{"meta":{"query_hash":"f950e0c20dfb","filters":{"venue":"Econometrics"},"cohort_total":38,"direct_labels_cover":0,"predictions_cover":38,"exported":38,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/f950e0c20dfb","api":"https://metacan.xera.ac/api/v1/cohort?venue=Econometrics"},"results":[{"id":"W2188313221","doi":"10.3390/econometrics6020028","title":"Decomposing Wage Distributions Using Recentered Influence Function Regressions","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":635,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Wage; Econometrics; Influence function; Economics; Variable (mathematics); Function (biology); Mathematics; Regression; Statistics; Labour economics; Mathematical analysis","score_opus":0.06970212019164312,"score_gpt":0.2693768982615971,"score_spread":0.19967477806995398,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188313221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22660476,0.00075594435,0.765974,0.00038795185,0.00007606322,0.00010313872,0.0007114957,0.0005415932,0.004845071],"genre_scores_gemma":[0.85670197,0.00076361,0.13562588,0.00009351935,0.00024913013,0.00010406128,0.0015317206,0.00022160183,0.004708572],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986071,0.0006399644,0.00006444048,0.0002909807,0.00026972027,0.0001277854],"domain_scores_gemma":[0.9963217,0.0020292613,0.0006260145,0.0005633126,0.00036918625,0.00009061978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035643526,0.00072709046,0.0007101639,0.0031173385,0.00032829744,0.0014260727,0.0010679201,0.0006392344,0.0036284558],"category_scores_gemma":[0.0135130985,0.00035355147,0.0010521399,0.0021165884,0.00043786876,0.0017420513,0.00088386366,0.001404542,0.0006076455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021515234,0.00031941794,0.08660056,0.00017789021,0.000686772,0.0006094809,0.0012039283,0.32371774,0.004557563,0.20347184,0.004135176,0.3743044],"study_design_scores_gemma":[0.000021013166,0.00006238096,0.040288124,0.000039055587,0.0000603,0.000089743895,0.00016972855,0.8593268,0.0010624183,0.09304435,0.0057796407,0.000056433677],"about_ca_topic_score_codex":0.0080282455,"about_ca_topic_score_gemma":0.0064774523,"teacher_disagreement_score":0.0080282455,"about_ca_system_score_codex":0.0006527353,"about_ca_system_score_gemma":0.00047407183,"threshold_uncertainty_score":0.018850327},"labels":[],"label_agreement":null},{"id":"W2263748184","doi":"10.3390/econometrics4010006","title":"Functional-Coefficient Spatial Durbin Models with Nonparametric Spatial Weights: An Application to Economic Growth","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Purdue University","keywords":"Nonparametric statistics; Econometrics; Mathematics; Statistics; Spatial analysis","score_opus":0.026488834281336478,"score_gpt":0.19275165552857182,"score_spread":0.16626282124723535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2263748184","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0872171,0.000507165,0.9070347,0.0008238462,0.000060341135,0.00007648573,0.00028656106,0.0002556524,0.003738183],"genre_scores_gemma":[0.8958746,0.0008098219,0.092671745,0.00015708637,0.000073738316,0.00025034102,0.00046669072,0.00013712756,0.0095588425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732625,0.0016594598,0.000092723545,0.00040390977,0.00029842538,0.00021915545],"domain_scores_gemma":[0.98937327,0.0069772867,0.0015175588,0.00083523866,0.0011097591,0.00018687257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0074598663,0.0010886712,0.0017066841,0.0014661568,0.0007504559,0.0018183499,0.0029328135,0.0017504259,0.0037498535],"category_scores_gemma":[0.020930551,0.0006966186,0.0012965982,0.0030300796,0.0023776633,0.0037660776,0.0026282456,0.0026782304,0.00043051873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000881951,0.000058784564,0.0067811715,0.00010032489,0.00010142349,0.0003537527,0.00034445923,0.6154252,0.00034501846,0.35149992,0.0014097844,0.02349194],"study_design_scores_gemma":[0.000011223128,0.000023015673,0.0006282874,0.000009571905,0.000015135937,0.000033456323,0.000078529934,0.91701955,0.00014032195,0.080998175,0.0010264548,0.000016187416],"about_ca_topic_score_codex":0.019106682,"about_ca_topic_score_gemma":0.01308063,"teacher_disagreement_score":0.019106682,"about_ca_system_score_codex":0.002439052,"about_ca_system_score_gemma":0.0013826084,"threshold_uncertainty_score":0.039452016},"labels":[],"label_agreement":null},{"id":"W2327955434","doi":"10.3390/econometrics4020020","title":"Recovering the Most Entropic Copulas from Preliminary Knowledge of Dependence","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Copula (linguistics); Mathematics; Estimator; Joint entropy; Entropy (arrow of time); Kullback–Leibler divergence; Statistical physics; Transfer entropy; Marginal distribution; Econometrics; Applied mathematics; Statistics; Principle of maximum entropy; Random variable; Physics; Thermodynamics","score_opus":0.03413583178190069,"score_gpt":0.21588493244182863,"score_spread":0.18174910065992794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2327955434","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030520257,0.0002550326,0.96806216,0.00014518964,0.0000130732615,0.000019615149,0.00015616942,0.00011809813,0.000710457],"genre_scores_gemma":[0.7480477,0.0012152297,0.24767612,0.00023676515,0.0001936319,0.00008917007,0.0009964887,0.0002179345,0.0013269288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987174,0.00056515983,0.00009078814,0.0002557279,0.0002627057,0.000108091146],"domain_scores_gemma":[0.9900289,0.00694068,0.00083616056,0.0013167341,0.00063210767,0.00024539296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004738172,0.0011821256,0.0015202261,0.0020122,0.00049288094,0.0015383193,0.001260694,0.000724965,0.0013134592],"category_scores_gemma":[0.02268898,0.0008462476,0.001112726,0.001301266,0.0016996808,0.0036543144,0.0022101158,0.0021642128,0.0003389258],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017414866,0.00014180956,0.0110833915,0.00032544517,0.00040510666,0.00052727247,0.00032912727,0.6425186,0.008691455,0.1891353,0.0027728567,0.14389561],"study_design_scores_gemma":[0.000006720741,0.00003255228,0.002157657,0.000025269048,0.000023884268,0.000086986576,0.000032802527,0.90019584,0.0015149591,0.09516668,0.00072371704,0.00003290473],"about_ca_topic_score_codex":0.0022040368,"about_ca_topic_score_gemma":0.0021992915,"teacher_disagreement_score":0.004738172,"about_ca_system_score_codex":0.00072489347,"about_ca_system_score_gemma":0.0015450029,"threshold_uncertainty_score":0.02505815},"labels":[],"label_agreement":null},{"id":"W2516855501","doi":"10.3390/econometrics4030036","title":"Nonparametric Regression with Common Shocks","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Yale University","keywords":"Estimator; Kernel regression; Mathematics; Kernel (algebra); Nonparametric statistics; Econometrics; Conditional probability distribution; Nonparametric regression; Kernel density estimation; Applied mathematics; Statistics; Conditional expectation; Conditional variance; Discrete mathematics","score_opus":0.12641734027638504,"score_gpt":0.35932534661929677,"score_spread":0.23290800634291173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516855501","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029149543,0.0005465206,0.96779335,0.0005901066,0.00006013854,0.000029742276,0.00013620972,0.00011865885,0.001575765],"genre_scores_gemma":[0.89678055,0.0012856633,0.09124316,0.00034175484,0.0004007402,0.0002097542,0.0005031089,0.00007442785,0.00916084],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9917679,0.004368398,0.0003918494,0.0016889814,0.001140416,0.00064244575],"domain_scores_gemma":[0.96798295,0.022597264,0.0042381906,0.0035327114,0.0012281577,0.00042075553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011793248,0.0010940655,0.0026262128,0.0020702376,0.0007433273,0.0030772504,0.0033407914,0.0032698603,0.002239654],"category_scores_gemma":[0.04776046,0.0011702987,0.0021084594,0.0029804674,0.003766854,0.005873023,0.0038895851,0.0036140569,0.00050447276],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008905017,0.000079906924,0.0045136865,0.00012539752,0.0002599058,0.00055631733,0.00013326416,0.34919816,0.0006067684,0.6239418,0.0007767498,0.019719016],"study_design_scores_gemma":[0.00001890219,0.000038120565,0.0009227619,0.000023597677,0.000039850544,0.00010808416,0.000035775698,0.81489253,0.0002447989,0.18237552,0.0012738487,0.000026211927],"about_ca_topic_score_codex":0.005611002,"about_ca_topic_score_gemma":0.003160171,"teacher_disagreement_score":0.011793248,"about_ca_system_score_codex":0.0017733739,"about_ca_system_score_gemma":0.0013708755,"threshold_uncertainty_score":0.062369347},"labels":[],"label_agreement":null},{"id":"W2542638586","doi":"10.3390/econometrics4040042","title":"Social Networks and Choice Set Formation in Discrete Choice Models","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Ministry of Agriculture and Forestry; University of Alberta","funders":"","keywords":"Choice set; Discrete choice; Set (abstract data type); Consumer choice; Social choice theory; Choice modelling; Econometrics; Computer science; Economics; Microeconomics; Mathematics","score_opus":0.15340921464175142,"score_gpt":0.23850560542046864,"score_spread":0.08509639077871722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542638586","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20071468,0.0010746173,0.7635867,0.0052254605,0.00012319285,0.00052619306,0.0007725948,0.0001535706,0.027822927],"genre_scores_gemma":[0.90449023,0.0009829103,0.08203314,0.0003251539,0.00012578143,0.00089768885,0.00036891314,0.00004020569,0.010735935],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9851836,0.011485373,0.00038218565,0.0012701193,0.0009411356,0.0007375116],"domain_scores_gemma":[0.9370194,0.055677116,0.003892924,0.0014092125,0.00088615925,0.0011152254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013896007,0.0010003877,0.0015632989,0.0016501986,0.0013760182,0.0040195924,0.0020696425,0.0023499741,0.010914423],"category_scores_gemma":[0.043695483,0.00087648514,0.0015067303,0.0017950687,0.0046787723,0.0057899416,0.002766742,0.003535928,0.00072927366],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000102385966,0.00014565587,0.0044203307,0.00008560429,0.00009776513,0.00013498022,0.00067187345,0.11919652,0.0001317026,0.8653891,0.00059342745,0.009030646],"study_design_scores_gemma":[0.000079077356,0.000055621316,0.0010794542,0.000036169014,0.000025914214,0.00004892781,0.0001688556,0.30373657,0.000103052735,0.692883,0.0017471412,0.000036244877],"about_ca_topic_score_codex":0.006679474,"about_ca_topic_score_gemma":0.0050712717,"teacher_disagreement_score":0.013896007,"about_ca_system_score_codex":0.0038281912,"about_ca_system_score_gemma":0.0015596078,"threshold_uncertainty_score":0.073489964},"labels":[],"label_agreement":null},{"id":"W2545558097","doi":"10.3390/econometrics6010009","title":"A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Blackberry (Canada)","funders":"International Association for Applied Econometrics","keywords":"Logit; Poisson distribution; Negative binomial distribution; Model selection; Econometrics; Mathematics; Gravity model of trade; Benchmark (surveying); Computer science; Mathematical optimization; Statistics; Economics; Geography","score_opus":0.1003522477064455,"score_gpt":0.21367812407846654,"score_spread":0.11332587637202103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545558097","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019416869,0.000039705734,0.9796592,0.000077477154,0.000010882452,0.00003109832,0.000080987564,0.00012424342,0.00055956637],"genre_scores_gemma":[0.4040561,0.00023088984,0.5919665,0.00010042039,0.00004519728,0.00027079938,0.0008910718,0.00009722262,0.0023417827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99787843,0.001368232,0.00010706242,0.00028398942,0.00025296965,0.00010924382],"domain_scores_gemma":[0.99484336,0.0034153282,0.0004943995,0.0007052103,0.0004811002,0.00006052604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047886,0.0006146292,0.00067467126,0.0022030638,0.0006416613,0.001009587,0.0018155972,0.001074817,0.0037525748],"category_scores_gemma":[0.016636286,0.00040228557,0.0020056732,0.0025615115,0.00091214257,0.0013791445,0.0014123593,0.0010180804,0.0006306238],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010632807,0.00014857427,0.0408003,0.00018786492,0.0003985503,0.0004669338,0.00062242494,0.4734661,0.004145584,0.28227437,0.002490279,0.19489272],"study_design_scores_gemma":[0.000016175254,0.00005672536,0.0054164766,0.000025431913,0.00003578709,0.0000771125,0.00008328758,0.92219627,0.0015247788,0.06856444,0.0019716378,0.000031861127],"about_ca_topic_score_codex":0.010135305,"about_ca_topic_score_gemma":0.010232763,"teacher_disagreement_score":0.010135305,"about_ca_system_score_codex":0.0007614071,"about_ca_system_score_gemma":0.0018638108,"threshold_uncertainty_score":0.025324821},"labels":[],"label_agreement":null},{"id":"W2765803834","doi":"10.3390/econometrics5040045","title":"An Interview with William A. Barnett","year":2017,"lang":"en","type":"article","venue":"Econometrics","topic":"Economic theories and models","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Download; Economics; Divisia monetary aggregates index; Order (exchange); Divisia index; Macro; Mathematical economics; Keynesian economics; Monetary policy; Finance; Computer science; Central bank; Mathematics; Statistics; Quantitative easing","score_opus":0.08510253998727861,"score_gpt":0.2415078674243901,"score_spread":0.1564053274371115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765803834","genre_codex":"commentary","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0048680827,0.0071593965,0.0004389304,0.94238776,0.010324343,0.000072221264,0.00022389911,0.000045720717,0.03447962],"genre_scores_gemma":[0.044280604,0.010188738,0.00067354133,0.7287931,0.0045299735,0.0003193234,0.0002176606,0.000116848605,0.21088018],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982173,0.0009718418,0.00006123396,0.00015784487,0.0003316429,0.0002600785],"domain_scores_gemma":[0.9920107,0.0041001965,0.00034379287,0.000110092966,0.0017979924,0.0016372929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036157728,0.0004570913,0.00059931504,0.0009363962,0.009532349,0.0023446667,0.0007884676,0.005533564,0.027047409],"category_scores_gemma":[0.018369239,0.000678441,0.000215286,0.0014805646,0.0019066383,0.0039275964,0.0019074532,0.011620407,0.0075761946],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000012673335,0.000018098535,0.00038293324,0.000029305218,0.0000010284317,0.00024029256,0.0041239797,0.000013625865,0.00008828959,0.0011200425,0.98976094,0.0042088563],"study_design_scores_gemma":[0.0000058409,0.000017215973,0.0016279902,0.00017688553,0.000001813379,0.00024175379,0.018825732,0.00004989266,0.00006404957,0.00089607545,0.97806853,0.000024168612],"about_ca_topic_score_codex":0.042459007,"about_ca_topic_score_gemma":0.08254665,"teacher_disagreement_score":0.042459007,"about_ca_system_score_codex":0.0041361046,"about_ca_system_score_gemma":0.003652379,"threshold_uncertainty_score":0.09048253},"labels":[],"label_agreement":null},{"id":"W2775457149","doi":"10.3390/econometrics5040053","title":"Reducing Approximation Error in the Fourier Flexible Functional Form","year":2017,"lang":"en","type":"article","venue":"Econometrics","topic":"Scientific Measurement and Uncertainty Evaluation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"National Institute of Food and Agriculture; Washington State University; U.S. Department of Agriculture","keywords":"Fourier series; Taylor series; Logarithm; Fourier transform; Mathematics; Applied mathematics; Function (biology); Transformation (genetics); Fourier analysis; Discrete Fourier series; Series (stratigraphy); Mathematical analysis; Algorithm; Short-time Fourier transform","score_opus":0.6308223987465845,"score_gpt":0.43812625063864336,"score_spread":0.19269614810794117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775457149","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011790528,0.000120015946,0.98552656,0.00042469162,0.00003480384,0.000048472426,0.00006211181,0.00016434722,0.0018283927],"genre_scores_gemma":[0.44274247,0.0003990736,0.5500385,0.00049957895,0.00014082433,0.00048181097,0.00037559852,0.0004123937,0.0049097077],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9832788,0.0109907165,0.0006657083,0.001170895,0.0033458327,0.0005480628],"domain_scores_gemma":[0.9119348,0.071757406,0.002341479,0.009446173,0.00414917,0.0003709168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.033296045,0.0011965032,0.001510164,0.0018881236,0.0007148189,0.0023285432,0.0023734448,0.002211603,0.004889079],"category_scores_gemma":[0.13099651,0.00064446,0.00167774,0.0016850366,0.0028644484,0.0044719367,0.0038865565,0.0040406403,0.0010756642],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024098557,0.00012449815,0.0046375273,0.00023578972,0.00017286242,0.00036675783,0.00048862374,0.3046692,0.0027523937,0.54437435,0.0036144785,0.13832259],"study_design_scores_gemma":[0.000030682346,0.00008193937,0.0008050444,0.00005990103,0.000027467646,0.00010249329,0.00006417783,0.83570004,0.001359646,0.15999551,0.0017401517,0.000032927524],"about_ca_topic_score_codex":0.0041851625,"about_ca_topic_score_gemma":0.002272085,"teacher_disagreement_score":0.033296045,"about_ca_system_score_codex":0.001594366,"about_ca_system_score_gemma":0.0027917165,"threshold_uncertainty_score":0.17608845},"labels":[],"label_agreement":null},{"id":"W2784539053","doi":"10.3390/econometrics6010004","title":"From the Classical Gini Index of Income Inequality to a New Zenga-Type Relative Measure of Risk: A Modeller’s Perspective","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Università degli Studi di Milano-Bicocca","keywords":"Lorenz curve; Inequality; Index (typography); Perspective (graphical); Measure (data warehouse); Point (geometry); Mathematics; Mathematical economics; Economic inequality; Econometrics; Population; Economics; Gini coefficient; Sociology; Computer science; Mathematical analysis; Demography","score_opus":0.10211086492542328,"score_gpt":0.33859164754742516,"score_spread":0.2364807826220019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2784539053","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029295817,0.0069299135,0.8823401,0.034377396,0.00089085556,0.00004741454,0.00044395,0.00011326065,0.045561362],"genre_scores_gemma":[0.81240046,0.00820527,0.16624333,0.002194702,0.002746364,0.00020665987,0.00019250238,0.00013309869,0.007677713],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998679,0.0006736381,0.000057265457,0.00024343007,0.00027707155,0.000069644055],"domain_scores_gemma":[0.99750215,0.0014901091,0.00035901184,0.00024865058,0.00025861664,0.00014149996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041595963,0.0008735986,0.00077945925,0.003188975,0.00086653244,0.0034383978,0.001401282,0.0014914938,0.0025679644],"category_scores_gemma":[0.009003471,0.0002848803,0.0009038833,0.00194952,0.0067688534,0.005910133,0.002527203,0.0040162173,0.00035152622],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000037131608,0.0000055251166,0.00027261654,0.000012161338,0.000006343198,0.000019575615,0.00009664979,0.0024287514,0.00007022884,0.9938211,0.0004536387,0.0028096936],"study_design_scores_gemma":[0.0000022302047,0.000012952226,0.00039931582,0.000021925833,0.000004960966,0.000033126504,0.0000525867,0.010961149,0.00006114999,0.98446935,0.0039699427,0.000011367857],"about_ca_topic_score_codex":0.0019402566,"about_ca_topic_score_gemma":0.001320511,"teacher_disagreement_score":0.0041595963,"about_ca_system_score_codex":0.0029569159,"about_ca_system_score_gemma":0.00092395005,"threshold_uncertainty_score":0.021998346},"labels":[],"label_agreement":null},{"id":"W2789871200","doi":"10.3390/econometrics6010006","title":"Estimating Unobservable Inflation Expectations in the New Keynesian Phillips Curve","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Unobservable; Phillips curve; Economics; Inflation (cosmology); New Keynesian economics; Econometrics; Bayesian probability; Econometric model; Keynesian economics; Real interest rate; Bayes estimator; Monetary policy; Statistics; Mathematics","score_opus":0.1871880601009416,"score_gpt":0.25905099928890984,"score_spread":0.07186293918796824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789871200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19440418,0.0004559742,0.79740334,0.0010455166,0.00007543299,0.000040920553,0.00022454682,0.00023143619,0.0061186505],"genre_scores_gemma":[0.9192054,0.00080550846,0.07429278,0.00012053226,0.0000723254,0.00006901173,0.000420092,0.00006484029,0.004949374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986343,0.0007345339,0.000065797525,0.00023421855,0.00022358626,0.00010755886],"domain_scores_gemma":[0.9952619,0.0035072775,0.00064076047,0.00031267566,0.00020978303,0.00006751247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029540171,0.0003854234,0.0009363046,0.0008893247,0.00027201738,0.0018853912,0.00087619544,0.00095395703,0.002268087],"category_scores_gemma":[0.022841929,0.00078724424,0.00089620764,0.001044468,0.00072774006,0.0022817466,0.0010699378,0.0020282287,0.000630488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015257514,0.00011354979,0.016282799,0.00013852978,0.00017978826,0.0002603414,0.00046094667,0.69977826,0.0011614519,0.21856509,0.0011852138,0.06172144],"study_design_scores_gemma":[0.00004317241,0.00005604819,0.0041489173,0.000025142146,0.00003922961,0.00005501283,0.00006789551,0.8588285,0.0006749805,0.133924,0.0020940914,0.000043050957],"about_ca_topic_score_codex":0.006200255,"about_ca_topic_score_gemma":0.0055163824,"teacher_disagreement_score":0.006200255,"about_ca_system_score_codex":0.00090781954,"about_ca_system_score_gemma":0.0011644035,"threshold_uncertainty_score":0.015622556},"labels":[],"label_agreement":null},{"id":"W2792431846","doi":"10.3390/econometrics6020015","title":"Income Inequality, Cohesiveness and Commonality in the Euro Area: A Semi-Parametric Boundary-Free Analysis","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Group cohesiveness; Inequality; Economics; Poverty; Polarization (electrochemistry); Income distribution; Gini coefficient; Context (archaeology); Distribution (mathematics); Economic inequality; Demographic economics; Political science; Geography; Mathematics; Economic growth; Law","score_opus":0.08346479628588342,"score_gpt":0.3307403691816921,"score_spread":0.24727557289580865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792431846","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.91121787,0.0002750624,0.08397584,0.00034122416,0.000015400485,0.00008138865,0.0003343704,0.00006313687,0.0036956305],"genre_scores_gemma":[0.99239457,0.00007946608,0.006601624,0.000018288916,0.000017949344,0.00006823912,0.00026763338,0.000013145854,0.00053906144],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964101,0.002588474,0.00009881335,0.00037417488,0.0002295714,0.00029890315],"domain_scores_gemma":[0.98079175,0.016001197,0.001276289,0.00089632836,0.000555157,0.00047927015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008632809,0.00039248433,0.00091962726,0.0019534354,0.0007389065,0.0029096801,0.001235715,0.000718586,0.004683774],"category_scores_gemma":[0.017209535,0.00033507723,0.0019500463,0.0015642167,0.0030101717,0.0017890842,0.0034887532,0.0013189561,0.00019080388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007400417,0.0003584095,0.2980358,0.00023119898,0.0011041792,0.0012140006,0.010048824,0.22871204,0.0015286022,0.3990369,0.0030666785,0.05592332],"study_design_scores_gemma":[0.000023823914,0.00015677066,0.09099063,0.000059973274,0.00012590429,0.00012421621,0.0030830142,0.80991083,0.00020613741,0.093300804,0.0019647938,0.00005306238],"about_ca_topic_score_codex":0.009270847,"about_ca_topic_score_gemma":0.003360778,"teacher_disagreement_score":0.009270847,"about_ca_system_score_codex":0.001051472,"about_ca_system_score_gemma":0.00066871283,"threshold_uncertainty_score":0.04565519},"labels":[],"label_agreement":null},{"id":"W2792450877","doi":"10.3390/econometrics6010014","title":"Statistical Inference on the Canadian Middle Class","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McGill University","funders":"","keywords":"Inference; Econometrics; Asymptotic distribution; Mathematics; Statistical inference; Sample (material); Population; Statistics; Class (philosophy); Confidence interval; Middle class; Sampling distribution; Distribution (mathematics); Asymptotic analysis; Normality; Sample size determination; Economics; Demography; Computer science; Sociology","score_opus":0.3147779715277801,"score_gpt":0.2509652500907745,"score_spread":0.06381272143700556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792450877","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36834985,0.022368146,0.2616323,0.024419405,0.0016717778,0.00058214576,0.029864589,0.0014961802,0.28961563],"genre_scores_gemma":[0.9315368,0.0051495438,0.041620966,0.0014538425,0.00029404144,0.00016760577,0.0043468922,0.00018503057,0.015245356],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9922328,0.002298911,0.00024539005,0.0013492084,0.003069291,0.00080429885],"domain_scores_gemma":[0.9741716,0.011274563,0.0010954925,0.0024676323,0.01021803,0.0007726461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013209458,0.00054686517,0.001072581,0.0054644113,0.0037115316,0.0030956916,0.0020725776,0.0006315747,0.0070811813],"category_scores_gemma":[0.07551865,0.00034465714,0.0011876482,0.0061560776,0.003719243,0.0011283127,0.0019697347,0.0017152285,0.0005509172],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034773655,0.00003494198,0.09956394,0.0003840544,0.00042316044,0.00034181052,0.0032648465,0.021000253,0.00058693724,0.6199853,0.058154542,0.19591248],"study_design_scores_gemma":[0.00013965978,0.000074470576,0.41090265,0.0011537106,0.0003788861,0.00021877798,0.003915296,0.12785004,0.0021087446,0.2845265,0.16838019,0.00035111033],"about_ca_topic_score_codex":0.97953576,"about_ca_topic_score_gemma":0.9618556,"teacher_disagreement_score":0.030589629,"about_ca_system_score_codex":0.030589629,"about_ca_system_score_gemma":0.03411648,"threshold_uncertainty_score":0.22194433},"labels":[],"label_agreement":null},{"id":"W2808934554","doi":"10.3390/econometrics6030032","title":"Econometric Fine Art Valuation by Combining Hedonic and Repeat-Sales Information","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Art History and Market Analysis","field":"Arts and Humanities","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Valuation (finance); Pooling; Econometrics; Computer science; Real estate; Context (archaeology); Predictive analytics; Hedonic regression; Economics; Artificial intelligence; Machine learning; Finance","score_opus":0.037424054083882684,"score_gpt":0.20575737902083543,"score_spread":0.16833332493695274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2808934554","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3921,0.0016053732,0.5944527,0.00074452517,0.00009399781,0.0001247958,0.0015197732,0.00047339682,0.008885411],"genre_scores_gemma":[0.9432972,0.00054525497,0.050252188,0.000078080004,0.00016066818,0.000041580995,0.002046346,0.000039994688,0.0035387273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990376,0.0003960297,0.00005134175,0.00022007486,0.0002151879,0.00007981498],"domain_scores_gemma":[0.99586225,0.0029371544,0.00049390865,0.00045480387,0.00018227208,0.000069666065],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039759013,0.0007161092,0.0009697848,0.0026228365,0.00024389468,0.0021895033,0.0011781389,0.00086411124,0.0030129694],"category_scores_gemma":[0.012220754,0.00051323586,0.00093086984,0.003494308,0.00061653985,0.0023036145,0.0009885979,0.0011404016,0.00050227134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014835465,0.00019844531,0.09497358,0.00010091045,0.0004973841,0.00022021773,0.00022646964,0.72659665,0.00082291354,0.02794324,0.0025144748,0.14575732],"study_design_scores_gemma":[0.0000096544245,0.000028826335,0.02181859,0.000013952525,0.00004191552,0.000027150894,0.00006804831,0.95796746,0.000310818,0.018509835,0.0011770941,0.000026540973],"about_ca_topic_score_codex":0.026502727,"about_ca_topic_score_gemma":0.030866988,"teacher_disagreement_score":0.026502727,"about_ca_system_score_codex":0.00084230804,"about_ca_system_score_gemma":0.00058353785,"threshold_uncertainty_score":0.052696943},"labels":[],"label_agreement":null},{"id":"W2887079090","doi":"10.3390/econometrics6030038","title":"Econometrics Best Paper Award 2018","year":2018,"lang":"en","type":"article","venue":"Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Econometrics; Mathematics; Statistics","score_opus":0.19519511109238868,"score_gpt":0.23977003416297282,"score_spread":0.04457492307058414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887079090","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016946611,0.005137977,0.0057187835,0.09249303,0.094062835,0.00027132063,0.023862224,0.002486559,0.7742726],"genre_scores_gemma":[0.0044167936,0.001692715,0.00096950826,0.00226024,0.008477931,0.000079511185,0.004850399,0.00057012984,0.97668284],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9964856,0.00044449812,0.00021180586,0.0004954188,0.001885662,0.00047701786],"domain_scores_gemma":[0.98327005,0.0022228207,0.00060286315,0.0016704528,0.009199204,0.0030345751],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0047911685,0.0010683378,0.0024220576,0.0045272005,0.001914745,0.0077324053,0.0016646999,0.0050492478,0.5740488],"category_scores_gemma":[0.023777556,0.0006202308,0.0011328548,0.0036266823,0.0010774445,0.0038201222,0.002695638,0.0037357057,0.49754956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000043211818,0.00003083054,0.00011644715,0.00003652777,0.000006273611,0.000015954012,0.000002816757,0.00008285627,0.000041094467,0.0028740347,0.9758016,0.020948423],"study_design_scores_gemma":[0.000058400077,0.000029766432,0.0015273974,0.00013153558,0.000015077443,0.00003455299,0.000028299502,0.00077246653,0.00017345304,0.009205154,0.988008,0.000015893653],"about_ca_topic_score_codex":0.0051678047,"about_ca_topic_score_gemma":0.013068522,"teacher_disagreement_score":0.5740488,"about_ca_system_score_codex":0.0042914567,"about_ca_system_score_gemma":0.0048412266,"threshold_uncertainty_score":0.60756767},"labels":[],"label_agreement":null},{"id":"W2922743875","doi":"10.3390/econometrics7010016","title":"Monte Carlo Inference on Two-Sided Matching Models","year":2019,"lang":"en","type":"article","venue":"Econometrics","topic":"Game Theory and Voting Systems","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Matching (statistics); Inference; Monte Carlo method; Parametric statistics; Conditional independence; Independence (probability theory); Statistical physics; Mathematics; Observable; Applied mathematics; Computer science; Mathematical optimization; Statistics; Artificial intelligence; Physics","score_opus":0.07711424203231552,"score_gpt":0.23913971675466517,"score_spread":0.16202547472234963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922743875","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06868227,0.00038480049,0.92558897,0.00071154296,0.000057992835,0.00013724396,0.00017643294,0.00021107437,0.0040497533],"genre_scores_gemma":[0.8177144,0.00049690856,0.17490578,0.00032071522,0.00015483328,0.0005405929,0.000559942,0.00009688969,0.0052100504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9892513,0.008148902,0.0002968392,0.0010823322,0.0007429757,0.00047765512],"domain_scores_gemma":[0.8828122,0.10769838,0.0033151815,0.0040005944,0.0013921756,0.00078141165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.020605939,0.000729946,0.0023485504,0.0018668381,0.0010751678,0.002550546,0.002850639,0.0028397457,0.005536966],"category_scores_gemma":[0.10102315,0.0012028654,0.0011895241,0.0020138314,0.003925017,0.0046078702,0.0023056495,0.0031816864,0.0005898423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023501992,0.00013308076,0.0044703977,0.000091527894,0.00012841838,0.00021929448,0.00018473122,0.58891594,0.00023486801,0.38909477,0.00095225027,0.015339634],"study_design_scores_gemma":[0.00003012359,0.000016094116,0.00026724074,0.000013482709,0.000010083685,0.00002372383,0.0000211464,0.8611559,0.00008281512,0.13811111,0.0002552277,0.000013052642],"about_ca_topic_score_codex":0.008950508,"about_ca_topic_score_gemma":0.007534177,"teacher_disagreement_score":0.020605939,"about_ca_system_score_codex":0.002240092,"about_ca_system_score_gemma":0.0015674804,"threshold_uncertainty_score":0.10897589},"labels":[],"label_agreement":null},{"id":"W2980316693","doi":"10.3390/econometrics7040043","title":"Likelihood Inference for Generalized Integer Autoregressive Time Series Models","year":2019,"lang":"en","type":"article","venue":"Econometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; Mathematics; Negative binomial distribution; Series (stratigraphy); STAR model; Binomial (polynomial); Applied mathematics; Inference; Quasi-likelihood; Integer (computer science); Count data; Statistics; Time series; Likelihood function; Overdispersion; Estimation theory; Computer science; Autoregressive integrated moving average; Artificial intelligence; Poisson distribution","score_opus":0.027097747589056292,"score_gpt":0.2614788993959245,"score_spread":0.23438115180686822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980316693","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033133116,0.00023730815,0.9953193,0.00018953899,0.000012909131,0.000007838526,0.00005369057,0.00013193482,0.0007339947],"genre_scores_gemma":[0.38291037,0.002320292,0.60539263,0.00031803417,0.00026553482,0.0002924252,0.0012352581,0.0003867031,0.006878778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9974082,0.0017871162,0.00009416721,0.00024181108,0.00037035413,0.00009830915],"domain_scores_gemma":[0.985811,0.012504484,0.0007291523,0.0005021231,0.00033474714,0.00011853174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0059597893,0.0008299743,0.0011692597,0.0013737489,0.00047295116,0.0017425516,0.001967326,0.0012660506,0.0028685196],"category_scores_gemma":[0.034920808,0.00076621573,0.0010919361,0.0020213828,0.0013875379,0.0029134692,0.0014981013,0.0025268437,0.00064721046],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048093436,0.00003650283,0.0010619533,0.00013000307,0.00009307565,0.00014958967,0.00012741919,0.4794114,0.0006576318,0.46968678,0.0019209628,0.0466766],"study_design_scores_gemma":[0.000006264769,0.0000040630907,0.00012171292,0.0000123376085,0.000005050102,0.00001804103,0.000011084999,0.7917218,0.00009760238,0.20741832,0.0005745628,0.000009127423],"about_ca_topic_score_codex":0.0042127585,"about_ca_topic_score_gemma":0.0040639243,"teacher_disagreement_score":0.0059597893,"about_ca_system_score_codex":0.0010360616,"about_ca_system_score_gemma":0.0011441289,"threshold_uncertainty_score":0.031518757},"labels":[],"label_agreement":null},{"id":"W3005313528","doi":"10.3390/econometrics8010004","title":"Correction: Ardia, D., et al. Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices. Econometrics 2016, 4, 14","year":2020,"lang":"en","type":"article","venue":"Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Econometrics; Bayesian probability; Stock (firearms); Mathematics; Economics; Statistics; Financial economics; Computer science; Geography","score_opus":0.11546052377460558,"score_gpt":0.2513354348740075,"score_spread":0.13587491109940192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005313528","genre_codex":"editorial","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002728466,0.0019329594,0.0033116345,0.047019366,0.9322479,0.000053531006,0.01170058,0.0010561458,0.002404959],"genre_scores_gemma":[0.045730557,0.011811721,0.026538623,0.106639855,0.48924646,0.0008185317,0.031991698,0.0064994614,0.2807232],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99091035,0.0017644604,0.0018517928,0.0012816453,0.0036253266,0.00056652457],"domain_scores_gemma":[0.88166463,0.02137132,0.0048257536,0.008125248,0.08137098,0.002642021],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0087596895,0.003243741,0.0027624983,0.0053514736,0.002671392,0.005528324,0.005406059,0.0054498203,0.117057525],"category_scores_gemma":[0.18074727,0.001655639,0.0037870347,0.00467917,0.001968345,0.0031377652,0.0027073491,0.011324213,0.061816763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024761212,0.0000033461858,0.00007990372,0.00011857542,0.000018310922,0.00005084199,0.000023486766,0.00006707502,0.000025262323,0.0005533379,0.9963081,0.0027269993],"study_design_scores_gemma":[0.0001728634,0.000025767367,0.002458592,0.00078401994,0.00013665014,0.00062102405,0.00011285667,0.0010958773,0.00042375817,0.005038015,0.989019,0.00011171186],"about_ca_topic_score_codex":0.04202918,"about_ca_topic_score_gemma":0.035241254,"teacher_disagreement_score":0.117057525,"about_ca_system_score_codex":0.0046592634,"about_ca_system_score_gemma":0.009287728,"threshold_uncertainty_score":0.39159644},"labels":[],"label_agreement":null},{"id":"W3005541375","doi":"10.3390/econometrics8010005","title":"Testing for Stochastic Dominance up to a Common Relative Poverty Line","year":2020,"lang":"en","type":"article","venue":"Econometrics","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Stochastic dominance; Poverty; Econometrics; Dominance (genetics); Statistics; Mathematics; Monte Carlo method; Fraction (chemistry); Economics; Economic growth","score_opus":0.14704589486872086,"score_gpt":0.34375294028815456,"score_spread":0.1967070454194337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005541375","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3940083,0.00018905729,0.5953527,0.00079480873,0.000054694712,0.00021666243,0.0005578616,0.0002348536,0.008591141],"genre_scores_gemma":[0.96835965,0.000056608475,0.030319907,0.00015211725,0.000046383764,0.00013764581,0.000431275,0.000023809534,0.00047262575],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.96337265,0.023809126,0.0013294888,0.0038673158,0.005728056,0.0018932835],"domain_scores_gemma":[0.7952092,0.18237644,0.00825193,0.0055565815,0.0065234625,0.0020823474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03321245,0.00073370466,0.0017838344,0.0031727946,0.0015058897,0.0023110118,0.001661692,0.0014229463,0.0047339173],"category_scores_gemma":[0.16174224,0.00040882808,0.001905399,0.0024542734,0.0038603297,0.004032512,0.0037350294,0.0016384841,0.00029346402],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011947127,0.00078764977,0.19171202,0.00036334473,0.0016106904,0.0016540122,0.0012990091,0.22598758,0.0060283695,0.3822106,0.0040572765,0.18309471],"study_design_scores_gemma":[0.0001868168,0.0013477107,0.044220697,0.00010501503,0.00013074667,0.0007886154,0.00082543003,0.6097812,0.0031423,0.33697793,0.0023640974,0.00012946335],"about_ca_topic_score_codex":0.0031545565,"about_ca_topic_score_gemma":0.0021580062,"teacher_disagreement_score":0.03321245,"about_ca_system_score_codex":0.001181489,"about_ca_system_score_gemma":0.002911421,"threshold_uncertainty_score":0.17564636},"labels":[],"label_agreement":null},{"id":"W3014285022","doi":"10.3390/econometrics8020012","title":"Simultaneous Indirect Inference, Impulse Responses and ARMA Models","year":2020,"lang":"en","type":"article","venue":"Econometrics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canada Mortgage and Housing Corporation; Carleton University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Autoregressive model; Autoregressive–moving-average model; Robustness (evolution); Monte Carlo method; Inference; Impulse response; Confidence interval; Mathematics; Impulse (physics); Statistical hypothesis testing; Statistics; Econometrics; Algorithm; Computer science; Applied mathematics; Artificial intelligence","score_opus":0.07463999120752818,"score_gpt":0.240615264452482,"score_spread":0.1659752732449538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014285022","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0039473968,0.00006399822,0.9951283,0.00006914388,0.000008382132,0.000028807359,0.000027983828,0.000109074455,0.0006169167],"genre_scores_gemma":[0.48539546,0.00040017182,0.5101194,0.00015293388,0.000095201816,0.00072304433,0.00040365942,0.00018990402,0.0025202814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99091315,0.0064778775,0.00030133204,0.0007939216,0.001176391,0.00033730117],"domain_scores_gemma":[0.9351773,0.05787687,0.0024683923,0.0026547082,0.0014486662,0.0003740127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012841514,0.0017179357,0.0023639863,0.0026993747,0.0006280138,0.0031904243,0.0026824304,0.0019560393,0.0051178406],"category_scores_gemma":[0.07641012,0.0015341836,0.002357788,0.0018001393,0.0026820786,0.0029657828,0.0041158767,0.0038543674,0.0007106135],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001059512,0.00008962536,0.0022500767,0.0001427159,0.00027797007,0.00013860191,0.00017275577,0.72266865,0.0007274729,0.23581518,0.00042116578,0.03718997],"study_design_scores_gemma":[0.00001623145,0.00003133059,0.00024814965,0.000025357192,0.000025137006,0.000032290194,0.000011089554,0.91678816,0.00040918344,0.08206444,0.00032668156,0.000021977996],"about_ca_topic_score_codex":0.001869179,"about_ca_topic_score_gemma":0.0012263119,"teacher_disagreement_score":0.012841514,"about_ca_system_score_codex":0.0010152676,"about_ca_system_score_gemma":0.0020099594,"threshold_uncertainty_score":0.067913175},"labels":[],"label_agreement":null},{"id":"W3023765545","doi":"10.3390/econometrics4010012","title":"Evolutionary Sequential Monte Carlo Samplers for Change-Point Models","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Agence Nationale de la Recherche","keywords":"Markov chain Monte Carlo; Particle filter; Monte Carlo method; Computer science; Algorithm; Marginal likelihood; Mathematical optimization; Inference; Heuristic; Mathematics; Bayesian probability; Artificial intelligence; Statistics; Kalman filter","score_opus":0.12649954573386915,"score_gpt":0.2851502513235134,"score_spread":0.15865070558964425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3023765545","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028024116,0.00015743899,0.99580896,0.00007377356,0.000024931403,0.000041740062,0.000032178395,0.00011695454,0.000941584],"genre_scores_gemma":[0.21289337,0.00063637487,0.77897644,0.00020085188,0.00012874516,0.0006794452,0.0004892554,0.0002851954,0.0057102954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843234,0.0008821996,0.00006014363,0.00022725087,0.000312857,0.00008515498],"domain_scores_gemma":[0.9908937,0.007343061,0.00041908683,0.00050060137,0.0006545532,0.00018896796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048389924,0.0009529614,0.0013570653,0.0014484326,0.0007341264,0.0012821277,0.0020128712,0.0015957322,0.0077650943],"category_scores_gemma":[0.021527598,0.00079525466,0.0012102321,0.0013245291,0.0015220497,0.0015305715,0.0017059977,0.0022547375,0.0010637328],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000085542546,0.000052259697,0.0014976482,0.00011290248,0.00009039972,0.00009737027,0.00010636473,0.7287289,0.00049726677,0.22546087,0.0013110307,0.041959547],"study_design_scores_gemma":[0.000014772143,0.000010192735,0.00008234763,0.000012508903,0.000007783452,0.00001545007,0.000005390756,0.95186466,0.00012693516,0.046858132,0.0009960652,0.0000057866037],"about_ca_topic_score_codex":0.0044906903,"about_ca_topic_score_gemma":0.005906313,"teacher_disagreement_score":0.0077650943,"about_ca_system_score_codex":0.0012469995,"about_ca_system_score_gemma":0.0014484429,"threshold_uncertainty_score":0.025976777},"labels":[],"label_agreement":null},{"id":"W3115856677","doi":"10.3390/econometrics10040033","title":"Detecting and Quantifying Structural Breaks in Climate","year":2022,"lang":"en","type":"article","venue":"Econometrics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Recession; Coronavirus disease 2019 (COVID-19); Precipitation; Climate change; Economics; Financial crisis; Econometrics; Natural resource economics; Macroeconomics; Geography; Meteorology; Geology; Oceanography","score_opus":0.027844985872753733,"score_gpt":0.24404500393292214,"score_spread":0.21620001806016842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115856677","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.918286,0.00034281862,0.076747485,0.00062284275,0.000019065059,0.00007190722,0.0012155286,0.00014290506,0.0025513512],"genre_scores_gemma":[0.99253875,0.00007327473,0.006709284,0.00002174993,0.000014363382,0.00002180663,0.00051628234,0.000008681865,0.00009584848],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99716336,0.0013401627,0.00022384622,0.0005411652,0.00034283713,0.000388644],"domain_scores_gemma":[0.9795598,0.0120156845,0.005504572,0.0015582257,0.0007427374,0.0006190012],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007058045,0.0005498205,0.00056931697,0.003349134,0.00065584347,0.0015795908,0.00064811716,0.0011197568,0.0016740025],"category_scores_gemma":[0.032574166,0.00036180092,0.0007334547,0.0025809559,0.0011113499,0.0024055955,0.002681678,0.0016749254,0.00015649214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014618094,0.00014804125,0.72749424,0.00009433094,0.00061669707,0.00013108725,0.00077,0.19229415,0.0021740806,0.019554237,0.00090879935,0.055668164],"study_design_scores_gemma":[0.000027546548,0.00023851387,0.5146894,0.00006148075,0.00010360891,0.00006576494,0.0012378955,0.43800235,0.0016569562,0.04195306,0.0019026116,0.000060830283],"about_ca_topic_score_codex":0.011186119,"about_ca_topic_score_gemma":0.010440376,"teacher_disagreement_score":0.011186119,"about_ca_system_score_codex":0.0013859617,"about_ca_system_score_gemma":0.00084340747,"threshold_uncertainty_score":0.037326932},"labels":[],"label_agreement":null},{"id":"W3116613610","doi":"10.3390/econometrics9010001","title":"Regularized Maximum Diversification Investment Strategy","year":2020,"lang":"en","type":"article","venue":"Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Sharpe ratio; Portfolio; Econometrics; Covariance matrix; Diversification (marketing strategy); Portfolio optimization; Mathematics; Market portfolio; Covariance; Investment strategy; Economics; Mathematical optimization; Computer science; Statistics; Capital asset pricing model; Financial economics; Finance; Business","score_opus":0.0938337129060445,"score_gpt":0.2110520457036113,"score_spread":0.1172183327975668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116613610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08830944,0.0006188551,0.90013844,0.00045355884,0.00005487871,0.0001069581,0.00011832207,0.0004540479,0.0097454935],"genre_scores_gemma":[0.82980096,0.00023137745,0.16293532,0.00028331092,0.000059278424,0.0001721003,0.0001828176,0.000072929,0.0062619303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994753,0.0001895486,0.00002844432,0.00010586666,0.00013467352,0.00006621302],"domain_scores_gemma":[0.9989994,0.0005123683,0.0001465068,0.000108832995,0.00016940798,0.00006339975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012155767,0.0005961751,0.0012725075,0.0005591465,0.00019105458,0.0008327405,0.00094823405,0.0010874714,0.0024484606],"category_scores_gemma":[0.0041944887,0.0002748692,0.00045139797,0.00038966196,0.0005940265,0.00075836503,0.0006045587,0.00065525156,0.00045354245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021544765,0.00010702305,0.0014193886,0.0001004166,0.00010686966,0.00023587,0.00006966402,0.8322448,0.004161632,0.04974143,0.0042675706,0.10732982],"study_design_scores_gemma":[0.000020310144,0.000038135524,0.0001928804,0.000009659142,0.000009493091,0.000042004005,0.000003757694,0.98851925,0.0005377138,0.01005035,0.0005703353,0.000006028056],"about_ca_topic_score_codex":0.0009125586,"about_ca_topic_score_gemma":0.00081765815,"teacher_disagreement_score":0.0024484606,"about_ca_system_score_codex":0.00060023397,"about_ca_system_score_gemma":0.0007881547,"threshold_uncertainty_score":0.00819093},"labels":[],"label_agreement":null},{"id":"W3122905291","doi":"10.3390/econometrics5040054","title":"Time-Varying Window Length for Correlation Forecasts","year":2017,"lang":"en","type":"article","venue":"Econometrics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Toronto Metropolitan University","funders":"","keywords":"Econometrics; Diversification (marketing strategy); Benchmark (surveying); Correlation; Asset (computer security); Economics; Computer science; Statistics; Mathematics; Geography","score_opus":0.05955999101534873,"score_gpt":0.24320070515921735,"score_spread":0.18364071414386862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122905291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12124553,0.001048276,0.8735572,0.00048652472,0.00018331343,0.00007288874,0.0009357506,0.0005831864,0.0018873133],"genre_scores_gemma":[0.8086737,0.0010219673,0.18600006,0.00009057094,0.00021013056,0.00014891948,0.0018276362,0.00018806975,0.0018389798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991818,0.0003500535,0.00005526215,0.0002102701,0.00012576029,0.00007685931],"domain_scores_gemma":[0.99272245,0.0053423275,0.00068614277,0.0006113929,0.0004795392,0.00015811728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004169005,0.0007293699,0.000654964,0.0011654511,0.0003555757,0.0013321204,0.0011196166,0.0010500702,0.0029835026],"category_scores_gemma":[0.025143303,0.00047965028,0.00067911734,0.0016386253,0.00027454784,0.0031175814,0.0006532136,0.0017300006,0.0006072379],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031059256,0.00009707027,0.010912472,0.000106349245,0.00016112637,0.00010338714,0.00018616271,0.82064277,0.0036427141,0.03706271,0.003215712,0.12355894],"study_design_scores_gemma":[0.000008059515,0.000017135895,0.0010534094,0.000014755085,0.000013908035,0.000011228268,0.000010781923,0.9898567,0.000502136,0.007938148,0.00056088954,0.000012884855],"about_ca_topic_score_codex":0.0072664157,"about_ca_topic_score_gemma":0.0059909434,"teacher_disagreement_score":0.0072664157,"about_ca_system_score_codex":0.00083858205,"about_ca_system_score_gemma":0.000845942,"threshold_uncertainty_score":0.022048056},"labels":[],"label_agreement":null},{"id":"W3123512584","doi":"10.3390/econometrics5010013","title":"Goodness-of-Fit Tests for Copulas of Multivariate Time Series","year":2017,"lang":"en","type":"article","venue":"Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Univariate; Mathematics; Multivariate statistics; Econometrics; Stochastic volatility; Goodness of fit; Series (stratigraphy); Diagonal; Volatility (finance); Bivariate analysis; Applied mathematics; Statistics","score_opus":0.15544532776182962,"score_gpt":0.3037739153566798,"score_spread":0.14832858759485018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123512584","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3628167,0.00088330015,0.6303624,0.00066010666,0.00015121301,0.00022266421,0.0006169388,0.0011382829,0.0031484228],"genre_scores_gemma":[0.95507187,0.00031040027,0.041628245,0.00018860854,0.00017432978,0.00026394048,0.0015045153,0.00038186857,0.0004761929],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.97127074,0.018697549,0.0016629678,0.0030038129,0.0044000815,0.0009648887],"domain_scores_gemma":[0.49372795,0.45772877,0.016018119,0.022329342,0.007261598,0.0029341816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05068724,0.0016658274,0.0024728172,0.0070124962,0.0011175675,0.0028469707,0.0033534614,0.0032035962,0.0045820954],"category_scores_gemma":[0.3563528,0.00072657305,0.003014147,0.003964674,0.005160742,0.0062787533,0.0036726764,0.004335195,0.0008958414],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017799501,0.00082574465,0.1932494,0.0009693307,0.0040371683,0.0024711993,0.0018967591,0.39289963,0.005333872,0.22671965,0.0060274624,0.16378991],"study_design_scores_gemma":[0.0001587287,0.0010339756,0.04916443,0.00016142386,0.0002281005,0.0013975895,0.0007026663,0.77427363,0.0028885866,0.16775668,0.0020440947,0.0001900493],"about_ca_topic_score_codex":0.0010896216,"about_ca_topic_score_gemma":0.0006693903,"teacher_disagreement_score":0.05068724,"about_ca_system_score_codex":0.0010269657,"about_ca_system_score_gemma":0.0014503325,"threshold_uncertainty_score":0.26806295},"labels":[],"label_agreement":null},{"id":"W3124170137","doi":"10.3390/econometrics4010014","title":"Return and Risk of Pairs Trading Using a Simulation-Based Bayesian Procedure for Predicting Stable Ratios of Stock Prices","year":2016,"lang":"en","type":"article","venue":"Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Statistical arbitrage; Econometrics; Stock (firearms); Arbitrage; Pairs trade; Economics; Bayesian probability; Mathematics; Statistics; Financial economics; Capital asset pricing model; Arbitrage pricing theory; Algorithmic trading; Risk arbitrage","score_opus":0.1148953318868552,"score_gpt":0.24815456061500218,"score_spread":0.133259228728147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124170137","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15763292,0.00011559126,0.84107125,0.000118725235,0.000011238809,0.00004920032,0.00004445493,0.00019127333,0.0007655232],"genre_scores_gemma":[0.84286976,0.00013876996,0.1559286,0.00003486044,0.000027828068,0.00012750129,0.00017017862,0.000048337624,0.0006542114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984731,0.00095530023,0.00006819274,0.00017129027,0.00026329822,0.00006885065],"domain_scores_gemma":[0.9815167,0.01547316,0.001349133,0.0008052308,0.0006088429,0.00024690412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0058267424,0.0006758876,0.0008387695,0.0013750532,0.00032781204,0.0009117203,0.0009157254,0.00094516954,0.0010881242],"category_scores_gemma":[0.025534976,0.00045227105,0.0008472625,0.0006757723,0.0007704073,0.0012407257,0.0012227189,0.0009706634,0.00015806698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017947848,0.00010595594,0.01047378,0.000025856054,0.0001024628,0.0000912605,0.00008879254,0.93266237,0.0015806333,0.024101257,0.0001631263,0.030425012],"study_design_scores_gemma":[0.00000386533,0.000019029903,0.0003603968,0.0000026885816,0.000004922755,0.000012848003,0.0000024272128,0.99672985,0.000293339,0.002528851,0.000034864825,0.000006983713],"about_ca_topic_score_codex":0.003276863,"about_ca_topic_score_gemma":0.002583724,"teacher_disagreement_score":0.0058267424,"about_ca_system_score_codex":0.00062964455,"about_ca_system_score_gemma":0.00081439805,"threshold_uncertainty_score":0.030815125},"labels":[],"label_agreement":null},{"id":"W3125356077","doi":"10.3390/econometrics3040864","title":"Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality","year":2015,"lang":"en","type":"article","venue":"Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Science Foundation","keywords":"Heteroscedasticity; Seasonality; Econometrics; Volatility (finance); Estimator; Parametric statistics; Spot contract; Economics; Mathematics; Statistics; Financial economics","score_opus":0.07085584415197826,"score_gpt":0.24441449188952236,"score_spread":0.1735586477375441,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125356077","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18832855,0.0003111405,0.8098776,0.00024889185,0.00001982486,0.0000137606885,0.0000680983,0.00009940689,0.0010328147],"genre_scores_gemma":[0.9321881,0.00024596867,0.06640298,0.000039203645,0.00005076349,0.000020263757,0.00014815592,0.000028177314,0.00087631645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943846,0.00028029137,0.00003848051,0.000091167356,0.000116358555,0.00003522741],"domain_scores_gemma":[0.993985,0.004685952,0.000509508,0.0005589253,0.00019991571,0.000060719827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023868154,0.00033240393,0.0005650082,0.0006247304,0.00016360848,0.0007995512,0.0006754349,0.0005072796,0.00059361],"category_scores_gemma":[0.017018583,0.0002885459,0.00044102356,0.0006169151,0.0003816716,0.0015421574,0.0009482034,0.001031997,0.000103444436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012024213,0.00012636805,0.031504504,0.00011301392,0.0002521149,0.00028387143,0.00024593563,0.7132637,0.0073841866,0.044352997,0.0005789213,0.20177412],"study_design_scores_gemma":[0.0000035756163,0.000023745835,0.003839327,0.000007066979,0.000011289306,0.00003626558,0.000014793844,0.98607546,0.00066062924,0.009019976,0.0002983838,0.000009502796],"about_ca_topic_score_codex":0.0018206864,"about_ca_topic_score_gemma":0.002324948,"teacher_disagreement_score":0.0023868154,"about_ca_system_score_codex":0.00015144315,"about_ca_system_score_gemma":0.00043829848,"threshold_uncertainty_score":0.012622774},"labels":[],"label_agreement":null},{"id":"W3137127976","doi":"10.3390/econometrics9030033","title":"On Spurious Causality, CO2, and Global Temperature","year":2021,"lang":"en","type":"preprint","venue":"Econometrics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Spurious relationship; Econometrics; Causality (physics); Measure (data warehouse); Series (stratigraphy); Cointegration; Information flow; Economics; Global temperature; Climatology; Climate change; Mathematics; Computer science; Global warming; Physics; Statistics; Geology; Data mining","score_opus":0.010863210631388771,"score_gpt":0.2132941889351913,"score_spread":0.20243097830380255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137127976","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40399006,0.008203864,0.52757204,0.029098062,0.0006688755,0.00006503284,0.0007873385,0.00024687155,0.029367842],"genre_scores_gemma":[0.9739621,0.0033231631,0.018639186,0.00092802674,0.00063512777,0.00004780255,0.0002627614,0.00006208024,0.002139784],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967699,0.0020951272,0.00017863608,0.00038707975,0.00042233808,0.00014702571],"domain_scores_gemma":[0.89466804,0.094112776,0.0055729165,0.0031588848,0.0019079059,0.00057939795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0120826885,0.0005831592,0.000953788,0.0023675945,0.00080688676,0.002276503,0.0009620548,0.0013739307,0.006537621],"category_scores_gemma":[0.09890838,0.00034114282,0.0006894453,0.0021217403,0.0052184793,0.0045107733,0.0029035208,0.0024389974,0.0002834871],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012764505,0.000032558473,0.017101929,0.00015298414,0.000119896904,0.00020677976,0.00034886616,0.01887421,0.00020305856,0.9335223,0.0021907082,0.027119104],"study_design_scores_gemma":[0.000020957908,0.000010894337,0.0032162347,0.000057187564,0.000039174996,0.000034386263,0.00010623256,0.042799283,0.00016223468,0.95247614,0.0010644506,0.000012791498],"about_ca_topic_score_codex":0.0036360058,"about_ca_topic_score_gemma":0.0019688443,"teacher_disagreement_score":0.0120826885,"about_ca_system_score_codex":0.0012058935,"about_ca_system_score_gemma":0.0010802215,"threshold_uncertainty_score":0.06390011},"labels":[],"label_agreement":null},{"id":"W3183655225","doi":"10.3390/econometrics9030029","title":"Special Issue “Celebrated Econometricians: Peter Phillips”","year":2021,"lang":"en","type":"article","venue":"Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Guelph","funders":"","keywords":"Phillips curve; Mathematical economics; Economics; Econometrics; Keynesian economics; Monetary policy","score_opus":0.11424329854832505,"score_gpt":0.22203080510233414,"score_spread":0.1077875065540091,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183655225","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00016180231,0.028950932,0.0018299797,0.32667735,0.6270866,0.00002308084,0.00046426576,0.00025174284,0.014554296],"genre_scores_gemma":[0.0028836206,0.017618667,0.0005070438,0.048450347,0.87879574,0.00003846661,0.00025230422,0.0003549272,0.051098872],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9969951,0.0007330758,0.0002483986,0.000754947,0.001051298,0.00021717345],"domain_scores_gemma":[0.9825669,0.008779699,0.0010359555,0.0009727555,0.0045198305,0.002124923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004331212,0.0015056769,0.0018286784,0.0025736624,0.0016046851,0.0060727624,0.0013925438,0.0062180324,0.0567774],"category_scores_gemma":[0.027617395,0.0006307795,0.0010595234,0.0020539048,0.0018380012,0.0049692662,0.0018702263,0.008819867,0.041130204],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00000846946,0.0000033757594,0.000034731114,0.000039286322,0.0000040840537,0.000019267958,0.0000075818243,0.000018995503,0.000015382691,0.0016151076,0.99424136,0.003992376],"study_design_scores_gemma":[0.000007650094,0.000008782141,0.0003218521,0.00013520678,0.0000074703858,0.000120782504,0.000022694057,0.00014380491,0.00007002646,0.006298464,0.9928518,0.000011448524],"about_ca_topic_score_codex":0.0016235659,"about_ca_topic_score_gemma":0.002019526,"teacher_disagreement_score":0.0567774,"about_ca_system_score_codex":0.0020934658,"about_ca_system_score_gemma":0.0020610471,"threshold_uncertainty_score":0.18993932},"labels":[],"label_agreement":null},{"id":"W3215433330","doi":"10.3390/econometrics9040041","title":"Second-Order Least Squares Estimation in Nonlinear Time Series Models with ARCH Errors","year":2021,"lang":"en","type":"article","venue":"Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Heteroscedasticity; Mathematics; Conditional expectation; Asymptotic distribution; Applied mathematics; Estimator; Series (stratigraphy); Autoregressive model; Nonlinear system; Autoregressive conditional heteroskedasticity; Strong consistency; Time series; Conditional variance; Consistency (knowledge bases); Conditional probability distribution; Statistics; Econometrics","score_opus":0.039427339525578114,"score_gpt":0.22162560658646632,"score_spread":0.1821982670608882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215433330","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019319154,0.00023937174,0.979763,0.00012801813,0.000014740411,0.000012853823,0.000036079913,0.00012988155,0.00035685985],"genre_scores_gemma":[0.64612716,0.0010693335,0.3438927,0.00010592012,0.00009314261,0.00015481049,0.00039120187,0.00017634616,0.007989424],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99902284,0.00057335023,0.00004837555,0.00015084489,0.0001623223,0.00004225612],"domain_scores_gemma":[0.99649507,0.002900939,0.0002909499,0.00012423383,0.00015712957,0.000031726988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027192817,0.0006955953,0.00080605596,0.00063625857,0.000305252,0.0008670581,0.000902506,0.0008583553,0.0008999938],"category_scores_gemma":[0.009310774,0.0006621283,0.00073421915,0.0010775616,0.00072667416,0.0010412997,0.0008017924,0.0011432023,0.00027460675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006031718,0.000054314594,0.0027307344,0.00017358274,0.00014323655,0.00013137647,0.00016870264,0.8979311,0.001998233,0.04174024,0.00058178906,0.054286364],"study_design_scores_gemma":[0.00000369237,0.0000064313526,0.00027948074,0.0000031370703,0.0000046273535,0.000009776396,0.000006155831,0.9928248,0.00019651125,0.0064514875,0.00020898633,0.0000049222845],"about_ca_topic_score_codex":0.007559885,"about_ca_topic_score_gemma":0.008489244,"teacher_disagreement_score":0.007559885,"about_ca_system_score_codex":0.0005899413,"about_ca_system_score_gemma":0.0012587232,"threshold_uncertainty_score":0.015031755},"labels":[],"label_agreement":null},{"id":"W4200291761","doi":"10.3390/econometrics9040045","title":"Does the Choice of Realized Covariance Measures Empirically Matter? A Bayesian Density Prediction Approach","year":2021,"lang":"en","type":"article","venue":"Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Young Scientists Fund; ShanghaiTech University; Faculty of Graduate Studies and Research, University of Alberta; National Natural Science Foundation of China; National Science Foundation","keywords":"Covariance; Estimator; Predictive power; Econometrics; Bayesian probability; Covariance function; Estimation of covariance matrices; Mathematics; Statistics; Computer science","score_opus":0.06894571475928057,"score_gpt":0.24215515892827982,"score_spread":0.17320944416899925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200291761","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12954715,0.0014614688,0.8579187,0.0039444612,0.00011696779,0.00011980786,0.00038693156,0.00025448125,0.0062500555],"genre_scores_gemma":[0.92793053,0.00086114305,0.06873541,0.00046963218,0.00022076408,0.00014274156,0.00035693913,0.00008379651,0.0011989744],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9927406,0.004493552,0.00034253838,0.0011024475,0.0010713653,0.00024960583],"domain_scores_gemma":[0.90093106,0.08400646,0.004365022,0.006243852,0.0037064913,0.0007471416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.025521168,0.0007690709,0.0016096391,0.0014095502,0.0007861906,0.0029871315,0.0020956926,0.0021295494,0.0029783752],"category_scores_gemma":[0.1691365,0.0006471053,0.0010022116,0.0012788415,0.002477998,0.007673046,0.001646309,0.003480856,0.00051310967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036765277,0.00031756365,0.12192646,0.00038139088,0.0010845633,0.00037587332,0.0009236141,0.10491633,0.0017887257,0.5555727,0.003966218,0.20837888],"study_design_scores_gemma":[0.00006653273,0.00019339993,0.029521175,0.00019236522,0.0001797587,0.00020126833,0.00023616104,0.35994887,0.00090196705,0.6059314,0.0025326128,0.00009446509],"about_ca_topic_score_codex":0.0042755343,"about_ca_topic_score_gemma":0.0034956199,"teacher_disagreement_score":0.025521168,"about_ca_system_score_codex":0.0011266528,"about_ca_system_score_gemma":0.0012175837,"threshold_uncertainty_score":0.13497049},"labels":[],"label_agreement":null},{"id":"W42490498","doi":"10.3390/econometrics3040825","title":"Bootstrap Tests for Overidentification in Linear Regression Models","year":2015,"lang":"en","type":"article","venue":"Econometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; McGill University","funders":"University of Exeter; Social Sciences and Humanities Research Council of Canada; Fonds de Recherche du Québec-Société et Culture; Canada Research Chairs; McGill University","keywords":"Mathematics; Inference; Nuisance parameter; Limit (mathematics); Statistics; Gaussian; Econometrics; Applied mathematics; Linear regression; Statistical hypothesis testing; Statistical physics; Mathematical analysis; Computer science; Estimator","score_opus":0.561144818298222,"score_gpt":0.4674180131797065,"score_spread":0.09372680511851555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W42490498","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05160627,0.00046777222,0.94596696,0.00049759675,0.00003948494,0.000049187107,0.000050229373,0.0003765555,0.0009459637],"genre_scores_gemma":[0.8314555,0.00056508725,0.16576524,0.00033914368,0.00020751238,0.0003297514,0.00031595238,0.0002545644,0.0007671894],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.958349,0.034067154,0.0012125605,0.0015203578,0.0041436176,0.0007072263],"domain_scores_gemma":[0.3541228,0.6143264,0.012489835,0.013506173,0.00443234,0.0011225066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05679958,0.0010341154,0.0019548472,0.0044989567,0.0010546141,0.0020213432,0.0027953014,0.0019588629,0.0022599052],"category_scores_gemma":[0.45068815,0.0008108002,0.0013118471,0.0029920407,0.0057568694,0.0043422007,0.0035076463,0.0031653154,0.00039033958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007004811,0.00031838534,0.03690575,0.00047119433,0.0010101902,0.0017251548,0.0016150424,0.3002445,0.0027338497,0.43276402,0.0031035352,0.21840785],"study_design_scores_gemma":[0.000063084386,0.00017368562,0.0037271262,0.00009851665,0.000058363057,0.00028000193,0.00016308896,0.6365593,0.0014459282,0.3564821,0.0008994143,0.000049390623],"about_ca_topic_score_codex":0.0008120678,"about_ca_topic_score_gemma":0.00060268404,"teacher_disagreement_score":0.05679958,"about_ca_system_score_codex":0.0010798423,"about_ca_system_score_gemma":0.0016052023,"threshold_uncertainty_score":0.30038846},"labels":[],"label_agreement":null},{"id":"W4380536698","doi":"10.3390/econometrics11020016","title":"Skill Mismatch, Nepotism, Job Satisfaction, and Young Females in the MENA Region","year":2023,"lang":"en","type":"article","venue":"Econometrics","topic":"Labor market dynamics and wage inequality","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Nepotism; Job satisfaction; Ethnic group; Demographic economics; Productivity; Immigration; Government (linguistics); Economics; Labour economics; Business; Psychology; Political science; Social psychology; Economic growth","score_opus":0.06085186344214988,"score_gpt":0.24081097715503139,"score_spread":0.1799591137128815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380536698","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9986519,0.00024164018,0.00003825612,0.0002828098,0.0000052447454,0.0000020405878,0.000105952895,9.0122455e-7,0.0006712945],"genre_scores_gemma":[0.9994413,0.0001420541,0.000022608672,0.000034813183,0.00000755045,0.0000023800364,0.00006875704,6.12273e-7,0.00027991072],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99972254,0.000073239244,0.00001566774,0.000038088932,0.000039451286,0.000111025875],"domain_scores_gemma":[0.99943084,0.00011528878,0.00021869934,0.000017943463,0.00004363117,0.00017355787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005355896,0.000113855,0.00025346648,0.00039212775,0.00094969483,0.0006667903,0.00021462972,0.00024500574,0.002439999],"category_scores_gemma":[0.0010115062,0.000117851865,0.00025613338,0.00059706444,0.00038422266,0.00038427138,0.00082472176,0.00047396903,0.00026621454],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024184363,0.000046200814,0.9947765,0.00001572764,0.000015861278,0.00013707255,0.0013530395,0.00007367353,0.00014697663,0.00028063732,0.00024163947,0.0028884697],"study_design_scores_gemma":[9.837237e-7,0.000025015901,0.99384016,0.000015986632,0.0000058754467,0.000058462294,0.005320454,0.00016181768,0.000037840702,0.00006254014,0.00046861471,0.0000023323623],"about_ca_topic_score_codex":0.04546036,"about_ca_topic_score_gemma":0.061777607,"teacher_disagreement_score":0.04546036,"about_ca_system_score_codex":0.0006048728,"about_ca_system_score_gemma":0.0006541072,"threshold_uncertainty_score":0.09039146},"labels":[],"label_agreement":null},{"id":"W4392459686","doi":"10.3390/econometrics12010007","title":"Public Debt and Economic Growth: A Panel Kink Regression Latent Group Structures Approach","year":2024,"lang":"en","type":"article","venue":"Econometrics","topic":"Fiscal Policy and Economic Growth","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Regression; Debt; Panel data; Group (periodic table); Regression analysis; Economics; Latent growth modeling; Econometrics; Mathematics; Statistics; Macroeconomics; Physics","score_opus":0.1182564774535008,"score_gpt":0.22626391334110102,"score_spread":0.10800743588760021,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392459686","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.58313465,0.00077334634,0.40752688,0.0018704002,0.0000662919,0.00009154784,0.0013323352,0.0003136505,0.004890935],"genre_scores_gemma":[0.982447,0.00028967508,0.013733485,0.00007350759,0.000037577785,0.00005530318,0.000435912,0.000022859604,0.0029047418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9978205,0.001518107,0.00004540803,0.0003440611,0.000110583445,0.00016122835],"domain_scores_gemma":[0.99243826,0.004968105,0.0013396572,0.0007624952,0.000308474,0.0001831144],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039213314,0.00043693272,0.0010953685,0.00089449715,0.00047891174,0.0014775554,0.0011575911,0.00107403,0.0052174027],"category_scores_gemma":[0.01208254,0.00038958492,0.0008481058,0.0014854146,0.0010743205,0.0017426884,0.0015681649,0.0015645467,0.0006611988],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005387379,0.00037290232,0.100897476,0.00017364396,0.00076593657,0.00060058763,0.00090681313,0.67061645,0.0010734702,0.16222414,0.0032304183,0.058599483],"study_design_scores_gemma":[0.000036605004,0.00007159787,0.015935725,0.000019787201,0.000088320885,0.00005190419,0.00019508925,0.91804534,0.0001913812,0.06432942,0.0009984659,0.00003641698],"about_ca_topic_score_codex":0.0142564,"about_ca_topic_score_gemma":0.010540787,"teacher_disagreement_score":0.0142564,"about_ca_system_score_codex":0.0011220301,"about_ca_system_score_gemma":0.0007827772,"threshold_uncertainty_score":0.028346837},"labels":[],"label_agreement":null},{"id":"W4400599731","doi":"10.3390/econometrics12030021","title":"Instrumental Variable Method for Regularized Estimation in Generalized Linear Measurement Error Models","year":2024,"lang":"en","type":"article","venue":"Econometrics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Mathematics; Observational error; Errors-in-variables models; Linear regression; Covariate; Ordinary least squares; Linear model; Statistics; Instrumental variable; Covariance; Feature selection; Regularization (linguistics); Generalized linear model; Design matrix; Computer science; Artificial intelligence","score_opus":0.3212068295993425,"score_gpt":0.44336532630283404,"score_spread":0.12215849670349155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400599731","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046633612,0.00044006328,0.9981925,0.00017972311,0.00004433221,0.000038610644,0.0000706863,0.00011598508,0.0004518133],"genre_scores_gemma":[0.09206537,0.0032381273,0.896416,0.0007166807,0.00064856926,0.001614255,0.00088465645,0.000403629,0.004012656],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9805562,0.015385137,0.0004919655,0.0015692742,0.0015852379,0.0004120937],"domain_scores_gemma":[0.9752736,0.020065337,0.0016035506,0.0017134866,0.0011727129,0.00017128894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018009065,0.0022167272,0.0028210336,0.0027966802,0.0008983496,0.0021951715,0.0043044575,0.0031227265,0.005233981],"category_scores_gemma":[0.05331344,0.0010747854,0.002796433,0.0039306697,0.0027795937,0.0027597633,0.0030841727,0.0056565115,0.0018200281],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012217097,0.000098409066,0.0024276066,0.0009391526,0.0006864621,0.00035678165,0.0004009933,0.14561848,0.0010646784,0.7290552,0.007104493,0.112125605],"study_design_scores_gemma":[0.00007592068,0.00008242347,0.00079980906,0.00025821623,0.0001368697,0.00013971051,0.00006593009,0.58561546,0.0010657193,0.3976132,0.0140536595,0.000093006216],"about_ca_topic_score_codex":0.0034170337,"about_ca_topic_score_gemma":0.0021649315,"teacher_disagreement_score":0.018009065,"about_ca_system_score_codex":0.0016065968,"about_ca_system_score_gemma":0.0032618882,"threshold_uncertainty_score":0.0952422},"labels":[],"label_agreement":null},{"id":"W4403840116","doi":"10.3390/econometrics12040030","title":"Impact of Areal Factors on Students’ Travel Mode Choices: A Bayesian Spatial Analysis","year":2024,"lang":"en","type":"article","venue":"Econometrics","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Bayesian probability; Mode (computer interface); Statistics; Econometrics; Geography; Computer science; Mathematics","score_opus":0.04819575063994822,"score_gpt":0.3668217151099597,"score_spread":0.31862596447001146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403840116","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.924487,0.0002290122,0.0727037,0.00037275424,0.000011705632,0.00010767861,0.00045183708,0.00005674063,0.0015797616],"genre_scores_gemma":[0.9864557,0.00018942646,0.011763888,0.000037842427,0.000011818858,0.000062552535,0.0005156848,0.000014435584,0.0009486455],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9936475,0.0043554525,0.00018338754,0.00084705726,0.0006144878,0.00035211482],"domain_scores_gemma":[0.9730234,0.022165762,0.0017425801,0.0014298391,0.0012225274,0.00041582124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012492863,0.00057194335,0.0009543442,0.001717016,0.0005566685,0.0013505464,0.0013131703,0.0009988686,0.003758478],"category_scores_gemma":[0.029161198,0.00065008283,0.0031908748,0.001300849,0.0011348255,0.0014169295,0.0018529095,0.0014015003,0.0003475651],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071247463,0.00051208085,0.52459645,0.00012235221,0.0014436902,0.00031940883,0.0010743359,0.38799682,0.0012044216,0.030642772,0.0010652314,0.050309885],"study_design_scores_gemma":[0.000039818235,0.00026440804,0.09562157,0.000039405826,0.0003733608,0.00009177171,0.00060199853,0.88994026,0.00029731056,0.01120685,0.0014710356,0.000052189454],"about_ca_topic_score_codex":0.038518906,"about_ca_topic_score_gemma":0.020749299,"teacher_disagreement_score":0.038518906,"about_ca_system_score_codex":0.0011816566,"about_ca_system_score_gemma":0.0013171842,"threshold_uncertainty_score":0.076589406},"labels":[],"label_agreement":null},{"id":"W4407410378","doi":"10.3390/econometrics13010006","title":"Data-Based Parametrization for Affine GARCH Models Across Multiple Time Scales—Roughness Implications","year":2025,"lang":"en","type":"article","venue":"Econometrics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria; Toronto Metropolitan University; Western University","funders":"","keywords":"Parametrization (atmospheric modeling); Affine transformation; Mathematics; Econometrics; Applied mathematics; Statistical physics; Pure mathematics; Physics","score_opus":0.13051108718155496,"score_gpt":0.3678939288095726,"score_spread":0.23738284162801765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407410378","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.248295,0.0007279186,0.7443033,0.001411138,0.00009994749,0.000052930514,0.00023496144,0.00033029955,0.0045445156],"genre_scores_gemma":[0.968309,0.00033632995,0.030071022,0.00011133671,0.00008548001,0.000045890756,0.00019358448,0.00007950186,0.000767904],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987692,0.00042642863,0.000087436456,0.00034661245,0.00026670718,0.00010366239],"domain_scores_gemma":[0.9903273,0.005968406,0.0011952377,0.0019417116,0.00039762357,0.00016975404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0051771216,0.0006921857,0.00067508424,0.0011115946,0.00054099894,0.0025739158,0.0012959926,0.0014642223,0.0012855075],"category_scores_gemma":[0.029692758,0.0004898214,0.0010382526,0.0008844635,0.0019618967,0.0047082454,0.0013140216,0.0031194792,0.0002141898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000106689586,0.000049656635,0.012295605,0.00011981986,0.00011058248,0.00045719123,0.00059688493,0.377199,0.006794573,0.57208544,0.0010000399,0.029184561],"study_design_scores_gemma":[0.000015438804,0.000036219153,0.005355494,0.00003072174,0.000029459334,0.00018034862,0.000109022985,0.7748456,0.0013129944,0.21634245,0.0016797209,0.0000625361],"about_ca_topic_score_codex":0.0019647027,"about_ca_topic_score_gemma":0.0010319002,"teacher_disagreement_score":0.0051771216,"about_ca_system_score_codex":0.00094252743,"about_ca_system_score_gemma":0.00053726434,"threshold_uncertainty_score":0.027379572},"labels":[],"label_agreement":null},{"id":"W4413224169","doi":"10.3390/econometrics13030031","title":"A Statistical Characterization of Median-Based Inequality Measures","year":2025,"lang":"en","type":"article","venue":"Econometrics","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Queen's University","funders":"McGill University","keywords":"Decile; Mathematics; Statistics; Inequality; Econometrics; Skewness; Income distribution; Economic inequality; Weighted arithmetic mean; Lorenz curve; Population; Demography; Gini coefficient; Mathematical analysis","score_opus":0.07513924230298803,"score_gpt":0.33578691733777855,"score_spread":0.2606476750347905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413224169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20400248,0.00096189283,0.77732277,0.0006838323,0.00006952223,0.000102807375,0.0011635219,0.00039505,0.015298085],"genre_scores_gemma":[0.9237437,0.00031200986,0.07318328,0.00016745603,0.0002003185,0.00021052505,0.001238482,0.000083627994,0.0008607228],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9967541,0.0012848942,0.00023004084,0.00059046847,0.00090845407,0.00023205255],"domain_scores_gemma":[0.97704166,0.015158795,0.003072305,0.0024071876,0.0019831432,0.00033683266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008388628,0.00039421747,0.0006990503,0.0041673603,0.00057781546,0.001950164,0.0010509263,0.0006517848,0.003108206],"category_scores_gemma":[0.04550732,0.0002264769,0.00062480546,0.002625275,0.0018791505,0.0028050002,0.0014019588,0.0010995955,0.0004434338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024136486,0.0001575378,0.13199353,0.00030688703,0.00025422487,0.00036299007,0.0011916433,0.03627178,0.005119301,0.59998655,0.0044220486,0.21969211],"study_design_scores_gemma":[0.000030217176,0.00042807034,0.16130051,0.0002682797,0.00007848233,0.0018198115,0.0010857248,0.24794164,0.0043793907,0.5682464,0.014291642,0.00012985412],"about_ca_topic_score_codex":0.00060655014,"about_ca_topic_score_gemma":0.0003470466,"teacher_disagreement_score":0.008388628,"about_ca_system_score_codex":0.00085409044,"about_ca_system_score_gemma":0.0005126133,"threshold_uncertainty_score":0.044363797},"labels":[],"label_agreement":null},{"id":"W4414341827","doi":"10.3390/econometrics13030036","title":"Integration and Risk Transmission Dynamics Between Bitcoin, Currency Pairs, and Traditional Financial Assets in South Africa","year":2025,"lang":"en","type":"article","venue":"Econometrics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Currency; Financial integration; Diversification (marketing strategy); Volatility (finance); Vector autoregression; Financial asset; Systemic risk","score_opus":0.03687602277137284,"score_gpt":0.21680486967999893,"score_spread":0.1799288469086261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414341827","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99776936,0.00040451912,0.0004488516,0.00019691519,0.0000028225738,0.000005663539,0.00013141922,0.0000024289009,0.0010379384],"genre_scores_gemma":[0.9993381,0.00025208865,0.00010574315,0.000007864892,0.000002755655,0.0000025314578,0.000076487915,0.0000010974605,0.00021319532],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99979776,0.000053170806,0.000017915128,0.000040712683,0.000031420626,0.00005904113],"domain_scores_gemma":[0.99922,0.00020680182,0.00041639362,0.000025269077,0.000071388,0.000060174723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003900048,0.00021446055,0.00021951529,0.0011878285,0.00026980127,0.0014296453,0.00013758901,0.00021359672,0.0018341866],"category_scores_gemma":[0.0018432575,0.00014600785,0.0001901022,0.0017955167,0.0003974631,0.001659564,0.00091155956,0.00036578506,0.00012890431],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026860106,0.000062174244,0.93032736,0.00017048202,0.00020673819,0.0033357327,0.005270826,0.008348073,0.003232838,0.011816221,0.0008581064,0.036102902],"study_design_scores_gemma":[0.0000131818815,0.00005571012,0.9610529,0.00015919116,0.0001173549,0.0006711313,0.010150711,0.018809184,0.00080244575,0.0034108448,0.0047298404,0.000027385193],"about_ca_topic_score_codex":0.013642628,"about_ca_topic_score_gemma":0.0114505235,"teacher_disagreement_score":0.013642628,"about_ca_system_score_codex":0.00075267005,"about_ca_system_score_gemma":0.00042421522,"threshold_uncertainty_score":0.027126431},"labels":[],"label_agreement":null}]}