{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":139,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":139,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"3590ab7278a7","filters":{"venue":"International Journal of Approximate Reasoning"}},"results":[{"id":"W2913932916","doi":"10.1016/j.ijar.2008.11.006","title":"Semantic hashing","year":2008,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1272,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science","authors":[{"name":"Ruslan Salakhutdinov","is_ca":true},{"name":"Geoffrey E. Hinton","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02107195537136535,"gpt":0.2914815013127126,"spread":0.2704095459413473,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001212357,0.000639363,0.00124724,0.001971594,0.001097613,0.002491669,0.001598587,0.001406902,0.02618463],"category_scores_gemma":[0.005177497,0.0004344449,0.001099498,0.002820567,0.001126424,0.005194831,0.002658057,0.001245309,0.0105097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008261332,"about_ca_system_score_gemma":0.001172123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000837283,"about_ca_topic_score_gemma":0.0009068098,"domain_scores_codex":[0.9983642,0.0003252973,0.0001165627,0.0004077923,0.0006440459,0.0001421871],"domain_scores_gemma":[0.9982477,0.000236694,0.00006456416,0.001088654,0.0003015933,0.00006072546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004939504,0.0001806235,0.001475257,0.0003825426,0.000111062,0.0001183002,0.000120408,0.01244027,0.00790289,0.2485935,0.04265484,0.6855264],"study_design_scores_gemma":[0.0001300102,0.0003259149,0.001777889,0.0001300946,0.000139275,0.001329598,0.0002783804,0.2030812,0.02079093,0.6412467,0.1306676,0.0001024225],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01768018,0.002804554,0.9341422,0.001034063,0.001035654,0.0003250262,0.002225187,0.004123753,0.03662942],"genre_scores_gemma":[0.4759957,0.002895761,0.4583693,0.001006611,0.0008508678,0.0003673864,0.009791374,0.0006985058,0.05002446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02618463,"threshold_uncertainty_score":0.08759636,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2892294644","doi":"10.1016/j.ijar.2018.09.005","title":"Three-way decision and granular computing","year":2018,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":593,"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":"Granular computing; Computer science; Field (mathematics); Artificial intelligence; Mathematics; Rough set","authors":[{"name":"Yiyu Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01417066434871474,"gpt":0.2726181675979136,"spread":0.2584475032491989,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004876676,0.0009583109,0.002835818,0.00286381,0.001385044,0.007191912,0.00176247,0.001795595,0.004736347],"category_scores_gemma":[0.01389588,0.0007862786,0.002791649,0.005448278,0.002782209,0.008259871,0.003348347,0.002201272,0.0004237226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001820715,"about_ca_system_score_gemma":0.001347043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003674953,"about_ca_topic_score_gemma":0.002461172,"domain_scores_codex":[0.9956269,0.001431319,0.000573107,0.0006231022,0.001308655,0.0004368842],"domain_scores_gemma":[0.9935327,0.003844748,0.0005688136,0.001010542,0.0005992973,0.0004438729],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000446978,0.0001514434,0.00154798,0.0003815636,0.0002915022,0.0004405301,0.0003990197,0.1814493,0.001214789,0.7056887,0.002174933,0.1058133],"study_design_scores_gemma":[0.00003143234,0.00003669259,0.0002858609,0.0000372599,0.00006494753,0.00009138209,0.00009268847,0.4329085,0.0005227976,0.5640008,0.00188558,0.00004208259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02620734,0.001202712,0.9660795,0.0006932807,0.0002708894,0.00007410678,0.0001977089,0.0002531817,0.005021345],"genre_scores_gemma":[0.5767007,0.001032982,0.4180217,0.0001648461,0.0001685515,0.0001507651,0.0002788278,0.00006205774,0.003419515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007191912,"threshold_uncertainty_score":0.02579063,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2001692054","doi":"10.1016/j.ijar.2007.05.019","title":"Probabilistic rough set approximations","year":2007,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":591,"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":"Rough set; Probabilistic logic; Dominance-based rough set approach; Set (abstract data type); Computer science; Mathematics; Bayesian probability; Data mining; Algorithm; Artificial intelligence","authors":[{"name":"Yiyu Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02091057240139605,"gpt":0.2906223636234414,"spread":0.2697117912220454,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003707706,0.0008420155,0.002019153,0.002418799,0.0008721926,0.003829521,0.001513897,0.001595487,0.005565643],"category_scores_gemma":[0.02133064,0.0009776461,0.002392369,0.002514946,0.001185204,0.004029773,0.001859405,0.002699552,0.001365911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001255633,"about_ca_system_score_gemma":0.0009531816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001188416,"about_ca_topic_score_gemma":0.0009410219,"domain_scores_codex":[0.9951575,0.001444315,0.000299222,0.0005564948,0.002336722,0.0002057364],"domain_scores_gemma":[0.9949006,0.002646951,0.0003379171,0.001288903,0.0007234863,0.0001021153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001250627,0.00005005057,0.0006623333,0.0002035496,0.0001933144,0.0001415329,0.0001609995,0.1538895,0.00114985,0.7151168,0.004408661,0.1238984],"study_design_scores_gemma":[0.0000192751,0.00004176136,0.0003663918,0.00005384283,0.00006905149,0.0001953201,0.00005100869,0.470478,0.001049677,0.5182032,0.009444702,0.0000277554],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006499681,0.001585318,0.9825184,0.0005600688,0.000186591,0.00004088627,0.0001450913,0.0001629009,0.008300999],"genre_scores_gemma":[0.4857244,0.003930883,0.4965858,0.0002783084,0.000508043,0.0002348517,0.000882537,0.0001166203,0.01173848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005565643,"threshold_uncertainty_score":0.01960844,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2049204733","doi":"10.1016/j.ijar.2004.11.004","title":"The investigation of the Bayesian rough set model","year":2005,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":397,"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":"Rough set; Parametric statistics; Probabilistic logic; Mathematics; Set (abstract data type); Dominance-based rough set approach; Bayesian probability; Data mining; Parametric model; Identification (biology); Algorithm; Applied mathematics; Computer science; Statistics","authors":[{"name":"Dominik Ślȩzak","is_ca":true},{"name":"Wojciech Ziarko","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01793624371466196,"gpt":0.2579911133955625,"spread":0.2400548696809005,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00900562,0.0005238408,0.001753828,0.001997558,0.001098037,0.004786307,0.002623808,0.002453465,0.00407087],"category_scores_gemma":[0.04235267,0.0007273739,0.001308959,0.002116074,0.00242067,0.008527196,0.00204496,0.00281849,0.0005081255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002485381,"about_ca_system_score_gemma":0.002196233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003522931,"about_ca_topic_score_gemma":0.002125959,"domain_scores_codex":[0.9956698,0.002392033,0.0001342624,0.0003677205,0.001208858,0.0002272435],"domain_scores_gemma":[0.9814409,0.01438551,0.001119802,0.001102092,0.001479455,0.0004723142],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003269436,0.0000233579,0.0002761616,0.00006496671,0.00003711557,0.0000609123,0.0001545668,0.01960581,0.0001276725,0.970708,0.0008566969,0.008052153],"study_design_scores_gemma":[0.00001412404,0.00002113374,0.0002143612,0.00003535592,0.00001972091,0.00007691513,0.00005569559,0.1352243,0.00006219464,0.8622011,0.002055592,0.00001952993],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04425165,0.00373335,0.898837,0.01593688,0.0003513274,0.00007729431,0.0002330234,0.0001065836,0.03647286],"genre_scores_gemma":[0.8051437,0.004760569,0.1780517,0.001175366,0.0008134664,0.0001755914,0.0002285864,0.00007193629,0.009579165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00900562,"threshold_uncertainty_score":0.04762685,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2072348410","doi":"10.1016/j.ijar.2011.07.006","title":"Distances in evidence theory: Comprehensive survey and generalizations","year":2011,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":264,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Interpretation (philosophy); Measure (data warehouse); Computer science; Set (abstract data type); Class (philosophy); Fuzzy set; Fuzzy logic; Trigonometric functions; Possibility theory; Mathematics; Base (topology); Function (biology); Artificial intelligence; Data mining","authors":[{"name":"Anne-Laure Jousselme","is_ca":true},{"name":"Patrick Maupin","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3666811522813856,"gpt":0.4523683107298756,"spread":0.08568715844849001,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02784562,0.001345442,0.004068748,0.01824064,0.001638909,0.007251709,0.002720994,0.002802692,0.003090061],"category_scores_gemma":[0.1239481,0.001561565,0.002133759,0.02095126,0.006954134,0.02174796,0.00465119,0.004731709,0.0003837704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003559869,"about_ca_system_score_gemma":0.002827178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003169395,"about_ca_topic_score_gemma":0.002309403,"domain_scores_codex":[0.9826823,0.007882087,0.002016613,0.001986373,0.005006381,0.0004260959],"domain_scores_gemma":[0.737027,0.2407865,0.00434914,0.007052694,0.009390503,0.001394161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000251325,0.0002118958,0.02394739,0.005063239,0.0009776473,0.0001813229,0.001000129,0.02338647,0.0002503966,0.445229,0.006132564,0.4933687],"study_design_scores_gemma":[0.00003954176,0.0001695219,0.006440344,0.001149662,0.0003425971,0.0005786856,0.0004881107,0.03161801,0.0002998401,0.9410359,0.01776454,0.00007322917],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0543662,0.6257202,0.2960736,0.01090821,0.000274542,0.0001102656,0.0005792174,0.00009833722,0.01186942],"genre_scores_gemma":[0.5572056,0.3088809,0.1277909,0.001008108,0.00240266,0.0002094034,0.0009620787,0.0000850493,0.001455284],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.02784562,"threshold_uncertainty_score":0.1472635,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1975251650","doi":"10.1016/j.ijar.2007.06.014","title":"Probabilistic approach to rough sets","year":2007,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":237,"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":"Rough set; Probabilistic logic; Reduct; Dependency (UML); Property (philosophy); Probabilistic relevance model; Computer science; Measure (data warehouse); Monotonic function; Set (abstract data type); Data mining; Mathematics; Computation; Artificial intelligence; Probabilistic analysis of algorithms; Algorithm","authors":[{"name":"Wojciech Ziarko","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01987984125818776,"gpt":0.2843185250988401,"spread":0.2644386838406523,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005837019,0.0010574,0.002589064,0.005028299,0.001321958,0.004948305,0.002823197,0.001850101,0.004588502],"category_scores_gemma":[0.02310684,0.001483826,0.002731916,0.004099086,0.003364177,0.006588361,0.00248106,0.003622536,0.0007836464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002208901,"about_ca_system_score_gemma":0.001460219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001634372,"about_ca_topic_score_gemma":0.001660617,"domain_scores_codex":[0.9924006,0.0026306,0.0004733525,0.0006473479,0.003597055,0.0002510226],"domain_scores_gemma":[0.9870933,0.009050102,0.0007565996,0.001379818,0.001470365,0.0002497963],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001379545,0.00001566558,0.0001830079,0.0001002575,0.0000719114,0.00005225039,0.00008506738,0.02463315,0.0001579516,0.9594277,0.0008085622,0.0144507],"study_design_scores_gemma":[0.000007498045,0.00001249072,0.0001307422,0.00001972294,0.00002758349,0.00006483799,0.00001958238,0.0654109,0.00009482499,0.9312893,0.002906124,0.00001636443],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002379872,0.001748442,0.989624,0.001075419,0.0001606977,0.00002773128,0.00009576005,0.00007870174,0.004809313],"genre_scores_gemma":[0.3938978,0.007256501,0.5840566,0.0007358264,0.001941679,0.0004511492,0.0004867664,0.0001141431,0.01105948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005837019,"threshold_uncertainty_score":0.03086948,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2056784354","doi":"10.1016/j.ijar.2010.01.004","title":"Gaussian kernel based fuzzy rough sets: Model, uncertainty measures and applications","year":2010,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":227,"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":"Mathematics; Rough set; Kernel embedding of distributions; Kernel method; Fuzzy number; Artificial intelligence; Fuzzy set; Pattern recognition (psychology); Fuzzy logic; Data mining; Computer science; Support vector machine","authors":[{"name":"Qinghua Hu","is_ca":false},{"name":"Lei Zhang","is_ca":false},{"name":"Degang Chen","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Daren Yu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01587560862535223,"gpt":0.2704050235837117,"spread":0.2545294149583594,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003032247,0.0005951534,0.001924274,0.001892305,0.0005739576,0.003186788,0.001737702,0.001434663,0.0007354498],"category_scores_gemma":[0.01123402,0.0004625804,0.001442788,0.002613455,0.001369586,0.003862978,0.001050891,0.001426585,0.0002336693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001581334,"about_ca_system_score_gemma":0.0009881636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003525195,"about_ca_topic_score_gemma":0.001740472,"domain_scores_codex":[0.9978659,0.0007829813,0.0001377078,0.0002093537,0.0008472019,0.0001568322],"domain_scores_gemma":[0.9959952,0.002029855,0.000515716,0.0004153166,0.0009170492,0.0001267523],"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":[0.0002390517,0.0001033842,0.002032507,0.0003093737,0.0002297114,0.000267647,0.0003983047,0.4131892,0.002447995,0.4907829,0.002121869,0.08787798],"study_design_scores_gemma":[0.000008543609,0.00002991315,0.0004389614,0.00001557101,0.00003601813,0.00007192014,0.00004214272,0.8773086,0.0005297749,0.1207596,0.0007254428,0.00003361414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02846746,0.001444102,0.9684573,0.0003029208,0.0000565545,0.00002232985,0.00007498344,0.0001046373,0.001069732],"genre_scores_gemma":[0.8297104,0.001937301,0.1654948,0.00007613377,0.0001276686,0.00008344651,0.0001796514,0.00004318122,0.002347281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003525195,"threshold_uncertainty_score":0.01603627,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1969535228","doi":"10.1016/j.ijar.2013.03.014","title":"Triangular fuzzy decision-theoretic rough sets","year":2013,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":188,"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":"Ranking (information retrieval); Mathematics; Range (aeronautics); Membership function; Fuzzy logic; Fuzzy number; Function (biology); Rough set; Weighted sum model; Mathematical optimization; Particle swarm optimization; Decision rule; Value (mathematics); Data mining; Artificial intelligence; Computer science; Fuzzy set; Decision analysis; Influence diagram; Statistics; Engineering","authors":[{"name":"Decui Liang","is_ca":true},{"name":"Dun Liu","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Hu Pei","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01052932443075401,"gpt":0.2609551256978646,"spread":0.2504258012671106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002199682,0.0005647194,0.001421327,0.002140555,0.0007666606,0.004090307,0.00107984,0.0007632315,0.003598694],"category_scores_gemma":[0.01254249,0.000294866,0.001227334,0.003006459,0.001398451,0.002631046,0.0009243059,0.001141148,0.000735399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001374204,"about_ca_system_score_gemma":0.0009111506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001908874,"about_ca_topic_score_gemma":0.001349057,"domain_scores_codex":[0.996994,0.0009603466,0.0003303604,0.0002681527,0.001275456,0.0001715408],"domain_scores_gemma":[0.9960104,0.001866646,0.0004254875,0.0005911493,0.000973442,0.0001327618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001804134,0.0000423552,0.0007781369,0.0003025386,0.0001348556,0.0002660586,0.0003324594,0.08584473,0.002231772,0.82706,0.003532968,0.07929359],"study_design_scores_gemma":[0.00002518772,0.00005617451,0.0003031528,0.00005085414,0.00006913426,0.000129169,0.0001418953,0.2751032,0.001032665,0.7174876,0.00556035,0.00004063841],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01620016,0.0008278216,0.9721142,0.0003892769,0.0001309872,0.00007976254,0.0004405546,0.0001425998,0.009674569],"genre_scores_gemma":[0.619342,0.001928185,0.373248,0.0001816731,0.0001970063,0.0002213664,0.00099722,0.00003562718,0.003848944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004090307,"threshold_uncertainty_score":0.01203883,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2004524042","doi":"10.1016/j.ijar.2012.10.003","title":"Soft clustering – Fuzzy and rough approaches and their extensions and derivatives","year":2012,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":179,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Saint Mary's University","funders":"Comisión Nacional de Investigación Científica y Tecnológica","keywords":"Cluster analysis; Fuzzy clustering; Data mining; Computer science; Soft computing; Fuzzy logic; Artificial intelligence; Mathematics; Pattern recognition (psychology)","authors":[{"name":"Georg Peters","is_ca":false},{"name":"Fernando Crespo","is_ca":false},{"name":"Pawan Lingras","is_ca":true},{"name":"Richard W. Weber","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03882875475085171,"gpt":0.2575238557774239,"spread":0.2186951010265722,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00524561,0.0008598419,0.001217875,0.003071092,0.001094015,0.003484539,0.001790986,0.001801259,0.002381984],"category_scores_gemma":[0.01453324,0.0006649829,0.001267585,0.003492567,0.004900523,0.006733888,0.002431193,0.003004324,0.0003795638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001810518,"about_ca_system_score_gemma":0.0009920185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002529353,"about_ca_topic_score_gemma":0.002163146,"domain_scores_codex":[0.9977639,0.001000537,0.0001214343,0.0002627126,0.0007375169,0.0001138906],"domain_scores_gemma":[0.9939421,0.003553418,0.0009077349,0.0006249144,0.0007447909,0.000226972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001422167,0.00001316451,0.0001876012,0.00008092486,0.00002589219,0.00004820929,0.0002183563,0.01701098,0.0002531791,0.9617584,0.0006370544,0.01975211],"study_design_scores_gemma":[0.00000313374,0.00001767549,0.0003301296,0.00002780999,0.0000150366,0.00006874951,0.00008260128,0.06226113,0.0001311119,0.9347884,0.002252773,0.00002138347],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03632928,0.01510617,0.9218633,0.00285035,0.0004018173,0.00003700206,0.0001041971,0.0001001913,0.02320776],"genre_scores_gemma":[0.6726864,0.01200858,0.2951997,0.0004890176,0.001430173,0.000122636,0.0001228893,0.0001054959,0.01783496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00524561,"threshold_uncertainty_score":0.02774179,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2795686572","doi":"10.1016/j.ijar.2018.01.008","title":"Local rough set: A solution to rough data analysis in big data","year":2018,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":159,"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":"Program for New Century Excellent Talents in University; Narodowe Centrum Nauki; Narodowe Centrum Badań i Rozwoju; National Natural Science Foundation of China","keywords":"Rough set; Big data; Reduction (mathematics); Computer science; Set (abstract data type); Context (archaeology); Dominance-based rough set approach; Data set; Data mining; Data reduction; Algorithm; Mathematics; Artificial intelligence","authors":[{"name":"Yuhua Qian","is_ca":false},{"name":"Xinyan Liang","is_ca":false},{"name":"Qi Wang","is_ca":false},{"name":"Jiye Liang","is_ca":false},{"name":"Bing Liu","is_ca":false},{"name":"Andrzej Skowron","is_ca":false},{"name":"Yiyu Yao","is_ca":true},{"name":"Jian-Min Ma","is_ca":false},{"name":"Chuangyin Dang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09721236274903315,"gpt":0.3403119297401003,"spread":0.2430995669910672,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005410707,0.001010266,0.003924305,0.00314984,0.00128613,0.004507856,0.003062655,0.002145463,0.001982361],"category_scores_gemma":[0.01361552,0.0007641343,0.003608465,0.004302396,0.001771169,0.004204857,0.004070625,0.003288861,0.0005549936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001207933,"about_ca_system_score_gemma":0.002538587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002774061,"about_ca_topic_score_gemma":0.002324899,"domain_scores_codex":[0.9955015,0.001638896,0.0003359093,0.000509145,0.001804085,0.0002105046],"domain_scores_gemma":[0.9954723,0.002643632,0.000387003,0.0006238476,0.0007113661,0.0001617831],"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":[0.0002304592,0.0001613468,0.001385155,0.001144753,0.0006883224,0.0004643178,0.0008323382,0.3735304,0.002905431,0.2815986,0.009421402,0.3276374],"study_design_scores_gemma":[0.00003124808,0.00006052108,0.0002384487,0.00006673192,0.0001185653,0.0001071737,0.0001472066,0.8015395,0.0009985041,0.1925357,0.004112136,0.00004418048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001342213,0.0003064318,0.9976184,0.0001821569,0.00005179263,0.00003152219,0.00004839456,0.0001156142,0.0003034104],"genre_scores_gemma":[0.1012003,0.001149485,0.8955023,0.0002151064,0.0002759456,0.0002160517,0.0002340706,0.00009150316,0.001115179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005410707,"threshold_uncertainty_score":0.02861488,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2060945930","doi":"10.1016/j.ijar.2013.03.015","title":"Analyzing uncertainties of probabilistic rough set regions with game-theoretic rough sets","year":2013,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Regina","funders":"Faculty of Graduate Studies and Research, University of Alberta; Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Probabilistic logic; Rough set; Mathematics; Categorization; Set (abstract data type); Boundary (topology); Order (exchange); Mathematical optimization; Computer science; Artificial intelligence; Statistics; Mathematical analysis","authors":[{"name":"Nouman Azam","is_ca":true},{"name":"JingTao Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01593423351887067,"gpt":0.2571861106340858,"spread":0.2412518771152151,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009348371,0.001060876,0.002243281,0.002226035,0.0008898909,0.004469311,0.002623987,0.001745231,0.001145169],"category_scores_gemma":[0.02852497,0.001360603,0.002794709,0.001611526,0.003112633,0.00574187,0.002894103,0.001882747,0.00009537789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003408432,"about_ca_system_score_gemma":0.001613707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006709842,"about_ca_topic_score_gemma":0.002906984,"domain_scores_codex":[0.9952947,0.002368915,0.0001992265,0.000602587,0.001054877,0.0004795917],"domain_scores_gemma":[0.9770703,0.0186231,0.001954009,0.0007337529,0.0009980259,0.0006207637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009231451,0.00002738363,0.0007435963,0.00005415877,0.00008961655,0.0001317562,0.0001560708,0.8855224,0.0004033555,0.109479,0.0001345812,0.003165822],"study_design_scores_gemma":[0.000007397436,0.00002614533,0.0002138323,0.000009672379,0.00002148069,0.00002014721,0.00004266413,0.9299892,0.0001353461,0.06939305,0.0001241733,0.00001693823],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1014195,0.0004664242,0.8950714,0.0003858627,0.00003418696,0.00006544741,0.00008451781,0.00005096695,0.002421628],"genre_scores_gemma":[0.9582443,0.0002843988,0.04046108,0.00004041893,0.00004222477,0.00006631976,0.00006161676,0.00002014563,0.0007795669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009348371,"threshold_uncertainty_score":0.04943955,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2017548357","doi":"10.1016/j.ijar.2004.08.001","title":"New directions in fuzzy automata","year":2004,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Formal Methods in Verification","field":"Computer Science","cited_by":149,"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":"Fuzzy logic; Computer science; Theoretical computer science; Automata theory; Automaton; Fuzzy set operations; Mathematics; Fuzzy set; Artificial intelligence","authors":[{"name":"M. Doostfatemeh","is_ca":true},{"name":"Stefan C. Kremer","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0189341741581855,"gpt":0.3102065770582431,"spread":0.2912724029000576,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00602626,0.000901488,0.002198347,0.002627769,0.001793864,0.005518719,0.002492764,0.00402876,0.01204628],"category_scores_gemma":[0.01670892,0.001035941,0.002293003,0.002170288,0.007906061,0.02289702,0.00253007,0.006738782,0.001536424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00364295,"about_ca_system_score_gemma":0.001287454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002690762,"about_ca_topic_score_gemma":0.002360929,"domain_scores_codex":[0.9975939,0.0009782963,0.000174241,0.0004572954,0.0006864808,0.0001097238],"domain_scores_gemma":[0.9873372,0.00936611,0.0002144637,0.001223791,0.001527168,0.0003312915],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002475742,0.00001652011,0.00009177738,0.00007126006,0.00001218061,0.00002027347,0.000118947,0.001369361,0.0001081073,0.9834912,0.00176012,0.01291547],"study_design_scores_gemma":[0.0000123578,0.000009632262,0.00002865842,0.00002475871,0.000007641724,0.00001703507,0.000044721,0.009936747,0.00007966611,0.9816648,0.008166091,0.000008003509],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01949813,0.05083645,0.8227901,0.0418344,0.005589284,0.00006614481,0.0002880392,0.000403649,0.05869379],"genre_scores_gemma":[0.5066133,0.03472858,0.403963,0.006196024,0.01377091,0.0003620524,0.0004574088,0.0002792025,0.03362947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01204628,"threshold_uncertainty_score":0.04029882,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1979464351","doi":"10.1016/j.ijar.2014.11.002","title":"Using interval information granules to improve forecasting in fuzzy time series","year":2014,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":140,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China; Canada Research Chairs","keywords":"Interval (graph theory); Computer science; Series (stratigraphy); Partition (number theory); Time series; Fuzzy logic; Benchmark (surveying); Data mining; Domain of discourse; Basis (linear algebra); Generalization; Artificial intelligence; Algorithm; Mathematics; Machine learning; Geography","authors":[{"name":"Wei Lu","is_ca":false},{"name":"Xueyan Chen","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Xiaodong Liu","is_ca":false},{"name":"Jianhua Yang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06884974176019318,"gpt":0.3731629320079009,"spread":0.3043131902477078,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001897927,0.0004546974,0.001002248,0.0009086784,0.0002984446,0.001487949,0.000607856,0.0006603373,0.001155063],"category_scores_gemma":[0.008726901,0.000338545,0.0005762511,0.001043619,0.000372927,0.002369913,0.0007159959,0.001002771,0.0001333142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003166847,"about_ca_system_score_gemma":0.0003365316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001832117,"about_ca_topic_score_gemma":0.00134134,"domain_scores_codex":[0.9995064,0.0001347926,0.00007160398,0.00007729082,0.0001636848,0.00004617993],"domain_scores_gemma":[0.9968225,0.002096121,0.0002250471,0.0003668726,0.0004326127,0.00005676668],"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":[0.001160112,0.0004565767,0.00446521,0.0003221055,0.0002633439,0.0002687631,0.0003299583,0.603906,0.01620383,0.02834684,0.002174675,0.3421026],"study_design_scores_gemma":[0.00001654808,0.00005473465,0.0005282859,0.00001974603,0.00003970578,0.00001421297,0.00001809992,0.9895158,0.002282819,0.007196086,0.0003036961,0.00001038372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.162104,0.001021948,0.8340186,0.0002183425,0.0002457719,0.00004580682,0.0001463645,0.0005038136,0.001695412],"genre_scores_gemma":[0.8716859,0.0003950669,0.1271267,0.00005598006,0.0001020077,0.0000515801,0.0001575652,0.00003624403,0.0003889017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001897927,"threshold_uncertainty_score":0.01003736,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2796438895","doi":"10.1016/j.ijar.2018.04.001","title":"A three-way clustering approach for handling missing data using GTRS","year":2018,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":124,"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; Higher Education Commision, Pakistan; Higher Education Commission, Pakistan","keywords":"Generality; Cluster analysis; Object (grammar); Data mining; Computer science; Cluster (spacecraft); Set (abstract data type); Missing data; Task (project management); Data set; Artificial intelligence; Machine learning; Engineering","authors":[{"name":"Mohammad Khan Afridi","is_ca":false},{"name":"Nouman Azam","is_ca":false},{"name":"JingTao Yao","is_ca":true},{"name":"Eisa Alanazi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09381619314104128,"gpt":0.335437071274018,"spread":0.2416208781329767,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00815751,0.001854332,0.003151133,0.005357639,0.002638303,0.003181395,0.006070855,0.003093529,0.003250994],"category_scores_gemma":[0.018737,0.001018238,0.006103376,0.005883977,0.001228678,0.003347218,0.003640678,0.002820159,0.002325999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001185819,"about_ca_system_score_gemma":0.003836736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01650234,"about_ca_topic_score_gemma":0.01715402,"domain_scores_codex":[0.9914525,0.002843651,0.0009524288,0.001668764,0.002544286,0.0005384383],"domain_scores_gemma":[0.9908004,0.002502981,0.0005801162,0.002278249,0.003581603,0.0002564593],"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":[0.0007948519,0.0002970778,0.003896294,0.0005787117,0.001332708,0.0006819869,0.002177134,0.2152,0.01650163,0.0296914,0.00813853,0.7207097],"study_design_scores_gemma":[0.00004780916,0.0001533095,0.001130366,0.00007156489,0.0002643923,0.0003655834,0.0004841297,0.9608441,0.006869171,0.02351865,0.00610807,0.0001429015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002583189,0.00006917324,0.9963704,0.00004594416,0.00004406288,0.00006860594,0.00007990194,0.0005607186,0.0001778987],"genre_scores_gemma":[0.03902062,0.0000763416,0.9590921,0.00006365563,0.00003216148,0.0001561957,0.0004766471,0.000183611,0.0008986669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01650234,"threshold_uncertainty_score":0.04314154,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2040833276","doi":"10.1016/j.ijar.2013.01.006","title":"A proof for the positive definiteness of the Jaccard index matrix","year":2013,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":118,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Defence Research and Development Canada; Université Laval","funders":"","keywords":"Jaccard index; Mathematics; Positive definiteness; Matrix (chemical analysis); Positive-definite matrix; Distance matrix; Metric (unit); Combinatorics; Euclidean distance; Discrete mathematics; Pure mathematics; Statistics; Eigenvalues and eigenvectors","authors":[{"name":"Mathieu Bouchard","is_ca":true},{"name":"Anne-Laure Jousselme","is_ca":true},{"name":"Pierre-Emmanuel Doré","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07018941519558516,"gpt":0.4050584867001693,"spread":0.3348690715045841,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006811082,0.001389971,0.001380612,0.004081302,0.001853657,0.003925711,0.002131817,0.002244202,0.01214652],"category_scores_gemma":[0.06483332,0.0009114419,0.001696317,0.003673736,0.005767226,0.006988593,0.00404761,0.006716897,0.002669278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001161646,"about_ca_system_score_gemma":0.001861694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001099909,"about_ca_topic_score_gemma":0.0008883926,"domain_scores_codex":[0.993883,0.001933153,0.0003726242,0.001198466,0.002318837,0.0002938591],"domain_scores_gemma":[0.9503359,0.03374124,0.002332645,0.003819248,0.008604182,0.00116687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005112164,0.00009523811,0.0005855016,0.0003506973,0.00006095954,0.0003957197,0.0003899567,0.006328358,0.005769973,0.9277881,0.009530549,0.04865384],"study_design_scores_gemma":[0.00002986096,0.0000660094,0.0008166402,0.0001052291,0.00003054272,0.0006860757,0.0000909874,0.05156148,0.001969717,0.9354197,0.009132253,0.00009143729],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00604888,0.001021041,0.9753879,0.001895846,0.0006234029,0.00008084845,0.0002530494,0.0001844472,0.01450455],"genre_scores_gemma":[0.3350433,0.002812854,0.6460418,0.002739653,0.002958892,0.0005521629,0.0005358617,0.0003767316,0.008938617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01214652,"threshold_uncertainty_score":0.0406341,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4386826606","doi":"10.1016/j.ijar.2023.109032","title":"The Dao of three-way decision and three-world thinking","year":2023,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":115,"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":"Viewpoints; Epistemology; Management science; Computer science; Simple (philosophy); Artificial intelligence; Philosophy; Engineering","authors":[{"name":"Yiyu Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02079070648084576,"gpt":0.2758599507515674,"spread":0.2550692442707217,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003112974,0.0005379049,0.0009112005,0.001668579,0.00152355,0.005322725,0.001432481,0.001065801,0.00736639],"category_scores_gemma":[0.00563133,0.0003729199,0.001837427,0.00165857,0.00679246,0.007601789,0.00244371,0.002485672,0.0007502821],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00237396,"about_ca_system_score_gemma":0.00212701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002982377,"about_ca_topic_score_gemma":0.001906315,"domain_scores_codex":[0.9977671,0.00116341,0.0001535945,0.0003121162,0.0004467348,0.0001570673],"domain_scores_gemma":[0.9979564,0.0009618899,0.0001768861,0.0003263565,0.0003400393,0.000238458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001134918,0.000005561924,0.00008142644,0.00001872865,0.00001099653,0.00001468112,0.0001425666,0.001704238,0.00005613323,0.9937285,0.0002600864,0.003965673],"study_design_scores_gemma":[0.000004806789,0.000005536771,0.00003644726,0.000006642909,0.000004246736,0.00001230828,0.00005519145,0.006880692,0.00004217775,0.9911061,0.001840287,0.000005629833],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02492367,0.001424549,0.8770204,0.003674005,0.0006111455,0.0001163454,0.0002220071,0.000160504,0.0918475],"genre_scores_gemma":[0.6644688,0.0009302409,0.3190122,0.0004896615,0.0002532772,0.0002987145,0.0001827902,0.00008092165,0.0142833],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00736639,"threshold_uncertainty_score":0.024643,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2550257869","doi":"10.1016/j.ijar.2016.10.010","title":"Long-term forecasting of time series based on linear fuzzy information granules and fuzzy inference system","year":2016,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":107,"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":"Natural Science Foundation of Beijing Municipality; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Time series; Data mining; Fuzzy logic; Adaptive neuro fuzzy inference system; Mathematics; Series (stratigraphy); Computer science; Term (time); Artificial intelligence; Autoregressive model; Normalization (sociology); Algorithm; Fuzzy control system; Machine learning; Econometrics","authors":[{"name":"Xiyang Yang","is_ca":false},{"name":"Fusheng Yu","is_ca":false},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05061357592872628,"gpt":0.3459787121040283,"spread":0.295365136175302,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008719586,0.0003568925,0.0009408816,0.0006668001,0.0003732115,0.001233826,0.0005614092,0.0005954851,0.0005534207],"category_scores_gemma":[0.003054642,0.0002684851,0.000587715,0.0007399071,0.0004252236,0.001438457,0.000360595,0.0005600447,0.00006747375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005600079,"about_ca_system_score_gemma":0.000432635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004779237,"about_ca_topic_score_gemma":0.002716532,"domain_scores_codex":[0.9997209,0.0000532282,0.00003849689,0.00006191148,0.00009312078,0.000032367],"domain_scores_gemma":[0.9990494,0.0005726826,0.0001217613,0.00004869618,0.000180883,0.00002659921],"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":[0.000283316,0.0001066256,0.002539157,0.0001307709,0.0001475053,0.0002412499,0.0001477055,0.908418,0.005564941,0.02013453,0.0007330704,0.06155317],"study_design_scores_gemma":[0.000002550945,0.000008040086,0.0002748896,0.000003090207,0.000009330264,0.000006080312,0.000003941903,0.9971399,0.0002158296,0.002300019,0.00003296743,0.000003369817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2013234,0.001003679,0.7953578,0.0002499814,0.0001075536,0.00003227165,0.0001351791,0.000209619,0.001580532],"genre_scores_gemma":[0.9738575,0.0002989593,0.02527797,0.00001745556,0.00004011029,0.00002980849,0.00007912776,0.000006371257,0.0003926157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004779237,"threshold_uncertainty_score":0.009502888,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2899640688","doi":"10.1016/j.ijar.2018.11.001","title":"A sequential three-way approach to multi-class decision","year":2018,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":90,"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":"University of California, Irvine; National Natural Science Foundation of China; National Science Foundation","keywords":"Disjoint sets; Granularity; Computer science; Class (philosophy); Granular computing; Data mining; Sequence (biology); Artificial intelligence; Focus (optics); Machine learning; Decision rule; Decision problem; Theoretical computer science; Algorithm; Mathematics; Rough set","authors":[{"name":"Xin Yang","is_ca":true},{"name":"Tianrui Li","is_ca":false},{"name":"Hamido Fujita","is_ca":false},{"name":"Dun Liu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03411708914131092,"gpt":0.3015305969140245,"spread":0.2674135077727136,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008478181,0.001122843,0.002250836,0.002191074,0.00177384,0.004197023,0.003360965,0.002023522,0.009649743],"category_scores_gemma":[0.01200953,0.001152092,0.003522752,0.002976245,0.002126451,0.005587921,0.003184062,0.002628404,0.001088722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001804423,"about_ca_system_score_gemma":0.003457509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005946512,"about_ca_topic_score_gemma":0.008758754,"domain_scores_codex":[0.9918444,0.002988012,0.0007743346,0.001206773,0.002663292,0.0005232217],"domain_scores_gemma":[0.9906335,0.006225394,0.0003875746,0.0007016477,0.001650083,0.0004017682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006714853,0.0004305068,0.002178874,0.0004814731,0.0005457695,0.000548726,0.001285922,0.273612,0.003819899,0.375667,0.004022886,0.3367355],"study_design_scores_gemma":[0.00005441454,0.0001524892,0.0002646418,0.0000304967,0.0001172983,0.0001329204,0.0001241852,0.7469283,0.001303468,0.2472527,0.003582963,0.00005603428],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003132734,0.00005706208,0.9952536,0.0001447273,0.00003728842,0.00007783426,0.00004352387,0.00008451391,0.001168769],"genre_scores_gemma":[0.1166811,0.000148674,0.8786941,0.000147726,0.00007834384,0.000369428,0.0002015152,0.00005083449,0.00362821],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009649743,"threshold_uncertainty_score":0.04483742,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2908761996","doi":"10.1016/j.ijar.2019.01.005","title":"Computational method for fuzzy arithmetic operations on triangular fuzzy numbers by extension principle","year":2019,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":86,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fuzzy number; Fuzzy logic; Mathematics; Fuzzy set operations; Arithmetic; Type-2 fuzzy sets and systems; Extension (predicate logic); Defuzzification; Norm (philosophy); Algorithm; Fuzzy mathematics; Mathematical optimization; Fuzzy set; Algebra over a field; Computer science; Artificial intelligence; Pure mathematics","authors":[{"name":"Nima Gerami Seresht","is_ca":true},{"name":"Aminah Robinson Fayek","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06825895460799343,"gpt":0.4426220762179773,"spread":0.3743631216099839,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001438399,0.0004531078,0.0009293827,0.001060625,0.0007260895,0.001381853,0.001414324,0.0005424729,0.005990517],"category_scores_gemma":[0.003488914,0.0002853832,0.001631874,0.001297557,0.001307484,0.002649665,0.001723114,0.001953286,0.0008513469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006235527,"about_ca_system_score_gemma":0.0008256716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009140744,"about_ca_topic_score_gemma":0.0008247938,"domain_scores_codex":[0.999102,0.000323622,0.00005513104,0.0001274268,0.0003379594,0.00005377053],"domain_scores_gemma":[0.9990821,0.0005598068,0.00003015,0.0001325822,0.0001708736,0.00002453128],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00007608845,0.00004712686,0.0001461169,0.0001524981,0.00003579537,0.00008173515,0.0001860875,0.0498448,0.003859237,0.84745,0.002374569,0.09574602],"study_design_scores_gemma":[0.00002118998,0.00005944542,0.0001222303,0.00003310239,0.00002516178,0.0001149777,0.00004054356,0.5346348,0.001711863,0.4562284,0.006988244,0.00002006611],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002448007,0.00008164036,0.9947214,0.00006406748,0.00004295589,0.00002351523,0.00001820507,0.00003189773,0.002568294],"genre_scores_gemma":[0.1631775,0.0003403154,0.8300466,0.0001067045,0.0001791808,0.0002447218,0.0001196998,0.00009071681,0.005694542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005990517,"threshold_uncertainty_score":0.02004021,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2040185067","doi":"10.1016/j.ijar.2010.07.001","title":"An inventory model with backorders with fuzzy parameters and decision variables","year":2010,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":80,"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":"Defuzzification; Fuzzy logic; Fuzzy number; Mathematics; Mathematical optimization; Sensitivity (control systems); Signed distance function; Variable (mathematics); Fuzzy set; Applied mathematics; Computer science; Artificial intelligence; Algorithm; Engineering","authors":[{"name":"Nima Kazemi","is_ca":false},{"name":"Ehsan Ehsani","is_ca":false},{"name":"Mohamad Y. Jaber","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01127789183008081,"gpt":0.2333147577278356,"spread":0.2220368658977547,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003082254,0.002173174,0.003280274,0.00191543,0.001208615,0.005669516,0.003438736,0.004383164,0.005634137],"category_scores_gemma":[0.00448598,0.002212698,0.001675457,0.003250804,0.002127687,0.005207444,0.001345915,0.002550857,0.0008380848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004282737,"about_ca_system_score_gemma":0.002728401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0291227,"about_ca_topic_score_gemma":0.01365965,"domain_scores_codex":[0.9982886,0.000588395,0.0001270049,0.0003938599,0.0003445223,0.0002576927],"domain_scores_gemma":[0.9971528,0.00189471,0.0003114746,0.0001013599,0.0003706928,0.0001689757],"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":[0.0001385028,0.00006859173,0.0003590662,0.00007915493,0.00004062647,0.0002281262,0.00006918352,0.9807171,0.0004506808,0.01487274,0.0003265887,0.002649549],"study_design_scores_gemma":[0.00003449962,0.0000402275,0.0001083627,0.000009280639,0.00003283621,0.00002416369,0.0000169957,0.9946364,0.0001121672,0.004732632,0.0002340756,0.00001843396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2412364,0.002248047,0.7251484,0.002695757,0.0004475712,0.0003964296,0.003371558,0.001028392,0.02342737],"genre_scores_gemma":[0.9502024,0.001022615,0.02837909,0.00013261,0.0001078463,0.000244864,0.0006627517,0.00005996144,0.01918792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0291227,"threshold_uncertainty_score":0.05790639,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2899562587","doi":"10.1016/j.ijar.2018.11.003","title":"Cost-sensitive approximate attribute reduction with three-way decisions","year":2018,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":76,"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":"National Natural Science Foundation of China","keywords":"Rough set; Reduction (mathematics); Attribute domain; Partition (number theory); Leverage (statistics); Computer science; Data mining; Mathematical optimization; Mathematics; Function (biology); Decision table; Algorithm; Artificial intelligence","authors":[{"name":"Yu Fang","is_ca":true},{"name":"Fan Min","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03916791693468549,"gpt":0.2927953427190965,"spread":0.253627425784411,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004286102,0.001035571,0.003272646,0.001709726,0.0008820667,0.002888305,0.002238646,0.001214011,0.002423492],"category_scores_gemma":[0.01024119,0.000745663,0.002809831,0.002555032,0.001240443,0.003521104,0.003180974,0.002176908,0.0003305839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001650896,"about_ca_system_score_gemma":0.001631411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003931333,"about_ca_topic_score_gemma":0.00252327,"domain_scores_codex":[0.9950286,0.001587905,0.0003330225,0.0006847883,0.001976953,0.000388718],"domain_scores_gemma":[0.9956155,0.0027442,0.0002085022,0.0007574693,0.0005576176,0.0001167506],"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":[0.0008042018,0.000310891,0.001380741,0.0003627671,0.0003540809,0.0002489027,0.0003632511,0.6077261,0.004063731,0.09496989,0.002581274,0.2868342],"study_design_scores_gemma":[0.00001801428,0.00006569334,0.0002118032,0.00001547998,0.00005850398,0.00006697459,0.00004279447,0.9490694,0.001383594,0.04843556,0.0006121288,0.00002011272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02259201,0.0002978127,0.9753294,0.0001818965,0.00005048628,0.00007226469,0.0001069554,0.0001624279,0.001206842],"genre_scores_gemma":[0.5515599,0.0003174755,0.4450764,0.0001038282,0.00005747004,0.0001916083,0.0003361416,0.00006229224,0.00229483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004286102,"threshold_uncertainty_score":0.02266729,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057532020","doi":"10.1016/j.ijar.2013.06.003","title":"Neighborhood rough sets based multi-label classification for automatic image annotation","year":2013,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":74,"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":"Rough set; Pattern recognition (psychology); Image (mathematics); Computer science; Artificial intelligence; Set (abstract data type); Automatic image annotation; Feature (linguistics); A priori and a posteriori; Multi-label classification; Feature vector; Task (project management); Contextual image classification; Data mining; Image retrieval; Machine learning","authors":[{"name":"Ying Yu","is_ca":true},{"name":"Witold Pedrycz","is_ca":true},{"name":"Duoqian Miao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03709836444817024,"gpt":0.3108034495814643,"spread":0.2737050851332941,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001674026,0.0006007075,0.001690182,0.003888839,0.001114715,0.002138253,0.001770181,0.001489378,0.001327544],"category_scores_gemma":[0.003988212,0.0004231058,0.001665893,0.002349321,0.0006703949,0.002094506,0.001406264,0.001132379,0.0007032942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001091064,"about_ca_system_score_gemma":0.001049113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004188067,"about_ca_topic_score_gemma":0.005306486,"domain_scores_codex":[0.9976489,0.0004958434,0.0001847323,0.0004371965,0.001064562,0.0001688192],"domain_scores_gemma":[0.998204,0.0006812624,0.0001906037,0.0002985553,0.0005623157,0.00006311998],"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":[0.0007917109,0.0003734114,0.003489619,0.0004953658,0.0003205972,0.000341993,0.0005021528,0.06006092,0.04510627,0.01590109,0.006557918,0.8660589],"study_design_scores_gemma":[0.00001923992,0.00008878783,0.001874475,0.00005233344,0.0001430505,0.0001650291,0.0001758093,0.9616255,0.01994595,0.01311968,0.002737183,0.00005301349],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03487565,0.0006238028,0.9618233,0.0001209959,0.00007570459,0.0001199513,0.0001974866,0.0008641421,0.001299048],"genre_scores_gemma":[0.4238307,0.0003955855,0.5725895,0.00009389583,0.00007773258,0.0002743608,0.0006964034,0.0001182332,0.00192351],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004188067,"threshold_uncertainty_score":0.008853197,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083278476","doi":"10.1016/j.ijar.2012.07.007","title":"A genetic design of linguistic terms for fuzzy rule based classifiers","year":2012,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":71,"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":"Generality; Interpretability; Fuzzy logic; Computer science; Artificial intelligence; Semantics (computer science); Fuzzy set; Syntax; Set (abstract data type); Natural language; Natural language processing; Mathematics; Programming language","authors":[{"name":"Nguyễn Cát Hồ","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Thang Long Duong","is_ca":false},{"name":"Thai Son Tran","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02640662868582433,"gpt":0.2683179417631839,"spread":0.2419113130773596,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001172814,0.0003903993,0.0006984257,0.0009676555,0.0006837471,0.001015767,0.001257182,0.001247723,0.001962729],"category_scores_gemma":[0.003589863,0.0004219514,0.0007742366,0.0007774623,0.0005610639,0.0007579286,0.0005968101,0.0008658985,0.0004924142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007728338,"about_ca_system_score_gemma":0.0009695806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003141854,"about_ca_topic_score_gemma":0.003498544,"domain_scores_codex":[0.999485,0.0001276114,0.00003710193,0.0001173819,0.0001878149,0.00004508641],"domain_scores_gemma":[0.999189,0.0002961986,0.00006344462,0.00006201123,0.0003558037,0.00003353528],"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":[0.0002495318,0.0002643891,0.0009911901,0.0002058736,0.0001125387,0.0002513486,0.0004505116,0.4796886,0.06425734,0.06961525,0.002338124,0.3815753],"study_design_scores_gemma":[0.00002974112,0.00009254835,0.0002065079,0.00002633448,0.00004871252,0.00006225277,0.00002329548,0.9818707,0.005249072,0.01071004,0.001663722,0.00001703257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01313143,0.0001145209,0.9845622,0.000119252,0.0000566809,0.00007776459,0.00002449147,0.0001977628,0.001715819],"genre_scores_gemma":[0.2538585,0.0001427897,0.7428691,0.0001468343,0.00005219663,0.0002228884,0.00008478346,0.00008675598,0.002536159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003141854,"threshold_uncertainty_score":0.006565928,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2972986690","doi":"10.1016/j.ijar.2019.09.001","title":"A three-way cluster ensemble approach for large-scale data","year":2019,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Saint Mary's University","funders":"National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Data mining; Correlation clustering; SPARK (programming language); CURE data clustering algorithm; Cluster (spacecraft); k-medians clustering; Granularity; Fuzzy clustering; Single-linkage clustering; Data stream clustering; Scale (ratio); Constrained clustering; Artificial intelligence","authors":[{"name":"Hong Yu","is_ca":false},{"name":"Yun Chen","is_ca":false},{"name":"Pawan Lingras","is_ca":true},{"name":"Guoyin Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03398508273410222,"gpt":0.3263792715860228,"spread":0.2923941888519206,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003291747,0.001091379,0.002516867,0.00252998,0.002423282,0.00230096,0.003713401,0.001842272,0.002508954],"category_scores_gemma":[0.00769264,0.00063834,0.002654906,0.004153103,0.0005987986,0.002750737,0.003005779,0.002481263,0.001245956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008389937,"about_ca_system_score_gemma":0.00192213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01596693,"about_ca_topic_score_gemma":0.02824194,"domain_scores_codex":[0.9973776,0.0006553183,0.0001713706,0.0005195985,0.001026028,0.0002501106],"domain_scores_gemma":[0.9955456,0.001145765,0.0001782367,0.0009914915,0.00188148,0.0002572939],"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":[0.0004140161,0.0003300303,0.005409164,0.0001786993,0.0009726165,0.0001865724,0.000544352,0.3126215,0.005953684,0.01385435,0.01261641,0.6469186],"study_design_scores_gemma":[0.000008613836,0.00002763321,0.000459768,0.000009950687,0.00005722808,0.00003985612,0.00008548503,0.9895542,0.0008846401,0.007399626,0.001454883,0.00001814528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005731296,0.0001997259,0.9925722,0.0001096006,0.00008060716,0.00005861086,0.0001385265,0.0006653697,0.000443955],"genre_scores_gemma":[0.1429332,0.0003114429,0.8519794,0.0001859902,0.0001902562,0.0002257771,0.001421441,0.0003039986,0.002448434],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01596693,"threshold_uncertainty_score":0.031748,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2549529522","doi":"10.1016/j.ijar.2016.11.005","title":"Gini objective functions for three-way classifications","year":2016,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":69,"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; Mitacs; University of Regina","keywords":"Gini coefficient; Disjoint sets; Impurity; Contradiction; Partition (number theory); Mathematics; Inequality; Econometrics; Computer science; Statistics; Physics; Combinatorics; Economic inequality; Mathematical analysis","authors":[{"name":"Zhang Yan","is_ca":true},{"name":"JingTao Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02547569359047094,"gpt":0.2754602482236599,"spread":0.249984554633189,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01512985,0.001359756,0.002499914,0.006080179,0.001393392,0.004429997,0.00279607,0.002348717,0.003589722],"category_scores_gemma":[0.02940928,0.0005888697,0.002216936,0.004584179,0.00196978,0.005020664,0.003089849,0.003839176,0.0007169225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00477336,"about_ca_system_score_gemma":0.00191043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003392417,"about_ca_topic_score_gemma":0.003261391,"domain_scores_codex":[0.9944642,0.00262299,0.0003565373,0.0006943143,0.001470138,0.0003916999],"domain_scores_gemma":[0.9868152,0.008824348,0.0007782522,0.001365824,0.001649549,0.0005668291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000479566,0.0002044171,0.007829119,0.0004406488,0.0005543315,0.0001455132,0.0007037517,0.2950092,0.001756615,0.4348089,0.01086903,0.247199],"study_design_scores_gemma":[0.00001683331,0.00005659639,0.002001119,0.00007526474,0.00004561608,0.00006058845,0.0001019622,0.7254768,0.000615307,0.2696046,0.00191049,0.0000347517],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04526526,0.001166847,0.9471609,0.0008662429,0.0001579472,0.0001494884,0.0007311431,0.0003627914,0.00413952],"genre_scores_gemma":[0.457681,0.0008798658,0.5294871,0.0002290034,0.0001852769,0.0008087118,0.0022031,0.0004710546,0.008054864],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01512985,"threshold_uncertainty_score":0.08001524,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3127632841","doi":"10.1016/j.ijar.2021.02.003","title":"New measures of alliance and conflict for three-way conflict analysis","year":2021,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":67,"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":"National Natural Science Foundation of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province","keywords":"Alliance; Measure (data warehouse); Conflict analysis; Construct (python library); Function (biology); Conflict resolution; Social psychology; Computer science; Mathematics; Psychology; Political science; Data mining; Law","authors":[{"name":"Guangming Lang","is_ca":true},{"name":"Yiyu Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04895104983420392,"gpt":0.2989493358967241,"spread":0.2499982860625201,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01216454,0.001708269,0.00150911,0.01091016,0.001593085,0.00607684,0.003262113,0.001762676,0.005051598],"category_scores_gemma":[0.04055454,0.0005284713,0.002070085,0.008994553,0.004248864,0.013632,0.003888909,0.00287718,0.0004942064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003289968,"about_ca_system_score_gemma":0.001691107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00172187,"about_ca_topic_score_gemma":0.00214857,"domain_scores_codex":[0.9891965,0.005002248,0.001373486,0.001226458,0.002643908,0.000557468],"domain_scores_gemma":[0.9731292,0.0167151,0.003804384,0.002061188,0.003028546,0.001261597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006161784,0.000378583,0.03254054,0.000510551,0.000978419,0.0002806225,0.002686226,0.07888712,0.002577781,0.7252596,0.003832635,0.1514518],"study_design_scores_gemma":[0.00009575719,0.0004756482,0.01401826,0.0001937562,0.0003753805,0.0003293653,0.002982479,0.432065,0.001610386,0.5407275,0.006866724,0.0002596776],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06346206,0.0009811561,0.9245736,0.00034458,0.0002075374,0.0003641531,0.0007993901,0.0002475184,0.009020015],"genre_scores_gemma":[0.6037992,0.000371957,0.3921182,0.00008666799,0.0001190496,0.001156646,0.0008809039,0.00006573243,0.001401719],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01216454,"threshold_uncertainty_score":0.06433296,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2040330356","doi":"10.1016/j.ijar.2011.09.002","title":"An interval set model for learning rules from incomplete information table","year":2011,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Central University Basic Research Fund of China; National Natural Science Foundation of China; University of Regina","keywords":"Interval (graph theory); Complete information; Table (database); Set (abstract data type); Mathematics; Interval data; Function (biology); Rule induction; Missing data; Computer science; Data mining; Mathematical optimization; Mathematical economics; Statistics; Combinatorics","authors":[{"name":"Huaxiong Li","is_ca":false},{"name":"Minhong Wang","is_ca":false},{"name":"Xianzhong Zhou","is_ca":false},{"name":"Jiabao Zhao","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04371686460606597,"gpt":0.2761555974031727,"spread":0.2324387327971067,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00220908,0.0006186757,0.00140717,0.001493102,0.0004353089,0.002798103,0.002853919,0.001284012,0.004202218],"category_scores_gemma":[0.009144136,0.0005595352,0.001640814,0.002230693,0.0007389146,0.003712432,0.0009691595,0.001688836,0.0007882441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142228,"about_ca_system_score_gemma":0.001157185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007430749,"about_ca_topic_score_gemma":0.005668489,"domain_scores_codex":[0.9982296,0.0004899322,0.0001897235,0.0004073809,0.0005717545,0.000111627],"domain_scores_gemma":[0.9962773,0.002674903,0.0002792649,0.0002944974,0.000386936,0.00008710325],"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":[0.0002186197,0.0001303796,0.0009622085,0.0002625594,0.0001855347,0.0004287044,0.0004296755,0.7706098,0.001396366,0.1352302,0.001932737,0.08821312],"study_design_scores_gemma":[0.0000168486,0.00003201492,0.0001027122,0.00002476834,0.00003744659,0.00004702811,0.00002001832,0.9528059,0.0002848934,0.0457106,0.0009029435,0.00001487504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01086795,0.0003380778,0.9859125,0.0002024995,0.00003714871,0.00005266364,0.0004039107,0.0002568196,0.001928304],"genre_scores_gemma":[0.4226434,0.0008093235,0.570994,0.0001497755,0.00008104384,0.0003620746,0.001452354,0.00006496201,0.00344312],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007430749,"threshold_uncertainty_score":0.01477504,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4212899608","doi":"10.1016/j.ijar.2022.02.001","title":"Symbols-Meaning-Value (SMV) space as a basis for a conceptual model of data science","year":2022,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Knowledge Management and Technology","field":"Decision Sciences","cited_by":64,"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; Meaning (existential); Viewpoints; Categorization; Knowledge space; Space (punctuation); Data science; Artificial intelligence; Epistemology; Knowledge management","authors":[{"name":"Yiyu Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1487588550546954,"gpt":0.4128438175589429,"spread":0.2640849625042476,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006139652,0.0007950122,0.001141817,0.004799085,0.001708363,0.009228865,0.003211939,0.002382743,0.006284206],"category_scores_gemma":[0.01666597,0.0007292368,0.002920585,0.004974817,0.007126886,0.01758642,0.004906539,0.004365662,0.00109593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002093724,"about_ca_system_score_gemma":0.002607543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002800301,"about_ca_topic_score_gemma":0.001915242,"domain_scores_codex":[0.9939534,0.003254638,0.0007275034,0.0006112235,0.001171001,0.0002822057],"domain_scores_gemma":[0.9910185,0.004763868,0.0005361949,0.001998978,0.00119958,0.0004828311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001070189,0.000008587067,0.0001154421,0.0000407169,0.000009779579,0.00003047151,0.0002452828,0.00170222,0.0001471661,0.9911771,0.0004122317,0.006100253],"study_design_scores_gemma":[0.000007120833,0.00001570943,0.00005229427,0.00003663661,0.00001544584,0.00006144519,0.0001383174,0.0217173,0.0001859512,0.9717636,0.005996726,0.000009481952],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006063661,0.0004641687,0.9829898,0.002633926,0.0001365543,0.00009863325,0.0003482604,0.0002661026,0.006998827],"genre_scores_gemma":[0.2795902,0.0005389798,0.7154253,0.0005619674,0.0001900536,0.000393195,0.0007131948,0.0001296234,0.002457578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009228865,"threshold_uncertainty_score":0.03246999,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2778314461","doi":"10.1016/j.ijar.2017.12.001","title":"Axiomatization of inconsistency indicators for pairwise comparisons","year":2017,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":63,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University","funders":"","keywords":"Pairwise comparison; Axiom; Triad (sociology); Matrix (chemical analysis); Mathematics; Statistics; Computer science; Psychology; Chemistry; Geometry","authors":[{"name":"Waldemar W. Koczkodaj","is_ca":true},{"name":"Roman Urban","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1547113767721053,"gpt":0.4652015120606433,"spread":0.310490135288538,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03085331,0.001661794,0.002205374,0.007098314,0.002283979,0.007602088,0.006475032,0.003431621,0.005720274],"category_scores_gemma":[0.1070378,0.001599272,0.006089144,0.007499042,0.006455108,0.01509448,0.007725699,0.01069667,0.001050727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003642181,"about_ca_system_score_gemma":0.003741511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002181788,"about_ca_topic_score_gemma":0.001844369,"domain_scores_codex":[0.9515008,0.02099826,0.006483104,0.006687962,0.01275828,0.001571679],"domain_scores_gemma":[0.875065,0.08803745,0.005931972,0.01391653,0.0153547,0.001694462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001113724,0.00008956873,0.001038179,0.00040562,0.0002247405,0.0002119462,0.0004813008,0.01443767,0.001058293,0.9370875,0.002304841,0.04254888],"study_design_scores_gemma":[0.00004777645,0.00004878817,0.0002807608,0.0001087078,0.0001498252,0.0002873038,0.0001199908,0.06850006,0.001615004,0.9256833,0.003108591,0.00004987108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00423844,0.000191896,0.9913257,0.0006159144,0.00009065372,0.0001017344,0.0002484205,0.0001900355,0.002997156],"genre_scores_gemma":[0.160557,0.0003714284,0.8349943,0.0005519861,0.0003209767,0.0006401789,0.001219593,0.0001250982,0.001219466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03085331,"threshold_uncertainty_score":0.1631698,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2046090861","doi":"10.1016/s0888-613x(00)00033-5","title":"Probabilistic satisfiability with imprecise probabilities","year":2000,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":58,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal; Group for Research in Decision Analysis; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Satisfiability; Probabilistic logic; Coherence (philosophical gambling strategy); Mathematics; Computer science; Algorithm; Discrete mathematics; Statistics","authors":[{"name":"Pierre Hansen","is_ca":true},{"name":"Brigitte Jaumard","is_ca":true},{"name":"Marcus Poggi de Aragão","is_ca":false},{"name":"Fabien Chauny","is_ca":true},{"name":"Sylvain Perron","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05687405436042048,"gpt":0.3749603033046183,"spread":0.3180862489441978,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01038155,0.001214679,0.002336329,0.003067277,0.001148744,0.005588565,0.002745895,0.002967425,0.003910187],"category_scores_gemma":[0.07846018,0.002183109,0.003273132,0.004011221,0.004562736,0.01442794,0.003727849,0.004632602,0.0003738897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002341246,"about_ca_system_score_gemma":0.001522454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003425285,"about_ca_topic_score_gemma":0.002512136,"domain_scores_codex":[0.9848176,0.007122644,0.00126264,0.001346212,0.004778017,0.0006729505],"domain_scores_gemma":[0.9384964,0.05439698,0.002388044,0.002707701,0.001627934,0.0003829921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002013685,0.00007926382,0.001287348,0.0005431193,0.0002834462,0.000769099,0.0005227625,0.463791,0.0006075658,0.4933878,0.001805909,0.03672126],"study_design_scores_gemma":[0.0000353689,0.00001633653,0.0001503278,0.00005308149,0.00005194451,0.0001245174,0.00005722561,0.3549188,0.0003370595,0.6436352,0.0005983151,0.00002178017],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03024634,0.0008609479,0.9619213,0.001875761,0.00008742953,0.0000796102,0.0002553449,0.0001359596,0.00453731],"genre_scores_gemma":[0.725463,0.001509881,0.2693992,0.0004496563,0.0004170799,0.000272714,0.000593934,0.00007208436,0.001822445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01038155,"threshold_uncertainty_score":0.05490357,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2043000136","doi":"10.1016/j.ijar.2005.06.016","title":"Fuzzy relational neural network","year":2005,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":55,"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":"Moore–Penrose pseudoinverse; Defuzzification; Computer science; Artificial neural network; Benchmark (surveying); Artificial intelligence; Fuzzy logic; Backpropagation; Neuro-fuzzy; Relational database; Machine learning; Fuzzy set operations; Data mining; Fuzzy control system; Fuzzy number; Fuzzy set; Algorithm; Mathematics","authors":[{"name":"Angelo Ciaramella","is_ca":false},{"name":"Roberto Tagliaferri","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Antonio Di Nola","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01612328464036428,"gpt":0.2654042246827835,"spread":0.2492809400424192,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006045042,0.0002825891,0.0006496041,0.0005708394,0.0004468068,0.001016185,0.000672564,0.0007511872,0.007549242],"category_scores_gemma":[0.002614123,0.0001675069,0.000407066,0.0006640108,0.0004019502,0.001246345,0.0005027197,0.0006666086,0.00138501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005549372,"about_ca_system_score_gemma":0.0004668007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002499232,"about_ca_topic_score_gemma":0.002198843,"domain_scores_codex":[0.9996945,0.00005799984,0.00002302474,0.00008347542,0.000120203,0.00002088359],"domain_scores_gemma":[0.9996459,0.0001107846,0.00002309265,0.00007417989,0.0001287008,0.0000172349],"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":[0.0003008838,0.00006905961,0.001522829,0.0002327282,0.0001359274,0.0002310999,0.0001022679,0.1318587,0.009324715,0.2770019,0.01224901,0.5669708],"study_design_scores_gemma":[0.00001832019,0.00003926826,0.0005905367,0.00003547274,0.00006609154,0.0001738272,0.0000259159,0.8796082,0.003595687,0.100025,0.01580214,0.00001962929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02914379,0.004671601,0.9373105,0.001217181,0.0006053562,0.000076513,0.0004699172,0.0008070805,0.02569808],"genre_scores_gemma":[0.6945667,0.003254731,0.2754511,0.0003151017,0.000285498,0.00008642141,0.0008352498,0.00006669734,0.02513855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007549242,"threshold_uncertainty_score":0.02525479,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2002457770","doi":"10.1016/j.ijar.2003.07.004","title":"P-FCM: a proximity-based fuzzy clustering for user-centered web applications","year":2003,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":51,"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":"Computer science; The Internet; Information retrieval; World Wide Web; Similarity (geometry); Interface (matter); User interface; Search engine; Fuzzy logic; Cluster analysis; Artificial intelligence","authors":[{"name":"Vincenzo Loia","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Sabrina Senatore","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02175185749308755,"gpt":0.2813758141385941,"spread":0.2596239566455065,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001288147,0.0008720492,0.001433725,0.002595817,0.001693334,0.001363332,0.00323612,0.001815073,0.002835729],"category_scores_gemma":[0.004212334,0.0004568521,0.001422744,0.003000612,0.0006130472,0.002132981,0.001534495,0.001149437,0.001433465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154161,"about_ca_system_score_gemma":0.001941203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0226314,"about_ca_topic_score_gemma":0.02325178,"domain_scores_codex":[0.9985019,0.0002307761,0.00008379792,0.0002978901,0.0007816389,0.0001040129],"domain_scores_gemma":[0.9990374,0.0002321684,0.00006282044,0.0002138923,0.0003997731,0.00005403729],"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":[0.0005048645,0.0002320405,0.001546014,0.0002439731,0.0001563371,0.0001501237,0.0003037057,0.1266196,0.01255472,0.01003216,0.008615393,0.8390411],"study_design_scores_gemma":[0.00001941646,0.00005720259,0.0006668048,0.00001704725,0.00003582451,0.0001276822,0.00006559259,0.9785572,0.008451013,0.008153332,0.003814113,0.00003477081],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007589911,0.0002014497,0.9893056,0.00006140968,0.00003478709,0.00007865241,0.0001964608,0.00186005,0.0006716385],"genre_scores_gemma":[0.1232889,0.0001814798,0.8732473,0.00007440463,0.00004229441,0.0001494717,0.0005198505,0.0001983709,0.002297826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0226314,"threshold_uncertainty_score":0.04499936,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1967107571","doi":"10.1016/j.ijar.2008.03.004","title":"Ontological approach to development of computing with words based systems","year":2008,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":50,"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":"Ontology; Computer science; Personalization; Semantics (computer science); Semantic Web; World Wide Web; Artificial intelligence; Programming language","authors":[{"name":"Marek Reformat","is_ca":true},{"name":"Cuong Ly","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03363973958946274,"gpt":0.264193166344021,"spread":0.2305534267545583,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002073854,0.0004271206,0.0006852731,0.00280682,0.001465016,0.004375023,0.002177503,0.001112378,0.003704937],"category_scores_gemma":[0.006128445,0.0005855768,0.001754551,0.002792727,0.004602065,0.006910541,0.0028551,0.001987154,0.0007288327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002163125,"about_ca_system_score_gemma":0.002547693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007409913,"about_ca_topic_score_gemma":0.006917888,"domain_scores_codex":[0.9978129,0.000787614,0.0002976445,0.0002389208,0.0007280447,0.0001348928],"domain_scores_gemma":[0.997888,0.0009682857,0.0001024339,0.0004501873,0.0004816231,0.0001094484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000007437093,0.00001500767,0.0002015039,0.00006581354,0.00001651052,0.00006671569,0.0003195688,0.003319603,0.0003053167,0.9819854,0.000463256,0.01323379],"study_design_scores_gemma":[0.000006584227,0.000009993094,0.000141211,0.00005812475,0.0000309312,0.00007664276,0.0001886526,0.0244205,0.0008277849,0.9547914,0.0194359,0.00001242179],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007174111,0.001085815,0.9730448,0.001757659,0.0001029244,0.00009108181,0.0001658586,0.0002275464,0.01635026],"genre_scores_gemma":[0.172353,0.001643109,0.8196058,0.0003316618,0.0001067083,0.0002237228,0.0004232802,0.0001013444,0.005211376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007409913,"threshold_uncertainty_score":0.01569468,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1979199064","doi":"10.1016/j.ijar.2012.05.002","title":"Granular fuzzy models: a study in knowledge management in fuzzy modeling","year":2012,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Canada Research Chairs","keywords":"Granular computing; Granularity; Fuzzy logic; Realization (probability); Computer science; Data mining; Quality (philosophy); Focus (optics); Artificial intelligence; Machine learning; Mathematics; Rough set; Statistics","authors":[{"name":"Witold Pedrycz","is_ca":true},{"name":"Mingli Song","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04273405980502918,"gpt":0.305348792387105,"spread":0.2626147325820758,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003595782,0.0006596253,0.001478902,0.002425531,0.00150173,0.00690477,0.002088481,0.002205894,0.003279245],"category_scores_gemma":[0.01572408,0.0005230044,0.001627286,0.005601878,0.003544923,0.01064976,0.002000207,0.002582971,0.0002637186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002625797,"about_ca_system_score_gemma":0.001546541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004186854,"about_ca_topic_score_gemma":0.002664818,"domain_scores_codex":[0.9979472,0.0009400217,0.0001372449,0.0002077846,0.0005980369,0.000169728],"domain_scores_gemma":[0.9920338,0.005662713,0.0006467859,0.0008188509,0.0004863717,0.0003514684],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000178131,0.00003077641,0.000303301,0.00007649411,0.0000348042,0.00006901065,0.0003377181,0.02407623,0.0001169929,0.9672217,0.0005226248,0.007192567],"study_design_scores_gemma":[0.00001254654,0.00002693837,0.0002082439,0.00007066126,0.0000319346,0.00006270746,0.0002667809,0.1497141,0.0001361267,0.8461388,0.003312377,0.0000185899],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07494235,0.009964823,0.8649972,0.007881775,0.0003975865,0.0001318659,0.000215757,0.0001580632,0.04131059],"genre_scores_gemma":[0.8632311,0.006330415,0.1239934,0.0003944222,0.0003850982,0.0001606247,0.0001551886,0.00004934624,0.005300409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00690477,"threshold_uncertainty_score":0.01905155,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1979302160","doi":"10.1016/s0888-613x(01)00047-0","title":"Fuzzy decision support system knowledge base generation using a genetic algorithm","year":2001,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Knowledge base; Computer science; Base (topology); Fuzzy logic; Data mining; Fuzzy set operations; A priori and a posteriori; Genetic algorithm; Artificial intelligence; Expert system; Set (abstract data type); Neuro-fuzzy; Machine learning; Fuzzy classification; Fuzzy set; Algorithm; Fuzzy control system; Mathematics","authors":[{"name":"Luc Baron","is_ca":true},{"name":"Sofiane Achiche","is_ca":true},{"name":"Marek Balazinski","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02497256994145624,"gpt":0.2767645031978608,"spread":0.2517919332564046,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007986805,0.0004694562,0.0007532157,0.001205964,0.0006861221,0.0008422787,0.001138082,0.001267048,0.002482664],"category_scores_gemma":[0.002620666,0.0003543654,0.0007044754,0.0007837315,0.0004606384,0.0008503784,0.0007142819,0.0006482988,0.0003652462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006999833,"about_ca_system_score_gemma":0.0008575215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006270824,"about_ca_topic_score_gemma":0.004214105,"domain_scores_codex":[0.9997378,0.00005346175,0.00001888283,0.00005963498,0.00009467807,0.00003565473],"domain_scores_gemma":[0.9994251,0.0002602576,0.00002814019,0.00005589737,0.0002093294,0.00002138795],"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":[0.0002658642,0.0002656936,0.001021764,0.00009204614,0.00009697017,0.0003148787,0.0002310854,0.5281433,0.01062997,0.009166857,0.002104318,0.4476673],"study_design_scores_gemma":[0.00003609476,0.00004822349,0.0001568862,0.000009780505,0.00003309431,0.00004191137,0.00001990174,0.9944941,0.002425411,0.00222633,0.0004999602,0.000008299261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07234604,0.0001908143,0.9204609,0.0002106873,0.00008624609,0.0002803083,0.00008269153,0.00107586,0.005266493],"genre_scores_gemma":[0.5329686,0.0001237558,0.4641874,0.0001347775,0.00003042659,0.0002494723,0.0001882577,0.00005970057,0.002057613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006270824,"threshold_uncertainty_score":0.01246864,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2468160993","doi":"10.1016/j.ijar.2016.06.013","title":"A semantically sound approach to Pawlak rough sets and covering-based rough sets","year":2016,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","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; Ministerio de Ciencia y Tecnología; Universiteit Gent","keywords":"Rough set; Partition (number theory); Mathematics; Computer science; Discrete mathematics; Artificial intelligence; Combinatorics","authors":[{"name":"Lynn D'eer","is_ca":false},{"name":"Chris Cornelis","is_ca":false},{"name":"Yiyu Yao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01843608064740648,"gpt":0.269350916280944,"spread":0.2509148356335375,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004530496,0.001070941,0.002365443,0.004537611,0.002205411,0.00747834,0.00255985,0.001949475,0.00282747],"category_scores_gemma":[0.01700316,0.001105834,0.004543961,0.005287666,0.004892613,0.01009996,0.0058714,0.006212802,0.0007184144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002002742,"about_ca_system_score_gemma":0.002088458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001669882,"about_ca_topic_score_gemma":0.001551624,"domain_scores_codex":[0.9917679,0.002389848,0.0008331868,0.0008700688,0.003636149,0.0005029143],"domain_scores_gemma":[0.9940978,0.002595764,0.0004139025,0.00150386,0.001144168,0.0002444516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002284727,0.00002175064,0.0001137361,0.00009056399,0.0000307607,0.0001033108,0.0002578946,0.005552886,0.0006035253,0.9764123,0.0008210923,0.0159692],"study_design_scores_gemma":[0.000009475145,0.0000266303,0.000107963,0.00003818724,0.00003634401,0.0001322714,0.0001191829,0.02918379,0.0005246669,0.9644716,0.005321788,0.00002823048],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005348109,0.0005371734,0.9882758,0.0007073009,0.0001813119,0.00007600343,0.0001398751,0.0001420283,0.004592312],"genre_scores_gemma":[0.2052656,0.00112165,0.7877543,0.0005877895,0.0005227186,0.0002723168,0.0004938241,0.0001254605,0.003856289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00747834,"threshold_uncertainty_score":0.02395982,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3007921036","doi":"10.1016/j.ijar.2020.02.005","title":"Three-way decision with incomplete information based on similarity and satisfiability","year":2020,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":44,"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":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Satisfiability; Equivalence (formal languages); Generalization; Mathematics; Similarity (geometry); Rough set; Boolean satisfiability problem; Equivalence relation; Similarity measure; Theoretical computer science; Computer science; Algorithm; Artificial intelligence; Discrete mathematics","authors":[{"name":"Junfang Luo","is_ca":true},{"name":"Mengjun Hu","is_ca":true},{"name":"Keyun Qin","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01489936376932271,"gpt":0.2357930965465411,"spread":0.2208937327772184,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005298134,0.000890707,0.003270177,0.00280474,0.001881032,0.004003672,0.002451081,0.002130251,0.002278487],"category_scores_gemma":[0.01696658,0.000923885,0.004118217,0.002840082,0.002364694,0.008667246,0.002834362,0.001782017,0.0001718681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001853911,"about_ca_system_score_gemma":0.00271445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004218359,"about_ca_topic_score_gemma":0.002886474,"domain_scores_codex":[0.9934342,0.001710851,0.0008683858,0.001282988,0.002170275,0.0005332702],"domain_scores_gemma":[0.9879943,0.008589668,0.0009454873,0.0006858137,0.001236516,0.0005482084],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001230528,0.0003955063,0.006730315,0.0007904474,0.0008513328,0.001215477,0.001386846,0.6384617,0.003576885,0.2474293,0.001701953,0.09622969],"study_design_scores_gemma":[0.00004271578,0.00009810291,0.0004618873,0.00003019603,0.0001366519,0.0001093177,0.0001551939,0.8627198,0.0009119707,0.1349474,0.0003360034,0.00005089211],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1062859,0.0003223636,0.8901879,0.0004912327,0.00006909936,0.0001309479,0.0001417488,0.00009097695,0.002279815],"genre_scores_gemma":[0.8040738,0.0002594121,0.1939291,0.0000871625,0.00007297342,0.0001849626,0.0003462558,0.00002378422,0.001022578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005298134,"threshold_uncertainty_score":0.02801955,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2409812824","doi":"10.1016/j.ijar.2007.10.005","title":"Probabilistic rough sets: Approximations, decision-makings, and applications","year":2007,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":42,"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":"Probabilistic logic; Rough set; Computer science; Mathematics; Artificial intelligence","authors":[{"name":"JingTao Yao","is_ca":true},{"name":"Yiyu Yao","is_ca":true},{"name":"Wojciech Ziarko","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.010500575117463,"gpt":0.2831935031452537,"spread":0.2726929280277907,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01216345,0.00151585,0.003779411,0.003450328,0.00105662,0.009361735,0.002399574,0.002758169,0.002277207],"category_scores_gemma":[0.04652734,0.001191067,0.002028923,0.006027337,0.005276436,0.01073554,0.002988973,0.004702789,0.0005117116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002313515,"about_ca_system_score_gemma":0.001884962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001883194,"about_ca_topic_score_gemma":0.001498734,"domain_scores_codex":[0.9866633,0.006977799,0.001074828,0.0008828397,0.00411562,0.0002855516],"domain_scores_gemma":[0.97282,0.02117863,0.002089426,0.001871016,0.001606786,0.0004341593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005820223,0.00004881369,0.0006221829,0.000568693,0.0001844428,0.0001161552,0.0004045391,0.04603703,0.0002779749,0.8873123,0.002252985,0.0621168],"study_design_scores_gemma":[0.00001377884,0.00002297835,0.0001952085,0.00009847101,0.00003925141,0.00007030926,0.0001163992,0.0539836,0.0001087199,0.9424514,0.002872759,0.00002709676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01286387,0.03269287,0.9434337,0.0044425,0.0004740478,0.00008385487,0.00026513,0.0001562491,0.005587748],"genre_scores_gemma":[0.4917692,0.03744429,0.4648476,0.0006556989,0.001669548,0.000478522,0.0005071877,0.00006285708,0.002565118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01216345,"threshold_uncertainty_score":0.06432724,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2591697403","doi":"10.1016/j.ijar.2017.04.005","title":"On normalization of inconsistency indicators in pairwise comparisons","year":2017,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","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":"University of Manitoba; Laurentian University","funders":"Ministerstwo Edukacji i Nauki; Ministerstvo Školství, Mládeže a Tělovýchovy","keywords":"Normalization (sociology); Pairwise comparison; Mathematics; Computer science; Econometrics; Statistics; Sociology","authors":[{"name":"Waldemar W. Koczkodaj","is_ca":true},{"name":"Jean-Pierre Magnot","is_ca":false},{"name":"Jiří Mazurek","is_ca":false},{"name":"James F. Peters","is_ca":true},{"name":"Hojjat Rakhshani","is_ca":false},{"name":"Michael Soltys","is_ca":false},{"name":"Dominik Strzałka","is_ca":false},{"name":"Jacek Szybowski","is_ca":false},{"name":"Arturo Tozzi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01810864377255716,"gpt":0.2893690540260443,"spread":0.2712604102534872,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04797159,0.002035537,0.00466642,0.01338695,0.002512308,0.006536995,0.006688864,0.002290181,0.003626555],"category_scores_gemma":[0.2029925,0.001197508,0.003832177,0.01777052,0.005527343,0.01469591,0.007393,0.0062658,0.0007159652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003837477,"about_ca_system_score_gemma":0.004369653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002898922,"about_ca_topic_score_gemma":0.002679801,"domain_scores_codex":[0.9392458,0.02450349,0.00566466,0.009321425,0.01948676,0.001778016],"domain_scores_gemma":[0.8197832,0.1221583,0.01041277,0.01980595,0.02556627,0.002273615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001185077,0.000387495,0.008688859,0.001657194,0.001044881,0.0003616793,0.001619531,0.08408729,0.005528941,0.3681616,0.005277921,0.5219995],"study_design_scores_gemma":[0.0001001914,0.0003563633,0.00613258,0.0005497841,0.0006454883,0.0006515813,0.000657995,0.4379638,0.00843702,0.5370525,0.00723593,0.0002168159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01098351,0.001000343,0.9847507,0.0002789925,0.0001646844,0.0001677893,0.0002762128,0.0002530855,0.002124696],"genre_scores_gemma":[0.1686038,0.0008888936,0.8269649,0.0001857648,0.0003142521,0.0006463331,0.0009834559,0.000292961,0.001119679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04797159,"threshold_uncertainty_score":0.253701,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3008638314","doi":"10.1016/j.ijar.2020.02.003","title":"Label distribution learning: A local collaborative mechanism","year":2020,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Text and Document Classification Technologies","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":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Ambiguity; Computer science; Artificial intelligence; Machine learning; Feature learning; Representation (politics); Pattern recognition (psychology)","authors":[{"name":"Suping Xu","is_ca":true},{"name":"Hengrong Ju","is_ca":true},{"name":"Lin Shang","is_ca":false},{"name":"Witold Pedrycz","is_ca":true},{"name":"Xibei Yang","is_ca":false},{"name":"Chun Li","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0166668925147492,"gpt":0.2669003154745818,"spread":0.2502334229598325,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01123617,0.001230187,0.002622547,0.003092006,0.003619496,0.004811798,0.009424263,0.004842258,0.011719],"category_scores_gemma":[0.02775939,0.001037653,0.001937623,0.004701944,0.002437192,0.009820425,0.01242648,0.00408987,0.003638513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001918971,"about_ca_system_score_gemma":0.002964992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004482917,"about_ca_topic_score_gemma":0.007033485,"domain_scores_codex":[0.991616,0.002773653,0.0004938073,0.001977005,0.002586063,0.0005535596],"domain_scores_gemma":[0.9773299,0.008463179,0.001055975,0.009253772,0.002993935,0.0009031407],"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":[0.001585966,0.001535808,0.004174915,0.0002805904,0.0003520279,0.0002177737,0.0008456961,0.06008149,0.01283623,0.07498785,0.01741615,0.8256854],"study_design_scores_gemma":[0.0002357703,0.0001871297,0.0006243092,0.00003528003,0.0001463094,0.0001997961,0.0001639663,0.8668132,0.01424122,0.1095221,0.007769173,0.00006170964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006738469,0.0001444155,0.989838,0.000360709,0.00005443379,0.00009991998,0.00008652839,0.001248868,0.001428705],"genre_scores_gemma":[0.3171343,0.0002127832,0.6692164,0.0005201558,0.0003503738,0.0004141781,0.0006568087,0.0004442349,0.01105076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.011719,"threshold_uncertainty_score":0.05942327,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2606186227","doi":"10.1016/j.ijar.2017.03.005","title":"Cost-sensitive three-way recommendations by learning pair-wise preferences","year":2017,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":36,"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":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Ranking (information retrieval); Recommender system; Preference; Set (abstract data type); Preference learning; Machine learning; Focus (optics); Quality (philosophy); Product (mathematics); Basis (linear algebra); Artificial intelligence; Function (biology); Data mining; Information retrieval; Mathematics; Statistics","authors":[{"name":"Jiajin Huang","is_ca":false},{"name":"Jian Wang","is_ca":false},{"name":"Yiyu Yao","is_ca":true},{"name":"Ning Zhong","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04503922916240923,"gpt":0.3126615651773456,"spread":0.2676223360149364,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004582859,0.001233881,0.002764117,0.002263947,0.0007420626,0.002373349,0.003060581,0.002606829,0.00302303],"category_scores_gemma":[0.0168552,0.0009091304,0.001854701,0.002644485,0.0006314146,0.004486953,0.002028357,0.002456449,0.0008745727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165242,"about_ca_system_score_gemma":0.001255248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003871513,"about_ca_topic_score_gemma":0.005179735,"domain_scores_codex":[0.99445,0.00174219,0.0004815653,0.001068784,0.001892591,0.0003649556],"domain_scores_gemma":[0.989948,0.006924095,0.0005317403,0.001027361,0.001231579,0.0003371379],"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":[0.002096579,0.001019965,0.0106557,0.0005581988,0.0009664966,0.0004646434,0.0004071642,0.4844514,0.006135452,0.0159753,0.008373187,0.4688959],"study_design_scores_gemma":[0.00004639698,0.0001495998,0.000601831,0.0000227958,0.00006978329,0.0001071119,0.00005850084,0.9864534,0.001217248,0.0108195,0.0004252142,0.00002871717],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08779326,0.0007354794,0.9075964,0.0005537297,0.0001295208,0.000173924,0.0004948389,0.0005331599,0.001989641],"genre_scores_gemma":[0.7219133,0.0003113215,0.2737985,0.0001978724,0.0001390292,0.0001875539,0.001083734,0.00006847359,0.0023001],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004582859,"threshold_uncertainty_score":0.02423674,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2121848472","doi":"10.1016/j.ijar.2006.06.026","title":"Fuzzy modelling through logic optimization","year":2006,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Canada Research Chairs","keywords":"Fuzzy logic; Interpretability; Computer science; Artificial intelligence; Neuro-fuzzy; Machine learning; Fuzzy control system","authors":[{"name":"A.F. Gobi","is_ca":true},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0161126519505137,"gpt":0.2411487894147334,"spread":0.2250361374642197,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008079061,0.00043304,0.00102255,0.0005290565,0.0004486882,0.001377609,0.0007580814,0.0008099446,0.003900082],"category_scores_gemma":[0.00208853,0.0004127066,0.0008442589,0.0005685271,0.0007517945,0.001332319,0.0009224456,0.0009894826,0.0005200226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009777844,"about_ca_system_score_gemma":0.0007110742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003531714,"about_ca_topic_score_gemma":0.002491867,"domain_scores_codex":[0.9996623,0.0001471766,0.0000141506,0.00003587075,0.0001202239,0.0000202503],"domain_scores_gemma":[0.9996334,0.000214073,0.00003473252,0.00004319194,0.00005913335,0.00001544654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003122909,0.00002537728,0.00009742906,0.0000406971,0.00002771225,0.00002525526,0.00004101923,0.6444486,0.0008561522,0.3321542,0.000829134,0.02142314],"study_design_scores_gemma":[0.000004429949,0.000004656372,0.00001444664,0.000004700623,0.000003968153,0.000004908035,0.000003850223,0.9231739,0.0001718167,0.07568083,0.0009293669,0.000003111441],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004686526,0.0002168367,0.9861665,0.0003435696,0.00004684488,0.00001889166,0.00002782629,0.00008100826,0.008411968],"genre_scores_gemma":[0.6131269,0.0008281093,0.3693605,0.000180349,0.0001064289,0.0001670088,0.0001275319,0.0001390058,0.01596419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003900082,"threshold_uncertainty_score":0.0130471,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2606056073","doi":"10.1016/j.ijar.2017.04.004","title":"Collaborative fuzzy clustering algorithm: Some refinements","year":2017,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Advanced Clustering Algorithms Research","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Cluster analysis; Partition (number theory); Computer science; Data mining; Fuzzy clustering; Fuzzy logic; Algorithm; Granular computing; Artificial intelligence; Mathematics; Rough set","authors":[{"name":"Yinghua Shen","is_ca":true},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01798900918128958,"gpt":0.3372030557690087,"spread":0.3192140465877191,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006780022,0.0009117055,0.002103499,0.002318438,0.001886218,0.002383287,0.004792922,0.00272255,0.004076994],"category_scores_gemma":[0.02243493,0.000756767,0.002526049,0.00489074,0.00193116,0.005763412,0.002824707,0.00304514,0.001950738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00151632,"about_ca_system_score_gemma":0.002499537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01199178,"about_ca_topic_score_gemma":0.009310338,"domain_scores_codex":[0.9951415,0.001548182,0.0003992514,0.001128288,0.001601831,0.0001808269],"domain_scores_gemma":[0.9905309,0.003295589,0.0002622847,0.002136621,0.003522196,0.0002524064],"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":[0.0004667889,0.0002803652,0.003046378,0.0006370282,0.0003294644,0.0002215928,0.0009965189,0.1727653,0.006894503,0.2914388,0.01274453,0.5101787],"study_design_scores_gemma":[0.00005280183,0.0001374828,0.0009238328,0.00005792215,0.0001232014,0.0004220452,0.0001712219,0.8823673,0.003328077,0.09896734,0.01337028,0.00007845586],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003304531,0.0008676832,0.9932099,0.000331449,0.0001640569,0.00004863009,0.00006206155,0.00009879893,0.001912902],"genre_scores_gemma":[0.09565048,0.001790959,0.8965848,0.0002329017,0.0005253028,0.0001855213,0.0002737047,0.0001176929,0.004638556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01199178,"threshold_uncertainty_score":0.03585666,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2059605406","doi":"10.1016/j.ijar.2013.10.012","title":"A construction of sound semantic linguistic scales using 4-tuple representation of term semantics","year":2013,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":35,"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":"Semantics (computer science); Computer science; Vagueness; Computational semantics; Representation (politics); Natural language processing; Well-founded semantics; Interpretation (philosophy); Rule-based machine translation; Operational semantics; Linguistics; Artificial intelligence; Denotational semantics; Programming language; Fuzzy logic","authors":[{"name":"Nguyễn Cát Hồ","is_ca":false},{"name":"Van‐Nam Huynh","is_ca":false},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1128497310563939,"gpt":0.4294184621617268,"spread":0.3165687311053329,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002871091,0.00065439,0.001267996,0.003498509,0.001116691,0.003932123,0.001604597,0.001102259,0.004589335],"category_scores_gemma":[0.01181423,0.0007336308,0.002460316,0.003898474,0.001678083,0.006535409,0.003299619,0.001953877,0.001341223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101713,"about_ca_system_score_gemma":0.00173146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003058309,"about_ca_topic_score_gemma":0.002226998,"domain_scores_codex":[0.9969648,0.0007951387,0.0005698039,0.0005596127,0.0009140145,0.0001967016],"domain_scores_gemma":[0.9963275,0.001316092,0.0003218301,0.0008023665,0.001005934,0.0002262859],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002907985,0.0001771085,0.001928102,0.000488781,0.0001298443,0.0004027012,0.001740163,0.02798715,0.0133179,0.7087982,0.006065302,0.238674],"study_design_scores_gemma":[0.00005108511,0.000191521,0.001015014,0.0001830554,0.0001598025,0.0002404668,0.0008892599,0.3113829,0.008492907,0.6536467,0.02363031,0.0001169513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009218509,0.0001250798,0.9869549,0.0001900155,0.00007576357,0.0001203592,0.0004541083,0.0008284617,0.002032791],"genre_scores_gemma":[0.1272476,0.0001294262,0.8706195,0.00008492617,0.00003618933,0.0002481843,0.0007220404,0.0001335828,0.000778569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004589335,"threshold_uncertainty_score":0.0153529,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2065433699","doi":"10.1016/j.ijar.2010.05.003","title":"Aggregating multiple classification results using fuzzy integration and stochastic feature selection","year":2010,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta; University of Manitoba; National Research Council Canada","funders":"","keywords":"Curse of dimensionality; Pattern recognition (psychology); Classifier (UML); Artificial intelligence; Feature selection; Computer science; Fuzzy logic; Feature vector; Random subspace method; Machine learning; Data mining; Mathematics","authors":[{"name":"Nick J. Pizzi","is_ca":true},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01114264503755551,"gpt":0.2845943143249133,"spread":0.2734516692873578,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006436764,0.001026257,0.002539559,0.003184404,0.0007520306,0.002320251,0.001356769,0.001004133,0.001265168],"category_scores_gemma":[0.01215358,0.0006168352,0.0017226,0.002675161,0.0004535174,0.002271713,0.001635745,0.0009666378,0.0004316601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007541497,"about_ca_system_score_gemma":0.0009947082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003001394,"about_ca_topic_score_gemma":0.00402654,"domain_scores_codex":[0.9964684,0.0005961518,0.0004521171,0.0004492379,0.001786073,0.0002480002],"domain_scores_gemma":[0.9928226,0.002870804,0.0004308445,0.000789,0.002915208,0.0001716178],"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":[0.0009013831,0.0003954047,0.009875047,0.0002045821,0.0006627109,0.0004156517,0.0002422019,0.1863437,0.01903424,0.004095092,0.002133449,0.7756967],"study_design_scores_gemma":[0.0000187135,0.00008994693,0.001721773,0.00001287954,0.0001338547,0.00006521655,0.00003764045,0.9871291,0.005324821,0.005105994,0.0003392717,0.00002088756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06576191,0.0003103969,0.9321674,0.0001512419,0.00009910177,0.00008489194,0.0000571606,0.0006455717,0.0007223982],"genre_scores_gemma":[0.6166984,0.0001484603,0.3815382,0.0000690003,0.0001086677,0.0000950106,0.0002659698,0.00006366179,0.001012588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006436764,"threshold_uncertainty_score":0.03404129,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1987500336","doi":"10.1016/j.ijar.2007.03.001","title":"Merging the local and global approaches to probabilistic satisfiability","year":2007,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Leibniz-Gemeinschaft","keywords":"Probabilistic logic; Satisfiability; Consistency (knowledge bases); Logical consequence; Set (abstract data type); Mathematics; Mathematical optimization; Computer science; Column generation; Algorithm; Discrete mathematics; Artificial intelligence","authors":[{"name":"Pierre Hansen","is_ca":true},{"name":"Sylvain Perron","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04887454489303928,"gpt":0.2829215156129502,"spread":0.2340469707199109,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008265549,0.001322614,0.003182468,0.003062075,0.001143274,0.005538836,0.004837295,0.002280293,0.00613485],"category_scores_gemma":[0.02781654,0.001665681,0.003289599,0.003779156,0.003749073,0.01630783,0.008473032,0.005635799,0.001001865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001393036,"about_ca_system_score_gemma":0.001953756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002149489,"about_ca_topic_score_gemma":0.006441467,"domain_scores_codex":[0.9927966,0.003292341,0.0004826116,0.0010849,0.001938609,0.0004049387],"domain_scores_gemma":[0.9775572,0.01284113,0.000901595,0.006355585,0.001889384,0.0004550996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003722655,0.0002285715,0.003274785,0.0009524662,0.0006727692,0.0002854118,0.001472388,0.1251604,0.004087499,0.4874845,0.004916811,0.3710921],"study_design_scores_gemma":[0.00004510413,0.00008952618,0.0007090785,0.00009901558,0.0002973283,0.0001355844,0.0002757815,0.3591777,0.003161597,0.6318382,0.004110446,0.00006060129],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005833756,0.000554003,0.9903927,0.0004562793,0.00002559579,0.00002729302,0.00009540184,0.0004476111,0.002167369],"genre_scores_gemma":[0.2863494,0.001443467,0.7070696,0.0004748899,0.0002348509,0.0001888927,0.0006586298,0.0006149014,0.00296534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008265549,"threshold_uncertainty_score":0.04371297,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2015699578","doi":"10.1016/j.ijar.2008.06.006","title":"Structural and parametric design of fuzzy inference systems using hierarchical fair competition-based parallel genetic algorithms and information granulation","year":2008,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":34,"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":"Adaptive neuro fuzzy inference system; Cluster analysis; Data mining; Fuzzy logic; Inference; Algorithm; Genetic algorithm; Fuzzy clustering; Computer science; Fuzzy control system; Mathematics; Mathematical optimization; Artificial intelligence","authors":[{"name":"Jeoung‐Nae Choi","is_ca":false},{"name":"Sung‐Kwun Oh","is_ca":false},{"name":"Witold Pedrycz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02632923453678215,"gpt":0.2514020253413799,"spread":0.2250727908045977,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002357019,0.000546972,0.001480799,0.0007613142,0.001124041,0.001349409,0.00179247,0.001207346,0.001811384],"category_scores_gemma":[0.005662472,0.000645716,0.0008927236,0.0006260414,0.001540598,0.00150379,0.001260815,0.001036124,0.0001477011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001485084,"about_ca_system_score_gemma":0.002075486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005819783,"about_ca_topic_score_gemma":0.006606308,"domain_scores_codex":[0.9992145,0.0002237283,0.00004861095,0.0001515976,0.0002505027,0.0001110225],"domain_scores_gemma":[0.998128,0.001124338,0.0001882258,0.0001312018,0.0003562999,0.00007193181],"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":[0.00005130163,0.00004410032,0.0001150564,0.00003046172,0.00001638067,0.00002660222,0.00006697819,0.9615234,0.001404401,0.01728855,0.0001424316,0.01929036],"study_design_scores_gemma":[0.000007431844,0.00001567117,0.00002035301,0.000001691846,0.000003571474,0.000004198396,0.00000365763,0.9952788,0.0002694903,0.004331912,0.00006056322,0.000002598942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0200077,0.00006895405,0.9782776,0.00008159226,0.00002280261,0.00005268389,0.00001022257,0.00009427941,0.001384131],"genre_scores_gemma":[0.7930042,0.00007974885,0.2055103,0.00005698344,0.00002640115,0.0001578005,0.00002911979,0.00002805143,0.001107356],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005819783,"threshold_uncertainty_score":0.01246524,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2018657423","doi":"10.1016/j.ijar.2013.02.012","title":"An axiomatic characterization of probabilistic rough sets","year":2013,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China; University of Regina","keywords":"Probabilistic logic; Rough set; Axiom; Mathematics; Equivalence relation; Equivalence (formal languages); Characterization (materials science); Set (abstract data type); Constructive; Dominance-based rough set approach; Relation (database); Discrete mathematics; Computer science; Data mining; Process (computing); Statistics","authors":[{"name":"Tong-Jun Li","is_ca":false},{"name":"Xiaoping Yang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01190864352483876,"gpt":0.2574340896150724,"spread":0.2455254460902336,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00593909,0.0009113164,0.001136708,0.004798722,0.002120057,0.005862711,0.002793494,0.001980458,0.004832893],"category_scores_gemma":[0.01538197,0.001103383,0.002894219,0.003685448,0.004150619,0.01188265,0.003948506,0.005197554,0.0008709739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001567833,"about_ca_system_score_gemma":0.001140379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009178639,"about_ca_topic_score_gemma":0.000878486,"domain_scores_codex":[0.9930508,0.002030327,0.0007699475,0.00100613,0.002760741,0.0003820826],"domain_scores_gemma":[0.9847456,0.008987989,0.001126294,0.001994045,0.002688373,0.0004578471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001116862,0.00001473895,0.0001742098,0.0000474192,0.00002096254,0.00006269323,0.0001461009,0.002021635,0.0003388041,0.9908133,0.0008968323,0.00545209],"study_design_scores_gemma":[0.000009263963,0.0000163351,0.0002153785,0.00002601058,0.00002209153,0.0001613316,0.00006215086,0.01891029,0.0003265239,0.9762592,0.003970137,0.00002127843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01659403,0.0006276148,0.9616493,0.002320999,0.0002088811,0.00007214373,0.0004742575,0.0002126189,0.01784021],"genre_scores_gemma":[0.5230433,0.001524216,0.4626289,0.001745987,0.001153944,0.0004809802,0.001769697,0.0001606429,0.007492328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00593909,"threshold_uncertainty_score":0.03140932,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2996609727","doi":"10.1016/j.ijar.2019.12.008","title":"Indices for rough set approximation and the application to confusion matrices","year":2019,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","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":"Brock University","funders":"National Natural Science Foundation of China","keywords":"Confusion; Confusion matrix; Converse; Upper and lower bounds; Mathematics; Estimator; Rough set; Universality (dynamical systems); Statistics; Odds; Computer science; Artificial intelligence; Mathematical analysis; Logistic regression","authors":[{"name":"Ivo Düntsch","is_ca":true},{"name":"Günther Gediga","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00987474744162942,"gpt":0.2730428514048785,"spread":0.2631681039632491,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01110239,0.001807926,0.002907769,0.009216412,0.001394979,0.005652164,0.003052903,0.001797078,0.002698372],"category_scores_gemma":[0.05555556,0.0007429332,0.002638327,0.008300735,0.002057653,0.005381736,0.002949773,0.00353406,0.001213282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002297774,"about_ca_system_score_gemma":0.002505285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003081522,"about_ca_topic_score_gemma":0.002057197,"domain_scores_codex":[0.9885091,0.00519005,0.001083491,0.0009512361,0.003887253,0.0003787965],"domain_scores_gemma":[0.9624842,0.02808666,0.001804187,0.002600599,0.004517801,0.0005065053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000389355,0.0001917866,0.001831124,0.0006660584,0.000311621,0.0001597862,0.000688319,0.1322568,0.003453645,0.4481986,0.006040294,0.4058126],"study_design_scores_gemma":[0.00002309043,0.0001129128,0.0007972994,0.0001633817,0.00007369417,0.0002063001,0.0001223565,0.6844479,0.002327205,0.3075303,0.004074658,0.0001208828],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002653478,0.0007523567,0.9952458,0.0001274306,0.00007063987,0.0000574205,0.000156973,0.000202078,0.0007338729],"genre_scores_gemma":[0.09604373,0.00121114,0.9000294,0.00008364669,0.000267565,0.0005097525,0.0004969521,0.000133086,0.001224781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01110239,"threshold_uncertainty_score":0.05871576,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1976622705","doi":"10.1016/j.ijar.2011.08.002","title":"Classification systems based on rough sets under the belief function framework","year":2011,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Saint Mary's University","funders":"","keywords":"Rough set; Dominance-based rough set approach; Decision table; Decision tree; Classifier (UML); Artificial intelligence; Computer science; Decision rule; Data mining; Decision tree learning; Mathematics; Machine learning; Pattern recognition (psychology)","authors":[{"name":"Salsabil Trabelsi","is_ca":false},{"name":"Zied Elouedi","is_ca":false},{"name":"Pawan Lingras","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04747247624804989,"gpt":0.2737894304972597,"spread":0.2263169542492098,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004480047,0.0006763425,0.003070227,0.002141921,0.001119636,0.005834437,0.002001757,0.001711075,0.001412056],"category_scores_gemma":[0.01553901,0.0005606325,0.001770768,0.002112806,0.001547864,0.005394385,0.001902608,0.001775543,0.0005663138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001441795,"about_ca_system_score_gemma":0.00139253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003872653,"about_ca_topic_score_gemma":0.002039695,"domain_scores_codex":[0.9954768,0.001560652,0.0004236531,0.0005747862,0.001635871,0.0003283114],"domain_scores_gemma":[0.9930758,0.004253644,0.0007438751,0.0005201428,0.001223438,0.0001831047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004198861,0.0001776719,0.002081122,0.0003615168,0.0005444871,0.0002926044,0.0008053409,0.4209396,0.002712837,0.3421889,0.002429516,0.2270465],"study_design_scores_gemma":[0.00003262308,0.00006392122,0.0003829239,0.00003735154,0.00009511544,0.00004802014,0.0000536656,0.8556298,0.0007177948,0.1421276,0.0007708249,0.00004034522],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02261111,0.0009580982,0.9733594,0.0005654967,0.00009837552,0.00006928808,0.00007615615,0.0002517962,0.002010231],"genre_scores_gemma":[0.718142,0.001252873,0.2779409,0.0001784879,0.0002533894,0.0002661161,0.0002597222,0.00003455726,0.001671897],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005834437,"threshold_uncertainty_score":0.02369303,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}