{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":31,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":31,"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":"ee675ae6172f","filters":{"venue":"Intelligent Data Analysis"}},"results":[{"id":"W1941659294","doi":"10.3233/ida-2002-6504","title":"The class imbalance problem: A systematic study1","year":2002,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":3298,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Class (philosophy); Psychology; Computer science; Artificial intelligence","authors":[{"name":"Nathalie Japkowicz","is_ca":true},{"name":"Shaju Stephen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0727591430494873,"gpt":0.2990844827479253,"spread":0.226325339698438,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0435316,0.00181782,0.004126335,0.01452691,0.002486984,0.004220188,0.002437891,0.003046876,0.001316685],"category_scores_gemma":[0.1366493,0.001109844,0.001646496,0.01508942,0.003597342,0.008225242,0.003179472,0.00155469,0.0005243706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001532807,"about_ca_system_score_gemma":0.005752864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001333362,"about_ca_topic_score_gemma":0.003719326,"domain_scores_codex":[0.9398562,0.03054205,0.007092546,0.007079821,0.01495332,0.000475927],"domain_scores_gemma":[0.7057753,0.2277032,0.02383411,0.0108086,0.0308395,0.001039294],"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":[0.0005613827,0.0004369939,0.05826699,0.02459065,0.001123318,0.0004579317,0.002834832,0.001893429,0.002641465,0.01065535,0.01330841,0.8832292],"study_design_scores_gemma":[0.0009975593,0.006010386,0.1308598,0.08992421,0.01286231,0.01568694,0.02211422,0.02640592,0.03569788,0.1713067,0.4868121,0.001321869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.08302546,0.5961828,0.3021837,0.006715476,0.001778301,0.001720653,0.00242397,0.0005389549,0.005430716],"genre_scores_gemma":[0.3978455,0.3633703,0.2190206,0.005006751,0.003028552,0.003885833,0.004914985,0.0004693569,0.002457951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0435316,"threshold_uncertainty_score":0.2302199,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2097627964","doi":"10.3233/ida-2006-10604","title":"A comprehensive survey of numeric and symbolic outlier mining techniques","year":2006,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":164,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Outlier; Computer science; Data science; Data mining; Anomaly detection; Information retrieval; Artificial intelligence","authors":[{"name":"Malik Agyemang","is_ca":true},{"name":"Ken Barker","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05757818261835768,"gpt":0.3155999382805493,"spread":0.2580217556621916,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00217431,0.001261982,0.001751003,0.005632131,0.0008214806,0.00234868,0.001942527,0.0008953192,0.002124123],"category_scores_gemma":[0.008656994,0.0003666418,0.001305687,0.01176343,0.0006979001,0.003203409,0.00111531,0.001016638,0.001740022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004486959,"about_ca_system_score_gemma":0.001382917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001379703,"about_ca_topic_score_gemma":0.001665903,"domain_scores_codex":[0.9958757,0.0005229542,0.0004601448,0.0004631293,0.002549689,0.0001284248],"domain_scores_gemma":[0.9953659,0.001914159,0.0005968254,0.0005148387,0.00151597,0.00009233544],"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":[0.00008667602,0.00009652825,0.005899849,0.001811145,0.0001388164,0.000182545,0.000104039,0.008737466,0.002701763,0.01185314,0.009955735,0.9584323],"study_design_scores_gemma":[0.0001181788,0.0008733958,0.02286707,0.003013207,0.000592262,0.00792382,0.001276207,0.3264349,0.02570822,0.1787441,0.4319972,0.0004515461],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01168531,0.05837675,0.9132336,0.002163061,0.000603342,0.0002244893,0.001338665,0.003000897,0.009373953],"genre_scores_gemma":[0.168859,0.1467828,0.6683145,0.001151783,0.001530787,0.0004991374,0.004918008,0.0004693225,0.00747478],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005632131,"threshold_uncertainty_score":0.01149893,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1935986945","doi":"10.3233/ida-2003-7406","title":"Measuring the interestingness of discovered knowledge: A principled approach","year":2003,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Data science","authors":[{"name":"Robert J. Hilderman","is_ca":true},{"name":"Howard J. Hamilton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1438015283348033,"gpt":0.3139100790409247,"spread":0.1701085507061213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01442173,0.001593405,0.001973616,0.01707756,0.001494627,0.007035897,0.003364461,0.001924457,0.001085624],"category_scores_gemma":[0.04682202,0.001312968,0.002417908,0.007292939,0.006247156,0.006630589,0.004134196,0.002893252,0.0002865968],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001833783,"about_ca_system_score_gemma":0.002248827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001111181,"about_ca_topic_score_gemma":0.00131476,"domain_scores_codex":[0.9863725,0.004013671,0.001017443,0.001726054,0.006629989,0.0002402271],"domain_scores_gemma":[0.9552965,0.03354724,0.003182217,0.004269389,0.003171867,0.0005327948],"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":[0.0004783334,0.0007581926,0.02046481,0.002193386,0.001219695,0.0005565252,0.004060088,0.1244199,0.01686479,0.2982759,0.002848531,0.5278599],"study_design_scores_gemma":[0.0001212123,0.0004458962,0.006873746,0.0003414766,0.0003651611,0.0009123462,0.001045496,0.3853829,0.00790676,0.5898452,0.006569999,0.0001899179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02514235,0.0006974085,0.9695759,0.001066529,0.00002768859,0.0004437435,0.0003167662,0.0004351425,0.002294432],"genre_scores_gemma":[0.2306008,0.000701546,0.7668145,0.0001820306,0.0001016325,0.0007593432,0.0003851996,0.00006635061,0.0003885934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01707756,"threshold_uncertainty_score":0.07627034,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3090132571","doi":"10.3233/ida-194747","title":"Recognition of speech emotion using custom 2D-convolution neural network deep learning algorithm","year":2020,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Emotion and Mood Recognition","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Artificial neural network; Feature extraction; Multilayer perceptron; Curse of dimensionality; Salient; Convolutional neural network; Speech recognition; Field (mathematics); Feature (linguistics); Machine learning; Perceptron; Pattern recognition (psychology)","authors":[{"name":"Kudakwashe Zvarevashe","is_ca":false},{"name":"Oludayo O. Olugbara","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1403660009736294,"gpt":0.3535415836457495,"spread":0.2131755826721201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003672547,0.0005636329,0.0004053131,0.0003405634,0.0001857188,0.0004000312,0.0006432422,0.0005837549,0.002296784],"category_scores_gemma":[0.0006279318,0.0002032641,0.0004400575,0.0002490029,0.0001652943,0.0004562602,0.0005259211,0.0005593469,0.0006641048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005124029,"about_ca_system_score_gemma":0.000431256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004532075,"about_ca_topic_score_gemma":0.006496082,"domain_scores_codex":[0.9997949,0.00002203029,0.00001577446,0.00006602142,0.00006595741,0.00003539315],"domain_scores_gemma":[0.9998457,0.00003656127,0.000013364,0.00001839779,0.00007697172,0.000008926053],"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":[0.0004924326,0.0003284044,0.004028765,0.0001335365,0.0001377382,0.0002204514,0.0001070119,0.1807443,0.08925813,0.00193807,0.005684576,0.7169266],"study_design_scores_gemma":[0.00000959157,0.00005505731,0.001179538,0.000005671015,0.00001411822,0.00006187143,0.00001171024,0.9840643,0.01337759,0.0003585008,0.0008543083,0.00000773481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1481572,0.0005873871,0.8415093,0.0003027482,0.0002018749,0.0001456994,0.0004522423,0.003581602,0.005062017],"genre_scores_gemma":[0.7283984,0.0003223342,0.2593021,0.0002603889,0.00003736807,0.0002273482,0.001257534,0.0000940509,0.01010057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004532075,"threshold_uncertainty_score":0.009011388,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1950860289","doi":"10.3233/ida-2008-12205","title":"Bridging the gap between data mining and decision support: A case-based reasoning and ontology approach","year":2008,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Bridging (networking); Ontology; Computer science; Decision support system; Data science; Data mining; Knowledge management; Epistemology","authors":[{"name":"Michel Charest","is_ca":true},{"name":"Sylvain Delisle","is_ca":true},{"name":"Ofelia Cervantes","is_ca":true},{"name":"Yanfen Shen","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1568791216608444,"gpt":0.3427810502239256,"spread":0.1859019285630813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01503317,0.001159386,0.001468834,0.008482897,0.00275308,0.01207827,0.005849223,0.005065547,0.004118749],"category_scores_gemma":[0.01445841,0.001065449,0.001989542,0.007157046,0.01133762,0.01271872,0.006605073,0.002966533,0.0006333228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004661876,"about_ca_system_score_gemma":0.004317735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007870259,"about_ca_topic_score_gemma":0.006699178,"domain_scores_codex":[0.9888542,0.007016029,0.0007848532,0.0004868874,0.002300445,0.0005576456],"domain_scores_gemma":[0.9874238,0.0101832,0.0006594376,0.0007209311,0.0006000058,0.000412672],"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":[0.00005015868,0.0001935761,0.0008611517,0.0002896097,0.00005228945,0.001191785,0.00170946,0.007484778,0.0003066181,0.9431674,0.00294278,0.04175038],"study_design_scores_gemma":[0.0001100576,0.0000629599,0.0003974087,0.0008133681,0.00007975745,0.001354377,0.002883456,0.06931026,0.00103536,0.8405449,0.08333867,0.00006943021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01238762,0.007000753,0.8955454,0.03261396,0.0004442135,0.0003473781,0.0001963545,0.0002053698,0.05125897],"genre_scores_gemma":[0.1947054,0.006317773,0.7918511,0.00177233,0.000337775,0.0004721835,0.000242666,0.0000508382,0.00424982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01503317,"threshold_uncertainty_score":0.07950395,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1917171227","doi":"10.3233/ida-2003-7203","title":"Rule quality for multiple-rule classifier: Empirical expertise and theoretical methodology1","year":2003,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Classifier (UML); Artificial intelligence; Data mining","authors":[{"name":"Ivan Brůha","is_ca":true},{"name":"Josef Tkadlec","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3013180650314298,"gpt":0.4482821620566228,"spread":0.146964097025193,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05140509,0.0009945773,0.00170122,0.007797023,0.0009758134,0.00587047,0.003965331,0.002242107,0.003252864],"category_scores_gemma":[0.2293044,0.0007393919,0.00155561,0.00654577,0.009668567,0.01322956,0.003384763,0.003538206,0.0006437841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003943492,"about_ca_system_score_gemma":0.001821331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001254384,"about_ca_topic_score_gemma":0.0007300204,"domain_scores_codex":[0.9626728,0.01669496,0.002449043,0.004557664,0.01310003,0.0005254788],"domain_scores_gemma":[0.6919765,0.2633453,0.01110201,0.02007232,0.01235176,0.00115214],"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":[0.0001077407,0.0001713387,0.01661466,0.0009092079,0.000142697,0.000194652,0.001385136,0.04892813,0.0009336821,0.7658739,0.001983076,0.1627557],"study_design_scores_gemma":[0.00002007428,0.0001499936,0.00325156,0.0002916983,0.00003875505,0.0004136641,0.0003502752,0.3774912,0.001566551,0.61295,0.003427194,0.00004900023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01625097,0.001507685,0.9768783,0.0008175603,0.00003605443,0.0001140199,0.0001109563,0.0001550739,0.004129382],"genre_scores_gemma":[0.4333797,0.001041561,0.5635796,0.0002453451,0.0002304984,0.0004091332,0.0002986497,0.0001173022,0.0006982457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05140509,"threshold_uncertainty_score":0.2718593,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2604199453","doi":"10.3233/ida-170874","title":"Efficiently mining high utility sequential patterns in static and streaming data","year":2017,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Institute for Information Industry, Ministry of Science and Technology, Taiwan","keywords":"Computer science; Data stream mining; Pruning; Data mining; Data stream; Tree (set theory); Sequential Pattern Mining; Decision tree; Machine learning","authors":[{"name":"Morteza Zihayat","is_ca":true},{"name":"Chengwei Wu","is_ca":false},{"name":"Aijun An","is_ca":true},{"name":"Vincent S. Tseng","is_ca":false},{"name":"Chien Liang Lin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1300096773392162,"gpt":0.3673476933726963,"spread":0.2373380160334802,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001872713,0.001162023,0.001587787,0.003860958,0.0008194018,0.001494852,0.001941163,0.0008935466,0.0005897746],"category_scores_gemma":[0.009227375,0.0007384574,0.001354017,0.005906557,0.0006126369,0.003574622,0.001324856,0.001056443,0.000501877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004634331,"about_ca_system_score_gemma":0.001698829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002551079,"about_ca_topic_score_gemma":0.004097372,"domain_scores_codex":[0.9977797,0.000356561,0.0003320965,0.0006248871,0.0007291996,0.0001775773],"domain_scores_gemma":[0.9940904,0.003044924,0.0008825358,0.0007432824,0.001000807,0.0002381607],"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":[0.0007445329,0.0004441545,0.07389707,0.001178306,0.0004791631,0.003139818,0.000902245,0.1669676,0.02500995,0.01097826,0.008164695,0.7080942],"study_design_scores_gemma":[0.00003882247,0.0001945167,0.004561078,0.00005020725,0.0000910545,0.001216386,0.000422194,0.9494213,0.01052001,0.02952076,0.003930159,0.00003347536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1547491,0.001146294,0.8369743,0.0004450712,0.00006374982,0.0003469596,0.002431015,0.00281662,0.001026942],"genre_scores_gemma":[0.4855084,0.0006947623,0.5073237,0.0001473628,0.00006135631,0.0002911127,0.004626025,0.0001537214,0.001193643],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003860958,"threshold_uncertainty_score":0.009904027,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1877442807","doi":"10.3233/ida-2004-8206","title":"Prediction of oil well production: A multiple-neural-network approach","year":2004,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"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; University of Regina","keywords":"Artificial neural network; Oil well; Production (economics); Oil production; Environmental science; Computer science; Petroleum engineering; Artificial intelligence; Geology; Economics","authors":[{"name":"Hanh H. Nguyen","is_ca":true},{"name":"Christine W. Chan","is_ca":true},{"name":"Malcolm Wilson","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07144835743049187,"gpt":0.283464590018772,"spread":0.2120162325882801,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067673,0.000807197,0.0006740694,0.0005278823,0.0002777965,0.0006953116,0.0009305732,0.0008893569,0.001019819],"category_scores_gemma":[0.001980481,0.0003373895,0.0005343439,0.0004892235,0.0002289252,0.001277353,0.0004981796,0.0008439662,0.0001619667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005333933,"about_ca_system_score_gemma":0.0003788581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004968191,"about_ca_topic_score_gemma":0.005420623,"domain_scores_codex":[0.9996924,0.0000887987,0.00001892925,0.00008432684,0.00008509966,0.0000304388],"domain_scores_gemma":[0.9995009,0.0002784699,0.00007251612,0.0000312076,0.00009316798,0.00002374765],"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":[0.00007397654,0.00005915877,0.001516821,0.00003005383,0.00005245317,0.0000560886,0.00001427844,0.9483906,0.001156562,0.0006799672,0.0001439169,0.04782606],"study_design_scores_gemma":[0.000001702081,0.00001229388,0.0001022048,9.91235e-7,0.000002922849,0.000003526722,0.000001296659,0.9994546,0.0001992397,0.0001949975,0.00002442147,0.000001827045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2448986,0.0008849995,0.7502744,0.0004409634,0.0001146425,0.00004120863,0.0001536642,0.0005267677,0.002664725],"genre_scores_gemma":[0.9372977,0.0002184988,0.06088306,0.00003095032,0.00004127865,0.00003756231,0.00006973666,0.00001949036,0.001401837],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004968191,"threshold_uncertainty_score":0.009878576,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1566313113","doi":"10.3233/ida-140686","title":"Hybrid probabilistic sampling with random subspace for imbalanced data learning","year":2014,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Alberta Innovates; China Scholarship Council","keywords":"Subspace topology; Probabilistic logic; Sampling (signal processing); Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning; Mathematics","authors":[{"name":"Peng Cao","is_ca":true},{"name":"Dazhe Zhao","is_ca":false},{"name":"Osmar R. Zai͏̈ane","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08624709222993643,"gpt":0.333776344110833,"spread":0.2475292518808965,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007610859,0.001092668,0.00269214,0.00217358,0.001034992,0.001477916,0.003161105,0.001436398,0.001827924],"category_scores_gemma":[0.01315385,0.0006583729,0.00163065,0.002906554,0.001718988,0.003149765,0.004106917,0.001995746,0.001115541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008273638,"about_ca_system_score_gemma":0.001693087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001744139,"about_ca_topic_score_gemma":0.002054589,"domain_scores_codex":[0.9915127,0.003884041,0.0003611181,0.001114619,0.002785881,0.0003416584],"domain_scores_gemma":[0.9938413,0.002707133,0.0004360957,0.001597679,0.001132024,0.0002857583],"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":[0.0005230897,0.0003499985,0.003745934,0.0002223801,0.0003188415,0.0001274402,0.0002532434,0.4033127,0.007408777,0.0700656,0.005532429,0.5081396],"study_design_scores_gemma":[0.00001718058,0.00004574079,0.0001917538,0.000004363761,0.000008867671,0.00003553217,0.000009897329,0.9846761,0.001036792,0.01280288,0.001157847,0.00001313175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002369235,0.0001125326,0.9968791,0.00005974748,0.00002260702,0.00003095467,0.00002180574,0.0003087372,0.0001953705],"genre_scores_gemma":[0.2003603,0.0003148505,0.7960692,0.0002574209,0.0002722334,0.0003631901,0.0005542804,0.0001873175,0.001621219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007610859,"threshold_uncertainty_score":0.04025054,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388465895","doi":"10.3233/ida-227006","title":"Resformer: Combine quadratic linear transformation with efficient sparse Transformer for long-term series forecasting","year":2023,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Transformer; Quadratic equation; Time series; Algorithm; Data mining; Artificial intelligence; Machine learning; Mathematics; Engineering; Voltage","authors":[{"name":"Gongguan Chen","is_ca":false},{"name":"Hua Wang","is_ca":false},{"name":"Yepeng Liu","is_ca":false},{"name":"Mingli Zhang","is_ca":true},{"name":"Fan Zhang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3240042070222831,"gpt":0.4432345663334256,"spread":0.1192303593111425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008338026,0.001001034,0.0007744705,0.0007026537,0.0002800515,0.0005560056,0.001096999,0.0006100371,0.003961525],"category_scores_gemma":[0.001934199,0.0004008023,0.0007944896,0.001127148,0.0003932885,0.001669238,0.001022813,0.001203637,0.001326013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003887872,"about_ca_system_score_gemma":0.0008895445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005574192,"about_ca_topic_score_gemma":0.005677863,"domain_scores_codex":[0.9996452,0.00007691564,0.00002565744,0.00009238049,0.0001132175,0.00004658181],"domain_scores_gemma":[0.9995856,0.0001712001,0.00003702211,0.00004956156,0.0001282629,0.00002841443],"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":[0.0002444717,0.0001840151,0.001009897,0.0001140777,0.00009157454,0.0001432857,0.00008685215,0.2706908,0.01937641,0.01007533,0.007929025,0.6900542],"study_design_scores_gemma":[0.000008910723,0.0000373416,0.00009640299,0.000002766501,0.000008184265,0.00002241277,0.000004766502,0.9951982,0.001961547,0.001853331,0.0008000191,0.000006169347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006905416,0.0002399538,0.9902592,0.0001408175,0.00005599917,0.00003104995,0.00006209571,0.001399515,0.0009060465],"genre_scores_gemma":[0.5163332,0.0009233034,0.4715273,0.0005606961,0.0002668556,0.0002227783,0.001029515,0.0004231709,0.008713317],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005574192,"threshold_uncertainty_score":0.01325268,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1560982279","doi":"10.3233/ida-2011-0506","title":"Future trends in business analytics and optimization","year":2011,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Analytics; Business analytics; Data science; Business intelligence; Computer science; Business; Business model; Knowledge management; Business analysis; Marketing","authors":[{"name":"Donald E. Brown","is_ca":false},{"name":"Fazel Famili","is_ca":true},{"name":"Gerhard Paaß","is_ca":false},{"name":"Kate Smith‐Miles","is_ca":false},{"name":"Lyn C. Thomas","is_ca":false},{"name":"Richard W. Weber","is_ca":false},{"name":"Ricardo Baeza‐Yates","is_ca":false},{"name":"Cristián Bravo","is_ca":false},{"name":"Gastón L’Huillier","is_ca":false},{"name":"Sebastián Maldonado","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07903561137995375,"gpt":0.2993903364623755,"spread":0.2203547250824217,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003628173,0.0007894641,0.0009679275,0.002286841,0.0004771995,0.004076196,0.001366981,0.002359089,0.01678072],"category_scores_gemma":[0.008391881,0.0003248779,0.0004650783,0.004895163,0.001481632,0.006981569,0.001124362,0.003348825,0.004708542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001646851,"about_ca_system_score_gemma":0.002040155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001115599,"about_ca_topic_score_gemma":0.001740638,"domain_scores_codex":[0.9983482,0.0005529275,0.00008644532,0.0002353241,0.0006663408,0.0001108079],"domain_scores_gemma":[0.9915589,0.004723342,0.0004327216,0.0003514082,0.002368599,0.0005650833],"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":[0.0001380311,0.0001773593,0.002349984,0.001699533,0.00004797561,0.00007487488,0.0001489566,0.00271672,0.00067279,0.2078949,0.09643757,0.6876413],"study_design_scores_gemma":[0.00003166913,0.0001384811,0.003073094,0.001428438,0.00004442516,0.000328025,0.000683415,0.01636637,0.0004702256,0.2516902,0.7256918,0.00005391998],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.007002785,0.743048,0.04619296,0.122437,0.004869591,0.00005141506,0.0004244733,0.0004853623,0.07548845],"genre_scores_gemma":[0.1076978,0.791069,0.05108253,0.01398342,0.01567579,0.0001281123,0.0009919658,0.0001709656,0.01920037],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01678072,"threshold_uncertainty_score":0.05613714,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111618184","doi":"10.3233/ida-2010-0417","title":"CliDaPa: A new approach to combining clinical data with DNA microarrays","year":2010,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"DNA microarray; Computer science; Data mining; Decision tree; Tree (set theory); Bayesian probability; Machine learning; Artificial intelligence; Gene expression; Gene; Mathematics; Biology","authors":[{"name":"Santiago González","is_ca":false},{"name":"L. Guerra","is_ca":false},{"name":"Vı́ctor Robles","is_ca":false},{"name":"José M. Peña","is_ca":false},{"name":"Fazel Famili","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1202609473562234,"gpt":0.3859514819374388,"spread":0.2656905345812154,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005743708,0.002777889,0.00255857,0.006449196,0.001017778,0.005397592,0.003897247,0.001562018,0.008406623],"category_scores_gemma":[0.01544816,0.001506722,0.002489282,0.006515239,0.00113054,0.003337648,0.005902549,0.003653872,0.005814111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008171888,"about_ca_system_score_gemma":0.00255253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002770848,"about_ca_topic_score_gemma":0.003598799,"domain_scores_codex":[0.9933256,0.001967967,0.0004183406,0.001559003,0.002477688,0.0002513218],"domain_scores_gemma":[0.9923901,0.003839602,0.0003530424,0.001848703,0.001311841,0.0002566815],"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":[0.0005392477,0.000251895,0.005969094,0.001070275,0.000702253,0.0007295819,0.0004276697,0.01512777,0.02879467,0.03712745,0.04110184,0.8681582],"study_design_scores_gemma":[0.0002273051,0.0003477307,0.003496101,0.0002472657,0.0005254283,0.002968892,0.0002993183,0.5119763,0.03598294,0.1870424,0.2565749,0.0003114947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001029495,0.0004837724,0.9864032,0.0003937645,0.0002131206,0.0001854886,0.0009609349,0.008313864,0.002016384],"genre_scores_gemma":[0.02373682,0.0007888027,0.9671655,0.0008878179,0.0003396995,0.0007160209,0.001984719,0.0007534263,0.003627321],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008406623,"threshold_uncertainty_score":0.03037602,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1632892920","doi":"10.3233/ida-2002-6204","title":"Boosting strategy for classification","year":2002,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Canadian Imperial Bank of Commerce (Canada)","funders":"","keywords":"Boosting (machine learning); Artificial intelligence; Computer science; Pattern recognition (psychology); Machine learning","authors":[{"name":"Huma Lodhi","is_ca":false},{"name":"Grigoris Karakoulas","is_ca":true},{"name":"John Shawe‐Taylor","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3118482477726015,"gpt":0.3669830752251207,"spread":0.0551348274525193,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002841031,0.00104655,0.002121652,0.001711456,0.0006452934,0.001813373,0.001979731,0.001567837,0.004615036],"category_scores_gemma":[0.005001955,0.0005268363,0.001400268,0.00163273,0.0008428146,0.001879427,0.001423661,0.002015184,0.005176905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007166605,"about_ca_system_score_gemma":0.0006572455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006992792,"about_ca_topic_score_gemma":0.0005377962,"domain_scores_codex":[0.9978666,0.0007304332,0.0001130952,0.000343969,0.0008216167,0.0001242796],"domain_scores_gemma":[0.9988662,0.0005155922,0.00006041414,0.000205348,0.0003004208,0.00005203005],"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":[0.0001058284,0.0001185103,0.0008492229,0.0003980505,0.0002609662,0.0001319051,0.0001802623,0.1101061,0.007528349,0.2155313,0.0168574,0.6479321],"study_design_scores_gemma":[0.00005024302,0.0001739827,0.0004079934,0.00009741072,0.00007221677,0.0002991505,0.00003228607,0.714772,0.004798845,0.2210786,0.05817013,0.00004720007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001401538,0.00162864,0.9920952,0.0002209661,0.0002011493,0.00007831989,0.0000369215,0.0003519419,0.00398542],"genre_scores_gemma":[0.14296,0.003059284,0.838517,0.0008357405,0.0009663522,0.0006510697,0.0003543807,0.0002346924,0.01242168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004615036,"threshold_uncertainty_score":0.01543885,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1737793783","doi":"10.3233/ida-2011-0496","title":"Robust learning intrusion detection for attacks on wireless networks","year":2011,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Research Nova Scotia; Dalhousie University","keywords":"Computer science; Intrusion detection system; Wireless network; Computer network; Computer security; Wireless; Artificial intelligence; Telecommunications","authors":[{"name":"Adetokunbo Makanju","is_ca":true},{"name":"A. Nur Zincir‐Heywood","is_ca":true},{"name":"Evangelos Milios","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09837774240311724,"gpt":0.2796497730222798,"spread":0.1812720306191625,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006766244,0.001211641,0.001568868,0.002280756,0.0004422436,0.001811694,0.001481686,0.001544028,0.0006685685],"category_scores_gemma":[0.04463968,0.0004198686,0.0008045864,0.001061107,0.001744426,0.003186183,0.001453356,0.001764758,0.0002296639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001652722,"about_ca_system_score_gemma":0.0008277498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001348638,"about_ca_topic_score_gemma":0.0008332355,"domain_scores_codex":[0.9960969,0.00120513,0.0002982885,0.0007230142,0.001389685,0.0002870293],"domain_scores_gemma":[0.9732186,0.018834,0.003657426,0.002039613,0.001976839,0.0002735968],"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":[0.0001254651,0.0001040295,0.00364638,0.00005400244,0.0001218979,0.00004377361,0.00003663807,0.932601,0.003771981,0.003540709,0.0002335279,0.05572056],"study_design_scores_gemma":[0.00000315765,0.00004912215,0.0003393977,0.00000339395,0.000007028614,0.00001933056,0.000005601402,0.9945498,0.002331064,0.002624359,0.00006219365,0.0000055111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1986617,0.0004636948,0.797598,0.0004566389,0.00005620478,0.000121398,0.00009085792,0.001157462,0.001393882],"genre_scores_gemma":[0.910919,0.000200066,0.08779981,0.00009818394,0.0000502697,0.00008428254,0.0001503884,0.0000885402,0.0006094953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006766244,"threshold_uncertainty_score":0.03578377,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1567354125","doi":"10.3233/ida-140658","title":"Interactive document clustering with feature supervision through reweighting1","year":2014,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Cluster analysis; Computer science; Feature (linguistics); Set (abstract data type); Feature selection; Data mining; Ask price; Information retrieval; Artificial intelligence; Machine learning; Pattern recognition (psychology)","authors":[{"name":"Yeming Hu","is_ca":true},{"name":"Evangelos Milios","is_ca":true},{"name":"James Blustein","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02899920739437957,"gpt":0.296214113902025,"spread":0.2672149065076454,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001774407,0.001384536,0.001583693,0.002753884,0.001112786,0.001550264,0.002838284,0.001345678,0.005740079],"category_scores_gemma":[0.005384294,0.0005993696,0.001237073,0.003441252,0.0008018115,0.001848933,0.002226921,0.001394192,0.004423703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000688198,"about_ca_system_score_gemma":0.001052873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007188525,"about_ca_topic_score_gemma":0.01703994,"domain_scores_codex":[0.9981539,0.0003418122,0.0001301488,0.0006071109,0.0006153019,0.0001517831],"domain_scores_gemma":[0.997018,0.000916306,0.0002009197,0.0009869592,0.0007579903,0.0001198611],"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":[0.0004194241,0.0002910613,0.00129437,0.0002004174,0.0001793141,0.00008673733,0.0003210593,0.0151667,0.04957822,0.002499973,0.01040683,0.9195559],"study_design_scores_gemma":[0.0001143025,0.0002506088,0.003387925,0.00004065151,0.0001453629,0.0003643292,0.0001726749,0.8679183,0.09291619,0.01034491,0.02423821,0.0001065916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01230782,0.0002435674,0.9760122,0.00008056335,0.00006900359,0.0001822888,0.0003349648,0.009567067,0.001202616],"genre_scores_gemma":[0.08902493,0.0001583989,0.9024267,0.00007934745,0.0001021423,0.0002683064,0.001594248,0.0009634387,0.005382349],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007188525,"threshold_uncertainty_score":0.01920247,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1892337079","doi":"10.3233/ida-2012-00563","title":"Periodic pattern analysis of non-uniformly sampled stock market data","year":2012,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Stock market; Data mining; Time series; Missing data; Transaction data; Database transaction; Algorithm; Biological data; Machine learning","authors":[{"name":"Faraz Rasheed","is_ca":true},{"name":"Reda Alhajj","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08593796217694967,"gpt":0.3167714887996536,"spread":0.2308335266227039,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00142811,0.0005001153,0.0007593648,0.00232114,0.0003562392,0.0006441778,0.000578442,0.0004360558,0.000670277],"category_scores_gemma":[0.005550854,0.000141647,0.0005579405,0.001927285,0.0002371414,0.0007521865,0.0003551527,0.0004836127,0.0002660499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002803791,"about_ca_system_score_gemma":0.0004313699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002475002,"about_ca_topic_score_gemma":0.001993549,"domain_scores_codex":[0.9989282,0.0002058096,0.000160495,0.0001945226,0.0004167614,0.00009409295],"domain_scores_gemma":[0.9971897,0.001399642,0.0003952097,0.0004074756,0.0005305677,0.00007738307],"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":[0.002123126,0.0007365625,0.0801429,0.000951808,0.0005853429,0.004082776,0.0007092764,0.1329981,0.0698293,0.005709612,0.006859324,0.6952718],"study_design_scores_gemma":[0.0000347225,0.0002388341,0.03166848,0.00002975904,0.00006065005,0.0006734309,0.0002335171,0.9494648,0.01226266,0.003807215,0.00150031,0.00002564135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6657234,0.001317969,0.3268239,0.0003369731,0.0001715143,0.0002156466,0.002150979,0.001552524,0.001707153],"genre_scores_gemma":[0.8824111,0.0004501186,0.1129434,0.00006192499,0.00006101844,0.0001033882,0.003314349,0.0000490463,0.0006057271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002475002,"threshold_uncertainty_score":0.007552683,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4294770356","doi":"10.3233/ida-216149","title":"Influence maximization based on network representation learning in social network","year":2022,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Random walk; Social network (sociolinguistics); Maximization; Representation (politics); Node (physics); Heuristic; Embedding; Theoretical computer science; Social network analysis; Artificial intelligence; Machine learning; Mathematical optimization; Mathematics; World Wide Web","authors":[{"name":"Zhibin Wang","is_ca":false},{"name":"Xiaoliang Chen","is_ca":true},{"name":"Xianyong Li","is_ca":false},{"name":"Yajun Du","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03793808942495143,"gpt":0.3282187782115389,"spread":0.2902806887865875,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001402094,0.001398053,0.001385469,0.002003763,0.0006812789,0.001281981,0.001833954,0.001274101,0.001420052],"category_scores_gemma":[0.0066992,0.0005792221,0.001274009,0.001883757,0.001112947,0.0027203,0.001724647,0.001912033,0.0004456632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134568,"about_ca_system_score_gemma":0.0009670178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00403751,"about_ca_topic_score_gemma":0.003894454,"domain_scores_codex":[0.9988914,0.000398959,0.0000515923,0.0003190546,0.0002363506,0.0001026711],"domain_scores_gemma":[0.9975605,0.001564954,0.0003142313,0.0001958067,0.000260013,0.0001045977],"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":[0.00009389172,0.0001116879,0.002496304,0.000244472,0.0001452331,0.000155907,0.0002290198,0.7705365,0.002546147,0.0476477,0.003761112,0.172032],"study_design_scores_gemma":[0.000003746613,0.0000098304,0.0001266749,0.000007231814,0.000008750996,0.00001881313,0.00000999397,0.9877346,0.0004242972,0.01122964,0.0004210294,0.000005351816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009178964,0.0002665689,0.9888633,0.0001852766,0.00002449511,0.00005305293,0.00007228319,0.0003095768,0.00104653],"genre_scores_gemma":[0.5529196,0.0008702415,0.4402648,0.0002984676,0.000212252,0.0004658956,0.0009531982,0.0002441003,0.003771435],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00403751,"threshold_uncertainty_score":0.009763658,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1696916719","doi":"10.3233/ida-2003-7202","title":"Iceberg-cube algorithms: An empirical evaluation on synthetic and real data","year":2003,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Crossover; Pruning; Algorithm; Point (geometry); Cube (algebra); Computer science; Set (abstract data type); Function (biology); Mathematics; Artificial intelligence; Combinatorics; Geometry","authors":[{"name":"Leah Findlater","is_ca":true},{"name":"Howard J. Hamilton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1953116273521738,"gpt":0.4085211079741043,"spread":0.2132094806219306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02012682,0.002250579,0.002272228,0.004294194,0.001683105,0.002590224,0.003821531,0.002564939,0.001341764],"category_scores_gemma":[0.05417858,0.000679586,0.001206058,0.006865019,0.001732252,0.005280395,0.00227937,0.001924502,0.0006086807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002306112,"about_ca_system_score_gemma":0.002610051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01062315,"about_ca_topic_score_gemma":0.0108062,"domain_scores_codex":[0.9894301,0.004354293,0.001221156,0.001294507,0.003231501,0.0004685252],"domain_scores_gemma":[0.9314313,0.05411328,0.00167616,0.005192518,0.006606987,0.0009796491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002548167,0.002416434,0.02003376,0.001653105,0.0008639675,0.0002782251,0.0005415792,0.7160094,0.001851526,0.006533066,0.02709514,0.2201755],"study_design_scores_gemma":[0.0002797604,0.0004878947,0.001943995,0.00006544912,0.00007314987,0.0001615181,0.0002360298,0.9862027,0.003007734,0.004782867,0.002721512,0.00003735735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8597104,0.006612657,0.1041504,0.00188391,0.0005395467,0.001231386,0.009359032,0.007513079,0.008999566],"genre_scores_gemma":[0.6073503,0.001767886,0.3711777,0.0005291632,0.0001297503,0.0008198126,0.01640904,0.0005574462,0.001258854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02012682,"threshold_uncertainty_score":0.1064421,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1888927200","doi":"10.3233/ida-2012-00557","title":"Feature ranking fusion for text classifier","year":2012,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo; Ontario Tech University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Computer science; Fusion; Machine learning; Natural language processing; Linguistics","authors":[{"name":"Masoud Makrehchi","is_ca":true},{"name":"Mohamed S. Kamel","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09784571685237035,"gpt":0.34321365686519,"spread":0.2453679400128196,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001966682,0.001046618,0.001959224,0.003470602,0.000818617,0.001630741,0.001114403,0.001267718,0.004750905],"category_scores_gemma":[0.004466045,0.0002322464,0.001357648,0.003370246,0.0002968886,0.002049007,0.0008757263,0.001018059,0.004605236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000711391,"about_ca_system_score_gemma":0.0009445066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001982544,"about_ca_topic_score_gemma":0.002142894,"domain_scores_codex":[0.9976234,0.0003833077,0.0001793722,0.0004089478,0.001134876,0.0002701042],"domain_scores_gemma":[0.9978343,0.0004509657,0.0001370762,0.0003081781,0.001177153,0.00009241597],"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":[0.000623573,0.0003137747,0.002100086,0.0002294955,0.0001798914,0.0001698874,0.00006540603,0.01924778,0.05403979,0.003595211,0.01965419,0.8997809],"study_design_scores_gemma":[0.00007071642,0.0005618205,0.006620081,0.00004658868,0.0003071811,0.0003534452,0.00008402846,0.9083929,0.05756773,0.01014059,0.0157492,0.00010567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04925236,0.002785372,0.9327549,0.0004777496,0.0005783925,0.0002764596,0.001353777,0.006273726,0.006247205],"genre_scores_gemma":[0.6586749,0.001056349,0.3239091,0.0003431913,0.0008828534,0.0004550589,0.004938137,0.0003013719,0.009439055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004750905,"threshold_uncertainty_score":0.01589334,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4385789921","doi":"10.3233/ida-220383","title":"Oversampling method based on GAN for tabular binary classification problems","year":2023,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Oversampling; Artificial intelligence; Regularization (linguistics); Binary number; Computer science; Resampling; Pattern recognition (psychology); Skew; Binary classification; Machine learning; Generalization; Class (philosophy); Algorithm; Mathematics; Support vector machine","authors":[{"name":"Jie Yang","is_ca":false},{"name":"Zhenhao Jiang","is_ca":false},{"name":"Tingting Pan","is_ca":false},{"name":"Yueqi Chen","is_ca":false},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1909662518164376,"gpt":0.4044797046661944,"spread":0.2135134528497568,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001881867,0.0008936266,0.001210215,0.000664232,0.0003036486,0.0006141274,0.001089489,0.0006784319,0.00133131],"category_scores_gemma":[0.003426255,0.0002586127,0.0007544082,0.0005510868,0.0006540608,0.0009651883,0.0007938198,0.001322197,0.000410525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006120262,"about_ca_system_score_gemma":0.0004988684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001613479,"about_ca_topic_score_gemma":0.001830194,"domain_scores_codex":[0.999293,0.0002906169,0.00003098727,0.0001405769,0.0001791978,0.00006565664],"domain_scores_gemma":[0.9988809,0.0005874321,0.0001126774,0.0001735914,0.000186929,0.00005846284],"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":[0.000310458,0.0001359724,0.002786487,0.0001371565,0.0001025859,0.0001141613,0.0001324047,0.6670389,0.007460626,0.01718881,0.006272383,0.2983201],"study_design_scores_gemma":[0.000006376673,0.00002407348,0.0001552338,0.000004843019,0.000005983222,0.00002735608,0.000005586899,0.9946433,0.0007401223,0.003923358,0.0004596969,0.00000391097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02716057,0.0007705725,0.9688962,0.0002288152,0.0000921629,0.00008119622,0.0001001095,0.000979016,0.001691348],"genre_scores_gemma":[0.7516256,0.0006410461,0.2427264,0.0004813076,0.0002011343,0.0002434865,0.0008132823,0.0001960866,0.00307148],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001881867,"threshold_uncertainty_score":0.009952366,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406855646","doi":"10.3233/ida-220449","title":"Detection of multi-size peach in orchard using RGB-D camera combined with an improved DEtection Transformer model","year":2023,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"RGB color model; Orchard; Artificial intelligence; Transformer; Computer science; Computer vision; Computer graphics (images); Horticulture; Engineering; Biology; Voltage; Electrical engineering","authors":[{"name":"Yu Yang","is_ca":true},{"name":"Xin Wang","is_ca":false},{"name":"Zhenfang Liu","is_ca":false},{"name":"Min Huang","is_ca":false},{"name":"Shangpeng Sun","is_ca":true},{"name":"Qibing Zhu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07605681010673757,"gpt":0.2869816376120741,"spread":0.2109248275053366,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002501171,0.0006739599,0.0004037744,0.000638791,0.0001448505,0.0003652652,0.0007120028,0.0003259031,0.0009833495],"category_scores_gemma":[0.0002697156,0.0002878873,0.0003171201,0.0003294623,0.0001572031,0.0005920259,0.0004718074,0.0002921851,0.0004116503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004337115,"about_ca_system_score_gemma":0.0004277886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005410696,"about_ca_topic_score_gemma":0.0125355,"domain_scores_codex":[0.9998116,0.00001340046,0.000006261683,0.00008115636,0.00006208218,0.00002549617],"domain_scores_gemma":[0.9999043,0.00001303792,0.00001096243,0.00001681369,0.00004532887,0.0000095569],"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":[0.0005136331,0.0002120269,0.01543286,0.0002317631,0.0001367851,0.0003217757,0.0001023101,0.07232188,0.404933,0.001295741,0.003203256,0.5012951],"study_design_scores_gemma":[0.00001509542,0.0001247422,0.01097661,0.000007348673,0.00004123925,0.0002479613,0.00002810344,0.9053764,0.08109244,0.0003887854,0.00167847,0.00002289211],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1534385,0.0003430581,0.8377334,0.0001069774,0.0000828787,0.0001055148,0.0004226854,0.004575692,0.003191342],"genre_scores_gemma":[0.7901633,0.0001951086,0.2049972,0.00009430213,0.0000188468,0.00006482755,0.0005400939,0.00006621772,0.003859989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005410696,"threshold_uncertainty_score":0.0107584,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4402514349","doi":"10.3233/ida-230765","title":"A deep learning-based neural style transfer optimization approach","year":2024,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brandon University","funders":"","keywords":"Transfer of learning; Artificial intelligence; Style (visual arts); Computer science; Deep learning; Artificial neural network; Machine learning; Geography","authors":[{"name":"Rhythm Bhardwaj","is_ca":false},{"name":"Nonita Sharma","is_ca":false},{"name":"Deepak Kumar Sharma","is_ca":false},{"name":"Gautam Srivastava","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03733929827427201,"gpt":0.2842154847685892,"spread":0.2468761864943171,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005746419,0.0009128925,0.0007894126,0.0006330352,0.0002785561,0.0007936074,0.001649145,0.001127698,0.00395351],"category_scores_gemma":[0.000705258,0.0004606266,0.0008894167,0.0007456566,0.000438296,0.0008580519,0.0008756387,0.001432916,0.0009526123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001332679,"about_ca_system_score_gemma":0.0008466758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004843543,"about_ca_topic_score_gemma":0.006040515,"domain_scores_codex":[0.999796,0.00003107863,0.000009837769,0.00005630168,0.00007153687,0.00003524431],"domain_scores_gemma":[0.9998156,0.00005062384,0.00002144186,0.00002834888,0.00006757075,0.00001641867],"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":[0.00005837415,0.00009118352,0.0003489853,0.00007785555,0.00008332345,0.00006434017,0.00004154185,0.7276278,0.0104107,0.009402984,0.003395961,0.2483969],"study_design_scores_gemma":[0.00000293276,0.00001189756,0.00003938334,0.000002989558,0.000004580503,0.000008711197,0.000001795948,0.9972205,0.0008122725,0.001491037,0.000401521,0.000002338406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01445398,0.0003667039,0.978372,0.0001512944,0.00006947818,0.00006278945,0.00008351268,0.001173982,0.005266279],"genre_scores_gemma":[0.5025292,0.0005544967,0.465683,0.0005440259,0.0001471227,0.0002415183,0.000567209,0.0004418502,0.02929156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004843543,"threshold_uncertainty_score":0.01322585,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1958712682","doi":"10.3233/ida-150771","title":"How much effort should be spent to detect fraudulent applications when engaged in classifier-based lending?","year":2015,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"Loan; Profit (economics); Computer science; Business; Classifier (UML); Actuarial science; Finance; Artificial intelligence; Economics; Microeconomics","authors":[{"name":"Mimi Chong","is_ca":true},{"name":"Cristián Bravo","is_ca":false},{"name":"Matt Davison","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1822484865133957,"gpt":0.3154700617981531,"spread":0.1332215752847574,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01167988,0.0009181389,0.001192198,0.002286216,0.001609639,0.005309931,0.001704597,0.003405175,0.003568133],"category_scores_gemma":[0.09108146,0.0006114196,0.0005249217,0.00158628,0.001922074,0.00980171,0.001439795,0.001976647,0.00325054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001770882,"about_ca_system_score_gemma":0.002899098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005435206,"about_ca_topic_score_gemma":0.006605877,"domain_scores_codex":[0.9898224,0.005639394,0.0005515165,0.001110548,0.001935981,0.000940179],"domain_scores_gemma":[0.9513078,0.02497066,0.006693905,0.005605034,0.009223784,0.002198755],"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":[0.0007589494,0.001325448,0.1635928,0.0004787391,0.0002486471,0.00025972,0.001000488,0.01042052,0.005241505,0.01298921,0.0212124,0.7824716],"study_design_scores_gemma":[0.000318629,0.001634049,0.4943703,0.001741437,0.000400418,0.002427331,0.01895466,0.2988529,0.01622633,0.1183714,0.046328,0.0003745316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6540969,0.00605832,0.1613879,0.1378566,0.001047821,0.0005622148,0.0005405998,0.001298084,0.03715153],"genre_scores_gemma":[0.9466927,0.001220679,0.04560784,0.00255909,0.0002446857,0.000149995,0.0001733999,0.0001128216,0.003238772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01167988,"threshold_uncertainty_score":0.06176978,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3118013975","doi":"10.3233/ida-194719","title":"Uni- and multivariate probability density models for numeric subgroup discovery","year":2020,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"","keywords":"Univariate; Kernel density estimation; Multivariate statistics; Curse of dimensionality; Exploratory data analysis; Variance (accounting); Computer science; Statistics; Data mining; Mathematics; Artificial intelligence; Pattern recognition (psychology); Estimator","authors":[{"name":"Marvin Meeng","is_ca":false},{"name":"Harm de Vries","is_ca":true},{"name":"Peter Flach","is_ca":false},{"name":"Siegfried Nijssen","is_ca":false},{"name":"Arno Knobbe","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1256607281594774,"gpt":0.3117743928853609,"spread":0.1861136647258835,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009522427,0.001090703,0.001985468,0.003830418,0.0008647166,0.002747174,0.003794468,0.00193429,0.004868919],"category_scores_gemma":[0.03514613,0.0007482608,0.002774577,0.004565386,0.001884718,0.004801149,0.002925275,0.003798906,0.00170544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002104058,"about_ca_system_score_gemma":0.001740596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006127077,"about_ca_topic_score_gemma":0.004651079,"domain_scores_codex":[0.9950019,0.002579903,0.0002359792,0.0009285064,0.0009442438,0.0003095057],"domain_scores_gemma":[0.9778335,0.01605053,0.001604659,0.002714397,0.001409837,0.0003869525],"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":[0.0002398794,0.0002116396,0.01101993,0.0002512923,0.000345161,0.0002233478,0.0005067338,0.3161451,0.00062795,0.5246367,0.006448483,0.1393438],"study_design_scores_gemma":[0.00001065719,0.00002017136,0.0006407356,0.00002574378,0.0000274314,0.00005671865,0.00003938352,0.7990014,0.0001404309,0.1977507,0.002265747,0.00002083024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004281031,0.0004408919,0.9935039,0.0003255424,0.0000333915,0.00006155171,0.0002978208,0.0002865432,0.0007694525],"genre_scores_gemma":[0.4738481,0.002249504,0.5112156,0.0005190495,0.0005052999,0.001086404,0.002388838,0.000267856,0.007919258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009522427,"threshold_uncertainty_score":0.05035996,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1576444319","doi":"10.3233/ida-2012-0549","title":"Improving multi-view semi-supervised learning with agreement-based sampling","year":2012,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"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","keywords":"Machine learning; Semi-supervised learning; Computer science; Co-training; Artificial intelligence; Supervised learning; Sampling (signal processing); Set (abstract data type); Relation (database); Independence (probability theory); Unsupervised learning; Quality (philosophy); Instance-based learning; Data mining; Mathematics; Artificial neural network; Statistics","authors":[{"name":"Jin Huang","is_ca":true},{"name":"Jelber Sayyad-Shirabad","is_ca":true},{"name":"Stan Matwin","is_ca":true},{"name":"Su Jiang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07268107837048085,"gpt":0.32335071251491,"spread":0.2506696341444292,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01616286,0.002431763,0.006033416,0.002296882,0.001677928,0.00294609,0.006437096,0.003832091,0.003079601],"category_scores_gemma":[0.05633185,0.001530552,0.002818048,0.002622504,0.002180443,0.006360752,0.005766747,0.004867542,0.002256926],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118924,"about_ca_system_score_gemma":0.002754127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004297629,"about_ca_topic_score_gemma":0.004268992,"domain_scores_codex":[0.9840853,0.008666559,0.0009338726,0.002411938,0.003332994,0.000569362],"domain_scores_gemma":[0.939038,0.03948702,0.00235808,0.009452324,0.008447405,0.001217224],"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":[0.001279825,0.0006832443,0.005489498,0.0004887558,0.0008251771,0.0002662855,0.0006663176,0.5364379,0.00493687,0.02112027,0.01161546,0.4161904],"study_design_scores_gemma":[0.00003143603,0.00005267296,0.0001350541,0.000009251284,0.00001926924,0.00003037441,0.00001808331,0.9929239,0.0006311256,0.005846591,0.0002910649,0.00001120888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005800221,0.0002319919,0.9924523,0.000104291,0.00004544457,0.00006722162,0.00005713175,0.0009170952,0.0003243399],"genre_scores_gemma":[0.3436053,0.0003489502,0.6494132,0.0006159651,0.0004251166,0.0005046791,0.002401565,0.0006810443,0.002004258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01616286,"threshold_uncertainty_score":0.08547837,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1556196612","doi":"10.3233/ida-2011-0501","title":"Mining sequential patterns with extensible knowledge representation","year":2011,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Data mining; Knowledge extraction; Representation (politics); Set (abstract data type); Pruning; Knowledge representation and reasoning; Association rule learning; K-optimal pattern discovery; Apriori algorithm; A priori and a posteriori; Machine learning; Artificial intelligence","authors":[{"name":"Shang Gao","is_ca":true},{"name":"Reda Alhajj","is_ca":true},{"name":"Jon Rokne","is_ca":true},{"name":"Jiwen Guan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.182989932994737,"gpt":0.3504488406659183,"spread":0.1674589076711813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002235093,0.0005923841,0.0006899339,0.003412938,0.0003878365,0.002141753,0.001634506,0.0007091454,0.001936173],"category_scores_gemma":[0.01044613,0.0004606525,0.001175604,0.003976878,0.0005520749,0.005211531,0.001872405,0.001014133,0.000581191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003811658,"about_ca_system_score_gemma":0.0007901784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001661055,"about_ca_topic_score_gemma":0.001978997,"domain_scores_codex":[0.9983642,0.0003489731,0.0003772831,0.0003263251,0.0005003909,0.00008293834],"domain_scores_gemma":[0.9958977,0.001833476,0.0004829148,0.001250113,0.0004542058,0.00008158022],"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":[0.0003991336,0.0003995098,0.008794835,0.000587207,0.0003257275,0.001328026,0.0006827902,0.07430415,0.009762631,0.06176614,0.005222535,0.8364274],"study_design_scores_gemma":[0.00008067258,0.0001911124,0.002229824,0.000238981,0.0002464168,0.0009410848,0.0002585297,0.7941144,0.01031503,0.1743866,0.01694213,0.00005525837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04141129,0.0005938362,0.9503316,0.0004484146,0.00005389416,0.0002656642,0.001480731,0.003188071,0.002226569],"genre_scores_gemma":[0.1798047,0.0007697383,0.8127372,0.0001781354,0.00004334829,0.0002724838,0.004763094,0.0000833661,0.001348038],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003412938,"threshold_uncertainty_score":0.0118205,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2198714519","doi":"10.3233/ida-150780","title":"A geometric density-based sample reduction method","year":2015,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Classifier (UML); Sample (material); Pattern recognition (psychology); Data mining; Sample space; Reduction (mathematics); Cluster (spacecraft); Artificial intelligence; Selection (genetic algorithm); Mathematics","authors":[{"name":"Mehdi Mohammadi","is_ca":true},{"name":"Bijan Raahemi","is_ca":true},{"name":"Ahmad Akbari","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1562675010934071,"gpt":0.3830509522967132,"spread":0.226783451203306,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001283553,0.0007804169,0.001299274,0.003056539,0.0006988215,0.001039902,0.002015274,0.0006738867,0.001552204],"category_scores_gemma":[0.006214283,0.0004853947,0.001351306,0.002387079,0.0007880278,0.001342422,0.00165667,0.001187981,0.0009943472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008251073,"about_ca_system_score_gemma":0.001364753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004433422,"about_ca_topic_score_gemma":0.003526648,"domain_scores_codex":[0.9978333,0.0002945624,0.0001241417,0.0003712425,0.001261477,0.000115256],"domain_scores_gemma":[0.9977168,0.0006705047,0.0001608179,0.0003565659,0.001032297,0.00006316582],"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":[0.0002766544,0.0002440163,0.005626719,0.0002712974,0.00020169,0.0001797699,0.0002664579,0.1355131,0.02667111,0.02049072,0.008955042,0.8013034],"study_design_scores_gemma":[0.00002817224,0.000078745,0.001979927,0.00001382043,0.000043913,0.0002548179,0.00005196483,0.9744794,0.01060547,0.005631232,0.006795132,0.00003737597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005914905,0.0001403221,0.9924495,0.00009115864,0.00004436443,0.00009298159,0.00007604998,0.0006097946,0.000580895],"genre_scores_gemma":[0.1304801,0.0003476583,0.865042,0.0001488712,0.0001568695,0.0003886608,0.0008769511,0.0001726881,0.002386211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004433422,"threshold_uncertainty_score":0.008815229,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1595816206","doi":"10.3233/ida-2000-43-414","title":"An architectural framework for hybrid intelligent systems: Implementation issues","year":2000,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Artificial neural network; Modularity (biology); Set (abstract data type); Hybrid system; Artificial intelligence; Programming language; Machine learning","authors":[{"name":"Narate Lertpalangsunti","is_ca":true},{"name":"Christine W. Chan","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0433581560745468,"gpt":0.3467810065893082,"spread":0.3034228505147614,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004567995,0.0009556723,0.0007520327,0.0009619293,0.001141038,0.005796882,0.004923571,0.002600671,0.0066493],"category_scores_gemma":[0.003840183,0.001210684,0.0018039,0.0008572168,0.002523158,0.006249368,0.003600288,0.00334662,0.002358512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001549569,"about_ca_system_score_gemma":0.001877373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00346532,"about_ca_topic_score_gemma":0.002781691,"domain_scores_codex":[0.9977772,0.0006939429,0.0002297553,0.0003480925,0.0007018695,0.0002491255],"domain_scores_gemma":[0.9987094,0.0004327464,0.00006834406,0.0003815431,0.0002619623,0.0001460688],"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":[0.00007098213,0.0001167515,0.0006477322,0.0004164454,0.0001213422,0.0004078442,0.001526012,0.06169259,0.006943849,0.8292072,0.004197229,0.09465203],"study_design_scores_gemma":[0.00008738733,0.0002141415,0.0004214416,0.0004778078,0.0001637185,0.0006701107,0.0004467454,0.3841222,0.008944571,0.3270432,0.2772867,0.000121932],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001963249,0.0001705874,0.9918283,0.0003200122,0.00003943128,0.00006376227,0.00002471916,0.001881858,0.003708065],"genre_scores_gemma":[0.05134536,0.0004311292,0.9420183,0.0001637928,0.00004612674,0.0003421397,0.0002149704,0.0005757621,0.004862362],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0066493,"threshold_uncertainty_score":0.02415818,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2107698269","doi":"10.3233/ida-2011-0514","title":"Functional characterization of drug-protein interactions network","year":2012,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computational biology; Drug; Drug discovery; Drug target; Interaction network; Computer science; Amino acid; Drug development; Mechanism (biology); Biology; Bioinformatics; Genetics; Gene; Biochemistry; Pharmacology","authors":[{"name":"Mona Okasha","is_ca":true},{"name":"Abdallah M. ElSkeikh","is_ca":true},{"name":"Mohammed Alshalalfa","is_ca":true},{"name":"Ghada Naji","is_ca":true},{"name":"Reda Alhajj","is_ca":true},{"name":"Jon Rokne","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07849948687403212,"gpt":0.3355607606022977,"spread":0.2570612737282655,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004572148,0.000397303,0.0002606679,0.004265801,0.0004463932,0.0005925043,0.000433627,0.000473095,0.002828483],"category_scores_gemma":[0.002387926,0.00009451965,0.0004910364,0.002037642,0.0002681771,0.001056147,0.0002994924,0.0002028738,0.0002778783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006185404,"about_ca_system_score_gemma":0.0003445221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002957827,"about_ca_topic_score_gemma":0.001721034,"domain_scores_codex":[0.9995814,0.0001104323,0.00003484643,0.00008227876,0.0001335572,0.00005747485],"domain_scores_gemma":[0.9985738,0.0006538593,0.0002448857,0.0001015415,0.0003549776,0.00007100801],"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":[0.0006044933,0.0003193584,0.1068061,0.001643889,0.0003892786,0.00163657,0.0006983982,0.3678127,0.09151258,0.1479561,0.007685997,0.2729344],"study_design_scores_gemma":[0.00001079573,0.0001204127,0.05600064,0.00006239201,0.0000905235,0.0008411201,0.0003749559,0.8858083,0.01365127,0.03277439,0.01022788,0.00003731375],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6113392,0.002156656,0.3498877,0.0008373032,0.00005933766,0.0003506429,0.008422844,0.0009015652,0.0260449],"genre_scores_gemma":[0.9627858,0.0006675489,0.0302152,0.00004077406,0.00003018937,0.0001485209,0.003878594,0.00003275157,0.002200696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004265801,"threshold_uncertainty_score":0.009462178,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4256380542","doi":"10.3233/ida-120564","title":"Guest Editorial","year":2013,"lang":"es","type":"editorial","venue":"Intelligent Data Analysis","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Illinois at Urbana-Champaign; Universidade Federal de Minas Gerais; KU Leuven; Università degli Studi di Pavia; Institut National des Sciences Appliquées de Lyon; Institute of Genetics; Indian National Science Academy","keywords":"Political science","authors":[{"name":"Ruggero G. Pensa","is_ca":false},{"name":"Francesca Cordero","is_ca":false},{"name":"Céine Rouveirol","is_ca":false},{"name":"Rushed Kanawati","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02963828349743141,"gpt":0.3297877078129713,"spread":0.3001494243155399,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002575194,0.001336488,0.001182218,0.002104492,0.001571545,0.006897776,0.002247164,0.003736527,0.3994198],"category_scores_gemma":[0.01457947,0.0004828765,0.001138194,0.001253151,0.0009281703,0.004306267,0.00282675,0.005084404,0.2579127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001691011,"about_ca_system_score_gemma":0.002867913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009487636,"about_ca_topic_score_gemma":0.001221656,"domain_scores_codex":[0.9972669,0.0003514227,0.0002218195,0.0007297784,0.001160588,0.0002695932],"domain_scores_gemma":[0.990716,0.00134563,0.0004901275,0.000914427,0.004247254,0.002286609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003062898,0.00001450657,0.00008505641,0.0001613497,0.000006233024,0.00008367786,0.00001942847,0.00004321344,0.000114323,0.002061826,0.9706284,0.02675119],"study_design_scores_gemma":[0.000007536888,0.000009205442,0.0001074606,0.0001055728,0.000003871744,0.0001050165,0.00002462828,0.00004955152,0.00007373008,0.001175671,0.9983334,0.000004406201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.0006430823,0.01207714,0.002399923,0.06515083,0.7463036,0.0001912423,0.003009632,0.00134031,0.1688842],"genre_scores_gemma":[0.007843008,0.0168173,0.002205104,0.03437922,0.3858451,0.0002345207,0.004494644,0.001461899,0.5467192],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.3994198,"threshold_uncertainty_score":0.8566548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2979163681","doi":"10.3233/ida-194486","title":"A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification","year":2020,"lang":"en","type":"preprint","venue":"Intelligent Data Analysis","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Dimensionality reduction; Pattern recognition (psychology); Principal component analysis; Linear discriminant analysis; Computer science; Support vector machine; Kernel principal component analysis; Kernel (algebra); Benchmark (surveying); Feature extraction; Local binary patterns; Dimension (graph theory); Machine learning; Kernel method; Histogram; Mathematics; Image (mathematics)","authors":[{"name":"Katherine Kempfert","is_ca":false},{"name":"Yishi Wang","is_ca":false},{"name":"Cuixian Chen","is_ca":true},{"name":"Samuel W. K. Wong","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1920095745022328,"gpt":0.4478469634700107,"spread":0.2558373889677779,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002862042,0.001037425,0.001261028,0.001973212,0.0004018668,0.001892745,0.0008266966,0.0008854179,0.001740873],"category_scores_gemma":[0.01029424,0.0003452418,0.00110786,0.002150255,0.0006905293,0.002190863,0.001075324,0.001058228,0.0005619713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006902309,"about_ca_system_score_gemma":0.0008223362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003339036,"about_ca_topic_score_gemma":0.001777433,"domain_scores_codex":[0.998174,0.0007289654,0.00014711,0.0002955729,0.0005619383,0.00009246374],"domain_scores_gemma":[0.9946549,0.002844298,0.0002966688,0.0007777027,0.001299423,0.0001270112],"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":[0.0005883278,0.0003229657,0.0033974,0.0006888522,0.0002419425,0.00007650672,0.0002426672,0.1159791,0.006880614,0.01463714,0.005381838,0.8515626],"study_design_scores_gemma":[0.00002791075,0.0002661897,0.003372864,0.00006123274,0.00005717277,0.0001631682,0.0000894269,0.9812506,0.004620295,0.0048678,0.00516935,0.00005394857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1060097,0.02677217,0.8564581,0.001383518,0.0004409014,0.0002219414,0.0002352168,0.001621108,0.006857285],"genre_scores_gemma":[0.4900041,0.01382086,0.4896221,0.0001974116,0.0003294819,0.0002641235,0.000734003,0.0005377032,0.004490197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003339036,"threshold_uncertainty_score":0.01513606,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}