{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":505,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":505,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"c1777bc12b53","filters":{"venue":"Expert Systems with Applications"}},"results":[{"id":"W4396766974","doi":"10.1016/j.eswa.2024.124167","title":"Artificial intelligence in education: A systematic literature review","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Online Learning and Analytics","field":"Computer Science","cited_by":681,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; University of Saskatchewan","funders":"","keywords":"Computer science; Systematic review; Artificial intelligence; Data science; MEDLINE","authors":[{"name":"Shan Wang","is_ca":true},{"name":"Fang Wang","is_ca":true},{"name":"Zhen Zhu","is_ca":false},{"name":"Jingxuan Wang","is_ca":false},{"name":"Tam Tran","is_ca":true},{"name":"Zhao Du","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01439528846662681,"gpt":0.3187294091367465,"spread":0.3043341206701197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008141275,0.001028803,0.003221989,0.025106,0.0009899475,0.003257818,0.001422421,0.00175718,0.005746521],"category_scores_gemma":[0.03328953,0.0007443466,0.002557038,0.02586463,0.001041123,0.003965787,0.002074146,0.001469086,0.0007067915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003014829,"about_ca_system_score_gemma":0.02097424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006308183,"about_ca_topic_score_gemma":0.02181578,"domain_scores_codex":[0.9951996,0.001521941,0.001693342,0.0003636669,0.001035099,0.0001863299],"domain_scores_gemma":[0.9621771,0.02948802,0.003462394,0.000444269,0.003918108,0.0005102742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00009334303,0.00006529994,0.001993252,0.737448,0.001368217,0.0003607581,0.001021624,0.0002319331,0.000245417,0.002009766,0.008304881,0.2468575],"study_design_scores_gemma":[0.00004249452,0.00009695826,0.004540022,0.8899293,0.005816453,0.0006762406,0.001434911,0.0001069431,0.0001489013,0.001131042,0.09603821,0.00003845733],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009911585,0.9966702,0.000280429,0.0007680431,0.0001282122,0.0001492686,0.0003249563,0.000007740626,0.0006800739],"genre_scores_gemma":[0.005245006,0.9929543,0.000801927,0.0004581473,0.00006453787,0.0001765263,0.0002049234,0.000003827126,0.00009084614],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.025106,"threshold_uncertainty_score":0.04305571,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4205471456","doi":"10.1016/j.eswa.2021.116429","title":"Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances","year":2021,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":544,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Alberta Oil Sands Technology and Research Authority; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Credit card fraud; Anomaly detection; Exploit; Computer science; Insider; Artificial intelligence; Business; Finance; Computer security; Credit card; Payment","authors":[{"name":"Waleed Hilal","is_ca":true},{"name":"S. Andrew Gadsden","is_ca":true},{"name":"John Yawney","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02676344441469306,"gpt":0.3305285859300074,"spread":0.3037651415153144,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001734217,0.001180651,0.001527705,0.006407836,0.0005020114,0.001863977,0.001713806,0.001549584,0.00323342],"category_scores_gemma":[0.00450435,0.0005300572,0.001022899,0.007395253,0.0008427688,0.003180168,0.0008439163,0.001760766,0.001972456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008831574,"about_ca_system_score_gemma":0.001955863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001914189,"about_ca_topic_score_gemma":0.002048791,"domain_scores_codex":[0.9990112,0.0001522796,0.0001503191,0.0001600224,0.000466805,0.00005936763],"domain_scores_gemma":[0.9958888,0.002634282,0.0003185525,0.0001036906,0.000940109,0.0001146103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004400338,0.00008802259,0.0006675661,0.01282529,0.00009453592,0.0001285915,0.0001050043,0.0007506951,0.0005004781,0.005551786,0.02496596,0.9542781],"study_design_scores_gemma":[0.00001537741,0.0001635133,0.002697349,0.01223484,0.0002840962,0.001791277,0.0002235558,0.001225789,0.0008157347,0.008585515,0.9718844,0.00007847069],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002945432,0.9956808,0.001410399,0.0006420445,0.0003030525,0.00001385818,0.00003978912,0.00003027485,0.001585161],"genre_scores_gemma":[0.001550433,0.9960038,0.001301924,0.0002402802,0.0004169469,0.00001149392,0.00006799899,0.000006019848,0.0004011118],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006407836,"threshold_uncertainty_score":0.01081693,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2021938316","doi":"10.1016/j.eswa.2011.04.222","title":"Forecasting stock indices with back propagation neural network","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":484,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education; Lanzhou University","keywords":"Artificial neural network; Stock (firearms); Computer science; Backpropagation; Stock market index; Stock price; Composite index; Econometrics; Index (typography); Stock market; Data mining; Artificial intelligence; Series (stratigraphy); Mathematics","authors":[{"name":"Jianzhou Wang","is_ca":false},{"name":"Jujie Wang","is_ca":false},{"name":"Zhe George Zhang","is_ca":true},{"name":"Shu-Po Guo","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.189853560597106,"gpt":0.3608030537518224,"spread":0.1709494931547164,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009728516,0.0007283429,0.0008533912,0.0009375727,0.0002190925,0.0008073431,0.0005871444,0.0008615156,0.0009011805],"category_scores_gemma":[0.003191705,0.0004306741,0.0004957065,0.000918054,0.0002428366,0.001152377,0.0002976637,0.001094734,0.0002849069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004380316,"about_ca_system_score_gemma":0.0004724663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01102965,"about_ca_topic_score_gemma":0.008671816,"domain_scores_codex":[0.9997633,0.00004842567,0.00002217381,0.00004496515,0.0000957803,0.00002533082],"domain_scores_gemma":[0.9989876,0.0005990404,0.0001015974,0.00005295755,0.0002350442,0.00002367604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002419959,0.0001879747,0.003963503,0.00008698916,0.0001473876,0.00007185492,0.00002836062,0.8191891,0.003956237,0.001439444,0.001135936,0.1695512],"study_design_scores_gemma":[0.000003750787,0.00000594306,0.0002064749,0.000001386225,0.000006881196,0.000001889348,7.909078e-7,0.9991791,0.0002943814,0.0002680455,0.0000295344,0.00000179163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2446136,0.001854577,0.7485633,0.0003460116,0.0003359133,0.00007228553,0.0001679398,0.001173858,0.002872535],"genre_scores_gemma":[0.8657986,0.0008204019,0.1291046,0.00006511412,0.0001289265,0.00006173913,0.0002472796,0.000044691,0.003728665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01102965,"threshold_uncertainty_score":0.02193093,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3115103108","doi":"10.1016/j.eswa.2020.114513","title":"Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":446,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Cluster analysis; Beijing; Convolutional neural network; Artificial intelligence; Air quality index; Artificial neural network; Deep learning; Data mining; Machine learning; Pattern recognition (psychology); Meteorology","authors":[{"name":"Rui Yan","is_ca":false},{"name":"Jiaqiang Liao","is_ca":false},{"name":"Jie Yang","is_ca":false},{"name":"Wei Sun","is_ca":true},{"name":"Mingyue Nong","is_ca":false},{"name":"Feipeng Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1115916191243101,"gpt":0.3171298078202858,"spread":0.2055381886959757,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002592976,0.0006567862,0.0004780493,0.0005028642,0.0002730151,0.0003800354,0.0005337361,0.0005069664,0.001109383],"category_scores_gemma":[0.0004419043,0.0002430217,0.0005045011,0.0008078665,0.0001433557,0.0006083585,0.0003395671,0.000372975,0.000314836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008536633,"about_ca_system_score_gemma":0.0005934968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06239182,"about_ca_topic_score_gemma":0.05192214,"domain_scores_codex":[0.9998747,0.000008252883,0.000007541459,0.00004972395,0.00002150233,0.00003832185],"domain_scores_gemma":[0.9998946,0.00002021192,0.00001668224,0.00001338981,0.00003907153,0.00001592997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006503841,0.0003011744,0.0696009,0.0001644667,0.0002786377,0.0004949289,0.0001441805,0.6202509,0.02334931,0.0008500856,0.008758505,0.2751565],"study_design_scores_gemma":[0.000004366315,0.0000148474,0.01728004,0.000002787181,0.00002050887,0.00001321568,0.00002533251,0.9807087,0.001472799,0.0002311876,0.0002173478,0.000008880311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9667359,0.0008108433,0.02663791,0.0004267084,0.0001869216,0.00001631133,0.001350554,0.0009121496,0.002922754],"genre_scores_gemma":[0.9952689,0.0001197403,0.002599077,0.00001974791,0.00002519464,0.000006853764,0.0006967664,0.00001420791,0.001249605],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06239182,"threshold_uncertainty_score":0.1240573,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4319335604","doi":"10.1016/j.eswa.2023.119619","title":"SAITS: Self-attention-based imputation for time series","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":442,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Ciena (Canada); Concordia University","funders":"Beijing Jiaotong University; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Imputation (statistics); Missing data; Artificial intelligence; Data mining; Multivariate statistics; Series (stratigraphy); Time series; Machine learning; Pattern recognition (psychology)","authors":[{"name":"Wenjie Du","is_ca":true},{"name":"David Côté","is_ca":true},{"name":"Yan Liu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009208493889557967,"gpt":0.2370432656503537,"spread":0.2278347717607958,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007958738,0.001224393,0.002111919,0.001586427,0.0009834793,0.00212605,0.004348394,0.002669006,0.01881425],"category_scores_gemma":[0.0389766,0.001143815,0.002126356,0.002089601,0.0006711509,0.00263227,0.003309466,0.004170025,0.01093233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005889043,"about_ca_system_score_gemma":0.002116097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004678273,"about_ca_topic_score_gemma":0.008351805,"domain_scores_codex":[0.9965253,0.001867648,0.0002347757,0.000600362,0.0005574768,0.0002144117],"domain_scores_gemma":[0.9877493,0.007217033,0.0005117565,0.002861236,0.001378075,0.0002826185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002066973,0.0006768066,0.01072894,0.0003794767,0.001341431,0.000276288,0.0002729306,0.1176199,0.003366306,0.02432902,0.07894026,0.7600017],"study_design_scores_gemma":[0.0001236869,0.0001041119,0.001031206,0.00004354214,0.0000775303,0.00007828636,0.00002680083,0.9643422,0.002674368,0.02325323,0.008205839,0.00003918054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004955954,0.0002084874,0.9763168,0.0002295334,0.0001947763,0.0000937213,0.001440504,0.0158927,0.0006674745],"genre_scores_gemma":[0.1417042,0.0003077267,0.8301973,0.0006999563,0.000411518,0.0007589821,0.01128996,0.003158464,0.01147187],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01881425,"threshold_uncertainty_score":0.06293988,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388923662","doi":"10.1016/j.eswa.2023.122666","title":"A comprehensive survey on applications of transformers for deep learning tasks","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":421,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transformer; Artificial intelligence; Machine learning; Data science; Electrical engineering; Engineering","authors":[{"name":"Saidul Islam","is_ca":true},{"name":"Hanae Elmekki","is_ca":true},{"name":"Ahmed Elsebai","is_ca":true},{"name":"Jamal Bentahar","is_ca":true},{"name":"Nagat Drawel","is_ca":true},{"name":"Gaith Rjoub","is_ca":true},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06332082183036745,"gpt":0.3130403542702057,"spread":0.2497195324398382,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001957915,0.001378178,0.001120912,0.003191938,0.0003318625,0.002188585,0.001498076,0.001070349,0.006432151],"category_scores_gemma":[0.009170354,0.000804448,0.001038683,0.004989816,0.0007101179,0.003953989,0.001955288,0.001850525,0.003271978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007832786,"about_ca_system_score_gemma":0.001598852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001412619,"about_ca_topic_score_gemma":0.001742245,"domain_scores_codex":[0.9984506,0.0003123282,0.000224888,0.0002569701,0.0006621662,0.0000930959],"domain_scores_gemma":[0.9968656,0.001891104,0.0001364861,0.0004253838,0.0006094954,0.00007186016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001142949,0.00005244322,0.0008351198,0.001779735,0.00005551599,0.00004851889,0.00004242472,0.00869375,0.002665224,0.03331271,0.007443093,0.9449572],"study_design_scores_gemma":[0.00007792997,0.0007290762,0.003365838,0.002190613,0.0003032794,0.002798769,0.0002283031,0.273726,0.0374199,0.2720114,0.4070143,0.0001346435],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.009557407,0.2035396,0.7668938,0.00111701,0.0005734008,0.000128695,0.00080599,0.00193577,0.01544838],"genre_scores_gemma":[0.1982407,0.3864289,0.3937096,0.001018626,0.00147043,0.0002856863,0.003240178,0.001163491,0.01444241],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.006432151,"threshold_uncertainty_score":0.02151769,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2054219113","doi":"10.1016/j.eswa.2011.04.005","title":"Application of fuzzy TOPSIS in evaluating sustainable transportation systems","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":318,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Sustainability; TOPSIS; Computer science; Multiple-criteria decision analysis; Fuzzy logic; Risk analysis (engineering); Quality (philosophy); Work (physics); Process (computing); Sustainable transport; Operations research; Business; Engineering; Artificial intelligence","authors":[{"name":"Anjali Awasthi","is_ca":true},{"name":"Satyaveer S. Chauhan","is_ca":true},{"name":"Hichem Omrani","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1614085459001022,"gpt":0.4177091967761213,"spread":0.2563006508760191,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005429551,0.0009383135,0.001425726,0.004253406,0.001136868,0.002156924,0.0006960592,0.0007517873,0.00181921],"category_scores_gemma":[0.009519253,0.0003429485,0.001050326,0.00417072,0.000604908,0.00114873,0.0007601206,0.0007229187,0.0001875],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001472389,"about_ca_system_score_gemma":0.001614492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008718177,"about_ca_topic_score_gemma":0.009568144,"domain_scores_codex":[0.9945844,0.002879926,0.000291169,0.0001601919,0.001918419,0.0001660196],"domain_scores_gemma":[0.9961345,0.002496736,0.0001727322,0.0001133443,0.001003927,0.00007869602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007525119,0.0003164698,0.007844256,0.001051348,0.00088598,0.0005451966,0.0008554652,0.4290133,0.01671256,0.0209415,0.001161747,0.5199196],"study_design_scores_gemma":[0.00004966857,0.0006461125,0.004418212,0.00008340606,0.0003039735,0.00017127,0.0004692265,0.9714087,0.006296628,0.01451398,0.00155696,0.00008182639],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2216764,0.001904602,0.7585431,0.0003135668,0.0001722389,0.0004041381,0.0001678031,0.0002701805,0.01654784],"genre_scores_gemma":[0.8450472,0.0006337521,0.1529904,0.00002079042,0.00002256413,0.0000943776,0.00004614843,0.00001312974,0.001131597],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008718177,"threshold_uncertainty_score":0.02871454,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1982490352","doi":"10.1016/j.eswa.2011.12.056","title":"An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sustainable Supply Chain Management","field":"Business, Management and Accounting","cited_by":307,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Supply chain; Purchasing; Computer science; Supply chain network; Profit (economics); Reverse logistics; Reuse; Operations research; Selection (genetic algorithm); Supply chain management; Closed loop; Integer programming; Linear programming; Mathematical optimization; Operations management; Business; Mathematics","authors":[{"name":"Saman Hassanzadeh Amin","is_ca":true},{"name":"Guoqing Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02001503350720926,"gpt":0.2532873810124063,"spread":0.233272347505197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001961648,0.00187328,0.002723918,0.001450551,0.0009197061,0.003244015,0.003223468,0.004087192,0.006570951],"category_scores_gemma":[0.002888525,0.001454388,0.001611864,0.001917229,0.001047225,0.002295975,0.001774868,0.001672167,0.0008263427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002264727,"about_ca_system_score_gemma":0.002443713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02420848,"about_ca_topic_score_gemma":0.01551709,"domain_scores_codex":[0.9989397,0.0003517438,0.0000527865,0.0002277188,0.0002616299,0.0001664918],"domain_scores_gemma":[0.9987531,0.0007196042,0.0001303168,0.00003954763,0.0002922855,0.00006514597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001355544,0.00001562081,0.00006036824,0.00002252284,0.00001887987,0.00003010781,0.00001545755,0.9960507,0.0001258149,0.001519969,0.0001011476,0.002025905],"study_design_scores_gemma":[0.00000571628,0.00001125127,0.00003055967,0.000003412898,0.000009015925,0.000003921824,0.000004049667,0.9990963,0.00003891053,0.0007032396,0.00009033108,0.000003431386],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01893671,0.0003397268,0.9700912,0.0002400529,0.00005845269,0.0001167297,0.0002501028,0.0003725856,0.009594399],"genre_scores_gemma":[0.858916,0.0005598813,0.1271696,0.0001272611,0.00006260353,0.0007343065,0.0004665751,0.0001188613,0.01184508],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02420848,"threshold_uncertainty_score":0.04813516,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2006725660","doi":"10.1016/j.eswa.2008.12.039","title":"Supplier selection: A hybrid model using DEA, decision tree and neural network","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":282,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Data envelopment analysis; Computer science; Purchasing; Artificial neural network; Selection (genetic algorithm); Decision tree; Supplier evaluation; Vendor; Operations research; Machine learning; Artificial intelligence; Decision tree model; Data mining; Supply chain management; Supply chain; Mathematical optimization; Business; Mathematics; Marketing","authors":[{"name":"Desheng Wu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07092476119191075,"gpt":0.343568813380332,"spread":0.2726440521884212,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002470421,0.000966699,0.002191865,0.001602475,0.0006764634,0.001962205,0.002243173,0.001766797,0.004026067],"category_scores_gemma":[0.003269206,0.0007586501,0.001029318,0.003349601,0.0004390744,0.002217651,0.0007380532,0.0009500531,0.0005292895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001613993,"about_ca_system_score_gemma":0.001656831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01288796,"about_ca_topic_score_gemma":0.01180036,"domain_scores_codex":[0.9987425,0.0006504288,0.00006302816,0.0001861694,0.0002615212,0.00009637787],"domain_scores_gemma":[0.9981336,0.001348363,0.000140996,0.00005909821,0.000261131,0.00005680758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006759253,0.00007230097,0.0005299198,0.00004291943,0.00008299116,0.00004246157,0.00001774086,0.9800541,0.0001684792,0.004199262,0.0003705433,0.01435175],"study_design_scores_gemma":[0.000005951242,0.000009474137,0.00008490217,0.000002519691,0.00001120778,0.000007087009,0.000003020879,0.998546,0.00004834684,0.001195884,0.00008199576,0.00000355726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06710571,0.000478587,0.9242399,0.0004133,0.00006195585,0.0001472956,0.0002743647,0.0002346826,0.007044306],"genre_scores_gemma":[0.8252373,0.0005405396,0.1649849,0.0001535555,0.00008371286,0.0003544763,0.0003425501,0.00006392971,0.008238953],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01288796,"threshold_uncertainty_score":0.02562588,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012323948","doi":"10.1016/j.eswa.2010.06.071","title":"Supplier selection and order allocation based on fuzzy SWOT analysis and fuzzy linear programming","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":263,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"","keywords":"SWOT analysis; Fuzzy logic; Computer science; Vagueness; Operations research; Selection (genetic algorithm); Context analysis; Order (exchange); Mathematical optimization; Artificial intelligence; Business; Mathematics; Marketing; Government (linguistics)","authors":[{"name":"Saman Hassanzadeh Amin","is_ca":true},{"name":"Jafar Razmi","is_ca":false},{"name":"Guoqing Zhang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0424467241313793,"gpt":0.3690723420025249,"spread":0.3266256178711456,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003261425,0.001200447,0.002499169,0.003640353,0.001149123,0.002680576,0.001227494,0.001075141,0.004315204],"category_scores_gemma":[0.005788657,0.001080819,0.001969077,0.004661853,0.0008458231,0.002331733,0.0008290668,0.000660466,0.0003889483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002117532,"about_ca_system_score_gemma":0.002536007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009382657,"about_ca_topic_score_gemma":0.00956654,"domain_scores_codex":[0.9972978,0.001188145,0.0001150274,0.0002164387,0.0009537609,0.000228857],"domain_scores_gemma":[0.9970475,0.00190962,0.0002714938,0.00009742572,0.0005987507,0.00007524134],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002721548,0.00009054287,0.001458276,0.000312536,0.0001405638,0.0001943779,0.0001879616,0.8946857,0.002534744,0.02446338,0.001167183,0.07449258],"study_design_scores_gemma":[0.00000859955,0.00004100985,0.0003003303,0.00001128603,0.00002051004,0.00002143413,0.00003692237,0.9898294,0.0005374845,0.008961301,0.0002196225,0.0000121714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04825976,0.0001373663,0.9454312,0.0001136681,0.00003798095,0.0002027381,0.0001035329,0.0001159202,0.005597766],"genre_scores_gemma":[0.7470037,0.0001892611,0.2495067,0.00004369467,0.00002763476,0.000231011,0.0001403654,0.0000510895,0.002806571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009382657,"threshold_uncertainty_score":0.01865608,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2012290523","doi":"10.1016/j.eswa.2011.09.058","title":"Gradient boosting trees for auto insurance loss cost modeling and prediction","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":251,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Royal Bank of Canada","funders":"","keywords":"Computer science; Gradient boosting; Softmax function; Support vector machine; Feature selection; Data mining; Parameterized complexity; Generalized linear model; Hinge loss; Artificial neural network; Data pre-processing; Machine learning; Artificial intelligence; Algorithm; Mathematical optimization; Random forest; Mathematics","authors":[{"name":"Leo Guelman","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04805340199553818,"gpt":0.2653546485366028,"spread":0.2173012465410646,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003945287,0.0006988691,0.001916794,0.001095439,0.0004706851,0.0009674121,0.001690314,0.001477408,0.001728938],"category_scores_gemma":[0.007469957,0.0007188075,0.0008285326,0.001206655,0.0004236295,0.001518502,0.0006556535,0.001922548,0.0007175068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007760111,"about_ca_system_score_gemma":0.0009020456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004047426,"about_ca_topic_score_gemma":0.004151616,"domain_scores_codex":[0.9991721,0.0003718915,0.00004902322,0.000129637,0.0001981928,0.00007917862],"domain_scores_gemma":[0.9972772,0.001864399,0.0001430592,0.0002252772,0.0004091862,0.00008087009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001070762,0.0001141603,0.001404484,0.00005536719,0.00006347003,0.0000440677,0.00003639313,0.8271843,0.0007528865,0.01206032,0.004449253,0.1537284],"study_design_scores_gemma":[0.000001624004,0.00000418365,0.0000644207,0.000002012904,0.000003162475,0.000003720029,9.69007e-7,0.9965428,0.00007814548,0.003147951,0.0001496426,0.000001444359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01726013,0.0008897771,0.9803104,0.0002324196,0.00006891465,0.00002933138,0.0001218998,0.0004975559,0.0005896931],"genre_scores_gemma":[0.6859136,0.001097189,0.305214,0.0002141195,0.0002979552,0.0002078765,0.0007745901,0.0002204207,0.006060367],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004047426,"threshold_uncertainty_score":0.02086496,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400340432","doi":"10.1016/j.eswa.2024.124678","title":"Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring","year":2024,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":251,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning; Physics","authors":[{"name":"Yuandi Wu","is_ca":true},{"name":"Brett Sicard","is_ca":true},{"name":"S. Andrew Gadsden","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03957793814183662,"gpt":0.3458485476791319,"spread":0.3062706095372952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001599026,0.001614051,0.001769835,0.003731596,0.0004310417,0.001752051,0.001772395,0.001531752,0.004401331],"category_scores_gemma":[0.00374894,0.0006409984,0.001400774,0.005029886,0.0007067258,0.002434902,0.001195768,0.001933842,0.002188791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006756581,"about_ca_system_score_gemma":0.001573125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00173295,"about_ca_topic_score_gemma":0.001574797,"domain_scores_codex":[0.9993368,0.0001362537,0.0000832412,0.000143058,0.0002579997,0.00004253305],"domain_scores_gemma":[0.9975079,0.001787225,0.0001525821,0.00008682546,0.0004108025,0.00005459036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005103268,0.0001046931,0.0008382107,0.01732936,0.0001725235,0.0001685749,0.0001382789,0.007071204,0.001003327,0.01630209,0.02157762,0.935243],"study_design_scores_gemma":[0.00002019867,0.0002802261,0.002769616,0.01108616,0.0004304131,0.001184354,0.0001858688,0.01613604,0.00205754,0.03011343,0.9355837,0.0001523258],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006411661,0.9820279,0.01229202,0.0006546074,0.0004170858,0.00002954225,0.00009194449,0.00009945392,0.003746318],"genre_scores_gemma":[0.006316512,0.9854015,0.005974035,0.0003088141,0.0007458269,0.00003886008,0.0001991807,0.00003383081,0.0009815132],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004401331,"threshold_uncertainty_score":0.0147239,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1974304087","doi":"10.1016/j.eswa.2007.09.034","title":"A type-2 fuzzy rule-based expert system model for stock price analysis","year":2007,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":190,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Fuzzy logic; Computer science; Econometrics; Stock price; Automotive industry; Stock (firearms); Expert system; Fuzzy set; Data mining; Artificial intelligence; Operations research; Mathematics; Engineering","authors":[{"name":"M.H. Fazel Zarandi","is_ca":false},{"name":"Babak Rezaee","is_ca":false},{"name":"İ.B. Türkşen","is_ca":true},{"name":"Elahe Neshat","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1175076733738869,"gpt":0.4130005603318492,"spread":0.2954928869579623,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007026035,0.0004934808,0.0009954884,0.0004188458,0.0003551123,0.001296496,0.001378503,0.001727196,0.00376962],"category_scores_gemma":[0.002280344,0.0003267172,0.0006093079,0.0005230558,0.0002483752,0.001103667,0.0003473114,0.0008438806,0.0009436382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005507344,"about_ca_system_score_gemma":0.0008142704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008726328,"about_ca_topic_score_gemma":0.007211137,"domain_scores_codex":[0.9996692,0.00007850085,0.00002705532,0.00007969782,0.0001198839,0.00002570302],"domain_scores_gemma":[0.999467,0.0002594849,0.00004086451,0.00002857328,0.0001881792,0.00001595399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001713567,0.0001012412,0.0005943903,0.0001169267,0.00009070049,0.0002709981,0.00007982229,0.9217629,0.003962445,0.006748875,0.001517499,0.06458294],"study_design_scores_gemma":[0.00001058939,0.00001775805,0.0001300324,0.000003942168,0.00001247206,0.00002024305,0.000002422636,0.9980624,0.0002909777,0.001143319,0.0003001301,0.000005661859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03942102,0.0004869195,0.9518233,0.0002594242,0.0001371513,0.00009993827,0.0003258783,0.0006505566,0.006795815],"genre_scores_gemma":[0.8004537,0.0004794058,0.1885355,0.0001704482,0.00008893397,0.0002807154,0.000380357,0.00004939688,0.009561627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008726328,"threshold_uncertainty_score":0.01735109,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1965671311","doi":"10.1016/j.eswa.2014.12.026","title":"Risk assessment of hydropower stations through an integrated fuzzy entropy-weight multiple criteria decision making method: A case study of the Xiangxi River","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water resources management and optimization","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydropower; Closeness; Computer science; Entropy (arrow of time); Fuzzy logic; Risk assessment; Fuzzy set; Operations research; Data mining; Mathematical optimization; Mathematics; Artificial intelligence; Engineering","authors":[{"name":"Yao Ji","is_ca":false},{"name":"Guohe Huang","is_ca":false},{"name":"Wei Sun","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01288814193261734,"gpt":0.3041931822560463,"spread":0.291305040323429,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003047127,0.0009579289,0.0007919559,0.001863735,0.0006821722,0.001351542,0.0008222066,0.0009658189,0.0008544869],"category_scores_gemma":[0.002753301,0.0004506797,0.0009359675,0.001149008,0.000470402,0.001160786,0.0007864606,0.0004985121,0.00004789648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001332672,"about_ca_system_score_gemma":0.001199816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006402535,"about_ca_topic_score_gemma":0.008500115,"domain_scores_codex":[0.9989191,0.0005792903,0.00005632143,0.000115325,0.0002594665,0.00007044445],"domain_scores_gemma":[0.9985379,0.00102584,0.0001251732,0.00004381668,0.0002131159,0.00005413796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004080583,0.000357022,0.01075868,0.0001986492,0.0002272987,0.0007030955,0.000498139,0.9048256,0.008733009,0.005657408,0.0003228897,0.06731017],"study_design_scores_gemma":[0.00001359694,0.0000785731,0.001603075,0.00001009324,0.00004737884,0.00003741111,0.0001144888,0.9956585,0.001412834,0.0009117547,0.0000911829,0.00002109801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7538544,0.000213906,0.2428965,0.000168506,0.00001506794,0.0001123671,0.00009240662,0.00007396245,0.00257291],"genre_scores_gemma":[0.9567912,0.00008582035,0.04240675,0.000006983439,0.000005256276,0.00004029829,0.00002905995,0.000006498631,0.0006281977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006402535,"threshold_uncertainty_score":0.01611495,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388036504","doi":"10.1016/j.eswa.2023.122380","title":"Experts and intelligent systems for smart homes’ Transformation to Sustainable Smart Cities: A comprehensive review","year":2023,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":159,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"National University of Computer and Emerging Sciences","keywords":"Smart city; Automation; Computer science; Home automation; Sustainability; Process (computing); Knowledge management; Data science; Architectural engineering; Engineering management; Process management; Business; Internet of Things; Engineering; Computer security; Telecommunications","authors":[{"name":"Noor Ul Huda","is_ca":false},{"name":"Ijaz Ahmed","is_ca":false},{"name":"Muhammad Adnan","is_ca":false},{"name":"Mansoor Ali","is_ca":true},{"name":"Faisal Naeem","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0464812757838032,"gpt":0.3030155111114415,"spread":0.2565342353276384,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001814353,0.001162393,0.002157932,0.00382387,0.0003297655,0.001981759,0.001142544,0.002411805,0.005789047],"category_scores_gemma":[0.003169578,0.0005292139,0.001147582,0.004057219,0.0006201415,0.00291172,0.001222207,0.001566748,0.001527272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008034403,"about_ca_system_score_gemma":0.002796894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002495774,"about_ca_topic_score_gemma":0.005987798,"domain_scores_codex":[0.9993974,0.0001342278,0.0001096852,0.0001062675,0.0002009683,0.00005144043],"domain_scores_gemma":[0.9975459,0.001592771,0.0002933768,0.00003521869,0.0004531141,0.00007966564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008698005,0.00008393325,0.0002792627,0.09094226,0.0003468971,0.0001118856,0.0001160454,0.0003919719,0.0005516467,0.003735438,0.03104187,0.8723119],"study_design_scores_gemma":[0.00008207592,0.0001570147,0.001786299,0.04609697,0.001295539,0.0005938259,0.0002070175,0.0002212101,0.0003910287,0.002625413,0.9464864,0.00005723105],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00004489126,0.9992462,0.00007098613,0.0001813144,0.0001065422,0.000004891583,0.00001280937,0.000002317359,0.0003299694],"genre_scores_gemma":[0.0004641846,0.9988678,0.0001368445,0.0002456967,0.0001031836,0.000006026349,0.00001551344,8.235452e-7,0.0001599871],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005789047,"threshold_uncertainty_score":0.01936626,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2237959143","doi":"10.1016/j.eswa.2016.01.002","title":"Malicious sequential pattern mining for automatic malware detection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke","funders":"Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Malware; Computer science; Executable; Data mining; Classifier (UML); Trojan; Cryptovirology; Intrusion detection system; Sequential Pattern Mining; System call; Artificial intelligence; Machine learning; Computer security; Operating system","authors":[{"name":"Yujie Fan","is_ca":false},{"name":"Yanfang Ye","is_ca":false},{"name":"Lifei Chen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01557217367219011,"gpt":0.2475662252126508,"spread":0.2319940515404607,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007711028,0.000710879,0.0007063558,0.003567757,0.0007455968,0.00079345,0.0008263281,0.0005710993,0.001496048],"category_scores_gemma":[0.003399845,0.0003444531,0.0008217377,0.001817767,0.0003519769,0.001003939,0.0005420207,0.0007320522,0.0007990344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382063,"about_ca_system_score_gemma":0.0009793343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002223353,"about_ca_topic_score_gemma":0.004183549,"domain_scores_codex":[0.9990802,0.0001653188,0.0001152567,0.0002519789,0.0003118892,0.00007531647],"domain_scores_gemma":[0.9977629,0.001056455,0.0002462802,0.0003416286,0.0005023944,0.00009022326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004766662,0.0004336999,0.02041536,0.0003432747,0.0002433045,0.0006928627,0.0001694706,0.02902675,0.05657515,0.005295506,0.005479482,0.8808486],"study_design_scores_gemma":[0.0000187459,0.0001903452,0.004449356,0.0000268621,0.00008970998,0.0009468088,0.00006367661,0.9545246,0.02673277,0.009229061,0.003706674,0.00002127911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1631972,0.001334612,0.8262768,0.000258253,0.0001194921,0.0002317376,0.001137709,0.005365267,0.002078986],"genre_scores_gemma":[0.6103246,0.0004347964,0.3840964,0.0000845445,0.00007803326,0.0001425188,0.001894455,0.000152639,0.002791936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003567757,"threshold_uncertainty_score":0.005004764,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089870669","doi":"10.1016/j.eswa.2011.09.160","title":"Comparison of term frequency and document frequency based feature selection metrics in text categorization","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":152,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Faculty of Graduate Studies and Research, University of Alberta; Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Feature selection; Discriminative model; Computer science; Term (time); Text categorization; Categorization; Word lists by frequency; Feature (linguistics); Frequency; Artificial intelligence; Selection (genetic algorithm); Pattern recognition (psychology); tf–idf; Data mining; Mathematics; Statistics","authors":[{"name":"Nouman Azam","is_ca":true},{"name":"JingTao Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03104747594243825,"gpt":0.2863871260576094,"spread":0.2553396501151711,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007369473,0.0007362022,0.001714788,0.008694715,0.0006183662,0.002287736,0.0008298984,0.001058735,0.0009872641],"category_scores_gemma":[0.02153089,0.000160184,0.0009252518,0.006065359,0.0003891039,0.003035834,0.0007324978,0.0007328248,0.0004117906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009292995,"about_ca_system_score_gemma":0.001007283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002635662,"about_ca_topic_score_gemma":0.003027074,"domain_scores_codex":[0.9952807,0.001381554,0.0006418823,0.000368515,0.002099152,0.0002282765],"domain_scores_gemma":[0.9680778,0.02402744,0.001336264,0.0008269638,0.0051425,0.0005889791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003741232,0.0007666025,0.04127194,0.001211128,0.0008361935,0.0001132483,0.0003835254,0.01296392,0.01714732,0.002503321,0.006852606,0.9122091],"study_design_scores_gemma":[0.0006886059,0.006505141,0.2174399,0.0003248268,0.001431435,0.001343719,0.001517268,0.7219796,0.0291134,0.009271628,0.009999031,0.0003855258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8060538,0.01900421,0.1651201,0.0008211363,0.0005060783,0.0003177764,0.002392485,0.001909372,0.003874871],"genre_scores_gemma":[0.8899647,0.002226198,0.1014041,0.00009452019,0.0003152272,0.000222533,0.003983019,0.0001574125,0.001632305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008694715,"threshold_uncertainty_score":0.03897399,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313831028","doi":"10.1016/j.eswa.2023.119509","title":"News-based intelligent prediction of financial markets using text mining and machine learning: A systematic literature review","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":151,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Computer science; Artificial intelligence; Stock market; Stock market prediction; Social media; Sentiment analysis; Artificial neural network; Deep learning; Stock (firearms); Financial market; Data science; Data mining; Finance; World Wide Web; Business; Engineering","authors":[{"name":"Matin N. Ashtiani","is_ca":true},{"name":"Bijan Raahemi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1097304280731196,"gpt":0.3816748664956033,"spread":0.2719444384224837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005197126,0.0009571357,0.002636535,0.009532619,0.0002690004,0.001925565,0.001410268,0.001205902,0.002572294],"category_scores_gemma":[0.02490064,0.0004728639,0.002984806,0.006506254,0.0004243301,0.002749145,0.000693468,0.0009012974,0.0004907463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005938649,"about_ca_system_score_gemma":0.004076004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002837863,"about_ca_topic_score_gemma":0.006448497,"domain_scores_codex":[0.9982327,0.0004347528,0.0006021755,0.0002773939,0.0004122871,0.00004064694],"domain_scores_gemma":[0.9637744,0.03145677,0.002444447,0.0003451958,0.001809159,0.0001699804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0003868445,0.0002751326,0.01193019,0.3250219,0.008014997,0.0002567383,0.0002747586,0.001610681,0.0006552723,0.001014071,0.006848834,0.6437106],"study_design_scores_gemma":[0.0005508004,0.001423522,0.05776611,0.6541032,0.1045976,0.00158036,0.001207085,0.01203301,0.003582248,0.008092107,0.1547312,0.0003327294],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00439369,0.9909293,0.00185446,0.0007109857,0.0001448688,0.0001639119,0.001159475,0.00003286527,0.0006103513],"genre_scores_gemma":[0.02901002,0.9627932,0.00557836,0.0006126445,0.0003374728,0.0002513538,0.001199237,0.00001299789,0.0002047561],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009532619,"threshold_uncertainty_score":0.02748531,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388099759","doi":"10.1016/j.eswa.2023.122156","title":"Financial fraud detection using graph neural networks: A systematic review","year":2023,"lang":"en","type":"review","venue":"Expert Systems with Applications","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":144,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Graph; Artificial neural network; Artificial intelligence; Machine learning; Theoretical computer science","authors":[{"name":"Bijan Raahemi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06262470066207466,"gpt":0.3396527258724665,"spread":0.2770280252103919,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006089294,0.001343928,0.004883243,0.008125808,0.0004024787,0.002039836,0.002188282,0.001520631,0.003440882],"category_scores_gemma":[0.03133549,0.0005719428,0.004421169,0.006847185,0.0008332419,0.002563997,0.001237646,0.001181137,0.0003982391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141776,"about_ca_system_score_gemma":0.004939363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004051532,"about_ca_topic_score_gemma":0.01493746,"domain_scores_codex":[0.996783,0.001100588,0.000884044,0.0003486075,0.0008140844,0.00006962369],"domain_scores_gemma":[0.9823139,0.01367751,0.002233208,0.0002843728,0.001340471,0.0001505063],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0003202207,0.00009157657,0.002696677,0.3698028,0.009754799,0.0001090318,0.000103217,0.0007231575,0.0001595294,0.0008858114,0.009572819,0.6057804],"study_design_scores_gemma":[0.0006556896,0.0007247751,0.01118863,0.7553541,0.09850694,0.001439527,0.0004969825,0.002684362,0.0008203189,0.006581341,0.1213734,0.0001738553],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006347402,0.9980107,0.0004098169,0.0003604669,0.0001052841,0.00007928715,0.0001739615,0.000008074073,0.0002177503],"genre_scores_gemma":[0.007595098,0.9904866,0.001146335,0.0003644845,0.0000832348,0.00007258823,0.0001559809,0.00000446777,0.00009113003],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008125808,"threshold_uncertainty_score":0.03220367,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2045584434","doi":"10.1016/j.eswa.2012.12.084","title":"Sentiment polarity detection in Spanish reviews combining supervised and unsupervised approaches","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"McGill University","keywords":"Computer science; Artificial intelligence; Machine learning; Classifier (UML); Sentiment analysis; Unsupervised learning; Polarity (international relations); Supervised learning; Natural language processing; Pattern recognition (psychology); Artificial neural network","authors":[{"name":"María Teresa Martín Valdivia","is_ca":false},{"name":"Eugenio Martínez‐Cámara","is_ca":false},{"name":"José M. Perea‐Ortega","is_ca":false},{"name":"Luís Alfonso Ureña López","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05847855461083074,"gpt":0.2670254764919242,"spread":0.2085469218810935,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001811107,0.0006298225,0.0005755359,0.002768425,0.0004204319,0.001167509,0.0002539972,0.0003616233,0.001068198],"category_scores_gemma":[0.007331188,0.0001353897,0.0004824927,0.001305315,0.0001503846,0.0005745712,0.0003831922,0.0003083528,0.001010616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000394324,"about_ca_system_score_gemma":0.0007422221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003000031,"about_ca_topic_score_gemma":0.005756351,"domain_scores_codex":[0.9984249,0.0006694747,0.0001390908,0.0002203556,0.0004250614,0.0001211804],"domain_scores_gemma":[0.9927845,0.001646283,0.0007368058,0.0002236124,0.004409327,0.0001994706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001513151,0.0004519216,0.2620333,0.001771367,0.0004855914,0.0008073703,0.002143784,0.002018102,0.09973361,0.001176103,0.03087968,0.596986],"study_design_scores_gemma":[0.0001699293,0.0008119774,0.7358437,0.0005028333,0.0009602722,0.001252651,0.004763535,0.1265225,0.06186768,0.002541757,0.0646139,0.0001493068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9135394,0.003505404,0.0557332,0.0006591586,0.0005058728,0.0004385067,0.004541989,0.001027404,0.02004913],"genre_scores_gemma":[0.95399,0.001091492,0.03293512,0.0001151353,0.0004501842,0.0002359234,0.004945677,0.0001231028,0.006113214],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003000031,"threshold_uncertainty_score":0.009578109,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4389752793","doi":"10.1016/j.eswa.2023.122946","title":"MSER: Multimodal speech emotion recognition using cross-attention with deep fusion","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":137,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Ministry of Science and ICT, South Korea; State Fund for Fundamental Research of Ukraine","keywords":"Computer science; Discriminative model; Robustness (evolution); Speech recognition; Artificial intelligence; Encoder; Feature (linguistics); Fusion mechanism; Pattern recognition (psychology); Fusion","authors":[{"name":"Mustaqeem Khan","is_ca":false},{"name":"Wail Gueaieb","is_ca":true},{"name":"Soonil Kwon","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05091831193250718,"gpt":0.3489223053887625,"spread":0.2980039934562553,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009895015,0.001522415,0.001091687,0.0008449072,0.000323135,0.0007719896,0.001000862,0.001089815,0.01026256],"category_scores_gemma":[0.001120752,0.0003676234,0.0009585264,0.0006077277,0.0002080037,0.001099935,0.00200229,0.001287875,0.005414827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000374916,"about_ca_system_score_gemma":0.0004510169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0034619,"about_ca_topic_score_gemma":0.006195279,"domain_scores_codex":[0.999486,0.00007473661,0.0000263265,0.0001730772,0.000142159,0.000097709],"domain_scores_gemma":[0.9997194,0.0000863225,0.00001795289,0.00004842877,0.00009779307,0.00002998646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000923071,0.0004440573,0.001517215,0.0002095523,0.0002854437,0.0002306489,0.00008558307,0.009229257,0.09884325,0.001571376,0.03318695,0.8534737],"study_design_scores_gemma":[0.0001194508,0.0005961608,0.01061553,0.00006639655,0.00022656,0.0004796032,0.0001084023,0.8657713,0.09855048,0.005478774,0.01786043,0.0001268737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08601354,0.003077655,0.8454103,0.0006198844,0.001304182,0.0005226515,0.008139846,0.04523836,0.009673579],"genre_scores_gemma":[0.419582,0.001232921,0.5281466,0.001267382,0.0004612667,0.0008101912,0.01840863,0.001568087,0.02852301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01026256,"threshold_uncertainty_score":0.03433162,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017302001","doi":"10.1016/j.eswa.2010.11.044","title":"A new nonparametric EWMA Sign Control Chart","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":136,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"National Science Council","keywords":"EWMA chart; Control chart; Chart; Statistics; X-bar chart; Computer science; Control limits; Nonparametric statistics; Normality; Shewhart individuals control chart; Step detection; Mathematics; Process (computing)","authors":[{"name":"Su‐Fen Yang","is_ca":false},{"name":"Jheng-Sian Lin","is_ca":false},{"name":"Smiley W. Cheng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04499023868176891,"gpt":0.3833046998646136,"spread":0.3383144611828447,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003539161,0.0008552864,0.001583485,0.001184289,0.0004644479,0.001831861,0.001529925,0.001288409,0.003036068],"category_scores_gemma":[0.01117231,0.0004373719,0.0006925294,0.001092733,0.0007792379,0.001909586,0.001270068,0.00191457,0.001080832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005405971,"about_ca_system_score_gemma":0.00165452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001783289,"about_ca_topic_score_gemma":0.001512733,"domain_scores_codex":[0.9971928,0.0007916927,0.0002033129,0.0005202317,0.001160289,0.0001316704],"domain_scores_gemma":[0.9958417,0.001480231,0.000469357,0.0005516766,0.001507386,0.0001496207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007654535,0.0002048741,0.001346796,0.0001917859,0.0001008289,0.000171587,0.00007468566,0.1100403,0.05204287,0.03582053,0.005498624,0.7937416],"study_design_scores_gemma":[0.00004107775,0.0001012722,0.0004122301,0.00001185024,0.00003042206,0.00008437163,0.000003051275,0.9831532,0.009109386,0.003444668,0.003572011,0.00003644833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001764246,0.00009299946,0.9970616,0.00003446787,0.00005300365,0.00002127069,0.00002952856,0.0006500773,0.0002928367],"genre_scores_gemma":[0.1754177,0.0003283331,0.8188439,0.0001632505,0.0002586394,0.0001889997,0.0003535424,0.0003901211,0.004055419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003539161,"threshold_uncertainty_score":0.01871711,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2003513713","doi":"10.1016/j.eswa.2008.02.036","title":"An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water resources management and optimization","field":"Engineering","cited_by":132,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Decision support system; Energy planning; Variety (cybernetics); Context (archaeology); Sustainable development; Energy management; Operations research; Management science; Energy (signal processing); Risk analysis (engineering); Artificial intelligence; Renewable energy","authors":[{"name":"Yanpeng Cai","is_ca":true},{"name":"Guohe Huang","is_ca":true},{"name":"Q.G. Lin","is_ca":true},{"name":"X.H. Nie","is_ca":true},{"name":"Qingkun Tan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01752184273349544,"gpt":0.2429244503849656,"spread":0.2254026076514702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006314255,0.0007589271,0.0009505927,0.0004409063,0.00033729,0.0009807704,0.001122873,0.0008473703,0.008211914],"category_scores_gemma":[0.00177751,0.0003886713,0.0003473147,0.0003814219,0.0002508075,0.0008707427,0.0007651815,0.0006009801,0.001049701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005828167,"about_ca_system_score_gemma":0.0007346373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005727025,"about_ca_topic_score_gemma":0.005484573,"domain_scores_codex":[0.9997668,0.00006910403,0.00001834395,0.00004875596,0.00007612572,0.00002089954],"domain_scores_gemma":[0.999556,0.0002389747,0.00004095164,0.00004043665,0.00008562317,0.00003790421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008103883,0.0002509695,0.0007472435,0.0001244066,0.0001013047,0.0002026736,0.00008231473,0.8540756,0.007335747,0.003788014,0.007851086,0.1246303],"study_design_scores_gemma":[0.00002725203,0.00001666138,0.00006554922,0.000001931542,0.000008785196,0.000007076892,0.000002040212,0.9979672,0.0006721935,0.0006912478,0.0005355361,0.000004494229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04706089,0.000171679,0.9224535,0.0003339377,0.00008276408,0.0001409721,0.0005709648,0.0223139,0.006871369],"genre_scores_gemma":[0.7680628,0.0001320438,0.2263087,0.0002045227,0.00005355384,0.0004100135,0.0006969557,0.00048613,0.003645306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008211914,"threshold_uncertainty_score":0.0274716,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2063373779","doi":"10.1016/j.eswa.2011.06.018","title":"Risk analysis in a linguistic environment: A fuzzy evidential reasoning-based approach","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":127,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Dempster–Shafer theory; Domain (mathematical analysis); Component (thermodynamics); Evidential reasoning approach; Task (project management); Fuzzy logic; Fuzzy set; Set (abstract data type); Artificial intelligence; Risk analysis (engineering); Machine learning; Data mining; Natural language processing; Decision support system; Mathematics","authors":[{"name":"Yong Deng","is_ca":false},{"name":"Rehan Sadiq","is_ca":true},{"name":"Wen Jiang","is_ca":false},{"name":"Solomon Tesfamariam","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1018287816704744,"gpt":0.3575505341706405,"spread":0.2557217525001662,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009380397,0.001352921,0.002093685,0.003598988,0.001283245,0.004905911,0.00314394,0.002199054,0.002479865],"category_scores_gemma":[0.01518754,0.000693578,0.002565762,0.001872614,0.002796114,0.005370306,0.002615901,0.002333179,0.0003661413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001434222,"about_ca_system_score_gemma":0.001647048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00127998,"about_ca_topic_score_gemma":0.001402777,"domain_scores_codex":[0.9948542,0.002544463,0.0003669532,0.0004317589,0.001632342,0.0001704228],"domain_scores_gemma":[0.9937834,0.004419508,0.0006399428,0.0002971776,0.0006945818,0.0001653984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009794454,0.0002828283,0.0009752339,0.0004724319,0.0004469198,0.0006274485,0.0008306645,0.4435613,0.002732765,0.471171,0.001249599,0.07755191],"study_design_scores_gemma":[0.00002073808,0.00006818028,0.0002102373,0.00008822596,0.000115571,0.0001143635,0.0001172937,0.6370589,0.0005973309,0.3605393,0.001021671,0.00004835384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004976893,0.0003378657,0.9920501,0.0003568666,0.00003122427,0.00002707085,0.00001725852,0.00002477033,0.002177894],"genre_scores_gemma":[0.4212665,0.001007891,0.5752121,0.0002061548,0.0002510971,0.0001668422,0.00005959145,0.0000331589,0.001796575],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009380397,"threshold_uncertainty_score":0.04960889,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2137180616","doi":"10.1016/j.eswa.2014.07.018","title":"OWA operator based link prediction ensemble for social network","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":126,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Badan Riset dan Inovasi Nasional; National Natural Science Foundation of China; McGill University","keywords":"Computer science; Benchmark (surveying); Link (geometry); Stability (learning theory); Operator (biology); Data mining; Variance (accounting); Social network (sociolinguistics); Ensemble learning; Algorithm; Artificial intelligence; Machine learning; Social media","authors":[{"name":"Yulin He","is_ca":false},{"name":"James N.K. Liu","is_ca":false},{"name":"Yanxing Hu","is_ca":false},{"name":"Xizhao Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01293051456868141,"gpt":0.2658279071817388,"spread":0.2528973926130574,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001917467,0.0009136961,0.0014915,0.002532164,0.0008261045,0.0009262935,0.001397825,0.001222831,0.002648709],"category_scores_gemma":[0.005114797,0.0003293034,0.000938711,0.002066595,0.0003537418,0.002120062,0.001209232,0.001368662,0.0009177196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004961946,"about_ca_system_score_gemma":0.001102279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01169103,"about_ca_topic_score_gemma":0.01630369,"domain_scores_codex":[0.9992037,0.0001955172,0.0000479649,0.0001877309,0.0002532962,0.0001118025],"domain_scores_gemma":[0.997507,0.00120477,0.0001353567,0.0003532468,0.0006687745,0.0001309361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003612301,0.0004277505,0.006170066,0.00009542715,0.0002875442,0.0001237428,0.00008948066,0.493038,0.003937562,0.005829894,0.01103912,0.4786002],"study_design_scores_gemma":[0.000002511117,0.000009703759,0.0002039391,0.000002164471,0.000009869608,0.000005720737,0.00000605527,0.9977102,0.0002647275,0.001575508,0.0002070482,0.000002644529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07459673,0.0006839275,0.9194549,0.0004010062,0.0002762412,0.0000911915,0.0008384822,0.001497947,0.002159523],"genre_scores_gemma":[0.7983769,0.0005112392,0.1891083,0.0002185497,0.0004102039,0.0002174056,0.003325203,0.0001847323,0.007647387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01169103,"threshold_uncertainty_score":0.02324599,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293581755","doi":"10.1016/j.eswa.2022.118710","title":"Social media-based COVID-19 sentiment classification model using Bi-LSTM","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":126,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Lakehead University","funders":"","keywords":"Misinformation; Computer science; Social media; Sentiment analysis; Coronavirus disease 2019 (COVID-19); Artificial intelligence; Public opinion; The Internet; Machine learning; Natural language processing; Data science; World Wide Web; Computer security; Political science","authors":[{"name":"Mohamed Arbane","is_ca":false},{"name":"Rachid Benlamri","is_ca":false},{"name":"Youcef Brik","is_ca":false},{"name":"Ayman Alahmar","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1436696653422719,"gpt":0.3919141200364346,"spread":0.2482444546941626,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006645755,0.00113996,0.0006649753,0.001300614,0.0004967947,0.0008958388,0.0008477006,0.001014154,0.004323467],"category_scores_gemma":[0.001183391,0.0002797316,0.0007488285,0.001207116,0.0001961262,0.001190493,0.0007540963,0.001488791,0.003813887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006660142,"about_ca_system_score_gemma":0.0009040646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008155222,"about_ca_topic_score_gemma":0.01098079,"domain_scores_codex":[0.9997259,0.00004021015,0.0000243231,0.00008039413,0.00006298123,0.00006623322],"domain_scores_gemma":[0.9995078,0.0001151949,0.00003606854,0.00002919967,0.0002799024,0.00003179403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008863549,0.001206152,0.01240368,0.0003659849,0.0003369621,0.0003602088,0.0002468145,0.04269419,0.04095422,0.002963527,0.04254842,0.8550335],"study_design_scores_gemma":[0.00001498358,0.00008854822,0.002803856,0.00002622198,0.0000694195,0.00005045085,0.00004953892,0.9872318,0.005731225,0.001318889,0.002595473,0.00001956066],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4231303,0.003910707,0.5083974,0.003759162,0.003777506,0.0005680069,0.01013468,0.01097203,0.03535016],"genre_scores_gemma":[0.8879075,0.0008667498,0.07648667,0.0006517704,0.0006345724,0.0002907721,0.009056676,0.0001892241,0.02391606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008155222,"threshold_uncertainty_score":0.0162155,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4364375123","doi":"10.1016/j.eswa.2023.120112","title":"Neural Network-based control using Actor-Critic Reinforcement Learning and Grey Wolf Optimizer with experimental servo system validation","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Adaptive Dynamic Programming Control","field":"Computer Science","cited_by":126,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Ministry of Education and Research, Romania; Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii; Corporation for National and Community Service","keywords":"Reinforcement learning; Computer science; Artificial neural network; Particle swarm optimization; Gradient descent; Convergence (economics); Process (computing); Controller (irrigation); Mathematical optimization; Servomechanism; Artificial intelligence; Machine learning; Control engineering; Mathematics; Engineering","authors":[{"name":"Iuliu Alexandru Zamfirache","is_ca":false},{"name":"Radu‐Emil Precup","is_ca":false},{"name":"Raul‐Cristian Roman","is_ca":false},{"name":"Emil M. Petriu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01517488254014964,"gpt":0.2595307435211804,"spread":0.2443558609810307,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002945866,0.0008833375,0.0009327244,0.0004718342,0.0005564335,0.0006851752,0.0008308581,0.001307712,0.001880793],"category_scores_gemma":[0.005399694,0.0004038852,0.0004411204,0.0002990795,0.0009616219,0.0006243238,0.000817609,0.001095545,0.0001898857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009873732,"about_ca_system_score_gemma":0.001155088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01182806,"about_ca_topic_score_gemma":0.007249158,"domain_scores_codex":[0.9993533,0.0002762162,0.00004552995,0.00007823422,0.0001860954,0.00006058713],"domain_scores_gemma":[0.9968759,0.001718682,0.0002808477,0.0002103506,0.0008568593,0.00005748789],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000185677,0.0001036071,0.0004655233,0.0001422766,0.00004246171,0.00003851455,0.0000676321,0.9782569,0.004492837,0.001690516,0.0002490863,0.01426492],"study_design_scores_gemma":[0.00001523171,0.00004600466,0.0001585258,0.000006204975,0.000005091617,0.000004133199,0.000003320964,0.9979431,0.001587852,0.0001669746,0.00005917253,0.000004389027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2935678,0.0005894112,0.6955472,0.0003288832,0.0001493587,0.0003450553,0.00008878723,0.0009478498,0.008435732],"genre_scores_gemma":[0.9722214,0.0000464333,0.02645336,0.00001927376,0.000004113007,0.0001172555,0.00003170552,0.00003224178,0.001074292],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01182806,"threshold_uncertainty_score":0.02351844,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4382281483","doi":"10.1016/j.eswa.2023.120854","title":"Integrated intelligent fault diagnosis approach of offshore wind turbine bearing based on information stream fusion and semi-supervised learning","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":123,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Turbine; Wind power; Offshore wind power; Fault (geology); Sensor fusion; Supervised learning; Condition monitoring; Artificial intelligence; Real-time computing; Artificial neural network; Engineering","authors":[{"name":"Yongchao Zhang","is_ca":true},{"name":"Kun Yu","is_ca":false},{"name":"Zihao Lei","is_ca":true},{"name":"Jian Ge","is_ca":true},{"name":"Yadong Xu","is_ca":true},{"name":"Zhixiong Li","is_ca":false},{"name":"Zhaohui Ren","is_ca":false},{"name":"Ke Feng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0120918666765608,"gpt":0.2485244883925244,"spread":0.2364326217159636,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002953953,0.000551427,0.0007305243,0.0006125178,0.0003253896,0.0005130299,0.0005509282,0.0004997394,0.0007025432],"category_scores_gemma":[0.0005842152,0.0002211461,0.0005106333,0.0003713339,0.0002100567,0.0008078777,0.0003960871,0.0004026506,0.0001838694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002200841,"about_ca_system_score_gemma":0.0005168198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002143941,"about_ca_topic_score_gemma":0.002720962,"domain_scores_codex":[0.9998092,0.00002147189,0.00001878188,0.00004667862,0.00007886176,0.00002495938],"domain_scores_gemma":[0.9997663,0.00005305942,0.00003809928,0.00001974504,0.0001103006,0.00001254744],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004717479,0.000237496,0.003864586,0.0002615572,0.0001610825,0.0003120903,0.0002390993,0.4368556,0.0431965,0.005465451,0.002147892,0.5067869],"study_design_scores_gemma":[0.000005111171,0.00004167324,0.0006144441,0.000002933403,0.00001523038,0.00002980013,0.000009538203,0.9959247,0.002443236,0.0007203969,0.0001885838,0.000004320166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04989659,0.0003070286,0.9478651,0.00009256321,0.00005648426,0.00003646679,0.0000438416,0.0004466502,0.001255237],"genre_scores_gemma":[0.9076585,0.0002280528,0.09061977,0.00003816146,0.00005998889,0.00005306896,0.0001306778,0.00001946078,0.001192233],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002143941,"threshold_uncertainty_score":0.004262865,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2022201359","doi":"10.1016/j.eswa.2013.07.002","title":"Cluster center initialization algorithm for K-modes clustering","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":120,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Initialization; Computer science; Cluster analysis; Cluster (spacecraft); Center (category theory); Algorithm; Data mining; Artificial intelligence; Pattern recognition (psychology); Computer network","authors":[{"name":"Shehroz S. Khan","is_ca":true},{"name":"Amir Ahmad","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02231767915479643,"gpt":0.304274552149248,"spread":0.2819568729944515,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001262775,0.001035586,0.001137247,0.001645635,0.001858366,0.001449107,0.002592851,0.001476532,0.006314137],"category_scores_gemma":[0.00413323,0.000676921,0.001027221,0.002037435,0.0006528165,0.001280901,0.001608813,0.002192117,0.004742485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132249,"about_ca_system_score_gemma":0.002779763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01117003,"about_ca_topic_score_gemma":0.01301561,"domain_scores_codex":[0.9988613,0.0002556399,0.00006858325,0.0002930375,0.0003914158,0.0001300878],"domain_scores_gemma":[0.9986067,0.000259615,0.00006609828,0.0002390339,0.0007589253,0.0000696179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008200851,0.000148942,0.001514862,0.0002758791,0.0001855982,0.0001137025,0.0004173579,0.1712034,0.01851998,0.02760586,0.03079143,0.7484029],"study_design_scores_gemma":[0.00006263667,0.00005237019,0.0008768007,0.00003691161,0.00004077988,0.0001317103,0.00009240861,0.9612038,0.01309927,0.01381938,0.0105216,0.0000623698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002550045,0.0001486374,0.9949216,0.00005938262,0.00008269643,0.00006701612,0.0001076167,0.000990444,0.001072557],"genre_scores_gemma":[0.07133662,0.0001695929,0.9226873,0.00007712089,0.00005278,0.000245731,0.0007641893,0.0004059235,0.004260613],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01117003,"threshold_uncertainty_score":0.02221,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1985369104","doi":"10.1016/j.eswa.2011.06.022","title":"An interactive method for dynamic intuitionistic fuzzy multi-attribute group decision making","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":118,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Closeness; Ranking (information retrieval); Group decision-making; Aggregate (composite); Operator (biology); Computer science; Measure (data warehouse); TOPSIS; Mathematics; Group (periodic table); Mathematical optimization; Data mining; Operations research; Artificial intelligence","authors":[{"name":"Zhi-xin Su","is_ca":true},{"name":"Mingyuan Chen","is_ca":true},{"name":"Guoping Xia","is_ca":false},{"name":"Li Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.157470969285867,"gpt":0.4766061745075416,"spread":0.3191352052216745,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003045121,0.0008871863,0.001220805,0.001258765,0.0009412621,0.001342204,0.002459954,0.001117824,0.01349617],"category_scores_gemma":[0.005883046,0.0005641378,0.001485529,0.001418945,0.0009214709,0.001644867,0.00274095,0.001719474,0.001570835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007252168,"about_ca_system_score_gemma":0.001313603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002048344,"about_ca_topic_score_gemma":0.003094235,"domain_scores_codex":[0.9977551,0.0008635446,0.000106129,0.0003185789,0.0008175837,0.0001390296],"domain_scores_gemma":[0.9962144,0.002702759,0.0001088285,0.0002906619,0.0005474979,0.0001359108],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006047616,0.0004518253,0.0007412603,0.0005522723,0.0003009765,0.0003941184,0.0009961657,0.1533511,0.01381574,0.1584322,0.007156305,0.6632033],"study_design_scores_gemma":[0.00007890935,0.0001187378,0.0002655359,0.0000445587,0.00006635802,0.0001304289,0.00006128663,0.9466369,0.002974545,0.04158086,0.007991001,0.00005095611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001092465,0.00002102049,0.9974589,0.00002292718,0.00001745875,0.00003377142,0.00001990209,0.00017338,0.001160226],"genre_scores_gemma":[0.05922018,0.00005336208,0.9378352,0.00005993964,0.00003239069,0.0003184594,0.00009555563,0.0001230947,0.002261785],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01349617,"threshold_uncertainty_score":0.04514915,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2398526858","doi":"10.1016/j.eswa.2016.05.027","title":"A hybrid intelligent fuzzy predictive model with simulation for supplier evaluation and selection","year":2016,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":116,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Adaptive neuro fuzzy inference system; Artificial neural network; Data mining; Machine learning; Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Selection (genetic algorithm); Parametric statistics; Supplier evaluation; Perceptron; Process (computing); Sensitivity (control systems); Supply chain management; Supply chain; Fuzzy control system; Engineering","authors":[{"name":"Madjid Tavana","is_ca":false},{"name":"Alireza Fallahpour","is_ca":false},{"name":"Debora Di Caprio","is_ca":true},{"name":"Francisco J. Santos‐Arteaga","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1372853844337297,"gpt":0.4290438737337837,"spread":0.291758489300054,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009178608,0.0006480088,0.001361127,0.00103314,0.0007263139,0.001389234,0.001753607,0.001929917,0.003119182],"category_scores_gemma":[0.002134126,0.0006431065,0.0009723345,0.001356247,0.0004758824,0.001434337,0.0007747761,0.0007967557,0.0004849119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001096546,"about_ca_system_score_gemma":0.001266645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01708251,"about_ca_topic_score_gemma":0.009072948,"domain_scores_codex":[0.9995962,0.0001547211,0.00002279837,0.00006800354,0.0001185104,0.00003979559],"domain_scores_gemma":[0.9992687,0.0004701684,0.00005915514,0.00004167319,0.0001311486,0.00002903409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002957979,0.00002428767,0.0001668986,0.00001477398,0.00001607552,0.00002921006,0.00001432564,0.9913071,0.0002143799,0.00202764,0.0001479259,0.00600784],"study_design_scores_gemma":[0.000002577986,0.000004557905,0.00001910834,0.000001169692,0.000002880812,0.000002593815,0.000001125695,0.9995075,0.00004947179,0.000359284,0.00004808901,0.000001747696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04501506,0.0002846638,0.945351,0.0002053538,0.00006794116,0.00007358244,0.0001478234,0.0006230254,0.008231509],"genre_scores_gemma":[0.9137836,0.0002545751,0.0819227,0.00008104115,0.00003333941,0.0002278687,0.0001662267,0.00005407743,0.003476681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01708251,"threshold_uncertainty_score":0.03396618,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2790858115","doi":"10.1016/j.eswa.2018.01.056","title":"A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Convolutional neural network; Image retrieval; Projection (relational algebra); Transfer of learning; Pattern recognition (psychology); Closing (real estate); Semantic gap; Similarity (geometry); Radon transform; Artificial intelligence; Image (mathematics); Linear search; Algorithm","authors":[{"name":"Amin Khatami","is_ca":false},{"name":"Morteza Babaie","is_ca":true},{"name":"Hamid R. Tizhoosh","is_ca":true},{"name":"Abbas Khosravi","is_ca":false},{"name":"Thanh Thi Nguyen","is_ca":false},{"name":"Saeid Nahavandi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02723945521666757,"gpt":0.3211081028218519,"spread":0.2938686476051844,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007942588,0.000648039,0.00129649,0.000916746,0.000385917,0.0005807691,0.001475491,0.0008753147,0.003769079],"category_scores_gemma":[0.001430687,0.0004880281,0.0009434907,0.0009967411,0.0003671532,0.001487799,0.00153497,0.000693059,0.000994009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003402429,"about_ca_system_score_gemma":0.001227413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003877825,"about_ca_topic_score_gemma":0.004565145,"domain_scores_codex":[0.9996537,0.00005489907,0.00002733894,0.00009571757,0.0001233405,0.00004501368],"domain_scores_gemma":[0.999633,0.00009155351,0.00002632025,0.00009773539,0.0001254562,0.00002587957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003668848,0.0002642059,0.0008770577,0.0001716766,0.0001260331,0.0001445105,0.00008430525,0.07114487,0.07191863,0.00444117,0.005485876,0.8449748],"study_design_scores_gemma":[0.00002488753,0.0001854109,0.0005122684,0.000009117273,0.00005851407,0.0002247328,0.00002318349,0.9784641,0.01651975,0.001956215,0.002004482,0.00001730762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02350084,0.0005068565,0.973465,0.0001000071,0.00005533532,0.0001012758,0.00009159589,0.001040818,0.001138256],"genre_scores_gemma":[0.3562463,0.0006974845,0.6347252,0.0002306021,0.0001138213,0.0002749384,0.0006740713,0.0002120257,0.006825571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003877825,"threshold_uncertainty_score":0.01260877,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2921247158","doi":"10.1016/j.eswa.2019.04.022","title":"A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":109,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Institute on Aging; Innovate UK; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Ulster University; Interreg; European Cooperation in Science and Technology; Invest Northern Ireland; Northern California Institute for Research and Education; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; AbbVie; European Commission; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Alzheimer's Association","keywords":"Support vector machine; Artificial intelligence; Machine learning; Computer science; Random forest; Clinical decision support system; Regression; Predictive power; Dementia; Decision support system; Kernel (algebra); Disease; Medicine; Statistics; Mathematics; Pathology","authors":[{"name":"Magda Bucholc","is_ca":false},{"name":"Xuemei Ding","is_ca":false},{"name":"Haiying Wang","is_ca":false},{"name":"David H. Glass","is_ca":false},{"name":"Hui Wang","is_ca":false},{"name":"Girijesh Prasad","is_ca":false},{"name":"Liam Maguire","is_ca":false},{"name":"Anthony J. Bjourson","is_ca":false},{"name":"Paula L. McClean","is_ca":false},{"name":"Stephen Todd","is_ca":false},{"name":"David P. Finn","is_ca":false},{"name":"KongFatt Wong‐Lin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03540534592713189,"gpt":0.3637991144329962,"spread":0.3283937685058643,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001764043,0.0009828939,0.001086111,0.002051868,0.0004848015,0.001336469,0.001126864,0.001232883,0.01287053],"category_scores_gemma":[0.006605826,0.0003247475,0.0003546557,0.001221441,0.0002352852,0.001013215,0.000753065,0.0005416602,0.00285091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005341297,"about_ca_system_score_gemma":0.001231024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006057467,"about_ca_topic_score_gemma":0.00504552,"domain_scores_codex":[0.9993026,0.0001762771,0.0001264724,0.000185713,0.0001732776,0.00003550762],"domain_scores_gemma":[0.9955368,0.002946721,0.0001850669,0.000221112,0.0008034699,0.0003069746],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004257914,0.00121765,0.0266432,0.0004160975,0.0002026554,0.001144716,0.0003066394,0.01188302,0.01355809,0.001297598,0.03957579,0.8994967],"study_design_scores_gemma":[0.002668395,0.003414133,0.04545207,0.0003622855,0.000765742,0.003897452,0.0004782379,0.8826751,0.02294767,0.007928751,0.02910192,0.000308349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3087412,0.001612676,0.5830751,0.002126113,0.0008692537,0.003825274,0.01131286,0.07874582,0.009691799],"genre_scores_gemma":[0.6144338,0.0005604626,0.3700492,0.0008247413,0.0003265354,0.001618623,0.00479475,0.0002586364,0.007133247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01287053,"threshold_uncertainty_score":0.04305619,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3083618710","doi":"10.1016/j.eswa.2020.113959","title":"Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Vehicle Routing Optimization Methods","field":"Engineering","cited_by":106,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea","keywords":"Computer science; Genetic algorithm; Pickup; Routing (electronic design automation); Vehicle routing problem; Operations research; Point (geometry); Delivery Performance; Set (abstract data type); Decision maker; Artificial intelligence; Industrial engineering; Machine learning; Computer network","authors":[{"name":"Hyungbin Park","is_ca":false},{"name":"Dongmin Son","is_ca":false},{"name":"Bonwoo Koo","is_ca":true},{"name":"Bongju Jeong","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02939381609448962,"gpt":0.259616303172509,"spread":0.2302224870780194,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001667267,0.001276082,0.00202742,0.001143238,0.0006379365,0.001485731,0.002533065,0.002459269,0.004666914],"category_scores_gemma":[0.00305635,0.000978991,0.001078369,0.001421371,0.0008792417,0.001333799,0.0008431355,0.001598711,0.0004250456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001647727,"about_ca_system_score_gemma":0.002390942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0160911,"about_ca_topic_score_gemma":0.00703777,"domain_scores_codex":[0.9993985,0.0001851846,0.00002467598,0.00009792721,0.0001364272,0.0001572949],"domain_scores_gemma":[0.9984292,0.001134745,0.0001172698,0.00003207372,0.0001847783,0.0001019378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000679053,0.00005064107,0.0001487832,0.00004968229,0.00002656944,0.00004202109,0.00003193432,0.9816524,0.0005594489,0.007670757,0.0007168961,0.008982969],"study_design_scores_gemma":[0.00001093595,0.00001822454,0.00002620069,0.000002960918,0.000005445397,0.000003731208,0.00000457982,0.9984953,0.00007071105,0.001251847,0.0001069192,0.000002983666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03314631,0.0003937221,0.9611732,0.0003599099,0.00008511525,0.000103866,0.00007286629,0.0002062434,0.004458806],"genre_scores_gemma":[0.7513312,0.0006166831,0.2319515,0.0002762946,0.00009702049,0.0003835097,0.0002761665,0.0002015054,0.01486611],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0160911,"threshold_uncertainty_score":0.03199488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1964189664","doi":"10.1016/j.eswa.2014.05.043","title":"A novel approach for multimodal medical image fusion","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":105,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Image fusion; Fuse (electrical); Artificial intelligence; Computer science; Compressed sensing; Robustness (evolution); Fusion; Computer vision; Pattern recognition (psychology); Gaussian; Image (mathematics)","authors":[{"name":"Zhaodong Liu","is_ca":false},{"name":"Hongpeng Yin","is_ca":false},{"name":"Yi Chai","is_ca":false},{"name":"Simon X. Yang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.008291136246485259,"gpt":0.2544536248195662,"spread":0.2461624885730809,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008066311,0.0007633348,0.0008691968,0.001289635,0.0005194571,0.001304053,0.001140973,0.001559003,0.003634203],"category_scores_gemma":[0.001403656,0.0004082789,0.001287819,0.001227786,0.0004727609,0.001428493,0.002572087,0.001211476,0.002085303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003509334,"about_ca_system_score_gemma":0.0005843254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000658128,"about_ca_topic_score_gemma":0.001143301,"domain_scores_codex":[0.9992212,0.0001208951,0.00004258381,0.0001404001,0.0004218013,0.0000531594],"domain_scores_gemma":[0.9996239,0.00007682735,0.00002791241,0.00008862714,0.0001573413,0.00002542331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000206436,0.0001387677,0.0004913677,0.0002841613,0.0002402349,0.0005217905,0.0001864933,0.02167638,0.2003328,0.05313823,0.008646642,0.7141367],"study_design_scores_gemma":[0.00003719848,0.0002150366,0.001001514,0.00004799516,0.0002014568,0.003379903,0.00008573459,0.850306,0.07043828,0.03733305,0.03686409,0.00008973417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00117188,0.0002058402,0.9970213,0.00009420684,0.00006448152,0.00003300235,0.00002828484,0.0002303075,0.001150649],"genre_scores_gemma":[0.05129145,0.000676426,0.9424853,0.000243845,0.0001950033,0.0001225517,0.0001786804,0.0001301457,0.004676695],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003634203,"threshold_uncertainty_score":0.01215768,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2013650127","doi":"10.1016/j.eswa.2010.07.004","title":"Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Water Systems and Optimization","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Program for New Century Excellent Talents in University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Department of Science and Technology, Ministry of Science and Technology, India; Shanghai Rising-Star Program; Natural Science Foundation of Chongqing; National Science Foundation","keywords":"Computer science; Flexibility (engineering); Intrusion; Similarity (geometry); Data mining; Process (computing); Intrusion detection system; Mathematical optimization; Artificial intelligence; Statistics; Mathematics","authors":[{"name":"Yong Deng","is_ca":false},{"name":"Wen Jiang","is_ca":false},{"name":"Rehan Sadiq","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.007357480738357826,"gpt":0.2226710859223034,"spread":0.2153136051839456,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001095694,0.0007279071,0.001386263,0.001256526,0.0004211441,0.0009642357,0.001875463,0.001662748,0.00167112],"category_scores_gemma":[0.003125651,0.0005428528,0.001102801,0.001247699,0.0006532376,0.001883855,0.00112677,0.0009574225,0.0004373465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007328228,"about_ca_system_score_gemma":0.00114444,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006397183,"about_ca_topic_score_gemma":0.003233419,"domain_scores_codex":[0.9994864,0.0001491816,0.00004260527,0.0001056443,0.000187106,0.00002899365],"domain_scores_gemma":[0.9988027,0.0005367503,0.0001452972,0.0001033307,0.0003363994,0.00007561463],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004716344,0.00004129883,0.0005012774,0.00005861508,0.00005656165,0.00004049347,0.00002561922,0.94676,0.002109179,0.005859429,0.000495964,0.04400437],"study_design_scores_gemma":[0.000002346908,0.000004764485,0.00001651468,8.154361e-7,0.000003138366,0.000004498272,9.047445e-7,0.9992431,0.0001395392,0.000460836,0.0001220374,0.000001538151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005663367,0.000107631,0.9932811,0.00007298245,0.00002998795,0.00002870791,0.0000323556,0.0001229109,0.0006609818],"genre_scores_gemma":[0.46062,0.0005161585,0.5326522,0.0002309801,0.0002271764,0.0002913338,0.0002964127,0.0002418747,0.004923888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006397183,"threshold_uncertainty_score":0.01271993,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3146978883","doi":"10.1016/j.eswa.2021.114920","title":"Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":103,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Benchmark (surveying); Computer science; Coronavirus disease 2019 (COVID-19); Convergence (economics); Gaussian; Economic shortage; Field (mathematics); Mathematical optimization; Pandemic; Lévy flight; Artificial intelligence; Machine learning; Random walk; Mathematics; Statistics","authors":[{"name":"Soheyl Khalilpourazari","is_ca":true},{"name":"Hossein Hashemi Doulabi","is_ca":true},{"name":"Aybike Özyüksel Çiftçioğlu","is_ca":false},{"name":"Gerhard‐Wilhelm Weber","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1603518404687845,"gpt":0.359060530684251,"spread":0.1987086902154666,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004593748,0.00132331,0.003535616,0.001037059,0.0007462366,0.00173735,0.001627642,0.003924717,0.002673634],"category_scores_gemma":[0.009701057,0.001119145,0.00129884,0.0008286044,0.001287702,0.001326369,0.001633034,0.002513711,0.0004402349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001224498,"about_ca_system_score_gemma":0.002696558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03269358,"about_ca_topic_score_gemma":0.01351354,"domain_scores_codex":[0.9992336,0.0004377782,0.00004371568,0.0001290281,0.00007301198,0.00008288282],"domain_scores_gemma":[0.9935976,0.005188738,0.0002180822,0.0001130711,0.0006798164,0.0002027557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004899361,0.00002386749,0.0002989627,0.00003437335,0.00003513819,0.00003175216,0.00002063746,0.9926361,0.00009691469,0.002046201,0.0003382563,0.004388866],"study_design_scores_gemma":[0.000004054747,0.000006959905,0.00002723541,0.00000235554,0.000002428197,0.000001673973,0.000001552634,0.9994056,0.0000156476,0.0005027865,0.00002785316,0.000001781238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05500094,0.00149392,0.9388586,0.000911308,0.0001364383,0.00009306952,0.0001542519,0.0007686629,0.002582783],"genre_scores_gemma":[0.8254313,0.0005910716,0.1671762,0.0004421772,0.0001082965,0.0002428417,0.0003376192,0.0002686112,0.00540192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03269358,"threshold_uncertainty_score":0.06500661,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3199582773","doi":"10.1016/j.eswa.2021.115950","title":"Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review","year":2021,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Obstructive Sleep Apnea Research","field":"Medicine","cited_by":103,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Student Research Committee, Tabriz University of Medical Sciences; Deputy for Research and Technology, Kermanshah University of Medical Sciences; Kermanshah University of Medical Sciences","keywords":"Support vector machine; Artificial intelligence; Machine learning; Computer science; Sleep apnea; Algorithm; Artificial neural network; Apnea; Hypopnea; Pattern recognition (psychology); Polysomnography; Medicine; Internal medicine","authors":[{"name":"Nader Salari","is_ca":false},{"name":"Amin Hosseinian‐Far","is_ca":false},{"name":"Masoud Mohammadi","is_ca":false},{"name":"Hooman Ghasemi","is_ca":false},{"name":"Habibolah Khazaie","is_ca":false},{"name":"Alireza Daneshkhah","is_ca":false},{"name":"Arash Ahmadi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03408396577353544,"gpt":0.3232978203401495,"spread":0.289213854566614,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006851814,0.001543042,0.009249511,0.005581476,0.0004167952,0.002223758,0.002029127,0.001695049,0.001733835],"category_scores_gemma":[0.02857494,0.0008744256,0.01101783,0.004387799,0.0007763496,0.002205784,0.001057421,0.001123919,0.0002277345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017421,"about_ca_system_score_gemma":0.00337691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00439208,"about_ca_topic_score_gemma":0.0121741,"domain_scores_codex":[0.9942451,0.001458227,0.002282396,0.0009074645,0.001005028,0.0001017031],"domain_scores_gemma":[0.972183,0.02219621,0.003530088,0.0004413569,0.001488435,0.0001607816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.001052439,0.0001007362,0.01272479,0.7060224,0.1179078,0.0001433445,0.0002089763,0.0007615775,0.0006601302,0.0002673125,0.001751976,0.1583985],"study_design_scores_gemma":[0.001326825,0.001208699,0.04064831,0.33121,0.6077183,0.0008137858,0.0004090068,0.001366615,0.0008236698,0.0008429872,0.01344291,0.0001890326],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002670698,0.9961211,0.0005241174,0.0001058263,0.00006905536,0.0001022975,0.0002562275,0.000009225066,0.0001414642],"genre_scores_gemma":[0.06918724,0.9259239,0.003137602,0.0006247271,0.0001976075,0.0002232237,0.0005941927,0.0000141629,0.00009739161],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009249511,"threshold_uncertainty_score":0.03623623,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2020287868","doi":"10.1016/j.eswa.2007.11.045","title":"A genetic fuzzy <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" altimg=\"si218.gif\" overflow=\"scroll\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-Modes algorithm for clustering categorical data","year":2007,"lang":"lv","type":"article","venue":"Expert Systems with Applications","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Algorithm; Fuzzy logic; Crossover; Categorical variable; Computer science; Genetic algorithm; Operator (biology); Cluster analysis; Mathematics; Artificial intelligence; Machine learning","authors":[{"name":"Guojun Gan","is_ca":true},{"name":"Junzheng Wu","is_ca":true},{"name":"Zhijing Yang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03440380589158171,"gpt":0.2949587150331948,"spread":0.2605549091416131,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001223863,0.0004290872,0.0006065027,0.001519586,0.001139924,0.00144051,0.002129828,0.001320394,0.006803934],"category_scores_gemma":[0.004124444,0.00035092,0.0010874,0.001649818,0.0007008978,0.0007040647,0.0008425842,0.001157946,0.002395255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001912308,"about_ca_system_score_gemma":0.002484758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02485346,"about_ca_topic_score_gemma":0.02555266,"domain_scores_codex":[0.9990734,0.0001482301,0.00004404081,0.0002589134,0.0004216585,0.0000537694],"domain_scores_gemma":[0.9991124,0.0002662407,0.00004191049,0.0001384516,0.0003944465,0.00004645674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000205394,0.0001891132,0.002304127,0.0001863163,0.0001334223,0.0001714688,0.0003514629,0.216504,0.0158596,0.08961814,0.01764373,0.6568332],"study_design_scores_gemma":[0.00004292267,0.00007471246,0.0006493525,0.00004807624,0.0000525729,0.0001478668,0.0000673916,0.9505681,0.007548164,0.02914919,0.01161047,0.00004110689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008415041,0.00007960118,0.9840109,0.0002324942,0.00007005004,0.00009829771,0.0003482506,0.0008099753,0.005935405],"genre_scores_gemma":[0.0777313,0.000104123,0.9109325,0.0001855394,0.00004074275,0.0001903005,0.0007285099,0.0001582689,0.009928742],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02485346,"threshold_uncertainty_score":0.04941761,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4313478995","doi":"10.1016/j.eswa.2022.119494","title":"Dynamic blockchain adoption for freshness-keeping in the fresh agricultural product supply chain","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":97,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Blockchain; Supply chain; Agriculture; Business; Product (mathematics); Cold chain; Commerce; Environmental economics; Industrial organization; Computer science; Marketing; Economics; Food science; Computer security; Chemistry","authors":[{"name":"Yuting Li","is_ca":false},{"name":"Chunqiao Tan","is_ca":false},{"name":"W.H. Ip","is_ca":true},{"name":"C.H. Wu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01917859603849657,"gpt":0.2435108359824423,"spread":0.2243322399439457,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00324339,0.0003635116,0.0004090507,0.0008151846,0.001039083,0.002138949,0.001218329,0.001173954,0.01359514],"category_scores_gemma":[0.009217113,0.0002574477,0.0003437125,0.001237106,0.0006681325,0.005358809,0.002730415,0.001106713,0.001797629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028712,"about_ca_system_score_gemma":0.002308292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004496883,"about_ca_topic_score_gemma":0.005619314,"domain_scores_codex":[0.9982158,0.000577508,0.00008523961,0.0003226834,0.000445597,0.0003531556],"domain_scores_gemma":[0.9923334,0.003352066,0.0004350403,0.001811845,0.001355759,0.0007118699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00418598,0.001789692,0.03722483,0.0006925358,0.0001704801,0.002741528,0.002933378,0.2463737,0.04545945,0.09306694,0.01324606,0.5521154],"study_design_scores_gemma":[0.0002965131,0.001061303,0.008598,0.0002051288,0.0001287578,0.0004842896,0.002189569,0.8703904,0.02072085,0.06427003,0.03153421,0.000120985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8528414,0.0006969433,0.1047647,0.001644574,0.0002020093,0.0003793261,0.0005330088,0.001311967,0.03762609],"genre_scores_gemma":[0.9899941,0.0001153551,0.006802089,0.00003941515,0.00001049618,0.00002937506,0.000175713,0.00002562681,0.002807903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01359514,"threshold_uncertainty_score":0.04548025,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2171666901","doi":"10.1016/j.eswa.2006.12.027","title":"Predicting opponent’s moves in electronic negotiations using neural networks","year":2006,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; HEC Montréal","funders":"","keywords":"Computer science; Negotiation; Artificial neural network; Artificial intelligence; Machine learning; Context (archaeology); Adversary; Process (computing); Set (abstract data type); Intelligent agent; Computer security","authors":[{"name":"Réal A. Carbonneau","is_ca":true},{"name":"Gregory E. Kersten","is_ca":true},{"name":"Rustam Vahidov","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01401987702492702,"gpt":0.2423584366861064,"spread":0.2283385596611794,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003033836,0.0005465575,0.0006722449,0.001200389,0.0007352256,0.001376403,0.001138861,0.001694574,0.002365709],"category_scores_gemma":[0.01618023,0.0006341503,0.0003370119,0.0008485285,0.0006416322,0.002719578,0.0008254231,0.001861932,0.0003375061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009660228,"about_ca_system_score_gemma":0.0005963319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009838804,"about_ca_topic_score_gemma":0.01172075,"domain_scores_codex":[0.999285,0.0003196897,0.00005154553,0.0001252421,0.0001156141,0.0001027594],"domain_scores_gemma":[0.9890563,0.009308738,0.0006016567,0.00017729,0.0006120634,0.000243891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001320414,0.000449099,0.02574462,0.0000650559,0.0001055325,0.0001745631,0.0002140184,0.8994917,0.001442034,0.004268137,0.000669603,0.06605522],"study_design_scores_gemma":[0.000007853333,0.00001615568,0.000762146,0.000002825629,0.000004297321,0.000004040403,0.00001915757,0.9978637,0.0002297226,0.001059633,0.00002714191,0.000003290754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9141839,0.0003748985,0.08089425,0.0004844225,0.00008483093,0.0000622424,0.00007354869,0.0001309605,0.003710954],"genre_scores_gemma":[0.9930328,0.00005532522,0.005820954,0.00002084322,0.00001182,0.00001479412,0.0000461457,0.000008394785,0.0009889711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009838804,"threshold_uncertainty_score":0.01956302,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385989064","doi":"10.1016/j.eswa.2023.121207","title":"A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":95,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"HORIZON EUROPE Framework Programme; Ministry of Science and Higher Education of the Russian Federation; Ministry of Education and Science of the Russian Federation","keywords":"Electricity price forecasting; Electricity market; Computer science; Bidding; Electricity; Smart grid; Process (computing); Demand response; Artificial intelligence; Econometrics; Mathematical optimization; Operations research; Microeconomics; Economics","authors":[{"name":"Alireza Pourdaryaei","is_ca":false},{"name":"Mohammad Mohammadi","is_ca":false},{"name":"Hamza Mubarak","is_ca":false},{"name":"Abdallah Abdellatif","is_ca":false},{"name":"Mazaher Karimi","is_ca":false},{"name":"Elena Gryazina","is_ca":false},{"name":"Vladimir Terzija","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01794151546354841,"gpt":0.2563422122477567,"spread":0.2384006967842083,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003857687,0.000551014,0.0004467704,0.0005267519,0.0002422243,0.0006335769,0.001151449,0.0007347185,0.00138738],"category_scores_gemma":[0.0006753072,0.0003134281,0.0005965135,0.0005579045,0.0003046342,0.001100346,0.0006209997,0.0006501693,0.0002301191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007406856,"about_ca_system_score_gemma":0.0006627091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01950988,"about_ca_topic_score_gemma":0.01395137,"domain_scores_codex":[0.9998546,0.00002461122,0.00000915261,0.0000437037,0.0000360675,0.00003179481],"domain_scores_gemma":[0.999882,0.00003589715,0.00002063314,0.0000104566,0.00004218165,0.000008801046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006365572,0.00006472355,0.001708887,0.00005952613,0.00008523878,0.0001983635,0.00005233321,0.8381524,0.00498251,0.01843111,0.001853397,0.1343479],"study_design_scores_gemma":[6.549921e-7,0.00000359383,0.00009776541,0.00000100784,0.000002434102,0.00000467494,0.000001185669,0.998844,0.0001658431,0.0007449129,0.0001326462,0.000001309017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02718531,0.0007945403,0.9674169,0.0002789304,0.0001073395,0.00003331317,0.00008892657,0.0005357192,0.00355904],"genre_scores_gemma":[0.8645409,0.0007462473,0.1281091,0.0002013058,0.0001835935,0.00008746583,0.0002386782,0.00006608087,0.005826639],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01950988,"threshold_uncertainty_score":0.03879267,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2049920566","doi":"10.1016/j.eswa.2008.02.014","title":"Building and evaluating a location-based service recommendation system with a preference adjustment mechanism","year":2008,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":95,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Recommender system; Personalization; Preference; Service (business); Measure (data warehouse); Term (time); Location-based service; Mechanism (biology); Index (typography); Variation (astronomy); Information retrieval; Data mining; World Wide Web; Telecommunications; Statistics","authors":[{"name":"Mu-Hsing Kuo","is_ca":true},{"name":"Liang-Chu Chen","is_ca":false},{"name":"Chien-Wen Liang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05867532360640439,"gpt":0.2870652731271769,"spread":0.2283899495207725,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002751135,0.0007493183,0.002147669,0.001333258,0.0008485218,0.001899356,0.00260273,0.002900929,0.002481545],"category_scores_gemma":[0.006860635,0.0007396159,0.001135629,0.001298438,0.0004261229,0.002218786,0.0009882047,0.00114197,0.001152253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001470711,"about_ca_system_score_gemma":0.001877696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02293091,"about_ca_topic_score_gemma":0.02072637,"domain_scores_codex":[0.9980392,0.0004311904,0.0002028158,0.0004689469,0.0006925722,0.000165299],"domain_scores_gemma":[0.9971424,0.001017881,0.0002094175,0.0003459154,0.001086476,0.0001978841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001212694,0.0009681827,0.01400764,0.0003320822,0.0007174615,0.0006078544,0.0001761153,0.488039,0.04115167,0.005437131,0.005276138,0.442074],"study_design_scores_gemma":[0.00002930602,0.00007863675,0.0006095601,0.000003096683,0.00005354262,0.00004877673,0.00001397941,0.9956872,0.002836904,0.0003682039,0.0002568522,0.00001384219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1348049,0.0003345082,0.8581367,0.0003728409,0.00009807033,0.0004121478,0.000201032,0.003155165,0.002484679],"genre_scores_gemma":[0.6476657,0.0001168927,0.34888,0.0001120929,0.00004916689,0.0001680628,0.0002631698,0.00006548189,0.002679462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02293091,"threshold_uncertainty_score":0.04559487,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1971891529","doi":"10.1016/j.eswa.2012.01.105","title":"A scoring model to detect abusive billing patterns in health insurance claims","year":2012,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Workplace Violence and Bullying","field":"Social Sciences","cited_by":93,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"National Research Foundation of Korea; Hongik University","keywords":"Psychological intervention; Health care; Decision tree; Computer science; Quarter (Canadian coin); Categorization; Actuarial science; Intervention (counseling); Data mining; Medicine; Artificial intelligence; Nursing; Business","authors":[{"name":"Hyunjung Shin","is_ca":false},{"name":"Hayoung Park","is_ca":false},{"name":"JunWoo Lee","is_ca":false},{"name":"Won Chul Jhee","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04169654788812277,"gpt":0.3407262214233228,"spread":0.2990296735352,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002835299,0.0007422277,0.0009367419,0.001728222,0.0005065502,0.00114108,0.001299788,0.001148898,0.001745541],"category_scores_gemma":[0.007865595,0.0002837651,0.0007490274,0.001016219,0.0002548006,0.0008533379,0.0005057963,0.0009309425,0.0006210985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018113,"about_ca_system_score_gemma":0.001160787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02014674,"about_ca_topic_score_gemma":0.01779552,"domain_scores_codex":[0.9991632,0.0002514841,0.0001047143,0.0002093919,0.0001600551,0.000111236],"domain_scores_gemma":[0.9950669,0.00292249,0.0003067644,0.0001934564,0.001323176,0.0001871617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008961141,0.00181456,0.1381739,0.0001361248,0.000408054,0.0003199234,0.0001723428,0.3414606,0.003540177,0.002230997,0.01120154,0.4996456],"study_design_scores_gemma":[0.000008434521,0.00005279138,0.004389969,0.000005840717,0.00002327536,0.0000257507,0.00001341677,0.9945071,0.0002282647,0.0006239859,0.0001145666,0.000006534609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5239797,0.0006149785,0.4643952,0.001477781,0.0002437806,0.0004524714,0.00205475,0.003799667,0.002981723],"genre_scores_gemma":[0.9428146,0.0001088014,0.05329175,0.0001455878,0.0000533262,0.0001850188,0.001148125,0.00003264114,0.002220242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02014674,"threshold_uncertainty_score":0.04005897,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4400620876","doi":"10.1016/j.eswa.2024.124780","title":"Alzheimer’s disease diagnosis from single and multimodal data using machine and deep learning models: Achievements and future directions","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":92,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Machine learning; Disease; Deep learning; Data science; Medicine; Pathology","authors":[{"name":"Ahmed Elazab","is_ca":false},{"name":"Changmiao Wang","is_ca":false},{"name":"M. Abdel-Aziz","is_ca":false},{"name":"Jian Zhang","is_ca":false},{"name":"Jason Gu","is_ca":true},{"name":"J. M. Górriz","is_ca":false},{"name":"Yu‐Dong Zhang","is_ca":false},{"name":"Chunqi Chang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09059684299010333,"gpt":0.3029390564757793,"spread":0.2123422134856759,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002441113,0.0008812307,0.001375565,0.001137053,0.0001487664,0.001671624,0.001055994,0.001133931,0.0009703241],"category_scores_gemma":[0.004138533,0.0002861971,0.000886913,0.001098874,0.0004326975,0.002139856,0.0008403383,0.001355997,0.0003890459],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006164499,"about_ca_system_score_gemma":0.001091024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004918866,"about_ca_topic_score_gemma":0.006695965,"domain_scores_codex":[0.9995384,0.000132167,0.00004108313,0.0001132654,0.0001210249,0.00005394763],"domain_scores_gemma":[0.9979749,0.001119427,0.0001274566,0.0001271651,0.0005334374,0.0001175556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0002674724,0.0003336868,0.02045722,0.00079301,0.0004745043,0.0001117407,0.0001095069,0.02975384,0.005111205,0.005447373,0.009672387,0.9274681],"study_design_scores_gemma":[0.00004227641,0.0003253102,0.01412821,0.00055839,0.0003809313,0.0003571778,0.000369779,0.9121548,0.005533012,0.046336,0.01970366,0.0001104737],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1162688,0.3125434,0.54588,0.0145812,0.0009028324,0.0001323337,0.001240033,0.001359195,0.007092332],"genre_scores_gemma":[0.6710929,0.0991428,0.2192314,0.001825776,0.002254386,0.0001452253,0.00220531,0.000126669,0.003975485],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.004918866,"threshold_uncertainty_score":0.01290995,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077832074","doi":"10.1016/j.eswa.2011.08.086","title":"Efficient content-based image retrieval using Multiple Support Vector Machines Ensemble","year":2011,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Content-based image retrieval; Image retrieval; Context (archaeology); Information retrieval; Process (computing); Matching (statistics); Digital image; Data mining; Feature vector; Pattern recognition (psychology); Artificial intelligence; Image (mathematics); Image processing","authors":[{"name":"Ela Yildizer","is_ca":false},{"name":"Ali Metin Balci","is_ca":false},{"name":"Mohammad Hassan","is_ca":false},{"name":"Reda Alhajj","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06270980461727851,"gpt":0.2733790069792933,"spread":0.2106692023620148,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00109485,0.001027735,0.002634403,0.002154747,0.0005123062,0.001038393,0.001302642,0.001215955,0.001689341],"category_scores_gemma":[0.002864196,0.0004693891,0.001243661,0.002416291,0.0002843002,0.002238178,0.001040991,0.00106345,0.001675631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003427908,"about_ca_system_score_gemma":0.0006311703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002092143,"about_ca_topic_score_gemma":0.002754164,"domain_scores_codex":[0.9988472,0.000202443,0.00009688454,0.0001977384,0.0005236482,0.0001319628],"domain_scores_gemma":[0.9983334,0.0004527864,0.0001198187,0.0002924143,0.0007551088,0.00004646419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004203886,0.0002930042,0.0007680303,0.0001248,0.000195013,0.00008990885,0.0000411719,0.04115129,0.04799359,0.001136696,0.004768772,0.9030174],"study_design_scores_gemma":[0.00001949364,0.00009263976,0.0005653574,0.00000548373,0.00007838733,0.00009015317,0.00001977854,0.9845102,0.01269107,0.0011428,0.0007688075,0.00001570924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03330259,0.0009164628,0.9630057,0.0001218762,0.0001482964,0.00006275349,0.0001205436,0.001458244,0.0008635776],"genre_scores_gemma":[0.3680151,0.0007731077,0.6246818,0.0001783992,0.000357289,0.0001502113,0.001271904,0.0001696187,0.00440262],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002634403,"threshold_uncertainty_score":0.005790174,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2042020602","doi":"10.1016/j.eswa.2013.12.043","title":"Multi-objective PSO algorithm for mining numerical association rules without a priori discretization","year":2014,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of British Columbia","funders":"","keywords":"Discretization; Computer science; Particle swarm optimization; A priori and a posteriori; Association rule learning; Algorithm; Data mining; Apriori algorithm; Numerical analysis; Measure (data warehouse); Mathematical optimization; Machine learning; Mathematics","authors":[{"name":"Vahid Beiranvand","is_ca":true},{"name":"Mohamad Mobasher-Kashani","is_ca":false},{"name":"Azuraliza Abu Bakar","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01305000528970371,"gpt":0.2739942227814187,"spread":0.260944217491715,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001456551,0.0006061878,0.00140279,0.001125087,0.000446239,0.0009656965,0.001254228,0.001079194,0.001973173],"category_scores_gemma":[0.005245327,0.0005955276,0.0009462404,0.001285616,0.0005449901,0.0009080441,0.0009455384,0.001111186,0.0004324076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003795493,"about_ca_system_score_gemma":0.001049846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003212174,"about_ca_topic_score_gemma":0.003138396,"domain_scores_codex":[0.9993579,0.0001756204,0.00006887547,0.0001303075,0.0002199976,0.00004735439],"domain_scores_gemma":[0.9982003,0.001204504,0.0001306712,0.0001310149,0.0002766555,0.00005680537],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001742232,0.0001533106,0.002613459,0.0001981192,0.0002076842,0.0001369535,0.00009861466,0.7346649,0.002733832,0.009745214,0.002074422,0.2471993],"study_design_scores_gemma":[0.00001716478,0.00002124787,0.0001718999,0.000007279299,0.000009516059,0.00002595881,0.000004602709,0.9978897,0.0001823704,0.001477955,0.0001890105,0.000003297128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01149069,0.0002527047,0.9867797,0.0001093277,0.00004337507,0.00004219866,0.00003770328,0.0002092079,0.001035084],"genre_scores_gemma":[0.2626972,0.0002578059,0.7345892,0.0001539017,0.00007237827,0.0002496668,0.0002188127,0.00005056534,0.00171039],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003212174,"threshold_uncertainty_score":0.007703006,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385776789","doi":"10.1016/j.eswa.2023.121180","title":"Secure hierarchical fog computing-based architecture for industry 5.0 using an attribute-based encryption scheme","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Cloud computing; Architecture; Field (mathematics); Encryption; Distributed computing; Computer security; Layer (electronics); Embedded system; Operating system","authors":[{"name":"Shruti","is_ca":false},{"name":"Shalli Rani","is_ca":false},{"name":"Gautam Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04640962118083454,"gpt":0.3115153368081297,"spread":0.2651057156272952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004833779,0.0003752348,0.0004918893,0.0005285597,0.0009520602,0.001436431,0.001195029,0.0005944536,0.001518254],"category_scores_gemma":[0.0004741977,0.0001990986,0.0004805411,0.0005375663,0.0004311313,0.001963572,0.001513752,0.0009097154,0.0006997232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008299904,"about_ca_system_score_gemma":0.001287061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002857395,"about_ca_topic_score_gemma":0.002902766,"domain_scores_codex":[0.9995134,0.00006509572,0.00003900779,0.0000722973,0.00015742,0.0001528687],"domain_scores_gemma":[0.999693,0.00002151602,0.00002760307,0.0001225679,0.00009505679,0.000040205],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003504564,0.0009258263,0.005502674,0.0007102179,0.0004821,0.001856858,0.001057737,0.08521952,0.1847996,0.3015178,0.07272921,0.3416938],"study_design_scores_gemma":[0.0001644752,0.000569311,0.0026636,0.00007774292,0.0002498709,0.0007688869,0.0002124248,0.7925088,0.08786251,0.0564763,0.05829373,0.0001524271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.173635,0.002214971,0.7779125,0.001130016,0.0006428435,0.0006657214,0.0004763848,0.009952688,0.03336987],"genre_scores_gemma":[0.9474928,0.0003227217,0.046042,0.0002652921,0.00004319048,0.00008542838,0.0003383704,0.00006773078,0.005342369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002857395,"threshold_uncertainty_score":0.006022036,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2789731619","doi":"10.1016/j.eswa.2018.03.021","title":"Adapting dynamic classifier selection for concept drift","year":2018,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Concept drift; Computer science; Classifier (UML); Artificial intelligence; Machine learning; A priori and a posteriori; Data mining","authors":[{"name":"Paulo Ricardo Lisboa de Almeida","is_ca":false},{"name":"Luiz S. Oliveira","is_ca":false},{"name":"Alceu S. Britto","is_ca":false},{"name":"Robert Sabourin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0181693000240935,"gpt":0.2960006961471375,"spread":0.277831396123044,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008375608,0.001395238,0.002782407,0.003033641,0.001145794,0.002545311,0.003930773,0.00251777,0.00242106],"category_scores_gemma":[0.02272746,0.0006927662,0.001329235,0.002579462,0.0006170745,0.003199107,0.00244916,0.00287149,0.001721167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001094838,"about_ca_system_score_gemma":0.002510848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003243024,"about_ca_topic_score_gemma":0.003559235,"domain_scores_codex":[0.9963273,0.0008399527,0.0002860606,0.001110258,0.001150284,0.0002861662],"domain_scores_gemma":[0.9886798,0.005913348,0.0004217226,0.001401398,0.003207877,0.0003758918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006725167,0.0007529039,0.009381339,0.0001780393,0.0004491344,0.0003203857,0.0002130977,0.1077642,0.01350598,0.003855002,0.01438634,0.8485209],"study_design_scores_gemma":[0.00004111945,0.0001017009,0.000792411,0.00001536196,0.00007271854,0.0001569906,0.00003772206,0.9898959,0.002673818,0.004293418,0.001903008,0.00001570682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07524928,0.00181227,0.9161742,0.0008310106,0.0007437671,0.0002989649,0.0003516283,0.002852138,0.001686776],"genre_scores_gemma":[0.6458833,0.0007026991,0.3443685,0.0007922705,0.0007803733,0.0004226434,0.001790221,0.0005438485,0.004716122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008375608,"threshold_uncertainty_score":0.04429501,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083584242","doi":"10.1016/j.eswa.2010.06.101","title":"Dynamic independent component analysis approach for fault detection and diagnosis","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Fault detection and isolation; Component (thermodynamics); Fault (geology); Data mining; Process (computing); Independent component analysis; Component analysis; Root cause; Artificial intelligence; Pattern recognition (psychology); Reliability engineering; Machine learning; Engineering","authors":[{"name":"George Stefatos","is_ca":false},{"name":"A. Ben Hamza","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.005600101482105121,"gpt":0.2219482992190848,"spread":0.2163481977369797,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000415965,0.001243517,0.001192927,0.001255302,0.0005235373,0.0009174515,0.001061479,0.0006882456,0.002238248],"category_scores_gemma":[0.001452521,0.0003530762,0.0007906341,0.001110949,0.0004071353,0.00082256,0.0005535249,0.001274118,0.001056985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000521305,"about_ca_system_score_gemma":0.0008290791,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005863418,"about_ca_topic_score_gemma":0.004455022,"domain_scores_codex":[0.999499,0.00009956495,0.0000293114,0.0001171279,0.0002129283,0.00004210341],"domain_scores_gemma":[0.9995422,0.0001682066,0.00002816505,0.00006397563,0.0001864326,0.00001095472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002112081,0.0000991879,0.0006178321,0.0003161918,0.0003277097,0.0001646673,0.0001005852,0.26851,0.01618852,0.05205149,0.005129825,0.6562828],"study_design_scores_gemma":[0.00001170738,0.00005715021,0.0004138122,0.00001216022,0.00006986555,0.00008286293,0.00001529984,0.9717196,0.00386208,0.01872315,0.005014042,0.000018309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001066308,0.0004736944,0.9971442,0.00004111595,0.00004863535,0.00001588354,0.00002305903,0.0002640955,0.000923056],"genre_scores_gemma":[0.3345509,0.002526755,0.6528236,0.000150504,0.0002577412,0.0002829931,0.00042744,0.0002149371,0.008765268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005863418,"threshold_uncertainty_score":0.01165861,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}