{"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"},"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006199573,0.000122654,0.0002844424,0.0002875037,0.00004489234,0.0002330055,0.0006380524,0.00005317991,0.000003780601],"category_scores_gemma":[0.00002691758,0.00008051756,0.0001080817,0.000304437,0.00002799175,0.002735239,0.00007802777,0.0001633056,0.0000309118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001429526,"about_ca_system_score_gemma":0.0001545593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004487126,"about_ca_topic_score_gemma":0.000005818022,"domain_scores_codex":[0.9980456,0.00003545394,0.001151772,0.00005986365,0.0005463262,0.0001610072],"domain_scores_gemma":[0.9979889,0.00003287067,0.001181992,0.0002309699,0.0004747235,0.00009050105],"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":[0.0001048494,0.00006475797,0.0005141854,0.0002841423,0.0001492394,0.00002205719,0.01859948,0.954924,0.00005846588,0.01417912,0.0007405943,0.01035914],"study_design_scores_gemma":[0.004905432,0.005347451,0.0008098372,0.004513694,0.00007976588,0.006305902,0.02276052,0.830909,0.005407955,0.000741245,0.117191,0.001028198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02753995,0.00009425783,0.9678209,0.0002444183,0.0009914688,0.0001426342,0.000004370045,0.0000219984,0.003139967],"genre_scores_gemma":[0.9811789,0.00006652323,0.01852887,0.0001307921,0.0000752972,0.000001872228,0.00000175255,0.000004210925,0.00001172403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.953639,"threshold_uncertainty_score":0.3283411,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001351233,0.0001291436,0.0002995665,0.0002598105,0.00004487832,0.0002301155,0.001255998,0.00008366749,0.000001302124],"category_scores_gemma":[0.0006941609,0.00009461749,0.00003215619,0.0002278364,0.00005922015,0.003588855,0.0002339597,0.0002581027,0.00001696548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009659,"about_ca_system_score_gemma":0.0001547429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009760422,"about_ca_topic_score_gemma":0.00000171681,"domain_scores_codex":[0.9981136,0.0001425164,0.00103537,0.0001235224,0.0004604348,0.0001245828],"domain_scores_gemma":[0.9966052,0.0002604786,0.00156875,0.0006564761,0.0007864097,0.0001227136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009484206,0.0005076478,0.0608161,0.000979377,0.0009387216,0.00001553881,0.05630567,0.05740239,0.01703307,0.2590288,0.06235243,0.4836718],"study_design_scores_gemma":[0.001272526,0.001233669,0.01091954,0.00127429,0.00003453498,0.0001692794,0.01319884,0.417071,0.04823048,0.001887337,0.5040334,0.0006750891],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07507265,0.0004770244,0.9212421,0.0001960548,0.0006396295,0.0002208979,0.00009033801,0.00007705297,0.001984222],"genre_scores_gemma":[0.9931608,0.0002162975,0.006401529,0.00008771746,0.00006216612,0.00000274592,0.00004832526,0.0000030457,0.00001732794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9180882,"threshold_uncertainty_score":0.3858389,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004318491,0.0001316182,0.0001998215,0.0002687593,0.0001439289,0.0005916406,0.001078062,0.00006285728,0.00001246021],"category_scores_gemma":[0.0001234675,0.00008535542,0.0001300322,0.0003424875,0.0000555724,0.001248469,0.00007966897,0.0002188755,0.00006971855],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008995743,"about_ca_system_score_gemma":0.000146038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005293284,"about_ca_topic_score_gemma":0.000001349095,"domain_scores_codex":[0.9977692,0.000122596,0.001341397,0.00008386061,0.0004592095,0.000223674],"domain_scores_gemma":[0.9972278,0.0004177561,0.001212415,0.000355001,0.0006811451,0.0001059192],"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":[0.000004726419,0.0000378924,0.0003767145,0.00004310804,0.00006198786,0.000005556102,0.002792024,0.0006833519,0.0001170564,0.003587671,0.003910419,0.9883795],"study_design_scores_gemma":[0.001106938,0.0004384691,0.001182233,0.0005155763,0.00006754164,0.00164055,0.01158783,0.5591515,0.03624678,0.0001237932,0.3873363,0.0006024632],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001451291,0.00008646582,0.9951153,0.0006617937,0.001602698,0.0001734453,0.00001352862,0.00003141509,0.0008640054],"genre_scores_gemma":[0.9149901,0.00001500734,0.08360515,0.001036247,0.0002865878,0.00001016296,0.00001749433,0.000007565749,0.00003169339],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9877771,"threshold_uncertainty_score":0.5705204,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006974103,0.0001139867,0.0002006339,0.0003181426,0.00009195897,0.0008254849,0.0004137496,0.00006839603,0.00002429356],"category_scores_gemma":[0.00009583835,0.0000888362,0.0001073725,0.0002530805,0.00001417548,0.005313002,0.00006495934,0.0002057898,0.0007617053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001396303,"about_ca_system_score_gemma":0.00004497979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001712504,"about_ca_topic_score_gemma":0.00000326657,"domain_scores_codex":[0.9983279,0.00005202774,0.0009073202,0.00006199627,0.000480966,0.0001697681],"domain_scores_gemma":[0.9978061,0.0000916745,0.0008752469,0.000212632,0.0008931353,0.0001211593],"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":[0.0001096611,0.0001514425,0.002389141,0.0004831726,0.000481357,0.00003390841,0.04137376,0.07295641,0.004571765,0.0341476,0.04754043,0.7957613],"study_design_scores_gemma":[0.0008754436,0.0008759424,0.006029048,0.0009784692,0.00002842664,0.002550117,0.005557073,0.7774924,0.0439749,0.00109639,0.1598213,0.0007205007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02710083,0.00007042575,0.9663028,0.0002210862,0.003428194,0.0002893156,0.000001222736,0.00004528021,0.002540801],"genre_scores_gemma":[0.9964986,0.00002995281,0.002911562,0.0001939897,0.0002808439,0.00001026435,0.000001402496,0.000004343116,0.0000689846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9693978,"threshold_uncertainty_score":0.9790435,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003113679,0.0001822625,0.0003170612,0.0002618025,0.000209959,0.0007390474,0.001156878,0.0001017802,0.000003511072],"category_scores_gemma":[0.0002933683,0.0001125055,0.0002247493,0.0002388867,0.00002661325,0.002119107,0.00005455855,0.0002010666,0.00004887383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001336044,"about_ca_system_score_gemma":0.0003820217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001021396,"about_ca_topic_score_gemma":0.000005262735,"domain_scores_codex":[0.9972298,0.0001548593,0.001695742,0.0001017677,0.0005336992,0.0002841122],"domain_scores_gemma":[0.9966906,0.0004521981,0.001438705,0.0004495153,0.0007981901,0.000170771],"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":[0.00001883045,0.00002686477,0.00002383062,0.0001235989,0.00009672658,0.000001388223,0.003073865,0.0003865508,0.00003739236,0.7627123,0.03594273,0.1975559],"study_design_scores_gemma":[0.0001490735,0.0002640649,0.000004558759,0.0001830698,0.000006479987,0.0003617974,0.00283406,0.01117106,0.00300407,0.003518051,0.9783595,0.0001442054],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0001158801,0.006158113,0.9795789,0.001475861,0.00399394,0.0005490538,0.000002406784,0.00006683411,0.00805907],"genre_scores_gemma":[0.9684184,0.009524765,0.02038948,0.000302279,0.0005586704,0.00007064398,0.000001566179,0.00001940601,0.0007148214],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9683025,"threshold_uncertainty_score":0.7126651,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001081327,0.00007926038,0.0001383926,0.0002038352,0.0001881627,0.0005186546,0.000433708,0.00003793783,0.00002613225],"category_scores_gemma":[0.00008963411,0.00005122188,0.00009160663,0.0003320872,0.00002395729,0.002059785,0.0000425596,0.0001723949,0.00008409814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006996476,"about_ca_system_score_gemma":0.00005791186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003262683,"about_ca_topic_score_gemma":0.000004441406,"domain_scores_codex":[0.9984139,0.0000868857,0.0008935609,0.00004161286,0.0004469699,0.0001170788],"domain_scores_gemma":[0.9982921,0.0000858903,0.0008417564,0.0001804818,0.0005348133,0.00006492093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001010804,0.0001305607,0.04829291,0.0001478178,0.0002395593,0.0000423997,0.01767632,0.2017949,0.00008294549,0.2936344,0.0212054,0.4166517],"study_design_scores_gemma":[0.0003403099,0.00008090967,0.001918763,0.0001029649,0.000009341686,0.002149582,0.002842019,0.2969819,0.0001230637,0.000216016,0.6950851,0.0001500118],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002169232,0.00008350732,0.9900547,0.00143442,0.001437333,0.0001422603,9.077042e-7,0.0000308058,0.004646795],"genre_scores_gemma":[0.9953919,0.000087674,0.003935657,0.0003775634,0.0001259148,0.000002244107,0.000002923699,0.0000024271,0.00007366955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9932227,"threshold_uncertainty_score":0.5001398,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009342683,0.0001424595,0.0004514342,0.0003726254,0.0006246417,0.0005069452,0.0004517169,0.00002329874,0.0001443841],"category_scores_gemma":[0.001619702,0.00009717495,0.00009840468,0.0005312947,0.00004021479,0.0008099007,0.0001536306,0.0003503097,0.00001384737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001456452,"about_ca_system_score_gemma":0.00008089701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009056901,"about_ca_topic_score_gemma":0.00000385939,"domain_scores_codex":[0.996366,0.000489698,0.001912912,0.000120784,0.000924499,0.0001860731],"domain_scores_gemma":[0.9938954,0.002886771,0.001991756,0.0002168679,0.0008992039,0.0001100528],"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":[0.003000706,0.0009441781,0.01994905,0.0002614314,0.0006744908,0.0006617914,0.05768974,0.6679376,0.0001654396,0.009324585,0.04177297,0.197618],"study_design_scores_gemma":[0.000664571,0.003096887,0.00003135445,0.00004335232,0.00006587442,0.03471716,0.07160314,0.7099015,0.00009620408,0.0005612141,0.1789885,0.0002302339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0553662,0.00002653643,0.9429162,0.0002090921,0.0002236282,0.0008717551,0.00003971206,0.00004076057,0.0003061446],"genre_scores_gemma":[0.8799726,8.807452e-7,0.1194097,0.00007958579,0.00004641697,0.0001319621,0.000008774404,0.0000121181,0.0003378798],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8246065,"threshold_uncertainty_score":0.4888484,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003330251,0.0002335824,0.0003507728,0.001021365,0.0002349686,0.001060002,0.001025725,0.0001400995,0.00001165247],"category_scores_gemma":[0.000324921,0.0001759787,0.0001639579,0.000845939,0.00002021695,0.01362536,0.00004545123,0.000442225,0.0005271869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005164701,"about_ca_system_score_gemma":0.0004112405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003750504,"about_ca_topic_score_gemma":3.027093e-8,"domain_scores_codex":[0.9947683,0.0001504261,0.002383783,0.00008453657,0.002273022,0.0003399839],"domain_scores_gemma":[0.994579,0.0001293947,0.002074435,0.0004470187,0.002557389,0.0002128037],"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":[0.001431538,0.0002014275,0.0006872615,0.001172991,0.00006410896,0.000008470997,0.009247165,0.5522454,0.0002440872,0.08408574,0.003191887,0.3474199],"study_design_scores_gemma":[0.0008642125,0.002209312,0.002117003,0.0007444338,0.00001372832,0.0001683873,0.0008880615,0.972643,0.004445682,0.000009154788,0.01563407,0.0002629306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1092518,0.00002926483,0.87791,0.0002909479,0.002256649,0.0007900308,0.00001070108,0.0001650333,0.009295506],"genre_scores_gemma":[0.9951518,0.00001758853,0.004027694,0.000623383,0.0001039476,0.000005011333,0.00001647634,0.000003383907,0.00005070103],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8859,"threshold_uncertainty_score":0.999977,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000456812,0.0001144535,0.0002190364,0.0002017709,0.0002606051,0.0002221147,0.00007577484,0.0000564355,0.00001954978],"category_scores_gemma":[0.004050069,0.0000947003,0.00007266246,0.0002066447,0.00003811825,0.001592391,0.00007915044,0.0002994808,0.00002105078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001335046,"about_ca_system_score_gemma":0.00004677435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001189992,"about_ca_topic_score_gemma":0.000007159126,"domain_scores_codex":[0.9984284,0.0002221831,0.0006614607,0.000106179,0.0004662726,0.000115496],"domain_scores_gemma":[0.9974125,0.00129511,0.0007393516,0.00009184665,0.0003968734,0.00006429995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003928854,0.00009001304,0.01307596,0.0002533393,0.0001473237,0.00006988766,0.004650292,0.1138469,0.780328,0.0002227591,0.0005509055,0.08637176],"study_design_scores_gemma":[0.0003866191,0.0001001939,0.0008519985,0.0002641855,0.00003019801,0.001238495,0.003532872,0.09242884,0.8722866,0.0000826416,0.02864229,0.0001550863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6186066,0.001743498,0.3765649,0.0002336709,0.002057823,0.0001253529,0.0000173655,0.00003357327,0.0006173012],"genre_scores_gemma":[0.9988649,0.0003720387,0.0003032795,0.000234007,0.0001673477,0.000001795808,0.000002333373,0.000006825413,0.00004745152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3802584,"threshold_uncertainty_score":0.4848604,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001839023,0.0001057104,0.000191242,0.0002908922,0.0001131971,0.0004207997,0.0009122577,0.00005416306,0.000001396939],"category_scores_gemma":[0.0002962109,0.00008492592,0.0001103367,0.000148737,0.00000835362,0.003567954,0.00004806295,0.0001542783,0.00001474796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009971287,"about_ca_system_score_gemma":0.0003884329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004905428,"about_ca_topic_score_gemma":9.815956e-7,"domain_scores_codex":[0.9981859,0.00006411706,0.001047426,0.00009095771,0.0004586283,0.0001529136],"domain_scores_gemma":[0.9978784,0.0001408779,0.0007755366,0.0005084801,0.0006014921,0.00009519881],"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":[0.00004393702,0.00006073583,0.0002047497,0.0001491956,0.00008029547,0.000001120392,0.001186439,0.9532749,0.0001334037,0.02523168,0.01327919,0.006354361],"study_design_scores_gemma":[0.0003708113,0.00007650615,0.000002417905,0.000131507,0.00001286332,0.00007503601,0.0002609799,0.9561417,0.0001109907,0.0001024767,0.04263553,0.00007914766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001954197,0.0001664218,0.9947038,0.0001070847,0.001644622,0.0002398031,0.0001025725,0.00004109535,0.001040388],"genre_scores_gemma":[0.9907153,0.00003544184,0.008771323,0.0002487039,0.00009312393,0.000009252951,0.00002836341,0.00000504773,0.00009347442],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9887611,"threshold_uncertainty_score":0.4057781,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004605,0.0000903195,0.0002704858,0.0005308832,0.00004342182,0.00009089767,0.0001150736,0.00008417152,7.722052e-7],"category_scores_gemma":[0.0002686299,0.00009152554,0.00002567561,0.0001984309,0.00001301489,0.0009974852,0.00001831427,0.0001071381,0.000007449609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009517232,"about_ca_system_score_gemma":0.00003343616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005074481,"about_ca_topic_score_gemma":0.00000275091,"domain_scores_codex":[0.9984428,0.00002831616,0.001211563,0.0001047371,0.0001197373,0.00009279496],"domain_scores_gemma":[0.99852,0.00002347281,0.0008144228,0.000124283,0.000309483,0.0002083945],"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":[0.000441803,0.0001807925,0.0134251,0.0006609235,0.00004277999,2.287875e-7,0.04470074,0.4193635,0.0006647906,0.2827187,0.0001603501,0.2376403],"study_design_scores_gemma":[0.0001758582,0.0002094475,0.006150171,0.00004153975,0.000004995166,0.00002129144,0.001000005,0.9885887,0.0002666948,0.002491463,0.0009543745,0.00009546203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.423404,0.00009975023,0.5757915,0.00006912007,0.00009676722,0.0002750559,0.00002524205,0.000004314049,0.0002342875],"genre_scores_gemma":[0.9908034,0.0000197814,0.009026863,0.00003835769,0.00006153287,0.000009600766,0.0000295614,0.000005811239,0.000005085079],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5692252,"threshold_uncertainty_score":0.3732303,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","open_science"],"consensus_categories":["scholarly_communication","open_science"],"category_scores_codex":[0.002721214,0.0001303734,0.0002536806,0.0005780567,0.00008160271,0.002838367,0.0429031,0.0001264243,0.000005608779],"category_scores_gemma":[0.02103581,0.0001029772,0.00005891385,0.0005874723,0.00002080948,0.03431664,0.01517712,0.0004448366,0.00009735643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001523052,"about_ca_system_score_gemma":0.0001498734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001493461,"about_ca_topic_score_gemma":3.884967e-7,"domain_scores_codex":[0.997284,0.00006493487,0.001398742,0.0001251298,0.0008857516,0.0002414671],"domain_scores_gemma":[0.9916018,0.0001278027,0.001450852,0.006034472,0.0006814232,0.0001036353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009653817,0.00003323178,0.00006813269,0.00002911782,0.0000348313,0.000013655,0.0003277172,0.0004222906,0.00003595315,0.01350364,0.7964433,0.1890785],"study_design_scores_gemma":[0.0003903707,0.0003130335,0.0002087068,0.0003459392,0.00001343151,0.001503292,0.0009790993,0.4253609,0.001452336,0.01916612,0.5499472,0.0003195608],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006605523,0.0005171737,0.9755416,0.01583858,0.002041278,0.000141121,0.00002028283,0.0001915743,0.005047842],"genre_scores_gemma":[0.8329666,0.000366835,0.1649012,0.001290266,0.0003625479,0.000001792423,0.00006383016,0.000006600888,0.00004040891],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.832306,"threshold_uncertainty_score":0.9981968,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001261742,0.0001489909,0.0002619206,0.000386747,0.0004398981,0.001428053,0.001218411,0.0000954218,0.000009561459],"category_scores_gemma":[0.000391056,0.0001111679,0.0001455868,0.0001371752,0.00004256443,0.009327324,0.0001309365,0.0003078828,0.0003385584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002096536,"about_ca_system_score_gemma":0.0002030687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003024156,"about_ca_topic_score_gemma":3.525415e-7,"domain_scores_codex":[0.9972742,0.00005763237,0.001303186,0.00007219886,0.001060621,0.0002321145],"domain_scores_gemma":[0.9957502,0.00008599985,0.002251723,0.000601801,0.001142308,0.0001679159],"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":[0.0009856781,0.0004893046,0.004570095,0.001103422,0.0002624871,0.000145899,0.03429548,0.1075561,0.00375737,0.1986503,0.0237096,0.6244743],"study_design_scores_gemma":[0.002338334,0.001567691,0.006584901,0.002623197,0.0000435152,0.001848771,0.004363306,0.7322087,0.03120197,0.0004266227,0.215704,0.001088987],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.27197,0.00007425636,0.7167859,0.0003598856,0.005468413,0.000341522,0.000006553599,0.00004384287,0.004949607],"genre_scores_gemma":[0.9955311,0.0001581505,0.003597761,0.0002412103,0.0002696536,0.000002535455,0.000002019624,0.000005373906,0.000192241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.723561,"threshold_uncertainty_score":0.9996086,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009585857,0.00008828999,0.0001941329,0.0003156865,0.00005448865,0.00018139,0.0002830204,0.00004981399,0.000006485118],"category_scores_gemma":[0.0001157382,0.00007166899,0.00007232728,0.0003740324,0.00001424197,0.001826636,0.00002662448,0.0001001677,0.00006713647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001153657,"about_ca_system_score_gemma":0.0001048684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001421025,"about_ca_topic_score_gemma":5.896094e-7,"domain_scores_codex":[0.9981487,0.00005815598,0.001137784,0.00006315749,0.0004756565,0.0001165012],"domain_scores_gemma":[0.997966,0.00003055314,0.001084667,0.0001525046,0.0006562719,0.0001100044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009564627,0.0002992283,0.004078918,0.0007223995,0.0001391336,0.000003343704,0.02294988,0.01236998,0.002016624,0.08616526,0.007830532,0.8633291],"study_design_scores_gemma":[0.001919755,0.001801085,0.001129806,0.00186124,0.00007408582,0.001437696,0.01329398,0.07376423,0.1133733,0.01061582,0.7795756,0.001153311],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06916417,0.00004090889,0.9281012,0.0001244761,0.000900157,0.0001848389,0.000001355928,0.00001231078,0.001470511],"genre_scores_gemma":[0.9834054,0.000006908874,0.01621375,0.0002846851,0.0000661691,0.00000682773,0.000002492909,0.000002718959,0.00001100933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9142413,"threshold_uncertainty_score":0.2922577,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00133328,0.0001095696,0.0001923527,0.0002505627,0.0001546912,0.0003621249,0.001076605,0.00005747642,0.00001382293],"category_scores_gemma":[0.0001104377,0.00009026984,0.00006799845,0.0004188784,0.00001782603,0.001459754,0.00006405912,0.0001092256,0.00007510046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009311083,"about_ca_system_score_gemma":0.0001530172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001518227,"about_ca_topic_score_gemma":2.201921e-7,"domain_scores_codex":[0.9981339,0.00003477291,0.001091404,0.0001208114,0.0004437979,0.000175316],"domain_scores_gemma":[0.9977998,0.0001248556,0.0007533046,0.0006262164,0.0005912597,0.0001045726],"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":[0.00005413816,0.0001775212,0.00008699469,0.0001094428,0.00007474871,0.000001420114,0.001905936,0.1605024,0.00004545675,0.004263864,0.02200635,0.8107717],"study_design_scores_gemma":[0.0002739193,0.00008338335,0.00003963783,0.00009585515,0.000009683981,0.00006200447,0.0004122002,0.7444226,0.0002705314,0.00000471127,0.2542433,0.00008215604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01900333,0.00008336231,0.9789182,0.0003364135,0.0009917095,0.00028865,0.0001196426,0.0000391223,0.0002195931],"genre_scores_gemma":[0.5832716,0.00006476751,0.4138349,0.000549441,0.0009059538,0.00008792336,0.0009625045,0.00002175907,0.0003012221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8106896,"threshold_uncertainty_score":0.3681097,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003585891,0.0001316861,0.0002272529,0.0003837243,0.0001197116,0.00111095,0.001271296,0.00005350827,0.0000027716],"category_scores_gemma":[0.0005378506,0.0000964704,0.00003663406,0.0002358352,0.00003356616,0.005528665,0.0003629019,0.0003101822,0.00002014778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003337209,"about_ca_system_score_gemma":0.00007629876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001362611,"about_ca_topic_score_gemma":0.00000224195,"domain_scores_codex":[0.9982906,0.00006901009,0.0009200999,0.000122348,0.0004342899,0.0001636323],"domain_scores_gemma":[0.9979934,0.0001892802,0.0008365972,0.0005771423,0.0002998947,0.0001037245],"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":[0.00003706758,0.00007939515,0.0001403563,0.000339398,0.000124242,0.000008552291,0.003597173,0.1119442,0.0002753598,0.4305368,0.006126659,0.4467908],"study_design_scores_gemma":[0.0001455882,0.00008248197,0.00001884328,0.0001086914,0.00001127055,0.00007925234,0.0003063642,0.9216349,0.00007324052,0.0006477061,0.07678195,0.0001096684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003984231,0.00005603836,0.9912395,0.0002728022,0.002299418,0.0001229588,0.00001096186,0.00003286956,0.001981209],"genre_scores_gemma":[0.3460867,0.0001548406,0.6517071,0.0005860624,0.0006717147,0.00001240091,0.00007445228,0.00001721804,0.0006894541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8096908,"threshold_uncertainty_score":0.999926,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000475787,0.0000658558,0.0001212603,0.0001704114,0.00006562954,0.0002664362,0.000217174,0.0000421528,0.000003008321],"category_scores_gemma":[0.00002869243,0.00005200228,0.00004309731,0.00007876176,0.00001300564,0.001229156,0.00004832706,0.00005057318,0.00001667451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005702205,"about_ca_system_score_gemma":0.0000386766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006194234,"about_ca_topic_score_gemma":3.55185e-7,"domain_scores_codex":[0.9989684,0.00002161591,0.0006650049,0.00005287518,0.0001919469,0.0001001317],"domain_scores_gemma":[0.9986901,0.00003462756,0.0004736175,0.0001325953,0.0006183973,0.0000506155],"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":[0.000242456,0.0002384375,0.01380521,0.002327685,0.0007084517,0.000009566479,0.1012745,0.02171042,0.005708787,0.2619339,0.05781004,0.5342306],"study_design_scores_gemma":[0.0007298195,0.000445635,0.0002452401,0.0002935277,0.00002030207,0.000314921,0.001433725,0.7017453,0.01478964,0.0007327974,0.2790172,0.000231814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01429244,0.00007265793,0.9833067,0.0003300506,0.001510745,0.0001955765,0.000001249164,0.00001249121,0.0002780588],"genre_scores_gemma":[0.9837898,0.00002806903,0.01513833,0.0004213054,0.0004031719,0.000005392912,0.000003441157,0.000003109747,0.0002073606],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9694974,"threshold_uncertainty_score":0.256925,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01112823,0.0001356065,0.0004945914,0.0009491611,0.00006623776,0.0003759867,0.0003836997,0.0001081012,0.00009165049],"category_scores_gemma":[0.004841962,0.00008999796,0.0004079917,0.0007055938,0.0000540842,0.002542315,0.00002015376,0.000161877,0.0001163049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009126568,"about_ca_system_score_gemma":0.0004113229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008117383,"about_ca_topic_score_gemma":0.000001117631,"domain_scores_codex":[0.9953547,0.0001650177,0.002912593,0.00008725793,0.00130151,0.0001789352],"domain_scores_gemma":[0.9951977,0.001755785,0.001204244,0.0001322052,0.001611194,0.00009885409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001294605,0.00004497039,0.0004232147,0.0008347835,0.0001757041,0.00000722302,0.01020349,0.004825316,0.001200357,0.009620919,0.1104928,0.8608766],"study_design_scores_gemma":[0.0003376047,0.0009900822,0.000123717,0.0006817468,0.00004778414,0.0005698667,0.004269594,0.03352348,0.01235825,0.004438931,0.9424548,0.0002042175],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02058219,0.0004806582,0.966244,0.0002304603,0.004503184,0.0004666017,0.00006035859,0.00002201134,0.007410491],"genre_scores_gemma":[0.9829816,0.00003982126,0.01361004,0.00005308782,0.0004642159,0.00001752653,0.000007505924,0.00001502807,0.002811174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9623994,"threshold_uncertainty_score":0.5796631,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001276721,0.0001370404,0.0002586999,0.0005242956,0.000134951,0.0003177564,0.0003286024,0.00008406981,0.00000631884],"category_scores_gemma":[0.0001717865,0.0001088645,0.00008339599,0.0005710318,0.00001903495,0.0028146,0.00005156854,0.0005286118,0.00002406326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003574108,"about_ca_system_score_gemma":0.000121324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001257468,"about_ca_topic_score_gemma":0.000006033491,"domain_scores_codex":[0.9984231,0.0001408168,0.0007601607,0.00008384466,0.0003516859,0.000240399],"domain_scores_gemma":[0.9980703,0.0001689876,0.001001471,0.000148313,0.0005154887,0.00009545035],"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":[0.00009901975,0.00004343593,0.02235395,0.0001393206,0.00006481722,0.002550868,0.01336788,0.9193575,0.00005884918,0.00809802,0.003570372,0.03029601],"study_design_scores_gemma":[0.0004677551,0.0002190756,0.0001895842,0.001190893,0.000008679767,0.03822508,0.005790811,0.86811,0.0003597185,0.0001079874,0.08508793,0.000242471],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0346764,0.0004650245,0.9621959,0.000339922,0.0009232124,0.0001202613,0.000001374713,0.00003052081,0.001247402],"genre_scores_gemma":[0.9922194,0.00009256905,0.007264612,0.0001210865,0.0001422378,0.000001927852,0.000001583758,0.000005014984,0.0001515864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.957543,"threshold_uncertainty_score":0.4439365,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001270927,0.0001243259,0.0002444424,0.0004475042,0.00009186465,0.0001189198,0.0001150613,0.0000447307,0.0001135865],"category_scores_gemma":[0.00001743393,0.0001031949,0.0002803073,0.0002203216,0.000008938285,0.001289293,0.000009323473,0.00014413,0.00002767509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001080757,"about_ca_system_score_gemma":0.00003970181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001463097,"about_ca_topic_score_gemma":1.693284e-7,"domain_scores_codex":[0.9983265,0.0000675318,0.001152521,0.00004596298,0.0002236238,0.0001838151],"domain_scores_gemma":[0.9978743,0.0001409919,0.001284327,0.000129534,0.0004661496,0.0001046816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009441968,0.0008608578,0.07290372,0.0002932015,0.001078157,1.394764e-7,0.00368149,0.09900399,0.0001318919,0.05566921,0.1545416,0.6108916],"study_design_scores_gemma":[0.0006947593,0.0003758822,0.0003533004,0.0002015027,0.0001138392,0.000003634452,0.001127662,0.1700567,0.002242454,0.0003702682,0.8242848,0.0001752356],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003398156,0.00002960565,0.9907726,0.0001654558,0.0009380233,0.0004565596,0.00004349729,0.00002669826,0.004169415],"genre_scores_gemma":[0.9966033,0.000005402881,0.001609784,0.00008973164,0.001390948,0.00006804304,0.0001885435,0.000007989756,0.0000362351],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9932052,"threshold_uncertainty_score":0.4208164,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110203,0.00009323106,0.0001846575,0.0003985643,0.0001383898,0.0006735576,0.0007156791,0.00004666734,0.000001086865],"category_scores_gemma":[0.00005296486,0.00007788761,0.00006367611,0.0005943208,0.000007921882,0.001435266,0.00004715171,0.00009997559,0.00007118774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492942,"about_ca_system_score_gemma":0.00008467647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005085617,"about_ca_topic_score_gemma":6.185173e-7,"domain_scores_codex":[0.9982288,0.00004973596,0.000966255,0.00008186974,0.0005129919,0.0001603777],"domain_scores_gemma":[0.9982316,0.00005848306,0.0007770959,0.0002830115,0.0005303996,0.0001193787],"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":[0.000006072369,0.0005030095,0.0007003717,0.00003709667,0.00006052285,0.000001420328,0.01124952,0.8016666,0.0001322417,0.1029642,0.02287667,0.05980236],"study_design_scores_gemma":[0.0001785412,0.000146069,0.002871629,0.0000627332,0.00001186002,0.0001280883,0.00220984,0.9259662,0.0007060907,0.00004288682,0.06746917,0.0002068846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01827225,0.00003772574,0.9740112,0.00009320778,0.0005085863,0.0001767205,0.0000228683,0.00004477329,0.006832646],"genre_scores_gemma":[0.8586429,0.000002823909,0.1408528,0.0001243722,0.0002478667,0.00001941586,0.00004368814,0.000004908475,0.00006121481],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8403707,"threshold_uncertainty_score":0.6495131,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006061047,0.0001240905,0.0002008718,0.0003354796,0.00008368171,0.0004458967,0.0006132777,0.00005481276,0.000005810725],"category_scores_gemma":[0.00004739021,0.00009386202,0.00008137735,0.0003441101,0.00001366534,0.002346036,0.0001008804,0.0001377306,0.0001504796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001020407,"about_ca_system_score_gemma":0.00009737431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000662389,"about_ca_topic_score_gemma":2.406951e-7,"domain_scores_codex":[0.9977958,0.00007213256,0.001093489,0.0001013399,0.000739818,0.0001974599],"domain_scores_gemma":[0.9986063,0.00005260561,0.0003252985,0.0003617198,0.0004823738,0.0001716716],"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":[0.000130916,0.0008232325,0.0009019959,0.0003561495,0.00006424846,0.00003543669,0.001108295,0.7698402,0.0007849252,0.0623179,0.1012704,0.06236636],"study_design_scores_gemma":[0.0002686298,0.0001311447,0.0005691779,0.0001177147,0.000007651909,0.0001670582,0.0001069973,0.9255016,0.001755826,0.00006246291,0.07117257,0.0001391923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002507727,0.0001223882,0.9948557,0.00009817696,0.0009586022,0.0002054278,0.00001509286,0.00003242207,0.001204446],"genre_scores_gemma":[0.7477868,0.00001514182,0.2510954,0.0004345607,0.000547933,0.00001957923,0.00006450617,0.000007837377,0.00002826683],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.745279,"threshold_uncertainty_score":0.4299792,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001374275,0.0001006994,0.0002556172,0.0002580026,0.0001325343,0.0004864972,0.002793708,0.0000575372,7.685943e-7],"category_scores_gemma":[0.0008323038,0.00008258912,0.00006464211,0.00006539252,0.00002865469,0.006390015,0.0003510483,0.00009672737,0.000003507514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005848041,"about_ca_system_score_gemma":0.0001443227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001129228,"about_ca_topic_score_gemma":0.00001950833,"domain_scores_codex":[0.9980931,0.00003069711,0.001275744,0.00008391426,0.0003951557,0.0001214242],"domain_scores_gemma":[0.9941353,0.0001501868,0.003655712,0.001160112,0.0008465763,0.00005206796],"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":[0.00004873617,0.00009736969,0.0005053887,0.0003687529,0.0001186891,0.000002354922,0.003292922,0.004039706,0.0002552811,0.01157252,0.003656412,0.9760419],"study_design_scores_gemma":[0.0002432308,0.0002588346,0.0002538747,0.0006057685,0.00001760147,0.00008728203,0.001892633,0.973816,0.001746412,0.0002071217,0.02076309,0.0001080907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01429761,0.00009716115,0.9840702,0.0001507492,0.000729438,0.0002255957,0.000220864,0.00003032705,0.0001780954],"genre_scores_gemma":[0.8095355,0.00009340465,0.1901226,0.0000160912,0.00005699703,0.000003030567,0.0001494017,0.000004691741,0.00001821277],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9759338,"threshold_uncertainty_score":0.5191451,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0008418923,0.0001464772,0.0003899758,0.0008057711,0.0001625799,0.001175856,0.0003771261,0.00005400618,0.0004821323],"category_scores_gemma":[0.00004512058,0.000115503,0.0001906562,0.001011749,0.00003594564,0.003922554,0.00007103278,0.0001192885,0.0002075907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005941819,"about_ca_system_score_gemma":0.00009555124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005766002,"about_ca_topic_score_gemma":8.907026e-7,"domain_scores_codex":[0.997579,0.0001055416,0.001365036,0.0001211011,0.0006458844,0.0001834125],"domain_scores_gemma":[0.9969305,0.000127522,0.001466749,0.0002560491,0.001062035,0.0001571801],"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":[0.00006531306,0.0009641418,0.1856848,0.0004657831,0.0205797,0.00002692941,0.06642001,0.5079415,0.003507281,0.01752305,0.1135931,0.08322842],"study_design_scores_gemma":[0.0002270474,0.00006671366,0.00207243,0.00007273557,0.0002737662,0.00007783539,0.003893341,0.985896,0.0009042352,0.00003823664,0.006288363,0.0001893301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1533329,0.0005599832,0.8435544,0.0002262638,0.0006789993,0.000294842,0.000008686871,0.00002636832,0.001317557],"genre_scores_gemma":[0.9911931,0.00005284293,0.007919164,0.0001445137,0.0001274347,0.00000719109,0.00002458348,0.00000508978,0.0005261383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8378601,"threshold_uncertainty_score":0.999861,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001401852,0.0001928631,0.0003083386,0.000314528,0.0001903133,0.00008079082,0.000245713,0.0001690913,0.0004069558],"category_scores_gemma":[0.00006491449,0.0001530517,0.0001488821,0.0002012387,0.00003008598,0.0006514685,0.00003249583,0.0006306247,0.000581024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002646193,"about_ca_system_score_gemma":0.00004420603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002638899,"about_ca_topic_score_gemma":0.000001219557,"domain_scores_codex":[0.9966969,0.0002444872,0.001888317,0.0001019276,0.000686336,0.0003820671],"domain_scores_gemma":[0.9970304,0.000113682,0.001848769,0.0001626582,0.0005400608,0.0003043835],"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":[0.006160814,0.0009375921,0.18739,0.001393529,0.00119004,0.00006035537,0.1667395,0.007294888,0.00002627594,0.002764896,0.1875612,0.438481],"study_design_scores_gemma":[0.004419472,0.009062635,0.0354639,0.003871365,0.0001416444,0.004477285,0.139148,0.5828184,0.0001372254,0.00002952613,0.2193203,0.001110174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9116692,0.0007888859,0.03503068,0.0007337416,0.04289929,0.0007454738,0.00001297001,0.00005302548,0.008066745],"genre_scores_gemma":[0.9898676,0.00002151643,0.00004270673,0.001805095,0.008114802,0.00001308164,0.00001264603,0.00001477513,0.0001077348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5755236,"threshold_uncertainty_score":0.7468082,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00104471,0.0001263471,0.0002501116,0.0002603366,0.0001380267,0.0001872633,0.0004627614,0.00007580382,9.621298e-7],"category_scores_gemma":[0.0001801467,0.00009384862,0.0001462778,0.0002197038,0.00001950245,0.001696508,0.00003437357,0.0001928612,0.00001581199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000103008,"about_ca_system_score_gemma":0.0002316567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001315227,"about_ca_topic_score_gemma":0.000001358716,"domain_scores_codex":[0.9979861,0.0000648769,0.001217346,0.00009074496,0.0004629088,0.0001780536],"domain_scores_gemma":[0.9977603,0.00006469478,0.0007079544,0.0002299293,0.001138454,0.00009866768],"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":[0.00006991319,0.00007158001,0.00006964127,0.0003793184,0.00004661725,0.000002734219,0.008910806,0.9218071,0.0001929091,0.02420072,0.000770361,0.0434783],"study_design_scores_gemma":[0.0002078889,0.0002057902,0.000009370676,0.0001714707,0.000006677868,0.0005306229,0.0001591761,0.9932047,0.0007567662,0.003080362,0.001554227,0.0001129087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02364603,0.0001114841,0.9745515,0.0003639491,0.0005801009,0.0004315492,0.000002036256,0.00004268598,0.0002706467],"genre_scores_gemma":[0.9656503,0.00002515673,0.03406183,0.00008632299,0.0001211366,0.00001902612,0.000003031379,0.00000415015,0.00002906606],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9420043,"threshold_uncertainty_score":0.3827035,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001149587,0.0001203064,0.000285404,0.0005321132,0.00006451646,0.0002466861,0.0004011131,0.00005895826,0.000001454197],"category_scores_gemma":[0.00005630548,0.00009165298,0.00006568296,0.0005160103,0.00002617662,0.003106694,0.00002846616,0.0001746874,0.000004377445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001979071,"about_ca_system_score_gemma":0.0002047882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000447773,"about_ca_topic_score_gemma":0.00000116511,"domain_scores_codex":[0.9974488,0.00004466958,0.001675694,0.00008018477,0.0005832332,0.0001674281],"domain_scores_gemma":[0.996941,0.0000351378,0.002313874,0.0001660557,0.0004782998,0.00006557896],"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":[0.000436159,0.0005013867,0.0113958,0.001407724,0.0002050628,0.00003162868,0.08833303,0.3245825,0.04063117,0.01328264,0.0003260466,0.5188669],"study_design_scores_gemma":[0.0007915644,0.0002856237,0.002177817,0.001604375,0.00001468246,0.0002747167,0.006710669,0.9410555,0.04644988,0.0001084322,0.0003228422,0.0002038417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4064564,0.00005466551,0.5927799,0.0001242168,0.0002797558,0.0001128691,6.21953e-7,0.000006257198,0.000185265],"genre_scores_gemma":[0.9888939,0.00001738028,0.01088204,0.0001326617,0.00005110151,0.000001210154,9.470083e-7,0.000003034621,0.00001769662],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6164731,"threshold_uncertainty_score":0.37375,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001761241,0.00009536749,0.0001741928,0.0003631257,0.00006862795,0.0001532509,0.0002900457,0.00005417203,0.00000683357],"category_scores_gemma":[0.0003648079,0.00008049249,0.00006032676,0.0005346544,0.00001407108,0.002105047,0.00003620809,0.000249816,0.00006097014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002520636,"about_ca_system_score_gemma":0.0001172227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008376294,"about_ca_topic_score_gemma":5.688376e-7,"domain_scores_codex":[0.997999,0.0001418115,0.001146644,0.00008175128,0.0004871684,0.000143693],"domain_scores_gemma":[0.9983547,0.00007772365,0.0006975498,0.0001483325,0.0006314961,0.00009014262],"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":[0.00008571202,0.0003045798,0.004061275,0.0003856091,0.0000686127,0.00001512366,0.01613641,0.2714107,0.01383689,0.6212533,0.001098185,0.07134368],"study_design_scores_gemma":[0.001079308,0.0004422448,0.004959044,0.001698681,0.000007373622,0.0007099504,0.002910033,0.2112363,0.1284185,0.000825876,0.6470393,0.0006733825],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004121911,0.0001693382,0.99371,0.000161953,0.0004421768,0.00021668,7.183468e-7,0.00003628005,0.001140969],"genre_scores_gemma":[0.9581792,0.00004627442,0.04143154,0.00009451216,0.00001877817,0.0000113516,0.000001485324,0.000003477175,0.0002134261],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9540573,"threshold_uncertainty_score":0.3282388,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008356837,0.0001506252,0.0003294208,0.0003847272,0.0000316125,0.0001057872,0.0004859528,0.0000426316,0.000005369543],"category_scores_gemma":[0.000001596519,0.0001260995,0.00006727542,0.0002115358,0.0000486411,0.0007571212,0.0001586438,0.00009691421,0.000005563881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002224715,"about_ca_system_score_gemma":0.00004105764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001024734,"about_ca_topic_score_gemma":0.00001720848,"domain_scores_codex":[0.997196,0.00009290604,0.001790364,0.0001073025,0.0006549403,0.0001585062],"domain_scores_gemma":[0.9980217,0.00004403191,0.001482836,0.000310087,0.00009138497,0.00004998547],"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":[0.0001037954,0.0002226352,0.002471582,0.002607593,0.0002351554,0.00002444812,0.003022967,0.8867082,0.0003102217,0.06030176,0.0005724306,0.04341922],"study_design_scores_gemma":[0.003169619,0.0008583541,0.004703588,0.003516328,0.00009991607,0.0001596537,0.008312103,0.9103463,0.005955857,0.000584537,0.0617048,0.0005889943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1748581,0.001282203,0.8217388,0.0001420391,0.0003459179,0.0008034749,0.000007735945,0.000014272,0.0008073504],"genre_scores_gemma":[0.9944272,0.001057742,0.004372125,0.00004465938,0.0000160558,0.00002068123,0.000009877289,0.000005101461,0.00004656284],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8195691,"threshold_uncertainty_score":0.5142189,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009603067,0.0001270705,0.0003142409,0.0003778142,0.00007986491,0.00008170382,0.0001950517,0.00003182898,8.467112e-7],"category_scores_gemma":[0.0003888949,0.00009943156,0.00006943369,0.0002420234,0.00001828064,0.005213401,0.00006538062,0.0001123154,0.000003224835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008266506,"about_ca_system_score_gemma":0.00009698228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001762175,"about_ca_topic_score_gemma":4.702011e-7,"domain_scores_codex":[0.9981056,0.0000529848,0.001190568,0.0001122662,0.0004004857,0.0001381115],"domain_scores_gemma":[0.9975144,0.000167332,0.001165727,0.0001959917,0.0008537085,0.0001027914],"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":[0.0008206398,0.0003489381,0.0006016152,0.001063897,0.0002429484,0.000004139447,0.02583179,0.06005472,0.004801031,0.7878725,0.005378182,0.1129796],"study_design_scores_gemma":[0.001980408,0.003596845,0.001884851,0.003083855,0.00006828592,0.001759557,0.02276661,0.4122893,0.04619505,0.0003956878,0.5048573,0.001122264],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008203205,0.000211646,0.990099,0.000123536,0.000452831,0.0004972641,0.00004388356,0.00001535661,0.000353297],"genre_scores_gemma":[0.7756541,0.00003229447,0.2238393,0.0001694112,0.000197652,0.00001668367,0.00004269229,0.000004786328,0.00004300924],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7874768,"threshold_uncertainty_score":0.4054702,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00295784,0.0001502601,0.0003165664,0.0005321455,0.00009261984,0.0004993194,0.0005561106,0.0001047337,0.000009567961],"category_scores_gemma":[0.0001730504,0.0001165546,0.0001350545,0.0002764976,0.00001284297,0.002472874,0.00005102422,0.0002291641,0.00006285553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003707626,"about_ca_system_score_gemma":0.0002471795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005604155,"about_ca_topic_score_gemma":0.000001611548,"domain_scores_codex":[0.9975096,0.0002276569,0.001307811,0.0000792467,0.0007028801,0.0001727953],"domain_scores_gemma":[0.9970732,0.0001215677,0.001412821,0.0001771854,0.001054477,0.0001607177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008802061,0.0001086301,0.0001927382,0.0001305957,0.00007309242,0.00000818647,0.01472401,0.001913975,0.000006647929,0.04407794,0.1897615,0.7489147],"study_design_scores_gemma":[0.0008435835,0.0004012638,0.00008675978,0.0003697017,0.000009819014,0.0001592374,0.006669326,0.2425716,0.001952715,0.0003185884,0.746324,0.0002933724],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002609252,0.00003523368,0.9858019,0.002639395,0.003482562,0.0002047008,0.00000612454,0.00009045846,0.007478642],"genre_scores_gemma":[0.9963977,0.0000110964,0.002134292,0.001054878,0.0003584311,0.00001080749,0.00001377747,0.000006270418,0.00001275883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9961368,"threshold_uncertainty_score":0.4814948,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008496821,0.000129526,0.0002113319,0.000249555,0.00007594112,0.0003959145,0.0005260497,0.0001285067,0.000005506831],"category_scores_gemma":[0.0002316052,0.0000958464,0.00009744579,0.000218975,0.00002147368,0.001664843,0.00001574631,0.0004139413,0.000008632956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007898286,"about_ca_system_score_gemma":0.0001503066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008830014,"about_ca_topic_score_gemma":0.000005882992,"domain_scores_codex":[0.9986414,0.00003990969,0.0006504334,0.00007428263,0.0004395005,0.0001544471],"domain_scores_gemma":[0.9981078,0.0002055988,0.0007327945,0.0001983652,0.0006595583,0.00009588469],"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":[0.000613142,0.0002331445,0.000147572,0.001802089,0.0001635544,0.00002580345,0.00717926,0.004487918,0.02238976,0.2856655,0.01399717,0.663295],"study_design_scores_gemma":[0.0004590488,0.0002501309,0.00002325604,0.0001581362,0.00001662687,0.0002192898,0.00009262743,0.9533032,0.007993977,0.001231922,0.03606926,0.0001825428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003739652,0.0007901888,0.9960285,0.001133288,0.00114868,0.000333052,0.00003366647,0.00009429682,0.0000644038],"genre_scores_gemma":[0.5743356,0.000005388687,0.4254448,0.0001051456,0.00006015675,0.000006034405,0.00002155508,0.000004928076,0.00001636366],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9488153,"threshold_uncertainty_score":0.3908503,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009243281,0.0001263656,0.0002412156,0.0005403329,0.0001352415,0.0006680596,0.0008759852,0.00006604852,0.000008147574],"category_scores_gemma":[0.0002594293,0.0001026182,0.000118377,0.0008456205,0.00001290925,0.002786483,0.00006725811,0.0001155375,0.00007732659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001248294,"about_ca_system_score_gemma":0.0000988204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001752556,"about_ca_topic_score_gemma":2.481011e-7,"domain_scores_codex":[0.997988,0.00002570386,0.001220495,0.00009593173,0.0004575973,0.0002122828],"domain_scores_gemma":[0.9971772,0.0002127624,0.00110288,0.0003116676,0.001051312,0.0001441839],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001790503,0.0001511025,0.0007825643,0.0001616838,0.0001634923,0.00001183492,0.00427842,0.01318346,0.00002701987,0.02573819,0.05021744,0.9052669],"study_design_scores_gemma":[0.0003384313,0.0002088468,0.0001837892,0.000179499,0.00001319539,0.0007555599,0.001303194,0.4096332,0.0002765775,0.00009646975,0.5868299,0.0001813657],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003423807,0.0003103097,0.9936925,0.0003099925,0.001237205,0.0003038773,0.00004439434,0.0000412142,0.0006366856],"genre_scores_gemma":[0.5614194,0.0007550572,0.434769,0.0008433103,0.001260332,0.000173903,0.0002065708,0.00003134894,0.0005411003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9050855,"threshold_uncertainty_score":0.6442115,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0009290083,0.0001728505,0.0003105496,0.0007341002,0.0001915744,0.001342179,0.001307315,0.00009031247,0.000006138812],"category_scores_gemma":[0.00008977762,0.0001400087,0.0001425735,0.000795766,0.00003033252,0.004763902,0.0002078192,0.0001771024,0.00003479629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001163906,"about_ca_system_score_gemma":0.0001102311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001305152,"about_ca_topic_score_gemma":0.00000370336,"domain_scores_codex":[0.9974163,0.00005039662,0.001517868,0.0001440886,0.0006123146,0.0002590179],"domain_scores_gemma":[0.9973994,0.0002183612,0.001375314,0.0004493437,0.0004423007,0.0001152199],"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":[0.00009405106,0.000461819,0.0004567591,0.0001508955,0.000273661,0.00001933147,0.02557118,0.09854322,0.0002594744,0.0840896,0.06782195,0.722258],"study_design_scores_gemma":[0.0006541532,0.00007495996,0.0002805672,0.0004214159,0.00002541141,0.0003874096,0.005122121,0.9573712,0.0005472272,0.0007045257,0.03413762,0.0002733876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01622958,0.0001720499,0.9777445,0.0001692715,0.0007680521,0.0002330636,0.00002595973,0.00005442154,0.004603076],"genre_scores_gemma":[0.7208874,0.00004095337,0.2779057,0.0002428516,0.0006313148,0.00003695282,0.000152409,0.00001195207,0.00009043107],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.858828,"threshold_uncertainty_score":0.9996945,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002858792,0.0001878539,0.0003899595,0.0005114244,0.0001481339,0.0004518715,0.000664963,0.00009656076,0.00004838696],"category_scores_gemma":[0.0002634776,0.0001282061,0.0001618374,0.0005784287,0.00003299974,0.00660058,0.0001395888,0.0002523524,0.0003874035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002096642,"about_ca_system_score_gemma":0.0001651412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000020211,"about_ca_topic_score_gemma":0.000004894001,"domain_scores_codex":[0.9959847,0.00005692996,0.00250703,0.0001035247,0.001011841,0.0003360016],"domain_scores_gemma":[0.9964731,0.0002452875,0.001727617,0.0004243152,0.0009049239,0.0002247267],"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":[0.0005953309,0.0003014314,0.001531021,0.002926856,0.000768636,0.0004318127,0.09371972,0.009070026,0.0008331222,0.4956857,0.1057264,0.2884099],"study_design_scores_gemma":[0.0006049469,0.0003188707,0.0003139983,0.002477705,0.00002027904,0.002514552,0.0146826,0.03221429,0.0054878,0.0001505068,0.9407642,0.0004502305],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002015709,0.0002891699,0.9910443,0.0002774245,0.002974311,0.0002258616,0.000006806024,0.00005769255,0.003108681],"genre_scores_gemma":[0.7924997,0.0004886145,0.2001713,0.002855191,0.001237067,0.00001657783,0.00001311471,0.00003115851,0.002687292],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8350378,"threshold_uncertainty_score":0.5228091,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001016182,0.00009341735,0.0002501663,0.0004029977,0.0000370717,0.000130623,0.002215764,0.0000604132,0.000004080599],"category_scores_gemma":[0.0004109303,0.00007196771,0.00004921696,0.000425964,0.00003143378,0.00783077,0.000238866,0.0001899911,0.00003882395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006477105,"about_ca_system_score_gemma":0.0000649935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008952787,"about_ca_topic_score_gemma":6.266754e-7,"domain_scores_codex":[0.9979606,0.00003002306,0.001331527,0.0000710725,0.0004755132,0.0001312496],"domain_scores_gemma":[0.9968576,0.0001022661,0.001633827,0.0008946136,0.000463142,0.00004854812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001691176,0.0003125049,0.001639785,0.0008404794,0.0003959884,0.000119477,0.01231713,0.1100321,0.005288888,0.09320007,0.02191175,0.7537727],"study_design_scores_gemma":[0.0007831508,0.0006048878,0.0001354393,0.001090179,0.00003240917,0.003604897,0.01030301,0.1716227,0.1080186,0.0006915548,0.7025954,0.0005178058],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003386354,0.0004350927,0.9939422,0.000106626,0.0008726936,0.0001008039,0.00001588883,0.00005591834,0.001084422],"genre_scores_gemma":[0.9386359,0.0003652372,0.0608015,0.0000521141,0.00009395131,0.000001229438,0.00001430592,0.000003801974,0.00003196112],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9352496,"threshold_uncertainty_score":0.5677119,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003304262,0.0001250267,0.0002897593,0.0006005117,0.0001296201,0.0004971381,0.0005387859,0.00005694411,0.00001872899],"category_scores_gemma":[0.0001362137,0.00009535265,0.00007203393,0.000765466,0.00003139867,0.002846091,0.00007041493,0.0001901628,0.00007145717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001405181,"about_ca_system_score_gemma":0.0001604536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004882081,"about_ca_topic_score_gemma":0.000006714929,"domain_scores_codex":[0.9973372,0.0002953891,0.001651667,0.0000914222,0.0004569996,0.0001673229],"domain_scores_gemma":[0.9974418,0.0001066138,0.001291182,0.0003177074,0.0007488788,0.00009382825],"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":[0.00001775241,0.0003418935,0.003019434,0.0003562861,0.00007585351,0.000576689,0.1586033,0.0009871082,0.0002027974,0.003863092,0.002629032,0.8293267],"study_design_scores_gemma":[0.0009756452,0.0007909475,0.00168691,0.0005451705,0.00003382064,0.01913246,0.08866599,0.8658978,0.0009691357,0.0002265816,0.02071465,0.00036083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1490485,0.0001400011,0.8481658,0.00009756292,0.001305985,0.0002769602,2.870929e-7,0.00002959975,0.0009353494],"genre_scores_gemma":[0.9615909,0.000005175479,0.03793037,0.0002395203,0.0001963562,0.00000999215,3.46115e-7,0.000003963549,0.00002331902],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8649107,"threshold_uncertainty_score":0.4793914,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001176098,0.0001166536,0.0001845629,0.0005975093,0.0001519211,0.001068529,0.0005642868,0.00006408118,0.00001917975],"category_scores_gemma":[0.0004387166,0.00009554195,0.000182414,0.0004391504,0.00002017759,0.004568626,0.00004557244,0.0001814216,0.0001783242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002199681,"about_ca_system_score_gemma":0.0002292367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001281027,"about_ca_topic_score_gemma":0.000001213231,"domain_scores_codex":[0.9980748,0.00005313488,0.001222553,0.00009045214,0.0003813686,0.0001776702],"domain_scores_gemma":[0.997628,0.0004847044,0.0005322787,0.0001950108,0.001062622,0.00009735717],"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":[0.0000132583,0.00002956281,0.00002430852,0.0001501411,0.0001275795,0.000006234225,0.02130121,0.009954578,0.00004736047,0.7397659,0.05644576,0.1721341],"study_design_scores_gemma":[0.00004740747,0.0001620869,0.000003806192,0.0005479094,0.00001600934,0.0002740189,0.009317123,0.3306511,0.001401338,0.001312652,0.6561325,0.0001340673],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003456643,0.0007472105,0.9896106,0.001806932,0.005385444,0.0003016239,0.00002017187,0.00008289527,0.001699445],"genre_scores_gemma":[0.9221612,0.0005914854,0.07540365,0.0003590585,0.0008141124,0.00009241214,0.00003328123,0.00001829183,0.0005265446],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9218155,"threshold_uncertainty_score":0.9999685,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001125314,0.0001215665,0.0002593977,0.0004302539,0.0001276114,0.0005621053,0.0004964646,0.00007216956,0.0001031342],"category_scores_gemma":[0.000549197,0.00009614671,0.0001913013,0.0001884293,0.00001169278,0.001942829,0.00003522282,0.0001648586,0.0002803312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001008177,"about_ca_system_score_gemma":0.00002734153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005227754,"about_ca_topic_score_gemma":3.729726e-7,"domain_scores_codex":[0.9979807,0.00008041328,0.00116657,0.00007431691,0.0005088383,0.0001892313],"domain_scores_gemma":[0.9977242,0.0003123624,0.001030676,0.0001631631,0.0006604173,0.0001092194],"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":[0.00002109403,0.00007028501,0.0008275312,0.0002465552,0.0000918345,0.00001011384,0.003018596,0.0008030944,0.00007560173,0.002229504,0.0039697,0.9886361],"study_design_scores_gemma":[0.0007144085,0.0006865724,0.0002219337,0.0009047758,0.0000533529,0.002141763,0.004767182,0.5329242,0.05160228,0.0001783224,0.4052691,0.0005360467],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01171575,0.0001036492,0.9824516,0.0003262649,0.001181678,0.0003268982,0.000007757964,0.00005612886,0.003830227],"genre_scores_gemma":[0.9889064,0.0002326689,0.00994737,0.000156755,0.0002107182,0.00003804294,0.00000169481,0.000008058997,0.000498301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9881001,"threshold_uncertainty_score":0.5420394,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009427791,0.0001158339,0.00022812,0.0008323589,0.00008769397,0.0006985773,0.000279319,0.00009236149,0.000004923459],"category_scores_gemma":[0.0001392376,0.00009719963,0.00007140775,0.0002836607,0.00001278832,0.01376225,0.00003690795,0.0001525152,0.00003061921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000145111,"about_ca_system_score_gemma":0.00008504539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000211994,"about_ca_topic_score_gemma":0.00004746443,"domain_scores_codex":[0.9981535,0.00005043751,0.001257368,0.00006864487,0.0003326015,0.0001374557],"domain_scores_gemma":[0.9981132,0.0001188012,0.001038505,0.0001551274,0.0005027298,0.00007169748],"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":[0.0002384512,0.0001172228,0.002524606,0.0003252703,0.0002313802,0.000001933815,0.03350601,0.07869623,0.005068081,0.002071384,0.001755961,0.8754635],"study_design_scores_gemma":[0.0006500446,0.0001366026,0.001206352,0.0002471331,0.00002371323,0.000126083,0.006966658,0.9118142,0.001128799,0.00004455399,0.07748058,0.0001752554],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5063528,0.0002790114,0.4919731,0.0004651542,0.0005752415,0.0001781046,0.00005771096,0.000024526,0.00009433235],"genre_scores_gemma":[0.9912283,0.000214653,0.008183194,0.0001187493,0.0001172514,0.000008479456,0.0001061252,0.000002997281,0.00002025681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8752882,"threshold_uncertainty_score":0.9977296,"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","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002633504,0.0001744646,0.000275994,0.0003781846,0.0009238435,0.001340557,0.0003790566,0.0001377561,9.112852e-7],"category_scores_gemma":[0.00007128295,0.0001295352,0.0001263349,0.0007544488,0.00002167279,0.004742557,0.0001237598,0.0003662507,0.000001428627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002017349,"about_ca_system_score_gemma":0.000054516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009687457,"about_ca_topic_score_gemma":0.00001644044,"domain_scores_codex":[0.9977448,0.0002427304,0.001198364,0.0001386454,0.0003545336,0.000320926],"domain_scores_gemma":[0.9978665,0.0001712984,0.0008951029,0.0002634892,0.000732681,0.00007089422],"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":[0.00004664215,0.00001177066,0.00001605759,0.0001090621,0.00004250606,6.808311e-7,0.001589177,0.9391801,0.00007101266,0.03176634,0.003643444,0.02352325],"study_design_scores_gemma":[0.0003090782,0.0001105611,0.0000111596,0.0002644354,0.00002316218,0.0001348327,0.002863429,0.9760066,0.0001765198,0.0004923978,0.01948786,0.0001199492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01108775,0.00798126,0.9730623,0.0003169743,0.006740188,0.0006516521,0.00000140923,0.00005570554,0.0001027989],"genre_scores_gemma":[0.9972518,0.0007234772,0.0004548799,0.0004787635,0.0009823681,0.00004544931,0.000008182206,0.000006402983,0.0000486842],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.986164,"threshold_uncertainty_score":0.9996961,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001231546,0.0001116538,0.0002725685,0.0003413704,0.00005623749,0.00007227781,0.0006949332,0.00005836132,0.000006621429],"category_scores_gemma":[0.00030492,0.00008589317,0.00007012739,0.0002746527,0.00002433181,0.006595387,0.00008369414,0.00008131871,0.000007520488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006542603,"about_ca_system_score_gemma":0.0001782024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004537007,"about_ca_topic_score_gemma":0.000001319719,"domain_scores_codex":[0.9979231,0.00004080918,0.001469702,0.0000871996,0.0003578142,0.0001213152],"domain_scores_gemma":[0.9958345,0.00008028047,0.002088783,0.0005408656,0.001392914,0.00006263518],"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":[0.0004229704,0.0002453331,0.0002664546,0.001710309,0.0002179416,0.000003014099,0.01195211,0.3175824,0.001158709,0.6160092,0.009009123,0.04142251],"study_design_scores_gemma":[0.0003894908,0.0002531744,0.000008646991,0.0005258748,0.0000142364,0.00005024455,0.002103743,0.9010127,0.01945711,0.00005591841,0.07599605,0.0001328725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001519939,0.0001394013,0.9977089,0.00004032321,0.0009966111,0.0003538447,0.00005628133,0.00002088346,0.0005317585],"genre_scores_gemma":[0.1755343,0.00005635992,0.823973,0.00008618436,0.0001095283,0.00001738198,0.0001799573,0.000008156334,0.00003511502],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6159533,"threshold_uncertainty_score":0.4781496,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001012534,0.0001172118,0.0002551528,0.0004116496,0.00005307226,0.0002824236,0.0005696918,0.00005027653,0.00003925064],"category_scores_gemma":[0.0003319788,0.00008693379,0.00009403983,0.0003622756,0.00002404902,0.003018135,0.00008288038,0.0002272313,0.0003484142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001232014,"about_ca_system_score_gemma":0.0000447191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002866658,"about_ca_topic_score_gemma":0.000003220607,"domain_scores_codex":[0.9980487,0.00009435524,0.001117452,0.0000737167,0.0004561069,0.0002096661],"domain_scores_gemma":[0.9985502,0.0001174381,0.0006594578,0.0002419214,0.0003063057,0.0001246982],"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":[0.00007090538,0.000593298,0.03100758,0.0003474228,0.0001221129,0.000185486,0.03338803,0.008036057,0.0000617045,0.749228,0.04711789,0.1298415],"study_design_scores_gemma":[0.001030079,0.0004116852,0.001151389,0.0009417573,0.00001279003,0.002519887,0.002874444,0.5208667,0.001019994,0.0003071623,0.4683966,0.000467495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01715938,0.001555509,0.9420847,0.0002209519,0.002161798,0.0001998132,0.000003043867,0.00005013618,0.03656469],"genre_scores_gemma":[0.9946333,0.00015944,0.004778855,0.00009806546,0.0001510498,0.000002976572,0.000001792788,0.00000364816,0.0001708645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9774739,"threshold_uncertainty_score":0.4478276,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00166314,0.000100556,0.0001777149,0.0002666782,0.0001134108,0.0003294518,0.0004208736,0.00006247268,0.000004387464],"category_scores_gemma":[0.0006431617,0.00007689129,0.00009350731,0.0002088068,0.00001216712,0.005353311,0.0000311314,0.0001368876,0.00009702784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001116817,"about_ca_system_score_gemma":0.00005713562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004883428,"about_ca_topic_score_gemma":1.280255e-7,"domain_scores_codex":[0.9983804,0.00009794887,0.0009474673,0.00005652276,0.0003337174,0.000183896],"domain_scores_gemma":[0.9972092,0.0003001865,0.001291544,0.0002284528,0.0008510348,0.0001196014],"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":[0.0002059703,0.0004384412,0.07666934,0.000600361,0.0002510385,5.736701e-7,0.02863636,0.01316387,0.002089293,0.6774012,0.06573294,0.1348105],"study_design_scores_gemma":[0.0003160738,0.0001338469,0.00576332,0.00006311303,0.000008247586,0.00006843422,0.001216926,0.3127693,0.001935008,0.00008787079,0.6775073,0.0001304898],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001506781,0.0003607119,0.9950023,0.0005183843,0.001663786,0.0002597006,0.00001953388,0.00003414691,0.0006346647],"genre_scores_gemma":[0.9735491,0.00003644829,0.02577535,0.000107861,0.0003431726,0.00002261888,0.0000830944,0.000003887163,0.00007851495],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9720423,"threshold_uncertainty_score":0.3881021,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003006265,0.00008493818,0.0002469002,0.0002480005,0.00004088548,0.00006296568,0.0002847018,0.00003825668,0.0000180101],"category_scores_gemma":[0.0001902117,0.00006220223,0.00009542472,0.0002853367,0.00001701641,0.00395797,0.00004233477,0.00008301365,0.00005631709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000105288,"about_ca_system_score_gemma":0.0001496945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001915762,"about_ca_topic_score_gemma":0.000001426353,"domain_scores_codex":[0.9971591,0.000148988,0.001514876,0.00004789054,0.001018823,0.0001102786],"domain_scores_gemma":[0.996172,0.00004815693,0.00172745,0.0002639332,0.001721135,0.00006733027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001350568,0.0001591563,0.001432412,0.0003785567,0.0002035925,0.00000975409,0.01520228,0.1566216,0.0008625683,0.3923888,0.008033649,0.4245726],"study_design_scores_gemma":[0.0004693161,0.0001903732,0.0001431877,0.0003163389,0.0000232582,0.0005519186,0.001854786,0.1407275,0.004600987,0.0001313439,0.8508616,0.0001293395],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01168577,0.000715073,0.9828219,0.00005618555,0.00170607,0.0002216283,0.000004196684,0.0000112963,0.002777849],"genre_scores_gemma":[0.9929389,0.0002174637,0.006511147,0.00005260296,0.0002155712,0.00000993358,0.000005944741,0.000003258909,0.00004519909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9812531,"threshold_uncertainty_score":0.2869433,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001242197,0.0001357881,0.0002568271,0.0001612454,0.0006147637,0.0002145732,0.0005507783,0.00004504768,4.598961e-7],"category_scores_gemma":[0.0001749065,0.00008464717,0.0001546783,0.0002083854,0.00004539067,0.00388088,0.00009942791,0.0001536851,0.00002778017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087056,"about_ca_system_score_gemma":0.0001132645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002457597,"about_ca_topic_score_gemma":0.000002545662,"domain_scores_codex":[0.997771,0.00006307641,0.001396184,0.00007465213,0.000456051,0.0002390963],"domain_scores_gemma":[0.9969464,0.0003858757,0.001347532,0.0002781857,0.0009486327,0.00009336381],"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":[0.00003340541,0.00006545918,0.0002599323,0.0001983838,0.0002047602,0.00001827626,0.01516909,0.03256856,0.00007373234,0.1082464,0.007977062,0.8351849],"study_design_scores_gemma":[0.0002753461,0.00008062685,0.00003215907,0.0001463211,0.000003513252,0.0007852225,0.002224215,0.546734,0.0006864642,0.00001606461,0.448912,0.0001041393],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005451603,0.0009740347,0.9949794,0.00008763095,0.002845087,0.0003106489,0.000009850784,0.0000356671,0.0002125362],"genre_scores_gemma":[0.1689315,0.0009385021,0.8279833,0.0002830233,0.001252601,0.00003309174,0.00001687021,0.00002205776,0.0005391578],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8350807,"threshold_uncertainty_score":0.4728328,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001649218,0.0001169953,0.0002352236,0.0003698945,0.0001190958,0.0006149957,0.00145602,0.00005138457,0.000005771033],"category_scores_gemma":[0.0002455222,0.00008767933,0.00004554712,0.0003359806,0.00002883819,0.01157574,0.0002605414,0.00008239771,0.00002309334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005811172,"about_ca_system_score_gemma":0.00009973767,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005913978,"about_ca_topic_score_gemma":6.953891e-7,"domain_scores_codex":[0.9975473,0.00002832343,0.001568901,0.00007634434,0.0005931205,0.0001859534],"domain_scores_gemma":[0.9959806,0.00009238512,0.002217198,0.0006522362,0.0009851849,0.00007235099],"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":[0.0005122205,0.0001455076,0.0007952132,0.001680124,0.0008011121,0.000004651786,0.02278563,0.005648231,0.00009534842,0.02381969,0.03354215,0.9101701],"study_design_scores_gemma":[0.0005916944,0.0008597719,0.00001982602,0.001222921,0.00004103388,0.0001296426,0.00264502,0.6303788,0.002836729,0.0002329852,0.3608264,0.0002152412],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002089032,0.00005198567,0.9955282,0.00009880382,0.0009717043,0.0003107062,0.00003605639,0.00001853647,0.0008950051],"genre_scores_gemma":[0.266809,0.00004454179,0.7322688,0.000299938,0.0003761965,0.000008279561,0.0001443405,0.000008182194,0.00004077387],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9099548,"threshold_uncertainty_score":0.839213,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01315602,0.00009464013,0.0002560618,0.0003467634,0.000139484,0.0001988294,0.0006326823,0.00003812372,0.00004219593],"category_scores_gemma":[0.004656446,0.00005300763,0.0002009454,0.0004730831,0.00007488031,0.002802125,0.00004558339,0.0001077341,0.00003717053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005522048,"about_ca_system_score_gemma":0.0001003944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001761263,"about_ca_topic_score_gemma":5.469178e-7,"domain_scores_codex":[0.9956981,0.0002415185,0.00234426,0.00006725924,0.001497825,0.0001510292],"domain_scores_gemma":[0.992221,0.003066357,0.002870416,0.0004178027,0.001339575,0.00008487319],"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":[0.000794917,0.0008729892,0.01060915,0.0007332234,0.0004101403,2.288844e-7,0.01440112,0.1266103,0.0002621659,0.5515664,0.1996815,0.09405785],"study_design_scores_gemma":[0.0005697642,0.00009542419,0.0051646,0.0001332137,0.00005961199,0.00002908341,0.03524717,0.09476523,0.0007898001,0.0001884631,0.862817,0.000140605],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004732744,0.0003064755,0.98887,0.0005244779,0.001313708,0.0005585077,0.0001499434,0.000006727926,0.003537419],"genre_scores_gemma":[0.9915719,0.00001745093,0.007707572,0.0001888686,0.0002301157,0.00002564082,0.00005932944,0.000003561495,0.000195585],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9868391,"threshold_uncertainty_score":0.5574536,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006479508,0.00008685383,0.0002068265,0.0004088203,0.00003492221,0.0001356489,0.0002769362,0.00003798886,0.000002359304],"category_scores_gemma":[0.0000408541,0.0000655335,0.00006392039,0.0002940164,0.00001842617,0.004282061,0.00004552204,0.000118622,0.00007069098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130107,"about_ca_system_score_gemma":0.00006804586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001219076,"about_ca_topic_score_gemma":0.000010419,"domain_scores_codex":[0.9981607,0.00004433135,0.00124481,0.00004650019,0.0003675154,0.0001361957],"domain_scores_gemma":[0.9986171,0.00004399688,0.0008096124,0.0001889397,0.0002969166,0.00004347206],"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":[0.00001497279,0.00004951471,0.001818057,0.0001192004,0.0000130931,0.00002369876,0.001627195,0.0418941,0.0001759787,0.9405644,0.005045922,0.008653865],"study_design_scores_gemma":[0.0004473222,0.0001207042,0.001452363,0.0004701086,0.000002753608,0.0006722028,0.00197948,0.01695232,0.002522654,0.0006248201,0.9745385,0.0002167626],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007611985,0.0004343417,0.9873579,0.00009184516,0.001121065,0.0001101214,0.000005122583,0.00001864055,0.003248973],"genre_scores_gemma":[0.9873182,0.0000632575,0.01218928,0.00007433932,0.0002161462,0.000004993757,0.000006000967,0.000003565561,0.000124272],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9797062,"threshold_uncertainty_score":0.3104391,"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","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004028288,0.0001154482,0.0002237899,0.0002328098,0.000126423,0.0001923894,0.000244723,0.00003663778,0.000004235988],"category_scores_gemma":[0.00005424024,0.0000708713,0.00005792884,0.0002399759,0.00002033224,0.006141637,0.00002999978,0.0001357159,0.0000287374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006119571,"about_ca_system_score_gemma":0.00009227129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001927857,"about_ca_topic_score_gemma":0.000001678513,"domain_scores_codex":[0.9984806,0.0000461624,0.0008784468,0.00005645371,0.0003942219,0.0001441403],"domain_scores_gemma":[0.9980421,0.0000500659,0.0009075333,0.0002907512,0.0005868322,0.0001227172],"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":[0.0001444809,0.0001058069,0.0001347196,0.0001653641,0.00009513829,0.001496424,0.01888107,0.0251613,0.0004030269,0.8787397,0.008647426,0.0660256],"study_design_scores_gemma":[0.0005089924,0.001004056,0.00006451026,0.0008351305,0.00001249122,0.06495138,0.01222444,0.006368062,0.004173597,0.000150464,0.9093759,0.0003309814],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005118411,0.0002898508,0.9917213,0.0001826487,0.0006011018,0.000135532,0.000007045,0.00003724272,0.001906829],"genre_scores_gemma":[0.9561767,0.00006087959,0.04327807,0.0002178181,0.0001453798,0.000004325534,0.000003203681,0.000003298338,0.0001103108],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9510583,"threshold_uncertainty_score":0.4452538,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}