{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":44,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":44,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"a48b1d7b7b81","filters":{"venue":"International Journal of Forecasting"}},"results":[{"id":"W2136118318","doi":"10.1016/j.ijforecast.2003.09.005","title":"Extreme value theory and Value-at-Risk: Relative performance in emerging markets","year":2004,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":422,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Windsor","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Colorado College; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Extreme value theory; Value at risk; Quantile; Econometrics; Generalized Pareto distribution; Percentile; Economics; Stock (firearms); Emerging markets; Covariance; Mathematics; Statistics; Risk management; Geography","authors":[{"name":"Ramazan Gençay","is_ca":true},{"name":"Faruk Selçuk∥","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04222271458268369,"gpt":0.2415933415593471,"spread":0.1993706269766634,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01021286,0.0006404867,0.001290264,0.001600909,0.0005223533,0.003878766,0.001274319,0.001930231,0.001737332],"category_scores_gemma":[0.0530456,0.0002745062,0.0004890877,0.00217977,0.002858892,0.008193194,0.001688509,0.002347047,0.0002675384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074529,"about_ca_system_score_gemma":0.0006699817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007718388,"about_ca_topic_score_gemma":0.0003988105,"domain_scores_codex":[0.9979382,0.00119553,0.00008216364,0.000243363,0.0004018778,0.0001388402],"domain_scores_gemma":[0.9669389,0.02737984,0.002336979,0.001113383,0.001560926,0.0006699251],"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":[0.00015607,0.00005784015,0.008561837,0.0001149357,0.0001295651,0.00009604645,0.0003233386,0.1499043,0.0004196205,0.7844753,0.001915808,0.0538453],"study_design_scores_gemma":[0.000008706043,0.0000367865,0.002223823,0.00002889636,0.00001849667,0.00005554993,0.00009670199,0.2003477,0.000141943,0.7963014,0.0007217736,0.00001818115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2836938,0.01643845,0.683131,0.004621572,0.0003511894,0.00003173027,0.0001355799,0.0001481776,0.01144839],"genre_scores_gemma":[0.9775169,0.003797085,0.01682058,0.0001105843,0.0004534861,0.00003057363,0.0001097081,0.00002770999,0.001133316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01021286,"threshold_uncertainty_score":0.0540114,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1979869620","doi":"10.1016/s0169-2070(03)00014-1","title":"Forecasting discrete valued low count time series","year":2003,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":185,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Econometrics; Autoregressive model; Series (stratigraphy); Poisson distribution; Contrast (vision); Conditional expectation; Conditional probability distribution; Statistics; Distribution (mathematics); Time series; Mathematics; Computer science; Economics; Artificial intelligence","authors":[{"name":"R. Keith Freeland","is_ca":true},{"name":"Brendan McCabe","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04716236610753581,"gpt":0.2402760115740578,"spread":0.1931136454665219,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008477535,0.000414434,0.0006259636,0.0009513766,0.0002113936,0.001550844,0.0005842107,0.0009918049,0.001385134],"category_scores_gemma":[0.007995937,0.0002720916,0.0002474704,0.0008112206,0.0003742326,0.001820911,0.000359599,0.0008652993,0.0001932784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004626435,"about_ca_system_score_gemma":0.0002109871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001605976,"about_ca_topic_score_gemma":0.001313025,"domain_scores_codex":[0.9998513,0.00003299153,0.00001208554,0.00003347303,0.00004934366,0.00002081853],"domain_scores_gemma":[0.9964658,0.002578902,0.0003382858,0.0001512123,0.0003437441,0.0001219827],"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":[0.0004404949,0.0001702864,0.02416028,0.0001021957,0.00009768974,0.0002612828,0.00008696422,0.8398685,0.009469818,0.02731412,0.001753228,0.09627501],"study_design_scores_gemma":[0.000002415685,0.000006193649,0.0004233085,0.000001222845,0.000002747047,0.000005545162,0.000003304653,0.9969407,0.0003093263,0.002260171,0.00004347683,0.00000147893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7026294,0.0004738523,0.2945709,0.0004603236,0.0002545733,0.00001775353,0.0001297743,0.0002630387,0.001200405],"genre_scores_gemma":[0.9853811,0.0002051925,0.01330602,0.000022565,0.00007786163,0.00000890234,0.0001493512,0.00001332411,0.0008357896],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001605976,"threshold_uncertainty_score":0.004633725,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4210761163","doi":"10.1016/j.ijforecast.2021.12.013","title":"Forecasting crude oil market volatility using variable selection and common factor","year":2022,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":111,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Volatility (finance); Econometrics; Feature selection; Lasso (programming language); Elastic net regularization; Economics; Principal component analysis; Model selection; Factor analysis; Regression; Implied volatility; Computer science; Machine learning; Statistics; Artificial intelligence; Mathematics","authors":[{"name":"Yaojie Zhang","is_ca":false},{"name":"M.I.M. Wahab","is_ca":true},{"name":"Yudong Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06444334607937233,"gpt":0.2539905274608675,"spread":0.1895471813814952,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001921501,0.0006691776,0.001180281,0.002929938,0.0005472977,0.001180666,0.0006510793,0.000697408,0.0008107164],"category_scores_gemma":[0.006583026,0.0003841154,0.001105351,0.002542551,0.0002866206,0.00119154,0.0005018199,0.0006623868,0.0001869269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000686294,"about_ca_system_score_gemma":0.001163576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02210397,"about_ca_topic_score_gemma":0.01753814,"domain_scores_codex":[0.9993762,0.0002006649,0.00004703807,0.0001524564,0.0001092684,0.0001143212],"domain_scores_gemma":[0.9957182,0.002833383,0.0003745178,0.0002774583,0.0006434608,0.0001530512],"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":[0.001614686,0.0005696929,0.1958898,0.00005757636,0.0006676087,0.0002656088,0.000129456,0.5686229,0.006353133,0.003877098,0.001831203,0.2201211],"study_design_scores_gemma":[0.00002393406,0.00004330081,0.008459522,0.000002101948,0.00003228229,0.00001173999,0.00001344689,0.9897591,0.0009090069,0.0006318932,0.00010515,0.000008507955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.936567,0.0002071193,0.06194287,0.00008466786,0.00004570621,0.00002659027,0.0002427375,0.0002195089,0.0006636989],"genre_scores_gemma":[0.9897451,0.00006984596,0.009410309,0.00000716562,0.00003083446,0.000008049711,0.0004533464,0.00001154908,0.0002638072],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02210397,"threshold_uncertainty_score":0.04395062,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1976663395","doi":"10.1016/s0169-2070(02)00073-0","title":"Modelling multinational telecommunications demand with limited data","year":2002,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Innovation Diffusion and Forecasting","field":"Decision Sciences","cited_by":75,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Guelph","funders":"","keywords":"Pooling; Gompertz function; Diffusion; Benchmark (surveying); Integrated Services Digital Network; Multinational corporation; Computer science; Innovation diffusion; Econometrics; Telecommunications; Estimation; Demand forecasting; Operations research; Economics; Engineering; Finance; Artificial intelligence","authors":[{"name":"Towhidul Islam","is_ca":true},{"name":"Denzil G. Fiebig","is_ca":false},{"name":"Nigel Meade","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3920235667463665,"gpt":0.3916633281536203,"spread":0.0003602385927462537,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002934654,0.001040636,0.001467729,0.000977396,0.0004654734,0.001804082,0.001816322,0.003377672,0.003960707],"category_scores_gemma":[0.02264822,0.001624312,0.0009666466,0.001470897,0.0008049496,0.003261251,0.0009452177,0.002098512,0.000452041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002166031,"about_ca_system_score_gemma":0.0009858604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04385322,"about_ca_topic_score_gemma":0.02372642,"domain_scores_codex":[0.9992124,0.0004073488,0.00005909791,0.0001465566,0.00006792883,0.0001067672],"domain_scores_gemma":[0.9744425,0.02327977,0.0008651447,0.0005028929,0.0005930032,0.0003166371],"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":[0.00009188845,0.00003920151,0.001974899,0.00002788757,0.0000190631,0.0000539373,0.00002450521,0.9951386,0.00007771209,0.001056064,0.0002636107,0.0012326],"study_design_scores_gemma":[0.000005906174,0.000007189791,0.0002172308,0.00000193505,0.000003276929,0.000003956137,0.000008178356,0.9989442,0.00003423283,0.0007280031,0.00004292227,0.000002938107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9251511,0.0004922311,0.06475803,0.002474123,0.0001109106,0.00006659445,0.003484745,0.0002255903,0.003236601],"genre_scores_gemma":[0.9907181,0.0001680767,0.004658222,0.00005701619,0.00004540407,0.00007343366,0.001075753,0.00003203249,0.003171994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04385322,"threshold_uncertainty_score":0.08719593,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2569456096","doi":"10.1016/j.ijforecast.2016.10.002","title":"Nowcasting with payments system data","year":2017,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Banking stability, regulation, efficiency","field":"Economics, Econometrics and Finance","cited_by":53,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"St. Francis Xavier University; McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Nowcasting; Payment; Economics; Business; Meteorology; Finance; Geography","authors":[{"name":"John W. Galbraith","is_ca":true},{"name":"Greg Tkacz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1053396827502767,"gpt":0.2866428780034912,"spread":0.1813031952532145,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004202559,0.0007960257,0.001064632,0.001832747,0.0003809693,0.002168242,0.0008878527,0.001517135,0.005149324],"category_scores_gemma":[0.0266372,0.0005628609,0.000683996,0.00226059,0.0002712591,0.003354738,0.0006785713,0.002497923,0.002141763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007764979,"about_ca_system_score_gemma":0.0008132414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01418003,"about_ca_topic_score_gemma":0.01355999,"domain_scores_codex":[0.9989783,0.0004082282,0.00007489738,0.000262659,0.0001686753,0.0001073149],"domain_scores_gemma":[0.9896993,0.005745468,0.0006781479,0.002523313,0.001110145,0.0002437249],"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":[0.001612362,0.0003350645,0.05585305,0.0002209391,0.0004000911,0.0004539583,0.0002158644,0.4905309,0.002579289,0.02319528,0.05388349,0.3707196],"study_design_scores_gemma":[0.00005384833,0.0000498161,0.00817852,0.00003205064,0.00005167108,0.00004477734,0.000055976,0.9616786,0.001115666,0.0226728,0.006038272,0.00002798598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5707762,0.003532929,0.3734198,0.007389467,0.005178052,0.0002207378,0.01870845,0.005289822,0.01548453],"genre_scores_gemma":[0.9468259,0.0009154332,0.03210019,0.000172005,0.0008433873,0.00004868805,0.0118529,0.0001371029,0.007104556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01418003,"threshold_uncertainty_score":0.02819502,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1998233328","doi":"10.1016/j.ijforecast.2009.10.011","title":"Forecasting national activity using lots of international predictors: An application to New Zealand","year":2010,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":47,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Deutsche Bundesbank; University of Victoria","keywords":"Econometrics; Regional science; Economics; Computer science; Geography","authors":[{"name":"Sandra Eickmeier","is_ca":false},{"name":"Tim Ng","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1211188788940058,"gpt":0.2940143766796164,"spread":0.1728954977856105,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00174466,0.0006776368,0.0009538516,0.001311532,0.0007663103,0.001004724,0.0008616121,0.0007088819,0.001217953],"category_scores_gemma":[0.008836757,0.0004156786,0.00069713,0.003233655,0.0004881216,0.001362574,0.0006594487,0.0007918303,0.0002007226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001148397,"about_ca_system_score_gemma":0.001022156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2424466,"about_ca_topic_score_gemma":0.208241,"domain_scores_codex":[0.9997485,0.00007017639,0.0000280733,0.00006347725,0.00006277207,0.00002685177],"domain_scores_gemma":[0.9970091,0.001909491,0.0002557642,0.0002664943,0.0004309427,0.0001282064],"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":[0.001713127,0.0005890429,0.2958148,0.0001728807,0.0005489758,0.0006809004,0.0007967405,0.508327,0.001459808,0.002064785,0.004743085,0.1830888],"study_design_scores_gemma":[0.00009347748,0.0001128652,0.0479686,0.00001418095,0.00009534362,0.0000523081,0.0003073043,0.9482604,0.0005504237,0.001518304,0.0009889932,0.00003780886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851288,0.0003498835,0.01144298,0.0004843475,0.00006332638,0.00003659347,0.0009921002,0.0002901159,0.00121184],"genre_scores_gemma":[0.9908635,0.0003610564,0.007310036,0.00001936721,0.0000386163,0.00002296862,0.0007341646,0.00002125444,0.0006290002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2424466,"threshold_uncertainty_score":0.4820708,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4384700499","doi":"10.1016/j.ijforecast.2023.05.002","title":"Bayesian forecasting in economics and finance: A modern review","year":2023,"lang":"en","type":"review","venue":"International Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":36,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada; Australian Research Council; Austrian Science Fund","keywords":"Bayesian probability; Bayesian econometrics; Computer science; Context (archaeology); Probabilistic logic; Computational finance; Field (mathematics); Probabilistic forecasting; Bayesian statistics; Artificial intelligence; Econometrics; Bayesian inference; Economics; Machine learning; Finance; Mathematics","authors":[{"name":"Gael M. Martin","is_ca":false},{"name":"David T. Frazier","is_ca":false},{"name":"Worapree Maneesoonthorn","is_ca":false},{"name":"Rubén Loaiza‐Maya","is_ca":false},{"name":"Florian Huber","is_ca":false},{"name":"Gary Koop","is_ca":false},{"name":"John M. Maheu","is_ca":true},{"name":"Didier Nibbering","is_ca":false},{"name":"Anastasios Panagiotelis","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1766026558421256,"gpt":0.3244632513444106,"spread":0.1478605955022851,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002773995,0.001200898,0.00241875,0.002998531,0.0002850289,0.001875928,0.001298522,0.002469735,0.004208936],"category_scores_gemma":[0.007452956,0.0005052494,0.0007458971,0.00569078,0.0007986226,0.003225307,0.001035864,0.002316889,0.001443971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051101,"about_ca_system_score_gemma":0.003105682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004429032,"about_ca_topic_score_gemma":0.004232313,"domain_scores_codex":[0.9993892,0.0001325433,0.00009407238,0.0001222306,0.0002303601,0.00003149973],"domain_scores_gemma":[0.994093,0.004384082,0.0003946599,0.0001417305,0.0008453932,0.0001412021],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005510615,0.00007162066,0.0004875349,0.009776835,0.0001492629,0.00005317053,0.00003946922,0.00180076,0.0002879409,0.008796118,0.02386534,0.9546168],"study_design_scores_gemma":[0.00008680118,0.0001714352,0.003779005,0.01902007,0.0009125007,0.0007244281,0.0001108368,0.004750811,0.0006624392,0.04631187,0.9233192,0.0001506304],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001148038,0.9969021,0.001466766,0.0007558707,0.0002210589,0.000004858083,0.00004117078,0.0000136924,0.0004796592],"genre_scores_gemma":[0.001249198,0.9966524,0.001082389,0.0003033384,0.0005001735,0.000007435858,0.0000464769,0.000004052901,0.0001545421],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004429032,"threshold_uncertainty_score":0.01467049,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1997761874","doi":"10.1016/j.ijforecast.2008.02.007","title":"Campaign trial heats as election forecasts: Measurement error and bias in 2004 presidential campaign polls","year":2008,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Polling; Presidential system; Presidential election; Opinion poll; Econometrics; Test (biology); General election; Economics; Political science; Computer science; Public opinion; Law; Politics","authors":[{"name":"Mark Pickup","is_ca":true},{"name":"Richard Johnston","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2514785077243227,"gpt":0.3926941115163439,"spread":0.1412156037920211,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06183433,0.0005402587,0.0008670709,0.002602423,0.0009138423,0.003277869,0.001278367,0.001847444,0.001695727],"category_scores_gemma":[0.3070544,0.0007466823,0.0008035011,0.003525332,0.001094028,0.002312726,0.001728459,0.002167973,0.000687377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001935579,"about_ca_system_score_gemma":0.001436325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02665544,"about_ca_topic_score_gemma":0.02499856,"domain_scores_codex":[0.968751,0.02209852,0.002132522,0.002376742,0.003608246,0.001032835],"domain_scores_gemma":[0.4840778,0.4361939,0.03461275,0.01859409,0.02503615,0.001485259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001363185,0.0001892982,0.9536046,0.00009069918,0.0006689053,0.00003797027,0.001942996,0.009076361,0.0002857916,0.001761998,0.004515569,0.02646266],"study_design_scores_gemma":[0.00009503555,0.0002243419,0.941304,0.00008957963,0.0004388833,0.00004043666,0.001049742,0.04883828,0.00141306,0.002816155,0.00361187,0.00007866199],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885311,0.0006043486,0.005292063,0.000781191,0.0003002965,0.00008639339,0.001048582,0.00007607789,0.003279977],"genre_scores_gemma":[0.9969469,0.0001027847,0.0005957468,0.0001231121,0.0001772814,0.00007807339,0.001059468,0.00003600451,0.0008806229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06183433,"threshold_uncertainty_score":0.327015,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3177942030","doi":"10.1016/j.ijforecast.2021.05.013","title":"Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model","year":2021,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Carleton University","funders":"","keywords":"Copula (linguistics); Probabilistic forecasting; Probabilistic logic; Wind power; Residual; Replicate; Computer science; Econometrics; Grid; Wind speed; Statistics; Mathematics; Meteorology; Artificial intelligence; Engineering; Algorithm","authors":[{"name":"Mario E. Arrieta-Prieto","is_ca":false},{"name":"Kristen R. Schell","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03774601430938498,"gpt":0.2473869508528618,"spread":0.2096409365434768,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00100821,0.000704217,0.001213908,0.0005437544,0.0004434703,0.001211069,0.001898734,0.001257866,0.001785934],"category_scores_gemma":[0.002377629,0.0007927215,0.001227002,0.001165838,0.0004592648,0.001646682,0.0007476346,0.001227238,0.0003979709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006775296,"about_ca_system_score_gemma":0.0008560495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0236366,"about_ca_topic_score_gemma":0.01567441,"domain_scores_codex":[0.9996167,0.0001030882,0.00002370139,0.0001404467,0.00005992322,0.00005603251],"domain_scores_gemma":[0.9990457,0.0005074595,0.0001489772,0.0000689339,0.0001726594,0.00005620622],"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":[0.00002029313,0.0000206243,0.0007107611,0.00001085798,0.00004453069,0.00004048746,0.00001165736,0.9943959,0.0002020637,0.001051158,0.0002004071,0.003291396],"study_design_scores_gemma":[0.000001081791,0.000003039668,0.000118368,6.22317e-7,0.000003118635,0.000003219982,0.000001379576,0.999647,0.00001339268,0.0001872405,0.00001991328,0.000001498996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2612084,0.0008870339,0.7304741,0.0008487619,0.0002150702,0.00007801541,0.0008347622,0.0005546085,0.004899243],"genre_scores_gemma":[0.9770868,0.0003641298,0.01856663,0.00007269635,0.00007399144,0.00006332717,0.0003772849,0.00005533374,0.00333975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0236366,"threshold_uncertainty_score":0.04699808,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2057363883","doi":"10.1016/j.ijforecast.2014.05.003","title":"Markov-switching mixed-frequency VAR models","year":2014,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Italy: Economic History and Contemporary Issues","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Bank of Canada","funders":"","keywords":"Econometrics; Inference; Business cycle; Markov chain Monte Carlo; Markov chain; Monte Carlo method; Sample (material); Vector autoregression; Sampling (signal processing); Computer science; Mathematics; Economics; Statistics; Artificial intelligence; Macroeconomics; Telecommunications","authors":[{"name":"Claudia Foroni","is_ca":false},{"name":"Pierre Guérin","is_ca":true},{"name":"Massimiliano Marcellino","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05233574697476035,"gpt":0.2176897034229946,"spread":0.1653539564482343,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003447124,0.001262784,0.002326768,0.001235599,0.0006229479,0.002664302,0.002991274,0.003267639,0.008129252],"category_scores_gemma":[0.01063538,0.001087191,0.001572658,0.001323987,0.001235103,0.003218101,0.001240219,0.002142134,0.001424078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009095802,"about_ca_system_score_gemma":0.0007878693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006771544,"about_ca_topic_score_gemma":0.005895139,"domain_scores_codex":[0.9984331,0.0007305823,0.00007800462,0.0003576782,0.0001691228,0.0002315812],"domain_scores_gemma":[0.9909078,0.006830719,0.0009200639,0.0005012931,0.0006028801,0.0002372527],"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":[0.0003749501,0.0001402903,0.003788815,0.0001297726,0.000274579,0.0002704608,0.0001719952,0.7869697,0.0008441746,0.1837422,0.002819608,0.02047351],"study_design_scores_gemma":[0.00001893365,0.00002111499,0.0002749061,0.000007589436,0.00002246665,0.00002297477,0.000007703457,0.9794978,0.00006699772,0.01977503,0.0002700222,0.00001453435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1450524,0.001760066,0.8428151,0.001540845,0.0004770834,0.00006873123,0.001137167,0.0007802905,0.006368247],"genre_scores_gemma":[0.9515239,0.001142032,0.02574056,0.0002207288,0.0004186792,0.0001437391,0.001012748,0.00009803016,0.0196997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008129252,"threshold_uncertainty_score":0.02719504,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1988812725","doi":"10.1016/s0169-2070(01)00112-1","title":"Bootstrap prediction intervals for single period regression forecasts","year":2002,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McMaster University","funders":"","keywords":"Percentile; Statistics; Prediction interval; Mathematics; Econometrics; Regression; Standard error; Interval (graph theory); Confidence interval; Regression analysis; Monte Carlo method; Sample size determination; Sample (material); Standard deviation","authors":[{"name":"Janice Lam","is_ca":true},{"name":"Michael R. Veall","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.3993399489601318,"gpt":0.4256177449625351,"spread":0.02627779600240332,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0146837,0.0006569389,0.001711847,0.002417304,0.0004964754,0.001372616,0.001539721,0.001333461,0.003160148],"category_scores_gemma":[0.1062321,0.0005822423,0.0008911018,0.001948557,0.0006826177,0.002598951,0.001126119,0.002047072,0.0005990281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005002922,"about_ca_system_score_gemma":0.0005142846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001018045,"about_ca_topic_score_gemma":0.0006512639,"domain_scores_codex":[0.996379,0.002019437,0.0001931094,0.0003142284,0.0009255401,0.0001687499],"domain_scores_gemma":[0.8856532,0.103673,0.00261355,0.004310869,0.003243283,0.0005060624],"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":[0.00200048,0.0002508344,0.006706987,0.0004921983,0.0004942546,0.000301544,0.0003195167,0.5210293,0.003802759,0.06124333,0.004876181,0.3984827],"study_design_scores_gemma":[0.00004259594,0.00009624341,0.002454937,0.00006193955,0.00006049976,0.00006838414,0.0000286874,0.967804,0.001257729,0.02729336,0.0008058473,0.00002587796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1000905,0.003568042,0.8920313,0.0002692075,0.0001909892,0.00004756855,0.0002818662,0.0009817599,0.002538692],"genre_scores_gemma":[0.8764806,0.00170246,0.1188495,0.00008755939,0.0002821714,0.0001516738,0.001111196,0.0003050133,0.001029784],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0146837,"threshold_uncertainty_score":0.07765573,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2000506406","doi":"10.1016/j.ijforecast.2009.04.002","title":"Electoral forecasting in France: A multi-equation solution","year":2009,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Electoral Systems and Political Participation","field":"Social Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Presidential system; Context (archaeology); Structural equation modeling; Presidential election; Econometrics; Work (physics); Field (mathematics); Key (lock); Computer science; Political science; Operations research; Applied mathematics; Mathematics; Law; History; Engineering; Politics; Computer security; Machine learning","authors":[{"name":"Richard Nadeau","is_ca":true},{"name":"Michael S. Lewis‐Beck","is_ca":false},{"name":"Éric Bélanger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1587935145315889,"gpt":0.3940765814986887,"spread":0.2352830669670998,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00275103,0.0006692752,0.00254779,0.001470912,0.001528099,0.002827346,0.001726551,0.003862602,0.006818253],"category_scores_gemma":[0.01104359,0.001014833,0.00176647,0.001715355,0.0005418063,0.001409372,0.001038822,0.002113209,0.0006246647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003145291,"about_ca_system_score_gemma":0.004797821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3827511,"about_ca_topic_score_gemma":0.1970779,"domain_scores_codex":[0.9990503,0.0004166502,0.00004931979,0.0001986108,0.000072791,0.0002123194],"domain_scores_gemma":[0.9919401,0.006690983,0.0004035415,0.0001421756,0.0005787863,0.0002443578],"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":[0.00006573281,0.0001036595,0.0113919,0.00004790837,0.0001202326,0.0002476802,0.0002339092,0.9650119,0.00009700543,0.01088022,0.002688394,0.009111479],"study_design_scores_gemma":[0.00003999624,0.00001913407,0.003031999,0.00001522237,0.00003640297,0.0000174385,0.0001537413,0.9931781,0.00004283182,0.002726857,0.0007165548,0.00002162817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9223703,0.001395414,0.05641513,0.00682458,0.0002565662,0.0001119742,0.002162574,0.0003135644,0.0101499],"genre_scores_gemma":[0.9761264,0.000579318,0.009924157,0.0002349851,0.0002014839,0.0001169617,0.0012173,0.00005451624,0.01154473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3827511,"threshold_uncertainty_score":0.7610465,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1993565439","doi":"10.1016/j.ijforecast.2004.10.002","title":"Content horizons for conditional variance forecasts","year":2004,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Econometrics; Volatility (finance); Autoregressive conditional heteroskedasticity; Realized variance; Conditional variance; Variance (accounting); Economics; Stochastic volatility; Statistics; Mathematics","authors":[{"name":"John W. Galbraith","is_ca":true},{"name":"Turgut Kıṣınbay","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1568203140430071,"gpt":0.2749748780340028,"spread":0.1181545639909957,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006967331,0.00118297,0.00143702,0.002571695,0.0009758589,0.002838161,0.001854075,0.001976037,0.009767051],"category_scores_gemma":[0.06636882,0.001181263,0.000906681,0.00169555,0.001192774,0.009215289,0.002310267,0.004002673,0.001226298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001880742,"about_ca_system_score_gemma":0.001398984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003678425,"about_ca_topic_score_gemma":0.002775249,"domain_scores_codex":[0.9982961,0.0005206032,0.0001197971,0.0002695774,0.0005207782,0.0002730888],"domain_scores_gemma":[0.9676073,0.02645592,0.001434022,0.002073995,0.001568027,0.000860649],"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":[0.0005245089,0.00009580307,0.001572668,0.0002405064,0.000106325,0.0001854559,0.0002845913,0.2605404,0.002750148,0.5829345,0.006264378,0.1445008],"study_design_scores_gemma":[0.00001175439,0.00001742132,0.0004597862,0.00004273151,0.00002316157,0.0000193855,0.00001530673,0.7756252,0.0006461545,0.222274,0.0008459867,0.00001915947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0460053,0.002082928,0.9419878,0.0009811374,0.000332871,0.00006417276,0.0005358325,0.0007728641,0.007237101],"genre_scores_gemma":[0.8463876,0.002763261,0.1395034,0.0002798009,0.001024098,0.0001869701,0.001800015,0.0005303394,0.007524488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009767051,"threshold_uncertainty_score":0.03684723,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1964796118","doi":"10.1016/s0169-2070(01)00117-0","title":"Normalization of seasonal factors in Winters’ methods","year":2003,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"","keywords":"Multiplicative function; Normalization (sociology); Seasonality; Mathematics; Smoothing; Renormalization; Seasonal adjustment; Series (stratigraphy); Exponential smoothing; Statistics; Econometrics; Applied mathematics; Mathematical analysis; Geology; Mathematical physics","authors":[{"name":"Blyth C. Archibald","is_ca":true},{"name":"Anne B. Koehler","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2247057654747922,"gpt":0.4749273167383505,"spread":0.2502215512635583,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006500887,0.000793419,0.0007715314,0.001397845,0.0007066271,0.001540478,0.001482557,0.0006547774,0.006614021],"category_scores_gemma":[0.01636259,0.0004351024,0.001541332,0.001966258,0.0006459783,0.001585849,0.001162242,0.00128107,0.00243817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001030491,"about_ca_system_score_gemma":0.001512227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004546716,"about_ca_topic_score_gemma":0.006831052,"domain_scores_codex":[0.9968293,0.001355744,0.000250321,0.0005967711,0.0007208333,0.0002469434],"domain_scores_gemma":[0.9954106,0.001534595,0.0002723291,0.001426698,0.001255131,0.0001006648],"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":[0.0005863573,0.0001618755,0.008007391,0.0003455689,0.0003757413,0.00008379901,0.0005457908,0.08208597,0.01627128,0.09129291,0.0103524,0.7898909],"study_design_scores_gemma":[0.00007319073,0.0001935748,0.0297462,0.0001312823,0.0001790419,0.0002284859,0.000291454,0.7637586,0.02434198,0.1056215,0.07531396,0.0001208638],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01369917,0.0003951405,0.981627,0.0001276625,0.0003033773,0.00006607184,0.0002755762,0.0005117992,0.00299409],"genre_scores_gemma":[0.2828591,0.0009880175,0.6963251,0.0001978146,0.0006059734,0.0005605962,0.002423984,0.001808604,0.01423086],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006614021,"threshold_uncertainty_score":0.03438038,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2739047936","doi":"10.1016/j.ijforecast.2017.05.002","title":"Nowcasting BRIC+M in real time","year":2017,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Bank of Canada","funders":"","keywords":"BRIC; Nowcasting; Dynamic factor; China; Emerging markets; Sample (material); Econometrics; Economics; Real gross domestic product; Macroeconomics; Geography","authors":[{"name":"Tatjana Dahlhaus","is_ca":true},{"name":"Justin-Damien Guénette","is_ca":true},{"name":"Garima Vasishtha","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1665136942621235,"gpt":0.2863217796964572,"spread":0.1198080854343337,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002041283,0.0005841962,0.0007426642,0.00127694,0.0002839177,0.001764697,0.0008197915,0.001436247,0.004493258],"category_scores_gemma":[0.01253081,0.0003485022,0.0003504748,0.001345592,0.0003025678,0.001613784,0.0006236352,0.002186806,0.001993575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000689155,"about_ca_system_score_gemma":0.00103903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01820818,"about_ca_topic_score_gemma":0.02121324,"domain_scores_codex":[0.9993911,0.0001209949,0.00003826907,0.0001503291,0.0002165755,0.0000827182],"domain_scores_gemma":[0.9968071,0.001123298,0.0003810082,0.000664976,0.0008557581,0.0001678429],"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":[0.001696993,0.0002002516,0.0225343,0.0003289485,0.0002044105,0.0002998558,0.0001495492,0.6739125,0.01045791,0.02185541,0.05607225,0.2122878],"study_design_scores_gemma":[0.00004842607,0.00005187081,0.008481815,0.00003579207,0.00003709579,0.00004289862,0.00004786517,0.9696144,0.002983697,0.007215244,0.01140934,0.00003143298],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.634791,0.007550318,0.2783076,0.0128986,0.01409862,0.000135319,0.01605762,0.006854019,0.02930677],"genre_scores_gemma":[0.9572371,0.0009493128,0.02600919,0.0003621588,0.001433869,0.00002761781,0.00562281,0.0002581215,0.008099698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01820818,"threshold_uncertainty_score":0.0362044,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2915228488","doi":"10.1016/j.ijforecast.2018.11.006","title":"Asymmetry in unemployment rate forecast errors","year":2019,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":29,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; Center for Interuniversity Research and Analysis on Organizations; McGill University","funders":"","keywords":"Survey of Professional Forecasters; Asymmetry; Economics; Unemployment; Econometrics; Unemployment rate; Forecast error; Horizon; Consensus forecast; Information asymmetry; Macroeconomics; Monetary policy; Finance; Mathematics","authors":[{"name":"John W. Galbraith","is_ca":true},{"name":"Simon van Norden","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09322400308235114,"gpt":0.254601075544843,"spread":0.1613770724624918,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007259674,0.0002618543,0.0008131298,0.001967105,0.0003826797,0.002701116,0.000547079,0.001451303,0.003891792],"category_scores_gemma":[0.08126483,0.0005456008,0.0004540676,0.001011916,0.0007295419,0.003530147,0.001230954,0.00141004,0.0005995296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008950193,"about_ca_system_score_gemma":0.0004021498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002237366,"about_ca_topic_score_gemma":0.00157465,"domain_scores_codex":[0.9980665,0.0007088648,0.0001733011,0.0002694658,0.0005149489,0.0002668983],"domain_scores_gemma":[0.9265935,0.05860923,0.007395447,0.003451238,0.003381494,0.000569076],"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":[0.00442121,0.0002069137,0.4858803,0.0003116772,0.0008537993,0.001307961,0.002794506,0.101571,0.01227961,0.2162076,0.008128497,0.1660369],"study_design_scores_gemma":[0.0001399447,0.0002149466,0.3530226,0.0001618581,0.000332059,0.0009165349,0.0008800045,0.3955297,0.003577305,0.2431257,0.001955603,0.0001437202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9634013,0.001170448,0.0229648,0.001701989,0.0001131157,0.00001778191,0.0004543196,0.0001246799,0.01005147],"genre_scores_gemma":[0.9990625,0.0001157518,0.0003395336,0.00003331543,0.0000658452,0.000001988497,0.00009415947,0.000009878574,0.0002771251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007259674,"threshold_uncertainty_score":0.03839326,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2903850923","doi":"10.1016/j.ijforecast.2019.09.006","title":"A three-frequency dynamic factor model for nowcasting Canadian provincial GDP growth","year":2020,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":25,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Bank of Canada","funders":"","keywords":"Nowcasting; Gross domestic product; Dynamic factor; Econometrics; Real gross domestic product; Economics; National accounts; Lag; Product (mathematics); Geography; Macroeconomics; Computer science; Mathematics; Meteorology","authors":[{"name":"Tony Chernis","is_ca":true},{"name":"Calista Cheung","is_ca":true},{"name":"Gabriella Velasco","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1563857037210321,"gpt":0.2518952886839546,"spread":0.09550958496292244,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001532029,0.0009602595,0.001077204,0.001079982,0.001940267,0.002524086,0.003103503,0.002364668,0.003953751],"category_scores_gemma":[0.004917793,0.0007598863,0.001128361,0.002128512,0.001017752,0.001406857,0.0007228822,0.002388211,0.000561843],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01215557,"about_ca_system_score_gemma":0.01286187,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9478424,"about_ca_topic_score_gemma":0.8820627,"domain_scores_codex":[0.9995097,0.00009545047,0.00002245152,0.0001254495,0.0001011437,0.0001458127],"domain_scores_gemma":[0.9988596,0.0003365287,0.00009221022,0.00005975131,0.0005411619,0.0001107549],"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":[0.00007550575,0.00002415791,0.002362901,0.00002426547,0.00004091224,0.00007012505,0.00007565034,0.9777008,0.0003154958,0.0110717,0.002259744,0.005978839],"study_design_scores_gemma":[0.00001040271,0.000003941225,0.0007166927,0.000003893433,0.00001121721,0.000005365129,0.00001747825,0.9973917,0.00004070412,0.001263714,0.0005201219,0.00001476201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5624092,0.003100812,0.3832438,0.009273314,0.001533768,0.0002599115,0.009736324,0.001935558,0.0285072],"genre_scores_gemma":[0.9687899,0.0008617502,0.01578566,0.0001575347,0.0001218773,0.00007457752,0.001849284,0.00008126997,0.01227828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9878444,"threshold_uncertainty_score":0.1049294,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2908925382","doi":"10.1016/j.ijforecast.2019.08.003","title":"A functional time series analysis of forward curves derived from commodity futures","year":2019,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Renmin University of China","keywords":"Futures contract; Heteroscedasticity; Econometrics; Series (stratigraphy); Economics; Commodity; Functional principal component analysis; Index (typography); Multivariate statistics; Functional data analysis; Time series; Autocorrelation; Financial economics; Mathematics; Statistics; Computer science; Finance","authors":[{"name":"Lajos Horváth","is_ca":false},{"name":"Zhenya Liu","is_ca":false},{"name":"Gregory Rice","is_ca":true},{"name":"Shixuan Wang","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02861054141609645,"gpt":0.2256483608638289,"spread":0.1970378194477325,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005435668,0.0003006186,0.0002226033,0.001054074,0.0002255531,0.0006122978,0.0002833272,0.000417843,0.001711042],"category_scores_gemma":[0.00198789,0.0001310023,0.0005900301,0.0007817593,0.0002028556,0.0006709759,0.0002106961,0.0005095325,0.0002141418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002969531,"about_ca_system_score_gemma":0.0003477434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004159505,"about_ca_topic_score_gemma":0.00161644,"domain_scores_codex":[0.9999259,0.00002487213,0.000004680199,0.00001456773,0.00002242728,0.00000759659],"domain_scores_gemma":[0.999607,0.0002203486,0.00002655782,0.00003790868,0.00009317325,0.00001501764],"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":[0.0003071354,0.0001211163,0.01420178,0.000203062,0.0001415624,0.0007255042,0.0004677454,0.5548158,0.08781875,0.1054991,0.002256957,0.2334414],"study_design_scores_gemma":[0.000002654285,0.00002512369,0.004198308,0.000005373228,0.00001079484,0.00007187668,0.00002293079,0.9895033,0.00212756,0.003009518,0.001010747,0.00001186361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5880094,0.0005133584,0.4041388,0.0002830016,0.0000960697,0.00004450505,0.0004736391,0.0003991011,0.00604211],"genre_scores_gemma":[0.9690492,0.000366885,0.02782742,0.00001173334,0.00004810673,0.00001122915,0.0004436473,0.00008974785,0.002152164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004159505,"threshold_uncertainty_score":0.008270621,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3123808201","doi":"10.1016/j.ijforecast.2015.09.005","title":"Betas and the myth of market neutrality","year":2016,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"HEC Montréal; University of Auckland; University of New South Wales; La Trobe University","keywords":"Construct (python library); Uncorrelated; Economics; Portfolio; Econometrics; Neutrality; Financial economics; Market portfolio; BETA (programming language); Financial market; Computer science; Finance; Statistics; Mathematics","authors":[{"name":"Nicolas Papageorgiou","is_ca":true},{"name":"Jonathan J. Reeves","is_ca":false},{"name":"Xuan Xie","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04358669826179985,"gpt":0.2277242802355491,"spread":0.1841375819737493,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01180839,0.0004131938,0.0007763962,0.001342959,0.001101518,0.005206611,0.001027117,0.002777253,0.004067475],"category_scores_gemma":[0.04638807,0.0003160269,0.00048736,0.0004459845,0.01073149,0.009364007,0.002170395,0.005185168,0.0005551867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048348,"about_ca_system_score_gemma":0.0006577132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003192508,"about_ca_topic_score_gemma":0.000129169,"domain_scores_codex":[0.9969929,0.00144166,0.0001186119,0.0006024566,0.0005656137,0.0002786714],"domain_scores_gemma":[0.977465,0.01566661,0.002060413,0.002230508,0.001526779,0.001050627],"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":[0.00006297962,0.00001744091,0.0007576371,0.00002055788,0.00002906049,0.00005958629,0.0002607144,0.001104601,0.000235108,0.9870645,0.001830916,0.008557023],"study_design_scores_gemma":[0.00001634,0.00001218623,0.0002551429,0.000009466899,0.000003552566,0.00004260448,0.00003480587,0.001667014,0.00006102804,0.9968026,0.001087938,0.000007234446],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3994204,0.007718772,0.1865947,0.2303686,0.00269581,0.00006393215,0.0003158398,0.0002555368,0.1725664],"genre_scores_gemma":[0.9884071,0.00081679,0.003704289,0.002711615,0.001441662,0.0000373079,0.00002679661,0.0000344964,0.002819958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01180839,"threshold_uncertainty_score":0.06244951,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2076209930","doi":"10.1016/s0169-2070(03)00064-5","title":"The evolution of consensus in macroeconomic forecasting","year":2003,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Queen's University","funders":"","keywords":"Economics; Consensus forecast; Econometrics","authors":[{"name":"Allan W. Gregory","is_ca":true},{"name":"James Yetman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09611602992980839,"gpt":0.2473993596999721,"spread":0.1512833297701637,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008203852,0.0002733323,0.000879062,0.002552933,0.001709062,0.003557994,0.001280728,0.002950888,0.005641425],"category_scores_gemma":[0.1100486,0.000452654,0.0005063303,0.002139776,0.002946426,0.00641883,0.002128764,0.002714255,0.0006505303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002008449,"about_ca_system_score_gemma":0.001214453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004224798,"about_ca_topic_score_gemma":0.002820523,"domain_scores_codex":[0.9973334,0.001369745,0.0001184351,0.0005520407,0.000405391,0.0002209598],"domain_scores_gemma":[0.9252759,0.05427169,0.005349884,0.005982678,0.006829043,0.002290751],"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":[0.0007001538,0.0001754363,0.04914799,0.0002868643,0.0002977139,0.0004344489,0.004827933,0.09655752,0.002755453,0.678825,0.01275448,0.153237],"study_design_scores_gemma":[0.00006423534,0.00007122013,0.01196614,0.00005359668,0.00004975178,0.0001417604,0.00115351,0.1675354,0.0006976782,0.8128679,0.005344213,0.00005452373],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8594274,0.003594438,0.08308477,0.01637267,0.0005319091,0.00004134532,0.0004346376,0.0003256671,0.03618716],"genre_scores_gemma":[0.9936835,0.0003457523,0.004377764,0.0001563608,0.0001188992,0.00001359519,0.00008626406,0.00003674648,0.001181133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008203852,"threshold_uncertainty_score":0.04338664,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3084216266","doi":"10.1016/j.ijforecast.2020.12.005","title":"Forecasting the Covid-19 Recession and Recovery: Lessons from the Financial Crisis","year":2020,"lang":"en","type":"preprint","venue":"International Journal of Forecasting","topic":"COVID-19 Pandemic Impacts","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations; Université du Québec à Montréal","funders":"","keywords":"Nowcasting; Recession; Financial crisis; Econometrics; Economic recovery; Coronavirus disease 2019 (COVID-19); Economics; Variety (cybernetics); Estimation; Computer science; Macroeconomics; Geography; Meteorology","authors":[{"name":"Claudia Foroni","is_ca":false},{"name":"Massimiliano Marcellino","is_ca":false},{"name":"Dalibor Stevanović","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2104775964110182,"gpt":0.3385988885855596,"spread":0.1281212921745414,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002450789,0.0005355551,0.0005036884,0.0009089555,0.0003984926,0.002501949,0.0004870538,0.002509515,0.003648636],"category_scores_gemma":[0.0177015,0.0002333033,0.0003145966,0.001332116,0.0005577084,0.003117816,0.0007743148,0.002700335,0.0003694065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010843,"about_ca_system_score_gemma":0.001030622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03657766,"about_ca_topic_score_gemma":0.02016793,"domain_scores_codex":[0.9997823,0.00009642302,0.00001208945,0.0000340258,0.00003310659,0.00004214033],"domain_scores_gemma":[0.9966011,0.002283554,0.0003236275,0.0001081902,0.0004372771,0.0002462282],"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":[0.0006234195,0.0002549129,0.1190168,0.000318128,0.000229087,0.0008373472,0.0009744345,0.4320376,0.0005050026,0.1564492,0.1340628,0.1546914],"study_design_scores_gemma":[0.00006929586,0.00005066642,0.0343303,0.0002641391,0.00005179918,0.0000752363,0.0019652,0.6522478,0.0002801298,0.2950432,0.01555899,0.00006323103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7471502,0.01739745,0.03462945,0.1410089,0.002047124,0.00005537446,0.00447259,0.0002886271,0.05295026],"genre_scores_gemma":[0.9878867,0.005428385,0.002081426,0.0007536865,0.0007446678,0.00001231087,0.0008745239,0.00005746321,0.002160956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03657766,"threshold_uncertainty_score":0.07272953,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2035277700","doi":"10.1016/j.ijforecast.2010.03.004","title":"Forecasting temperature to price CME temperature derivatives","year":2010,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Economics","authors":[{"name":"Debbie J. Dupuis","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0440088545689243,"gpt":0.2551162907764979,"spread":0.2111074362075736,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002918469,0.000246062,0.0002431009,0.0004636958,0.0001721622,0.0005358684,0.0002974618,0.0005346457,0.001496475],"category_scores_gemma":[0.002549323,0.000161179,0.0002923027,0.0003956885,0.0001524771,0.0008375304,0.0001755682,0.0007012869,0.0001819494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005026687,"about_ca_system_score_gemma":0.0003343586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007087621,"about_ca_topic_score_gemma":0.007288305,"domain_scores_codex":[0.9999353,0.00001179992,0.000003297032,0.00001612836,0.00002013104,0.000013314],"domain_scores_gemma":[0.999683,0.000141869,0.00004290346,0.00002672496,0.00007574756,0.00002968091],"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":[0.0004700582,0.0001943993,0.03731645,0.00005056128,0.0001076719,0.0002118347,0.0000634227,0.8535274,0.02264704,0.006885908,0.002746811,0.07577848],"study_design_scores_gemma":[0.000005857704,0.000009782033,0.002614672,0.000001410532,0.000004869577,0.000007879328,0.000003273185,0.995346,0.001283959,0.0005911391,0.0001281673,0.000002988911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9172596,0.0002700015,0.0744096,0.0004231902,0.0002281047,0.00003092996,0.0002431136,0.0004026661,0.006732767],"genre_scores_gemma":[0.9949653,0.00004855546,0.004356799,0.00001621246,0.00002967335,0.000002955156,0.0000652969,0.00001526245,0.0004999858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007087621,"threshold_uncertainty_score":0.01409274,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4221029418","doi":"10.1016/j.ijforecast.2022.02.004","title":"Analysing differences between scenarios","year":2022,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"demographic modeling and climate adaptation","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":"University of Victoria","funders":"Robertson Foundation; Institute for New Economic Thinking","keywords":"Econometrics; Sample (material); Covariate; Sample size determination; Variable (mathematics); Computer science; Statistics; Economics; Mathematics","authors":[{"name":"David F. Hendry","is_ca":false},{"name":"Felix Pretis","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2837804020029268,"gpt":0.3990886874996576,"spread":0.1153082854967308,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03552547,0.0006830912,0.0007765479,0.002862478,0.0008051202,0.003149027,0.001691335,0.001827861,0.01098295],"category_scores_gemma":[0.2375631,0.0003659218,0.001874467,0.002993741,0.001966532,0.005663406,0.003913346,0.002834247,0.0007153193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002410949,"about_ca_system_score_gemma":0.001049411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001703519,"about_ca_topic_score_gemma":0.001294639,"domain_scores_codex":[0.9618977,0.02575389,0.002254956,0.004047192,0.005121824,0.0009244398],"domain_scores_gemma":[0.7413337,0.2282054,0.009311375,0.01253366,0.007583003,0.00103284],"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":[0.003463282,0.0006758675,0.1840085,0.001990893,0.003474732,0.001023248,0.007085636,0.1967741,0.00328399,0.3636922,0.01519007,0.2193374],"study_design_scores_gemma":[0.0002518703,0.002185442,0.1256086,0.0008615114,0.0005880297,0.000956962,0.009131487,0.1853236,0.003741393,0.6134124,0.05760852,0.0003301242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6284339,0.002221733,0.3050992,0.002928907,0.000721685,0.001321848,0.01308783,0.0006272126,0.04555779],"genre_scores_gemma":[0.9502246,0.0002406803,0.04260781,0.0002873115,0.00005514934,0.0007945925,0.004882677,0.0001526932,0.0007544821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03552547,"threshold_uncertainty_score":0.1878789,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392203267","doi":"10.1016/j.ijforecast.2024.02.001","title":"The short-term predictability of returns in order book markets: A deep learning perspective","year":2024,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":10,"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":"Engineering and Physical Sciences Research Council","keywords":"Predictability; Computer science; Representation (politics); Inference; Perspective (graphical); Term (time); Order (exchange); Machine learning; Artificial intelligence; Order book; High-frequency trading; Statistical inference; Econometrics; Data science; Algorithmic trading; Economics; Financial economics; Mathematics; Finance; Statistics","authors":[{"name":"Lorenzo Lucchese","is_ca":false},{"name":"Mikko S. Pakkanen","is_ca":true},{"name":"Almut E. D. Veraart","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07891228350161848,"gpt":0.4145048008010231,"spread":0.3355925172994046,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001429269,0.0004818657,0.0005015379,0.0009548113,0.000292603,0.001490689,0.0007743019,0.0006080957,0.0008491316],"category_scores_gemma":[0.008704592,0.000386951,0.0003742003,0.0008628975,0.001103454,0.003524803,0.0008498346,0.002005308,0.0001108524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007368522,"about_ca_system_score_gemma":0.0005606784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003682337,"about_ca_topic_score_gemma":0.00286881,"domain_scores_codex":[0.9997301,0.00008363138,0.00001577983,0.00005557573,0.00007233711,0.00004270818],"domain_scores_gemma":[0.9953485,0.003349365,0.0006640374,0.0002996822,0.0002032002,0.0001351799],"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":[0.0002192358,0.0001360259,0.03843055,0.0001287679,0.0001371207,0.0003219879,0.0002775762,0.7700629,0.009626078,0.1080065,0.001257234,0.07139596],"study_design_scores_gemma":[0.000004023382,0.00001941285,0.004808457,0.000007822764,0.000007470559,0.00002122054,0.00001867005,0.9589229,0.0007609821,0.03521772,0.0002022243,0.000009074434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6115739,0.001394044,0.379655,0.00263169,0.00005176103,0.00002149555,0.0004251328,0.0003597999,0.003887077],"genre_scores_gemma":[0.9898782,0.0003423117,0.008969348,0.00005511908,0.00005369201,0.000006595527,0.000126338,0.00002685633,0.0005416467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003682337,"threshold_uncertainty_score":0.007558823,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3123361201","doi":"10.1016/j.ijforecast.2017.06.004","title":"Beta forecasting at long horizons","year":2017,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"La Trobe University","keywords":"BETA (programming language); Econometrics; Autoregressive model; Economics; Investment (military); Capital asset pricing model; Statistics; Mathematics; Computer science","authors":[{"name":"Tolga Cenesizoglu","is_ca":true},{"name":"Fabio de Oliveira Ferrazoli Ribeiro","is_ca":false},{"name":"Jonathan J. Reeves","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.125925120106616,"gpt":0.2840119903615495,"spread":0.1580868702549335,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001561942,0.0005014278,0.000550609,0.0008482784,0.0003511349,0.001581752,0.0004345986,0.0009397101,0.002953077],"category_scores_gemma":[0.00919247,0.0003659424,0.0003147814,0.0008667335,0.0002763346,0.002833354,0.0004176694,0.001532567,0.0007258011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004150749,"about_ca_system_score_gemma":0.0003187502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002059734,"about_ca_topic_score_gemma":0.001720273,"domain_scores_codex":[0.9998037,0.00004655601,0.00001107289,0.00004210629,0.00005982969,0.00003668487],"domain_scores_gemma":[0.9972869,0.001626498,0.0002889187,0.0002591432,0.0004075304,0.0001310349],"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":[0.0003261747,0.0001050484,0.02274215,0.0001084411,0.0001683469,0.0003601456,0.0001771069,0.6768286,0.006138527,0.0832805,0.01067607,0.1990889],"study_design_scores_gemma":[0.000006421279,0.00001854978,0.004074086,0.00001941069,0.00002172378,0.00003490927,0.00001786857,0.9464103,0.0009659714,0.04750539,0.0009117579,0.0000136866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5652512,0.00800064,0.3963313,0.003320589,0.00091257,0.00002006824,0.000567449,0.0009754273,0.02462079],"genre_scores_gemma":[0.9862983,0.001230615,0.008171752,0.00006992327,0.0002428232,0.000007249084,0.0002566226,0.00006150272,0.003661206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002953077,"threshold_uncertainty_score":0.009879053,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2524232512","doi":"10.1016/j.ijforecast.2016.06.004","title":"Monte Carlo forecast evaluation with persistent data","year":2016,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; Université Laval; Carleton University","funders":"","keywords":"Parametric statistics; Monte Carlo method; Computer science; Unit root; Nuisance parameter; Econometrics; Benchmark (surveying); Mathematics; Statistics; Applied mathematics; Estimator","authors":[{"name":"Lynda Khalaf","is_ca":true},{"name":"Charles J. Saunders","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.5114945704313234,"gpt":0.4599525797243991,"spread":0.05154199070692433,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006441284,0.0006299837,0.001419462,0.001116741,0.0005746295,0.001670518,0.001077519,0.001825996,0.002300128],"category_scores_gemma":[0.03113227,0.0006394973,0.0006598516,0.0008020091,0.000748891,0.001946801,0.0009148651,0.001590502,0.0002260159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216593,"about_ca_system_score_gemma":0.001529957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01218893,"about_ca_topic_score_gemma":0.008337115,"domain_scores_codex":[0.9983884,0.000904417,0.0001133575,0.0001654014,0.0003035971,0.000124864],"domain_scores_gemma":[0.9658647,0.03059957,0.0006560021,0.0009497554,0.001622139,0.0003079691],"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":[0.0002021088,0.00004336306,0.0007752746,0.00002941219,0.00003109229,0.00002826782,0.00001639947,0.9850184,0.0001793589,0.003093231,0.0002582118,0.01032486],"study_design_scores_gemma":[0.000006009053,0.00001040432,0.00005457826,0.000002400669,0.000003269974,0.000002279298,0.000001938745,0.9990537,0.00009066234,0.000749561,0.00002336117,0.000001856835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3617287,0.001349154,0.6303056,0.0008601857,0.0002232961,0.0001367494,0.0003034615,0.0007817383,0.004311012],"genre_scores_gemma":[0.9287547,0.0002315494,0.06888352,0.00008420466,0.00007162391,0.00009827149,0.0003119719,0.00006815902,0.001496016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01218893,"threshold_uncertainty_score":0.03406513,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3122237717","doi":"10.1016/j.ijforecast.2013.02.004","title":"The financial content of inflation risks in the euro area","year":2013,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Italy: Economic History and Contemporary Issues","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Inflation (cosmology); Economics; Monetary policy; Downside risk; Quarter (Canadian coin); Predictive power; Survey data collection; Financial market; Monetary economics; Finance","authors":[{"name":"Philippe Andrade","is_ca":false},{"name":"Valère Fourel","is_ca":false},{"name":"Éric Ghysels","is_ca":false},{"name":"Julien Idier","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1840502176630777,"gpt":0.2509758654990337,"spread":0.06692564783595598,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001080687,0.0001659416,0.0002331568,0.001467735,0.0002579803,0.002063005,0.0003314909,0.0009392111,0.001921917],"category_scores_gemma":[0.009881432,0.0001619107,0.0001655771,0.001557267,0.0005681828,0.001366724,0.0005518071,0.0009371208,0.0002163779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005813384,"about_ca_system_score_gemma":0.0001939232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001912662,"about_ca_topic_score_gemma":0.001674166,"domain_scores_codex":[0.9998149,0.00006209619,0.00001586971,0.00002919733,0.00005043779,0.00002747939],"domain_scores_gemma":[0.9961579,0.002104836,0.001089531,0.0001466143,0.0003574594,0.0001435951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001331642,0.0002584278,0.6397989,0.0003145397,0.0002628276,0.002042659,0.003981115,0.03319383,0.003715542,0.1793001,0.01236224,0.1234382],"study_design_scores_gemma":[0.00005040798,0.0001019873,0.875342,0.0002113731,0.0001763937,0.000598599,0.001544833,0.03297212,0.0007213868,0.07798509,0.01024781,0.00004808541],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742867,0.005154826,0.001007471,0.00468023,0.00008427358,0.00000457418,0.0002237749,0.00002736873,0.01453066],"genre_scores_gemma":[0.9983387,0.000810574,0.00006991268,0.00005124935,0.0002050412,0.000001308074,0.00006439982,0.000005935435,0.0004527864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002063005,"threshold_uncertainty_score":0.006429493,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4395115909","doi":"10.1016/j.ijforecast.2024.04.002","title":"Coupling LSTM neural networks and state-space models through analytically tractable inference","year":2024,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":8,"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; Hydro-Québec","keywords":"Inference; State space; Coupling (piping); Computer science; Artificial neural network; State (computer science); Artificial intelligence; Mathematical economics; Mathematics; Algorithm; Engineering","authors":[{"name":"Van-Dai Vuong","is_ca":true},{"name":"Luong Ha Nguyen","is_ca":true},{"name":"James A. Goulet","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04056806359197044,"gpt":0.2830901977272691,"spread":0.2425221341352986,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003516488,0.001083217,0.0008241947,0.0006324954,0.0003834056,0.00181475,0.001589575,0.001495258,0.002863395],"category_scores_gemma":[0.02285763,0.0009509211,0.0006195088,0.0007912604,0.000994476,0.004533768,0.002585836,0.003120903,0.0007070943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001305352,"about_ca_system_score_gemma":0.001620802,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004438369,"about_ca_topic_score_gemma":0.004610043,"domain_scores_codex":[0.9983546,0.0007886069,0.000108981,0.0002809838,0.000375517,0.00009126899],"domain_scores_gemma":[0.9935782,0.005273377,0.0003250248,0.0005123692,0.0002441873,0.00006681952],"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":[0.00007954826,0.00007026513,0.000553837,0.0001057859,0.0000797707,0.00008722714,0.000177668,0.8476257,0.002809846,0.08157514,0.0009504021,0.06588474],"study_design_scores_gemma":[0.000004156925,0.00000908629,0.00004567882,0.000005755599,0.000005363278,0.000008791127,0.000008188199,0.9567899,0.0004731065,0.04231841,0.0003269602,0.000004683966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009638431,0.0001191511,0.9880726,0.0002664117,0.00002353561,0.00002271537,0.00004922479,0.0004867017,0.001321214],"genre_scores_gemma":[0.6457527,0.0004487938,0.3497357,0.0002642616,0.0001073301,0.0002229305,0.0002917923,0.000277645,0.002898929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004438369,"threshold_uncertainty_score":0.01859713,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4200264291","doi":"10.1016/j.ijforecast.2021.12.012","title":"A flexible framework for intervention analysis applied to credit-card usage during the coronavirus pandemic","year":2021,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Housing Market and Economics","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Bank of Canada; Toronto Metropolitan University","funders":"","keywords":"Multinomial logistic regression; Econometrics; Multinomial distribution; Credit card; Distribution (mathematics); Counterfactual thinking; Intervention (counseling); Economics; Categorical variable; Markov chain; Actuarial science; Computer science; Statistics; Mathematics; Finance","authors":[{"name":"Anson T. Y. Ho","is_ca":true},{"name":"Lealand Morin","is_ca":false},{"name":"Harry J. Paarsch","is_ca":false},{"name":"Kim P. Huynh","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08727238293481902,"gpt":0.3035024273511003,"spread":0.2162300444162813,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01385508,0.001143361,0.002921146,0.001761523,0.001290492,0.003982561,0.004262039,0.004189712,0.01249085],"category_scores_gemma":[0.03501828,0.001275714,0.002556613,0.001730072,0.002793169,0.003030737,0.003539082,0.004571843,0.0007035288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00234487,"about_ca_system_score_gemma":0.004014697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02969976,"about_ca_topic_score_gemma":0.01795232,"domain_scores_codex":[0.9946789,0.003701422,0.0001696073,0.000588385,0.0002432912,0.0006184818],"domain_scores_gemma":[0.9796901,0.01673924,0.00115141,0.001040326,0.0006902636,0.0006887187],"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":[0.0001278296,0.000177455,0.005510757,0.00009360807,0.000286074,0.0004120894,0.000401216,0.4364978,0.0003650971,0.5374804,0.003062471,0.01558515],"study_design_scores_gemma":[0.00005266669,0.00005114372,0.0009500563,0.00002601255,0.00004919044,0.00004621843,0.000120847,0.8323842,0.00005219552,0.164818,0.001419724,0.00002963691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03896061,0.0006299953,0.949542,0.003908879,0.0002280097,0.0001744916,0.0008328001,0.0003036121,0.005419647],"genre_scores_gemma":[0.8634063,0.0009077814,0.1187253,0.0005533878,0.0005815774,0.0006057278,0.0007510838,0.0001721318,0.01429674],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02969976,"threshold_uncertainty_score":0.07327354,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2145139914","doi":"10.1016/j.ijforecast.2004.12.007","title":"Clustered panel data models: an efficient approach for nowcasting from poor data","year":2005,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":8,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"HEC Montréal","keywords":"Nowcasting; Computer science; Inference; Panel data; Realization (probability); Cluster analysis; Econometrics; Data mining; Field (mathematics); Missing data; Time series; Machine learning; Statistics; Artificial intelligence; Mathematics; Geography","authors":[{"name":"Michel Mouchart","is_ca":false},{"name":"Jeroen V.K. Rombouts","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.698356989627895,"gpt":0.472676036577904,"spread":0.225680953049991,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009294,0.001383944,0.003106485,0.001991875,0.0008877174,0.002063174,0.003690324,0.002363868,0.004689218],"category_scores_gemma":[0.02796861,0.001501543,0.0017727,0.003891607,0.0006777644,0.002739868,0.002080192,0.003882597,0.001106679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009754256,"about_ca_system_score_gemma":0.001996665,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01258406,"about_ca_topic_score_gemma":0.0157316,"domain_scores_codex":[0.9973014,0.001790014,0.000120705,0.0003361405,0.0002875405,0.0001641827],"domain_scores_gemma":[0.9859611,0.009710783,0.0007270359,0.002394423,0.0009853618,0.0002212718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001821248,0.00006543776,0.001509343,0.0001180584,0.0003141722,0.0001214001,0.0001059342,0.8605292,0.0004497572,0.03210849,0.005966919,0.09852915],"study_design_scores_gemma":[0.00001364937,0.00001500811,0.0002406144,0.000009384252,0.00003267598,0.00001212252,0.00001638063,0.971225,0.0001983741,0.0273856,0.0008363136,0.00001485393],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004094063,0.0001639502,0.9945432,0.0001641096,0.00004916865,0.00003473877,0.0002691874,0.000349535,0.0003321416],"genre_scores_gemma":[0.3119692,0.001187372,0.6771479,0.0002658236,0.000404741,0.0004174248,0.002848454,0.0003948454,0.005364236],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01258406,"threshold_uncertainty_score":0.04915196,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2914021772","doi":"10.1016/j.ijforecast.2019.01.002","title":"Editorial: Forecasting in sports","year":2019,"lang":"en","type":"editorial","venue":"International Journal of Forecasting","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"University of Liverpool","keywords":"Kurtosis; Mathematics; Gaussian; Power law; Applied mathematics; Probability distribution; Univariate; Econometrics; Statistical physics; Statistics; Computer science; Multivariate statistics","authors":[{"name":"Ian G. McHale","is_ca":false},{"name":"Tim B. Swartz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.03410099152663625,"gpt":0.2511039997057466,"spread":0.2170030081791103,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01078302,0.00671754,0.006368095,0.01129147,0.004674054,0.01374113,0.004800021,0.02121635,0.0299616],"category_scores_gemma":[0.05224609,0.001575271,0.00370679,0.00465319,0.002828545,0.005972746,0.002599341,0.01630406,0.02312705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004297017,"about_ca_system_score_gemma":0.004159456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003762442,"about_ca_topic_score_gemma":0.009236324,"domain_scores_codex":[0.9916038,0.001497627,0.001206437,0.00124022,0.003924382,0.0005275282],"domain_scores_gemma":[0.9546373,0.01843258,0.003668024,0.001422506,0.0165603,0.005279308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002142722,0.000007897821,0.00002132033,0.00008257241,0.00001487928,0.00004013901,0.000004625582,0.00002191733,0.0000158885,0.0001117709,0.9970866,0.002571088],"study_design_scores_gemma":[0.0001418882,0.00004170916,0.0006802042,0.0009702569,0.0001273526,0.0001882126,0.00006845467,0.0005633975,0.0001221262,0.002480468,0.9945737,0.00004235229],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00001884436,0.002946894,0.00008140974,0.02478733,0.9711357,0.00001299121,0.00009852243,0.00003846324,0.0008799089],"genre_scores_gemma":[0.0002968452,0.002237159,0.00006497328,0.009092811,0.9817123,0.00001596217,0.00005998482,0.00003238902,0.006487558],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.0299616,"threshold_uncertainty_score":0.1002316,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2613616238","doi":"10.1016/j.ijforecast.2019.02.009","title":"Fiscal Surprises at the FOMC","year":2019,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"","keywords":"Economics; Business cycle; Fiscal policy; Monetary policy; Revenue; Monetary economics; Macroeconomics; Forecast error; Econometrics; Finance","authors":[{"name":"Dean Croushore","is_ca":false},{"name":"Simon van Norden","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1002253673714677,"gpt":0.2430940668527515,"spread":0.1428686994812838,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001623654,0.0002347219,0.0005641725,0.0009476069,0.001521535,0.003844711,0.0004503044,0.002608744,0.007075517],"category_scores_gemma":[0.01761712,0.0001931966,0.0002706812,0.0007522136,0.0005277439,0.002249656,0.0008693919,0.004156964,0.000982357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004064,"about_ca_system_score_gemma":0.002016516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03338468,"about_ca_topic_score_gemma":0.02486363,"domain_scores_codex":[0.9994774,0.00006114834,0.00002299111,0.00008576215,0.0001748275,0.000177916],"domain_scores_gemma":[0.9960699,0.0009807941,0.000697114,0.0002250053,0.001270383,0.0007567102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002870455,0.0003745577,0.08158192,0.0002010206,0.0002512938,0.003464909,0.002331399,0.03867353,0.001802905,0.3549047,0.4115437,0.1019995],"study_design_scores_gemma":[0.0003234191,0.0005115748,0.1942648,0.0004841424,0.0002979644,0.0007781685,0.005225353,0.1551821,0.005137836,0.3054326,0.3320645,0.0002975132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7204896,0.003041112,0.003998279,0.1261484,0.006263414,0.00003324846,0.003091929,0.0006778796,0.1362561],"genre_scores_gemma":[0.9903998,0.0003081541,0.0002094557,0.001061119,0.0008827125,0.000005322099,0.0002703944,0.00005018928,0.006812837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03338468,"threshold_uncertainty_score":0.06638074,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4391234986","doi":"10.1016/j.ijforecast.2023.12.010","title":"Instance-based meta-learning for conditionally dependent univariate multi-step forecasting","year":2024,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Dalhousie University","funders":"Service Public de Wallonie; Dalhousie University","keywords":"Univariate; Predictability; Computer science; Constraint (computer-aided design); Regularization (linguistics); Series (stratigraphy); Key (lock); Time series; Machine learning; Artificial intelligence; sort; Multivariate statistics; Data mining; Econometrics; Statistics; Mathematics","authors":[{"name":"Vítor Cerqueira","is_ca":true},{"name":"Luı́s Torgo","is_ca":true},{"name":"Gianluca Bontempi","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.09765793202472836,"gpt":0.2994176505733588,"spread":0.2017597185486305,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00725267,0.001925698,0.004452527,0.002368783,0.0007208958,0.002475968,0.004607197,0.0038144,0.002929631],"category_scores_gemma":[0.01478712,0.001720595,0.003235546,0.002059068,0.001031608,0.002640767,0.002398907,0.004463165,0.00081801],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001467879,"about_ca_system_score_gemma":0.001887332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005734378,"about_ca_topic_score_gemma":0.006084035,"domain_scores_codex":[0.9980143,0.001023964,0.0001565079,0.0003663437,0.000259446,0.0001794172],"domain_scores_gemma":[0.989012,0.008814773,0.000432099,0.0007504166,0.0006988098,0.0002919692],"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":[0.0002113273,0.000134695,0.0009679068,0.00009556943,0.0004690818,0.00005843462,0.00003337274,0.9384537,0.000368554,0.005042138,0.001361631,0.05280356],"study_design_scores_gemma":[0.000008436598,0.00001446194,0.00004315423,0.000006942988,0.00002310527,0.000005201257,0.000001665429,0.9969469,0.00008216833,0.002781081,0.00008306701,0.000003843365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01978355,0.002191881,0.9748437,0.000530083,0.0001367502,0.0000578707,0.000308161,0.001310289,0.0008376164],"genre_scores_gemma":[0.64126,0.001278245,0.3508555,0.0006686135,0.0004223477,0.0004209455,0.001775988,0.0004519369,0.002866451],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00725267,"threshold_uncertainty_score":0.03835618,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3081731416","doi":"10.1016/j.ijforecast.2024.08.006","title":"Time-varying parameters as ridge regressions","year":2025,"lang":"en","type":"preprint","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Ridge; Computation; Regression; Volatility (finance); Variation (astronomy); Computer science; Dual (grammatical number); Econometrics; Mathematical optimization; Mathematics; Algorithm; Applied mathematics; Statistics; Geology","authors":[{"name":"Philippe Goulet Coulombe","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1448460943910487,"gpt":0.2962258197600307,"spread":0.151379725368982,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006701749,0.001015038,0.001848435,0.001587996,0.000389808,0.002363918,0.002139268,0.002272479,0.003290284],"category_scores_gemma":[0.02631061,0.001437498,0.001330149,0.002243198,0.002013521,0.003849095,0.001726068,0.003453364,0.001055915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007870151,"about_ca_system_score_gemma":0.0007382671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00357378,"about_ca_topic_score_gemma":0.002774544,"domain_scores_codex":[0.9981343,0.001078813,0.00008764928,0.0003022074,0.0002692424,0.000127784],"domain_scores_gemma":[0.9843841,0.01129543,0.001222398,0.002086839,0.0006910109,0.0003201923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001564677,0.00009169041,0.003060661,0.0001808555,0.0003036154,0.0002076308,0.000258238,0.6424509,0.003041131,0.2822601,0.004227073,0.06376171],"study_design_scores_gemma":[0.000006875891,0.00000520954,0.000291826,0.000009950089,0.00001395635,0.00001310219,0.000007539592,0.9495453,0.0001869308,0.04941133,0.0004979888,0.000009974687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04188818,0.001946833,0.9521672,0.0007962173,0.0001421761,0.00002803135,0.0003081449,0.0009754336,0.001747788],"genre_scores_gemma":[0.759672,0.003914573,0.2067563,0.0003883836,0.0007500192,0.0002184864,0.001601038,0.00174199,0.02495727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006701749,"threshold_uncertainty_score":0.03544265,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4406431708","doi":"10.1016/j.ijforecast.2024.12.004","title":"Modeling and predicting failure in US credit unions","year":2025,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"","funders":"Queen's University; National Natural Science Foundation of China; Queen's University Belfast","keywords":"Economics; Econometrics","authors":[{"name":"Qiao Peng","is_ca":false},{"name":"Donal McKillop","is_ca":false},{"name":"Barry Quinn","is_ca":false},{"name":"Kailong Liu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.01702468248504705,"gpt":0.2367820613871745,"spread":0.2197573789021275,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00162171,0.0007145954,0.0005207863,0.001063349,0.0005438905,0.001028505,0.0007572254,0.001388857,0.001186469],"category_scores_gemma":[0.005617892,0.0003442266,0.0004862631,0.001048934,0.0003837148,0.0009022481,0.0004591842,0.001293435,0.0001891557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001436383,"about_ca_system_score_gemma":0.001117512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1494569,"about_ca_topic_score_gemma":0.09648154,"domain_scores_codex":[0.999768,0.00007418238,0.00001828553,0.00004628413,0.00002409596,0.00006932365],"domain_scores_gemma":[0.9974994,0.001777957,0.0002151077,0.00008252794,0.0002518329,0.0001731732],"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":[0.0002408864,0.0003495765,0.1954746,0.000017005,0.00005359675,0.00009618632,0.0001266687,0.7903069,0.0002334362,0.0007347564,0.001649971,0.01071658],"study_design_scores_gemma":[0.000007452801,0.00002632612,0.0129295,0.000003950192,0.00001082702,0.000006164487,0.00007837729,0.986209,0.0001037984,0.0005289277,0.00009153724,0.000004220143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976314,0.0001074993,0.001335813,0.000259788,0.00001655027,0.000007758183,0.0002668853,0.00002932448,0.0003450266],"genre_scores_gemma":[0.9989136,0.00005927935,0.0003599741,0.00001623519,0.000007526525,0.000007518046,0.00033544,0.00000271845,0.0002977538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1494569,"threshold_uncertainty_score":0.297174,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2594098966","doi":"10.1016/j.ijforecast.2018.05.008","title":"Does foreign sector help forecast domestic variables in DSGE models?","year":2016,"lang":"en","type":"preprint","venue":"International Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Narodowym Centrum Nauki","keywords":"Dynamic stochastic general equilibrium; Small open economy; Benchmark (surveying); Econometrics; Open economy; Economics; Monte Carlo method; Economy; Macroeconomics; Statistics; Monetary policy; Mathematics; Geography","authors":[{"name":"Marcin Kolasa","is_ca":false},{"name":"Michał Rubaszek","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1307958069869149,"gpt":0.2620816582627192,"spread":0.1312858512758044,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004892934,0.0007136158,0.001059518,0.0006367262,0.0004470593,0.002774631,0.0006454985,0.001278862,0.003447809],"category_scores_gemma":[0.02272048,0.0003536728,0.0004377726,0.0009067834,0.0006014354,0.003702578,0.0009960913,0.001450643,0.0006900471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008392727,"about_ca_system_score_gemma":0.001063985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02500612,"about_ca_topic_score_gemma":0.01324275,"domain_scores_codex":[0.9993718,0.0003551202,0.00003548813,0.000115586,0.00005191029,0.00007006944],"domain_scores_gemma":[0.9933523,0.004334021,0.0008086732,0.0006984264,0.0005039643,0.0003025578],"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":[0.0005720092,0.00007050639,0.07528018,0.0001275489,0.0002094957,0.0002720374,0.0003951251,0.8540826,0.0006708659,0.02617564,0.005271722,0.03687226],"study_design_scores_gemma":[0.00008448276,0.00008047421,0.01093601,0.00009717083,0.00008576784,0.00006929661,0.0003658692,0.9539669,0.001146602,0.02879128,0.004321666,0.00005444264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9054078,0.002462927,0.06581804,0.00625617,0.0004001635,0.0000387468,0.001376377,0.0009157475,0.01732393],"genre_scores_gemma":[0.994127,0.0005576208,0.003583262,0.0001622385,0.00005320812,0.000007212991,0.0004227403,0.00006595023,0.001020738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02500612,"threshold_uncertainty_score":0.04972112,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408437940","doi":"10.1016/j.ijforecast.2025.02.009","title":"Carpe diem: Can daily oil prices improve model-based forecasts of the real price of crude oil?","year":2025,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Market Dynamics and Volatility","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":"Wilfrid Laurier University; Bank of Canada","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Crude oil; Oil price; Economics; Crack spread; Econometrics; Petroleum engineering; Monetary economics; Engineering","authors":[{"name":"Amor Aniss Benmoussa","is_ca":true},{"name":"Reinhard Ellwanger","is_ca":true},{"name":"Stephen Snudden","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0265528071892245,"gpt":0.2450421826276918,"spread":0.2184893754384673,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002834113,0.0008331072,0.001185516,0.001082398,0.0005568707,0.001653978,0.001381975,0.00145733,0.004418063],"category_scores_gemma":[0.0205917,0.0004289964,0.0005912131,0.0009108533,0.00049714,0.003629833,0.001164088,0.001829407,0.001320958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008078984,"about_ca_system_score_gemma":0.001214953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01141066,"about_ca_topic_score_gemma":0.01080419,"domain_scores_codex":[0.9991767,0.0003138238,0.0000320787,0.0001588016,0.0002554749,0.00006312512],"domain_scores_gemma":[0.9965243,0.001833493,0.000274814,0.0005859567,0.0006573353,0.0001241562],"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":[0.001055845,0.0002114441,0.0124837,0.0002189019,0.0003381605,0.0001863636,0.000147828,0.5665788,0.002746772,0.02300779,0.02515268,0.3678717],"study_design_scores_gemma":[0.00005959054,0.00008218209,0.002056316,0.00003101111,0.00002866702,0.00003455783,0.00003749228,0.9647269,0.001785376,0.02687823,0.004233175,0.00004653862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2527743,0.00851039,0.6548709,0.02470222,0.003275691,0.0001801024,0.002464808,0.005539607,0.04768201],"genre_scores_gemma":[0.9050986,0.001287741,0.08208912,0.0007819681,0.00061731,0.00006699028,0.0009941808,0.0002763166,0.008787955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01141066,"threshold_uncertainty_score":0.02268845,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1536054321","doi":"10.1016/j.ijforecast.2013.07.006","title":"The Value of Multivariate Model Sophistication: An Application to Pricing Dow Jones Industrial Average Options","year":2012,"lang":"en","type":"preprint","venue":"International Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University; Center for Interuniversity Research and Analysis on Organizations; HEC Montréal","funders":"","keywords":"Econometrics; Multivariate statistics; Sophistication; Economics; Multivariate normal distribution; Conditional variance; Laplace transform; Volatility (finance); Mathematics; Statistics; Autoregressive conditional heteroskedasticity","authors":[{"name":"Jeroen V.K. Rombouts","is_ca":true},{"name":"Lars Stentoft","is_ca":true},{"name":"Francesco Violante","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1032437091315808,"gpt":0.3000268907316721,"spread":0.1967831816000913,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00809504,0.001082176,0.001749838,0.001889586,0.0009946347,0.004867341,0.001751302,0.003212799,0.003737163],"category_scores_gemma":[0.08277182,0.001063964,0.001528485,0.002242903,0.002783178,0.01001579,0.003793311,0.005074295,0.0001539373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001172435,"about_ca_system_score_gemma":0.0009715919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002806294,"about_ca_topic_score_gemma":0.002270164,"domain_scores_codex":[0.9984855,0.0007846164,0.00007878702,0.0002097578,0.0003174251,0.0001238641],"domain_scores_gemma":[0.9267458,0.06450172,0.003172116,0.00313278,0.001352089,0.001095491],"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":[0.0002765449,0.0001740871,0.01578365,0.0001864483,0.0002332253,0.0007433166,0.0004974894,0.4561426,0.001766946,0.4703386,0.002160406,0.05169662],"study_design_scores_gemma":[0.00001602773,0.00002062428,0.001283157,0.00002330587,0.00003786114,0.0000813447,0.00003081002,0.872144,0.000235257,0.1257803,0.0003150325,0.00003245286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4089542,0.003155517,0.5606979,0.008937176,0.000201006,0.00006933929,0.0002880412,0.000457299,0.0172397],"genre_scores_gemma":[0.9673727,0.00101809,0.02943572,0.0001866381,0.0003104402,0.00002235031,0.00005722633,0.0001349697,0.001461864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00809504,"threshold_uncertainty_score":0.04281116,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2067541510","doi":"10.1016/j.ijforecast.2011.10.001","title":"Testing time series data compatibility for benchmarking","year":2011,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; Compatibility (geochemistry); Series (stratigraphy); Computer science; Econometrics; Time series; Statistics; Data mining; Mathematics; Machine learning; Engineering; Economics","authors":[{"name":"Benoît Quennevillle","is_ca":true},{"name":"Christian Gagné","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.2960590050499919,"gpt":0.273547270225132,"spread":0.02251173482485991,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07381718,0.001224562,0.001100781,0.003058929,0.001413084,0.003993968,0.003039583,0.004752725,0.01119352],"category_scores_gemma":[0.3572116,0.0005788769,0.002317966,0.003413275,0.002672796,0.007536402,0.004621628,0.002036494,0.002786942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009715526,"about_ca_system_score_gemma":0.00230259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047976,"about_ca_topic_score_gemma":0.0004424757,"domain_scores_codex":[0.8948767,0.06847287,0.01324804,0.009827874,0.01122521,0.002349252],"domain_scores_gemma":[0.4654988,0.4111683,0.0165336,0.06528819,0.03725736,0.004253751],"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":[0.02734645,0.006559141,0.5250131,0.001463938,0.00419106,0.004140791,0.004824418,0.06001095,0.03528035,0.07699376,0.01466766,0.2395084],"study_design_scores_gemma":[0.01011702,0.02385944,0.2037625,0.0009221435,0.003158948,0.004389971,0.009574241,0.5289273,0.07528953,0.1017673,0.03770102,0.0005306422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8740698,0.0002566568,0.1075814,0.001681634,0.0005898718,0.0007002985,0.002240705,0.0007954234,0.01208414],"genre_scores_gemma":[0.9689255,0.0000530233,0.0271948,0.0002049508,0.0001090444,0.0003060647,0.002393098,0.0002451401,0.0005683885],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07381718,"threshold_uncertainty_score":0.3903872,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4392301000","doi":"10.1016/j.ijforecast.2024.02.003","title":"Dynamic prediction of the National Hockey League draft with rank-ordered logit models","year":2024,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Simon Fraser University","funders":"Canadian Statistical Sciences Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Rank (graph theory); League; Logit; Econometrics; Computer science; Operations research; Bayesian probability; Economics; Mathematics; Machine learning; Artificial intelligence","authors":[{"name":"Brendan Kumagai","is_ca":true},{"name":"Ryker Moreau","is_ca":true},{"name":"Kimberly Kroetch","is_ca":true},{"name":"Tim B. Swartz","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06111843759515651,"gpt":0.2403695094263556,"spread":0.1792510718311991,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004176063,0.0007066022,0.001226107,0.0014339,0.0003831637,0.00223432,0.001294827,0.001437711,0.007767522],"category_scores_gemma":[0.0116452,0.0006233199,0.0009065013,0.001209685,0.0005509481,0.001816601,0.0007333303,0.002123734,0.001877957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361165,"about_ca_system_score_gemma":0.000956535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03669725,"about_ca_topic_score_gemma":0.03374506,"domain_scores_codex":[0.9990674,0.0004096925,0.00004085468,0.0001920698,0.00007949534,0.0002106181],"domain_scores_gemma":[0.9878988,0.009508745,0.0009064278,0.0003986474,0.0007654807,0.0005219125],"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":[0.0007954311,0.0004739397,0.05515829,0.00005952453,0.0001258747,0.0001586549,0.00009659996,0.9153573,0.0002741883,0.006621223,0.004792514,0.01608655],"study_design_scores_gemma":[0.0000173079,0.00004807048,0.003902532,0.000005507458,0.00001121036,0.000005955147,0.00003779235,0.9936537,0.00007352141,0.002079889,0.0001546811,0.000009865072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9577278,0.0004419651,0.0333313,0.001298645,0.0001685745,0.00006279961,0.002925532,0.0003133955,0.003729824],"genre_scores_gemma":[0.9915868,0.0001024116,0.001531506,0.00004373552,0.00004146704,0.00002689582,0.001652589,0.00001617178,0.004998346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03669725,"threshold_uncertainty_score":0.07296735,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W7117487734","doi":"10.1016/j.ijforecast.2025.12.002","title":"Quantile-based modeling of scale dynamics in financial returns for Value-at-Risk and Expected Shortfall forecasting","year":2025,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Downside risk; Quantile; Expected shortfall; Leverage effect; Value at risk; Stock (firearms); Autoregressive conditional heteroskedasticity; Leverage (statistics)","authors":[{"name":"Xiaochun Liu","is_ca":false},{"name":"Richard Luger","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04708907652607817,"gpt":0.2606651301296184,"spread":0.2135760536035402,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001678048,0.000704261,0.000890491,0.0006348636,0.0002568776,0.001059727,0.001658919,0.001001665,0.002453364],"category_scores_gemma":[0.006189247,0.0004390971,0.0008240235,0.0009203863,0.0005236199,0.001381286,0.0008991593,0.001599734,0.0004910691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005685681,"about_ca_system_score_gemma":0.0006601231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00662352,"about_ca_topic_score_gemma":0.003727688,"domain_scores_codex":[0.9996008,0.000148285,0.00002180855,0.00007657307,0.00009125525,0.00006123398],"domain_scores_gemma":[0.9983771,0.0009677025,0.0002668935,0.0001522161,0.0001726723,0.00006335385],"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":[0.00002376069,0.00003177859,0.002244934,0.00003492655,0.00003348779,0.00008157537,0.00004388302,0.9453782,0.001146297,0.03496044,0.0008353748,0.01518529],"study_design_scores_gemma":[0.000001416668,0.000004333924,0.0001977586,0.000002864887,0.000002921234,0.00000803165,0.000002397653,0.9942982,0.00006472242,0.005218237,0.0001953965,0.000003792034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01994492,0.0004552095,0.9774687,0.0002321861,0.00003707615,0.00002352933,0.0002125611,0.0003472891,0.001278492],"genre_scores_gemma":[0.8933143,0.001343711,0.0993295,0.0001770857,0.0001934299,0.0001330562,0.0006254939,0.0001668994,0.004716537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00662352,"threshold_uncertainty_score":0.01316988,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4229012345","doi":"10.1016/j.ijforecast.2022.03.011","title":"Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage","year":2022,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal","funders":"Belgian Federal Science Policy Office; Fonds De La Recherche Scientifique - FNRS","keywords":"Shrinkage; Robust optimization; Econometrics; Proposition; Economics; Computer science; Mathematical optimization; Mathematics; Machine learning","authors":[{"name":"Francesco Roccazzella","is_ca":false},{"name":"Paolo Gambetti","is_ca":false},{"name":"Frédéric Vrins","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07683419412777104,"gpt":0.3396715372834413,"spread":0.2628373431556703,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007884257,0.003627043,0.003416495,0.00306222,0.001914927,0.005358724,0.003065392,0.006161109,0.1071053],"category_scores_gemma":[0.110231,0.002264529,0.001871879,0.00572692,0.001657366,0.005573357,0.004651461,0.008263317,0.03631823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001594418,"about_ca_system_score_gemma":0.004624581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005033276,"about_ca_topic_score_gemma":0.00745731,"domain_scores_codex":[0.9924825,0.001518484,0.001024564,0.001555685,0.002885326,0.0005334144],"domain_scores_gemma":[0.9560938,0.01264884,0.003260346,0.01161871,0.01522646,0.001151821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001367332,0.0001085983,0.002090446,0.001107726,0.0005362225,0.001382282,0.0003059958,0.0358092,0.007340608,0.03945662,0.7388473,0.1716477],"study_design_scores_gemma":[0.0003398714,0.0001697481,0.005504862,0.000558381,0.0002956421,0.002236935,0.0003301321,0.5353054,0.0264722,0.09452619,0.3336906,0.000569976],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.009563885,0.002994136,0.6063334,0.0263226,0.3250338,0.0002900289,0.005770944,0.0141146,0.00957652],"genre_scores_gemma":[0.3393165,0.003549203,0.4639683,0.006409589,0.03384621,0.0004762627,0.007916839,0.0124662,0.1320509],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1071053,"threshold_uncertainty_score":0.3583028,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4387223121","doi":"10.1016/j.ijforecast.2023.09.001","title":"The profitability of lead–lag arbitrage at high frequency","year":2023,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"HEC Montréal; Université de Montréal","funders":"","keywords":"Statistical arbitrage; Lag; Lagging; Predictability; Arbitrage; High-frequency trading; Profitability index; Econometrics; Trading strategy; Lead–lag compensator; Price discovery; Pairs trade; Profit (economics); Lead (geology); Order (exchange); Algorithmic trading; Economics; Computer science; Financial economics; Arbitrage pricing theory; Capital asset pricing model; Risk arbitrage; Microeconomics; Alternative trading system; Statistics; Finance; Futures contract; Mathematics","authors":[{"name":"Cédric Poutré","is_ca":true},{"name":"Georges Dionne","is_ca":true},{"name":"Gabriel Yergeau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05944428443614971,"gpt":0.2452565094830188,"spread":0.1858122250468691,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001480082,0.0001757953,0.000400769,0.0008121329,0.000507889,0.002083653,0.0004451467,0.001368752,0.004459249],"category_scores_gemma":[0.02370567,0.0002080314,0.0001857145,0.0005565324,0.0007424216,0.00279148,0.0005782967,0.001095416,0.0004268224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005671957,"about_ca_system_score_gemma":0.0004099438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009668137,"about_ca_topic_score_gemma":0.0006860237,"domain_scores_codex":[0.999552,0.0001052542,0.00002534963,0.00006524528,0.0001319165,0.0001202642],"domain_scores_gemma":[0.9794645,0.01661677,0.001742983,0.00061664,0.0009343462,0.0006247126],"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":[0.006344622,0.0009240148,0.2678017,0.0003389403,0.000280497,0.006233627,0.0008735165,0.2808796,0.04717333,0.2379973,0.01260909,0.1385438],"study_design_scores_gemma":[0.0001932886,0.0005930531,0.1093467,0.00004305947,0.0001344157,0.00212876,0.0006894991,0.7332562,0.009340397,0.1426152,0.001559848,0.00009971873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887096,0.0005000542,0.003779313,0.001029885,0.00004254966,0.000004324518,0.00006247391,0.00006112222,0.005810699],"genre_scores_gemma":[0.999597,0.00003732201,0.0001007583,0.000008585838,0.00002296988,5.157308e-7,0.00001440516,0.000003319952,0.0002151633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004459249,"threshold_uncertainty_score":0.01491773,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4413143033","doi":"10.1016/j.ijforecast.2025.07.003","title":"Optimal text-based time-series indices","year":2025,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université de Sherbrooke; HEC Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de Recherche du Québec-Société et Culture","keywords":"Series (stratigraphy); Time series; Econometrics; Computer science; Economics; Machine learning; Geology","authors":[{"name":"David Ardia","is_ca":true},{"name":"Keven Bluteau","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02245109836544681,"gpt":0.2435335135979772,"spread":0.2210824152325304,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002613453,0.001363838,0.001352496,0.003097352,0.0005040072,0.002054057,0.00143243,0.001430677,0.002566156],"category_scores_gemma":[0.01324612,0.000564686,0.0008208206,0.002751906,0.0008400741,0.003190021,0.001060468,0.0012338,0.0007323386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0010843,"about_ca_system_score_gemma":0.001455826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001537946,"about_ca_topic_score_gemma":0.002053163,"domain_scores_codex":[0.9985765,0.0004404783,0.0001424891,0.000363533,0.0003768561,0.0001001305],"domain_scores_gemma":[0.9959136,0.002362698,0.0005530987,0.000225392,0.0008225333,0.0001227319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003129473,0.0003646505,0.004064149,0.0002427757,0.0001669043,0.0001576464,0.000197672,0.7346644,0.01179305,0.0272116,0.003433612,0.2173906],"study_design_scores_gemma":[0.00003559476,0.00007523403,0.0005250272,0.0000173976,0.00002765473,0.0000196435,0.00002916736,0.9775887,0.003135973,0.01770235,0.0008270222,0.00001629314],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05982692,0.0006024515,0.9343576,0.000472475,0.000107621,0.0001785142,0.0005424347,0.0008324558,0.003079427],"genre_scores_gemma":[0.4044598,0.0004331791,0.5892588,0.0002424556,0.0002086225,0.0005417735,0.002072072,0.0003758823,0.002407386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003097352,"threshold_uncertainty_score":0.01382142,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}