004583137,0.000012967288,0.99070483,0.0063638994,0.0014176924,0.00059940666,0.000007028803],"about_ca_topic_score_codex":0.008999766,"about_ca_topic_score_gemma":0.010454973,"teacher_disagreement_score":0.008999766,"about_ca_system_score_codex":0.0006406561,"about_ca_system_score_gemma":0.0006386647,"threshold_uncertainty_score":0.017894745},"labels":[],"label_agreement":null},{"id":"W4392667187","doi":"10.1109/tai.2024.3375260","title":"Adaptive Learning for Soil Classification in Laser-Induced Breakdown Spectroscopy Streaming","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Laser-induced spectroscopy and plasma","field":"Engineering","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 Alberta; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Laser-induced breakdown spectroscopy; Spectroscopy; Materials science; Computer science; Laser; Environmental science; Physics; Optics","score_opus":0.05069460419608276,"score_gpt":0.29388009413397137,"score_spread":0.2431854899378886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392667187","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10832044,0.00044456634,0.8858201,0.00025180547,0.000069918315,0.00010084295,0.00035620446,0.00352724,0.0011088891],"genre_scores_gemma":[0.8036209,0.00023580722,0.19086507,0.00023760046,0.00008550942,0.00023356438,0.0014173959,0.0002086494,0.00309548],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996675,0.000059804486,0.000018861563,0.00013297454,0.000071023984,0.000049868784],"domain_scores_gemma":[0.9994025,0.0002390394,0.000072943156,0.00008869654,0.00015826426,0.000038623653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085579016,0.000710122,0.00070794014,0.0006547057,0.0003232312,0.00069390825,0.0016449145,0.0010200532,0.0012788549],"category_scores_gemma":[0.002314298,0.00031449462,0.0007111165,0.00059948413,0.0005121214,0.0011522779,0.0011216,0.0011602488,0.0007177838],"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":[0.00036283568,0.00042701146,0.0071368776,0.00018948458,0.00009998628,0.00024247535,0.00021305114,0.4617833,0.038685296,0.0026773121,0.0037645684,0.48441786],"study_design_scores_gemma":[0.0000053932417,0.000029854253,0.00057661993,0.000004707062,0.000005202041,0.000025277435,0.000017736369,0.9930137,0.00403528,0.0018774782,0.00040092666,0.000007818473],"about_ca_topic_score_codex":0.002625351,"about_ca_topic_score_gemma":0.0023412104,"teacher_disagreement_score":0.002625351,"about_ca_system_score_codex":0.0005858344,"about_ca_system_score_gemma":0.0006093012,"threshold_uncertainty_score":0.005220115},"labels":[],"label_agreement":null},{"id":"W4396215031","doi":"10.1109/tai.2024.3394797","title":"Redefining Real-Time Road Quality Analysis With Vision Transformers on Edge Devices","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Software deployment; SAFER; Architecture; Benchmarking; Real-time computing; Transformer; Benchmark (surveying); Artificial intelligence; Transport engineering; Computer security; Engineering; Software engineering","score_opus":0.024552822713205656,"score_gpt":0.30037607460040516,"score_spread":0.2758232518871995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396215031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16846068,0.00066519564,0.8020155,0.0003075568,0.00023392713,0.00021012331,0.0013147105,0.017475467,0.009316904],"genre_scores_gemma":[0.8198333,0.00036944548,0.17338361,0.00023736981,0.00005082858,0.000074307856,0.0015091966,0.00030147997,0.00424045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998388,0.000012304202,0.000005511445,0.000053329506,0.000060513812,0.000029432931],"domain_scores_gemma":[0.99983394,0.000028787137,0.000016974323,0.00003677474,0.000068228335,0.000015379546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023338228,0.00058260816,0.00041150197,0.0006890591,0.00016830335,0.0006678257,0.0009129155,0.00042870102,0.002586542],"category_scores_gemma":[0.00093144603,0.00021731161,0.0002740648,0.00039594926,0.00018805098,0.0011280623,0.0007932701,0.00054599694,0.0012808456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005637242,0.00028022355,0.008725021,0.00022762295,0.00010351881,0.00030153184,0.00015065158,0.04432005,0.14477085,0.003137402,0.019879933,0.7775394],"study_design_scores_gemma":[0.0000262435,0.00015687165,0.006586114,0.000030455829,0.000047385347,0.00026226175,0.0000797067,0.92037886,0.061782695,0.0030024832,0.007611678,0.000035254725],"about_ca_topic_score_codex":0.0048326515,"about_ca_topic_score_gemma":0.008833872,"teacher_disagreement_score":0.0048326515,"about_ca_system_score_codex":0.00043420584,"about_ca_system_score_gemma":0.00043760735,"threshold_uncertainty_score":0.009609044},"labels":[],"label_agreement":null},{"id":"W4396629560","doi":"10.1109/tai.2024.3396422","title":"Remaining Useful Life Prediction via Frequency Emphasizing Mix-Up and Masked Reconstruction","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","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 British Columbia","funders":"Singapore Institute of Manufacturing Technology; National University of Singapore","keywords":"Leverage (statistics); Computer science; Bottleneck; Artificial intelligence; Machine learning; Autoencoder; Domain (mathematical analysis); Data mining; Deep learning; Mathematics","score_opus":0.03230652616771762,"score_gpt":0.2861934823015627,"score_spread":0.2538869561338451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396629560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12304422,0.00024498435,0.8742939,0.00012760484,0.00002737105,0.000025477852,0.00020261205,0.00092739606,0.0011064697],"genre_scores_gemma":[0.8985281,0.00014765601,0.09894151,0.000055863064,0.000027061133,0.000042621523,0.00042890495,0.00006285316,0.0017653642],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999785,0.000030988107,0.000011482409,0.00006968303,0.00006944109,0.000033375218],"domain_scores_gemma":[0.9994918,0.00019466665,0.00009867728,0.000078927376,0.000107388325,0.000028587534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000595757,0.0006256355,0.00053253456,0.0007709049,0.00018922458,0.00043467234,0.00075152906,0.00059403275,0.00095255295],"category_scores_gemma":[0.0015878917,0.00026936896,0.0005185661,0.0004372702,0.00036032527,0.0010323137,0.0005973689,0.00067961623,0.00029660398],"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":[0.00055802625,0.00018609488,0.010996738,0.0001112674,0.00006848491,0.0002714625,0.00010804398,0.6575587,0.0316167,0.0056205676,0.0022549438,0.29064903],"study_design_scores_gemma":[0.0000020094908,0.00001790729,0.0005457406,0.0000024667356,0.0000051265038,0.000019022495,0.0000034078469,0.9949071,0.0033864372,0.0009257241,0.00018070343,0.0000043528867],"about_ca_topic_score_codex":0.001733934,"about_ca_topic_score_gemma":0.0023393333,"teacher_disagreement_score":0.001733934,"about_ca_system_score_codex":0.00037213112,"about_ca_system_score_gemma":0.00041700937,"threshold_uncertainty_score":0.0034477115},"labels":[],"label_agreement":null},{"id":"W4400228131","doi":"10.1109/tai.2024.3421176","title":"StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Classifier (UML); Stacking; Identification (biology); Artificial intelligence; Antimicrobial; Computational biology; Computer science; Ensemble learning; Machine learning; Pattern recognition (psychology); Biology; Chemistry; Microbiology","score_opus":0.033656301419975644,"score_gpt":0.3175476373886711,"score_spread":0.2838913359686954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400228131","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072368175,0.0026892754,0.9177482,0.0003929834,0.00037283395,0.00016758498,0.00045644902,0.0034415568,0.0023630038],"genre_scores_gemma":[0.6235196,0.0013450596,0.3672436,0.0005179188,0.00032200318,0.000306912,0.0019639868,0.0001614819,0.004619513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989802,0.00021093358,0.00008524462,0.00021559847,0.00036490816,0.00014304258],"domain_scores_gemma":[0.9987797,0.00049360417,0.000094649695,0.000106593034,0.00045977955,0.00006559157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016952213,0.0017402032,0.0018043279,0.0020853807,0.00074819336,0.0008866113,0.0015701939,0.0015762901,0.0015236442],"category_scores_gemma":[0.0027481618,0.00042174573,0.0015172455,0.001388885,0.00030067828,0.0015410396,0.0010789174,0.0019544873,0.00096993725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003598135,0.00029826871,0.0065485365,0.0001462365,0.00050260243,0.00020808038,0.00011656546,0.1648327,0.018545765,0.001615345,0.006817091,0.800009],"study_design_scores_gemma":[0.000008358612,0.00012506302,0.00068150094,0.000012104039,0.000057051715,0.00007285545,0.000022486358,0.99112916,0.005531462,0.0011420419,0.0011947467,0.000023222286],"about_ca_topic_score_codex":0.0037467552,"about_ca_topic_score_gemma":0.003768692,"teacher_disagreement_score":0.0037467552,"about_ca_system_score_codex":0.0004997563,"about_ca_system_score_gemma":0.0009118176,"threshold_uncertainty_score":0.008965254},"labels":[],"label_agreement":null},{"id":"W4400411150","doi":"10.1109/tai.2024.3424427","title":"An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient-Boosting and Fuzzy Rule-Based Models","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Fuzzy Logic and Control Systems","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":"University of Alberta","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Boosting (machine learning); Ensemble learning; Computer science; Artificial intelligence; Gradient boosting; Fuzzy rule; Fusion; Fuzzy logic; Machine learning; Fuzzy control system; Random forest","score_opus":0.06118054267137102,"score_gpt":0.29249241259162284,"score_spread":0.23131186992025182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400411150","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030462954,0.00030490174,0.9956256,0.00007376695,0.000038659793,0.000025938676,0.00002139301,0.00024528554,0.0006181169],"genre_scores_gemma":[0.36124626,0.00069957797,0.634846,0.00027561435,0.00020959615,0.00020499778,0.000291332,0.00012144003,0.0021051646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862134,0.0003917609,0.00008016834,0.0002511139,0.00054830743,0.00010737156],"domain_scores_gemma":[0.9987828,0.00036434684,0.00010920131,0.00017082308,0.0004995361,0.00007321354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003653549,0.001163976,0.0020657866,0.0014098744,0.0006695703,0.0014983538,0.0020406973,0.0014968358,0.001147265],"category_scores_gemma":[0.0041708048,0.0005072854,0.0016316972,0.0012363796,0.0005380992,0.0020573519,0.001677011,0.001662492,0.00064626],"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":[0.00009478274,0.000121589524,0.0012718962,0.000115793155,0.00028897496,0.00015806324,0.00014249404,0.7136683,0.0070251734,0.029216392,0.002883443,0.24501313],"study_design_scores_gemma":[0.000004259909,0.00003926769,0.00010915745,0.000010032524,0.000027863873,0.00004069975,0.0000057574157,0.9889509,0.0008567581,0.008854133,0.0010893102,0.000011915951],"about_ca_topic_score_codex":0.002847435,"about_ca_topic_score_gemma":0.0031495509,"teacher_disagreement_score":0.003653549,"about_ca_system_score_codex":0.0005966005,"about_ca_system_score_gemma":0.0011376787,"threshold_uncertainty_score":0.019322097},"labels":[],"label_agreement":null},{"id":"W4400878183","doi":"10.1109/tai.2024.3432028","title":"Two-Stage Representation Refinement Based on Convex Combination for 3-D Human Poses Estimation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Human Pose and Action 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":"Higher Education Discipline Innovation Project; National Natural Science Foundation of China","keywords":"Stage (stratigraphy); Representation (politics); Regular polygon; Estimation; Computer science; Mathematics; Artificial intelligence; Pattern recognition (psychology); Biology; Geometry; Economics; Political science","score_opus":0.09487364321996045,"score_gpt":0.3731276419340904,"score_spread":0.27825399871413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400878183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004987381,0.00014894822,0.992942,0.000064634136,0.000024377681,0.000045390352,0.000070431,0.0010520457,0.0006648249],"genre_scores_gemma":[0.28880113,0.00073006377,0.6996983,0.00039905717,0.00012909135,0.00019693638,0.001712278,0.00054757996,0.007785513],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899906,0.00014382483,0.000045425084,0.00031529844,0.0003703616,0.00012613123],"domain_scores_gemma":[0.9993082,0.0001777883,0.000075403674,0.00016485061,0.00022129172,0.00005241783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007494975,0.0018756891,0.0014776946,0.0011053112,0.00037525716,0.000791613,0.0017539148,0.0009107383,0.0035797667],"category_scores_gemma":[0.0023538463,0.0009631724,0.0014273328,0.0010225202,0.00060151395,0.0014666329,0.0016221474,0.0015744669,0.0017898048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035191243,0.00013384748,0.001109686,0.00012216742,0.00014377416,0.00024980403,0.00011007494,0.32162482,0.038652178,0.003958317,0.0059950515,0.62754846],"study_design_scores_gemma":[0.0000071943878,0.00004983053,0.0003647742,0.000006413178,0.000015339583,0.00009145854,0.000012194658,0.99018717,0.006550153,0.0014913818,0.0012100168,0.000013967104],"about_ca_topic_score_codex":0.0104875965,"about_ca_topic_score_gemma":0.01495448,"teacher_disagreement_score":0.0104875965,"about_ca_system_score_codex":0.0007211637,"about_ca_system_score_gemma":0.0011297469,"threshold_uncertainty_score":0.020853102},"labels":[],"label_agreement":null},{"id":"W4401725036","doi":"10.1109/tai.2024.3446759","title":"Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","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 Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Federated learning; Econometrics; Artificial intelligence; Economics","score_opus":0.042682084210825914,"score_gpt":0.28434595824568926,"score_spread":0.24166387403486334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401725036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016187273,0.00021024131,0.98145735,0.0004039577,0.000027414268,0.00004875502,0.000081458515,0.000685887,0.0008976362],"genre_scores_gemma":[0.8023589,0.00026218314,0.19351164,0.00050934893,0.00010102413,0.00019662525,0.00033657468,0.00013688339,0.0025868085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9948608,0.0015622737,0.00028681417,0.0012260234,0.0014663619,0.00059778575],"domain_scores_gemma":[0.98973817,0.005423017,0.0007966823,0.0025558453,0.0011462666,0.0003400429],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005653673,0.001101999,0.0019418695,0.00079246133,0.0010976802,0.0021960041,0.003634341,0.002063741,0.0014822918],"category_scores_gemma":[0.020182041,0.00052262645,0.0011069482,0.0016495733,0.001682233,0.0043081045,0.0042496263,0.0031279933,0.0005255901],"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":[0.0007216936,0.00026725445,0.0020167509,0.00017900586,0.00010839357,0.00026635255,0.00021212696,0.72717136,0.006507873,0.073059365,0.004736968,0.18475291],"study_design_scores_gemma":[0.000027192084,0.000046910056,0.000107110944,0.000006589214,0.000009340739,0.000079252844,0.000020582153,0.971332,0.0021738724,0.025738414,0.00044851177,0.0000102380545],"about_ca_topic_score_codex":0.0015422534,"about_ca_topic_score_gemma":0.0012070736,"teacher_disagreement_score":0.005653673,"about_ca_system_score_codex":0.0018431436,"about_ca_system_score_gemma":0.0029288463,"threshold_uncertainty_score":0.029899895},"labels":[],"label_agreement":null},{"id":"W4404562684","doi":"10.1109/tai.2024.3502577","title":"DGeC: Dynamically and Globally Enhanced Convolution","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Semiconductor materials and devices","field":"Engineering","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":"Natural Science Foundation of Fujian Province; Natural Science Foundation for Distinguished Young Scholars of Hunan Province; National Natural Science Foundation of China","keywords":"Computer science; Convolution (computer science); Artificial intelligence","score_opus":0.02191339940162017,"score_gpt":0.2596169886145709,"score_spread":0.2377035892129507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404562684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037948485,0.0008957298,0.9516051,0.00025052592,0.0001240774,0.00005377834,0.00018638467,0.005447603,0.0034884047],"genre_scores_gemma":[0.5232675,0.000486906,0.4662518,0.00053579814,0.00008385379,0.00010248244,0.0009063856,0.0005702617,0.007795049],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996177,0.000044703014,0.000019483465,0.00013032125,0.000109051776,0.00007867532],"domain_scores_gemma":[0.9996424,0.00008299061,0.000026414526,0.00013420699,0.00008570328,0.000028255152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006049002,0.0010721643,0.0007440708,0.0005168124,0.00026157685,0.00072803965,0.0014870797,0.00077634066,0.0024738912],"category_scores_gemma":[0.0015188277,0.00033024748,0.0005237312,0.00055216573,0.0006210056,0.0017521239,0.001591463,0.0012935558,0.0009035832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005081659,0.00019059777,0.0024780724,0.00017570716,0.0001621434,0.00038361872,0.00013473452,0.22647713,0.08643733,0.018249627,0.013031814,0.65177107],"study_design_scores_gemma":[0.000025134868,0.0000940127,0.00064924685,0.000011607818,0.000027202272,0.00023633259,0.000017593415,0.95920914,0.028024497,0.0055573876,0.00612946,0.000018494095],"about_ca_topic_score_codex":0.0045122444,"about_ca_topic_score_gemma":0.0070290244,"teacher_disagreement_score":0.0045122444,"about_ca_system_score_codex":0.00066963345,"about_ca_system_score_gemma":0.0011656476,"threshold_uncertainty_score":0.008971989},"labels":[],"label_agreement":null},{"id":"W4406948310","doi":"10.1109/tai.2025.3535456","title":"SAMScore: A Content Structural Similarity Metric for Image Translation Evaluation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Natural Language Processing Techniques","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":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; National Cancer Institute; Foundation for the National Institutes of Health","keywords":"Translation (biology); Similarity (geometry); Structural similarity; Metric (unit); Content (measure theory); Artificial intelligence; Computer science; Image (mathematics); Mathematics; Pattern recognition (psychology); Natural language processing; Biology; Mathematical analysis; Engineering; Biochemistry","score_opus":0.12617588977865285,"score_gpt":0.38295597615255395,"score_spread":0.2567800863739011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406948310","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22298133,0.0080628935,0.7273244,0.0005195685,0.0009707484,0.0013495887,0.009002395,0.013640749,0.016148364],"genre_scores_gemma":[0.61586463,0.001213172,0.3578304,0.00034810434,0.00022727642,0.0010016618,0.017052898,0.0015853028,0.0048764544],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99499243,0.0012873407,0.000628116,0.0005831654,0.002314756,0.00019420049],"domain_scores_gemma":[0.9926615,0.0033332568,0.0006455069,0.00083903177,0.0022445542,0.00027617376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0038077,0.0018725314,0.0015370022,0.00635851,0.00068324123,0.0019385279,0.0015017599,0.0018248248,0.0045653186],"category_scores_gemma":[0.01899596,0.00023609822,0.0010648724,0.0040817456,0.00085964444,0.003090767,0.001856428,0.00096928177,0.0020558957],"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":[0.001530589,0.00043048288,0.014543998,0.002364534,0.0009588961,0.00039658128,0.00048657533,0.043600626,0.050388016,0.008069257,0.046026763,0.83120364],"study_design_scores_gemma":[0.00029059665,0.0027782347,0.029394167,0.00029588258,0.00043911,0.002225346,0.0007412342,0.82447,0.084423475,0.019324178,0.035358246,0.00025951248],"about_ca_topic_score_codex":0.002129144,"about_ca_topic_score_gemma":0.0032706656,"teacher_disagreement_score":0.00635851,"about_ca_system_score_codex":0.0010129344,"about_ca_system_score_gemma":0.0009074157,"threshold_uncertainty_score":0.02013725},"labels":[],"label_agreement":null},{"id":"W4407825966","doi":"10.1109/tai.2025.3544590","title":"SecureLLAMA: Secure FPGAs Using LLAMA Large Language Models","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Physical Unclonable Functions (PUFs) and Hardware Security","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":"Ontario Tech University","funders":"","keywords":"Field-programmable gate array; Computer science; Programming language; Parallel computing; Computer architecture; Embedded system","score_opus":0.03582729545104406,"score_gpt":0.2982673236581781,"score_spread":0.262440028207134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407825966","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.058105413,0.0012076317,0.8449928,0.0014541614,0.00019810561,0.0006095661,0.014094222,0.06640626,0.012931859],"genre_scores_gemma":[0.44017968,0.00097562844,0.5187366,0.0006870517,0.000077927296,0.0009532716,0.027081871,0.0039673788,0.0073406347],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902344,0.00029873202,0.000100870486,0.0001653343,0.0003301073,0.000081519705],"domain_scores_gemma":[0.99709296,0.0015320559,0.0002631322,0.0007835527,0.0002614715,0.00006678134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011865298,0.0009395656,0.0004016405,0.0010305047,0.0005718545,0.0021719022,0.0014058662,0.0010756985,0.006701657],"category_scores_gemma":[0.00740809,0.00057488837,0.0015040174,0.00057569327,0.00074875966,0.003979604,0.0015743596,0.0014929239,0.0023203073],"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":[0.0013487331,0.00037995446,0.014802559,0.0011321568,0.00029249248,0.0009015235,0.0006599311,0.51006985,0.012999691,0.1626189,0.07012372,0.22467045],"study_design_scores_gemma":[0.000073856885,0.00010689325,0.00046391456,0.00007787032,0.000028791887,0.00020264201,0.000052347004,0.91019326,0.006668769,0.042918358,0.039179314,0.000034001572],"about_ca_topic_score_codex":0.005898893,"about_ca_topic_score_gemma":0.012467153,"teacher_disagreement_score":0.006701657,"about_ca_system_score_codex":0.000927727,"about_ca_system_score_gemma":0.0018424507,"threshold_uncertainty_score":0.022419274},"labels":[],"label_agreement":null},{"id":"W4407948409","doi":"10.1109/tai.2025.3544591","title":"Enhancement of Robot Dynamics Learning by Integrating Analytical Models into Deep Neural Networks: A Data Fusion Perspective","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Neural Networks and Applications","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perspective (graphical); Deep learning; Artificial intelligence; Artificial neural network; Computer science; Sensor fusion; Fusion; Dynamics (music); Machine learning; Psychology","score_opus":0.04404852999620597,"score_gpt":0.3245595524312559,"score_spread":0.28051102243504994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407948409","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01788354,0.00069548475,0.97865456,0.00036137752,0.00005180852,0.000021480027,0.00007941015,0.00063345267,0.0016188005],"genre_scores_gemma":[0.75415623,0.0015318843,0.24066833,0.00019513936,0.00008717291,0.00009847021,0.00040444385,0.00008406682,0.002774208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979955,0.000033688968,0.000016623339,0.00004893682,0.00007997548,0.00002125223],"domain_scores_gemma":[0.9995912,0.0001424947,0.00006210166,0.000057161807,0.00013008509,0.000017019196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079534145,0.0008342278,0.0005164544,0.0005695883,0.00016108558,0.00061172846,0.0006841789,0.00066385075,0.0009706375],"category_scores_gemma":[0.0017215795,0.0003953725,0.0005359077,0.00047231046,0.00033294878,0.0011976355,0.0007806754,0.001022981,0.00029730183],"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":[0.00006265764,0.00007551398,0.0011727873,0.00015312938,0.00006838599,0.00007819248,0.000071345006,0.76439035,0.015712945,0.006225087,0.0008310955,0.21115856],"study_design_scores_gemma":[0.000001629941,0.00001793497,0.00019346636,0.0000070885662,0.0000059019017,0.000011516977,0.000006013114,0.9947606,0.0022563527,0.0021228578,0.00061163935,0.0000049911882],"about_ca_topic_score_codex":0.0030434697,"about_ca_topic_score_gemma":0.0036266511,"teacher_disagreement_score":0.0030434697,"about_ca_system_score_codex":0.00049942173,"about_ca_system_score_gemma":0.0007270669,"threshold_uncertainty_score":0.0060515404},"labels":[],"label_agreement":null},{"id":"W4408099475","doi":"10.1109/tai.2025.3527806","title":"Guest Editorial: Operationalizing Responsible AI","year":2025,"lang":"en","type":"editorial","venue":"IEEE Transactions on Artificial Intelligence","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","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":"Polytechnique Montréal","funders":"","keywords":"Operationalization; Political science; Psychology; Philosophy; Epistemology","score_opus":0.06067420846163491,"score_gpt":0.41459446612175516,"score_spread":0.35392025766012025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408099475","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":[0.000017073246,0.003757748,0.00015658172,0.046349984,0.9478985,0.000013168344,0.00004153175,0.000023969273,0.0017414138],"genre_scores_gemma":[0.00037828332,0.0021875598,0.00012073163,0.018820714,0.9716376,0.000022050406,0.000022082182,0.000034365617,0.006776657],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.98832256,0.0030046902,0.0012921853,0.0011289865,0.0056269187,0.0006246196],"domain_scores_gemma":[0.9505042,0.022828832,0.0024483658,0.001242685,0.018093659,0.004882358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015916316,0.0040364573,0.004250236,0.0065158005,0.005034245,0.014041964,0.004434677,0.02688078,0.017838383],"category_scores_gemma":[0.058749374,0.0013590921,0.0025383364,0.0025747973,0.005739118,0.006358116,0.0028569049,0.02527351,0.010308771],"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":[0.000015147669,0.0000060151415,0.000011783591,0.0000833485,0.00001108705,0.000048727085,0.000009832179,0.00001551113,0.000015683723,0.0006400146,0.99712414,0.0020187006],"study_design_scores_gemma":[0.000065735505,0.00001610669,0.00016880994,0.0005218013,0.00006001484,0.00014244809,0.000055691933,0.00021969125,0.00007460134,0.0042191623,0.99443096,0.000024971307],"about_ca_topic_score_codex":0.002972957,"about_ca_topic_score_gemma":0.009622422,"teacher_disagreement_score":0.02688078,"about_ca_system_score_codex":0.0059392224,"about_ca_system_score_gemma":0.005935628,"threshold_uncertainty_score":0.084174514},"labels":[],"label_agreement":null},{"id":"W4409048322","doi":"10.1109/tai.2025.3556375","title":"TSTNet: Temporal Semantic Transformer-Based Computing Power Network for Automatic Driving in the Internet of Vehicles","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Graph Theory and Algorithms","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":"Computer science; Transformer; The Internet; Power network; Computer network; Real-time computing; World Wide Web; Electrical engineering; Power (physics); Engineering; Electric power system","score_opus":0.026363905753761765,"score_gpt":0.2882913611853399,"score_spread":0.26192745543157814,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409048322","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074486196,0.000796778,0.89478344,0.00058552553,0.00037370296,0.00020873135,0.00046408683,0.007793788,0.020507779],"genre_scores_gemma":[0.8471102,0.00041843407,0.14370367,0.00032495148,0.000057203924,0.00015546214,0.0013090733,0.0002002794,0.006720774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998286,0.000024249579,0.000011259219,0.000039624447,0.000060459486,0.000035836092],"domain_scores_gemma":[0.9998073,0.00003481806,0.000013636119,0.000035016626,0.0000827536,0.00002658625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002340905,0.00036109384,0.00023247482,0.00043544456,0.0004068311,0.00059094833,0.0012066334,0.000278341,0.0033465105],"category_scores_gemma":[0.0006097273,0.0001219182,0.00020311667,0.0004628656,0.0003214972,0.0013152123,0.0009276037,0.00053056085,0.00068703253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001141419,0.000463618,0.0036176972,0.00032927273,0.000118911485,0.00049829634,0.00034603977,0.15681423,0.06682154,0.072208464,0.06975338,0.6278872],"study_design_scores_gemma":[0.000035990353,0.00012792244,0.0005077494,0.000014473017,0.00003240715,0.00012478883,0.00007594435,0.9457109,0.015889416,0.017648239,0.019802263,0.00002994646],"about_ca_topic_score_codex":0.0043405765,"about_ca_topic_score_gemma":0.00855126,"teacher_disagreement_score":0.0043405765,"about_ca_system_score_codex":0.00061730476,"about_ca_system_score_gemma":0.00086293294,"threshold_uncertainty_score":0.011195183},"labels":[],"label_agreement":null},{"id":"W4409098630","doi":"10.1109/tai.2025.3556983","title":"iLeAD: An EMG-Based Adaptive Shared Control Framework for Exoskeleton Assistance via Deep Reinforcement Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Stroke Rehabilitation and Recovery","field":"Medicine","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Exoskeleton; Reinforcement learning; Reinforcement; Computer science; Control (management); Artificial intelligence; Engineering; Simulation; Structural engineering","score_opus":0.038321279111332276,"score_gpt":0.3313001407627515,"score_spread":0.29297886165141923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409098630","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004771239,0.00017596451,0.992576,0.0000902111,0.000037370228,0.000026991134,0.000032594864,0.0009357249,0.0013540017],"genre_scores_gemma":[0.77885544,0.0002727938,0.21362032,0.00022056844,0.000053641295,0.00027024173,0.0001496086,0.00016904088,0.0063883187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998134,0.000042768308,0.00001038406,0.00005151026,0.000054036478,0.000028029586],"domain_scores_gemma":[0.9998385,0.00006013347,0.00002336715,0.000020819894,0.000036270772,0.000020907999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056608743,0.0006237235,0.00052601635,0.00017859627,0.0001921738,0.00050339964,0.0012593,0.0006530175,0.002263637],"category_scores_gemma":[0.00091118045,0.0002936255,0.00042286768,0.00013740468,0.0004764189,0.000466796,0.0010971052,0.0010714409,0.00039877245],"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":[0.00010815052,0.000099675875,0.00055528135,0.00009181986,0.00006593113,0.00013287956,0.000079073616,0.86326,0.009053398,0.007957449,0.0019065993,0.11668981],"study_design_scores_gemma":[0.0000072719686,0.00003076476,0.000054533426,0.000004674316,0.000004030212,0.000012991496,0.0000029191322,0.99681073,0.00066115335,0.0016717643,0.0007351408,0.000003988961],"about_ca_topic_score_codex":0.0035774715,"about_ca_topic_score_gemma":0.005033992,"teacher_disagreement_score":0.0035774715,"about_ca_system_score_codex":0.0003754225,"about_ca_system_score_gemma":0.0008047417,"threshold_uncertainty_score":0.0075725913},"labels":[],"label_agreement":null},{"id":"W4409098678","doi":"10.1109/tai.2025.3556990","title":"A Cervical Cell Classification Framework Based on Multiview Supervised Contrastive Learning","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Supervised learning; Natural language processing; Artificial neural network","score_opus":0.04593820595481749,"score_gpt":0.30938432836576346,"score_spread":0.26344612241094595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409098678","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027127698,0.00048245827,0.9694165,0.00033611918,0.000055223296,0.00008220945,0.00025600538,0.0008696901,0.0013741887],"genre_scores_gemma":[0.60960984,0.0007531639,0.37735486,0.0007989799,0.0002580321,0.00027298415,0.0021388705,0.000217935,0.008595309],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959904,0.00004830311,0.000016736594,0.00013458259,0.00013783839,0.000063530795],"domain_scores_gemma":[0.9996427,0.00007463052,0.000057359546,0.000052547904,0.0001301289,0.000042661784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008064662,0.0007545412,0.00093678606,0.0012493744,0.00032104025,0.0008036209,0.0021521316,0.0011525962,0.0014312494],"category_scores_gemma":[0.0011560268,0.00029406985,0.0011654178,0.00057938776,0.00055702834,0.0008760302,0.0011087813,0.0011385601,0.00059047935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003270249,0.00033606504,0.007153768,0.00017362052,0.00017584594,0.00028601062,0.00013178783,0.3796492,0.04469008,0.012641209,0.008151613,0.5462838],"study_design_scores_gemma":[0.0000074015475,0.000040789266,0.00034326382,0.000005997913,0.000015323585,0.00006993934,0.000007198374,0.99282795,0.004011087,0.0019683475,0.0006949273,0.00000767215],"about_ca_topic_score_codex":0.006203176,"about_ca_topic_score_gemma":0.006618991,"teacher_disagreement_score":0.006203176,"about_ca_system_score_codex":0.0009510249,"about_ca_system_score_gemma":0.0010670668,"threshold_uncertainty_score":0.012334108},"labels":[],"label_agreement":null},{"id":"W4409882931","doi":"10.1109/tai.2025.3564900","title":"Prescribed Performance Resilient Motion Coordination With Actor–Critic Reinforcement Learning Design for UAV-USV Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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":"","keywords":"Reinforcement learning; Motion (physics); Reinforcement; Computer science; Aeronautics; Engineering; Artificial intelligence; Structural engineering","score_opus":0.04943525313310071,"score_gpt":0.2841132150108918,"score_spread":0.2346779618777911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409882931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018029133,0.00045314748,0.9759424,0.00020032405,0.00007300106,0.000072225885,0.000020359856,0.00017595093,0.005033479],"genre_scores_gemma":[0.9675968,0.00027080043,0.029121071,0.00007239614,0.000036883914,0.00021884331,0.000033133107,0.000028505812,0.0026214572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993793,0.00017899649,0.000031535004,0.00014532132,0.00017541555,0.00008943267],"domain_scores_gemma":[0.9991345,0.00033359884,0.00018556135,0.00004288936,0.00025133498,0.00005222336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013764093,0.0014281316,0.0010725423,0.00038135223,0.00038576467,0.001081295,0.0012064988,0.0012214449,0.0014906299],"category_scores_gemma":[0.0017300384,0.0004823318,0.0006164452,0.00027520276,0.0011606822,0.0004894415,0.001308812,0.0010721975,0.0002503853],"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":[0.00003214551,0.000016580123,0.00018766386,0.00005720745,0.000022202508,0.000078555444,0.00004849451,0.9890315,0.0015362181,0.0039014437,0.0001896749,0.004898293],"study_design_scores_gemma":[0.000005225217,0.000030774954,0.000024904451,0.0000033713447,0.0000036931335,0.0000040516675,0.0000036757403,0.9992411,0.00014950738,0.00039111063,0.00014046353,0.0000020590621],"about_ca_topic_score_codex":0.004696604,"about_ca_topic_score_gemma":0.0028386265,"teacher_disagreement_score":0.004696604,"about_ca_system_score_codex":0.00091497716,"about_ca_system_score_gemma":0.0011027182,"threshold_uncertainty_score":0.009338558},"labels":[],"label_agreement":null},{"id":"W4410393925","doi":"10.1109/tai.2025.3567434","title":"Deep3BPP: Identification of Blood–Brain Barrier Penetrating Peptides Using Word Embedding Feature Extraction Method and CNN-LSTM","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Identification (biology); Computer science; Artificial intelligence; Word embedding; Feature extraction; Extraction (chemistry); Feature (linguistics); Embedding; Pattern recognition (psychology); Chemistry; Chromatography; Biology; Philosophy; Botany","score_opus":0.0210132262035905,"score_gpt":0.358332301106191,"score_spread":0.3373190749026005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410393925","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.122390695,0.0021441397,0.8576118,0.0005907765,0.00038143434,0.0002035904,0.0020037629,0.010588909,0.0040848805],"genre_scores_gemma":[0.63493776,0.0015664769,0.3428147,0.0005664161,0.000115016875,0.00032676884,0.0061213304,0.00035845986,0.01319297],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982435,0.00001761287,0.000012220667,0.00006253316,0.00004571412,0.00003749487],"domain_scores_gemma":[0.9998312,0.000048152917,0.000027737417,0.000016131331,0.00006235624,0.000014406789],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002977381,0.0013907672,0.00060472375,0.00059708406,0.00022627381,0.0005364011,0.0008333899,0.0008121491,0.0030879593],"category_scores_gemma":[0.00065254903,0.0003674382,0.00069879653,0.0005786551,0.00020848255,0.0015015776,0.00072985276,0.0010156251,0.0017312898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044450146,0.00029171127,0.0029982424,0.00041639557,0.00016811889,0.00048565972,0.000105501706,0.054121368,0.12019529,0.0021864276,0.013712281,0.8048745],"study_design_scores_gemma":[0.00002292335,0.00015710764,0.001209593,0.000019084928,0.00003588557,0.00017933239,0.000029275041,0.9629616,0.03054994,0.0023987272,0.0024146766,0.000021724927],"about_ca_topic_score_codex":0.004303287,"about_ca_topic_score_gemma":0.0051465672,"teacher_disagreement_score":0.004303287,"about_ca_system_score_codex":0.0004829715,"about_ca_system_score_gemma":0.0008036209,"threshold_uncertainty_score":0.01033026},"labels":[],"label_agreement":null},{"id":"W4410427524","doi":"10.1109/tai.2025.3570676","title":"Leveraging Long-Term Multivariate History Representation for Time Series Forecasting","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Time Series Analysis and Forecasting","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":"Mitacs","keywords":"Term (time); Multivariate statistics; Series (stratigraphy); Representation (politics); Computer science; Time series; Econometrics; Artificial intelligence; Machine learning; Mathematics; Political science; Geology","score_opus":0.08926465082639921,"score_gpt":0.30374780910079024,"score_spread":0.21448315827439102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410427524","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10114056,0.0019459173,0.8907994,0.000513585,0.00019582124,0.00003986447,0.0008597605,0.001938218,0.002566947],"genre_scores_gemma":[0.9092132,0.001333866,0.08387978,0.00019981628,0.00016029709,0.000057677196,0.0020069305,0.00012696614,0.0030214493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998373,0.00002521738,0.000012981859,0.00005236042,0.000046396723,0.000025747751],"domain_scores_gemma":[0.9996748,0.00013587238,0.00004601194,0.00004704445,0.00007629459,0.000020077583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004344,0.0007928915,0.000523166,0.00071906106,0.00020471915,0.0005270889,0.00077824766,0.0005237565,0.0011730583],"category_scores_gemma":[0.0017865425,0.00030616776,0.0005574522,0.0010679979,0.00022054411,0.0012832193,0.00053473643,0.0010252929,0.00042865385],"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":[0.00019515809,0.00009684424,0.006344778,0.00011980676,0.00012513372,0.0002083757,0.00012653682,0.5755963,0.010974472,0.007027491,0.004966013,0.39421922],"study_design_scores_gemma":[0.0000027555827,0.000012575108,0.0005821693,0.000005946516,0.000011900753,0.000013365404,0.00000689483,0.9962746,0.00080875197,0.0017698552,0.0005048078,0.000006436214],"about_ca_topic_score_codex":0.010820194,"about_ca_topic_score_gemma":0.014098892,"teacher_disagreement_score":0.010820194,"about_ca_system_score_codex":0.00043513998,"about_ca_system_score_gemma":0.0005630165,"threshold_uncertainty_score":0.021514416},"labels":[],"label_agreement":null},{"id":"W4410427734","doi":"10.1109/tai.2025.3570665","title":"Ranking Time-Frequency Contrastive Learning for Multivariate Time Series Classification","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Time Series Analysis and Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"Fundamental Research Funds for the Central Universities","keywords":"Multivariate statistics; Ranking (information retrieval); Series (stratigraphy); Computer science; Artificial intelligence; Multivariate analysis; Time series; Statistics; Machine learning; Pattern recognition (psychology); Mathematics; Geology","score_opus":0.03505252385596043,"score_gpt":0.28561051993643466,"score_spread":0.25055799608047424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410427734","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038439266,0.00057247083,0.95806724,0.00023514216,0.00008224799,0.00007672405,0.0001027364,0.0010069949,0.0014171547],"genre_scores_gemma":[0.68007714,0.00046887738,0.31415945,0.0003467313,0.00022441952,0.0002116615,0.00072334567,0.00020145922,0.0035869305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986588,0.00041873948,0.00007815556,0.00033291793,0.0003959577,0.00011552703],"domain_scores_gemma":[0.9963115,0.0017561369,0.00039158377,0.00055255246,0.0008256415,0.0001625724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033264314,0.0012173008,0.001165112,0.001586385,0.00057325396,0.0013246457,0.0018212126,0.0012655008,0.0028749506],"category_scores_gemma":[0.008479645,0.000299047,0.0009296705,0.0014473494,0.0008165945,0.0018740223,0.0016372497,0.0023392965,0.001459196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004395232,0.00060627045,0.00508648,0.00020109398,0.00021198698,0.0001650706,0.00015485371,0.14261416,0.025633432,0.011980973,0.0050239637,0.80788213],"study_design_scores_gemma":[0.000011437297,0.00013018507,0.00082889886,0.000011383987,0.000023951996,0.000049514103,0.000021291851,0.98763627,0.004439821,0.006030629,0.00079910876,0.000017524993],"about_ca_topic_score_codex":0.0014315534,"about_ca_topic_score_gemma":0.0025117656,"teacher_disagreement_score":0.0033264314,"about_ca_system_score_codex":0.00068004057,"about_ca_system_score_gemma":0.0008652345,"threshold_uncertainty_score":0.017592072},"labels":[],"label_agreement":null},{"id":"W4410582155","doi":"10.1109/tai.2025.3569517","title":"Unsupervised Domain Adaptation With Source Data for Estimating Occupancy and Recognizing Activities in Smart Buildings","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Context-Aware Activity Recognition Systems","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":"Occupancy; Domain adaptation; Adaptation (eye); Computer science; Domain (mathematical analysis); Pattern recognition (psychology); Artificial intelligence; Engineering; Mathematics; Psychology; Architectural engineering","score_opus":0.09931836748195774,"score_gpt":0.31703966070077155,"score_spread":0.2177212932188138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410582155","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039290715,0.00032821717,0.9582244,0.000103392085,0.00006940886,0.000041651056,0.0001732162,0.0007788161,0.0009902585],"genre_scores_gemma":[0.7062182,0.00036975468,0.28813475,0.00022233276,0.00012882735,0.00016700733,0.001536712,0.00017338527,0.0030490528],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944645,0.00015252341,0.000020537946,0.00020765113,0.000110656154,0.0000622191],"domain_scores_gemma":[0.9991738,0.00036393057,0.000076960496,0.0001752073,0.0001623617,0.000047743317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067624333,0.0008350099,0.00068984093,0.00065529905,0.000249238,0.0005476562,0.0011447939,0.0006103433,0.0007480508],"category_scores_gemma":[0.0026270424,0.0003278305,0.0008854007,0.000655363,0.0006349494,0.0010696398,0.0011412138,0.0015122376,0.00054564915],"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":[0.00022698964,0.00022175428,0.009361619,0.00014259436,0.00014941994,0.00018931081,0.0002257914,0.5789722,0.019325964,0.006087195,0.003940038,0.38115707],"study_design_scores_gemma":[0.0000045900506,0.0000234995,0.0013331929,0.000007955917,0.0000082362085,0.000049809205,0.000032463064,0.9906086,0.004016619,0.002738574,0.0011637857,0.000012768526],"about_ca_topic_score_codex":0.0030048057,"about_ca_topic_score_gemma":0.0036805207,"teacher_disagreement_score":0.0030048057,"about_ca_system_score_codex":0.00038087158,"about_ca_system_score_gemma":0.00058567413,"threshold_uncertainty_score":0.0059746504},"labels":[],"label_agreement":null},{"id":"W4411019390","doi":"10.1109/tai.2025.3576201","title":"pFedBL: Federated Bayesian Learning With Personalized Prior","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","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 British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Bayesian probability; Federated learning; Artificial intelligence; Machine learning","score_opus":0.03914861593838184,"score_gpt":0.30016257062921936,"score_spread":0.26101395469083755,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411019390","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033778078,0.00024041106,0.9929128,0.00041378973,0.000023873688,0.000076689685,0.00018002452,0.0016497466,0.0011248401],"genre_scores_gemma":[0.40809786,0.0007476879,0.57979566,0.0015988613,0.00014739834,0.00072316447,0.0015878627,0.0005603631,0.0067411256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951303,0.0015493452,0.0002480674,0.0010927786,0.0014463525,0.0005332436],"domain_scores_gemma":[0.99393964,0.002810903,0.0003870463,0.0011902882,0.001274122,0.0003979851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075200275,0.0015438308,0.0026451412,0.0012385466,0.001156024,0.0031582818,0.0062094876,0.0033148366,0.0064418856],"category_scores_gemma":[0.019692963,0.0012753359,0.0012719568,0.001751271,0.0017956704,0.0067872317,0.006411362,0.0047619427,0.0023647028],"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":[0.00042275377,0.00043362455,0.002150992,0.00020948845,0.00010792101,0.00022179335,0.00025363217,0.6903482,0.0017847935,0.071214125,0.012927213,0.21992545],"study_design_scores_gemma":[0.000023994931,0.00002087539,0.000084339656,0.000014971261,0.0000065012,0.000036349764,0.000015134221,0.96674144,0.00052663445,0.031385414,0.0011321838,0.0000121149305],"about_ca_topic_score_codex":0.012551656,"about_ca_topic_score_gemma":0.016027689,"teacher_disagreement_score":0.012551656,"about_ca_system_score_codex":0.0035845647,"about_ca_system_score_gemma":0.0049135424,"threshold_uncertainty_score":0.039770246},"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":"W4411639781","doi":"10.1109/tai.2025.3582067","title":"Retraction Notice: Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Notice; Intrusion detection system; Computer science; Health care; Artificial intelligence; Political science","score_opus":0.0467453150505133,"score_gpt":0.3036530900805404,"score_spread":0.25690777503002715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411639781","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":[0.00035710438,0.0020742582,0.0018379416,0.21779147,0.7753263,0.000044726916,0.0004612393,0.00039639554,0.0017105811],"genre_scores_gemma":[0.016112922,0.0068561155,0.0063721016,0.18584618,0.6647233,0.00030948973,0.0009454259,0.00077567954,0.118058816],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9935514,0.0010792891,0.0010870675,0.0008093464,0.0029052051,0.0005676776],"domain_scores_gemma":[0.95317835,0.0194609,0.0018756292,0.0021188867,0.021251626,0.0021145195],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.007318655,0.0022647907,0.0020845889,0.0017655081,0.0023913463,0.0036465342,0.0040138355,0.015775148,0.016822629],"category_scores_gemma":[0.08434854,0.00080704165,0.0016801712,0.0013947801,0.0033626487,0.0025089132,0.0017545403,0.022109177,0.012492399],"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":[0.000031572927,0.000010887478,0.00004171814,0.00007389144,0.000013059493,0.00009936289,0.00002753375,0.000046520647,0.00012721415,0.0010477176,0.9929386,0.0055418983],"study_design_scores_gemma":[0.00009036893,0.000094338764,0.0013407603,0.00024815946,0.00006712255,0.00052774756,0.00006955095,0.0014977937,0.001073071,0.0030935502,0.9918275,0.000069995986],"about_ca_topic_score_codex":0.008596731,"about_ca_topic_score_gemma":0.008250104,"teacher_disagreement_score":0.98422486,"about_ca_system_score_codex":0.004035631,"about_ca_system_score_gemma":0.006314104,"threshold_uncertainty_score":0.056277335},"labels":[{"model":"gemma","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"gpt","categories":["research_integrity"],"domain":null,"study_design":"not_applicable","genre":"editorial","about_ca_system":false,"about_ca_topic":false,"confidence":"high"}],"label_agreement":"agree"},{"id":"W4413847135","doi":"10.1109/tai.2025.3603547","title":"Online Safety Analysis for LLMs: A Benchmark, an Assessment, and a Path Forward","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Digital Rights Management and Security","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":"Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Benchmark (surveying); Path (computing); Path analysis (statistics); Computer science; Risk analysis (engineering); Business; Geography; Machine learning; Computer network; Cartography","score_opus":0.04363054649827682,"score_gpt":0.35130561222885176,"score_spread":0.30767506573057496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413847135","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16822232,0.012407756,0.71269226,0.011104567,0.0014215031,0.0014904131,0.007068321,0.07440076,0.011192148],"genre_scores_gemma":[0.38055986,0.0027334993,0.5858439,0.0017807705,0.00026525484,0.0010172748,0.01888646,0.006254885,0.002658117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9722011,0.011808475,0.0018692944,0.0030016275,0.01021967,0.0008998912],"domain_scores_gemma":[0.90281445,0.054808535,0.0042429706,0.022557495,0.013946172,0.0016303124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.022693817,0.00327206,0.001245033,0.005103182,0.0012111611,0.004103936,0.005432634,0.0029579732,0.0036116056],"category_scores_gemma":[0.0931398,0.0008669482,0.002175464,0.0019923693,0.0025230965,0.0074504805,0.0059985016,0.0046356856,0.0018807093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019327534,0.0020716076,0.023062598,0.004902824,0.0007215538,0.0009573769,0.0016351453,0.2119838,0.02967329,0.03218723,0.08028225,0.61058956],"study_design_scores_gemma":[0.0004191469,0.0015560696,0.005818815,0.0011658525,0.00021595533,0.0007262318,0.0013973296,0.85597235,0.036337588,0.050754186,0.04543318,0.00020334932],"about_ca_topic_score_codex":0.0066591934,"about_ca_topic_score_gemma":0.007700219,"teacher_disagreement_score":0.022693817,"about_ca_system_score_codex":0.0028553065,"about_ca_system_score_gemma":0.005312631,"threshold_uncertainty_score":0.12001777},"labels":[],"label_agreement":null},{"id":"W4414321910","doi":"10.1109/tai.2025.3610590","title":"Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Reinforcement Learning in Robotics","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 New Brunswick; University of Windsor; Toronto Metropolitan University","funders":"","keywords":"Generalization; Inverse; Transfer (computing); Stability (learning theory); Calculus (dental)","score_opus":0.03715181315305159,"score_gpt":0.2995708591001717,"score_spread":0.2624190459471201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414321910","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":[0.008214992,0.6322766,0.3478768,0.0025707977,0.00029192734,0.000078818084,0.0001301218,0.00024178415,0.008318222],"genre_scores_gemma":[0.26542985,0.57305926,0.15284963,0.0011683557,0.0028127327,0.00027851394,0.0005344722,0.00023028911,0.003636796],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99777156,0.00058785285,0.00025890663,0.00064360414,0.0006454478,0.000092480244],"domain_scores_gemma":[0.98248416,0.014509058,0.00048690106,0.000805641,0.00159855,0.00011563304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005758259,0.0013862223,0.002589431,0.0018439534,0.00031793438,0.0026700215,0.0024073047,0.0017733265,0.0023332015],"category_scores_gemma":[0.024900198,0.0007147221,0.0011211423,0.0028469341,0.0016526041,0.00540449,0.001853953,0.002269119,0.000758059],"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":[0.0001264283,0.00015011443,0.001618788,0.0050015887,0.00026172033,0.00006209358,0.00013608791,0.04911924,0.0006780318,0.065176554,0.0040878444,0.87358147],"study_design_scores_gemma":[0.00009003968,0.00068744057,0.0065338057,0.004544764,0.0007240117,0.00092462194,0.00040565222,0.47715858,0.0038709966,0.42417642,0.080719985,0.00016364036],"about_ca_topic_score_codex":0.0027534293,"about_ca_topic_score_gemma":0.0017413774,"teacher_disagreement_score":0.005758259,"about_ca_system_score_codex":0.0013649052,"about_ca_system_score_gemma":0.0021199833,"threshold_uncertainty_score":0.030452907},"labels":[],"label_agreement":null},{"id":"W4414348218","doi":"10.1109/tai.2025.3609733","title":"Towards Vox Populi in Federated Learning: A Fair and Inclusive Client Selection Framework","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Privacy-Preserving Technologies in Data","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":"Polytechnique Montréal","funders":"","keywords":"Selection (genetic algorithm); Government (linguistics); The Internet; Interoperability; Key (lock)","score_opus":0.03421824515450956,"score_gpt":0.3255538070190496,"score_spread":0.29133556186454007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414348218","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066133435,0.00021445817,0.988008,0.0007234692,0.000057027493,0.00011557968,0.000050551804,0.0013692683,0.0028483362],"genre_scores_gemma":[0.41455483,0.00032053492,0.57015604,0.0008770621,0.00015885224,0.00032067005,0.00026263864,0.00071615505,0.012633214],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9891228,0.0052042035,0.00041123436,0.001301602,0.0028958465,0.0010643568],"domain_scores_gemma":[0.99082667,0.0033389346,0.0003222013,0.0038408295,0.0008998983,0.0007714699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015530884,0.0007251443,0.0016611479,0.0016251333,0.0023871525,0.008523799,0.0058541885,0.0036308789,0.0048219035],"category_scores_gemma":[0.020139288,0.00080110977,0.0011815875,0.00171148,0.004094007,0.010913928,0.014196963,0.005376149,0.001705146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004571567,0.0002793239,0.0011843198,0.000117753836,0.00009264905,0.00022576899,0.0006741692,0.061101686,0.002404353,0.8136162,0.0069982945,0.112848215],"study_design_scores_gemma":[0.000043081745,0.00005392176,0.00011926909,0.000056311066,0.00003349228,0.00012944438,0.00022953495,0.4671706,0.004037593,0.5157347,0.012356898,0.00003524505],"about_ca_topic_score_codex":0.002415126,"about_ca_topic_score_gemma":0.0032127255,"teacher_disagreement_score":0.015530884,"about_ca_system_score_codex":0.0022476024,"about_ca_system_score_gemma":0.004180979,"threshold_uncertainty_score":0.082136154},"labels":[],"label_agreement":null},{"id":"W4415706964","doi":"10.1109/tai.2025.3627517","title":"MaxDiv: Zero-Shot Machine Unlearning via Distributionally Divergent Erasing Samples","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Domain Adaptation and Few-Shot Learning","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":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Forgetting; Order (exchange); Training set; Machine translation; Empirical research","score_opus":0.07158567658853181,"score_gpt":0.31499383529742236,"score_spread":0.24340815870889054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415706964","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01808113,0.00038929403,0.9781296,0.00022766928,0.000053402116,0.000066097375,0.00011863686,0.0019041657,0.0010301169],"genre_scores_gemma":[0.515983,0.0003464844,0.47624487,0.0008373572,0.0001347479,0.00029489564,0.001163406,0.0005150508,0.0044802018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817884,0.0007536211,0.00009137571,0.0004886304,0.00034863685,0.00013884132],"domain_scores_gemma":[0.99555606,0.0025505123,0.00020974097,0.0012463862,0.0002850628,0.00015227421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033416091,0.0013282563,0.001662617,0.00096519117,0.00078419055,0.0013003758,0.0036814387,0.0017699323,0.0026233878],"category_scores_gemma":[0.01141658,0.0006087767,0.000855209,0.0008837857,0.0024208205,0.0047493116,0.005012431,0.0027935212,0.00082957617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083386543,0.0003118621,0.0017441228,0.00032228464,0.0001789574,0.0002740437,0.00036614,0.24145813,0.011890512,0.053046837,0.0071796305,0.68239355],"study_design_scores_gemma":[0.00003306957,0.0001414774,0.00014334344,0.000019447383,0.000014099835,0.00012337796,0.000033522847,0.94139636,0.008461242,0.048166547,0.0014471881,0.000020281544],"about_ca_topic_score_codex":0.0010389581,"about_ca_topic_score_gemma":0.0016554231,"teacher_disagreement_score":0.0036814387,"about_ca_system_score_codex":0.0007815302,"about_ca_system_score_gemma":0.0012921368,"threshold_uncertainty_score":0.01767236},"labels":[],"label_agreement":null},{"id":"W4416582631","doi":"10.1109/tai.2025.3635093","title":"Visual Safety Mapping for UAV Landings Using Ordinal Regression Networks","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Robotics and Sensor-Based Localization","field":"Engineering","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":"National Research Council Canada; Université du Québec en Outaouais","funders":"","keywords":"Generalization; Software deployment; Range (aeronautics); Terrain; Inference; Pixel; Code (set theory)","score_opus":0.05271422195112541,"score_gpt":0.3185727156665666,"score_spread":0.2658584937154412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416582631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15033199,0.0010541356,0.8344262,0.000740423,0.00018113932,0.000103047794,0.001902923,0.006456,0.004804115],"genre_scores_gemma":[0.84910476,0.0003907252,0.14036407,0.0002146888,0.00008855736,0.000103977734,0.00417291,0.00023316765,0.0053271893],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964464,0.00008037617,0.000015045711,0.000120975325,0.00006856572,0.000070445334],"domain_scores_gemma":[0.9994684,0.00018564177,0.000088486006,0.00006929046,0.0001508549,0.000037306883],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060095754,0.0012556805,0.0006674089,0.001100786,0.0003011855,0.0007836381,0.0015783814,0.0006315019,0.0022779505],"category_scores_gemma":[0.002135219,0.00040717446,0.00081308995,0.00084761623,0.00031099777,0.00098725,0.0009069636,0.0013224517,0.0010754747],"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":[0.00024231206,0.00017824066,0.006280612,0.00007953753,0.000090171074,0.00009881854,0.00006906348,0.7168753,0.0035355408,0.001410603,0.006335585,0.26480427],"study_design_scores_gemma":[0.000003917315,0.000010638693,0.00037362837,0.0000056252097,0.0000042752604,0.0000071771815,0.000010354418,0.9977983,0.0004623257,0.0010559679,0.00026466302,0.0000031827356],"about_ca_topic_score_codex":0.010951744,"about_ca_topic_score_gemma":0.013572737,"teacher_disagreement_score":0.010951744,"about_ca_system_score_codex":0.00087751925,"about_ca_system_score_gemma":0.0006096526,"threshold_uncertainty_score":0.02177602},"labels":[],"label_agreement":null},{"id":"W4417438495","doi":"10.1109/tai.2025.3644335","title":"Fully Perturbed Self-Ensemble Framework Using Cascaded Parallel CNN-Transformer for Semisupervised Medical Image Segmentation","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Advanced Neural Network Applications","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":"Segmentation; Exploit; Transformer; Image segmentation; Deep learning; Labeled data; Medical imaging; Pattern recognition (psychology)","score_opus":0.0519471167204318,"score_gpt":0.3542854030578109,"score_spread":0.30233828633737914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417438495","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023051694,0.00033395036,0.97377396,0.0001599204,0.000036932728,0.000044786735,0.00006440697,0.0012678978,0.0012664174],"genre_scores_gemma":[0.71795475,0.00037915417,0.27545196,0.00041985454,0.00009382943,0.0001422509,0.00058116706,0.0003094926,0.0046675582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994597,0.00011684976,0.000023665552,0.00018390272,0.00015095518,0.00006484804],"domain_scores_gemma":[0.9995129,0.00014301964,0.00006167599,0.00010670475,0.00013138421,0.00004425044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010309834,0.0010625058,0.00096110156,0.000493822,0.00038022638,0.0006230723,0.0020486542,0.0012161459,0.001399465],"category_scores_gemma":[0.0019039006,0.00056021265,0.0008494079,0.0004438918,0.00070428045,0.0017381551,0.0013388513,0.001181382,0.00045207512],"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":[0.00026777937,0.00011639656,0.0027556028,0.00012621371,0.00018862673,0.00028342524,0.0001835952,0.6763425,0.026527522,0.008750117,0.003971888,0.28048643],"study_design_scores_gemma":[0.0000033999374,0.000023841983,0.000103273014,0.0000021338526,0.000009509416,0.00004081974,0.0000053746135,0.9956375,0.0022996268,0.0015300106,0.0003400382,0.000004553],"about_ca_topic_score_codex":0.0045415633,"about_ca_topic_score_gemma":0.006078228,"teacher_disagreement_score":0.0045415633,"about_ca_system_score_codex":0.0007230747,"about_ca_system_score_gemma":0.0009554183,"threshold_uncertainty_score":0.0090302825},"labels":[],"label_agreement":null},{"id":"W7116441454","doi":"10.1109/tai.2025.3646194","title":"Harnessing Teacher’s Explanation for Improved Knowledge Distillation","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Explainable Artificial Intelligence (XAI)","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":"Distillation; Variety (cybernetics); Class (philosophy); Work (physics); Training set","score_opus":0.06975399240409494,"score_gpt":0.34683743239050235,"score_spread":0.2770834399864074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116441454","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.126905,0.0008806157,0.8606891,0.00127657,0.00008338711,0.00011940802,0.00077683426,0.006395359,0.002873762],"genre_scores_gemma":[0.7126598,0.00027271427,0.28151113,0.00034221399,0.000046788246,0.00013982425,0.0018811271,0.00020770683,0.0029386193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988103,0.00048597294,0.000068798276,0.00033793738,0.0002048878,0.00009204024],"domain_scores_gemma":[0.99498993,0.0028295342,0.00023942246,0.0014184932,0.000362537,0.00016001199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018457806,0.0011313624,0.0008774929,0.0008493674,0.00045406655,0.0011803331,0.0019095052,0.0013634196,0.0028279086],"category_scores_gemma":[0.009815061,0.00044107967,0.0008644081,0.00085643464,0.0009902027,0.004115773,0.0036393704,0.0032624572,0.0010115139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006017811,0.00058375427,0.013154126,0.000674976,0.00019204093,0.00029133083,0.0010199972,0.21227658,0.01693437,0.034840766,0.010607815,0.7088224],"study_design_scores_gemma":[0.00007870788,0.00012227133,0.00081371085,0.00003836377,0.000041962885,0.00008768165,0.00011180332,0.9492084,0.011683591,0.032072578,0.0057125585,0.000028391883],"about_ca_topic_score_codex":0.0021606046,"about_ca_topic_score_gemma":0.004615731,"teacher_disagreement_score":0.0028279086,"about_ca_system_score_codex":0.00069700246,"about_ca_system_score_gemma":0.001494431,"threshold_uncertainty_score":0.009761512},"labels":[],"label_agreement":null}]}