{"meta":{"query_hash":"a063d9cef302","filters":{"venue":"Grey Systems Theory and Application"},"cohort_total":10,"direct_labels_cover":0,"predictions_cover":10,"exported":10,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/a063d9cef302","api":"https://metacan.xera.ac/api/v1/cohort?venue=Grey+Systems+Theory+and+Application"},"results":[{"id":"W2134900285","doi":"10.1108/20439371211273267","title":"Commercial bank credit risk management based on grey incidence analysis","year":2012,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Credit risk; Loan; Credit history; Risk management; Actuarial science; Asset (computer security); Economics; Business; Finance; Computer science","score_opus":0.013837952339914883,"score_gpt":0.226044461388894,"score_spread":0.21220650904897911,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134900285","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.141841,0.00048722938,0.84345984,0.0005038371,0.000041991003,0.00016260684,0.00012427269,0.0001845834,0.013194622],"genre_scores_gemma":[0.9765461,0.00031588168,0.021552604,0.00002472318,0.000023509318,0.000052643132,0.00007594722,0.0000143149855,0.0013942757],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982729,0.0005322616,0.00010682647,0.00029120196,0.0006269062,0.00016987426],"domain_scores_gemma":[0.9972916,0.0013288256,0.00047460874,0.00019367784,0.00059721636,0.00011410376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022381542,0.00053490774,0.0009010127,0.0034785648,0.0006823771,0.0027613149,0.0009866905,0.00054952817,0.0028554054],"category_scores_gemma":[0.0068177106,0.00028103503,0.001317385,0.0018299652,0.001667437,0.002750689,0.0018850269,0.0009834855,0.00019268149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012565112,0.000113609894,0.027499879,0.00027495655,0.00018085496,0.00037070122,0.0011877884,0.60696346,0.004170437,0.24150583,0.0019655833,0.11564131],"study_design_scores_gemma":[0.000009121766,0.000037369897,0.004499838,0.000032883345,0.000039299204,0.00005079389,0.00022328476,0.90346086,0.0010828173,0.08967816,0.0008601776,0.000025425408],"about_ca_topic_score_codex":0.0066729,"about_ca_topic_score_gemma":0.002416774,"teacher_disagreement_score":0.0066729,"about_ca_system_score_codex":0.0023817807,"about_ca_system_score_gemma":0.0014231143,"threshold_uncertainty_score":0.017281175},"labels":[],"label_agreement":null},{"id":"W2166279149","doi":"10.1108/20439371211260162","title":"The optimized GPM(1,1) for forecasting small sample oscillating series","year":2012,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Sample (material); Series (stratigraphy); Range (aeronautics); Computer science; Variable (mathematics); Power (physics); Time series; Value (mathematics); Industrial engineering; Operations research; Mathematical optimization; Engineering; Mathematics; Machine learning","score_opus":0.11112063111047962,"score_gpt":0.3379260838433426,"score_spread":0.22680545273286298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166279149","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.073795386,0.00066109264,0.9196716,0.00048639617,0.00007155868,0.00008455241,0.00022928863,0.0003309051,0.004669261],"genre_scores_gemma":[0.88140523,0.0005231451,0.11415112,0.00009726471,0.00003529302,0.00017281348,0.00026559865,0.00007648992,0.0032729828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996855,0.00013264801,0.000013138003,0.00007400745,0.000066330984,0.000028353712],"domain_scores_gemma":[0.99946326,0.00036977298,0.000059485137,0.000020606329,0.000075570955,0.000011321269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00097143813,0.00057085056,0.0007313448,0.0004939387,0.00029697322,0.00076153484,0.000853353,0.0008337473,0.0014428557],"category_scores_gemma":[0.0031811479,0.00031656545,0.0006965545,0.00071493356,0.00031968538,0.00073856296,0.00042354243,0.0011736512,0.00019769727],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010956001,0.000009063678,0.00033507543,0.00002972744,0.00001242688,0.000033008208,0.000013963553,0.98606,0.00031554454,0.0029436878,0.00036852967,0.0098680165],"study_design_scores_gemma":[0.0000025345016,0.0000072394278,0.00013602742,0.0000024788824,0.0000038939165,0.000004311093,0.0000026900445,0.99801046,0.00011461345,0.0015662994,0.00014683712,0.0000026451737],"about_ca_topic_score_codex":0.010338413,"about_ca_topic_score_gemma":0.0069378433,"teacher_disagreement_score":0.010338413,"about_ca_system_score_codex":0.0007899468,"about_ca_system_score_gemma":0.0009435792,"threshold_uncertainty_score":0.02055651},"labels":[],"label_agreement":null},{"id":"W2171037160","doi":"10.1108/20439371111181224","title":"Dynamic analysis of Bayesian audit strategies with tests of controls and reliability modeling","year":2011,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Audit; Reliability (semiconductor); Bayesian probability; Computer science; Sampling (signal processing); Sample (material); Econometrics; Data mining; Accounting; Artificial intelligence; Mathematics; Business","score_opus":0.034600457136807206,"score_gpt":0.3414693549848222,"score_spread":0.306868897848015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171037160","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5997531,0.00037488993,0.39169914,0.000543036,0.00003840498,0.00050782436,0.00017143118,0.0002668126,0.006645464],"genre_scores_gemma":[0.98073775,0.00005667754,0.01841661,0.000029873176,0.000006110763,0.00011760054,0.00004031719,0.000014637013,0.00058045384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9806209,0.014579208,0.0005464794,0.0013035096,0.0021883969,0.0007615448],"domain_scores_gemma":[0.7716693,0.19863063,0.014280559,0.009454558,0.005122956,0.0008420216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.031822503,0.00087123615,0.0013546391,0.001405318,0.00053805276,0.002454041,0.001625703,0.0013454375,0.0026907416],"category_scores_gemma":[0.17890677,0.00068746944,0.0009812228,0.0010906684,0.002226036,0.0035178026,0.0015951678,0.0019471528,0.00020660009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007493799,0.0002588284,0.010077962,0.00011383569,0.000187268,0.000094412884,0.0003546573,0.8836851,0.0010291473,0.07564082,0.00036764875,0.027440919],"study_design_scores_gemma":[0.00006388198,0.00031797623,0.002837238,0.000026721476,0.000042263855,0.000036258054,0.00008298727,0.9678752,0.0008705967,0.027496785,0.00031302648,0.000037116362],"about_ca_topic_score_codex":0.0068189767,"about_ca_topic_score_gemma":0.002829766,"teacher_disagreement_score":0.031822503,"about_ca_system_score_codex":0.0030416443,"about_ca_system_score_gemma":0.0022194732,"threshold_uncertainty_score":0.1682955},"labels":[],"label_agreement":null},{"id":"W2171293692","doi":"10.1108/20439371211260234","title":"Grey relational evaluation of innovation competency in an aviation industry cluster","year":2012,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Weighting; Aviation; Grey relational analysis; Analytic hierarchy process; Relation (database); Originality; Process (computing); Computer science; Operations research; Engineering; Industrial engineering; Data mining; Mathematics","score_opus":0.12380854314690197,"score_gpt":0.3931378872409777,"score_spread":0.2693293440940757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171293692","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.72391224,0.00028973777,0.25510162,0.00030496024,0.00001973434,0.00030835145,0.00013493822,0.0000786783,0.019849788],"genre_scores_gemma":[0.9883217,0.00007101159,0.01105531,0.00000602303,0.0000029617242,0.000044496952,0.000039418843,0.0000031667541,0.00045598918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9973997,0.0011262953,0.00015230733,0.00021009192,0.00094488595,0.0001667341],"domain_scores_gemma":[0.99633884,0.0017150608,0.0005349319,0.00015093974,0.0010007692,0.00025947668],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033978845,0.00042264725,0.0005144199,0.0045897965,0.0006688413,0.0020960504,0.0005455932,0.00042491258,0.0016526885],"category_scores_gemma":[0.00883979,0.00012587842,0.00062476704,0.0028549929,0.0012241651,0.00163891,0.0014681462,0.00038054024,0.00010141549],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000430151,0.00030834638,0.115176395,0.00078453356,0.0004425922,0.00064815383,0.006457313,0.47749096,0.013032663,0.18484013,0.0017587682,0.19863002],"study_design_scores_gemma":[0.00002301143,0.0002918898,0.04375494,0.00010616132,0.00014591448,0.00013406093,0.0034894762,0.9079595,0.005512267,0.036258426,0.0022505475,0.00007369601],"about_ca_topic_score_codex":0.008256883,"about_ca_topic_score_gemma":0.0058203763,"teacher_disagreement_score":0.008256883,"about_ca_system_score_codex":0.0036990198,"about_ca_system_score_gemma":0.001886567,"threshold_uncertainty_score":0.026838362},"labels":[],"label_agreement":null},{"id":"W2733172450","doi":"10.1108/gs-05-2017-0011","title":"Forecasting the total energy consumption in China using a new-structure grey system model","year":2017,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Energy consumption; Smoothness; China; Computer science; Consumption (sociology); Econometrics; Economics; Mathematics; Engineering; Geography","score_opus":0.08134494366514301,"score_gpt":0.33028316533792373,"score_spread":0.24893822167278074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2733172450","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48778465,0.00060133735,0.4996836,0.0008277359,0.00010524665,0.00010439457,0.00053826347,0.00037774784,0.009977036],"genre_scores_gemma":[0.9863258,0.000217349,0.01111818,0.000024392024,0.000011156411,0.000041621784,0.00017623798,0.000008564619,0.002076705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999828,0.000044726236,0.000010538871,0.000046158766,0.000047294416,0.000023317998],"domain_scores_gemma":[0.9998017,0.00007834266,0.000026798607,0.000011891753,0.00006813829,0.000013093983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004463826,0.00043113474,0.00046150028,0.00044311452,0.0003071255,0.0006537511,0.00071093015,0.0006137874,0.0009806929],"category_scores_gemma":[0.0010329216,0.0002140477,0.00072172855,0.00058668485,0.00035259218,0.0006388026,0.00047836473,0.00053922797,0.000106725936],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000127128005,0.000007221519,0.0012001463,0.000021237514,0.000014684515,0.0000349827,0.000029813253,0.9915099,0.00052953267,0.0016134524,0.00019783038,0.0048284093],"study_design_scores_gemma":[0.0000013954374,0.000003845548,0.00025342824,0.0000011281945,0.00000290944,0.0000017061911,0.0000033703648,0.9991679,0.000060360217,0.00043847773,0.00006334655,0.0000020975344],"about_ca_topic_score_codex":0.044305578,"about_ca_topic_score_gemma":0.022858566,"teacher_disagreement_score":0.044305578,"about_ca_system_score_codex":0.0009047566,"about_ca_system_score_gemma":0.0010233271,"threshold_uncertainty_score":0.08809537},"labels":[],"label_agreement":null},{"id":"W3016134273","doi":"10.1108/gs-11-2019-0055","title":"Identifying the factors of China's seasonal retail sales of consumer goods using a data grouping approach–based GRA method","year":2020,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Per capita; Economics; Econometrics; China; Originality; Grey relational analysis; Value (mathematics); Statistics; Mathematics; Geography","score_opus":0.26679653729639496,"score_gpt":0.40645180712489837,"score_spread":0.1396552698285034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016134273","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8564458,0.0005059006,0.13512318,0.0005555282,0.000050664483,0.00030529284,0.000987571,0.00036372998,0.0056623747],"genre_scores_gemma":[0.98248196,0.0001736395,0.015877644,0.000019121133,0.000014425696,0.00009794319,0.00056356745,0.0000150292735,0.0007566984],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988979,0.00027974785,0.00011339155,0.00030629564,0.00030183553,0.00010079666],"domain_scores_gemma":[0.9982191,0.00083001534,0.00031026785,0.00015798914,0.0004090789,0.00007360979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013077429,0.0005801022,0.0005517535,0.0033850828,0.00054269633,0.0012430465,0.0005964743,0.00040264698,0.002239505],"category_scores_gemma":[0.0053216824,0.00024972062,0.0012559234,0.0039067585,0.00050375075,0.001085237,0.00080505083,0.0003945903,0.00018662162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026513461,0.0002197448,0.5704846,0.0007456147,0.0008556675,0.0015859922,0.004287614,0.13022968,0.00727623,0.023111211,0.004552441,0.25638604],"study_design_scores_gemma":[0.000024620285,0.00013801268,0.24289219,0.0000903313,0.00031785248,0.00019890587,0.0018049357,0.73875624,0.0022381116,0.009432192,0.004036837,0.000069789574],"about_ca_topic_score_codex":0.020197932,"about_ca_topic_score_gemma":0.013397998,"teacher_disagreement_score":0.020197932,"about_ca_system_score_codex":0.0014065278,"about_ca_system_score_gemma":0.0016005688,"threshold_uncertainty_score":0.040160716},"labels":[],"label_agreement":null},{"id":"W3049128818","doi":"10.1108/gs-09-2019-0032","title":"Study on the reliability assessment and early-warning method of online auditing based on the perspective of IT control","year":2020,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Audit; Reliability (semiconductor); Analytic hierarchy process; Computer science; Warning system; Audit risk; Risk analysis (engineering); Pairwise comparison; Reliability engineering; Operational auditing; Process management; Accounting; Operations research; Engineering; Internal audit; Artificial intelligence; Business; Power (physics)","score_opus":0.12613380954087725,"score_gpt":0.4489537912818131,"score_spread":0.32281998174093585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3049128818","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4893758,0.0015895963,0.4777089,0.002347972,0.000162115,0.0011520628,0.00009659625,0.00030924156,0.027257651],"genre_scores_gemma":[0.9490972,0.00029528176,0.049405586,0.000068383844,0.00003143589,0.00021992248,0.000027501652,0.00001555636,0.00083904143],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9746789,0.01640227,0.0011051942,0.0011504538,0.006174949,0.00048819993],"domain_scores_gemma":[0.9527444,0.026111169,0.0056569595,0.0021582604,0.012618213,0.0007110163],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017168524,0.0005603808,0.00047701056,0.0030033032,0.0007978464,0.0021854255,0.00094156084,0.0005826631,0.0017170993],"category_scores_gemma":[0.04852402,0.00027647824,0.0006482546,0.0021827456,0.0016390294,0.0034138984,0.0012891877,0.0011597836,0.00016036957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059618085,0.00079544587,0.080594316,0.0023671135,0.00022973379,0.00046545707,0.02530471,0.036213536,0.0091831405,0.08849383,0.0034490991,0.75230736],"study_design_scores_gemma":[0.00026636184,0.0035254061,0.1324374,0.0020793849,0.00065862597,0.0011796206,0.032462258,0.7080175,0.02124153,0.075867474,0.021876348,0.00038800167],"about_ca_topic_score_codex":0.0028639378,"about_ca_topic_score_gemma":0.0018222124,"teacher_disagreement_score":0.017168524,"about_ca_system_score_codex":0.00237229,"about_ca_system_score_gemma":0.0035998554,"threshold_uncertainty_score":0.09079689},"labels":[],"label_agreement":null},{"id":"W3110356145","doi":"10.1108/gs-07-2020-0090","title":"Identifying influence patterns of regional agricultural drought vulnerability using a two-phased grey rough combined model","year":2020,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Geography; Agriculture; Vulnerability (computing); Environmental resource management; Computer science; Environmental science","score_opus":0.02328512292127108,"score_gpt":0.2671854997920658,"score_spread":0.24390037687079472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110356145","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40448004,0.0004138724,0.5830376,0.00084185536,0.000052334664,0.0002352446,0.00052394666,0.00023317784,0.010181887],"genre_scores_gemma":[0.97963744,0.00013929256,0.018255757,0.000023438131,0.000010140744,0.000095404896,0.00012924185,0.000008448355,0.0017007877],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928623,0.00025738752,0.00004392619,0.00015901861,0.00015803441,0.000095391646],"domain_scores_gemma":[0.999161,0.0004581632,0.00015148555,0.000041886997,0.00013673602,0.000050694773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009170924,0.0005353341,0.0006214778,0.0015861636,0.0003895268,0.0014025591,0.0009707369,0.0006454207,0.0019059986],"category_scores_gemma":[0.002746765,0.00036948745,0.0014263551,0.0010160615,0.0006700774,0.0009527653,0.00094583444,0.0005255605,0.00012932735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048579735,0.000029231509,0.0069682132,0.000072702875,0.00011241267,0.00024345667,0.00021902463,0.96765804,0.0011775155,0.009877836,0.00036911128,0.013223906],"study_design_scores_gemma":[0.000005064864,0.000023403909,0.0017846398,0.000007754095,0.000033155822,0.000018964509,0.000054624008,0.992886,0.00014541169,0.0047834096,0.00024779912,0.00000977382],"about_ca_topic_score_codex":0.013786429,"about_ca_topic_score_gemma":0.010966071,"teacher_disagreement_score":0.013786429,"about_ca_system_score_codex":0.0012830915,"about_ca_system_score_gemma":0.0008617271,"threshold_uncertainty_score":0.027412355},"labels":[],"label_agreement":null},{"id":"W3200121749","doi":"10.1108/gs-03-2021-0041","title":"A hybrid predictive framework for evaluating P2P credit risks","year":2021,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Data mining; Randomness; Support vector machine; Machine learning; Feature selection; Flexibility (engineering); Key (lock); Artificial intelligence; Selection (genetic algorithm); Representativeness heuristic; Cluster analysis","score_opus":0.029917032357163406,"score_gpt":0.2924527461511255,"score_spread":0.26253571379396207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200121749","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044548683,0.00041633612,0.9524461,0.0003392645,0.00003086659,0.000071747265,0.00014766367,0.00026440257,0.0017348836],"genre_scores_gemma":[0.9194569,0.0002798984,0.07883461,0.00008053566,0.00006544107,0.00014083869,0.0002083566,0.00002337175,0.0009099692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998847,0.00039060213,0.00006241486,0.00025050205,0.00035310956,0.000096258256],"domain_scores_gemma":[0.99801743,0.0011631428,0.00022735604,0.000090169706,0.00042533746,0.000076542456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002801132,0.00097329385,0.00091863464,0.0019139742,0.00047105626,0.0016724406,0.0015692823,0.00095469574,0.0010369315],"category_scores_gemma":[0.0061936295,0.00030590605,0.00072411465,0.0014626642,0.0009288268,0.0017332317,0.0010852608,0.0009854824,0.00015732057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000040581835,0.000045825545,0.0038176107,0.00004286955,0.00005985254,0.00008277984,0.000065317094,0.9458266,0.00067905657,0.01059726,0.0005645146,0.03817767],"study_design_scores_gemma":[9.798628e-7,0.000007095524,0.00021236147,0.0000026135008,0.0000041923486,0.0000059507206,0.000006225041,0.99779606,0.000076214485,0.0018148561,0.00007118288,0.0000024047692],"about_ca_topic_score_codex":0.009594616,"about_ca_topic_score_gemma":0.0056093396,"teacher_disagreement_score":0.009594616,"about_ca_system_score_codex":0.0013121088,"about_ca_system_score_gemma":0.0010909437,"threshold_uncertainty_score":0.01907754},"labels":[],"label_agreement":null},{"id":"W4414819000","doi":"10.1108/gs-05-2025-0057","title":"Novel method for flood-affected area prediction based on non-equigap multivariable grey model","year":2025,"lang":"en","type":"article","venue":"Grey Systems Theory and Application","topic":"Grey System Theory Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institute on Governance","funders":"","keywords":"Multivariable calculus; Flood myth; Warning system; Population; Equidistant; Nonlinear system; Flood warning; Flood forecasting","score_opus":0.04790912013401627,"score_gpt":0.36190119939928,"score_spread":0.31399207926526373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414819000","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01978895,0.00018921468,0.9780532,0.000108910506,0.00003677828,0.00003128476,0.000065108274,0.0004089049,0.001317752],"genre_scores_gemma":[0.88719547,0.00035506667,0.109066755,0.00006815459,0.000047577716,0.00011619355,0.00019553817,0.000059046768,0.0028961145],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997284,0.000054646538,0.000017675457,0.00007847248,0.00008825314,0.000032596385],"domain_scores_gemma":[0.99975735,0.00010409877,0.000028148823,0.00001631402,0.00007922635,0.00001482807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045354376,0.00050901575,0.00073550834,0.0006748954,0.00036844137,0.0007065483,0.00095535954,0.0005593371,0.002193408],"category_scores_gemma":[0.0012025029,0.00026068877,0.00078621035,0.00065313984,0.00026394628,0.0006670782,0.00073770236,0.0006493293,0.000290178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000108048385,0.00005007151,0.0030489585,0.00013944242,0.00006991406,0.00018070884,0.0001318537,0.81994545,0.007320506,0.005512686,0.0016160053,0.16187629],"study_design_scores_gemma":[0.0000022462896,0.0000087494545,0.00022632211,0.0000022419256,0.0000050264644,0.000009210629,0.000005588692,0.99861777,0.00027138015,0.0006462149,0.00020204339,0.0000032157654],"about_ca_topic_score_codex":0.012330605,"about_ca_topic_score_gemma":0.007984776,"teacher_disagreement_score":0.012330605,"about_ca_system_score_codex":0.0004999585,"about_ca_system_score_gemma":0.0008272711,"threshold_uncertainty_score":0.024517655},"labels":[],"label_agreement":null}]}