{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":50,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":50,"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":"e4cb37757b38","filters":{"venue":"Journal of Intelligent Information Systems"}},"results":[{"id":"W1987149779","doi":"10.1023/b:jiis.0000029668.88665.1a","title":"Interval Set Clustering of Web Users with Rough K-Means","year":2004,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":523,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Data mining; Web mining; Rough set; Fuzzy clustering; Set (abstract data type); Information retrieval; Machine learning; Web page; World Wide Web","authors":[{"name":"Pawan Lingras","is_ca":true},{"name":"Chad West","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02651878657574229,"gpt":0.2466337281872999,"spread":0.2201149416115576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002449439,0.0005508547,0.001172563,0.003658762,0.001190225,0.002348727,0.001580929,0.001022894,0.001397177],"category_scores_gemma":[0.007936334,0.0003848354,0.0014276,0.003484356,0.0004956325,0.001778397,0.001040715,0.0008187009,0.0006556385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228493,"about_ca_system_score_gemma":0.001236097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01288431,"about_ca_topic_score_gemma":0.007988765,"domain_scores_codex":[0.9979873,0.0006584743,0.0001886604,0.0003396314,0.0006302668,0.0001956781],"domain_scores_gemma":[0.9975158,0.001131958,0.0001896181,0.0003805914,0.0007005593,0.00008142851],"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":[0.002064795,0.0006110775,0.01757067,0.0003560113,0.0005180814,0.0001586307,0.002103481,0.358755,0.007631472,0.01133683,0.006247416,0.5926465],"study_design_scores_gemma":[0.00002350768,0.0000805197,0.004334484,0.00001763444,0.00005857837,0.00003750814,0.0003467714,0.9841856,0.002974206,0.007035276,0.0008552668,0.00005068138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2554064,0.0003937657,0.7395896,0.0001577965,0.00006618898,0.0002355039,0.0004575447,0.001261149,0.002432058],"genre_scores_gemma":[0.6842603,0.0001405466,0.3131332,0.00002370093,0.00003603859,0.0001516326,0.0008437765,0.0001239781,0.00128683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01288431,"threshold_uncertainty_score":0.02561867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W752888290","doi":"10.1007/s10844-015-0368-1","title":"Types of minority class examples and their influence on learning classifiers from imbalanced data","year":2015,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":287,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of Ottawa","keywords":"Computer science; Outlier; Machine learning; Artificial intelligence; Identification (biology); Class (philosophy); Classifier (UML); Neighbourhood (mathematics); Data mining; Data type; Mathematics","authors":[{"name":"Krystyna Napierała","is_ca":false},{"name":"Jerzy Stefanowski","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07618345526224594,"gpt":0.2870352410138041,"spread":0.2108517857515581,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01447692,0.0008253405,0.00106036,0.002456373,0.001011576,0.00258091,0.000873751,0.001124423,0.0005305763],"category_scores_gemma":[0.07782118,0.0003881007,0.0006653268,0.001303614,0.001891854,0.003527752,0.001675586,0.001393664,0.0002380791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007369442,"about_ca_system_score_gemma":0.000432668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008182069,"about_ca_topic_score_gemma":0.0008984889,"domain_scores_codex":[0.9898114,0.004118625,0.000768271,0.001571038,0.003348244,0.0003824949],"domain_scores_gemma":[0.882947,0.09750053,0.006125462,0.006334731,0.006018253,0.001073913],"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":[0.003427533,0.0005729983,0.2269756,0.001109336,0.0004647976,0.001285757,0.004555419,0.1915412,0.03758106,0.009722107,0.003360027,0.5194042],"study_design_scores_gemma":[0.0000633952,0.000859607,0.1044574,0.0003031906,0.0003190578,0.001942618,0.003068864,0.7985876,0.06197484,0.02283254,0.005373615,0.0002172188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8546156,0.001783219,0.1397681,0.000571628,0.000130509,0.0001461919,0.0001972341,0.000318057,0.002469531],"genre_scores_gemma":[0.9580126,0.0003137265,0.04093641,0.00005524167,0.00005938441,0.00006170933,0.0002262549,0.00007597881,0.0002587917],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01447692,"threshold_uncertainty_score":0.07656223,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2052496634","doi":"10.1007/s10844-006-0006-z","title":"Constraint-based sequential pattern mining: the pattern-growth methods","year":2007,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":233,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Simon Fraser University; National Science Foundation","keywords":"Sequential Pattern Mining; Computer science; Constraint (computer-aided design); Data mining; Point (geometry); Artificial intelligence; Mathematics","authors":[{"name":"Jian Pei","is_ca":true},{"name":"Jiawei Han","is_ca":false},{"name":"Wei Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04073344562841342,"gpt":0.3292330749489349,"spread":0.2884996293205215,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004593479,0.001543776,0.002678273,0.004161199,0.0009722202,0.002356693,0.004278783,0.001356875,0.003021284],"category_scores_gemma":[0.02172062,0.0008862916,0.002053272,0.008666292,0.001209194,0.004052677,0.001782616,0.002951365,0.001336409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008071624,"about_ca_system_score_gemma":0.003552192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006587581,"about_ca_topic_score_gemma":0.008069252,"domain_scores_codex":[0.9963036,0.0009912582,0.0003343472,0.0007489662,0.001469998,0.0001518419],"domain_scores_gemma":[0.9845999,0.009689311,0.001042564,0.001765534,0.002563521,0.0003391607],"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":[0.0004930506,0.0004632476,0.008692825,0.001365225,0.000530222,0.0004030888,0.00020497,0.1048208,0.004852985,0.03952989,0.01292851,0.8257152],"study_design_scores_gemma":[0.00006378006,0.0001117592,0.001041217,0.0000766235,0.0001115441,0.000368294,0.00005718877,0.9401612,0.002650973,0.04912446,0.00619279,0.0000401099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00747499,0.001794766,0.987452,0.0006003351,0.00009173618,0.0002421883,0.0006912326,0.0006777401,0.0009750847],"genre_scores_gemma":[0.09248449,0.002475891,0.9001809,0.0002999555,0.0002456716,0.0005136831,0.001640873,0.0002431965,0.001915409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006587581,"threshold_uncertainty_score":0.02429295,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2029677459","doi":"10.1007/s10844-013-0254-7","title":"Cost-sensitive three-way email spam filtering","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":193,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Focus (optics); Set (abstract data type); Machine learning; Function (biology); Binary classification; Data mining; Binary number; Artificial intelligence; Support vector machine","authors":[{"name":"Bing Zhou","is_ca":false},{"name":"Yiyu Yao","is_ca":true},{"name":"Jigang Luo","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03493937407291117,"gpt":0.2448378195062222,"spread":0.209898445433311,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003353337,0.001208244,0.003134714,0.002288636,0.00127837,0.003343592,0.002799868,0.002890083,0.004601202],"category_scores_gemma":[0.01430804,0.0006590459,0.001369945,0.001799193,0.001139913,0.003195563,0.003486871,0.001271292,0.001544294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001724291,"about_ca_system_score_gemma":0.002002686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002631472,"about_ca_topic_score_gemma":0.00315563,"domain_scores_codex":[0.9948515,0.001334487,0.000286387,0.0006114369,0.002023914,0.0008921486],"domain_scores_gemma":[0.9884901,0.005298423,0.0008158379,0.002577049,0.00230161,0.0005170557],"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":[0.006673631,0.001373222,0.01405311,0.0005443795,0.0005004414,0.0005908735,0.0003821404,0.2933176,0.05914922,0.04230156,0.01579249,0.5653213],"study_design_scores_gemma":[0.00003519285,0.0001983401,0.002642649,0.00000976575,0.00007045316,0.0002702863,0.00005233088,0.9784757,0.008226288,0.009012884,0.0009595606,0.0000465421],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3333803,0.001230676,0.6507083,0.001130058,0.0003745342,0.0002382303,0.0005548882,0.003708663,0.008674366],"genre_scores_gemma":[0.9190474,0.0001630386,0.07456764,0.0001886781,0.0001427314,0.00005550263,0.0003135121,0.0001232978,0.005398242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004601202,"threshold_uncertainty_score":0.01773441,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1915750711","doi":"10.1023/a:1020945620934","title":"The Wisdom Web: New Challenges for Web Intelligence (WI)","year":2003,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Computer science","authors":[{"name":"Jiming Liu","is_ca":false},{"name":"Ning Zhong","is_ca":false},{"name":"Yiyu Yao","is_ca":true},{"name":"Zbigniew W. Raś","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05274164898155752,"gpt":0.281940192305971,"spread":0.2291985433244135,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00451384,0.0004385099,0.001002504,0.002373372,0.003053423,0.01985836,0.001418581,0.004583838,0.01246273],"category_scores_gemma":[0.01155511,0.0003946046,0.0005048034,0.004610193,0.005434523,0.03437277,0.004054806,0.004871026,0.005006276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001743037,"about_ca_system_score_gemma":0.003926281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002950619,"about_ca_topic_score_gemma":0.006286937,"domain_scores_codex":[0.9983358,0.0004482913,0.0001085853,0.0001784932,0.0008281218,0.0001008181],"domain_scores_gemma":[0.9930602,0.002897955,0.0004317562,0.001135648,0.001381513,0.001092922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004044482,0.00005562197,0.001353544,0.0003715755,0.00004254865,0.0000913604,0.0009946788,0.0004790933,0.0004171523,0.4662595,0.1838031,0.3460913],"study_design_scores_gemma":[0.000009667514,0.00001473022,0.000661011,0.0001968983,0.00001929313,0.0001423325,0.001522065,0.002281254,0.0001638009,0.6975151,0.2974506,0.0000232396],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.01697325,0.1403407,0.1411998,0.5333729,0.01068665,0.00007240006,0.0008299572,0.001518044,0.1550063],"genre_scores_gemma":[0.3931873,0.1935319,0.2208012,0.06361608,0.02763658,0.0002289556,0.001936234,0.0006991302,0.09836259],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01985836,"threshold_uncertainty_score":0.04169202,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1585071718","doi":"10.1023/a:1008788926897","title":"Temporal Granularity: Completing the Puzzle","year":2001,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"","keywords":"Granularity; Computer science; Temporal database; Semantics (computer science); Data mining; Theoretical computer science; Programming language","authors":[{"name":"Iqbal A. Goralwalla","is_ca":true},{"name":"Yuri Leontiev","is_ca":true},{"name":"M. TAMER ÖZSU","is_ca":true},{"name":"Duane Szafron","is_ca":true},{"name":"Carlo Combi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02279624169315993,"gpt":0.2435481714601876,"spread":0.2207519297670277,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003932808,0.0004807175,0.001151411,0.001005185,0.001563324,0.004002863,0.002249668,0.00237675,0.01128429],"category_scores_gemma":[0.02596997,0.0007785507,0.001218756,0.001863927,0.003863173,0.01798595,0.003925386,0.005457491,0.001521225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001259408,"about_ca_system_score_gemma":0.002106493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003175238,"about_ca_topic_score_gemma":0.003356478,"domain_scores_codex":[0.9974539,0.0009386215,0.0002103943,0.0004954069,0.0006259034,0.0002758413],"domain_scores_gemma":[0.986286,0.008362919,0.0007491165,0.003109023,0.0008927306,0.0006002158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002870647,0.00006510175,0.0007516935,0.000293309,0.00004505842,0.0001741992,0.0005804024,0.009965823,0.001270579,0.8720789,0.01542555,0.0990623],"study_design_scores_gemma":[0.00004203419,0.00001445926,0.0002387937,0.00004904406,0.00001808073,0.0000998779,0.0002332134,0.01540576,0.0003597357,0.9683409,0.0151852,0.00001309335],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05408571,0.01054842,0.8147202,0.05824507,0.0017091,0.0001118486,0.0007470002,0.0009565446,0.05887608],"genre_scores_gemma":[0.6832803,0.006119988,0.2900922,0.003661565,0.001756989,0.0001678345,0.0007910557,0.0007447507,0.01338528],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01128429,"threshold_uncertainty_score":0.03774971,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4280502440","doi":"10.1007/s10844-022-00713-9","title":"A case study comparing machine learning with statistical methods for time series forecasting: size matters","year":2022,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Canada Research Chairs","keywords":"Computer science; Machine learning; Series (stratigraphy); Artificial intelligence; Statistical learning; Time series; Sample size determination; Sample (material); Simple (philosophy); Code (set theory); Work (physics); Statistics; Mathematics","authors":[{"name":"Vítor Cerqueira","is_ca":true},{"name":"Luı́s Torgo","is_ca":true},{"name":"Carlos Soares","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1641113147290004,"gpt":0.4184983651093765,"spread":0.2543870503803761,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007895894,0.0004843086,0.0006922652,0.001383634,0.00098499,0.002267257,0.001311381,0.002819106,0.004184491],"category_scores_gemma":[0.03937849,0.000229033,0.0009505615,0.00245673,0.000751999,0.002815774,0.0008149481,0.00151693,0.0005109473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001286936,"about_ca_system_score_gemma":0.0007249439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008760795,"about_ca_topic_score_gemma":0.006684101,"domain_scores_codex":[0.9962108,0.002660367,0.0001623131,0.0002081573,0.0005927073,0.0001656098],"domain_scores_gemma":[0.9181728,0.07547711,0.001055737,0.002001735,0.002850636,0.0004419352],"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":[0.006717105,0.007886447,0.102181,0.002512915,0.0006160868,0.006968742,0.003568867,0.3459429,0.008647501,0.07907283,0.01958403,0.4163017],"study_design_scores_gemma":[0.0009560619,0.005321656,0.03565985,0.0003661345,0.0004564451,0.002384471,0.007066887,0.8769123,0.01297818,0.03763502,0.0200995,0.0001634773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9093139,0.0025185,0.05925667,0.005722304,0.0002901037,0.0004184088,0.0007091593,0.0002135283,0.02155747],"genre_scores_gemma":[0.971432,0.0006314176,0.02523927,0.0001761987,0.00009899418,0.0001057553,0.0001791487,0.00004596752,0.002091234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008760795,"threshold_uncertainty_score":0.041758,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2087859840","doi":"10.1007/s10844-009-0096-5","title":"Evaluating information retrieval system performance based on user preference","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Relevance (law); Information retrieval; Preference; Rank (graph theory); Precision and recall; Measure (data warehouse); Relevance feedback; Document retrieval; Data mining; Artificial intelligence; Image retrieval; Statistics","authors":[{"name":"Bing Zhou","is_ca":true},{"name":"Yiyu Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07183585331902738,"gpt":0.3096554805127487,"spread":0.2378196271937213,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00439729,0.0004529562,0.0006554505,0.001687248,0.0003880383,0.001324401,0.0002804422,0.0008868991,0.001732357],"category_scores_gemma":[0.02918341,0.000164888,0.0006145542,0.001056226,0.0002206238,0.001458719,0.0003296096,0.0003624563,0.0006676685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004118551,"about_ca_system_score_gemma":0.0003644148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002019558,"about_ca_topic_score_gemma":0.002394897,"domain_scores_codex":[0.9968977,0.001508201,0.0003936879,0.0001888235,0.0008006527,0.0002109832],"domain_scores_gemma":[0.9566723,0.0360355,0.001836147,0.001006465,0.003564788,0.0008847522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02355006,0.0027289,0.6423898,0.0007342655,0.001621492,0.0003177202,0.000896187,0.01721783,0.06221827,0.0006039595,0.002331186,0.2453902],"study_design_scores_gemma":[0.0006456163,0.01851963,0.6232491,0.00004643995,0.00169492,0.0008141388,0.001039023,0.3064464,0.04573683,0.0006757414,0.0008721117,0.0002599691],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954938,0.0002315208,0.002980398,0.00004721873,0.00001459521,0.00003192725,0.0000769563,0.00009124496,0.001032348],"genre_scores_gemma":[0.9975552,0.00005438249,0.001977703,0.00001615745,0.00001246623,0.00001236203,0.0001157366,0.00001186234,0.0002441216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00439729,"threshold_uncertainty_score":0.02325535,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3188021992","doi":"10.1007/s10844-021-00653-w","title":"Depression detection from sMRI and rs-fMRI images using machine learning","year":2021,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial intelligence; Computer science; Receiver operating characteristic; Discriminative model; Resting state fMRI; Pattern recognition (psychology); Major depressive disorder; Machine learning; Naive Bayes classifier; Connectome; Support vector machine; Functional connectivity; Psychology; Amygdala; Neuroscience","authors":[{"name":"Marzieh Mousavian","is_ca":false},{"name":"Jianhua Chen","is_ca":false},{"name":"Zachary Traylor","is_ca":false},{"name":"Steven G. Greening","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0399839761284182,"gpt":0.2686873334650831,"spread":0.2287033573366649,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004121763,0.0005515852,0.0002958076,0.001261036,0.0001674709,0.0004778607,0.0002637777,0.0004056409,0.001923815],"category_scores_gemma":[0.001434994,0.0001531129,0.0003163358,0.0005835204,0.0001486294,0.0004021485,0.0002163286,0.0002561593,0.0005568237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001037082,"about_ca_system_score_gemma":0.0001673638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000913472,"about_ca_topic_score_gemma":0.002988019,"domain_scores_codex":[0.9998974,0.00002866688,0.0000104366,0.00002524469,0.00002212887,0.00001607576],"domain_scores_gemma":[0.9997763,0.00008577642,0.00004395214,0.00001928688,0.00005963392,0.00001503391],"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":[0.002828812,0.0002785026,0.08289273,0.0006584221,0.0007699223,0.001735744,0.0003391689,0.005342366,0.3580518,0.001445014,0.005742755,0.5399148],"study_design_scores_gemma":[0.000220235,0.001032457,0.6639816,0.0001409177,0.0008561774,0.009707225,0.000574596,0.2156401,0.09438968,0.007687833,0.005638344,0.0001309628],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8224831,0.002216632,0.16331,0.0005036551,0.0001140599,0.0004872497,0.002546854,0.0009522738,0.007386182],"genre_scores_gemma":[0.9530573,0.0006115913,0.04357862,0.0001051672,0.0001372998,0.0001596504,0.0008967281,0.00006386891,0.001389748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001923815,"threshold_uncertainty_score":0.006435871,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1504222647","doi":"10.1023/a:1020990805004","title":"A Data Cube Model for Prediction-Based Web Prefetching","year":2003,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Caching and Content Delivery","field":"Computer Science","cited_by":46,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Hong Kong University of Science and Technology; University of Hong Kong; Simon Fraser University; U.S. Environmental Protection Agency","keywords":"Computer science; Instruction prefetch; Web server; Data cube; Web analytics; Data mining; Online analytical processing; Data Web; Web page; Web application; The Internet; Cluster analysis; Data warehouse; World Wide Web; Web modeling; Web intelligence; Machine learning; Cache; Operating system","authors":[{"name":"Qiang Yang","is_ca":false},{"name":"Joshua Zhexue Huang","is_ca":false},{"name":"Michael K. Ng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08014086763330527,"gpt":0.2762867701949062,"spread":0.1961459025616009,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001510009,0.0007640546,0.001619836,0.00116122,0.0006313307,0.002198876,0.00254269,0.001048565,0.00271394],"category_scores_gemma":[0.005815122,0.0006343896,0.0009748028,0.00282382,0.0006087846,0.00301708,0.0008706097,0.001409494,0.0006427838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001751772,"about_ca_system_score_gemma":0.002209832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03038782,"about_ca_topic_score_gemma":0.02345954,"domain_scores_codex":[0.9991819,0.0001863062,0.00007948065,0.0001370112,0.0003062074,0.0001090146],"domain_scores_gemma":[0.9970112,0.001541226,0.000180546,0.0004666584,0.0006949278,0.0001054612],"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":[0.0002214285,0.0001135073,0.001454117,0.00006332753,0.00005890516,0.00006822908,0.00006735808,0.9238988,0.0007927738,0.03109945,0.003634064,0.03852817],"study_design_scores_gemma":[0.000002895682,0.000004442483,0.00002769531,0.000001577269,0.000004976546,0.000004509934,0.000002776985,0.9949308,0.0001099108,0.004767601,0.000140341,0.000002493871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04350967,0.0009619627,0.9468336,0.0009136731,0.0001736902,0.0001109402,0.001983969,0.002584409,0.002928258],"genre_scores_gemma":[0.7548878,0.001337489,0.2368778,0.0002264262,0.0001346074,0.0003776321,0.002035243,0.0002628645,0.00386021],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03038782,"threshold_uncertainty_score":0.06042188,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1964236997","doi":"10.1007/s10844-014-0350-3","title":"An approach to structure determination and estimation of hierarchical Archimedean Copulas and its application to Bayesian classification","year":2015,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":41,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Estimator; Copula (linguistics); Computer science; Bayesian probability; Hierarchical database model; Econometrics; Artificial intelligence; Machine learning; Data mining; Mathematics; Statistics","authors":[{"name":"Jan Górecki","is_ca":false},{"name":"Marius Hofert","is_ca":true},{"name":"Martin Holeňa","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05024089782877226,"gpt":0.2764964665452292,"spread":0.2262555687164569,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005734603,0.0008774328,0.001839323,0.002676714,0.001266906,0.001867001,0.002486836,0.001949507,0.001823931],"category_scores_gemma":[0.02304307,0.001438622,0.002434318,0.002371586,0.00133234,0.002254233,0.002131123,0.003518421,0.0006072518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001254145,"about_ca_system_score_gemma":0.003159846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009458244,"about_ca_topic_score_gemma":0.01021677,"domain_scores_codex":[0.9978023,0.001157125,0.0001389219,0.0002810553,0.0004904094,0.0001301198],"domain_scores_gemma":[0.9910678,0.006305437,0.0004892763,0.0007866424,0.001171237,0.0001796506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008070141,0.000247469,0.003271175,0.0001862093,0.0002556479,0.0001394217,0.0004773598,0.4563159,0.003054035,0.2611423,0.003544987,0.2712848],"study_design_scores_gemma":[0.000006492412,0.00001380408,0.0003468668,0.00001357053,0.00001452367,0.00002254243,0.0000132463,0.942158,0.0002363607,0.05651713,0.0006430098,0.00001447865],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00232337,0.00009860926,0.9972047,0.0000731001,0.00001000007,0.000019895,0.00001986054,0.00005876027,0.0001917327],"genre_scores_gemma":[0.1200872,0.0005762177,0.8769332,0.0001487537,0.0001387635,0.0002404482,0.0003361136,0.0001060956,0.00143324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009458244,"threshold_uncertainty_score":0.03032786,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2116581216","doi":"10.1007/s10844-008-0075-2","title":"(α, k)-anonymous data publishing","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Anonymity; Computer science; k-anonymity; Scalability; Data anonymization; Data publishing; Identification (biology); Information sensitivity; Distortion (music); Data mining; Information privacy; Information retrieval; Theoretical computer science; Publishing; Computer security; Database; Computer network","authors":[{"name":"Raymond Chi-Wing Wong","is_ca":false},{"name":"Jiuyong Li","is_ca":false},{"name":"Ada W. C. Fu","is_ca":false},{"name":"Ke Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0717649415843026,"gpt":0.2993217077262449,"spread":0.2275567661419423,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005850801,0.0005534447,0.001611168,0.001542063,0.002735032,0.006633439,0.002705083,0.00215709,0.005968103],"category_scores_gemma":[0.03134988,0.0007148852,0.001108202,0.003521566,0.002322949,0.009533457,0.005257918,0.00247149,0.003391532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001533143,"about_ca_system_score_gemma":0.002749579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005524505,"about_ca_topic_score_gemma":0.0005201217,"domain_scores_codex":[0.9908096,0.002408453,0.001471215,0.00177518,0.002282842,0.001252836],"domain_scores_gemma":[0.9603363,0.01056578,0.002955155,0.02041652,0.004770884,0.0009553809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00171657,0.0002085056,0.004197273,0.0004011888,0.0001919243,0.0008917084,0.0008405036,0.01769942,0.006034372,0.8113193,0.01898597,0.1375133],"study_design_scores_gemma":[0.0001085912,0.0001099326,0.0005859581,0.00006432978,0.0001350224,0.002409458,0.0002275216,0.06954839,0.01101085,0.8860932,0.02962473,0.0000820043],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0861574,0.001326098,0.8584931,0.005830494,0.0009007459,0.0002866579,0.002059299,0.002115848,0.04283042],"genre_scores_gemma":[0.7963005,0.001129968,0.1589456,0.001086593,0.0005718057,0.0002348237,0.0009574719,0.0003230744,0.04045017],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006633439,"threshold_uncertainty_score":0.03094238,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2616566908","doi":"10.1007/s10844-017-0466-3","title":"Query expansion using pseudo relevance feedback on wikipedia","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Information Retrieval and Search Behavior","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Query expansion; Relevance feedback; Information retrieval; Web search query; Web query classification; Robustness (evolution); Query optimization; Relevance (law); Vocabulary; Data mining; Artificial intelligence; Search engine","authors":[{"name":"Andisheh Keikha","is_ca":true},{"name":"Faezeh Ensan","is_ca":false},{"name":"Ebrahim Bagheri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05628626845690768,"gpt":0.3131827004064152,"spread":0.2568964319495075,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001173943,0.0007736418,0.001077134,0.002718774,0.0006415393,0.0008016679,0.0007925017,0.0007381851,0.003794057],"category_scores_gemma":[0.009874912,0.0003414944,0.0004857449,0.00166378,0.0002831625,0.002072193,0.0007205973,0.0005678282,0.001367302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000468177,"about_ca_system_score_gemma":0.001110575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006949602,"about_ca_topic_score_gemma":0.01141124,"domain_scores_codex":[0.9984561,0.0006795233,0.0001019591,0.0001951626,0.0004607528,0.0001064943],"domain_scores_gemma":[0.9943886,0.00318289,0.0001449788,0.0003717532,0.001773596,0.0001381689],"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":[0.003346558,0.001325702,0.005161133,0.001442941,0.0002417771,0.000915695,0.0005098092,0.07134277,0.09503468,0.005661633,0.05728664,0.7577307],"study_design_scores_gemma":[0.000139144,0.0004631045,0.002862112,0.00004188925,0.000163101,0.0004244044,0.0001557595,0.962411,0.02270832,0.004592099,0.005973357,0.00006569458],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5530398,0.008480536,0.4026006,0.001697153,0.001194943,0.0008084235,0.003783114,0.01108467,0.01731075],"genre_scores_gemma":[0.8663148,0.00077548,0.121987,0.0001977669,0.0002926665,0.0001728029,0.003225625,0.0002647767,0.006768985],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006949602,"threshold_uncertainty_score":0.01381832,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1559282679","doi":"10.1023/a:1023505917953","title":"Application of Temporal Descriptors to Musical Instrument Sound Recognition","year":2003,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Music and Audio Processing","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Mashhad University of Medical Sciences; McGill University","keywords":"Computer science; TRACE (psycholinguistics); Representation (politics); Process (computing); Musical; Feature extraction; Artificial intelligence; Information retrieval; Multimedia; Programming language","authors":[{"name":"Alicja Wieczorkowska","is_ca":false},{"name":"Jakub Wróblewski","is_ca":false},{"name":"Piotr Synak","is_ca":false},{"name":"Dominik Ślȩzak","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03994754284207759,"gpt":0.2571475041979413,"spread":0.2171999613558637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057665,0.0002631239,0.0004533534,0.001193242,0.0002985146,0.000844448,0.0004110054,0.0003262827,0.001944842],"category_scores_gemma":[0.001477299,0.0001624092,0.0003554459,0.001514696,0.0002931531,0.0005954962,0.0004856409,0.0003933625,0.000629948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000218373,"about_ca_system_score_gemma":0.0004603263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003102296,"about_ca_topic_score_gemma":0.002996559,"domain_scores_codex":[0.9998229,0.00003498786,0.00001803496,0.00003307603,0.00006610025,0.00002492813],"domain_scores_gemma":[0.9993331,0.0003045485,0.00005009153,0.00008236312,0.00019567,0.00003428098],"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":[0.0004367138,0.0001221242,0.002336895,0.0001442376,0.00004675878,0.0001386573,0.00007985715,0.01434094,0.1285452,0.009369606,0.002049981,0.842389],"study_design_scores_gemma":[0.0001309747,0.0004791729,0.007893438,0.00004634284,0.0001704366,0.0008283283,0.000245591,0.8616823,0.09774426,0.01263779,0.01807674,0.00006473697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08748488,0.002565606,0.9044912,0.0001486508,0.000261082,0.00006634598,0.0002786347,0.0008909257,0.003812708],"genre_scores_gemma":[0.6137742,0.002533072,0.375912,0.00009081292,0.0002362345,0.00007885804,0.0008551404,0.0001354821,0.006384095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003102296,"threshold_uncertainty_score":0.006506145,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1592511765","doi":"10.1023/a:1008778107391","title":"Efficient Rule-Based Attribute-Oriented Induction for Data Mining","year":2000,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Generalization; Data mining; Rule induction; Backtracking; Knowledge extraction; Algorithm; Mathematics","authors":[{"name":"David W. Cheung","is_ca":false},{"name":"Hoi-Yee Hwang","is_ca":false},{"name":"Ada W. C. Fu","is_ca":false},{"name":"Jiawei Han","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05392687639899999,"gpt":0.2939089391231826,"spread":0.2399820627241826,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002548057,0.000863224,0.002059331,0.003084752,0.001167278,0.001818,0.003092032,0.0008348242,0.003099253],"category_scores_gemma":[0.008384484,0.0005999374,0.001321244,0.003639959,0.0005298617,0.001945818,0.001632272,0.001750043,0.003353157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006250458,"about_ca_system_score_gemma":0.003046272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003138729,"about_ca_topic_score_gemma":0.004888,"domain_scores_codex":[0.9973052,0.0006347904,0.0003746135,0.0004572004,0.001044182,0.0001839833],"domain_scores_gemma":[0.9933447,0.003959911,0.0002790999,0.0009030667,0.001387393,0.0001259364],"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":[0.0002431165,0.0004879603,0.00268369,0.0003456901,0.0001523634,0.0002172527,0.0001249223,0.02195751,0.007131893,0.005573401,0.01090009,0.9501821],"study_design_scores_gemma":[0.0001580013,0.000200401,0.002102792,0.00009922385,0.0002472527,0.0005526741,0.0001153069,0.9160793,0.01955506,0.05083288,0.009994853,0.00006227291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01333135,0.0006974979,0.9756806,0.0002560854,0.0001049825,0.0004036175,0.001043052,0.006521692,0.001961109],"genre_scores_gemma":[0.1112273,0.0004675879,0.8811151,0.0002306419,0.00009144783,0.0004354167,0.004027521,0.0002406025,0.002164351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003138729,"threshold_uncertainty_score":0.01347554,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2146327344","doi":"10.1007/s10844-010-0141-4","title":"Probabilistic skylines on uncertain data: model and bounding-pruning-refining methods","year":2010,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Skyline; Computer science; Uncertain data; Pruning; Probabilistic logic; Bounding overwatch; Data mining; Benchmark (surveying); Object (grammar); Reachability; Algorithm; Artificial intelligence","authors":[{"name":"Bin Jiang","is_ca":true},{"name":"Jian Pei","is_ca":true},{"name":"Xuemin Lin","is_ca":false},{"name":"Yidong Yuan","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08668100124270832,"gpt":0.3626044773832188,"spread":0.2759234761405105,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008488044,0.001497241,0.003603464,0.003402336,0.001253472,0.002656722,0.005596582,0.003054044,0.002998798],"category_scores_gemma":[0.03922344,0.002187331,0.002243533,0.005066263,0.00215721,0.00843206,0.00399705,0.004007502,0.0005751728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001665397,"about_ca_system_score_gemma":0.002110472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01183131,"about_ca_topic_score_gemma":0.01066275,"domain_scores_codex":[0.9966546,0.00154602,0.000178792,0.0004789846,0.0009388869,0.0002026411],"domain_scores_gemma":[0.9736161,0.02030917,0.001241442,0.002446726,0.001939211,0.000447401],"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":[0.0003073909,0.00008130062,0.001536451,0.0002820411,0.00008413462,0.00008676304,0.0002501321,0.8417814,0.0008055669,0.06498857,0.004721238,0.08507497],"study_design_scores_gemma":[0.00000991277,0.000009731883,0.00008600319,0.00001485369,0.000007810225,0.00001518498,0.0000077645,0.9852346,0.0001518796,0.01405619,0.0004003731,0.000005853909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005233508,0.0005915838,0.9930905,0.0002164525,0.00002337852,0.00003445336,0.0001092797,0.0002530692,0.0004478393],"genre_scores_gemma":[0.1982242,0.001451254,0.7958088,0.0001576137,0.0002729702,0.0002214101,0.001118502,0.0003883088,0.002356968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01183131,"threshold_uncertainty_score":0.04488963,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2886779119","doi":"10.1007/s10844-018-0521-8","title":"Topic and sentiment aware microblog summarization for twitter","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Topic Modeling","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automatic summarization; Computer science; Microblogging; Social media; Information retrieval; Categorization; Context (archaeology); Multi-document summarization; Sentiment analysis; Process (computing); Task (project management); Representation (politics); World Wide Web; Data science; Natural language processing; Artificial intelligence","authors":[{"name":"Syed Muhammad Ali","is_ca":true},{"name":"Zeinab Noorian","is_ca":true},{"name":"Ebrahim Bagheri","is_ca":true},{"name":"Chen Ding","is_ca":true},{"name":"Feras Al‐Obeidat","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02844098469120135,"gpt":0.2675110641839439,"spread":0.2390700794927425,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008231418,0.001083692,0.001035188,0.003379062,0.0006260596,0.001106799,0.000594018,0.0006651182,0.002633354],"category_scores_gemma":[0.002168661,0.0003003342,0.0008685229,0.001907728,0.0001139213,0.001293322,0.0007281846,0.0007238587,0.002851967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003519346,"about_ca_system_score_gemma":0.0006037042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003937739,"about_ca_topic_score_gemma":0.008925427,"domain_scores_codex":[0.9995993,0.00007593518,0.00004483548,0.00009622681,0.0001076103,0.00007613662],"domain_scores_gemma":[0.9990206,0.0002808105,0.0001037384,0.00009949605,0.0004194384,0.00007587958],"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":[0.0015701,0.0005463861,0.008823134,0.0006120751,0.0003890212,0.0004085421,0.0004439766,0.01842118,0.07755902,0.001260095,0.05312812,0.8368383],"study_design_scores_gemma":[0.00008465598,0.000500238,0.01914026,0.00004928873,0.0004187853,0.000295105,0.0006790474,0.9171374,0.03706776,0.003270698,0.02127753,0.00007923057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.328041,0.007181666,0.606696,0.002036121,0.00159829,0.0007443164,0.0207247,0.02534258,0.007635492],"genre_scores_gemma":[0.6524033,0.001844129,0.2885358,0.0001888074,0.001715452,0.0003899742,0.04158211,0.0007864573,0.01255398],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003937739,"threshold_uncertainty_score":0.008809447,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392345811","doi":"10.1007/s10844-024-00851-2","title":"Ensemble of temporal Transformers for financial time series","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Time series; Heteroscedasticity; Estimator; Transformer; Artificial intelligence; Machine learning; Autoregressive integrated moving average; Embedding; Ensemble learning; Deep learning; Autoregressive conditional heteroskedasticity; Data mining; Econometrics; Finance; Volatility (finance); Statistics","authors":[{"name":"Kenniy Olorunnimbe","is_ca":true},{"name":"Herna L. Viktor","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07442814628610717,"gpt":0.3738199868221773,"spread":0.2993918405360702,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001929995,0.0007329475,0.0009988943,0.001757848,0.0003882341,0.001322083,0.0009315667,0.0006653233,0.002241426],"category_scores_gemma":[0.00562943,0.0003963061,0.0009523976,0.00162698,0.0002530837,0.002333391,0.0008752321,0.001081166,0.0007630592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004577675,"about_ca_system_score_gemma":0.0008521492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00472535,"about_ca_topic_score_gemma":0.00616587,"domain_scores_codex":[0.9994979,0.0001188771,0.000056361,0.0001266777,0.0001322037,0.00006797254],"domain_scores_gemma":[0.9983448,0.0007135252,0.0001075975,0.0002942865,0.0004521303,0.00008772297],"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":[0.0004885698,0.0001838503,0.00679977,0.00012392,0.0003113608,0.0001067638,0.0001051483,0.3117112,0.005657109,0.01130523,0.003655144,0.6595519],"study_design_scores_gemma":[0.000005023051,0.00003191803,0.0007780999,0.000006674978,0.00003491926,0.00002526771,0.00001382407,0.9937996,0.0008217778,0.00383225,0.000643971,0.000006619598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07965861,0.001218434,0.9157322,0.000149617,0.0001911947,0.00004249632,0.0004942199,0.001239465,0.001273696],"genre_scores_gemma":[0.8595192,0.001367238,0.1340187,0.00005104905,0.000162395,0.00007453785,0.001917928,0.0001529079,0.002735981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00472535,"threshold_uncertainty_score":0.01020694,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1581863564","doi":"10.1023/a:1011208900462","title":"Feature Weight Maintenance in Case Bases Using Introspective Learning","year":2001,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"University of California, Irvine; Simon Fraser University","keywords":"Computer science","authors":[{"name":"Zhong Zhang","is_ca":true},{"name":"Qiang Yang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0177176875493175,"gpt":0.2514292455076879,"spread":0.2337115579583704,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002467877,0.0005116655,0.0007439889,0.001744153,0.0006055419,0.001502388,0.002456244,0.0008708037,0.002551196],"category_scores_gemma":[0.0191374,0.000681114,0.0005774166,0.001155837,0.0007211125,0.003787292,0.001805648,0.001452221,0.0004921887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006775816,"about_ca_system_score_gemma":0.000960713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003919929,"about_ca_topic_score_gemma":0.005524733,"domain_scores_codex":[0.998703,0.0002717216,0.0001526155,0.0002852287,0.0004604823,0.0001268673],"domain_scores_gemma":[0.9861622,0.007237557,0.001153189,0.002885208,0.002211132,0.0003507528],"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":[0.0003483017,0.000491044,0.007071643,0.00008935409,0.00007671735,0.000201338,0.0004206664,0.07728655,0.006034894,0.004935353,0.002180556,0.9008636],"study_design_scores_gemma":[0.00004160362,0.00009960784,0.001347021,0.00003565755,0.0000649037,0.0001117683,0.00009150062,0.9725471,0.008544688,0.01617976,0.0009183662,0.00001805617],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1397096,0.0002097123,0.8541995,0.0002180133,0.00003604696,0.0001514465,0.0001519467,0.003545997,0.001777792],"genre_scores_gemma":[0.6779828,0.00009889608,0.32002,0.00007897778,0.00003607366,0.00006996095,0.00044496,0.0001637881,0.001104566],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003919929,"threshold_uncertainty_score":0.01305151,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2067656157","doi":"10.1007/s10844-012-0227-2","title":"Folksonomy link prediction based on a tripartite graph for tag recommendation","year":2012,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":26,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Folksonomy; Link (geometry); Graph; Information retrieval; World Wide Web; Data mining; Database; Computer network; Theoretical computer science","authors":[{"name":"Majdi Rawashdeh","is_ca":true},{"name":"Heung-Nam Kim","is_ca":true},{"name":"Jihad Mohamad Alja’am","is_ca":false},{"name":"Abdulmotaleb El Saddik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02655069947119669,"gpt":0.2834824826696471,"spread":0.2569317831984504,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000526607,0.0006025606,0.0009841893,0.006996925,0.0009749165,0.001058502,0.001313439,0.001128695,0.002377949],"category_scores_gemma":[0.00427071,0.0003267764,0.0008927381,0.005641398,0.0002529822,0.002195593,0.0006634624,0.000784983,0.001512236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007292879,"about_ca_system_score_gemma":0.0008776225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01742674,"about_ca_topic_score_gemma":0.03675396,"domain_scores_codex":[0.9993029,0.0001416539,0.00005658291,0.0002257154,0.0002037141,0.0000695],"domain_scores_gemma":[0.9976137,0.001135095,0.0002131177,0.0003099664,0.000623556,0.0001047046],"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":[0.001164729,0.001254085,0.04759292,0.0007635782,0.0007605365,0.0005323011,0.0003976017,0.1230667,0.01820828,0.0107328,0.03509461,0.7604318],"study_design_scores_gemma":[0.00001756365,0.0000528814,0.004880422,0.00002538124,0.00010303,0.0001685614,0.00006824872,0.9850215,0.002158427,0.005862896,0.001612311,0.00002872848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2012367,0.002047446,0.7779199,0.0006739642,0.000190949,0.0003779183,0.007867591,0.00353296,0.006152549],"genre_scores_gemma":[0.7150823,0.0008217028,0.2682517,0.0001446604,0.0001412306,0.0002414821,0.009917192,0.0001230278,0.005276687],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01742674,"threshold_uncertainty_score":0.03465056,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2080533345","doi":"10.1007/s10844-006-2618-8","title":"An efficient approach to mining indirect associations","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Association rule learning; Data mining; struct; Set (abstract data type); Efficient algorithm; Algorithm","authors":[{"name":"Qian Wan","is_ca":true},{"name":"Aijun An","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02252393769148645,"gpt":0.2616728185588945,"spread":0.2391488808674081,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001915375,0.001118523,0.002108886,0.006299553,0.001778276,0.002794539,0.002972592,0.001312467,0.003464394],"category_scores_gemma":[0.01078205,0.0008748615,0.001564426,0.006367627,0.0008341443,0.003498367,0.00287042,0.001834282,0.001813278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005896495,"about_ca_system_score_gemma":0.002895413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003121452,"about_ca_topic_score_gemma":0.008400929,"domain_scores_codex":[0.9969333,0.0004771571,0.0003598729,0.0005330103,0.001521001,0.0001756979],"domain_scores_gemma":[0.9928856,0.003989911,0.0004019902,0.001209463,0.001342564,0.0001704508],"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":[0.0005114841,0.0006336015,0.01006617,0.0003707875,0.0003788724,0.0005444264,0.0003219015,0.01975709,0.008724493,0.02591242,0.01191524,0.9208635],"study_design_scores_gemma":[0.0002231088,0.0002721995,0.004759545,0.0001202611,0.0004925171,0.00204022,0.0003865475,0.817296,0.01051875,0.1451983,0.01860032,0.00009218982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03194429,0.001283068,0.9598985,0.0004310162,0.0001204547,0.0003372825,0.001423103,0.002320612,0.002241585],"genre_scores_gemma":[0.1574575,0.0006328305,0.8329218,0.000171518,0.0001647512,0.0003477275,0.003670403,0.0001122716,0.004521069],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006299553,"threshold_uncertainty_score":0.01158959,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1991329811","doi":"10.1007/s10844-006-0242-2","title":"Genetic algorithms based approach to database vertical partition","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Partition (number theory); Crossover; Computer science; String (physics); Cluster analysis; Algorithm; Genetic algorithm; Partition problem; Constraint (computer-aided design); Theoretical computer science; Artificial intelligence; Mathematics; Combinatorics; Machine learning","authors":[{"name":"Jun Du","is_ca":true},{"name":"Reda Alhajj","is_ca":true},{"name":"Ken Barker","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02254776506449425,"gpt":0.2472101069966373,"spread":0.2246623419321431,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006953975,0.0003806079,0.0006520809,0.001553524,0.000730925,0.001154881,0.001530908,0.00101884,0.002892171],"category_scores_gemma":[0.002242106,0.0003380442,0.0006001336,0.00169564,0.0006891694,0.0009914458,0.0007628328,0.0009125046,0.000376895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001217376,"about_ca_system_score_gemma":0.001353866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01163497,"about_ca_topic_score_gemma":0.009597778,"domain_scores_codex":[0.9995111,0.000138303,0.00002214603,0.00008057088,0.0001955183,0.00005235597],"domain_scores_gemma":[0.9993072,0.0002771533,0.00003936159,0.0001016222,0.0002468096,0.00002789782],"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":[0.0001056857,0.0001442229,0.00103942,0.0000532149,0.00007100583,0.0001005629,0.0001962803,0.6963532,0.005582802,0.05301024,0.00261608,0.2407274],"study_design_scores_gemma":[0.00001088479,0.00002180051,0.0001583425,0.000005958064,0.00001462049,0.00002722729,0.00003266346,0.9853086,0.001017375,0.01239982,0.0009969814,0.000005684584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02010192,0.000248415,0.974936,0.0002439724,0.00005986424,0.00007769575,0.00006024143,0.0004568493,0.003815037],"genre_scores_gemma":[0.3682278,0.0003154599,0.623494,0.0002047097,0.00006903042,0.0001652355,0.0002294211,0.0001214522,0.007172758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01163497,"threshold_uncertainty_score":0.02313453,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2606013202","doi":"10.1007/s10844-017-0460-9","title":"Dynamic adaptation of online ensembles for drifting data streams","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"","keywords":"Computer science; Data stream mining; Data mining; Concept drift; Construct (python library); Scalability; Resource (disambiguation); Data stream; Predictive analytics; Adaptation (eye); Return on investment; Analytics; Machine learning; Database","authors":[{"name":"M. Kehinde Olorunnimbe","is_ca":true},{"name":"Herna L. Viktor","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08983309585543633,"gpt":0.3500855087539786,"spread":0.2602524128985423,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002948612,0.0008501869,0.001384008,0.0009306628,0.000541183,0.001099418,0.001504465,0.001031681,0.001255589],"category_scores_gemma":[0.01107327,0.0004989625,0.0007245091,0.000928139,0.0004253981,0.001912515,0.001613098,0.001842514,0.0005029661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004571163,"about_ca_system_score_gemma":0.0007673228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002727751,"about_ca_topic_score_gemma":0.003453366,"domain_scores_codex":[0.9990907,0.000225905,0.0000793769,0.000297565,0.0002072427,0.00009932573],"domain_scores_gemma":[0.9964082,0.001776183,0.0002099385,0.0005105322,0.0009322609,0.0001629085],"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":[0.0004943339,0.0004746904,0.005818569,0.0001003775,0.0002244168,0.000134531,0.0002191772,0.5409848,0.01250901,0.004075922,0.003586267,0.4313778],"study_design_scores_gemma":[0.000004748347,0.00002551016,0.0002774681,0.000003406152,0.00001089029,0.00001671965,0.000007670913,0.9979949,0.0006909694,0.0007671136,0.0001965874,0.000003979921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09041092,0.0006808632,0.9063251,0.0002143575,0.0003112919,0.00008506505,0.0001309952,0.0008643987,0.0009770747],"genre_scores_gemma":[0.8507737,0.0003746514,0.1454271,0.0001297001,0.0002383979,0.0001480116,0.00049664,0.000151828,0.002260006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002948612,"threshold_uncertainty_score":0.01559395,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2029146157","doi":"10.1007/s10844-013-0273-4","title":"Dream sentiment analysis using second order soft co-occurrences (SOSCO) and time course representations","year":2013,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Dream; Computer science; Sentiment analysis; Representation (politics); Artificial intelligence; Natural language processing; Annotation; Scale (ratio); Domain (mathematical analysis); Feature (linguistics); Linguistics; Psychology; Mathematics","authors":[{"name":"Amir H. Razavi","is_ca":true},{"name":"Stan Matwin","is_ca":true},{"name":"Joseph De Koninck","is_ca":true},{"name":"Ray Reza Amini","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02410377205739697,"gpt":0.3056176712115789,"spread":0.2815138991541819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006156411,0.0004165418,0.000418733,0.00409466,0.0004937194,0.001015926,0.0004750754,0.0003551873,0.003928563],"category_scores_gemma":[0.002544228,0.0001340686,0.0008044465,0.003402469,0.0002314936,0.001043918,0.0006652454,0.0005532493,0.00092415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005069197,"about_ca_system_score_gemma":0.0006408347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005560923,"about_ca_topic_score_gemma":0.009040698,"domain_scores_codex":[0.9995871,0.0000840155,0.00004403225,0.00009324641,0.0001204778,0.0000710241],"domain_scores_gemma":[0.9989815,0.0004101683,0.0001431683,0.00009721672,0.0002928043,0.00007504348],"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":[0.002257397,0.0005500881,0.06022866,0.0004886604,0.0004301901,0.0006067328,0.0009964096,0.03703146,0.02966761,0.02641967,0.01584417,0.825479],"study_design_scores_gemma":[0.00004423154,0.0001618364,0.0379275,0.00004373345,0.0001673061,0.000336455,0.0008420082,0.9260485,0.008667309,0.01688924,0.008798881,0.00007305614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4327035,0.0008402744,0.5411112,0.0004414746,0.0003337552,0.0003106846,0.007164931,0.003000386,0.0140939],"genre_scores_gemma":[0.9100627,0.0001736462,0.0816415,0.00002636257,0.0001035897,0.0001382163,0.003955902,0.0001119998,0.003785978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005560923,"threshold_uncertainty_score":0.01314241,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2885581957","doi":"10.1007/s10844-018-0519-2","title":"Predicting future personal life events on twitter via recurrent neural networks","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Mental Health via Writing","field":"Psychology","cited_by":19,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Event (particle physics); Identification (biology); Phone; Variety (cybernetics); Social media; Task (project management); Personal life; Data science; Baseline (sea); World Wide Web; Personally identifiable information; Mobile phone; Internet privacy; Human–computer interaction; Artificial intelligence; Computer security; Telecommunications","authors":[{"name":"Maryam Khodabakhsh","is_ca":false},{"name":"Mohsen Kahani","is_ca":false},{"name":"Ebrahim Bagheri","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04665183238553716,"gpt":0.3480449519037792,"spread":0.301393119518242,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000472008,0.0005982322,0.0004626993,0.001348299,0.0003243774,0.0007256852,0.000530322,0.0006372061,0.002077078],"category_scores_gemma":[0.002598921,0.0002306949,0.000453426,0.0009638467,0.0001440564,0.00129191,0.0006170936,0.001015438,0.001517505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004650428,"about_ca_system_score_gemma":0.0002306866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008465464,"about_ca_topic_score_gemma":0.01903205,"domain_scores_codex":[0.9998077,0.00004424576,0.0000149409,0.00005384316,0.00003671511,0.00004262311],"domain_scores_gemma":[0.9990133,0.0005674132,0.0001248453,0.00005730955,0.0001681423,0.00006897237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001571931,0.001618249,0.4570378,0.0002354028,0.0005703432,0.0008997925,0.0004488478,0.1667304,0.009777212,0.002467418,0.0290728,0.3295697],"study_design_scores_gemma":[0.00000818879,0.00007160992,0.03209667,0.00001835762,0.00004964496,0.00004803144,0.00009412139,0.9641263,0.00100167,0.001496155,0.0009739251,0.00001516077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.965885,0.001098934,0.02120407,0.001490193,0.0004255835,0.00004986594,0.004958336,0.0005363901,0.004351575],"genre_scores_gemma":[0.9912711,0.0002788561,0.002751726,0.00005797869,0.0001383006,0.00002116415,0.00301732,0.00001457401,0.002449002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008465464,"threshold_uncertainty_score":0.01683241,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2127372347","doi":"10.1007/s10844-008-0073-4","title":"A join tree probability propagation architecture for semantic modeling","year":2008,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","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","keywords":"Computer science; Bayesian network; Inference; Benchmark (surveying); Conditional probability; Tree (set theory); Architecture; Node (physics); Artificial intelligence; Semantics (computer science); Bayesian inference; Theoretical computer science; Computation; Task (project management); Machine learning; Bayesian probability; Data mining; Algorithm; Programming language","authors":[{"name":"Cory J. Butz","is_ca":true},{"name":"Hong Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05922168174488613,"gpt":0.2606621188796231,"spread":0.201440437134737,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003273211,0.0006880449,0.0007972945,0.002108742,0.0009501939,0.002727452,0.002900381,0.001345664,0.005681541],"category_scores_gemma":[0.008080981,0.0007058841,0.001593923,0.002197472,0.0009497685,0.00544096,0.001933487,0.002023213,0.001966982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001249897,"about_ca_system_score_gemma":0.002253505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008687283,"about_ca_topic_score_gemma":0.01122484,"domain_scores_codex":[0.9985384,0.0003596982,0.0001032308,0.0003149926,0.0006047041,0.0000790501],"domain_scores_gemma":[0.9972519,0.001327197,0.0001525077,0.0005989619,0.0005624873,0.0001069832],"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":[0.0002430723,0.0001759656,0.002275034,0.0002477052,0.0001805185,0.0002208341,0.0006508294,0.2913917,0.00814835,0.2381126,0.00977187,0.4485816],"study_design_scores_gemma":[0.00001674035,0.00002825345,0.0001468308,0.00001739959,0.00004992105,0.00007231931,0.00002621636,0.897269,0.003525994,0.09214015,0.006678876,0.00002832189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001078079,0.0000344754,0.9967166,0.00007699897,0.00001099967,0.00002886275,0.00009165471,0.001365753,0.0005965818],"genre_scores_gemma":[0.07833146,0.0002118077,0.9179949,0.0001191511,0.00005481398,0.0001795798,0.0007167581,0.0003584363,0.002033069],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008687283,"threshold_uncertainty_score":0.01900667,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1995119722","doi":"10.1007/s10844-009-0098-3","title":"Identification of a dominating instrument in polytimbral same-pitch mixes using SVM classifiers with non-linear kernel","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Music and Audio Processing","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"National Research Council Canada; University of North Carolina at Charlotte; Universidade Federal do ABC; McGill University; Louisiana State University; National Science Foundation","keywords":"Musical instrument; Computer science; Support vector machine; Speech recognition; Classifier (UML); Identification (biology); Octave (electronics); Pattern recognition (psychology); Training set; Artificial intelligence; Test set; Set (abstract data type); Kernel (algebra); Harmonic; Noise (video); Acoustics; Mathematics; Physics","authors":[{"name":"Alicja Wieczorkowska","is_ca":false},{"name":"Elżbieta Kubera","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02451496179729403,"gpt":0.2697191419646219,"spread":0.2452041801673278,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007251578,0.0005833302,0.000691912,0.001688421,0.0005661635,0.0009651262,0.0004101713,0.0008161204,0.001778835],"category_scores_gemma":[0.001189091,0.0002644861,0.0005950278,0.0008005858,0.0004648496,0.00113103,0.0008068694,0.0006196119,0.001291675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001586651,"about_ca_system_score_gemma":0.0002439919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005081376,"about_ca_topic_score_gemma":0.0008035424,"domain_scores_codex":[0.9996367,0.00006386858,0.00002857796,0.00009830148,0.00009978914,0.00007278018],"domain_scores_gemma":[0.9991303,0.0003395586,0.00009412429,0.00009155968,0.0002485385,0.00009591401],"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":[0.002869586,0.0003164747,0.02783602,0.0002359229,0.0001243224,0.0002938093,0.0002408301,0.009705501,0.4157013,0.001431425,0.0006990543,0.5405458],"study_design_scores_gemma":[0.00007602463,0.0005988391,0.06291529,0.00006516059,0.0002699044,0.000705717,0.0006724052,0.7799878,0.1497064,0.002192228,0.002739467,0.00007091256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.59304,0.000472965,0.4024533,0.00008667955,0.000107824,0.00006746684,0.0001449634,0.0004943105,0.003132391],"genre_scores_gemma":[0.924682,0.0001652658,0.07308278,0.00002096525,0.00004112967,0.00002851073,0.0001922281,0.00006438964,0.001722787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001778835,"threshold_uncertainty_score":0.005950809,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2157831974","doi":"10.1023/a:1025876613117","title":"Filtering Multi-Instance Problems to Reduce Dimensionality in Relational Learning","year":2003,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Natural Sciences and Engineering Research Council of Canada","keywords":"Inductive logic programming; Datalog; Computer science; Curse of dimensionality; Statistical relational learning; Artificial intelligence; Expressive power; Task (project management); Representation (politics); Set (abstract data type); Logic programming; Relational database; Selection (genetic algorithm); Theoretical computer science; Value (mathematics); Programming language; Machine learning; Data mining","authors":[{"name":"Érick Alphonse","is_ca":false},{"name":"Stan Matwin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05261365240362178,"gpt":0.2875201723504531,"spread":0.2349065199468313,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006188616,0.001080802,0.003514585,0.001989599,0.001276413,0.002531702,0.00313841,0.002902959,0.003192104],"category_scores_gemma":[0.02269927,0.0009940781,0.002399171,0.002440759,0.001008939,0.00605621,0.002237404,0.005495017,0.0009042809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008928899,"about_ca_system_score_gemma":0.001240802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004224698,"about_ca_topic_score_gemma":0.005138468,"domain_scores_codex":[0.9955723,0.001937348,0.0004634739,0.0007226365,0.001029483,0.000274816],"domain_scores_gemma":[0.9864264,0.009215307,0.000503186,0.002497682,0.001086934,0.0002706663],"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":[0.0004816983,0.001100674,0.004127909,0.0003959665,0.000545614,0.0001939936,0.0004430372,0.2041542,0.003827047,0.03616751,0.01640197,0.7321603],"study_design_scores_gemma":[0.00003569654,0.00008012573,0.0004107372,0.00002482357,0.0000715419,0.00005013934,0.00004837975,0.9659517,0.002152462,0.02950287,0.001656147,0.00001531637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02670259,0.001488269,0.9690641,0.0007388462,0.000177986,0.0000999708,0.0002540278,0.0008931314,0.000581124],"genre_scores_gemma":[0.2527458,0.0009405183,0.7402606,0.0005359517,0.0004252165,0.000282149,0.001906407,0.0002651222,0.002638254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006188616,"threshold_uncertainty_score":0.03272891,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1972766481","doi":"10.1007/s10844-006-0005-0","title":"Dynamic management of UDDI registries in a wireless environment of web services: Concepts, architecture, operation, and deployment","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph; Université Laval","funders":"","keywords":"Computer science; WS-I Basic Profile; Web service; World Wide Web; Devices Profile for Web Services; SOAP; Web development; Web application security","authors":[{"name":"Zakaria Maamar","is_ca":false},{"name":"Hamdi Yahyaoui","is_ca":true},{"name":"Qusay H. Mahmoud","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00538153121096114,"gpt":0.2114839510980228,"spread":0.2061024198870617,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01191316,0.0004676562,0.0009851618,0.00239222,0.002301326,0.009073921,0.003480425,0.001054073,0.001046479],"category_scores_gemma":[0.01388165,0.001108926,0.0005351887,0.002415202,0.001326823,0.00780809,0.003735097,0.002213805,0.0007421704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001574481,"about_ca_system_score_gemma":0.002881356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003598616,"about_ca_topic_score_gemma":0.003239239,"domain_scores_codex":[0.9960613,0.001031995,0.0006032074,0.0006531833,0.001167043,0.000483296],"domain_scores_gemma":[0.9894115,0.001714965,0.001227901,0.004518456,0.001803723,0.001323275],"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":[0.001168107,0.00180155,0.04984412,0.0004002709,0.0002824567,0.001691596,0.005266066,0.03313936,0.04466577,0.3593307,0.0298619,0.4725482],"study_design_scores_gemma":[0.000222395,0.0005087677,0.01000119,0.0002355941,0.0004175655,0.001907709,0.00184034,0.6710494,0.06385032,0.07038862,0.1793058,0.0002723736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1385629,0.001647824,0.8270202,0.002623676,0.0003895303,0.000849209,0.0002829497,0.01289955,0.01572414],"genre_scores_gemma":[0.7710674,0.001149726,0.2163622,0.0003613053,0.0002351498,0.0003452571,0.001375573,0.0009445499,0.008158752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01191316,"threshold_uncertainty_score":0.0630036,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2111675636","doi":"10.1007/s10844-009-0087-6","title":"VIREX and VRXQuery: interactive approach for visual querying of relational databases to produce XML","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; XML validation; XML Schema Editor; Document Structure Description; XML database; Information retrieval; XML Schema (W3C); Efficient XML Interchange; Streaming XML; Database; XML Encryption; XML; World Wide Web","authors":[{"name":"Anthony Lo","is_ca":true},{"name":"Tansel Özyer","is_ca":false},{"name":"Keivan Kianmehr","is_ca":true},{"name":"Reda Alhajj","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03452150153576006,"gpt":0.3076422029281448,"spread":0.2731207013923847,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004868628,0.001368144,0.001394214,0.00211242,0.0008419341,0.005381862,0.004917837,0.002117457,0.01674627],"category_scores_gemma":[0.008464496,0.001508443,0.002305813,0.001689512,0.002098753,0.005347992,0.004567808,0.002653733,0.003673209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009925985,"about_ca_system_score_gemma":0.001289347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005525323,"about_ca_topic_score_gemma":0.005616025,"domain_scores_codex":[0.9957498,0.001315722,0.000383873,0.0005374349,0.001677492,0.000335554],"domain_scores_gemma":[0.9960636,0.002329832,0.0001474407,0.0008167832,0.0004196568,0.0002225796],"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":[0.003172555,0.000538758,0.002714519,0.002200426,0.0005369417,0.001679197,0.005811147,0.01810617,0.0911622,0.2499904,0.1044527,0.519635],"study_design_scores_gemma":[0.000895493,0.0005075486,0.00167566,0.0004649781,0.0004195766,0.001480854,0.001412566,0.3328503,0.1736801,0.09678353,0.3893055,0.0005239974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004339222,0.000375793,0.9504659,0.0002670288,0.0000698245,0.0001982587,0.0007798651,0.03993739,0.003566775],"genre_scores_gemma":[0.08310638,0.0007787023,0.8840368,0.0006196863,0.00006824767,0.0004483807,0.005312611,0.01181642,0.01381284],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01674627,"threshold_uncertainty_score":0.05602181,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W883825559","doi":"10.1007/s10844-015-0364-5","title":"Towards context-aware media recommendation based on social tagging","year":2015,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Social media; Context (archaeology); World Wide Web; Data science; Information retrieval; Recommender system","authors":[{"name":"Mohammed F. Alhamid","is_ca":false},{"name":"Majdi Rawashdeh","is_ca":false},{"name":"M. Anwar Hossain","is_ca":false},{"name":"Abdulhameed Alelaiwi","is_ca":false},{"name":"Abdulmotaleb El Saddik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0908986974637839,"gpt":0.3053854391696131,"spread":0.2144867417058292,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001036573,0.0007791218,0.001282544,0.002945751,0.0009006182,0.001869495,0.001412683,0.001427468,0.001217399],"category_scores_gemma":[0.004359113,0.0005371773,0.001015351,0.003185786,0.0003541585,0.00240637,0.001370823,0.001320324,0.001950862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004213483,"about_ca_system_score_gemma":0.0009314811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01148338,"about_ca_topic_score_gemma":0.03031055,"domain_scores_codex":[0.9987211,0.0003416581,0.0000870467,0.0003314595,0.0004012539,0.0001175335],"domain_scores_gemma":[0.9975655,0.0009576422,0.0001450668,0.0004820643,0.0007281947,0.0001215355],"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":[0.0007956031,0.00115494,0.02015013,0.0005093559,0.0006715321,0.000460838,0.0006279547,0.03679129,0.05743453,0.01054772,0.02314708,0.8477091],"study_design_scores_gemma":[0.00004239667,0.000116326,0.004151138,0.00005649702,0.0001967227,0.000263805,0.000232661,0.9661,0.01288305,0.009205364,0.006678131,0.00007404039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07799388,0.003087579,0.9067494,0.0006652114,0.000501396,0.0002321296,0.0008705195,0.003093976,0.00680594],"genre_scores_gemma":[0.5518497,0.001452178,0.4365214,0.0003711235,0.0004289237,0.0001547359,0.001787446,0.0001673401,0.007267203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01148338,"threshold_uncertainty_score":0.02283305,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2114580886","doi":"10.1007/s10844-010-0130-7","title":"A vector-space dynamic feature for phrase-based statistical machine translation","year":2010,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Institute for Infocomm Research; McGill University","keywords":"Computer science; Phrase; Machine translation; Feature vector; Artificial intelligence; Feature (linguistics); Translation (biology); Natural language processing; Context (archaeology); Feature selection; Support vector machine; Function (biology); Decoding methods; Vector space; Vector space model; Feature engineering; Algorithm; Linguistics","authors":[{"name":"Marta R. Costa‐jussà","is_ca":false},{"name":"Rafael E. Banchs","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01104179445258608,"gpt":0.2851133414040508,"spread":0.2740715469514647,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007198855,0.0005418954,0.0009047428,0.001609654,0.0005962663,0.000912906,0.0008456854,0.0008281308,0.006909385],"category_scores_gemma":[0.002125017,0.0002201401,0.0007424739,0.002846689,0.0003003929,0.001406975,0.001111824,0.0008527219,0.00332702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003806266,"about_ca_system_score_gemma":0.0009388211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002853685,"about_ca_topic_score_gemma":0.00309319,"domain_scores_codex":[0.9993368,0.0001722219,0.00007553618,0.0001586994,0.00018084,0.00007586437],"domain_scores_gemma":[0.9992107,0.0002712563,0.00006905888,0.0001480445,0.0002644408,0.00003637939],"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":[0.0007078913,0.0001546119,0.0007779434,0.0001570102,0.00006816407,0.0001763935,0.0000502956,0.009700661,0.04437185,0.008430603,0.01059492,0.9248098],"study_design_scores_gemma":[0.0001248621,0.0005330485,0.003721004,0.00004209085,0.0001137893,0.0006242741,0.00009851355,0.9139028,0.04093364,0.01766381,0.02214398,0.00009807409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02715882,0.0008017231,0.9613603,0.0002436874,0.0002766343,0.0001215853,0.002106223,0.005954913,0.001976215],"genre_scores_gemma":[0.3872654,0.0006689263,0.5963452,0.0001768952,0.0002865927,0.0004382579,0.008728821,0.0006517966,0.005438205],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006909385,"threshold_uncertainty_score":0.02311414,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1548844896","doi":"10.1023/a:1011276003319","title":"Parametric Algorithms for Mining Share Frequent Itemsets","year":2001,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Association rule learning; Data mining; Classifier (UML); Factor (programming language); Measure (data warehouse); Artificial intelligence; Machine learning","authors":[{"name":"Brock Barber","is_ca":true},{"name":"Howard J. Hamilton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05764236875585299,"gpt":0.3054972750066852,"spread":0.2478549062508322,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005834327,0.001046949,0.002769585,0.004538072,0.001425973,0.003225713,0.005075788,0.001956051,0.003355054],"category_scores_gemma":[0.05133324,0.001304859,0.001944623,0.007409663,0.001620343,0.008893788,0.004468921,0.002767143,0.001225793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000731761,"about_ca_system_score_gemma":0.001426397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008768218,"about_ca_topic_score_gemma":0.0009673053,"domain_scores_codex":[0.9947867,0.001857856,0.0006254358,0.0008136889,0.001617426,0.0002988146],"domain_scores_gemma":[0.9651115,0.02466286,0.001996682,0.005467183,0.002387729,0.0003740355],"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":[0.0009813713,0.0005219766,0.008917091,0.0006090943,0.0003446308,0.0003597903,0.0004937036,0.2315021,0.002327765,0.08677959,0.00436526,0.6627976],"study_design_scores_gemma":[0.00006027678,0.0001981477,0.001067594,0.00004694034,0.00007070995,0.0004247099,0.0001665907,0.8438859,0.001101582,0.1507212,0.002222012,0.00003439942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01819463,0.0006125144,0.9795009,0.0001774777,0.00002897434,0.00009119334,0.0002310762,0.00054941,0.0006138992],"genre_scores_gemma":[0.443268,0.001303721,0.5496728,0.0001254894,0.000225558,0.0007697054,0.002361646,0.0002036094,0.002069427],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005834327,"threshold_uncertainty_score":0.03085524,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056806761","doi":"10.1007/s10844-006-9951-9","title":"Mining changing regions from access-constrained snapshots: a cluster-embedded decision tree approach","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Data mining; Decision tree; Set (abstract data type); Tree (set theory); Spatial analysis; Simple (philosophy); Cluster (spacecraft); Data set; Artificial intelligence; Remote sensing","authors":[{"name":"Irene Pekerskaya","is_ca":true},{"name":"Jian Pei","is_ca":true},{"name":"Ke Wang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02951470207297184,"gpt":0.2779731110032382,"spread":0.2484584089302664,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001642519,0.001000815,0.00235648,0.004049677,0.0008677755,0.001844223,0.002494954,0.001474118,0.001239987],"category_scores_gemma":[0.006673712,0.0006704652,0.001191405,0.005816633,0.0004732984,0.002603733,0.001327396,0.001186163,0.0004357928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005623962,"about_ca_system_score_gemma":0.001067209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009156527,"about_ca_topic_score_gemma":0.01188873,"domain_scores_codex":[0.9987133,0.0002377926,0.0001315114,0.0004396278,0.0003217177,0.0001560379],"domain_scores_gemma":[0.9958429,0.002575806,0.0004102001,0.0003910247,0.0006021833,0.0001778925],"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":[0.0009094322,0.0005682787,0.03598553,0.0004172612,0.0006275675,0.0006681907,0.0004452124,0.6330989,0.005701635,0.006209303,0.005102747,0.310266],"study_design_scores_gemma":[0.00001569548,0.00004474369,0.001895343,0.00001199499,0.00007067819,0.00008087198,0.00009004043,0.9899312,0.0006174605,0.006729564,0.0004977208,0.00001465489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1295346,0.001133527,0.8646136,0.000476314,0.00006791686,0.0002046111,0.001922782,0.001062525,0.00098417],"genre_scores_gemma":[0.6895126,0.0004526509,0.3052921,0.0001083544,0.000110216,0.0001521184,0.003385958,0.0001108485,0.0008751955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009156527,"threshold_uncertainty_score":0.01820648,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002963734","doi":"10.1007/s10844-006-0036-6","title":"Is it DSS or OLTP: automatically identifying DBMS workloads","year":2007,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); Queen's University","funders":"Microsoft; International Business Machines Corporation","keywords":"Online transaction processing; Computer science; Workload; Online analytical processing; Database; Benchmark (surveying); Decision support system; Construct (python library); Transaction processing; Data mining; Operating system; Database transaction; Data warehouse; Programming language","authors":[{"name":"Said Elnaffar","is_ca":false},{"name":"Pat Martin","is_ca":true},{"name":"Berni Schiefer","is_ca":true},{"name":"Sam Lightstone","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05905425229981971,"gpt":0.3379666765231631,"spread":0.2789124242233434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006048151,0.0004797427,0.0003814186,0.002160119,0.0002776617,0.001358036,0.000470782,0.0004836812,0.001469859],"category_scores_gemma":[0.00350832,0.0002082306,0.0001973793,0.001565947,0.0001374838,0.002123487,0.0004612188,0.0003244776,0.0009691272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002444644,"about_ca_system_score_gemma":0.0005532512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002277089,"about_ca_topic_score_gemma":0.00404406,"domain_scores_codex":[0.9995443,0.00005677936,0.00006031124,0.00009360557,0.0001749003,0.00007005814],"domain_scores_gemma":[0.998045,0.0006982498,0.0002701395,0.0002688158,0.0004564415,0.0002613759],"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":[0.001752256,0.0003053963,0.3345341,0.0004364433,0.0001862502,0.000408818,0.0004785805,0.00495819,0.08515031,0.002355344,0.04039717,0.5290371],"study_design_scores_gemma":[0.0002339118,0.001031142,0.1928604,0.0001406659,0.0003701136,0.001932298,0.00210705,0.6271382,0.1098896,0.0123624,0.0518439,0.00009020863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9114044,0.001236967,0.05955266,0.0008612234,0.0003147899,0.0001218451,0.006497189,0.01443045,0.005580438],"genre_scores_gemma":[0.9512878,0.0004074608,0.04001266,0.0001736469,0.0001409837,0.00003868677,0.006213787,0.0002435661,0.001481526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002277089,"threshold_uncertainty_score":0.004917145,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2513699951","doi":"10.1023/a:1012857830230","title":"Tracing Lineage of Array Data","year":2001,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Data Storage Technologies","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Computer science; Lineage (genetic); Data structure; TRACE (psycholinguistics); Computation; Theoretical computer science; Algorithm; Programming language","authors":[{"name":"A. Marathe","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05793276575853196,"gpt":0.2990096459833188,"spread":0.2410768802247868,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002559961,0.0003488254,0.0006700267,0.003559922,0.001680748,0.003416196,0.001597114,0.001229935,0.00259848],"category_scores_gemma":[0.02731483,0.0009357918,0.0004237544,0.00424694,0.001389245,0.005328228,0.00306147,0.001771514,0.0009391396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293418,"about_ca_system_score_gemma":0.001785572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003464098,"about_ca_topic_score_gemma":0.004726025,"domain_scores_codex":[0.997337,0.0005390046,0.0002020332,0.0004444085,0.001224915,0.0002525613],"domain_scores_gemma":[0.9757645,0.009304507,0.001872214,0.007526483,0.004825678,0.0007067584],"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":[0.001493214,0.0002519789,0.1172171,0.0004557022,0.000147494,0.001095286,0.005573646,0.05334599,0.09397699,0.2540029,0.02275487,0.4496847],"study_design_scores_gemma":[0.00006953613,0.0002552944,0.01185623,0.0001859893,0.0001163967,0.0009205433,0.001853605,0.6575907,0.0873813,0.1807649,0.05890876,0.00009669219],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5010061,0.003564223,0.4741846,0.002448274,0.0004181858,0.0001732377,0.00333462,0.005167046,0.009703755],"genre_scores_gemma":[0.7617925,0.001038163,0.2265031,0.0003067006,0.0001132635,0.000109287,0.003532756,0.001123973,0.005480408],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003559922,"threshold_uncertainty_score":0.01353854,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2799430123","doi":"10.1007/s10844-018-0505-8","title":"Granular methods in automatic music genre classification: a case study","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Music and Audio Processing","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scalability; Rough set; Artificial intelligence; Machine learning; Focus (optics); Set (abstract data type); Statistical classification; Granular computing; Algorithm; Fuzzy set; Fuzzy logic; Data mining; Database","authors":[{"name":"Arshia Sathya Ulaganathan","is_ca":true},{"name":"Sheela Ramanna","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08951643055732517,"gpt":0.3717848257713616,"spread":0.2822683952140364,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004823324,0.000496919,0.0007132337,0.002463984,0.001290377,0.002747676,0.001613837,0.001748148,0.002361967],"category_scores_gemma":[0.01508898,0.0003040374,0.0005774141,0.003829834,0.0009672684,0.001496347,0.001561531,0.001267453,0.0005599804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008258971,"about_ca_system_score_gemma":0.0007967884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006794664,"about_ca_topic_score_gemma":0.008932672,"domain_scores_codex":[0.9968277,0.001241835,0.0002982011,0.0003514028,0.001077861,0.0002030251],"domain_scores_gemma":[0.9881886,0.008824733,0.000490287,0.001194659,0.0009338378,0.0003679575],"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":[0.002802524,0.002429619,0.05885338,0.001294455,0.000245605,0.003505618,0.004723688,0.03916558,0.02014456,0.01448168,0.007494791,0.8448585],"study_design_scores_gemma":[0.0007540461,0.001687931,0.05727757,0.0005792035,0.0005214853,0.005999263,0.008313942,0.809901,0.03859159,0.04645682,0.02967652,0.0002405449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7488911,0.002862049,0.2315026,0.001709708,0.0001899426,0.000665131,0.0009076453,0.001019668,0.01225207],"genre_scores_gemma":[0.8211468,0.0006043849,0.1750219,0.0001565237,0.00005022144,0.000119794,0.0003417787,0.0001237288,0.00243495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006794664,"threshold_uncertainty_score":0.02550852,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403236745","doi":"10.1007/s10844-024-00896-3","title":"Persuasive explanations for path reasoning recommendations","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Artificial Intelligence in Games","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Path (computing); Artificial intelligence; Data science; Information retrieval; Programming language","authors":[{"name":"Havva Alizadeh Noughabi","is_ca":false},{"name":"Behshid Behkamal","is_ca":false},{"name":"Fattane Zarrinkalam","is_ca":true},{"name":"Mohsen Kahani","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04292240770245533,"gpt":0.3238636200771864,"spread":0.2809412123747311,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002918858,0.001004766,0.0005778196,0.002407686,0.00105203,0.002561286,0.002039867,0.003708921,0.03248779],"category_scores_gemma":[0.05097871,0.0006605482,0.0008733963,0.001176459,0.00075704,0.005662021,0.001954693,0.00305683,0.002915148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000899245,"about_ca_system_score_gemma":0.001524835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004607864,"about_ca_topic_score_gemma":0.007790574,"domain_scores_codex":[0.9968913,0.00144486,0.0002120516,0.0003649343,0.0009253094,0.0001615929],"domain_scores_gemma":[0.964146,0.02949697,0.0009034369,0.00228128,0.00272588,0.0004464981],"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":[0.001752732,0.0008387161,0.009020448,0.001264298,0.0003431605,0.001367077,0.00324313,0.07647894,0.003505806,0.4388486,0.06872587,0.3946112],"study_design_scores_gemma":[0.0004220498,0.000162607,0.001247997,0.0003099128,0.0002667127,0.0003777764,0.0006992927,0.5814744,0.002967125,0.3863688,0.02563532,0.00006805518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07280083,0.001206425,0.8675808,0.009386054,0.0009282521,0.0006540363,0.002677794,0.004828514,0.03993727],"genre_scores_gemma":[0.7084255,0.0005349796,0.2774239,0.0005624261,0.0002564106,0.0002788957,0.002562597,0.000232502,0.009722802],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03248779,"threshold_uncertainty_score":0.1086825,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1512994456","doi":"10.1023/a:1015568521453","title":"Learning Prosodic Patterns for Mandarin Speech Synthesis","year":2002,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; University of Alberta","funders":"Chinese Academy of Sciences","keywords":"Naturalness; Computer science; Prosody; Intelligibility (philosophy); Speech synthesis; Speech recognition; Cluster analysis; Artificial intelligence; Decision tree; Artificial neural network; Natural language processing","authors":[{"name":"Yiqiang Chen","is_ca":false},{"name":"Wen Gao","is_ca":false},{"name":"Tingshao Zhu","is_ca":true},{"name":"Charles X. Ling","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03588713324134827,"gpt":0.2437557515907929,"spread":0.2078686183494446,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004791535,0.0005170045,0.0004451274,0.0004829282,0.0002355177,0.0003950749,0.0004744842,0.0004821911,0.003075183],"category_scores_gemma":[0.001672475,0.000433442,0.0003849708,0.0004140539,0.0001687229,0.0005775858,0.0006250506,0.0007598375,0.0007232505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001956131,"about_ca_system_score_gemma":0.0003518865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00155012,"about_ca_topic_score_gemma":0.003006499,"domain_scores_codex":[0.9998108,0.00005501442,0.00001423111,0.00006883206,0.00002994578,0.00002129479],"domain_scores_gemma":[0.9995546,0.0002522233,0.00002761558,0.00004859032,0.00009118613,0.000025743],"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":[0.0004099193,0.0001042488,0.001310816,0.00007840311,0.00005006065,0.00007594562,0.00007593635,0.03413103,0.03088453,0.001002232,0.001444092,0.9304327],"study_design_scores_gemma":[0.00007902429,0.0002228839,0.002334846,0.00001770888,0.00006279707,0.00007787797,0.00008042553,0.9727631,0.01847449,0.004471454,0.001400146,0.00001518269],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1737383,0.0007835721,0.8190946,0.0001656242,0.0001033934,0.00009592728,0.0003787675,0.002293221,0.003346693],"genre_scores_gemma":[0.6167891,0.0004807817,0.3776247,0.00008182262,0.00006033072,0.0001615584,0.0009329877,0.0002087345,0.003659929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003075183,"threshold_uncertainty_score":0.01028752,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2144630894","doi":"10.1007/s10844-005-0861-z","title":"Post-Supervised Template Induction for Information Extraction from Lists and Tables in Dynamic Web Sources","year":2005,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Row; Machine learning; Data mining; Artificial intelligence; Dynamic programming; Supervised learning; Exploit; Information extraction; Unsupervised learning; Row and column spaces; Pattern recognition (psychology); Information retrieval; Algorithm; Database; Artificial neural network","authors":[{"name":"Zhichun Shi","is_ca":true},{"name":"Evangelos Milios","is_ca":true},{"name":"A. Nur Zincir‐Heywood","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01489196065337485,"gpt":0.2572530941832417,"spread":0.2423611335298668,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001847651,0.001158675,0.001475934,0.006852447,0.001332252,0.002130615,0.002776451,0.001601572,0.005792176],"category_scores_gemma":[0.008698764,0.0009154249,0.002182258,0.006612774,0.000728832,0.003833742,0.001860369,0.001610818,0.007070547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009410227,"about_ca_system_score_gemma":0.003583924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005343763,"about_ca_topic_score_gemma":0.01038548,"domain_scores_codex":[0.9980123,0.0003539316,0.0002726688,0.0005484342,0.000627723,0.0001849267],"domain_scores_gemma":[0.9922516,0.004099844,0.0005878345,0.001307745,0.001547319,0.0002057085],"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":[0.0003700226,0.000276894,0.004875461,0.000700468,0.0001542041,0.0005648833,0.000341124,0.009101298,0.02239806,0.006334269,0.02634237,0.9285409],"study_design_scores_gemma":[0.0001310795,0.0002575979,0.005995976,0.0002169765,0.0004823359,0.001367594,0.0006372416,0.7902204,0.1105617,0.04768267,0.04231462,0.0001318182],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01731248,0.0007059392,0.9506807,0.0003116849,0.0001041329,0.0003657209,0.005603068,0.02338484,0.001531519],"genre_scores_gemma":[0.1102483,0.000573981,0.8583773,0.0001367313,0.0001178225,0.0003950212,0.02522999,0.001163075,0.003757755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006852447,"threshold_uncertainty_score":0.01937675,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4412468738","doi":"10.1007/s10844-025-00964-2","title":"Leveraging large language models, graph neural networks, and explainable AI for revolutionizing the next-generation network intrusion detection systems","year":2025,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Network Security and Intrusion Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Intrusion detection system; Graph; Artificial intelligence; Artificial neural network; Theoretical computer science","authors":[{"name":"Samar AboulEla","is_ca":true},{"name":"Rasha Kashef","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03068257555907937,"gpt":0.2456461130529806,"spread":0.2149635374939012,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263042,0.0008289669,0.0006703767,0.0012111,0.0004007058,0.001565615,0.001078723,0.0007876644,0.002019071],"category_scores_gemma":[0.005859169,0.0003354804,0.0008400612,0.0009778808,0.001023117,0.00425966,0.001381108,0.003035775,0.0005418109],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000850037,"about_ca_system_score_gemma":0.001052453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007765597,"about_ca_topic_score_gemma":0.01490002,"domain_scores_codex":[0.9994259,0.0002594383,0.00003081995,0.0001210852,0.000125996,0.00003666618],"domain_scores_gemma":[0.9962513,0.002691431,0.0002383516,0.0004641841,0.0002818269,0.00007292595],"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":[0.0002026624,0.0003415588,0.005394567,0.0003582668,0.0004480028,0.0002723762,0.0003783997,0.5124978,0.007749659,0.1705366,0.01302177,0.2887983],"study_design_scores_gemma":[0.000006930969,0.0000186378,0.0002472964,0.00001500179,0.00002865176,0.00001869384,0.00002380063,0.8651251,0.0006381892,0.1318542,0.002011323,0.00001219503],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04544782,0.003202032,0.9316967,0.009934975,0.0003945428,0.00007549226,0.000973731,0.002371066,0.00590357],"genre_scores_gemma":[0.7419568,0.003435076,0.2457798,0.001759247,0.0004815311,0.0001143514,0.001505592,0.0003599889,0.00460768],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007765597,"threshold_uncertainty_score":0.01544076,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2053340216","doi":"10.1007/s10844-011-0166-3","title":"Tableaux-based optimization of schema mappings for data integration","year":2011,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Schema (genetic algorithms); Assertion; Tuple; Conceptual schema; Star schema; Database schema; Data integration; Semi-structured model; Theoretical computer science; Information retrieval; Programming language; Data mining; Database design; Mathematics; Discrete mathematics","authors":[{"name":"Md. Anisur Rahman","is_ca":true},{"name":"Mehedi Masud","is_ca":false},{"name":"Iluju Kiringa","is_ca":true},{"name":"Abdulmotaleb El Saddik","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0965137520039541,"gpt":0.282461807509628,"spread":0.1859480555056739,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002339642,0.0008411811,0.00125181,0.001462279,0.0008256339,0.002653634,0.001966806,0.001021249,0.007830597],"category_scores_gemma":[0.008368305,0.0007561611,0.001642303,0.002696307,0.0008922236,0.003249732,0.002131267,0.001834801,0.001380966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001481738,"about_ca_system_score_gemma":0.001827071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00452665,"about_ca_topic_score_gemma":0.008287015,"domain_scores_codex":[0.9973378,0.0009629296,0.0002214689,0.0003705246,0.0008530741,0.0002542088],"domain_scores_gemma":[0.9963694,0.00207408,0.0001608742,0.0007550134,0.0005430508,0.0000976915],"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":[0.001124896,0.0005394304,0.002088802,0.0004836894,0.0002459977,0.0004755248,0.0005837764,0.2231211,0.02310091,0.2011979,0.02833039,0.5187076],"study_design_scores_gemma":[0.0001305973,0.000115888,0.0004928257,0.00005222608,0.00009342877,0.0001887063,0.0001983811,0.8034242,0.01460508,0.1698562,0.01079646,0.0000460546],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03321804,0.0005687361,0.9547516,0.0004229647,0.0001350031,0.0001911851,0.0007958474,0.004199731,0.00571693],"genre_scores_gemma":[0.2501301,0.0002981201,0.7416713,0.0002082696,0.00005615158,0.0002114792,0.00208,0.0009624069,0.00438217],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007830597,"threshold_uncertainty_score":0.02619594,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2139714950","doi":"10.1023/a:1016524013831","title":"Hypothetical Temporal Reasoning in Databases","year":2002,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Logic, Reasoning, and Knowledge","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Predicate (mathematical logic); Data integrity; Relational database; Temporal database; Database schema; Temporal logic; Programming language; Database; Theoretical computer science; State (computer science); Database theory; Database design; Information retrieval","authors":[{"name":"Marcelo Arenas","is_ca":false},{"name":"Leopoldo Bertossi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04761731362541245,"gpt":0.2615235239113944,"spread":0.2139062102859819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007634674,0.000438833,0.000781575,0.002190855,0.002604711,0.007861656,0.002446954,0.003025445,0.00978213],"category_scores_gemma":[0.03877252,0.0009466606,0.001620179,0.002639891,0.006314681,0.01993454,0.002658244,0.00350864,0.00105668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002691361,"about_ca_system_score_gemma":0.0015775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003221443,"about_ca_topic_score_gemma":0.002485164,"domain_scores_codex":[0.994727,0.002927671,0.0005065664,0.0006201794,0.0009788275,0.0002397933],"domain_scores_gemma":[0.9788507,0.01602021,0.0007196212,0.002372983,0.001499804,0.0005367841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007922784,0.00003443048,0.0003377879,0.00006773524,0.0000183077,0.00008988485,0.0002833691,0.003082256,0.0001572529,0.9853054,0.001779532,0.008764882],"study_design_scores_gemma":[0.00001705477,0.000005358314,0.00005146113,0.00001388025,0.00001219911,0.00005457475,0.00008245242,0.01167576,0.00016107,0.9854929,0.002426626,0.000006508142],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08828324,0.006924246,0.798193,0.02225874,0.0006626588,0.0001891887,0.0008858475,0.0007036749,0.08189943],"genre_scores_gemma":[0.8021438,0.00268597,0.1839863,0.0009936374,0.0006360197,0.0001290368,0.001006914,0.00007513999,0.008343234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00978213,"threshold_uncertainty_score":0.04037648,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2014504390","doi":"10.1007/s10844-012-0224-5","title":"Iterative classification for multiple target attributes","year":2012,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Exploit; Machine learning; Artificial intelligence; Data mining; Scheme (mathematics); Computation; Artificial neural network; Decision tree; Algorithm","authors":[{"name":"Hongyu Guo","is_ca":true},{"name":"Sylvain Létourneau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05481460153402744,"gpt":0.2974954544465248,"spread":0.2426808529124973,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004733506,0.0008752387,0.001421336,0.002222168,0.001151948,0.002550394,0.002516505,0.001834799,0.003828191],"category_scores_gemma":[0.01202918,0.0006149415,0.00214398,0.002038286,0.0006163092,0.00280731,0.002399093,0.00255791,0.002505128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183398,"about_ca_system_score_gemma":0.001910772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003299134,"about_ca_topic_score_gemma":0.004947718,"domain_scores_codex":[0.9960358,0.0009992004,0.0003109767,0.0008365887,0.001434706,0.0003827929],"domain_scores_gemma":[0.9914954,0.003872199,0.0003133178,0.001510249,0.002656832,0.0001520474],"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":[0.0005241163,0.0004984868,0.00889915,0.0001779275,0.0002626894,0.0001824992,0.0003380343,0.07075813,0.01645286,0.01251585,0.006092614,0.8832976],"study_design_scores_gemma":[0.00002255699,0.00008832201,0.001470739,0.00001666271,0.00005792266,0.0001223461,0.00007169286,0.9741122,0.01096225,0.01061458,0.002442638,0.00001813061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04626707,0.000243545,0.9496691,0.0002107588,0.0000909446,0.0001549339,0.0001233992,0.001186772,0.002053455],"genre_scores_gemma":[0.3625032,0.0001523755,0.6280318,0.000129013,0.00008651536,0.0002867463,0.0008446425,0.0002571273,0.007708644],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004733506,"threshold_uncertainty_score":0.02503347,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1533605675","doi":"10.1023/a:1008736910058","title":"Partial Evaluation of Views","year":2001,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Computer science; Tuple; Query optimization; Materialized view; Information retrieval; Spatial query; Query language; Web query classification; Web search query; Sargable; Data warehouse; View; Database; Data mining; Theoretical computer science; Search engine; Database design","authors":[{"name":"Parke Godfrey","is_ca":true},{"name":"Jarek Gryz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08159113884016463,"gpt":0.3311095683310115,"spread":0.2495184294908469,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003753965,0.000873761,0.001316196,0.001767391,0.000815751,0.004667271,0.00159848,0.00107052,0.01766439],"category_scores_gemma":[0.01680346,0.0007061168,0.001367405,0.002019213,0.001341976,0.00966284,0.003326092,0.001492358,0.002492078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001149714,"about_ca_system_score_gemma":0.001355115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002056965,"about_ca_topic_score_gemma":0.003119457,"domain_scores_codex":[0.9936678,0.001728533,0.0004141759,0.0007766302,0.002894088,0.0005188377],"domain_scores_gemma":[0.9844775,0.004816166,0.0004394524,0.006509059,0.00336352,0.0003941828],"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":[0.001865837,0.0002325636,0.00674589,0.0009531261,0.0004622949,0.0005590335,0.0009074874,0.01975284,0.02092471,0.3690465,0.03320391,0.5453458],"study_design_scores_gemma":[0.0001946437,0.0005776476,0.003680528,0.0003538446,0.0006843595,0.0009659561,0.000768506,0.1798038,0.0564789,0.6408142,0.1155333,0.0001443161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1575148,0.007690447,0.7396354,0.002693847,0.0009241792,0.0004362598,0.005571377,0.00815681,0.07737692],"genre_scores_gemma":[0.7574735,0.002087248,0.202471,0.0005314723,0.0005315428,0.0001663376,0.00688402,0.00142539,0.02842943],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01766439,"threshold_uncertainty_score":0.0590933,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1982859232","doi":"10.1007/s10844-008-0074-3","title":"The Multi-Tree Cubing algorithm for computing iceberg cubes","year":2008,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Data cube; Computation; Algorithm; Partition (number theory); Online analytical processing; Pruning; Tree (set theory); Sorting; Trie; Data mining; Data structure; Data warehouse; Mathematics","authors":[{"name":"Xing Li","is_ca":true},{"name":"Howard J. Hamilton","is_ca":true},{"name":"Kamran Karimi","is_ca":true},{"name":"Liqiang Geng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0363375668273497,"gpt":0.2791748354478105,"spread":0.2428372686204608,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00106588,0.0006495422,0.00177049,0.001668597,0.001208491,0.002237627,0.00220293,0.00104474,0.004291506],"category_scores_gemma":[0.004223722,0.0006795896,0.0009652439,0.002800928,0.0007180116,0.003645906,0.002154259,0.001728283,0.001037367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001103491,"about_ca_system_score_gemma":0.001890981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01204547,"about_ca_topic_score_gemma":0.01141445,"domain_scores_codex":[0.9993319,0.0001323191,0.00005772865,0.0001067533,0.000269177,0.000101967],"domain_scores_gemma":[0.9986313,0.000530579,0.00006990236,0.0002798541,0.000405991,0.0000823937],"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":[0.0005881116,0.0001382999,0.001770784,0.000391662,0.0001161607,0.00008726482,0.000301585,0.3436376,0.005116564,0.0868113,0.01854429,0.5424963],"study_design_scores_gemma":[0.00003448939,0.00004090302,0.0001687165,0.00002732027,0.00001609985,0.00005265126,0.00005771068,0.9502225,0.002175291,0.04198631,0.005201642,0.00001644607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0140237,0.0006638715,0.9814461,0.0002223485,0.00007277284,0.00006407761,0.0002873312,0.0009869918,0.002232887],"genre_scores_gemma":[0.09821608,0.0003933683,0.8981792,0.00009672136,0.000035634,0.0001286207,0.0008298377,0.000258075,0.001862443],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01204547,"threshold_uncertainty_score":0.0239507,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2887728628","doi":"10.1007/s10844-018-0524-5","title":"REMI: A framework of reusable elements for mining heterogeneous data with missing information","year":2018,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto","funders":"Horizon 2020 Framework Programme; European Commission","keywords":"Computer science; Data mining; Information retrieval; Missing data; Data science; Machine learning","authors":[{"name":"Avigdor Gal","is_ca":false},{"name":"Dimitrios Gunopulos","is_ca":false},{"name":"Νικόλαος Παναγιώτου","is_ca":false},{"name":"Nicoló Rivetti","is_ca":false},{"name":"Arik Senderovich","is_ca":true},{"name":"Nikolas Zygouras","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0448779938772994,"gpt":0.29895527494351,"spread":0.2540772810662106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0107277,0.001999781,0.00196536,0.004961996,0.00127278,0.004279385,0.00755761,0.001928853,0.00573899],"category_scores_gemma":[0.02917471,0.002086861,0.005298679,0.004620505,0.001455262,0.006629773,0.007132689,0.003513947,0.003291581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008210196,"about_ca_system_score_gemma":0.003106045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00525815,"about_ca_topic_score_gemma":0.008169355,"domain_scores_codex":[0.9945179,0.00150127,0.0008601147,0.00110131,0.001725816,0.0002936276],"domain_scores_gemma":[0.9895065,0.004948091,0.0005519491,0.00363678,0.001025065,0.0003316589],"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":[0.0009721207,0.0004663279,0.007795957,0.002874517,0.001557551,0.0017001,0.003194449,0.04484431,0.01582823,0.1682067,0.04142951,0.7111303],"study_design_scores_gemma":[0.0001869763,0.0002480849,0.001916182,0.0009003578,0.0007989649,0.001103702,0.0007352755,0.5758427,0.03525294,0.2150962,0.1676667,0.0002519376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008510892,0.00008529869,0.9865764,0.00008248744,0.00001603761,0.0001524985,0.0005693531,0.01132765,0.0003391347],"genre_scores_gemma":[0.008371622,0.0001053536,0.986813,0.00007472889,0.00001483564,0.0002515072,0.002558297,0.001157594,0.0006531152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0107277,"threshold_uncertainty_score":0.0567342,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1983388548","doi":"10.1007/s10844-012-0229-0","title":"Reducing the size of databases for multirelational classification: a subgraph-based approach","year":2012,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa; National Research Council Canada","funders":"","keywords":"Computer science; Database; Preprocessor; Tuple; Data mining; Relational database; Data pre-processing; Schema (genetic algorithms); Machine learning; Artificial intelligence","authors":[{"name":"Hongyu Guo","is_ca":true},{"name":"Herna L. Viktor","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3453831662634866,"gpt":0.421210654523675,"spread":0.07582748826018837,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648178,0.0009828124,0.002760458,0.004511752,0.001379269,0.002483575,0.003293507,0.001371466,0.003001158],"category_scores_gemma":[0.01294419,0.0008008998,0.002673316,0.005991275,0.001020151,0.005780153,0.002671729,0.001320185,0.0008374558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296954,"about_ca_system_score_gemma":0.002605044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057202,"about_ca_topic_score_gemma":0.01939943,"domain_scores_codex":[0.9970862,0.0009115334,0.0002560114,0.0005691987,0.0008861643,0.00029087],"domain_scores_gemma":[0.9864702,0.005950574,0.0007711134,0.00442918,0.001947329,0.0004316931],"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":[0.0007949385,0.000726792,0.00951367,0.000914754,0.0005427439,0.0004489266,0.0007295593,0.1846844,0.03062847,0.03792151,0.01816648,0.7149278],"study_design_scores_gemma":[0.00007123321,0.0001626064,0.00237532,0.00005448109,0.0002997083,0.0002975422,0.0004904006,0.9083661,0.006351848,0.07638277,0.005107142,0.00004090392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07274996,0.001019298,0.9175188,0.001373487,0.0001058594,0.0003395637,0.001455453,0.003044857,0.002392752],"genre_scores_gemma":[0.3389491,0.0006730571,0.6530453,0.0003168546,0.0001238166,0.0002368763,0.00390146,0.0005650625,0.002188371],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01057202,"threshold_uncertainty_score":0.02102095,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2070598375","doi":"10.1007/s10844-006-0368-2","title":"Holes in joins","year":2006,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Joins; Computer science; Join (topology); Sketch; Tuple; Query optimization; Cartesian product; Product (mathematics); Materialized view; Data warehouse; Data mining; Information retrieval; Theoretical computer science; Database; Algorithm; View; Programming language; Database design; Mathematics","authors":[{"name":"Jarek Gryz","is_ca":true},{"name":"Dongming Liang","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01195421222928267,"gpt":0.2346485700349911,"spread":0.2226943578057085,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002093046,0.0007840983,0.001965994,0.003066629,0.004200002,0.006827016,0.001423453,0.001790385,0.01900527],"category_scores_gemma":[0.01082317,0.00125439,0.001168874,0.003917478,0.004521073,0.01878226,0.005242018,0.003951509,0.002086416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001501801,"about_ca_system_score_gemma":0.00083281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00123432,"about_ca_topic_score_gemma":0.001019099,"domain_scores_codex":[0.9977404,0.0004797308,0.0001682246,0.0006056179,0.0007018532,0.0003041867],"domain_scores_gemma":[0.9943515,0.003280886,0.0004399287,0.0008892375,0.0005830597,0.0004554452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000689029,0.00001334119,0.0002428125,0.000030491,0.000007716939,0.00004728132,0.0002992006,0.0002327168,0.0002095487,0.9896193,0.002844983,0.006383727],"study_design_scores_gemma":[0.00001712192,0.000009405153,0.00009775937,0.00001288983,0.00001197142,0.00008492687,0.0001387948,0.001108641,0.0002171184,0.9894193,0.008874883,0.000007150547],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2500614,0.007894242,0.4577724,0.01402348,0.002168076,0.0002146578,0.001939545,0.002122662,0.2638035],"genre_scores_gemma":[0.8623587,0.002572897,0.07146817,0.001431771,0.001326379,0.0001542796,0.001372836,0.0008784882,0.05843647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01900527,"threshold_uncertainty_score":0.06357896,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145832235","doi":"10.1007/s10844-009-0091-x","title":"Queries with CASE expressions","year":2009,"lang":"en","type":"article","venue":"Journal of Intelligent Information Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"IBM (Canada); York University","funders":"","keywords":"Computer science; Query optimization; Expression (computer science); Information retrieval; Query language; Programming language","authors":[{"name":"Jarek Gryz","is_ca":true},{"name":"Qiong Wang","is_ca":true},{"name":"Xiaoyan Qian","is_ca":true},{"name":"Calisto Zuzarte","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01360373531177958,"gpt":0.2505480904407343,"spread":0.2369443551289548,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00506111,0.001353608,0.001362476,0.003132954,0.002005772,0.008830975,0.002350362,0.002427464,0.03832301],"category_scores_gemma":[0.02267131,0.001420138,0.001832301,0.003650693,0.002595038,0.01827001,0.005647872,0.003127883,0.01189243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001711533,"about_ca_system_score_gemma":0.001431255,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002366354,"about_ca_topic_score_gemma":0.001663526,"domain_scores_codex":[0.9876057,0.003231396,0.001513386,0.001766205,0.004940663,0.0009427056],"domain_scores_gemma":[0.9914185,0.004421268,0.0003837256,0.002118052,0.001394754,0.000263737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005578013,0.0001763762,0.00199807,0.0004228594,0.00009083209,0.001391763,0.001615159,0.002637933,0.00563488,0.7731026,0.07760647,0.1347654],"study_design_scores_gemma":[0.00010555,0.00005424003,0.0004524809,0.0001677473,0.0001599933,0.001977305,0.0007826188,0.03248085,0.01424219,0.5918359,0.3576426,0.00009856393],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02303825,0.002097911,0.7850337,0.008074305,0.001057859,0.0006833853,0.006843672,0.0137219,0.159449],"genre_scores_gemma":[0.4320307,0.002914647,0.4307539,0.003359373,0.00154062,0.0005674443,0.0199601,0.005545006,0.1033281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03832301,"threshold_uncertainty_score":0.1282033,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}