{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":79,"total_is_capped":false,"direct_labels_cover":1,"predictions_cover":79,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"94bbfaffb4a2","filters":{"venue":"Journal of Forecasting"}},"results":[{"id":"W3121356139","doi":"10.1002/(sici)1099-131x(200004)19:3<201::aid-for753>3.0.co;2-4","title":"Neural network versus econometric models in forecasting inflation","year":2000,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":113,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Econometric model; Autoregressive integrated moving average; Econometrics; Autoregressive model; Bayesian vector autoregression; Artificial neural network; Inflation (cosmology); Computer science; Mean squared error; Bayesian probability; Economics; Time series; Statistics; Machine learning; Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.3101054711190852,"gpt":0.3916595949918031,"spread":0.08155412387271788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01677379,0.0002785014,0.0007512355,0.001539828,0.0002349286,0.0003423074,0.0009264946,0.0001518751,0.0009525046],"category_scores_gemma":[0.01705647,0.0002296361,0.0003499245,0.004183603,0.00007172704,0.001923183,0.0001185391,0.0007175252,0.00002090752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002416422,"about_ca_system_score_gemma":0.0001458089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000204826,"about_ca_topic_score_gemma":0.00004068849,"domain_scores_codex":[0.9939829,0.0006774645,0.002804074,0.0004139458,0.001394092,0.0007275143],"domain_scores_gemma":[0.9859095,0.0109709,0.001954249,0.000372836,0.0005514603,0.0002411139],"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":[0.0004027985,0.00001379291,0.007545351,0.000002530804,0.00001124812,0.00004264209,0.000189017,0.5029147,0.000002461689,0.00004805297,0.0003088322,0.4885186],"study_design_scores_gemma":[0.001741629,0.0003593063,0.009384464,0.0001312759,0.00002374455,0.0004665972,0.000175018,0.9563287,0.0000121891,0.03025693,0.0008871204,0.0002329972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9421666,0.0003896032,0.006011405,0.0001121302,0.001662772,0.000163248,0.000001978296,0.00001920294,0.04947307],"genre_scores_gemma":[0.9440668,0.00001289713,0.05436768,0.00005145126,0.001147773,0.00000311657,7.501612e-7,0.00003251768,0.0003170166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4882856,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1965402578","doi":"10.1002/for.1134","title":"Forecasting volatility with support vector machine‐based GARCH model","year":2009,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":86,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"University of British Columbia; Fudan University; Kyungpook National University; Deutsche Forschungsgemeinschaft","keywords":"Autoregressive conditional heteroskedasticity; Support vector machine; Volatility (finance); Econometrics; Artificial neural network; Computer science; Normality; Artificial intelligence; Machine learning; Economics; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.2387891771350272,"gpt":0.4069124056440709,"spread":0.1681232285090437,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.01933934,0.0004493998,0.001034356,0.0009453387,0.0004331504,0.0003959191,0.001378824,0.0001605108,0.0002840522],"category_scores_gemma":[0.02479393,0.0002973345,0.0004830889,0.001623247,0.0001528801,0.0009140064,0.000110152,0.001025166,0.00000657823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002068611,"about_ca_system_score_gemma":0.0007983659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007792095,"about_ca_topic_score_gemma":0.00002015069,"domain_scores_codex":[0.9920773,0.0006084535,0.002512677,0.0006150731,0.003338817,0.0008477184],"domain_scores_gemma":[0.9884859,0.005270088,0.002908808,0.0007209308,0.002121758,0.0004924578],"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":[0.001981179,0.0002507416,0.05216986,0.00002936409,0.00006454108,0.0004454714,0.0009535609,0.07932065,0.0007629661,0.0001063333,0.002436998,0.8614783],"study_design_scores_gemma":[0.00130781,0.001524447,0.007347111,0.0002019582,0.00006610491,0.001429692,0.0001156615,0.9761888,0.0007181636,0.01021535,0.0005510881,0.0003337583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5864266,0.0001005901,0.3996862,0.0007172847,0.0003954563,0.0002512194,0.00001547465,0.00004820215,0.01235898],"genre_scores_gemma":[0.7159501,6.16476e-7,0.2831417,0.0002499929,0.0002835245,0.000001824336,0.000001373193,0.00003085509,0.0003400106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8968682,"threshold_uncertainty_score":0.9999479,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3015884958","doi":"10.1002/for.2691","title":"Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Volatility (finance); Cryptocurrency; Econometrics; Markov chain; Jump; Realized variance; Stochastic volatility; Computer science; Robustness (evolution); Economics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.05797193086110943,"gpt":0.2472648654775768,"spread":0.1892929346164674,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001106183,0.0001973791,0.0003809165,0.0001582464,0.0002806724,0.0001295893,0.001386212,0.0001430171,0.000008012928],"category_scores_gemma":[0.0008282587,0.0001719728,0.0001996693,0.0008389304,0.00005578029,0.0005315164,0.0003560662,0.0008961738,0.000002578475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000501603,"about_ca_system_score_gemma":0.0001428271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000428133,"about_ca_topic_score_gemma":9.286779e-7,"domain_scores_codex":[0.9980236,0.0000809649,0.0008132324,0.0003543116,0.0003687867,0.0003591189],"domain_scores_gemma":[0.997942,0.0001931225,0.0009529232,0.0003791986,0.0003077415,0.0002250653],"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":[0.00008859724,0.0004127786,0.01110384,0.000314735,0.0001626747,0.0001531863,0.008896589,0.0005148146,0.001499234,0.03652442,0.003844757,0.9364843],"study_design_scores_gemma":[0.0004336461,0.0002032806,0.0003771084,0.00006562819,0.00001998727,0.0006339211,0.0001588274,0.9838065,0.000402257,0.01161502,0.002089757,0.0001940724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1711588,0.0004086875,0.8228156,0.003245958,0.00013811,0.0001560209,0.00000115094,0.0001086655,0.001967054],"genre_scores_gemma":[0.7340358,0.000005046748,0.2655509,0.0001706683,0.0002176078,0.000004910544,4.50446e-7,0.00000930151,0.000005322637],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9832917,"threshold_uncertainty_score":0.7012846,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077565477","doi":"10.1002/for.1007","title":"The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries","year":2007,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":76,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Univariate; Benchmark (surveying); Econometrics; Real gross domestic product; Multivariate statistics; Economics; Computer science; Statistics; Geography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1109682136512708,"gpt":0.2608456533671137,"spread":0.1498774397158428,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004439153,0.0001189864,0.0002767713,0.0003372372,0.00022447,0.0001520217,0.0005499033,0.000055957,0.000008787889],"category_scores_gemma":[0.0002294217,0.00007254542,0.0001052876,0.0003469352,0.00005084822,0.000433563,0.00002151646,0.0002336377,0.00001730737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009685696,"about_ca_system_score_gemma":0.00001921913,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006394502,"about_ca_topic_score_gemma":0.00166453,"domain_scores_codex":[0.9982502,0.00002984602,0.001173472,0.0001367022,0.00007571949,0.0003340408],"domain_scores_gemma":[0.9983582,0.000467894,0.0007129239,0.0003238622,0.0000301642,0.0001069593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008085942,0.0002215543,0.521186,0.00003602619,0.0002055428,0.00001759534,0.06028476,0.09264624,0.00003822582,0.02015631,0.007819816,0.2965793],"study_design_scores_gemma":[0.0006307684,0.001969066,0.5509586,0.00008861393,0.00003286633,0.0001525728,0.006007227,0.06154239,0.0001864829,0.005525987,0.3724625,0.0004429433],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905824,0.0001817377,0.005770016,0.002336013,0.0001917613,0.0003114217,0.00005526008,0.000002624347,0.0005687881],"genre_scores_gemma":[0.9980697,0.00001598826,0.0007893603,0.0007297743,0.0003484249,0.000007497743,0.000001861749,0.0000128469,0.00002452689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3646427,"threshold_uncertainty_score":0.2958317,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2887488964","doi":"10.1002/for.2541","title":"Workforce forecasting models: A systematic review","year":2018,"lang":"en","type":"review","venue":"Journal of Forecasting","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Workforce; Scope (computer science); Workforce planning; Reliability (semiconductor); Computer science; Workforce management; Relevance (law); Analytics; Management science; Data science; Economics; Political science; Economic growth","retraction":null,"screen_n_in":null,"score":{"opus":0.6639334249707961,"gpt":0.4800621873498996,"spread":0.1838712376208965,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03201059,0.0008749038,0.01059396,0.001651168,0.0004081738,0.001034249,0.003677458,0.0003566183,0.0001640617],"category_scores_gemma":[0.05062277,0.0004834891,0.004147019,0.003136482,0.00009893414,0.001018075,0.0006609685,0.001106503,0.0002182356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003161239,"about_ca_system_score_gemma":0.0007072463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002461964,"about_ca_topic_score_gemma":0.000004010281,"domain_scores_codex":[0.9784951,0.001786093,0.01334944,0.000790673,0.004840938,0.0007377584],"domain_scores_gemma":[0.9572634,0.01304073,0.02427586,0.001603338,0.003378921,0.0004378022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000005190566,0.00001861555,3.251565e-7,0.5098401,0.0001722325,0.0002262471,0.0001104926,0.00005937076,7.031923e-9,0.0003240659,0.01543274,0.4738106],"study_design_scores_gemma":[0.00009520623,0.0001008599,8.473631e-9,0.7006966,0.001450332,0.009917691,0.00009214789,0.01123474,1.016205e-8,0.006352425,0.2697223,0.0003375995],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[8.420178e-7,0.9678913,0.02310375,0.00001277812,0.001785052,0.001484879,0.0000129996,0.00002408539,0.005684298],"genre_scores_gemma":[0.00002607195,0.9903442,0.005900535,0.0001055194,0.001709231,0.0000346648,0.000001199415,0.0001076295,0.001770905],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.473473,"threshold_uncertainty_score":0.9997617,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2994808218","doi":"10.1002/for.2639","title":"A predictive model of train delays on a railway line","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Department of Science and Technology of Sichuan Province; China Scholarship Council","keywords":"Computer science; Artificial neural network; Python (programming language); Random forest; Predictive modelling; Artificial intelligence; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02369394031512286,"gpt":0.201390566858223,"spread":0.1776966265431001,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004150551,0.0001088807,0.0002835632,0.0001603961,0.00001853662,0.000009410122,0.0001265423,0.00006142945,0.00001502718],"category_scores_gemma":[0.00006706815,0.00008609586,0.0001299501,0.0001300062,0.00001202495,0.0001204251,0.000009342201,0.0002097874,0.000003323034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005079971,"about_ca_system_score_gemma":0.00003335389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004319441,"about_ca_topic_score_gemma":0.0000023711,"domain_scores_codex":[0.9989864,0.00001319703,0.0005183666,0.00006659755,0.0002436683,0.000171738],"domain_scores_gemma":[0.9994295,0.00007667595,0.0002259503,0.00009492147,0.0001085218,0.00006437684],"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":[0.00003484326,0.00002216772,0.0001015567,0.00006558938,0.00004124928,0.000005186916,0.0007772128,0.9788641,0.01274553,0.0001125252,0.0001025254,0.007127503],"study_design_scores_gemma":[0.0004555056,0.0004865551,0.00006160069,0.0004084885,0.00001326315,0.00006566342,0.0002004426,0.9949042,0.003143899,0.0001062479,0.00007511489,0.00007899907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9415874,0.0001583455,0.04605033,0.00001236129,0.0003876247,0.00005911412,0.000006666842,0.00001934366,0.01171875],"genre_scores_gemma":[0.997615,0.0000101824,0.002047075,0.000008301729,0.0001763439,9.454014e-7,3.818803e-7,0.00002273114,0.0001189783],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05602759,"threshold_uncertainty_score":0.3510887,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2098806386","doi":"10.1002/for.986","title":"Non‐linear, non‐parametric, non‐fundamental exchange rate forecasting","year":2006,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true},"ca_institutions":"Bank of Canada; Lakehead University","funders":"University of British Columbia; Lakehead University","keywords":"Random walk; Mean squared error; Linear model; Exchange rate; Parametric statistics; Mathematics; Econometrics; Statistics; Computer science; Economics","retraction":null,"screen_n_in":null,"score":{"opus":0.1667260819266446,"gpt":0.3864074929030265,"spread":0.2196814109763819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.02667341,0.0006524907,0.001481877,0.002433702,0.0007098903,0.000721356,0.001842867,0.0002912585,0.0005062389],"category_scores_gemma":[0.02098538,0.0005072031,0.0009304675,0.004173057,0.000232982,0.001400331,0.0005711314,0.00118745,0.00009783766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003947689,"about_ca_system_score_gemma":0.0003362256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001359122,"about_ca_topic_score_gemma":0.00006148523,"domain_scores_codex":[0.9901366,0.0005997885,0.003995101,0.0008328951,0.003128791,0.001306779],"domain_scores_gemma":[0.9829635,0.008981432,0.005091217,0.0007471831,0.001739373,0.0004773199],"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":[0.001061748,0.0005811527,0.1774347,0.0001540902,0.0002401697,0.001793469,0.001770049,0.03327526,0.008517159,0.00004038524,0.04559403,0.7295378],"study_design_scores_gemma":[0.00496105,0.001729014,0.06294987,0.0009344164,0.0002388367,0.005227418,0.001734445,0.8886695,0.008722804,0.01051473,0.01298265,0.001335267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8722522,0.000267112,0.1016999,0.0002150826,0.003093243,0.0004248553,0.00001764686,0.00004053879,0.02198941],"genre_scores_gemma":[0.8496812,0.000007799463,0.1448139,0.0001516654,0.002668124,0.00001019538,0.000003699902,0.00009255012,0.002570919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8553942,"threshold_uncertainty_score":0.999738,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2909775723","doi":"10.1002/for.2569","title":"The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":56,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Futures contract; Volatility (finance); Economics; Futures market; Econometrics; Stock market; Jump; Stock market volatility; Financial economics; Stock (firearms)","retraction":null,"screen_n_in":null,"score":{"opus":0.04662875508698446,"gpt":0.2303333250277361,"spread":0.1837045699407516,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005843381,0.000185108,0.0004676718,0.0001527203,0.0001716652,0.0001349284,0.0006496179,0.00009062521,0.0002640948],"category_scores_gemma":[0.002201933,0.0001099334,0.0002568785,0.0003707682,0.00004185642,0.0003776065,0.00008077062,0.0005696654,0.000002307904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000111555,"about_ca_system_score_gemma":0.00005622567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001235138,"about_ca_topic_score_gemma":0.0001662812,"domain_scores_codex":[0.9977771,0.0001307859,0.001347477,0.0002215854,0.0001802938,0.0003427566],"domain_scores_gemma":[0.9956169,0.002099857,0.001764622,0.0003290319,0.0001201518,0.00006943795],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005149923,0.00007037774,0.9367744,0.00008379172,0.00007348773,0.000005784925,0.001357847,0.0001685534,0.00002918833,0.002406077,0.00324694,0.05526857],"study_design_scores_gemma":[0.0005069324,0.0003517968,0.4703989,0.000428732,0.0000142547,0.00007838521,0.001724936,0.5033552,0.00001075999,0.01742417,0.005515954,0.0001899947],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551384,0.003552104,0.00008473135,0.000605389,0.0004782538,0.0002868094,0.00001502578,0.000003614381,0.03983563],"genre_scores_gemma":[0.9981833,0.0001146494,0.0004632438,0.00004740226,0.000284174,0.000002488889,6.424898e-7,0.00001230569,0.000891782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5031866,"threshold_uncertainty_score":0.4482954,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3193155117","doi":"10.1002/for.993","title":"Forecasting volatility","year":2006,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":55,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Volatility (finance); Econometrics; Forward volatility; Implied volatility; Stochastic volatility; Volatility smile; Economics; Realized variance; Volatility swap; Volatility risk premium","retraction":null,"screen_n_in":null,"score":{"opus":0.08245622204750369,"gpt":0.2241866021693835,"spread":0.1417303801218798,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00165588,0.0001345805,0.0004443607,0.0002305579,0.0001593273,0.00006717134,0.0001883847,0.00008786804,0.00007291303],"category_scores_gemma":[0.0007494918,0.0001418767,0.0002432995,0.0002404066,0.00003486768,0.0004518168,0.00003912798,0.0003048107,0.00001257965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059127,"about_ca_system_score_gemma":0.00003382385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002418892,"about_ca_topic_score_gemma":0.00003482886,"domain_scores_codex":[0.9979655,0.00001303549,0.001464379,0.0001784946,0.00007230659,0.0003063161],"domain_scores_gemma":[0.9981949,0.0001214694,0.001324164,0.0001394822,0.0001562819,0.00006372095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000118218,0.0002095301,0.9117231,0.0001105218,0.00005399071,0.00007713697,0.0006796826,0.006962231,0.0000956947,0.03683462,0.001603404,0.04153183],"study_design_scores_gemma":[0.0006473268,0.0001375286,0.02166963,0.0001072062,0.00001143578,0.0001500789,0.00005644805,0.8113256,0.0001322541,0.1551959,0.01032437,0.0002422065],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9386166,0.002010122,0.04349183,0.0001082225,0.0005466116,0.00006272214,0.00001491678,0.00001334465,0.01513563],"genre_scores_gemma":[0.9854399,0.00001129006,0.01354546,0.00002765739,0.0007891805,8.720726e-7,0.000001719006,0.00001882778,0.0001651036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8900535,"threshold_uncertainty_score":0.5785563,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2138636187","doi":"10.1002/for.1061","title":"Forecasting commodity prices: GARCH, jumps, and mean reversion","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":54,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Bank of Canada; Carleton University; Université Laval","funders":"Mitacs","keywords":"Mean reversion; Autoregressive conditional heteroskedasticity; Econometrics; Futures contract; Random walk; Economics; Convenience yield; Volatility (finance); Spot contract; Jump; Jump diffusion; Stochastic volatility; Financial economics; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.1035771672582072,"gpt":0.2277795601652178,"spread":0.1242023929070106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001823571,0.0001314179,0.0004292245,0.0002140799,0.0002900132,0.00003976619,0.000173931,0.00007641061,0.00007059461],"category_scores_gemma":[0.0006012926,0.0001317114,0.00013857,0.0001748593,0.00007617591,0.0003819229,0.00009499283,0.0003401452,0.000002104215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000087386,"about_ca_system_score_gemma":0.00002653329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008558161,"about_ca_topic_score_gemma":0.0000216839,"domain_scores_codex":[0.9985628,0.00002259658,0.0009032156,0.0001861703,0.0000763122,0.0002489218],"domain_scores_gemma":[0.9981762,0.0002176893,0.0012155,0.0001428991,0.0001102026,0.0001374708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001084485,0.00009855525,0.9816499,0.0001534456,0.00008018463,0.0001155581,0.002184036,0.00008720969,0.00001583103,0.002377631,0.001363854,0.0117653],"study_design_scores_gemma":[0.001421762,0.0003565354,0.05378899,0.0002426552,0.00002132269,0.001794905,0.0002470517,0.9016073,0.00002028514,0.02197845,0.01811874,0.0004019761],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853792,0.001186619,0.005509237,0.0001444865,0.0003058482,0.00007695771,0.00002546076,0.000007860234,0.007364297],"genre_scores_gemma":[0.9896239,0.0002164911,0.009719675,0.00005170881,0.0002055536,5.895737e-7,0.000002336782,0.0000148428,0.0001648757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.927861,"threshold_uncertainty_score":0.5371035,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1484796914","doi":"10.1002/for.1242","title":"The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand","year":2011,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Demand forecasting; Computer science; Product (mathematics); Econometrics; Operations research; Economics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.6489373783846855,"gpt":0.4564700294472598,"spread":0.1924673489374256,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.009541326,0.0001854383,0.0005021608,0.0003284978,0.0004109344,0.00007367581,0.001208675,0.0001011317,0.0002059905],"category_scores_gemma":[0.01396264,0.0001183775,0.0004727818,0.0008182839,0.0004129643,0.000515579,0.0001169071,0.0004083997,0.000002509586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004951808,"about_ca_system_score_gemma":0.0002480534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001664122,"about_ca_topic_score_gemma":0.000007527355,"domain_scores_codex":[0.9956537,0.0002430669,0.002269088,0.0002264382,0.001303107,0.0003045482],"domain_scores_gemma":[0.9797425,0.01407189,0.004097033,0.0003858299,0.001552545,0.0001501569],"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":[0.001568425,0.0006741691,0.00410101,0.00007449491,0.0004119408,0.00003762389,0.003764282,0.00146657,0.01308237,0.05932359,0.01407261,0.9014229],"study_design_scores_gemma":[0.003317474,0.003286953,0.02290293,0.0009083571,0.0003199848,0.001957779,0.003227916,0.2035891,0.08359011,0.6703734,0.005876588,0.0006494268],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6499809,0.0002843097,0.3259629,0.0003786168,0.001139362,0.0003290676,0.00008170129,0.00002270197,0.02182044],"genre_scores_gemma":[0.6984902,0.00001544946,0.3011735,0.00001257569,0.0002554928,0.000006992837,0.000001749129,0.00001317497,0.00003086372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9007735,"threshold_uncertainty_score":0.9943432,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2068799179","doi":"10.1002/for.1197","title":"Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes","year":2010,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":47,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Arch; Econometrics; Sampling (signal processing); Prediction interval; Volatility (finance); Computer science; Representation (politics); Nonlinear system; Statistics; Mathematics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.08742118243897747,"gpt":0.2697760400854953,"spread":0.1823548576465179,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001153764,0.00006878406,0.0002205045,0.0002297393,0.00006649347,0.00005149476,0.00005299393,0.00005434118,0.000003499507],"category_scores_gemma":[0.001104497,0.00007034713,0.00003515108,0.00008727882,0.0000454109,0.0001637277,0.00002060658,0.0002301168,1.330578e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001862657,"about_ca_system_score_gemma":0.00004195575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002577605,"about_ca_topic_score_gemma":0.0001068749,"domain_scores_codex":[0.9990509,0.000005639405,0.0006516709,0.0001250093,0.00003784543,0.0001289708],"domain_scores_gemma":[0.9991441,0.0002764897,0.0003520186,0.00003657592,0.0001494571,0.00004129383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001910012,0.0001026811,0.95456,0.0006856705,0.00002805788,0.000003217519,0.01446606,0.006884436,0.0002581212,0.006061558,0.00002793572,0.01673125],"study_design_scores_gemma":[0.0006770001,0.0002181852,0.1647547,0.0001747653,0.000004929168,0.00005630995,0.0004080881,0.8069656,0.00005369312,0.02637337,0.0002239661,0.00008943216],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760361,0.0008356499,0.02249669,0.0001435165,0.0001845224,0.0001167693,0.00003995195,0.000003491231,0.0001433562],"genre_scores_gemma":[0.992136,0.00003979772,0.007640543,0.00001155578,0.0001466262,0.000003347094,0.00000175664,0.000007324386,0.00001302688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8000812,"threshold_uncertainty_score":0.2868673,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2151212654","doi":"10.1002/for.872","title":"Forecasting some low‐predictability time series using diffusion indices","year":2003,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":38,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University; McGill University; Center for Interuniversity Research and Analysis on Organizations","funders":"McGill University","keywords":"Predictability; Index (typography); Econometrics; Diffusion; Horizon; Series (stratigraphy); Economics; Time horizon; Consensus forecast; Investment (military); Value (mathematics); Time series; Computer science; Statistics; Mathematics; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.04451176708216419,"gpt":0.2211933733830687,"spread":0.1766816063009045,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003186562,0.0001946103,0.0005855033,0.0002791121,0.0002617604,0.0001153403,0.0002091349,0.0001243249,0.0003787422],"category_scores_gemma":[0.002073334,0.0001954197,0.0002422797,0.000263624,0.00007518801,0.001000819,0.00006734858,0.0003588515,0.000005253423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001849686,"about_ca_system_score_gemma":0.00006325803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003019089,"about_ca_topic_score_gemma":0.000006207348,"domain_scores_codex":[0.9977786,0.00006088141,0.001428764,0.0002522428,0.0001046361,0.0003748841],"domain_scores_gemma":[0.997481,0.000209604,0.00183289,0.0002015517,0.0001277623,0.0001471191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001053417,0.0001876504,0.988308,0.0001953037,0.00009878272,0.00004167375,0.0006738186,0.0007439357,0.0003579401,0.006232513,0.00005173189,0.003003382],"study_design_scores_gemma":[0.0009712821,0.0002608049,0.01185384,0.0002686074,0.00002863979,0.0005030627,0.0001839701,0.9111514,0.000167101,0.07248843,0.001712346,0.0004105214],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903871,0.0008598009,0.003181539,0.00004690392,0.0006273108,0.0001072484,0.00003599801,0.00001221638,0.004741869],"genre_scores_gemma":[0.9893054,0.00002657804,0.01017821,0.00002712769,0.0002595202,9.176077e-7,0.00000205513,0.00002576458,0.0001744033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9764541,"threshold_uncertainty_score":0.7968985,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3124307699","doi":"10.1002/for.2396","title":"Monthly Beta Forecasting with Low‐, Medium‐ and High‐Frequency Stock Returns","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"BETA (programming language); Estimator; Econometrics; Portfolio; Stock (firearms); Economics; Expected return; Statistics; Mathematics; Computer science; Financial economics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.04960559386694081,"gpt":0.212383496422006,"spread":0.1627779025550652,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001184372,0.0001920325,0.0005500072,0.0002648751,0.0001673577,0.00006574745,0.000195793,0.0001074255,0.00004144665],"category_scores_gemma":[0.0006958795,0.0001365918,0.0001077406,0.0001840195,0.00008004108,0.0007283119,0.00005216018,0.0002695064,0.000005358575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097374,"about_ca_system_score_gemma":0.00006394882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000988649,"about_ca_topic_score_gemma":0.00008999764,"domain_scores_codex":[0.9981523,0.00001615802,0.001091678,0.0002637298,0.0001020985,0.0003740122],"domain_scores_gemma":[0.9982905,0.000224452,0.0009765213,0.0001687781,0.0001822613,0.0001574622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003589923,0.0001187967,0.8851188,0.0001956319,0.0002106617,0.0002038873,0.002253835,0.0006311709,0.0008236339,0.01992081,0.0002023363,0.08996143],"study_design_scores_gemma":[0.01882069,0.006579641,0.3800147,0.009983749,0.0003171033,0.002623633,0.001084466,0.2431465,0.00439862,0.3234812,0.005649372,0.003900354],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976679,0.001366021,0.01913377,0.000655289,0.0003327982,0.0001025349,0.00004587452,0.00001340434,0.001671255],"genre_scores_gemma":[0.9864069,0.00009724694,0.01287505,0.0000383551,0.0004803883,0.000002303354,9.309834e-7,0.00003203267,0.00006683296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5051041,"threshold_uncertainty_score":0.5570054,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2105499812","doi":"10.1002/for.2411","title":"Predicting Systemic Risk with Entropic Indicators","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Predictability; Systemic risk; Economics; Econometrics; Volatility (finance); Skewness; Risk management; Financial crisis; Actuarial science; Financial economics; Statistics; Mathematics; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.01830630305456996,"gpt":0.1886435778991798,"spread":0.1703372748446098,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001301312,0.00009548352,0.0003121893,0.000261913,0.00008677896,0.000033957,0.0001597371,0.00005152053,0.00009242239],"category_scores_gemma":[0.0006089097,0.0000618358,0.00009549663,0.0001693556,0.00003243152,0.0002328122,0.00002691303,0.0001734182,0.000005583042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001201396,"about_ca_system_score_gemma":0.00002782168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002120894,"about_ca_topic_score_gemma":0.000007766592,"domain_scores_codex":[0.9987897,0.00002357208,0.0007895179,0.000137666,0.00005846193,0.0002010832],"domain_scores_gemma":[0.9975078,0.0001817057,0.002032504,0.0001313737,0.0000551198,0.00009155326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003375522,0.00001439267,0.9875064,0.00002260782,0.00006147537,0.000009684382,0.0001163541,0.0000157596,0.00001126179,0.001453749,0.00001755235,0.01073705],"study_design_scores_gemma":[0.005594841,0.00131044,0.8428434,0.002069482,0.000104979,0.001471724,0.0003729255,0.1165316,0.00008248509,0.02361058,0.005264284,0.0007433546],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971784,0.0005673457,0.02437387,0.00007805366,0.0002704329,0.00006085651,0.00003302538,0.00000843632,0.002823998],"genre_scores_gemma":[0.9983084,0.0000831574,0.00123356,0.000007470384,0.0002152141,0.000001142207,2.16733e-7,0.0000140224,0.0001367815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.144663,"threshold_uncertainty_score":0.2521591,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2994694912","doi":"10.1002/for.2641","title":"Forecasting of dependence, market, and investment risks of a global index portfolio","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":31,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Vine copula; Portfolio; Economics; Value at risk; Portfolio optimization; Downside risk; Econometrics; CVAR; Financial economics; Diversification (marketing strategy); Index (typography); Expected shortfall; Risk management; Copula (linguistics); Business; Finance; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.06847074415771963,"gpt":0.2600649460413053,"spread":0.1915942018835857,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002023809,0.0001271653,0.0005927705,0.0001664321,0.00003149406,0.0000220243,0.0001726374,0.0000890744,0.0002145368],"category_scores_gemma":[0.0004672115,0.0001272177,0.0001614092,0.0002500555,0.00005276594,0.0002386367,0.0000975096,0.000182268,5.561847e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008493905,"about_ca_system_score_gemma":0.00005675751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001845756,"about_ca_topic_score_gemma":0.00002725999,"domain_scores_codex":[0.9981438,0.0000224742,0.001360753,0.0001689986,0.0000987717,0.0002051933],"domain_scores_gemma":[0.9969143,0.0001407782,0.002549952,0.0001574597,0.0001425045,0.00009498237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009369531,0.00004564423,0.989581,0.0001466004,0.00007699828,0.000006721,0.00006691952,0.0001514365,0.000007428263,0.005581027,0.00005246072,0.00419008],"study_design_scores_gemma":[0.0008935711,0.0002815076,0.4147645,0.0002315354,0.00001841484,0.0001725129,0.0001136352,0.5448604,0.00001489955,0.03820265,0.0003025836,0.0001438624],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9469322,0.00101486,0.001156086,0.00001863154,0.0002378377,0.0001161865,0.00005195286,0.000002019527,0.05047024],"genre_scores_gemma":[0.9966286,0.00004563502,0.003133634,0.00002059709,0.00004746904,6.631321e-7,6.300842e-7,0.000009552111,0.0001131909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5748165,"threshold_uncertainty_score":0.5187787,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3014694269","doi":"10.1002/for.2689","title":"Predictive modeling of consumer color preference: Using retail data and merchandise images","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Color perception and design","field":"Psychology","cited_by":25,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Multinomial logistic regression; Popularity; Sample (material); Preference; Consumer behaviour; Product (mathematics); Computer science; Order (exchange); Function (biology); Advertising; Econometrics; Business; Economics; Mathematics; Machine learning; Microeconomics; Psychology","retraction":null,"screen_n_in":null,"score":{"opus":0.4915125811581008,"gpt":0.3769196897903212,"spread":0.1145928913677796,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005353421,0.00009407497,0.0002794359,0.00008160342,0.00006264786,0.00002175934,0.0002002083,0.00006479723,0.0003328982],"category_scores_gemma":[0.0004096718,0.00007961806,0.00004431676,0.0001187296,0.00007556556,0.0002469952,0.0001166137,0.000248148,0.000002331907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001685188,"about_ca_system_score_gemma":0.00007277516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001585084,"about_ca_topic_score_gemma":0.000001866388,"domain_scores_codex":[0.9988949,0.0001103015,0.0005084241,0.0001670987,0.0001788841,0.000140345],"domain_scores_gemma":[0.9989261,0.0001432407,0.000440968,0.000137382,0.0002223938,0.0001299115],"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":[0.02471812,0.001133339,0.1450045,0.001225319,0.003668417,0.0009891783,0.1857225,0.07294062,0.1938079,0.0005415078,0.02388758,0.3463609],"study_design_scores_gemma":[0.001112751,0.0004935749,0.0008871891,0.0001494757,0.0001862462,0.0002618431,0.004544826,0.9918905,0.0001113841,0.00009741759,0.00016513,0.00009967636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9279376,0.0008559816,0.06938046,0.000155818,0.0001689946,0.0001101901,0.00006116053,0.000009189377,0.001320615],"genre_scores_gemma":[0.9929537,0.00005325598,0.006710985,0.00007678651,0.0001675151,6.458135e-7,0.000002840343,0.00001139263,0.00002288479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9189498,"threshold_uncertainty_score":0.3645002,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2129179837","doi":"10.1002/for.982","title":"Gamma stochastic volatility models","year":2006,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Waterloo","funders":"","keywords":"Econometrics; Autoregressive model; Stochastic volatility; Estimator; Autoregressive conditional heteroskedasticity; Volatility (finance); Autocorrelation; Forward volatility; Kurtosis; Economics; Financial models with long-tailed distributions and volatility clustering; STAR model; Mathematics; Statistics; Autoregressive integrated moving average; Time series","retraction":null,"screen_n_in":null,"score":{"opus":0.07953029440778929,"gpt":0.2146513048953332,"spread":0.1351210104875439,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001122407,0.0001201184,0.0004065745,0.0002069852,0.0001119744,0.00005233667,0.0001688588,0.00007893922,0.00004373117],"category_scores_gemma":[0.0003013819,0.0001271093,0.0002030799,0.0001742435,0.00003094604,0.0005144635,0.00003024385,0.0002628291,0.00001286408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009786914,"about_ca_system_score_gemma":0.00003493297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002155731,"about_ca_topic_score_gemma":0.000023696,"domain_scores_codex":[0.9982938,0.00001021819,0.001209074,0.0001614848,0.00006791318,0.0002574843],"domain_scores_gemma":[0.9986231,0.00008665938,0.0009552698,0.0001365218,0.0001370164,0.00006146264],"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":[0.0002347534,0.0003699543,0.0767116,0.0001126764,0.00008071989,0.00004956381,0.001215279,0.687309,0.0001058483,0.2067532,0.001449874,0.02560755],"study_design_scores_gemma":[0.0002855116,0.00005218302,0.003021739,0.00003760529,0.00000549578,0.00003047773,0.00001795636,0.7162745,0.00001202485,0.2798552,0.0003070488,0.0001002623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5894043,0.001239411,0.4043196,0.00006428974,0.0002894438,0.00004534805,0.00001278431,0.000008454314,0.004616435],"genre_scores_gemma":[0.9920968,0.000006964502,0.007216731,0.00002099864,0.0005058497,9.48961e-7,0.000001358761,0.00001606099,0.0001343015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4026925,"threshold_uncertainty_score":0.5183367,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2123871236","doi":"10.1002/for.1046","title":"Linear and threshold forecasts of output and inflation using stock and housing prices","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Bank of Canada","funders":"","keywords":"Economics; Econometrics; Inflation (cosmology); Stock (firearms); Equity (law); Real gross domestic product; Asset (computer security); Monetary economics; Financial economics; Macroeconomics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.2604066037751568,"gpt":0.2580949808547658,"spread":0.002311622920390999,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005090134,0.00009685809,0.0003577564,0.0002633923,0.0001235525,0.00002657865,0.00004198719,0.00005928368,0.0000063781],"category_scores_gemma":[0.00009838067,0.0000988755,0.00004264016,0.00006333803,0.00007672708,0.000564994,0.00003819806,0.0001241883,3.555295e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002522426,"about_ca_system_score_gemma":0.00001202212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009921143,"about_ca_topic_score_gemma":0.000005721443,"domain_scores_codex":[0.9990284,0.000005532675,0.0006751827,0.0001135653,0.0000263564,0.0001509026],"domain_scores_gemma":[0.9987625,0.00006761624,0.00101082,0.00005711764,0.00002159075,0.00008032432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007924472,0.00002333329,0.9721131,0.0001203294,0.00009765709,0.00001499895,0.002924538,0.01244607,0.0001319475,0.0005024857,0.00002941277,0.01151683],"study_design_scores_gemma":[0.0009630743,0.0002605877,0.1567704,0.0001273241,0.00002286012,0.001087842,0.0001254372,0.8370825,0.000165299,0.002977751,0.0002300233,0.0001868604],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949758,0.002424778,0.001979277,0.000050825,0.00008288347,0.00005695297,0.000009710441,0.000002531952,0.0004172121],"genre_scores_gemma":[0.991085,0.0002923958,0.008392011,0.00002888671,0.0001768803,1.549835e-7,4.015767e-7,0.00001119545,0.00001307482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8246365,"threshold_uncertainty_score":0.4032026,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2300215694","doi":"10.1002/for.2398","title":"Forecasting Errors, Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Econometrics; Exchange rate; Trading strategy; Profitability index; Currency; Economics; Multivariate statistics; Benchmark (surveying); Computer science; Statistics; Finance; Mathematics; Monetary economics","retraction":null,"screen_n_in":null,"score":{"opus":0.1923253549047189,"gpt":0.2702042780884004,"spread":0.07787892318368153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002813193,0.0001419516,0.0004991676,0.0002121228,0.00012791,0.00002082369,0.0001595059,0.00005913553,0.0002104628],"category_scores_gemma":[0.002715135,0.0000952826,0.0001876755,0.0001350136,0.000140827,0.0005824066,0.00005048016,0.0001711245,0.000001650563],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006358461,"about_ca_system_score_gemma":0.00003278906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001961927,"about_ca_topic_score_gemma":0.00002521222,"domain_scores_codex":[0.9981397,0.00005378639,0.001349912,0.000170251,0.00003189932,0.0002544885],"domain_scores_gemma":[0.9963515,0.0009558087,0.002358109,0.0001612838,0.00006297177,0.0001103542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006429838,0.0004201808,0.6367082,0.001138245,0.000757401,0.0002322066,0.01120337,0.0008091915,0.001348158,0.007802065,0.002380868,0.3365571],"study_design_scores_gemma":[0.01275895,0.003935608,0.2063025,0.002883997,0.0003838993,0.03433114,0.003054047,0.5151059,0.01177663,0.1948656,0.01237886,0.002222912],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952344,0.001468479,0.0008078456,0.0006110862,0.0003849621,0.0001314848,0.000140015,0.000003517443,0.00121822],"genre_scores_gemma":[0.9987102,0.0001176445,0.0007992452,0.00002171253,0.0002759632,0.00000238141,6.010179e-7,0.00001318547,0.00005902794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5142967,"threshold_uncertainty_score":0.3885511,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4283741364","doi":"10.1002/for.2885","title":"Predicting earnings management through machine learning ensemble classifiers","year":2022,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Saint Mary's University; Concordia University","funders":"Social Sciences and Humanities Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Artificial intelligence; Computer science; Ensemble forecasting; Support vector machine; Ensemble learning; Machine learning; Principal component analysis; Random subspace method; Context (archaeology); Feature selection; Pattern recognition (psychology); Classifier (UML)","retraction":null,"screen_n_in":null,"score":{"opus":0.04131222352818766,"gpt":0.2561634504400872,"spread":0.2148512269118995,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001608104,0.0001264399,0.0002022088,0.000231249,0.0006492697,0.0001227859,0.001071534,0.00002630837,0.00003250747],"category_scores_gemma":[0.0002165161,0.0001244323,0.0001046573,0.0005725196,0.00002575434,0.0009277394,0.0007247868,0.0008693906,0.000002539265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002212174,"about_ca_system_score_gemma":0.00005032513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008931564,"about_ca_topic_score_gemma":5.539089e-7,"domain_scores_codex":[0.9980178,0.0001873044,0.0005977693,0.0002243541,0.0006782981,0.0002944243],"domain_scores_gemma":[0.9982416,0.0001463599,0.00116779,0.0002601127,0.0001210087,0.00006310423],"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":[0.0001914235,0.0004579089,0.1286565,0.0002778329,0.0005154076,0.001639909,0.01636925,0.04922246,0.01851786,0.07403336,0.01419376,0.6959243],"study_design_scores_gemma":[0.001430884,0.001248161,0.004578315,0.000235136,0.0000623571,0.003165222,0.002661399,0.7740473,0.007654974,0.007430702,0.1969118,0.0005737561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04265976,0.0001409463,0.9436274,0.0007300116,0.0005199466,0.0001281271,0.000002522309,0.0002069461,0.01198432],"genre_scores_gemma":[0.7428754,0.00002596901,0.2563777,0.0001507015,0.00008477298,0.00001079539,0.000003295971,0.00001543177,0.0004559455],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7248248,"threshold_uncertainty_score":0.5074202,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3112242143","doi":"10.1002/for.2752","title":"Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Jump; Futures contract; Volatility (finance); Leverage effect; Leverage (statistics); Econometrics; Futures market; Economics; R&D intensity; Financial economics; Mathematics; Statistics; Autoregressive conditional heteroskedasticity; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.02852553310382301,"gpt":0.1995735015606402,"spread":0.1710479684568172,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00215918,0.0001745347,0.0005652276,0.00007383636,0.0002784373,0.00008789641,0.0003905433,0.00008065114,0.00004202218],"category_scores_gemma":[0.002071604,0.0001110482,0.0002980076,0.000262649,0.0001028898,0.0002004954,0.0002673416,0.0005750904,5.155467e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004119918,"about_ca_system_score_gemma":0.00002537899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001063778,"about_ca_topic_score_gemma":0.00003374192,"domain_scores_codex":[0.9984312,0.00007332429,0.0009551701,0.0002031016,0.000104853,0.0002323159],"domain_scores_gemma":[0.9978024,0.0003430789,0.00143781,0.0002123066,0.000105826,0.00009862054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000194598,0.00002720253,0.9296164,0.0001838431,0.0001306646,0.000006382264,0.002407947,0.0001435459,0.0001552237,0.0009069435,0.0001292976,0.06609797],"study_design_scores_gemma":[0.0004988122,0.0002177265,0.1468755,0.0001242784,0.00003820097,0.0001112149,0.0002373079,0.8388351,0.0002110916,0.01067445,0.002031063,0.0001451973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914557,0.00241051,0.0004327178,0.001193747,0.000364603,0.00009352732,0.00003097368,0.000005712868,0.004012515],"genre_scores_gemma":[0.9990775,0.00004259898,0.0003394139,0.0001359098,0.000341968,0.000001180253,6.313123e-7,0.0000174548,0.00004334755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8386916,"threshold_uncertainty_score":0.4528414,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2580857550","doi":"10.1002/for.2518","title":"Exchange rate forecasting and the performance of currency portfolios","year":2018,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Thompson Rivers University","funders":"Oesterreichische Nationalbank","keywords":"Currency; Exchange rate; Portfolio; Economics; Econometrics; Profitability index; Liberian dollar; Pound (networking); Trading strategy; Financial economics; Monetary economics; Computer science; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.1241625151993179,"gpt":0.2340993400482817,"spread":0.1099368248489638,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002650782,0.0001167712,0.0004573168,0.0002113354,0.0001501105,0.00003628305,0.0001876511,0.00004637199,0.0001846952],"category_scores_gemma":[0.0004170198,0.00008933368,0.0001241495,0.0001289083,0.0002135332,0.0004329375,0.00005544784,0.0001821007,0.00001389802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002613054,"about_ca_system_score_gemma":0.00001488471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006911997,"about_ca_topic_score_gemma":0.000005221591,"domain_scores_codex":[0.9985366,0.00002260274,0.001040645,0.0001171293,0.00003016369,0.0002528226],"domain_scores_gemma":[0.9977871,0.0001660712,0.00179287,0.0001322892,0.00005071195,0.00007096522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002077975,0.000198444,0.7414969,0.0009625279,0.0009099236,0.00003731401,0.02424464,0.003008857,0.0001373622,0.02268018,0.005791505,0.1984544],"study_design_scores_gemma":[0.005243511,0.001359328,0.03919292,0.0004529163,0.00006494888,0.001099766,0.0003030536,0.9273786,0.0007912745,0.01411725,0.009552546,0.0004439156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893911,0.001969428,0.0003147419,0.000224724,0.0004813659,0.00008310892,0.00001424728,0.000003095884,0.007518174],"genre_scores_gemma":[0.9978809,0.000380291,0.0007078447,0.0001076535,0.0007661306,0.000001229989,5.18142e-7,0.00001229671,0.0001431365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9243697,"threshold_uncertainty_score":0.3642922,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083119721","doi":"10.1002/1099-131x(200103)20:2<145::aid-for787>3.0.co;2-5","title":"Cross-correlations and predictability of stock returns","year":2001,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Predictability; Econometrics; Stock (firearms); Economics; Financial economics; Mathematics; Statistics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.07026754148763727,"gpt":0.2520216582286761,"spread":0.1817541167410389,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007354579,0.00006228779,0.000344592,0.0001609655,0.00007694666,0.00003820726,0.00008302044,0.00003961149,0.0003773556],"category_scores_gemma":[0.0003684856,0.00006048887,0.0001424354,0.0002133995,0.00005040083,0.0002538831,0.00003088445,0.0001140818,0.000002306697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003140766,"about_ca_system_score_gemma":0.00001209358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363679,"about_ca_topic_score_gemma":0.00003877256,"domain_scores_codex":[0.998792,0.000008270679,0.0009584663,0.00009518162,0.00003934546,0.0001067615],"domain_scores_gemma":[0.9985027,0.00008281418,0.001118357,0.0001117943,0.0001284166,0.00005592634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000192736,0.00002408266,0.9942418,0.00002224047,0.00006047099,0.000003382103,0.0003338172,0.0006542652,0.00001076207,0.002825629,0.00004529048,0.001759003],"study_design_scores_gemma":[0.000742703,0.0002830431,0.9043504,0.00009426681,0.0000364065,0.0003621685,0.0002712651,0.07222909,0.00001677818,0.01316925,0.008288656,0.0001559638],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9886929,0.001652512,0.003933228,0.00008830033,0.0001581343,0.00004122013,0.00002220623,0.000003033394,0.005408482],"genre_scores_gemma":[0.9983125,0.00004183925,0.00105777,0.00000541757,0.0001411907,4.744408e-7,6.914005e-7,0.000006049752,0.0004340922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08989138,"threshold_uncertainty_score":0.4131779,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2089502686","doi":"10.1002/for.1079","title":"Power transformation models and volatility forecasting","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University","funders":"","keywords":"Econometrics; Autoregressive model; Volatility (finance); Heteroscedasticity; Realized variance; Economics; Independent and identically distributed random variables; Forecast error; Computer science; Mathematics; Statistics; Random variable","retraction":null,"screen_n_in":null,"score":{"opus":0.138524728483377,"gpt":0.2322359245395563,"spread":0.09371119605617934,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001121143,0.0001198769,0.0003805778,0.0002038516,0.0002365228,0.00003131759,0.00009919717,0.00008334317,0.00002547781],"category_scores_gemma":[0.0003264972,0.0001243912,0.0001352199,0.0001507788,0.00004487865,0.001139383,0.00002083908,0.0002661076,0.000002010451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005822103,"about_ca_system_score_gemma":0.00003040197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003275755,"about_ca_topic_score_gemma":0.000005421298,"domain_scores_codex":[0.9984453,0.00001177715,0.001109174,0.0001428662,0.00006812136,0.0002227029],"domain_scores_gemma":[0.9989038,0.00007959142,0.0007270038,0.00009020964,0.0001110494,0.00008841059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000864011,0.0005085644,0.6546893,0.0005336456,0.0003083275,0.0002126201,0.1089066,0.04719632,0.0002150377,0.08713527,0.0006715972,0.09875871],"study_design_scores_gemma":[0.0005063574,0.0001295667,0.005128168,0.00006226538,0.000004977733,0.0003807481,0.0001666364,0.9423376,0.00003361119,0.05064023,0.0004716943,0.0001380999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8361971,0.001277435,0.1561999,0.0000789312,0.0001870094,0.00006734774,0.00001108955,0.000008653796,0.00597257],"genre_scores_gemma":[0.9922338,0.0001239515,0.007462758,0.00003353921,0.0001073097,8.869779e-7,9.149298e-7,0.00001341576,0.00002343671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8951413,"threshold_uncertainty_score":0.5072525,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3003465369","doi":"10.1002/for.2676","title":"Using the yield curve to forecast economic growth","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Econometrics; Yield (engineering); Yield curve; Quarter (Canadian coin); Economics; Interest rate; Gross domestic product; Product (mathematics); Time series; Sample (material); Nowcasting; Series (stratigraphy); Computer science; Mathematics; Machine learning; Macroeconomics","retraction":null,"screen_n_in":null,"score":{"opus":0.368215246217224,"gpt":0.2699758576157541,"spread":0.09823938860146991,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008349626,0.0001510319,0.0004476304,0.0001521689,0.0001475981,0.0001134422,0.000388848,0.00005751778,0.0003637467],"category_scores_gemma":[0.0005961089,0.0001305769,0.0002421286,0.0001211385,0.00002984796,0.000438449,0.00007616577,0.0002757146,0.0001923739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001397532,"about_ca_system_score_gemma":0.00003160278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000253024,"about_ca_topic_score_gemma":0.00001322946,"domain_scores_codex":[0.9984121,0.00001417758,0.001032812,0.0001821397,0.00002901469,0.0003297651],"domain_scores_gemma":[0.9984767,0.0001523771,0.000969061,0.0001329913,0.00001987983,0.000248969],"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":[0.0006972765,0.0001115887,0.3597475,0.0001879258,0.001069813,0.0001218719,0.02068994,0.5111619,0.0004210896,0.04227903,0.05276098,0.01075112],"study_design_scores_gemma":[0.00132711,0.001030942,0.006518856,0.0001519118,0.00006175964,0.0008176118,0.0008640729,0.9344459,0.0009571463,0.01765347,0.035347,0.0008242384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9713022,0.0004019059,0.01424932,0.007027283,0.000683795,0.0001325138,0.00006228292,0.000008774414,0.006131989],"genre_scores_gemma":[0.9925147,0.00002086053,0.003552723,0.002337903,0.00151141,9.399355e-7,5.741097e-7,0.00002480252,0.0000360866],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.423284,"threshold_uncertainty_score":0.5324773,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2947491227","doi":"10.1002/for.2615","title":"The dynamic effect of macroeconomic news on the euro/US dollar exchange rate","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; Brock University","funders":"","keywords":"Economics; Recession; Volatility (finance); Monetary economics; Liberian dollar; Us dollar; Sovereign debt; Debt; European debt crisis; Great recession; Exchange rate; Sovereignty; International economics; Econometrics; Macroeconomics; Keynesian economics; European union; Finance; European integration; Political science","retraction":null,"screen_n_in":null,"score":{"opus":0.04667145162187038,"gpt":0.2236261918606523,"spread":0.1769547402387819,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003506208,0.000166558,0.0005137225,0.0001432006,0.0001614497,0.000081771,0.0004310644,0.00005830817,0.0002978169],"category_scores_gemma":[0.0003728094,0.0001028639,0.0003048164,0.00008922214,0.00005476502,0.0002000135,0.0000551282,0.0003075057,0.0003479632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001074315,"about_ca_system_score_gemma":0.0000147385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000769507,"about_ca_topic_score_gemma":0.0000224977,"domain_scores_codex":[0.9983922,0.00008670676,0.000985721,0.0001519369,0.00003038719,0.0003530369],"domain_scores_gemma":[0.9967167,0.001111872,0.001774679,0.0003178336,0.0000144934,0.00006437983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005213333,0.0002826116,0.6422935,0.001014918,0.003235536,0.00009308252,0.005294675,0.1327025,0.001670097,0.03474916,0.03442419,0.1390265],"study_design_scores_gemma":[0.009769022,0.009316447,0.1093032,0.0007451396,0.0001667481,0.0007622581,0.0006207647,0.622839,0.004653109,0.02804347,0.212229,0.001551828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879914,0.001213873,0.00004064446,0.001201827,0.001026022,0.0002113459,0.00004055413,0.000003764667,0.008270589],"genre_scores_gemma":[0.9983425,0.0003587831,0.00003697255,0.0003009086,0.00021636,0.000002280099,0.000001079386,0.00002377368,0.0007174145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5329902,"threshold_uncertainty_score":0.4472479,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2610239431","doi":"10.1002/for.2472","title":"The impact of parameter and model uncertainty on market risk predictions from GARCH‐type models","year":2017,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Econometrics; Pooling; Bayesian probability; Mathematics; Linear model; Autoregressive conditional heteroskedasticity; Bayesian vector autoregression; Statistics; Computer science; Volatility (finance); Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.1088418909732672,"gpt":0.2853854384292182,"spread":0.176543547455951,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001180646,0.0001012851,0.0003108667,0.00008422336,0.0005242959,0.0001160057,0.0002417339,0.00006608255,0.00001015758],"category_scores_gemma":[0.001843003,0.00007306709,0.0001892569,0.00003586157,0.00008666499,0.0003404044,0.00005532882,0.0003119675,8.243867e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006430952,"about_ca_system_score_gemma":0.00004774595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001183868,"about_ca_topic_score_gemma":0.00004454719,"domain_scores_codex":[0.9989691,0.00001792284,0.0006572568,0.0001298243,0.00005396758,0.000171864],"domain_scores_gemma":[0.9975888,0.0003976649,0.001542657,0.0002842556,0.0001194478,0.00006713437],"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":[0.000487901,0.00004994061,0.1200845,0.000008071996,0.0001535591,0.000001755314,0.0006702394,0.8591154,0.000008192188,0.002405917,0.0004044446,0.01661008],"study_design_scores_gemma":[0.0002670948,0.0001713105,0.02984636,0.00006441299,0.00001137642,0.000002758213,0.00001845818,0.7747602,0.000003710921,0.1947782,0.00002270451,0.00005342853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642304,0.0007839556,0.03151891,0.00006002173,0.0001829937,0.0000613489,0.0001914795,0.000002692147,0.002968254],"genre_scores_gemma":[0.9961948,0.0008990197,0.002703689,0.000004885977,0.0001232252,8.123649e-7,6.882283e-7,0.00001061325,0.00006225485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1923723,"threshold_uncertainty_score":0.4032513,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392771668","doi":"10.1002/for.3095","title":"Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"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":"Demand forecasting; Supply chain; Cluster analysis; Vendor; Downstream (manufacturing); Computer science; Missing data; Demand management; Demand patterns; Supply and demand; Supply chain management; Operations research; Business; Marketing; Economics; Artificial intelligence; Machine learning; Microeconomics","retraction":null,"screen_n_in":null,"score":{"opus":0.1641339135098591,"gpt":0.3699132245148424,"spread":0.2057793110049832,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006322549,0.0003499699,0.0006534416,0.0009021258,0.0003378355,0.0009920154,0.001277899,0.000107024,0.00001326604],"category_scores_gemma":[0.002094914,0.0002185046,0.0001500128,0.001179981,0.0001336412,0.001412811,0.000409463,0.0005636277,0.000001434929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002064893,"about_ca_system_score_gemma":0.0003658543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001326022,"about_ca_topic_score_gemma":0.000413023,"domain_scores_codex":[0.996018,0.0001007405,0.001605724,0.0007332897,0.0009205719,0.0006217078],"domain_scores_gemma":[0.9953664,0.002221223,0.001005261,0.0006613496,0.0005252775,0.0002204957],"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":[0.0006479092,0.00007084993,0.005157593,0.000318439,0.0001176156,0.000392206,0.002544348,0.02542176,0.002860933,0.0001562297,0.0003112466,0.9620008],"study_design_scores_gemma":[0.001284393,0.0005310439,0.000511453,0.00307055,0.00009612222,0.001929149,0.001471452,0.9890088,0.0004056723,0.0005248979,0.0008601175,0.0003062911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3770235,0.0002321851,0.6211011,0.0006437346,0.0001790652,0.0005145846,0.00004910026,0.00005897572,0.0001977059],"genre_scores_gemma":[0.7743174,0.000005594387,0.2252212,0.00003185134,0.0002685368,0.00002189885,0.000009788699,0.00005368947,0.00007003628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9635871,"threshold_uncertainty_score":0.9566027,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2040735861","doi":"10.1002/for.1044","title":"On forecasting counts","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Series (stratigraphy); Econometrics; Negative binomial distribution; Bayesian probability; Poisson distribution; Computer science; Probabilistic forecasting; Time series; Statistics; Mathematics; Probabilistic logic","retraction":null,"screen_n_in":null,"score":{"opus":0.09448738222617091,"gpt":0.2687520753828352,"spread":0.1742646931566643,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004956391,0.00006897256,0.0001332289,0.00009585077,0.000101245,0.00002717732,0.0002860943,0.00003286213,0.00001431758],"category_scores_gemma":[0.0002287409,0.00005265733,0.00007983888,0.0001427394,0.00001613423,0.0002133474,0.0000357484,0.0001937228,0.00001055516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002383599,"about_ca_system_score_gemma":0.00005961258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.326399e-7,"about_ca_topic_score_gemma":9.329468e-8,"domain_scores_codex":[0.9992661,0.00003465483,0.0002445477,0.0000825084,0.0002250754,0.0001471399],"domain_scores_gemma":[0.9992935,0.0001636905,0.0002541607,0.0001104274,0.0001108075,0.00006739897],"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":[0.00001696914,0.00005060376,0.0002371843,0.00001528052,0.00002889543,0.0008209439,0.0008457456,0.0002768645,0.0002071162,0.04869161,0.016256,0.9325528],"study_design_scores_gemma":[0.001523185,0.001211705,0.0006137889,0.0007492154,0.00002927092,0.02425208,0.00002000641,0.7203771,0.003193466,0.2272293,0.02017002,0.0006309244],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01330368,0.0001056171,0.9516495,0.0001154072,0.0005439582,0.00002048985,2.259018e-7,0.00001249822,0.03424857],"genre_scores_gemma":[0.4159521,0.000008731113,0.5832328,0.0001795584,0.0002685696,2.424091e-7,4.804937e-8,0.000006267131,0.0003517123],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9319218,"threshold_uncertainty_score":0.2147304,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3037592550","doi":"10.1002/for.2716","title":"Forecast performance and bubble analysis in noncausal MAR(1, 1) processes","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Futures contract; Econometrics; Estimator; Nonlinear system; Bubble; Term (time); Gaussian; Variance (accounting); Mathematics; Series (stratigraphy); Lévy process; Applied mathematics; Economics; Statistical physics; Computer science; Statistics; Financial economics; Physics; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.05588153369421907,"gpt":0.2193155033465456,"spread":0.1634339696523266,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008249106,0.00009644675,0.0004446023,0.0002834456,0.00004851062,0.00005961213,0.0001258338,0.00004738119,0.00007882672],"category_scores_gemma":[0.0005486432,0.0000972131,0.00008355744,0.0008611139,0.00002490098,0.0003446031,0.00004603051,0.0002054752,0.000001631282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003684773,"about_ca_system_score_gemma":0.0000307779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001837181,"about_ca_topic_score_gemma":0.00005317149,"domain_scores_codex":[0.9988323,0.000009355206,0.0007727003,0.0001628135,0.00004580252,0.0001769876],"domain_scores_gemma":[0.9990005,0.00008223356,0.0006717753,0.00006500845,0.0000772693,0.0001032305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005460602,0.0000201954,0.9938161,0.00014585,0.00008258925,0.00001012083,0.0004915659,0.0006213232,0.000002745589,0.0001420242,0.00003110615,0.004581811],"study_design_scores_gemma":[0.0004418676,0.0001660598,0.1788161,0.00003959109,0.00003352425,0.00002231134,0.00007737811,0.8180465,0.000007493074,0.00104355,0.001179551,0.0001260366],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910749,0.0008463518,0.003645566,0.0004023552,0.00005822473,0.00005042912,0.00001691877,0.000003735659,0.003901473],"genre_scores_gemma":[0.9973465,0.0001742583,0.002254603,0.00006845053,0.0001023226,9.963903e-7,0.00000161481,0.000008633756,0.00004265148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8174252,"threshold_uncertainty_score":0.3964235,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3195601859","doi":"10.1002/for.2942","title":"Worse than you think: Public debt forecast errors in advanced and developing economies","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Fiscal Policies and Political Economy","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Balliol College, University of Oxford; University of Oxford; Georgetown University; Queen's University; London School of Economics and Political Science; University of Illinois at Urbana-Champaign","keywords":"Economics; Recession; Debt; Emerging markets; Monetary economics; Gross domestic product; Real gross domestic product; Macroeconomics","retraction":null,"screen_n_in":null,"score":{"opus":0.08271555542471594,"gpt":0.2510026769835279,"spread":0.1682871215588119,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00102202,0.000163922,0.0005250853,0.0005466305,0.0001172861,0.0001243688,0.0002081095,0.00009484314,0.00005124597],"category_scores_gemma":[0.0004288733,0.0001686463,0.0001274682,0.0003935605,0.00007788908,0.0007290187,0.0001095578,0.0002877765,0.0000576648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001647517,"about_ca_system_score_gemma":0.00006493594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001016241,"about_ca_topic_score_gemma":0.0001174909,"domain_scores_codex":[0.9980499,0.00001366065,0.001143865,0.0002040377,0.00003414621,0.0005543884],"domain_scores_gemma":[0.9988239,0.0001916596,0.0006524806,0.0001171799,0.00004250509,0.0001722232],"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":[0.00005631815,0.00004404323,0.3410444,0.0001924124,0.0001264223,0.0000874828,0.002718267,0.001398631,0.000006403327,0.6203101,0.0006879069,0.03332756],"study_design_scores_gemma":[0.003328716,0.0004411702,0.3583339,0.0007055235,0.00001696509,0.0004138431,0.005424086,0.06149203,0.00008110896,0.4897424,0.07890321,0.001116983],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798694,0.0007326965,0.0002048091,0.005252019,0.0004616359,0.00008774102,0.00002121926,0.00002010043,0.01335037],"genre_scores_gemma":[0.9960548,0.0002417019,0.002950612,0.0002517992,0.0001950115,0.000004047526,0.000002884396,0.00002600016,0.0002731439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1305677,"threshold_uncertainty_score":0.6877197,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2343721382","doi":"10.1002/for.2416","title":"Yield Curve Forecasting with the Burg Model","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Autoregressive model; Yield curve; Econometrics; Yield (engineering); Impulse response; Economics; Mathematics; Recursion (computer science); Bond; Applied mathematics; Algorithm; Finance; Mathematical analysis","retraction":null,"screen_n_in":null,"score":{"opus":0.2127904944067084,"gpt":0.2237022503601404,"spread":0.010911755953432,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001288981,0.0001483227,0.0003653142,0.0001612852,0.0001604443,0.0000627416,0.0002924757,0.00005809855,0.0001588613],"category_scores_gemma":[0.0004353595,0.00008102855,0.0001623188,0.00009791162,0.00006617844,0.0005876663,0.00003912729,0.0002065417,0.00003843232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008642505,"about_ca_system_score_gemma":0.00002770006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004865915,"about_ca_topic_score_gemma":0.00001835535,"domain_scores_codex":[0.9986771,0.00001028987,0.0007487928,0.0001545964,0.00004545772,0.0003637943],"domain_scores_gemma":[0.998041,0.0003473905,0.00128181,0.0001882668,0.0000348019,0.0001067625],"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":[0.001289294,0.0002982107,0.3361132,0.0001771023,0.001676499,0.0002594928,0.00985072,0.3874509,0.0007321407,0.04818001,0.04841628,0.1655561],"study_design_scores_gemma":[0.002490631,0.0008959529,0.003895751,0.0006202557,0.0000583502,0.00180764,0.0003362516,0.9319546,0.000510724,0.04411422,0.01257316,0.0007424538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9241554,0.0004422366,0.05972011,0.003712979,0.000213623,0.00008165523,0.0000334631,0.00000895556,0.01163157],"genre_scores_gemma":[0.9956554,0.00003503026,0.002585774,0.0003065402,0.0004977711,0.000001911931,2.49991e-7,0.00002372875,0.000893653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5445037,"threshold_uncertainty_score":0.3304248,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1528064681","doi":"10.1002/for.2317","title":"Bayesian Analysis of Asymmetric Stochastic Conditional Duration Model","year":2014,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Particle filter; Markov chain Monte Carlo; Computer science; Econometrics; Mathematics; Bayesian probability; Applied mathematics; Statistics; Kalman filter","retraction":null,"screen_n_in":null,"score":{"opus":0.05247981638408884,"gpt":0.2394227167075427,"spread":0.1869429003234538,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001379357,0.00008649192,0.0005152565,0.001250695,0.0000738681,0.00002385865,0.0001224081,0.00006451602,0.00003109903],"category_scores_gemma":[0.001379458,0.000092767,0.0002938134,0.0008862831,0.00002538379,0.0002701513,0.00001658149,0.0001513632,0.000002903652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006077138,"about_ca_system_score_gemma":0.00003075305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002323501,"about_ca_topic_score_gemma":0.0000106688,"domain_scores_codex":[0.9983709,0.00001365921,0.00126279,0.0001245875,0.00008570123,0.0001423495],"domain_scores_gemma":[0.9978794,0.0001725558,0.001590178,0.000103105,0.0001953846,0.00005939387],"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":[0.00002452333,0.00004501546,0.01595993,0.00001504747,0.0001990639,5.483911e-7,0.0002324528,0.9422076,0.00002620447,0.03762461,0.00003197499,0.003632998],"study_design_scores_gemma":[0.0002461077,0.00007652298,0.008535938,0.0000189767,0.0001134903,0.000004548144,0.00001651611,0.9376338,0.00002209359,0.05323284,0.0000198019,0.00007940135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.31257,0.0001430412,0.6861027,0.00004278735,0.00008165883,0.00002504405,0.00003003817,0.000002603985,0.001002034],"genre_scores_gemma":[0.9883943,0.000007080415,0.01140922,0.00002700486,0.0001202909,7.390627e-7,0.000009556622,0.00000849703,0.00002332483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6758243,"threshold_uncertainty_score":0.3782929,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2346528261","doi":"10.1002/for.2426","title":"Bayesian Forecasting for Time Series of Categorical Data","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Montréal","funders":"Indian Statistical Institute","keywords":"Categorical variable; Bayesian probability; Frequentist inference; Computer science; Time series; Series (stratigraphy); Econometrics; Autoregressive model; Bayesian average; Data mining; Variable-order Bayesian network; Artificial intelligence; Machine learning; Bayesian inference; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.3132649776571354,"gpt":0.3992068342915667,"spread":0.08594185663443132,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005767814,0.0001618583,0.0005235582,0.000313498,0.0001859455,0.00009322526,0.001791189,0.00009143433,0.0001246883],"category_scores_gemma":[0.0146562,0.00009075287,0.0002198382,0.0005644544,0.0001758591,0.0009731162,0.0003746024,0.0001340312,0.000008361447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004732411,"about_ca_system_score_gemma":0.0001643504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005178957,"about_ca_topic_score_gemma":0.000005878603,"domain_scores_codex":[0.9966509,0.00007311822,0.001776007,0.0003263986,0.0008472214,0.0003263811],"domain_scores_gemma":[0.9921337,0.003478804,0.00223846,0.0007597543,0.001237641,0.000151638],"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":[0.0003287071,0.0001340793,0.003509856,0.00003925335,0.00007727822,0.00002523431,0.0002769876,0.0001392384,0.01432812,0.005802542,0.07616306,0.8991756],"study_design_scores_gemma":[0.002552702,0.002652378,0.00053544,0.001085756,0.0002290967,0.004259882,0.0006065326,0.2471942,0.03037882,0.560735,0.1489262,0.0008439275],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04801304,0.0001120744,0.9460369,0.002996975,0.0002803248,0.0003117692,0.0001773411,0.00003812293,0.002033492],"genre_scores_gemma":[0.7953036,0.000005600864,0.2033349,0.00002867348,0.0004521235,0.00000582902,0.000003806071,0.00002429094,0.0008410897],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8983317,"threshold_uncertainty_score":0.9936438,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4293226250","doi":"10.1002/for.2903","title":"A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information","year":2022,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Volatility (finance); Economics; Futures contract; Econometrics; Stock market; Leverage effect; Futures market; Financial economics; Stock (firearms); Oil price; Leverage (statistics); Monetary economics; Autoregressive conditional heteroskedasticity; Statistics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03749744999776277,"gpt":0.214238741639791,"spread":0.1767412916420282,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003765156,0.0001421396,0.0004968416,0.0002680332,0.000270556,0.00003058227,0.0003921347,0.00005552733,0.0005442565],"category_scores_gemma":[0.001321827,0.00012454,0.0002763835,0.0003598652,0.00005855252,0.0005110069,0.0001861003,0.0004784021,3.489862e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001805021,"about_ca_system_score_gemma":0.00007923901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001436251,"about_ca_topic_score_gemma":0.00004219181,"domain_scores_codex":[0.9976139,0.00007822795,0.001755584,0.0001186121,0.0002025566,0.0002311269],"domain_scores_gemma":[0.9955596,0.0005112024,0.003449858,0.0002389503,0.0001830927,0.0000573075],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005136011,0.0004154195,0.6860787,0.001202431,0.0009571295,0.000020526,0.01120497,0.008992814,0.00005202929,0.007867383,0.00482912,0.2732435],"study_design_scores_gemma":[0.00131473,0.0004375531,0.02570961,0.00005255444,0.00003411595,0.0000613001,0.0008666197,0.9523026,0.00001594029,0.002683721,0.01636395,0.0001573556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821327,0.0007862068,0.001061456,0.0001472936,0.001001973,0.000091708,0.0003154202,0.000004574172,0.01445872],"genre_scores_gemma":[0.9985513,0.00004324914,0.001141319,0.00002776623,0.0001307332,0.000004125891,0.00000732398,0.00001180145,0.00008236921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9433097,"threshold_uncertainty_score":0.5959226,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1606487396","doi":"10.1002/for.2264","title":"Forecasting Simultaneously High‐Dimensional Time Series: A Robust Model‐Based Clustering Approach","year":2013,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Booth University College","funders":"","keywords":"Outlier; Autoregressive model; Cluster analysis; Principal component analysis; Robustness (evolution); Computer science; Series (stratigraphy); Autoregressive integrated moving average; Econometrics; Time series; Data mining; Artificial intelligence; Mathematics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.1493626557280337,"gpt":0.3303635394458337,"spread":0.1810008837178,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001029882,0.0003053331,0.0006643848,0.0001615711,0.0002321315,0.00009112366,0.0002431748,0.0001258357,0.0001191313],"category_scores_gemma":[0.00385786,0.0002422315,0.0001936399,0.000177231,0.00007624368,0.0005820333,0.0001060434,0.0005152465,0.000005583167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001211936,"about_ca_system_score_gemma":0.000115073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006258253,"about_ca_topic_score_gemma":0.000001485644,"domain_scores_codex":[0.9974118,0.0001183644,0.001125552,0.0002587295,0.000557143,0.0005283608],"domain_scores_gemma":[0.9957744,0.002110725,0.0009527616,0.0002008153,0.0006911893,0.0002701168],"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":[0.0001029196,0.0001067261,0.000004990065,0.0001856011,0.00004796074,0.00009456288,0.000215278,0.966445,0.00109897,0.0009471987,0.0003247226,0.03042604],"study_design_scores_gemma":[0.0006497312,0.000229131,8.891186e-7,0.0003308046,0.00006466544,0.0009820192,0.00007688369,0.9017729,0.0001769457,0.09546587,0.000009600856,0.0002405212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05762452,0.00002446779,0.9406259,0.0001021254,0.0001176929,0.0002647999,0.00001314422,0.00003918686,0.001188135],"genre_scores_gemma":[0.238093,6.860964e-7,0.7612147,0.00007695204,0.0002150095,0.000009029839,0.000002468304,0.00005767211,0.0003304308],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1804685,"threshold_uncertainty_score":0.9877914,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2194956916","doi":"10.1002/for.2397","title":"Bayesian Analysis of a Threshold Stochastic Volatility Model","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Stochastic volatility; Econometrics; Threshold model; Volatility (finance); Particle filter; Threshold limit value; Markov chain Monte Carlo; Mathematics; Bayesian probability; Financial models with long-tailed distributions and volatility clustering; Statistics; Forward volatility; Kalman filter","retraction":null,"screen_n_in":null,"score":{"opus":0.08219115624416455,"gpt":0.2486953045450947,"spread":0.1665041483009302,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00134753,0.0001138034,0.0007022216,0.0006412017,0.00005833246,0.00001561709,0.0002020613,0.00007828997,0.00006966881],"category_scores_gemma":[0.0007871174,0.00009052984,0.0004410509,0.000519138,0.00004712298,0.0003280735,0.00003761171,0.0001351835,0.000002265313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008736247,"about_ca_system_score_gemma":0.00004690864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002686775,"about_ca_topic_score_gemma":0.00002837087,"domain_scores_codex":[0.9981251,0.00000853947,0.001395779,0.000174595,0.00008100502,0.0002149991],"domain_scores_gemma":[0.9979877,0.0001424147,0.001403512,0.0002045264,0.000177802,0.00008407211],"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":[0.0002997993,0.0002353891,0.4703912,0.00006814163,0.001264712,0.000008212514,0.001900468,0.4749075,0.0006751335,0.0291428,0.00009827225,0.02100841],"study_design_scores_gemma":[0.0003206077,0.00006625836,0.00626829,0.00007084401,0.0001189278,0.000003239605,0.00001751208,0.9509816,0.00003287665,0.04201179,0.00001114387,0.00009688876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4986812,0.0002685468,0.5003923,0.00006126049,0.00006807246,0.00002727077,0.00003883501,0.000002973856,0.0004595077],"genre_scores_gemma":[0.9948819,0.00001730047,0.004939707,0.00001541503,0.00007315761,7.63931e-7,5.77578e-7,0.00001121082,0.00005993452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4962007,"threshold_uncertainty_score":0.36917,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2160439741","doi":"10.1002/for.2353","title":"A Simple Linear Regression Approach to Modeling and Forecasting Mortality Rates","year":2015,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Taiwan University; University of Waterloo; National Science Council","keywords":"Econometrics; Statistics; Linear regression; Regression; Mortality rate; Regression analysis; Lag; Logarithm; Linear model; Mathematics; Simple linear regression; Computer science; Demography","retraction":null,"screen_n_in":null,"score":{"opus":0.2162571731317099,"gpt":0.375555006464455,"spread":0.1592978333327451,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00626024,0.0001809145,0.0003939421,0.0002749651,0.0005585563,0.0001725317,0.0003012843,0.00009077173,0.000003653976],"category_scores_gemma":[0.001960052,0.0001490644,0.0001402369,0.0005379774,0.0001045218,0.0005828917,0.000141882,0.0003078635,0.000001397514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001169262,"about_ca_system_score_gemma":0.000148372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008923464,"about_ca_topic_score_gemma":0.0002645487,"domain_scores_codex":[0.9972774,0.000259094,0.0007948271,0.0002431428,0.0009521045,0.0004733654],"domain_scores_gemma":[0.997972,0.0001220787,0.0006338565,0.0001546569,0.0006747929,0.0004426045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003199119,0.000375426,0.6511738,0.0002743953,0.0003256508,0.0001458843,0.05577015,0.1941224,0.0001356517,0.00424019,0.002149077,0.09096741],"study_design_scores_gemma":[0.001462291,0.0002993051,0.007757626,0.0005160449,0.0001652712,0.00007850058,0.04056167,0.9337334,0.00007297136,0.01049699,0.004196865,0.0006590455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9633254,0.0003420663,0.01963773,0.0001416111,0.0003634992,0.0002749533,0.000002906728,0.00003101455,0.01588086],"genre_scores_gemma":[0.9745828,0.00004163662,0.02433966,0.00009300201,0.0008676685,0.000004990494,0.000001579661,0.00002053057,0.00004815897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.739611,"threshold_uncertainty_score":0.6078671,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2077421290","doi":"10.1002/for.929","title":"Unemployment variation over the business cycles: a comparison of forecasting models","year":2004,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial neural network; Unemployment; Business cycle; Econometrics; Linear model; Linear regression; Computer science; Series (stratigraphy); Aggregate (composite); Regression; Economics; Mathematics; Artificial intelligence; Statistics; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.2191910576277073,"gpt":0.2730355441331966,"spread":0.05384448650548929,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001149301,0.0001409671,0.0005190694,0.0002277141,0.000136816,0.00005870365,0.0002484712,0.00006953631,0.00005126544],"category_scores_gemma":[0.0002602643,0.0001179234,0.0001775904,0.0002246549,0.00004318626,0.0006051786,0.00004394046,0.0002179363,0.000007464632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001781405,"about_ca_system_score_gemma":0.00004264854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007524699,"about_ca_topic_score_gemma":0.00002913518,"domain_scores_codex":[0.9980195,0.0000149456,0.001497142,0.000136527,0.00006541103,0.0002665377],"domain_scores_gemma":[0.9971591,0.0001315315,0.002414891,0.0001661122,0.00006174585,0.00006657448],"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":[0.00004847047,0.0001007903,0.03051388,0.00004340601,0.0001166132,0.000003307991,0.004300828,0.9527273,0.00002063107,0.00958136,0.00009790554,0.002445515],"study_design_scores_gemma":[0.001512235,0.0002032061,0.04364873,0.0002291435,0.00003689565,0.0001233648,0.0003332975,0.8569817,0.0001399888,0.09625887,0.000318966,0.0002135795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9524527,0.0006733163,0.04415608,0.0004535558,0.0003974119,0.0001058465,0.00002293495,0.000005409462,0.001732732],"genre_scores_gemma":[0.9958383,0.00003155898,0.003653528,0.00008947866,0.0003461393,0.000001658684,0.000001834602,0.00001882092,0.00001865334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09574558,"threshold_uncertainty_score":0.4808776,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4319440180","doi":"10.1002/for.2956","title":"Using a machine learning approach and big data to augment WASDE forecasts: Empirical evidence from US corn yield","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Manitoba","funders":"","keywords":"Yield (engineering); Computer science; Machine learning; Agriculture; Econometrics; Economics; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.6783027688674481,"gpt":0.3642247543382261,"spread":0.314078014529222,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00106382,0.0001927599,0.000344242,0.00005258692,0.0002661595,0.0002067322,0.0004891723,0.00009860023,0.00004904093],"category_scores_gemma":[0.002213059,0.00007137268,0.0000750935,0.0007711896,0.00002935455,0.0003918188,0.0006183055,0.0004195382,0.000004732884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007554871,"about_ca_system_score_gemma":0.00001547962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005575506,"about_ca_topic_score_gemma":0.0006197842,"domain_scores_codex":[0.9982573,0.00009407978,0.0004759413,0.0003260502,0.0004467812,0.0003998197],"domain_scores_gemma":[0.9981112,0.0009431246,0.0004188748,0.00008658558,0.0001304555,0.0003097635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002956977,0.0001064656,0.2371841,0.00005873305,0.0001107157,0.0002423953,0.002279511,0.001976169,0.471929,8.435457e-7,0.004281299,0.2815351],"study_design_scores_gemma":[0.0009227681,0.002658181,0.4921993,0.004309275,0.0003526848,0.002543025,0.009100562,0.4714475,0.004067425,0.0001859802,0.01093449,0.001278828],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970146,0.0005799275,0.0001156746,0.001686673,0.0002447652,0.0001517631,0.0001315948,0.00003843147,0.00003653305],"genre_scores_gemma":[0.9942588,0.0001705041,0.003711907,0.0002293659,0.001501596,0.000001580766,0.00008172325,0.000003312989,0.00004117104],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4694713,"threshold_uncertainty_score":0.2910494,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3124669735","doi":"10.1002/for.2757","title":"Convolution‐based filtering and forecasting: An application to WTI crude oil prices","year":2021,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; West Texas Intermediate; Econometrics; Convolution (computer science); Series (stratigraphy); Component (thermodynamics); Hodrick–Prescott filter; Time series; Mathematics; Autoregressive–moving-average model; Commodity; Computer science; Applied mathematics; Economics; Statistics; Artificial intelligence; Finance; Artificial neural network; Business cycle","retraction":null,"screen_n_in":null,"score":{"opus":0.06610561391593361,"gpt":0.2471949982617378,"spread":0.1810893843458042,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001173981,0.0001057033,0.0003123931,0.0001755406,0.0001280001,0.0001163769,0.000125499,0.00005678366,0.00005946814],"category_scores_gemma":[0.0006460448,0.0001188402,0.00007968539,0.000232118,0.00001951701,0.0002932105,0.00005451166,0.0001547636,0.000002082634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008034673,"about_ca_system_score_gemma":0.00004169067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003527465,"about_ca_topic_score_gemma":0.00005579489,"domain_scores_codex":[0.998722,0.00001810941,0.0007851458,0.0002238677,0.00005663752,0.0001942538],"domain_scores_gemma":[0.9985994,0.0001235051,0.0007731147,0.000165264,0.00018252,0.0001562543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002328559,0.00034142,0.6872638,0.0005632688,0.0001351581,0.00006826187,0.001090774,0.005830534,0.00557074,0.02445147,0.000147224,0.2743044],"study_design_scores_gemma":[0.0005642138,0.0001375574,0.03452552,0.0001013624,0.00001033856,0.0001173811,0.0001000003,0.9511224,0.0002271726,0.00601417,0.0068634,0.0002164709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8539128,0.0005804205,0.1420293,0.0003011157,0.0001842511,0.00004059595,0.0000239503,0.000008392407,0.00291917],"genre_scores_gemma":[0.9569747,0.00001937669,0.0425707,0.0001092771,0.0001808914,0.000003751642,0.00000568495,0.00001428256,0.0001213362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9452919,"threshold_uncertainty_score":0.4846164,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3125713255","doi":"10.1002/for.2552","title":"An analysis on the predictability of CAPM beta for momentum returns","year":2018,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Capital asset pricing model; Predictability; Momentum (technical analysis); BETA (programming language); Economics; Econometrics; Financial economics; Stock (firearms); Estimator; Trading strategy; Mathematics; Statistics; Computer science; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.07226554214504533,"gpt":0.2533654612180952,"spread":0.1810999190730498,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001758785,0.00008416246,0.0003661441,0.0002102796,0.0001181039,0.00004096793,0.0002273939,0.00004436476,0.0001381975],"category_scores_gemma":[0.0004159942,0.00006021048,0.0002509639,0.0003079843,0.0001128829,0.0002256075,0.00001416712,0.00009808248,0.000001444411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005104461,"about_ca_system_score_gemma":0.00002463391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004216363,"about_ca_topic_score_gemma":0.00003283026,"domain_scores_codex":[0.9988641,0.00001910465,0.0007796697,0.0001275659,0.00005316193,0.0001564025],"domain_scores_gemma":[0.9982685,0.0001654459,0.001152243,0.0002056843,0.0001625488,0.0000456081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0005025648,0.0004781915,0.4788146,0.0001073085,0.001149146,0.000002423658,0.003042551,0.0009491115,0.0002946493,0.5093119,0.002684931,0.002662624],"study_design_scores_gemma":[0.001389564,0.007858497,0.6403624,0.0001567288,0.0004314054,0.0000107628,0.001691311,0.1350828,0.003518102,0.2003042,0.008754573,0.0004395879],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9893421,0.0001343117,0.002129557,0.0004682773,0.0002950659,0.0001166461,0.0000821867,0.000003148995,0.007428709],"genre_scores_gemma":[0.9985238,0.00001567754,0.0009698851,0.00007708788,0.0003590136,0.000003048003,0.000001985642,0.000007267429,0.00004222297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3090077,"threshold_uncertainty_score":0.2455312,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2105292169","doi":"10.1002/for.2248","title":"Long‐Term Forecasting of Global Carbon Dioxide Emissions: Reducing Uncertainties Using a Per Capita Approach","year":2013,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Per capita; Econometrics; Range (aeronautics); Economics; Environmental science; Monte Carlo method; Term (time); Quartile; Yield (engineering); Statistics; Natural resource economics; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.02727958173295202,"gpt":0.2298092324937757,"spread":0.2025296507608236,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004172626,0.0002353273,0.0003851963,0.00001528631,0.0001562558,0.00003474871,0.0003166299,0.000101668,0.0002892254],"category_scores_gemma":[0.0001207177,0.0001912469,0.0001827943,0.0001765566,0.0002603994,0.0004804501,0.0002766939,0.0002686492,0.000003669478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007477346,"about_ca_system_score_gemma":0.00003452492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001262045,"about_ca_topic_score_gemma":0.000008463098,"domain_scores_codex":[0.9979458,0.00005837019,0.0008016491,0.0002277502,0.0005352158,0.0004312028],"domain_scores_gemma":[0.998574,0.00005851966,0.0009552427,0.0001798289,0.00002940206,0.0002030427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002468502,0.00008701914,0.5178935,0.00004504048,0.00004439806,0.00001476426,0.0008461977,0.451492,0.009014004,0.000004154515,0.00002056603,0.02051365],"study_design_scores_gemma":[0.0005679326,0.0002219988,0.1259505,0.0003831274,0.000124051,0.001635666,0.003140857,0.866824,0.0004113043,0.0003606875,0.00001088164,0.0003690078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9847644,0.0002196562,0.01193048,0.00002378281,0.0001925807,0.0001588568,0.000001001921,0.000008919786,0.002700307],"genre_scores_gemma":[0.892951,0.0000144367,0.1067559,0.00001954095,0.0001378622,0.000002236409,6.021093e-7,0.0000228065,0.00009567195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4153319,"threshold_uncertainty_score":0.7798824,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2894383657","doi":"10.1002/for.2556","title":"Does geographic location matter to stock return predictability?","year":2018,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Ottawa","funders":"","keywords":"Predictability; Random walk; Portfolio; Econometrics; Stock (firearms); Variance (accounting); Modern portfolio theory; Random walk hypothesis; Economics; Financial economics; Stock market; Statistics; Geography; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.03457976435843414,"gpt":0.2234409740936416,"spread":0.1888612097352074,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008799692,0.00009641806,0.0002465069,0.0002694249,0.000109683,0.00008433359,0.0001665448,0.00005407505,0.0003905033],"category_scores_gemma":[0.0003261988,0.00006927699,0.00008837583,0.0002727789,0.00007156353,0.0004217009,0.00003333455,0.0001264457,0.00007608408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000509312,"about_ca_system_score_gemma":0.00002360918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004906887,"about_ca_topic_score_gemma":0.00002437668,"domain_scores_codex":[0.9988547,0.0000125825,0.0007308554,0.0001510084,0.0000509545,0.0001999204],"domain_scores_gemma":[0.9989373,0.0000372374,0.0006092351,0.0001409732,0.0001928672,0.00008239223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007735185,0.00005583619,0.9844407,0.00005512884,0.00003777485,0.000003347456,0.0007104289,0.00003216248,0.00005357498,0.007257541,0.004486207,0.002789945],"study_design_scores_gemma":[0.0004616136,0.001026435,0.8886852,0.0002435063,0.00001412701,0.00003726489,0.0001820899,0.002341601,0.0002364634,0.07386805,0.03262635,0.0002773514],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9787691,0.000203189,0.00337663,0.001266376,0.001286109,0.0001176491,0.00001163738,0.000008273998,0.01496106],"genre_scores_gemma":[0.9958234,0.00001222591,0.002551482,0.0004655798,0.0008501805,0.000002977524,6.784001e-7,0.00001198913,0.0002814666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09575555,"threshold_uncertainty_score":0.4275737,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391611445","doi":"10.1002/for.3074","title":"Two‐stage credit risk prediction framework based on three‐way decisions with automatic threshold learning","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Particle swarm optimization; Machine learning; Optimal decision; Binary decision diagram; Credit risk; Data mining; Artificial intelligence; Decision tree; Finance; Algorithm; Business","retraction":null,"screen_n_in":null,"score":{"opus":0.02629509532868281,"gpt":0.2388271137773722,"spread":0.2125320184486894,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009746987,0.0002093369,0.0002594338,0.0005192026,0.0004379877,0.0006453298,0.0001777249,0.00009845686,0.0001848115],"category_scores_gemma":[0.001164699,0.0001482184,0.0001777017,0.000710048,0.00004131964,0.001155426,0.00004385999,0.0009984813,0.00003095756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007846831,"about_ca_system_score_gemma":0.00006026776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005364212,"about_ca_topic_score_gemma":0.00004302304,"domain_scores_codex":[0.9982356,0.00001552695,0.0005799544,0.0002190323,0.0006744375,0.0002754161],"domain_scores_gemma":[0.9985446,0.0003993003,0.000656869,0.0001431652,0.0002281506,0.00002785308],"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":[0.0004241063,0.0001961675,0.1977469,0.0004777978,0.0001593533,0.0005597817,0.0001479696,0.486738,0.00007315491,0.009984314,0.00281083,0.3006816],"study_design_scores_gemma":[0.0004921375,0.0002260931,0.03414441,0.00439284,0.0001863787,0.00002393758,0.0001221682,0.9529928,0.000008889144,0.003235594,0.004022936,0.0001518409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8102238,0.0002063837,0.1804032,0.0002148131,0.001852839,0.0001617145,0.00001249492,0.0002344234,0.006690375],"genre_scores_gemma":[0.9927676,0.00001140583,0.003179193,0.00009992565,0.003833913,0.000005957221,0.00001007762,0.00004452419,0.00004740764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4662548,"threshold_uncertainty_score":0.6222931,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1542665681","doi":"10.1002/for.1267","title":"Global Capital Flows, Time‐Varying Fundamentals and Transitional Exchange Rate Dynamics","year":2011,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Economics; Exchange rate; Sharpe ratio; Pound (networking); Monetary economics; Liberian dollar; Econometrics; Capital flows; Markov chain; Equity (law); Financial economics; Portfolio; Mathematics; Microeconomics; Statistics; Finance","retraction":null,"screen_n_in":null,"score":{"opus":0.05583244939862192,"gpt":0.2042839428720714,"spread":0.1484514934734495,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006402077,0.0001152547,0.0003928617,0.0001443215,0.0001213063,0.00006412384,0.0001020398,0.00004585037,0.001207541],"category_scores_gemma":[0.0000353282,0.0001192972,0.0001737524,0.000169438,0.00003168994,0.0003423173,0.00003442069,0.00008530127,0.0000272601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001336294,"about_ca_system_score_gemma":0.00001328775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002397081,"about_ca_topic_score_gemma":0.00009216988,"domain_scores_codex":[0.9988881,0.0000157393,0.0007191065,0.0001418759,0.00004518381,0.0001899431],"domain_scores_gemma":[0.9990923,0.00002778073,0.0006478514,0.00007493005,0.00005871612,0.00009839363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009818439,0.0008695438,0.6538207,0.0008340405,0.004096905,0.0009373952,0.01893436,0.002465353,0.0003184311,0.2220886,0.002150113,0.09250277],"study_design_scores_gemma":[0.004185136,0.001120689,0.1305175,0.0004610542,0.0002473307,0.003405125,0.002226532,0.7331022,0.0000396326,0.1181789,0.00519885,0.001316983],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975906,0.002064972,0.00632432,0.0001068175,0.0002518641,0.00005968617,0.0001875212,0.000007486468,0.01509139],"genre_scores_gemma":[0.9964731,0.00004186096,0.003060145,0.00003883491,0.0001862743,9.856118e-7,0.000006439954,0.00001084942,0.0001815012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7306368,"threshold_uncertainty_score":0.9997055,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3037145279","doi":"10.1002/for.2717","title":"A causal model for short‐term time series analysis to predict incoming Medicare workload","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Computer science; Term (time); Time series; Ensemble forecasting; Ensemble learning; Series (stratigraphy); Machine learning; Interval (graph theory); Artificial intelligence; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.1885171881366612,"gpt":0.3865930759151706,"spread":0.1980758877785094,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002469872,0.0001656909,0.0005873039,0.0004423941,0.0002459274,0.0002242888,0.0008909448,0.00008299664,0.00006547233],"category_scores_gemma":[0.005726126,0.0001226541,0.0004497557,0.001877593,0.00005548816,0.0003788073,0.0002090844,0.0002418582,0.00000737806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000538089,"about_ca_system_score_gemma":0.0001328564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002151361,"about_ca_topic_score_gemma":0.00001262586,"domain_scores_codex":[0.9969907,0.00004758993,0.001312028,0.0003121986,0.001042704,0.0002947218],"domain_scores_gemma":[0.9969974,0.0007946615,0.0006675483,0.0002612329,0.000844088,0.0004350976],"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":[0.0009456613,0.0001386506,0.03673225,0.00005461248,0.001211014,0.00008293998,0.01302046,0.3629499,0.007077076,0.0005616509,0.07786796,0.4993578],"study_design_scores_gemma":[0.000153603,0.0003397919,0.0004336903,0.00009179656,0.0002934037,0.0000497553,0.000285039,0.9935487,0.0003736261,0.003030804,0.001253103,0.0001466665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2081878,0.00004876898,0.7860808,0.004841097,0.00005336575,0.0002459342,0.00006167108,0.00004859209,0.0004319721],"genre_scores_gemma":[0.800441,0.000002842698,0.1986776,0.0003080349,0.0003695685,0.00001656612,0.000002754083,0.00001620773,0.0001653677],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6305988,"threshold_uncertainty_score":0.6855121,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2999793792","doi":"10.1002/for.2650","title":"Short‐run wavelet‐based covariance regimes for applied portfolio management","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Portfolio; Wavelet; Econometrics; Computer science; Project portfolio management; Portfolio optimization; Economics; Financial economics; Mathematics; Statistics; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.08140277547360625,"gpt":0.2344701920577541,"spread":0.1530674165841479,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009967348,0.0001280951,0.0004192301,0.0001213703,0.00008556288,0.00006166918,0.000227142,0.00005618425,0.00009227186],"category_scores_gemma":[0.0001634993,0.0001361339,0.0002036616,0.0001875335,0.00002040484,0.0001133523,0.00003716711,0.0001611413,0.000003440579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006114533,"about_ca_system_score_gemma":0.000019072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001921147,"about_ca_topic_score_gemma":5.944275e-7,"domain_scores_codex":[0.9985626,0.000006143136,0.0009227194,0.0002180554,0.00005825102,0.0002321774],"domain_scores_gemma":[0.9988084,0.0000883802,0.0007731994,0.0001360652,0.0000678999,0.0001260315],"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":[0.002279291,0.0004829343,0.08022045,0.002094136,0.00116672,0.0002321973,0.0009047213,0.005726609,0.00009874848,0.5832317,0.01946528,0.3040972],"study_design_scores_gemma":[0.001377431,0.00018938,0.007048227,0.00006072173,0.00003521956,0.00001372474,0.00007982529,0.9209533,0.00003424433,0.01927693,0.05064863,0.0002823483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0902849,0.0006362705,0.8239509,0.002105967,0.0005737519,0.0005695702,0.0001028343,0.00002641186,0.08174942],"genre_scores_gemma":[0.9415105,0.00002286226,0.05756857,0.0004059557,0.0002697996,0.000007574282,0.000005362683,0.00002115105,0.0001882148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9152267,"threshold_uncertainty_score":0.5551379,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4393868764","doi":"10.1002/for.3123","title":"An infinite hidden Markov model with stochastic volatility","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Philosophy and Social Science Foundation of Hunan Province; Social Sciences and Humanities Research Council of Canada; National Natural Science Foundation of China","keywords":"Stochastic volatility; Econometrics; Volatility (finance); Markov chain; Hidden Markov model; Economics; Mathematics; Financial economics; Statistical physics; Computer science; Statistics; Artificial intelligence; Physics","retraction":null,"screen_n_in":null,"score":{"opus":0.05895035572317358,"gpt":0.2527799162886512,"spread":0.1938295605654776,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001266223,0.0001470467,0.0003796401,0.0003149854,0.000102036,0.0001642432,0.0001917049,0.00007992361,0.00004374334],"category_scores_gemma":[0.0002282314,0.0001324842,0.000130618,0.0002634271,0.00003782336,0.0008396105,0.00002394218,0.0004173909,0.00001159399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001012939,"about_ca_system_score_gemma":0.0001094111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003840444,"about_ca_topic_score_gemma":0.00001991955,"domain_scores_codex":[0.9985499,0.00001175038,0.0008738911,0.0002320894,0.00008136444,0.0002510477],"domain_scores_gemma":[0.9990883,0.0001012074,0.0004003517,0.0001764609,0.0001141492,0.000119496],"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":[0.0009974563,0.0004201582,0.07778236,0.0006797413,0.0004283754,0.0002681755,0.01215681,0.5011617,0.0001983192,0.04935186,0.0006877661,0.3558673],"study_design_scores_gemma":[0.0002137036,0.0002317659,0.001129018,0.0001942102,0.00001642392,0.00006990319,0.00005373821,0.9678565,0.00000571035,0.02994229,0.0001375815,0.000149212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6108794,0.001202379,0.3864177,0.00007931662,0.0002112144,0.00005251674,0.00002738352,0.00001940632,0.001110679],"genre_scores_gemma":[0.9697444,0.00001548953,0.02978424,0.00003140383,0.0003031683,0.000001636916,0.000002048638,0.00002819084,0.00008934923],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4666947,"threshold_uncertainty_score":0.5402548,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}