{"meta":{"query_hash":"dbd1e8cdbb52","filters":{"venue":"Journal of Financial Econometrics"},"cohort_total":46,"direct_labels_cover":0,"predictions_cover":46,"exported":46,"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/dbd1e8cdbb52","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Financial+Econometrics"},"results":[{"id":"W1935652809","doi":"10.1093/jjfinec/nbi021","title":"The Accuracy of Density Forecasts from Foreign Exchange Options","year":2005,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Currency; Volatility (finance); Foreign exchange; Economics; Econometrics; Implied volatility; Valuation of options; Volatility smile; Financial economics; Monetary economics","score_opus":0.06170027182426852,"score_gpt":0.24195639897466797,"score_spread":0.18025612715039946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1935652809","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":[0.9658828,0.0015087362,0.024627224,0.0005680316,0.00010873824,0.000013888873,0.002154713,0.00049965526,0.0046362495],"genre_scores_gemma":[0.9962096,0.00027991898,0.0016460586,0.000025536312,0.000048375874,0.0000028138675,0.0015667276,0.000020918917,0.00020016395],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983374,0.0005237716,0.00014483185,0.00029733015,0.00060057134,0.00009615447],"domain_scores_gemma":[0.9728783,0.01815991,0.0034144707,0.0023531956,0.0029481675,0.00024605333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048104646,0.0004492122,0.0005703883,0.0022163296,0.0002646922,0.0021400189,0.00049587805,0.00088811846,0.0010066647],"category_scores_gemma":[0.052933563,0.00033556187,0.00038102502,0.0010826864,0.00036828898,0.0024140265,0.00079203065,0.00082479534,0.0005523239],"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":[0.0008341591,0.00007084823,0.42604545,0.00016599767,0.00047868252,0.00027655315,0.0004849204,0.39605588,0.0027183883,0.006160446,0.006039878,0.16066876],"study_design_scores_gemma":[0.00006200707,0.000107865904,0.25169063,0.00016661792,0.0001100068,0.000337734,0.000386272,0.72068316,0.005485792,0.01713922,0.0036634626,0.00016727243],"about_ca_topic_score_codex":0.0068292567,"about_ca_topic_score_gemma":0.00428556,"teacher_disagreement_score":0.0068292567,"about_ca_system_score_codex":0.0004978122,"about_ca_system_score_gemma":0.00027187867,"threshold_uncertainty_score":0.025440514},"labels":[],"label_agreement":null},{"id":"W1977073876","doi":"10.1093/jjfinec/nbs001","title":"Converting Tail-VaR to VaR: An Econometric Study","year":2012,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","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 Toronto","funders":"","keywords":"Value at risk; Econometrics; Vector autoregression; Economics; Currency; Risk management; Monetary economics; Finance","score_opus":0.13825375613268828,"score_gpt":0.373279548651269,"score_spread":0.23502579251858072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977073876","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":[0.28423014,0.0053443126,0.6809118,0.002822735,0.00022728917,0.00007853937,0.00034470463,0.00035291168,0.025687542],"genre_scores_gemma":[0.9532432,0.0033725647,0.035455804,0.00033429012,0.00041156774,0.000036707566,0.00025214034,0.00009593606,0.00679776],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876773,0.0006908565,0.00005889105,0.00015113622,0.00024628997,0.000084975065],"domain_scores_gemma":[0.9806239,0.017211469,0.00089763344,0.0006159744,0.0005140779,0.000136818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004063411,0.0003781248,0.0006663437,0.0011558766,0.00025776325,0.0019781562,0.0005465374,0.0009334365,0.0035867454],"category_scores_gemma":[0.026225291,0.00027326404,0.00067711197,0.0020311205,0.0009843493,0.0019087347,0.0010454078,0.0022020661,0.00052067754],"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":[0.00013584533,0.00026433772,0.034174632,0.00022435847,0.00016286258,0.00089461997,0.0006411338,0.2535847,0.0031144293,0.5704604,0.004909997,0.13143267],"study_design_scores_gemma":[0.000039628823,0.00011551793,0.018816547,0.00008479851,0.000060640567,0.0003408985,0.00037511767,0.72321904,0.00085286173,0.2485763,0.0074374485,0.00008122053],"about_ca_topic_score_codex":0.0026900251,"about_ca_topic_score_gemma":0.0013990393,"teacher_disagreement_score":0.004063411,"about_ca_system_score_codex":0.00060345454,"about_ca_system_score_gemma":0.00043791367,"threshold_uncertainty_score":0.02148962},"labels":[],"label_agreement":null},{"id":"W2047818254","doi":"10.1093/jjfinec/nbl002","title":"Sample and Implied Volatility in GARCH Models","year":2006,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","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":"Western University","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Volatility (finance); Sample (material); Library science; Economics; History; Econometrics; Computer science","score_opus":0.0523948598413272,"score_gpt":0.22253244344706163,"score_spread":0.17013758360573444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2047818254","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":[0.15873812,0.0032335764,0.82228124,0.0015268158,0.00015515415,0.000104687824,0.0019074416,0.00092117593,0.011131837],"genre_scores_gemma":[0.8866904,0.0030610363,0.10079625,0.0003580462,0.00029803283,0.00046502956,0.0038811655,0.0002182324,0.004231756],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972439,0.0013961775,0.00011013707,0.0003797706,0.000767165,0.00010299127],"domain_scores_gemma":[0.9778756,0.018279953,0.0008851559,0.0018365625,0.0009584537,0.00016429924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0073894137,0.00041790016,0.0008569722,0.001232203,0.00035763741,0.0020285915,0.0013027492,0.0012762478,0.0047879475],"category_scores_gemma":[0.07636863,0.0004892936,0.0005518087,0.0016001009,0.0011165465,0.0031957182,0.0013830918,0.001975462,0.00076287345],"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":[0.00014894361,0.00007673404,0.017507903,0.000222638,0.00017974855,0.00023043637,0.0004197491,0.10786018,0.0009848134,0.77498317,0.0047968156,0.09258886],"study_design_scores_gemma":[0.000037761634,0.000054416643,0.010129047,0.00010739729,0.000045288212,0.00023257203,0.00009761062,0.32812214,0.000699547,0.6559753,0.004456445,0.000042473028],"about_ca_topic_score_codex":0.0015933303,"about_ca_topic_score_gemma":0.0013407883,"teacher_disagreement_score":0.0073894137,"about_ca_system_score_codex":0.0009877891,"about_ca_system_score_gemma":0.00074518274,"threshold_uncertainty_score":0.039079428},"labels":[],"label_agreement":null},{"id":"W2057986178","doi":"10.1093/jjfinec/nbq026","title":"Asymmetric Stochastic Conditional Duration Model--A Mixture-of-Normal Approach","year":2011,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Waterloo","funders":"","keywords":"Duration (music); Bivariate analysis; Econometrics; IBM; Extension (predicate logic); Function (biology); Mathematics; Economics; Computer science; Statistics","score_opus":0.07416067455532886,"score_gpt":0.2193675854834982,"score_spread":0.14520691092816934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057986178","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":[0.028331771,0.0005904935,0.9661632,0.00055155123,0.00010952426,0.00007175889,0.0005818066,0.00029856258,0.003301351],"genre_scores_gemma":[0.8072551,0.0021654963,0.16628698,0.0003200744,0.00051022705,0.0004766278,0.0020824345,0.00029385043,0.02060932],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754626,0.0009452132,0.00013730982,0.00064419094,0.0004341655,0.0002928539],"domain_scores_gemma":[0.9923167,0.004429842,0.001097957,0.0008071813,0.0009230664,0.0004251523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0054299966,0.0011057926,0.0018712593,0.0022349877,0.00062260544,0.002456365,0.004203495,0.0023106972,0.005710202],"category_scores_gemma":[0.015580261,0.0009955766,0.0018291118,0.0021552828,0.0016628696,0.0047189244,0.0021852218,0.0031252438,0.001421156],"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":[0.0002167823,0.000092859416,0.0066887224,0.00011711447,0.00016707028,0.00032264725,0.00036007425,0.30821076,0.0013876972,0.6455833,0.0024829905,0.034370065],"study_design_scores_gemma":[0.000018646824,0.000027248612,0.0009218456,0.000020846142,0.000045963425,0.00017483933,0.000027655764,0.8925383,0.00020368515,0.10351548,0.0024649268,0.000040570783],"about_ca_topic_score_codex":0.00737714,"about_ca_topic_score_gemma":0.0047899904,"teacher_disagreement_score":0.00737714,"about_ca_system_score_codex":0.0015384429,"about_ca_system_score_gemma":0.0015886077,"threshold_uncertainty_score":0.028716922},"labels":[],"label_agreement":null},{"id":"W2089301165","doi":"10.1093/jjfinec/nbu024","title":"Robust Conditional Variance and Value-at-Risk Estimation","year":2014,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Variance (accounting); Value (mathematics); Conditional variance; Estimation; History; Mathematics; Econometrics; Economics; Statistics; Management; Accounting; Autoregressive conditional heteroskedasticity","score_opus":0.03905248508523772,"score_gpt":0.21271185236529785,"score_spread":0.17365936728006015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089301165","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":[0.0037219615,0.00019251464,0.99542385,0.00008343769,0.000016640051,0.000012039017,0.00004373985,0.00012600036,0.00037978304],"genre_scores_gemma":[0.6228659,0.0016319731,0.37042975,0.00022285078,0.00021990294,0.00022618462,0.00090368476,0.00033394125,0.003165769],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99424994,0.0032596262,0.00022731943,0.000851765,0.0011038289,0.00030759047],"domain_scores_gemma":[0.977372,0.016863322,0.0022558453,0.0021209822,0.0012348405,0.00015296155],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00948208,0.0011198475,0.001771807,0.0018713741,0.00031275803,0.0021728904,0.002424635,0.0014922523,0.002007203],"category_scores_gemma":[0.05969006,0.00073583436,0.0014735879,0.0020985333,0.0014964318,0.0031313985,0.0020358486,0.0023847236,0.0005504471],"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":[0.000059114132,0.000049388695,0.0034957712,0.00013741748,0.00031703664,0.0000980222,0.00006471968,0.7062552,0.0011486134,0.21971855,0.0014055427,0.06725063],"study_design_scores_gemma":[0.0000066179964,0.000022737164,0.00047298835,0.000022839831,0.000026859412,0.00004242611,0.00000790577,0.91047007,0.00063918286,0.08760916,0.0006528612,0.000026339478],"about_ca_topic_score_codex":0.0028340064,"about_ca_topic_score_gemma":0.0015988195,"teacher_disagreement_score":0.00948208,"about_ca_system_score_codex":0.0008889993,"about_ca_system_score_gemma":0.0012997462,"threshold_uncertainty_score":0.05014664},"labels":[],"label_agreement":null},{"id":"W2111132118","doi":"10.1093/jjfinec/nbm010","title":"Positivity Conditions for a Bivariate Autoregressive Volatility Specification","year":2007,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","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 Toronto","funders":"","keywords":"Bivariate analysis; Econometrics; Autoregressive model; Volatility (finance); Positive definiteness; Economics; Mathematics; Stochastic volatility; Forward volatility; Statistics; Positive-definite matrix","score_opus":0.0688075641789845,"score_gpt":0.2785529360611291,"score_spread":0.2097453718821446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111132118","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":[0.019083615,0.00020748239,0.9658441,0.0012767458,0.000071465874,0.00006976364,0.0005386608,0.00013853124,0.012769674],"genre_scores_gemma":[0.8501662,0.0018180178,0.12625642,0.0010161817,0.000406934,0.0007047297,0.0018706277,0.00039855656,0.017362423],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975783,0.0010581753,0.00016867243,0.00037693902,0.0005028505,0.00031510898],"domain_scores_gemma":[0.9794688,0.014576688,0.0020531209,0.0007015154,0.0026241855,0.00057558605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0062079076,0.0012358774,0.0008824461,0.0013089399,0.00063132704,0.0018461532,0.0012510219,0.0011144668,0.018049067],"category_scores_gemma":[0.028361814,0.0007695239,0.0015714878,0.0010891629,0.0021071807,0.004112532,0.0020106426,0.0034527227,0.002482239],"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":[0.000030985877,0.000059192625,0.0025701073,0.00014096677,0.00004173457,0.00062357076,0.00024866968,0.041440308,0.003342901,0.9347696,0.0031040849,0.013627938],"study_design_scores_gemma":[0.000028938042,0.00004730915,0.0013043373,0.00009097101,0.000030805884,0.00031038694,0.00010904026,0.26117417,0.0014961661,0.73208344,0.0032711034,0.000053278072],"about_ca_topic_score_codex":0.0034939263,"about_ca_topic_score_gemma":0.0033183866,"teacher_disagreement_score":0.018049067,"about_ca_system_score_codex":0.0008230649,"about_ca_system_score_gemma":0.0024205786,"threshold_uncertainty_score":0.0603801},"labels":[],"label_agreement":null},{"id":"W2121795782","doi":"10.1093/jjfinec/nbu011","title":"Bootstrap Inference for Pre-averaged Realized Volatility based on Nonoverlapping Returns","year":2014,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","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":"Université de Montréal","funders":"","keywords":"Inference; Volatility (finance); Economics; Econometrics; Financial economics; Computer science; Artificial intelligence","score_opus":0.0712902200500417,"score_gpt":0.28337984148578294,"score_spread":0.21208962143574123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121795782","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":[0.033582356,0.00026187196,0.9653847,0.00007719004,0.000026471698,0.000016897158,0.000058615657,0.00017683019,0.00041510715],"genre_scores_gemma":[0.7884783,0.00056647393,0.20888792,0.00013060373,0.00016708391,0.00014033211,0.0005938929,0.00013864457,0.0008966616],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99744713,0.0014478035,0.0001295523,0.0003738001,0.00045556203,0.00014611534],"domain_scores_gemma":[0.97551185,0.019090975,0.0013369883,0.0028009114,0.000994647,0.0002646004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0072500505,0.0005030044,0.0010691676,0.0012209638,0.00035144264,0.0013360427,0.0015550917,0.00089038746,0.0018148593],"category_scores_gemma":[0.046291962,0.0004196837,0.0008166268,0.0010588287,0.0016237097,0.0026166902,0.0011301474,0.0016606806,0.00030449775],"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":[0.00025395924,0.00015452062,0.017717123,0.00023598842,0.00030168833,0.00040233394,0.0002526617,0.4174985,0.0059559946,0.40923205,0.0017072625,0.14628786],"study_design_scores_gemma":[0.000016744329,0.00008138341,0.0035124898,0.00003731011,0.000020147498,0.00012730368,0.00003786572,0.83443636,0.0021958891,0.15858744,0.0009135914,0.00003353834],"about_ca_topic_score_codex":0.0009795364,"about_ca_topic_score_gemma":0.0009391155,"teacher_disagreement_score":0.0072500505,"about_ca_system_score_codex":0.0004984874,"about_ca_system_score_gemma":0.00056876993,"threshold_uncertainty_score":0.038342416},"labels":[],"label_agreement":null},{"id":"W2142367888","doi":"10.1093/jjfinec/nbm012","title":"Components of Market Risk and Return","year":2007,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":43,"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":"Econometrics; Volatility (finance); Univariate; Economics; Variance risk premium; Equity premium puzzle; Equity (law); Bivariate analysis; Realized variance; Risk premium; Financial economics; Volatility risk premium; Variance (accounting); Conditional variance; Stochastic volatility; Autoregressive conditional heteroskedasticity; Multivariate statistics; Statistics; Mathematics","score_opus":0.03930983273742125,"score_gpt":0.22469968978368782,"score_spread":0.18538985704626657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142367888","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":[0.29102418,0.0017404052,0.6783735,0.002860817,0.00014369507,0.00010742596,0.0009260026,0.0005644998,0.024259474],"genre_scores_gemma":[0.9795379,0.0009096274,0.012420748,0.000096082076,0.00011500704,0.000039696122,0.00035227073,0.00006912748,0.006459483],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992955,0.00016253826,0.00003825687,0.00018235986,0.00022431285,0.000097047516],"domain_scores_gemma":[0.9978036,0.001028843,0.0005988488,0.00020945887,0.00023646142,0.00012273637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013811542,0.00090271584,0.00059918885,0.0011849327,0.0002672947,0.0032756035,0.00083153136,0.0013116285,0.004174005],"category_scores_gemma":[0.010690116,0.00055662845,0.0007989115,0.000997215,0.00085353764,0.0047164867,0.000980691,0.0014542679,0.00077208155],"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":[0.000081738486,0.000057092166,0.030498903,0.000048419446,0.00015778768,0.00028378106,0.00026871424,0.2646117,0.001923775,0.6701566,0.0018072366,0.03010428],"study_design_scores_gemma":[0.000024817246,0.000050106068,0.022468315,0.0000285086,0.00006641755,0.00020153423,0.00008502513,0.61457443,0.0007305037,0.3572837,0.004412847,0.00007386167],"about_ca_topic_score_codex":0.005930029,"about_ca_topic_score_gemma":0.002793945,"teacher_disagreement_score":0.005930029,"about_ca_system_score_codex":0.001115017,"about_ca_system_score_gemma":0.0009231502,"threshold_uncertainty_score":0.013963401},"labels":[],"label_agreement":null},{"id":"W2155263147","doi":"10.1093/jjfinec/nbu007","title":"Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?","year":2014,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":96,"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":"Random walk; Sample (material); History; Library science; Statistics; Computer science; Mathematics","score_opus":0.10054330283859052,"score_gpt":0.24359619305026745,"score_spread":0.14305289021167694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155263147","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":[0.6641302,0.0016591453,0.32344863,0.005071371,0.00027311043,0.000043018885,0.0002790853,0.0006727156,0.004422685],"genre_scores_gemma":[0.98603976,0.0005221878,0.011821749,0.00030886923,0.00021869986,0.000014081224,0.00026401482,0.00007253874,0.0007381103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990069,0.00056123297,0.000041671097,0.00021087022,0.0001075153,0.00007176729],"domain_scores_gemma":[0.9741354,0.02144966,0.0017657008,0.0015916612,0.00075365306,0.00030394804],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006674715,0.0009212598,0.0013255046,0.00055878743,0.000488381,0.0016094829,0.00074293005,0.0011828667,0.00100142],"category_scores_gemma":[0.04761288,0.0005430122,0.0004220011,0.00054995547,0.0009898536,0.0052398853,0.0009629829,0.0024436447,0.00039938267],"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":[0.0012407067,0.00018214308,0.18319486,0.00016600409,0.00052010105,0.0006433826,0.0006298957,0.56989956,0.0013437588,0.057050105,0.0062882164,0.17884125],"study_design_scores_gemma":[0.00005260787,0.00011354243,0.012402984,0.00004218965,0.00004596419,0.00009072365,0.00009282552,0.9259115,0.00085075176,0.059623316,0.00073657325,0.000037020505],"about_ca_topic_score_codex":0.0033614952,"about_ca_topic_score_gemma":0.0035746254,"teacher_disagreement_score":0.006674715,"about_ca_system_score_codex":0.0002881462,"about_ca_system_score_gemma":0.00063129904,"threshold_uncertainty_score":0.03529972},"labels":[],"label_agreement":null},{"id":"W2161340333","doi":"10.1093/jjfinec/nbi001","title":"Optimal Estimation of the Risk Premium for the Long Run and Asset Allocation: A Case of Compounded Estimation Risk","year":2005,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Estimator; Econometrics; Economics; Portfolio; Risk premium; Compounding; Asset allocation; Asset (computer security); Bias of an estimator; Statistics; Mathematics; Minimum-variance unbiased estimator; Finance; Computer science","score_opus":0.026841912953148712,"score_gpt":0.2361683486014079,"score_spread":0.20932643564825917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161340333","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":[0.13849244,0.0007736698,0.8568655,0.001005811,0.000036141268,0.000067378365,0.00010739589,0.00010816432,0.0025435546],"genre_scores_gemma":[0.8652365,0.0006110991,0.13133581,0.0001407532,0.000092226015,0.00009002046,0.00012770765,0.00005695676,0.002309026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99665076,0.0015800404,0.00020810666,0.00068195665,0.00058700104,0.0002920031],"domain_scores_gemma":[0.9687377,0.025288299,0.0029760823,0.0018187985,0.00079606875,0.00038304046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010260807,0.00083381054,0.0016596967,0.00090040296,0.00033927802,0.002555669,0.0010906024,0.0017277638,0.0019517776],"category_scores_gemma":[0.061099306,0.0008882178,0.0007949213,0.0007708442,0.002268968,0.0047518024,0.0020720323,0.0019804584,0.00017914767],"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":[0.00018277449,0.000079952275,0.009462618,0.00015539676,0.00021476456,0.00048793928,0.0002855808,0.53075397,0.0021843107,0.40610856,0.0009515339,0.04913254],"study_design_scores_gemma":[0.000036089314,0.00008096665,0.0031708116,0.000042663538,0.000053758973,0.000121404475,0.000042304935,0.73012155,0.0014847327,0.26415095,0.0006473519,0.000047483092],"about_ca_topic_score_codex":0.0024453425,"about_ca_topic_score_gemma":0.0013823188,"teacher_disagreement_score":0.010260807,"about_ca_system_score_codex":0.0014721209,"about_ca_system_score_gemma":0.0017974337,"threshold_uncertainty_score":0.054264963},"labels":[],"label_agreement":null},{"id":"W2170655528","doi":"10.1093/jjfinec/nbq028","title":"Estimation and Inference in ARCH Models in the Presence of Outliers","year":2010,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":15,"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":"Outlier; Econometrics; Inference; Arch; Heteroscedasticity; Autoregressive model; Autoregressive conditional heteroskedasticity; Likelihood function; Computer science; Statistics; Mathematics; Estimation theory; Artificial intelligence; Volatility (finance)","score_opus":0.05958538013722739,"score_gpt":0.2631700049195858,"score_spread":0.20358462478235842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170655528","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":[0.05157317,0.0004632717,0.94600004,0.0006275977,0.00004224008,0.000018912673,0.00010290691,0.00036497205,0.00080687215],"genre_scores_gemma":[0.81828827,0.0008455659,0.17829172,0.00030193146,0.0002160805,0.000100898724,0.00044596216,0.00020365219,0.0013058854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.987421,0.008797492,0.0006571267,0.0011799281,0.0014938773,0.0004506582],"domain_scores_gemma":[0.8096867,0.16865416,0.008902175,0.008810842,0.003350744,0.00059535383],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02851758,0.0009335335,0.0017210526,0.0017318183,0.00089543127,0.002293074,0.00189336,0.0021287876,0.0016508469],"category_scores_gemma":[0.19941604,0.0009669827,0.001253496,0.0023099554,0.0027485616,0.0047326772,0.0028784918,0.00444064,0.00034824054],"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":[0.00030121588,0.00009227905,0.033831637,0.0002249474,0.00045697807,0.000691546,0.0007615957,0.675331,0.001277113,0.19874087,0.0020396465,0.08625116],"study_design_scores_gemma":[0.00002038301,0.000044088392,0.0022629113,0.0000340963,0.000037898353,0.00011690102,0.00009551206,0.83908,0.0010686149,0.15653972,0.00066654297,0.000033361426],"about_ca_topic_score_codex":0.005752436,"about_ca_topic_score_gemma":0.003979878,"teacher_disagreement_score":0.02851758,"about_ca_system_score_codex":0.001073201,"about_ca_system_score_gemma":0.001581308,"threshold_uncertainty_score":0.15081716},"labels":[],"label_agreement":null},{"id":"W2233974190","doi":"10.1093/jjfinec/nbz026","title":"Testing for the Diffusion Matrix in a Continuous-Time Markov Process Model with Applications to the Term Structure of Interest Rates","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Stochastic processes and financial applications","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":"Bank of Canada","funders":"","keywords":"Mathematics; Statistics; Econometrics; Yield curve; Parametric statistics; Applied mathematics; Affine transformation; Range (aeronautics); Diagonal; Statistical physics; Interest rate; Economics","score_opus":0.031678220719343814,"score_gpt":0.254450362547254,"score_spread":0.2227721418279102,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2233974190","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.63537616,0.000107581734,0.36164728,0.0006171501,0.000031689084,0.00009011527,0.00016800944,0.00024346258,0.0017186259],"genre_scores_gemma":[0.983452,0.000039578736,0.01611401,0.00004053989,0.00001931323,0.000041206706,0.00011051174,0.000010677096,0.0001720603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.986537,0.009266339,0.0006025913,0.001643687,0.0014347901,0.0005156429],"domain_scores_gemma":[0.768738,0.21394964,0.007493369,0.004703666,0.00294992,0.0021653965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.018945873,0.00075608224,0.0011936584,0.0020706938,0.0007663165,0.002132898,0.0015016499,0.0017473758,0.0026062278],"category_scores_gemma":[0.15252404,0.0005204017,0.0015166245,0.0017072138,0.0028450869,0.0035608574,0.0020351568,0.0019503726,0.00020615636],"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":[0.0011924099,0.0008619599,0.22924694,0.00022766626,0.0014040632,0.000722765,0.00081517984,0.39935422,0.008733531,0.26080954,0.0012505724,0.09538113],"study_design_scores_gemma":[0.000062583735,0.0003043656,0.01018809,0.0000175723,0.000037497546,0.000084366024,0.00012402682,0.9417036,0.0010705462,0.04621441,0.00014757908,0.00004543326],"about_ca_topic_score_codex":0.0039947117,"about_ca_topic_score_gemma":0.0021749216,"teacher_disagreement_score":0.018945873,"about_ca_system_score_codex":0.0010267895,"about_ca_system_score_gemma":0.0025995097,"threshold_uncertainty_score":0.10019654},"labels":[],"label_agreement":null},{"id":"W2361402095","doi":"10.1093/jjfinec/nbz034","title":"Bayesian Nonparametric Estimation of<i>Ex Post</i>Variance","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"McMaster University","funders":"","keywords":"Estimator; Heteroscedasticity; Nonparametric statistics; Econometrics; Realized variance; Variance (accounting); Statistics; Variance-based sensitivity analysis; Bayesian probability; Bayes estimator; Mathematics; Monte Carlo method; One-way analysis of variance; Economics; Analysis of variance; Volatility (finance)","score_opus":0.020625000809580376,"score_gpt":0.219540958526668,"score_spread":0.19891595771708762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2361402095","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":[0.027360108,0.00016591595,0.9707206,0.00014014801,0.00002178202,0.000017898801,0.00009869129,0.00016242314,0.0013124243],"genre_scores_gemma":[0.8215196,0.00046011904,0.17380695,0.0001180512,0.00011407017,0.00010577006,0.00048248994,0.00013606787,0.0032568441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978403,0.0009955289,0.000107939166,0.00033241074,0.00058862433,0.00013534447],"domain_scores_gemma":[0.99146044,0.0056541725,0.0011183592,0.0008749063,0.0007790474,0.00011311207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004865295,0.00047600133,0.0009740476,0.0011160137,0.00026222056,0.001640458,0.0015554071,0.00082714105,0.002233136],"category_scores_gemma":[0.023282891,0.0005575717,0.00058563624,0.00088533695,0.0013742737,0.0020819958,0.001358273,0.0015336236,0.00036487085],"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":[0.00020128893,0.00021000116,0.0097288,0.0002782695,0.00030997972,0.00021050968,0.00015878152,0.5148746,0.007549833,0.2800096,0.0021514928,0.18431677],"study_design_scores_gemma":[0.000013254348,0.000033388416,0.003687662,0.0000457188,0.000021913183,0.000066339395,0.000020280535,0.9140802,0.002071478,0.07900885,0.0009146542,0.000036315512],"about_ca_topic_score_codex":0.0020920099,"about_ca_topic_score_gemma":0.0017100818,"teacher_disagreement_score":0.004865295,"about_ca_system_score_codex":0.00065737107,"about_ca_system_score_gemma":0.0009507018,"threshold_uncertainty_score":0.025730431},"labels":[],"label_agreement":null},{"id":"W2411006838","doi":"10.1093/jjfinec/nbx033","title":"Estimating Systematic Risk under Extremely Adverse Market Conditions*","year":2017,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Estimator; Econometrics; Conditional expectation; Mathematics; Statistics; Regression; Linear regression; Stock market; Economics","score_opus":0.0612280543598787,"score_gpt":0.25928932555789325,"score_spread":0.19806127119801453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2411006838","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":[0.7857416,0.0003252428,0.2124982,0.00029954332,0.000017271252,0.000022566079,0.0001526208,0.00015227043,0.0007907295],"genre_scores_gemma":[0.9940118,0.00008170905,0.005492745,0.000022347043,0.000024492452,0.000007859977,0.00010561358,0.0000074651184,0.00024605403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987387,0.00064661534,0.000068061556,0.00022720746,0.00020960736,0.000109729306],"domain_scores_gemma":[0.9801062,0.014285939,0.003448465,0.0009990085,0.00085342146,0.0003070343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003981171,0.0006005344,0.00086300774,0.0011226508,0.0002048582,0.0009230597,0.00053682696,0.0007806408,0.00076462637],"category_scores_gemma":[0.02546939,0.0003889029,0.0003838397,0.0005845939,0.0007263467,0.0011763109,0.0012297882,0.00094624906,0.00013540672],"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":[0.00039126462,0.00015902292,0.27036327,0.00011783121,0.00048204663,0.0009985982,0.00019224423,0.6382188,0.0052378816,0.02253796,0.001022188,0.0602789],"study_design_scores_gemma":[0.00001600217,0.00010409391,0.02895897,0.00002255065,0.000038890154,0.0001249948,0.000056606423,0.9472005,0.0019376777,0.021336395,0.0001755199,0.000027759948],"about_ca_topic_score_codex":0.0028594204,"about_ca_topic_score_gemma":0.002337609,"teacher_disagreement_score":0.003981171,"about_ca_system_score_codex":0.00030020336,"about_ca_system_score_gemma":0.0005196633,"threshold_uncertainty_score":0.021054685},"labels":[],"label_agreement":null},{"id":"W2523656441","doi":"10.1093/jjfinec/nbz024","title":"Nonparametric Dynamic Conditional Beta","year":2019,"lang":"en","type":"article","venue":"Journal of Financial 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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick; McMaster University","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Econometrics; Conditional variance; Economics; BETA (programming language); Mathematics; Beta distribution; Conditional probability distribution; Autoregressive model; Heteroscedasticity; Conditional expectation; Nonparametric statistics; Volatility (finance); Statistics; Computer science","score_opus":0.025902139629020722,"score_gpt":0.22581377605308214,"score_spread":0.19991163642406143,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2523656441","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":[0.04632125,0.0003130847,0.9441758,0.00037348174,0.000046208406,0.00003410985,0.0005599182,0.0004586541,0.0077174776],"genre_scores_gemma":[0.9166512,0.0006046044,0.07314963,0.00014592326,0.00014654858,0.00010181082,0.0012110767,0.00017969418,0.007809428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989968,0.0004197774,0.000032718483,0.00021767405,0.00022431533,0.00010864729],"domain_scores_gemma":[0.99616134,0.0021093055,0.0005198795,0.00064048875,0.0004126883,0.0001563205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002670489,0.0004974894,0.00091565266,0.0016210089,0.00034079677,0.0017243121,0.0014647458,0.0008892574,0.007333986],"category_scores_gemma":[0.011492862,0.0004755761,0.0010108805,0.0012907418,0.0012806345,0.0019876664,0.0014364566,0.0014890175,0.0008174057],"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":[0.000067396715,0.00005744123,0.003827163,0.000067444416,0.00008186108,0.00018151962,0.00010241188,0.56924266,0.0011681519,0.36689034,0.0025122005,0.055801395],"study_design_scores_gemma":[0.0000074269483,0.000010765321,0.0012465402,0.000021510765,0.000009811841,0.00007545529,0.000010116026,0.89684814,0.0002185742,0.100229114,0.0013064644,0.000015971302],"about_ca_topic_score_codex":0.0039154817,"about_ca_topic_score_gemma":0.0031609596,"teacher_disagreement_score":0.007333986,"about_ca_system_score_codex":0.0010669718,"about_ca_system_score_gemma":0.00086649024,"threshold_uncertainty_score":0.024534643},"labels":[],"label_agreement":null},{"id":"W2595890985","doi":"10.1093/jjfinec/nbx006","title":"Rejoinder on: Nonparametric Tail Risk, Stock Returns, and the Macroeconomy","year":2017,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Monetary Policy and Economic Impact","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":"Université de Montréal","funders":"","keywords":"Nonparametric statistics; Stock (firearms); Econometrics; Perspective (graphical); Point (geometry); Economics; Regression; Actuarial science; Computer science; Mathematics; Statistics; Engineering","score_opus":0.09890273097241006,"score_gpt":0.2396905257005058,"score_spread":0.14078779472809574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595890985","genre_codex":"commentary","genre_gemma":"commentary","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"commentary","genre_consensus":"commentary","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018483674,0.027388105,0.024120836,0.8604521,0.07253907,0.000055982026,0.00028791436,0.00025276706,0.013054886],"genre_scores_gemma":[0.058319174,0.032727256,0.044998087,0.5025147,0.29284957,0.0005341581,0.00040552055,0.0015063827,0.066145234],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99035466,0.0033633248,0.00084380625,0.002146537,0.0030119882,0.00027979913],"domain_scores_gemma":[0.9602379,0.025627166,0.001156873,0.0050479416,0.0071078306,0.0008222169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01306185,0.000986073,0.0017902383,0.0020565053,0.002121282,0.004853876,0.00504455,0.012835737,0.006140149],"category_scores_gemma":[0.04440399,0.0004750648,0.0013193331,0.0013715567,0.008051569,0.013456358,0.0062542236,0.024818119,0.0049211327],"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":[0.00006212444,0.00004314411,0.00047902486,0.00021115884,0.0000367172,0.00017271361,0.0006261082,0.00026576387,0.00037917882,0.34112072,0.62722623,0.02937704],"study_design_scores_gemma":[0.000031686683,0.00003643492,0.00060644577,0.00041185488,0.000026035268,0.00034563255,0.00033944394,0.0010208391,0.00055155886,0.40920714,0.58734596,0.00007702336],"about_ca_topic_score_codex":0.0014741732,"about_ca_topic_score_gemma":0.0018588797,"teacher_disagreement_score":0.01306185,"about_ca_system_score_codex":0.0015153172,"about_ca_system_score_gemma":0.0015782726,"threshold_uncertainty_score":0.069078505},"labels":[],"label_agreement":null},{"id":"W2600709751","doi":"10.1093/jjfinec/nbx009","title":"Forecasting Stock Returns Using Option-Implied State Prices*","year":2017,"lang":"en","type":"article","venue":"Journal of Financial 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":"Carleton University","funders":"","keywords":"Economics; Econometrics; Quantile; Skewness; Capital asset pricing model; Volatility (finance); Equity premium puzzle; Equity (law); Stock (firearms); Risk aversion (psychology); Financial economics; Valuation of options; Expected utility hypothesis","score_opus":0.14764977923118888,"score_gpt":0.2658294531670003,"score_spread":0.11817967393581141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2600709751","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":[0.9443574,0.00023426981,0.05214317,0.00039199597,0.000025609,0.000020337482,0.0008807306,0.00026909623,0.0016774385],"genre_scores_gemma":[0.99642855,0.00007042773,0.0027827565,0.000011861526,0.000012658504,0.0000050822036,0.00048119586,0.000004747319,0.00020262705],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997923,0.00006921336,0.000014906088,0.000050398572,0.000044764452,0.000028374863],"domain_scores_gemma":[0.99793196,0.0012355926,0.0003576561,0.0001784307,0.00019836194,0.00009816267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013116259,0.00051004323,0.0004379932,0.0010573101,0.00016925478,0.0010401267,0.0004998911,0.00075300573,0.001478024],"category_scores_gemma":[0.007577331,0.0003601697,0.00053630857,0.0009011647,0.00023357973,0.001385819,0.00044842932,0.0009414747,0.00031041395],"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":[0.0003579917,0.00021685647,0.24373597,0.000054833876,0.00024237165,0.00020769062,0.00012369739,0.6716172,0.0026036915,0.016241185,0.0019453096,0.06265315],"study_design_scores_gemma":[0.0000049743076,0.0000139031235,0.008022172,0.0000038131705,0.000005292831,0.000008900544,0.000007625865,0.9889798,0.000210456,0.0026448516,0.00009207049,0.0000060769476],"about_ca_topic_score_codex":0.008549907,"about_ca_topic_score_gemma":0.008963516,"teacher_disagreement_score":0.008549907,"about_ca_system_score_codex":0.00046119306,"about_ca_system_score_gemma":0.00034058583,"threshold_uncertainty_score":0.017000258},"labels":[],"label_agreement":null},{"id":"W2892347549","doi":"10.1093/jjfinec/nby017","title":"Pseudo-True SDFs in Conditional Asset Pricing Models*","year":2018,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Capital asset pricing model; Estimator; Stochastic discount factor; Econometrics; Smoothing; Kernel density estimation; Affine transformation; Inference; Conditional expectation; Kernel (algebra); Mathematics; Computer science; Statistics; Artificial intelligence","score_opus":0.054240056836378066,"score_gpt":0.23109893563238343,"score_spread":0.17685887879600537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892347549","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":[0.04922539,0.00028560028,0.9480893,0.00050460285,0.00004266629,0.000021753893,0.00015250473,0.00018421302,0.0014940392],"genre_scores_gemma":[0.8806851,0.00053235324,0.11348034,0.00024151383,0.00014675765,0.000106173145,0.0004388642,0.00015225784,0.0042166705],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.997512,0.0013263791,0.00011375017,0.00048026038,0.000405326,0.00016225436],"domain_scores_gemma":[0.9838112,0.011620518,0.0016775881,0.0013619626,0.0010652746,0.00046353272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006517875,0.0006532503,0.0010753917,0.0013095047,0.0004307416,0.0022806223,0.0017395571,0.0014943848,0.003801651],"category_scores_gemma":[0.031401757,0.00065744,0.0011001644,0.0011481587,0.002429224,0.003722391,0.0020037128,0.0028700721,0.0003808881],"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":[0.000053646585,0.000039580133,0.0040472853,0.000090374255,0.000065885564,0.0002176455,0.0001820127,0.23485254,0.0005641135,0.7396378,0.0012626201,0.018986525],"study_design_scores_gemma":[0.000006592213,0.00001618777,0.00061315385,0.00001777277,0.0000085528545,0.000042812266,0.000018345001,0.81239235,0.00016889647,0.1860364,0.00066110824,0.000017835271],"about_ca_topic_score_codex":0.0024215174,"about_ca_topic_score_gemma":0.0015689601,"teacher_disagreement_score":0.006517875,"about_ca_system_score_codex":0.0012831991,"about_ca_system_score_gemma":0.001014309,"threshold_uncertainty_score":0.0344702},"labels":[],"label_agreement":null},{"id":"W2920992240","doi":"10.1093/jjfinec/nbz003","title":"Realized Peaks over Threshold: A Time-Varying Extreme Value Approach with High-Frequency-Based Measures*","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"Fondation HEC","keywords":"Econometrics; Extreme value theory; Sample (material); Index (typography); Conditional probability distribution; Economics; Asset (computer security); Statistics; Mathematics; Computer science","score_opus":0.05140794329571589,"score_gpt":0.21357770027770598,"score_spread":0.1621697569819901,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2920992240","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":[0.029578043,0.00020716514,0.9687384,0.00012580646,0.000027470598,0.000023400487,0.000068743626,0.0001528187,0.0010782221],"genre_scores_gemma":[0.8567536,0.0004502916,0.14016944,0.00012410365,0.00020452472,0.00009857786,0.00026048408,0.00014597416,0.0017930266],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913543,0.0004020644,0.00004855832,0.00018218637,0.00014878315,0.000082943625],"domain_scores_gemma":[0.99380857,0.004299513,0.0007376653,0.00062108226,0.00034032128,0.0001926818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003000653,0.00079551234,0.00085914665,0.0012201141,0.00038418823,0.0020681296,0.0020154305,0.0013063574,0.0015647009],"category_scores_gemma":[0.012926275,0.0004927588,0.0011385985,0.0012579844,0.00094446377,0.0026498656,0.0014807619,0.0021473516,0.00030739472],"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":[0.00019222261,0.00018001073,0.015862131,0.00016138828,0.00032571665,0.0005647037,0.0004251581,0.6986438,0.004360038,0.15229622,0.0016054277,0.12538315],"study_design_scores_gemma":[0.0000062620175,0.000039290906,0.0018168285,0.000015502928,0.000021301428,0.000057685524,0.000039516715,0.95083517,0.00038246726,0.04612357,0.0006386451,0.00002382482],"about_ca_topic_score_codex":0.0017220057,"about_ca_topic_score_gemma":0.0013718049,"teacher_disagreement_score":0.003000653,"about_ca_system_score_codex":0.0005288606,"about_ca_system_score_gemma":0.0004967701,"threshold_uncertainty_score":0.01586914},"labels":[],"label_agreement":null},{"id":"W2947983066","doi":"10.1093/jjfinec/nbz017","title":"Exact Inference in Long-Horizon Predictive Quantile Regressions with an Application to Stock Returns","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":14,"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; Bank of Canada; Western University","funders":"","keywords":"Quantile; Predictability; Econometrics; Resampling; Predictive power; Inference; Quantile regression; Statistics; Stock (firearms); Mathematics; Economics; Computer science; Artificial intelligence","score_opus":0.06042048668930893,"score_gpt":0.2609681538786446,"score_spread":0.20054766718933564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947983066","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":[0.032851227,0.00016796036,0.96582794,0.00017936157,0.000017893197,0.000042917323,0.000064142805,0.00047954943,0.000369062],"genre_scores_gemma":[0.76724035,0.00021402517,0.23067766,0.00017131554,0.000102403465,0.00015133026,0.00026166855,0.00015030488,0.0010308814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.993737,0.0040522544,0.00027139584,0.00078043307,0.00082925713,0.00032962207],"domain_scores_gemma":[0.9182002,0.06968921,0.0035149918,0.005692427,0.002310661,0.0005924812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021986293,0.0007278378,0.0016684136,0.0011277592,0.00065524934,0.0012158436,0.0026255995,0.0014244391,0.0028909333],"category_scores_gemma":[0.09138258,0.00092175894,0.0011395328,0.001542611,0.0020765055,0.0022328363,0.002065533,0.00254173,0.0003332454],"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":[0.00029188793,0.00019852251,0.021662263,0.00011294688,0.0003191005,0.0004256802,0.0002331859,0.8229335,0.0020977585,0.05789521,0.00066155236,0.093168505],"study_design_scores_gemma":[0.000024113819,0.000030164701,0.0015422016,0.000011529018,0.000016188544,0.000026593716,0.000011578117,0.9788963,0.0006183717,0.018687539,0.00012218978,0.000013165635],"about_ca_topic_score_codex":0.01126856,"about_ca_topic_score_gemma":0.009407238,"teacher_disagreement_score":0.021986293,"about_ca_system_score_codex":0.00096934824,"about_ca_system_score_gemma":0.0021584313,"threshold_uncertainty_score":0.116276026},"labels":[],"label_agreement":null},{"id":"W2993673477","doi":"10.1093/jjfinec/nbaa010","title":"The Term Structures of Expected Loss and Gain Uncertainty*","year":2020,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Stochastic processes and financial applications","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":false,"ca_institutions":"Bank of Canada","funders":"","keywords":"Term (time); Economics; Downside risk; Econometrics; Jump; Replicate; Jump diffusion; Financial economics; Mathematics; Statistics; Physics","score_opus":0.03268828762466609,"score_gpt":0.2226496247333031,"score_spread":0.189961337108637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2993673477","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":[0.9200683,0.00056859007,0.06891315,0.00084175094,0.000044060056,0.000029659928,0.00070474646,0.0001763147,0.008653336],"genre_scores_gemma":[0.99740523,0.000071518356,0.0016547133,0.000019599467,0.000016464304,0.000007756424,0.0001344756,0.000014589805,0.0006757114],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.999332,0.00015206343,0.000050648017,0.00012312099,0.00026266338,0.00007951184],"domain_scores_gemma":[0.9878457,0.0075045326,0.0025019464,0.0007419872,0.0010760439,0.00032968778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023373286,0.00027933792,0.00036463977,0.000991949,0.00020225436,0.0018473353,0.0005931929,0.0007276478,0.002346105],"category_scores_gemma":[0.020248938,0.0002538917,0.0003728358,0.0007568165,0.0009709718,0.0025899606,0.0009028199,0.0010962663,0.00028043013],"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":[0.0006335817,0.0002214219,0.10848159,0.00031576084,0.0003125834,0.0010522756,0.0010001219,0.35005778,0.027854433,0.4209561,0.0042131827,0.084901206],"study_design_scores_gemma":[0.000028027482,0.0001531825,0.12000902,0.000057457677,0.00007627027,0.00035180667,0.00016785246,0.6271675,0.0033803328,0.2465685,0.0019060933,0.0001339958],"about_ca_topic_score_codex":0.0011563187,"about_ca_topic_score_gemma":0.001023484,"teacher_disagreement_score":0.002346105,"about_ca_system_score_codex":0.000823086,"about_ca_system_score_gemma":0.000318025,"threshold_uncertainty_score":0.012361109},"labels":[],"label_agreement":null},{"id":"W3048718700","doi":"10.1093/jjfinec/nbab016","title":"Conditional Inferences Based on Vine Copulas with Applications to Credit Spread Data of Corporate Bonds","year":2021,"lang":"en","type":"preprint","venue":"Journal of Financial Econometrics","topic":"Credit Risk and Financial Regulations","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":"University of British Columbia","funders":"","keywords":"Vine copula; Copula (linguistics); Econometrics; Bond; Corporate bond; Inference; Credit risk; Conditional probability distribution; Conditional dependence; Economics; Tail dependence; Actuarial science; Computer science; Statistics; Mathematics; Artificial intelligence; Finance","score_opus":0.13694780005439722,"score_gpt":0.2817727479036607,"score_spread":0.14482494784926347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048718700","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":[0.033784516,0.0005894875,0.96272653,0.00037450367,0.00006664573,0.000069449925,0.0006365448,0.0006882838,0.0010641069],"genre_scores_gemma":[0.607663,0.0022380329,0.38065833,0.00036532685,0.00062863,0.00031071698,0.0046771253,0.000617538,0.002841374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9960091,0.00218311,0.00023525927,0.00082247297,0.0005204013,0.00022961911],"domain_scores_gemma":[0.9490225,0.040635116,0.0024510832,0.0039884364,0.0031083475,0.0007945907],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010814352,0.0012502075,0.0018269557,0.0039245565,0.0008320383,0.0025097046,0.0019457134,0.0011044656,0.0043090764],"category_scores_gemma":[0.08185119,0.0010336929,0.002444169,0.0034435324,0.0012752084,0.00289937,0.0024499844,0.0042346064,0.0007146292],"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":[0.000116317024,0.00018810204,0.012570754,0.00021409812,0.00048815043,0.00046417248,0.000412382,0.7670964,0.0014307136,0.11084541,0.0049839397,0.10118958],"study_design_scores_gemma":[0.000006571888,0.000008085712,0.0008969819,0.000017558961,0.000011049107,0.000026520525,0.00002095197,0.96858037,0.00017892488,0.029914167,0.00032683674,0.000011981095],"about_ca_topic_score_codex":0.013541064,"about_ca_topic_score_gemma":0.009229297,"teacher_disagreement_score":0.013541064,"about_ca_system_score_codex":0.0011391959,"about_ca_system_score_gemma":0.0014107514,"threshold_uncertainty_score":0.057192445},"labels":[],"label_agreement":null},{"id":"W3091500235","doi":"10.1093/jjfinec/nbaa044","title":"Multilevel and Tail Risk Management","year":2020,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","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":"Carleton University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Econometrics; Computer science; Value at risk; Expected shortfall; Sample (material); Sample size determination; Risk management; Limiting; CVAR; Economics; Statistics; Mathematics; Finance","score_opus":0.059060284675277956,"score_gpt":0.21552707516668856,"score_spread":0.1564667904914106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3091500235","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":[0.1794791,0.0008562118,0.8075672,0.0012986795,0.00007979675,0.0001073579,0.0006867181,0.00079633086,0.009128628],"genre_scores_gemma":[0.93086684,0.00017182056,0.066620395,0.00016938242,0.000077256234,0.00008536835,0.00026250226,0.000089266185,0.0016572264],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9942028,0.002735317,0.00034284126,0.0009639943,0.0013024135,0.00045265906],"domain_scores_gemma":[0.9306743,0.045447275,0.009134169,0.009263751,0.004025953,0.0014544607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010191559,0.0006870895,0.0015050253,0.0031142726,0.00072982354,0.00315281,0.0023072108,0.0016137748,0.008658896],"category_scores_gemma":[0.059752863,0.00048401835,0.0014273599,0.0020502352,0.0026847674,0.0043549137,0.004052544,0.0028015492,0.0006346835],"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":[0.00038847775,0.00021850404,0.13674091,0.0003002091,0.00059099455,0.0006755388,0.001144583,0.23107998,0.0024475309,0.37180993,0.004177336,0.25042605],"study_design_scores_gemma":[0.000024171466,0.00016088045,0.0152843455,0.00011260984,0.000075771575,0.00017949479,0.00016803703,0.58261883,0.0012185135,0.39822173,0.001863355,0.00007224419],"about_ca_topic_score_codex":0.0025342384,"about_ca_topic_score_gemma":0.002325355,"teacher_disagreement_score":0.010191559,"about_ca_system_score_codex":0.0012752577,"about_ca_system_score_gemma":0.0008071163,"threshold_uncertainty_score":0.05389875},"labels":[],"label_agreement":null},{"id":"W3121518889","doi":"10.1093/jjfinec/nbaa047","title":"Testing for Endogeneity of Covid-19 Patient Assignments","year":2020,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"COVID-19 epidemiological studies","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":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche; Ontario Ministry of Health and Long-Term Care; York University","keywords":"Endogeneity; Coronavirus disease 2019 (COVID-19); Isolation (microbiology); Econometrics; Inference; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Statistics; Economics; Medicine; Computer science; Mathematics; Disease; Virology; Biology; Infectious disease (medical specialty); Internal medicine; Outbreak","score_opus":0.5435313000926142,"score_gpt":0.4171606358329832,"score_spread":0.126370664259631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121518889","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":[0.9093848,0.00023266173,0.08365986,0.0012575488,0.000060239672,0.00037738174,0.0021904844,0.00011052353,0.0027265374],"genre_scores_gemma":[0.99140376,0.00005410397,0.006329284,0.00009524774,0.000018081939,0.00009331645,0.00077808887,0.000010070547,0.0012180425],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9773465,0.016230946,0.00084276585,0.0028055059,0.0014846683,0.0012896783],"domain_scores_gemma":[0.81694824,0.14264286,0.024893897,0.010973589,0.0029202306,0.001621164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.030257938,0.00042475946,0.0011052433,0.0011028774,0.001129768,0.0013650658,0.0019374093,0.0010996835,0.005913731],"category_scores_gemma":[0.10557954,0.0004704415,0.0010161725,0.001799155,0.002127043,0.0008279707,0.0020811579,0.0014815927,0.00030797964],"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":[0.00046203414,0.00018771787,0.94776195,0.000068951864,0.00063755113,0.00022529432,0.0009692768,0.010550611,0.00053791486,0.01606116,0.0013133214,0.021224298],"study_design_scores_gemma":[0.00031392273,0.0006153722,0.77928424,0.0001185674,0.000572408,0.00023475944,0.0023037847,0.18115076,0.0022733775,0.028313939,0.0047355397,0.00008333075],"about_ca_topic_score_codex":0.06420431,"about_ca_topic_score_gemma":0.04611852,"teacher_disagreement_score":0.06420431,"about_ca_system_score_codex":0.0024555367,"about_ca_system_score_gemma":0.0029135342,"threshold_uncertainty_score":0.16002119},"labels":[],"label_agreement":null},{"id":"W3122203669","doi":"10.1093/jjfinec/nbj001","title":"Incomplete Information, Heterogeneity, and Asset Pricing","year":2005,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Capital asset pricing model; Economics; Asset (computer security); Financial economics; Finance; Computer science","score_opus":0.03442978217883723,"score_gpt":0.2192372283780304,"score_spread":0.18480744619919315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122203669","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":[0.67939085,0.0031467872,0.28212523,0.007524245,0.00009370559,0.000058589598,0.0005285243,0.00011146327,0.027020631],"genre_scores_gemma":[0.99368894,0.00051790813,0.0035607954,0.000097261865,0.000062150546,0.000018607967,0.000065254884,0.0000053565695,0.001983527],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99932206,0.00029047206,0.00003277155,0.00010564378,0.00012194258,0.00012713393],"domain_scores_gemma":[0.9934075,0.0041414476,0.001521491,0.00045161162,0.00023349425,0.00024440812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001915136,0.00045879348,0.00091208256,0.00056943734,0.00043934805,0.0022984836,0.0010631365,0.0014141882,0.0023006366],"category_scores_gemma":[0.012272564,0.00032864755,0.0004520555,0.0007660464,0.001512108,0.0031740193,0.0008912057,0.0011001932,0.0002017232],"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":[0.000106367435,0.00006390764,0.0046472615,0.00006674216,0.000110177214,0.0004599172,0.00018428947,0.24019736,0.00043300574,0.7430278,0.001020459,0.009682677],"study_design_scores_gemma":[0.000038418908,0.00002755444,0.0017589183,0.000017928101,0.000028049551,0.00007494261,0.000055277975,0.28359252,0.0001437083,0.7134114,0.000832648,0.00001869264],"about_ca_topic_score_codex":0.0033047383,"about_ca_topic_score_gemma":0.0021446764,"teacher_disagreement_score":0.0033047383,"about_ca_system_score_codex":0.0011658343,"about_ca_system_score_gemma":0.0005421581,"threshold_uncertainty_score":0.010128379},"labels":[],"label_agreement":null},{"id":"W3122654098","doi":"10.1093/jjfinec/nbr010","title":"Microinformation, Nonlinear Filtering, and Granularity","year":2011,"lang":"en","type":"preprint","venue":"Journal of Financial 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":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Granularity; Kalman filter; Gaussian; Computation; State space; State variable; Filter (signal processing); Nonlinear system; Computer science; State (computer science); Tuple; Mathematics; Mathematical optimization; Applied mathematics; Algorithm; Statistics","score_opus":0.06868105097341858,"score_gpt":0.22746897349222564,"score_spread":0.15878792251880708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122654098","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":[0.048526224,0.0009885315,0.94407576,0.000874913,0.000088499524,0.000018323202,0.00014560192,0.00021933172,0.005062802],"genre_scores_gemma":[0.8841987,0.0012756586,0.11104458,0.00020727832,0.00020813681,0.00005042443,0.00015675326,0.00007620814,0.0027823125],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985367,0.00033845936,0.00009891778,0.00043886693,0.00043055665,0.00015653911],"domain_scores_gemma":[0.9888158,0.00704385,0.0016730849,0.0018040794,0.00048153833,0.00018161519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026341781,0.0004757831,0.00080545055,0.0010000688,0.00046027757,0.002497261,0.00087030104,0.00092120806,0.0016752653],"category_scores_gemma":[0.018017508,0.00058064517,0.0006265109,0.0012371106,0.0018433682,0.005573254,0.0018899542,0.0016140192,0.00025771532],"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":[0.00021174784,0.00004742397,0.012090807,0.00019301218,0.0001242652,0.0003004293,0.00048144133,0.28336704,0.004971924,0.5464337,0.0012988112,0.15047945],"study_design_scores_gemma":[0.000014429402,0.000042093896,0.0059733223,0.00004959164,0.000033283966,0.00015995192,0.000073805124,0.5798526,0.0023822691,0.40930614,0.0020490703,0.000063445004],"about_ca_topic_score_codex":0.0032513768,"about_ca_topic_score_gemma":0.0024619985,"teacher_disagreement_score":0.0032513768,"about_ca_system_score_codex":0.0011786316,"about_ca_system_score_gemma":0.0007113117,"threshold_uncertainty_score":0.013931036},"labels":[],"label_agreement":null},{"id":"W3123037888","doi":"10.1093/jjfinec/nbz022","title":"Positional Portfolio Management","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","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":"University of Toronto","funders":"Agence Nationale de la Recherche","keywords":"Unobservable; Portfolio; Position (finance); Competitor analysis; Project portfolio management; Asset allocation; Variance (accounting); Econometrics; Asset (computer security); Replicating portfolio; Rate of return on a portfolio; Function (biology); Economics; Modern portfolio theory; Portfolio optimization; Computer science; Financial economics; Finance; Accounting","score_opus":0.021321049227597057,"score_gpt":0.1971227183081128,"score_spread":0.17580166908051573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123037888","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":[0.38894948,0.004791458,0.55090636,0.003586532,0.00016104424,0.00033299965,0.00041509088,0.00041251353,0.050444417],"genre_scores_gemma":[0.96707165,0.00086312916,0.024145756,0.00016279638,0.00007640263,0.00007035105,0.00015855412,0.000026092297,0.007425165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838746,0.000802198,0.00006910511,0.0002332224,0.00032797168,0.00017995306],"domain_scores_gemma":[0.9963825,0.0017997188,0.00086378714,0.00035197538,0.00034183013,0.00026026528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031687946,0.00073036767,0.00073722407,0.0007665301,0.0005059159,0.0026684199,0.00095996226,0.0010637423,0.0065369857],"category_scores_gemma":[0.009301656,0.00025198364,0.00032603552,0.0010457658,0.00075858005,0.0023838822,0.0009623973,0.0008454838,0.0008439228],"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":[0.00021884701,0.00044284982,0.026498307,0.00024976005,0.00030096574,0.00024319522,0.00039306746,0.29334486,0.0029301632,0.26475164,0.0094499495,0.40117636],"study_design_scores_gemma":[0.00010998033,0.0006290594,0.0068433275,0.00008473953,0.00007055724,0.0002035366,0.00025931184,0.6823009,0.0019131884,0.2913836,0.016154904,0.0000468811],"about_ca_topic_score_codex":0.0015545046,"about_ca_topic_score_gemma":0.0015826698,"teacher_disagreement_score":0.0065369857,"about_ca_system_score_codex":0.0010672743,"about_ca_system_score_gemma":0.0011076835,"threshold_uncertainty_score":0.021868408},"labels":[],"label_agreement":null},{"id":"W3123827901","doi":"10.1093/jjfinec/nbx007","title":"Nonparametric Tail Risk, Stock Returns, and the Macroeconomy","year":2017,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":47,"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","funders":"","keywords":"Tail risk; Econometrics; Economics; Portfolio; Risk premium; Risk measure; Nonparametric statistics; Stock (firearms); Expected shortfall; Market risk; Systematic risk; Financial economics; Excess return","score_opus":0.032525448758373626,"score_gpt":0.22383859099933692,"score_spread":0.1913131422409633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123827901","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":[0.44424108,0.0029672408,0.54607964,0.0007711042,0.00006034217,0.000038088045,0.00031702055,0.00034142606,0.0051840334],"genre_scores_gemma":[0.98664016,0.0005784979,0.011353829,0.00005773997,0.0000622978,0.000019496878,0.00015810096,0.000029175259,0.0011005993],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9994797,0.00022582336,0.000034172546,0.00008814405,0.00011774298,0.00005445364],"domain_scores_gemma":[0.98936975,0.00683202,0.0019951556,0.0009817193,0.00046449134,0.0003569698],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032059024,0.0005184161,0.0005626538,0.0012392139,0.00023179357,0.001464416,0.0007366556,0.0008587025,0.0014323327],"category_scores_gemma":[0.023100968,0.00029292706,0.0004207252,0.0009988125,0.0013735448,0.00349965,0.0013183804,0.0010240728,0.00017529371],"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":[0.00026317275,0.00016404175,0.114644125,0.00014037672,0.0003362846,0.00058759336,0.00021609559,0.40560558,0.0036873491,0.37639594,0.0012835976,0.09667586],"study_design_scores_gemma":[0.000017525668,0.00007127185,0.03258327,0.000046553047,0.000033725373,0.00027949348,0.00005166297,0.6441874,0.000980254,0.32071066,0.0009742182,0.00006392851],"about_ca_topic_score_codex":0.001440056,"about_ca_topic_score_gemma":0.0014434782,"teacher_disagreement_score":0.0032059024,"about_ca_system_score_codex":0.0005975315,"about_ca_system_score_gemma":0.00043993583,"threshold_uncertainty_score":0.0169546},"labels":[],"label_agreement":null},{"id":"W3123979795","doi":"10.1093/jjfinec/nbh004","title":"Backtesting Value-at-Risk: A Duration-Based Approach","year":2004,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","cited_by":404,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Duration (music); Monte Carlo method; Econometrics; Value (mathematics); Sample (material); Market risk; Key (lock); Risk management; Computer science; Economics; Statistics; Mathematics; Finance","score_opus":0.03617379821538192,"score_gpt":0.21335118629440572,"score_spread":0.1771773880790238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123979795","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":[0.1769257,0.00070925674,0.8156784,0.0006124718,0.00014821274,0.0003521736,0.00043155672,0.00066470535,0.004477503],"genre_scores_gemma":[0.87792397,0.00024640776,0.11843434,0.0003274496,0.00015433543,0.00053071685,0.00070764095,0.000175058,0.001500066],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9549764,0.035179608,0.0014406481,0.003449047,0.004191623,0.0007625866],"domain_scores_gemma":[0.50056344,0.45953837,0.011609979,0.020902786,0.00505866,0.002326787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.048562497,0.0012895106,0.0025632456,0.0037567574,0.00064402126,0.002656026,0.0035029142,0.002771796,0.006190144],"category_scores_gemma":[0.2647948,0.0005943077,0.0017833547,0.0023279064,0.0030990986,0.006077923,0.003158519,0.0039383597,0.00064583414],"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":[0.004049175,0.001345138,0.07801404,0.0004557716,0.002387968,0.0010118252,0.0010365546,0.44668972,0.0035769471,0.13428646,0.0043041874,0.32284227],"study_design_scores_gemma":[0.00022289068,0.0015735883,0.011025589,0.000078316494,0.0001941482,0.00027343875,0.00012331187,0.8799754,0.0017170085,0.10331818,0.0013817685,0.00011651608],"about_ca_topic_score_codex":0.0013021468,"about_ca_topic_score_gemma":0.0009038446,"teacher_disagreement_score":0.048562497,"about_ca_system_score_codex":0.0010775838,"about_ca_system_score_gemma":0.0010962443,"threshold_uncertainty_score":0.2568261},"labels":[],"label_agreement":null},{"id":"W3125553128","doi":"10.1093/jjfinec/nbx022","title":"Non-affine GARCH Option Pricing Models, Variance-Dependent Kernels, and Diffusion Limits*","year":2017,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":35,"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; Université du Québec à Montréal","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Variance (accounting); Affine transformation; Econometrics; Economics; Valuation of options; Mathematics; Diffusion; Financial economics; Physics; Volatility (finance); Geometry; Accounting","score_opus":0.05192064071237137,"score_gpt":0.24425052427344912,"score_spread":0.19232988356107775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125553128","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":[0.29574725,0.0020000408,0.6938593,0.0013335353,0.00012778451,0.00003404691,0.00008306513,0.00018182812,0.006633203],"genre_scores_gemma":[0.9817649,0.0006940392,0.013717342,0.00006926127,0.00010047265,0.000021117934,0.000051697785,0.00003549672,0.0035456934],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9992787,0.00031179588,0.000048058064,0.0001257238,0.00014126532,0.00009446932],"domain_scores_gemma":[0.9953229,0.0024602287,0.0011165183,0.00037149477,0.0004206914,0.00030821265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002320538,0.0009328363,0.0010195563,0.0009616608,0.00055556756,0.0023178072,0.0014838115,0.0019560757,0.0013558283],"category_scores_gemma":[0.012862524,0.0005795648,0.0010781249,0.001020232,0.0022813147,0.004680425,0.0014127176,0.002040037,0.00020203984],"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":[0.000053183314,0.00008309291,0.0029575536,0.00010200067,0.00007942782,0.0005593617,0.00020562927,0.16236788,0.003732853,0.82092917,0.00059046946,0.008339446],"study_design_scores_gemma":[0.000009756494,0.000025584759,0.00074451,0.000007993605,0.000015701324,0.0001207713,0.000030518382,0.77005297,0.000495525,0.228109,0.00036402795,0.000023634382],"about_ca_topic_score_codex":0.002755875,"about_ca_topic_score_gemma":0.00126061,"teacher_disagreement_score":0.002755875,"about_ca_system_score_codex":0.0009820065,"about_ca_system_score_gemma":0.00067881256,"threshold_uncertainty_score":0.012272358},"labels":[],"label_agreement":null},{"id":"W3125989840","doi":"10.1093/jjfinec/nbaa032","title":"Selective Linear Segmentation for Detecting Relevant Parameter Changes","year":2020,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université Laval","funders":"","keywords":"Segmentation; Econometrics; Computer science; Economics; Mathematics; Artificial intelligence","score_opus":0.05229557163017287,"score_gpt":0.2510993745318345,"score_spread":0.19880380290166164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125989840","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":[0.12290002,0.00022256136,0.8740593,0.00016755844,0.00001713947,0.000048370774,0.00016923134,0.0010900901,0.0013257322],"genre_scores_gemma":[0.8433222,0.00009105156,0.15494457,0.00008488541,0.000038971222,0.00007790065,0.00043612666,0.00017602324,0.00082820334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99903035,0.00035110972,0.00005337824,0.00026131453,0.00019379915,0.000110159206],"domain_scores_gemma":[0.99286324,0.004823623,0.00092360785,0.00074103364,0.0004712934,0.00017724444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022684527,0.00061317964,0.00081954035,0.0025874528,0.0004653687,0.0010307049,0.0010226254,0.0012700133,0.002002752],"category_scores_gemma":[0.012321596,0.00051762833,0.0006548302,0.001516763,0.0007728681,0.0012513483,0.001228536,0.0011939405,0.0005529138],"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":[0.00096876913,0.00027990295,0.037374564,0.00024741347,0.0003298763,0.0005591105,0.00058930035,0.45649293,0.042798698,0.023284238,0.0030235217,0.4340517],"study_design_scores_gemma":[0.000009108146,0.000035737863,0.0041422504,0.000008935966,0.00001554362,0.00006253424,0.000030914947,0.9830526,0.004801652,0.007243827,0.00057953486,0.00001730103],"about_ca_topic_score_codex":0.0037495345,"about_ca_topic_score_gemma":0.0037930897,"teacher_disagreement_score":0.0037495345,"about_ca_system_score_codex":0.00068833376,"about_ca_system_score_gemma":0.0007637114,"threshold_uncertainty_score":0.011996865},"labels":[],"label_agreement":null},{"id":"W3136715134","doi":"10.1093/jjfinec/nbaa029","title":"Regulatory Capital and Incentives for Risk Model Choice under Basel 3*","year":2020,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Banking stability, regulation, efficiency","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":"Western University","funders":"","keywords":"Capital requirement; Basel III; Basel II; Risk-adjusted return on capital; Risk-weighted asset; Capital adequacy ratio; Portfolio; Economics; Incentive; Capital (architecture); Basel I; Bank regulation; Economic capital; Market liquidity; Business; Actuarial science; Financial economics; Finance; Financial capital; Microeconomics; Capital formation","score_opus":0.038666958504906035,"score_gpt":0.22788507912316544,"score_spread":0.1892181206182594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136715134","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":[0.8376092,0.00047444188,0.12966198,0.0031528794,0.00006916304,0.00013802714,0.00022269263,0.00028307343,0.028388591],"genre_scores_gemma":[0.99569917,0.00004026177,0.0036293417,0.000096460535,0.000012448878,0.000026432834,0.000033976507,0.000011747309,0.0004500703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9930011,0.0049266345,0.00031760425,0.0005630494,0.00063076644,0.0005608719],"domain_scores_gemma":[0.8385528,0.13381542,0.017794127,0.005714044,0.0025437125,0.0015799257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.021617863,0.00045582853,0.0008275692,0.00054634985,0.00059634075,0.002772546,0.00076864223,0.0015028979,0.0050511598],"category_scores_gemma":[0.09517975,0.0004136807,0.00053133187,0.00034390323,0.0015750567,0.0023876978,0.0016495326,0.0019995254,0.00029674795],"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":[0.0014097746,0.00056894566,0.029902093,0.0001686289,0.00011379217,0.00027581482,0.0006018653,0.6023239,0.002197143,0.33113375,0.0038465394,0.027457844],"study_design_scores_gemma":[0.00022654975,0.0003659183,0.010519202,0.00011044828,0.000044392233,0.000091638925,0.00024115473,0.7438479,0.0015572088,0.241112,0.0017950082,0.00008866563],"about_ca_topic_score_codex":0.0021094268,"about_ca_topic_score_gemma":0.0015317576,"teacher_disagreement_score":0.021617863,"about_ca_system_score_codex":0.0014199081,"about_ca_system_score_gemma":0.0016122506,"threshold_uncertainty_score":0.11432755},"labels":[],"label_agreement":null},{"id":"W3174445774","doi":"10.1093/jjfinec/nbz038","title":"On Frequent Batch Auctions for Stocks","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Common value auction; Matching (statistics); Stock (firearms); Economics; Limit (mathematics); Stock market; Order (exchange); Econometrics; Microeconomics; Financial economics; Mathematics; Statistics; Finance; Engineering","score_opus":0.11350207204799134,"score_gpt":0.36872322371665806,"score_spread":0.2552211516686667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174445774","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":[0.45760944,0.0026830237,0.5100855,0.0021515856,0.0006970951,0.00044572685,0.0006046855,0.00059631816,0.025126675],"genre_scores_gemma":[0.95686233,0.0006960965,0.032655194,0.00026016263,0.00029704478,0.00019709421,0.0002115333,0.00009816202,0.00872236],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9953701,0.0022234055,0.00025956304,0.00046290434,0.0009475368,0.0007365042],"domain_scores_gemma":[0.911117,0.0747459,0.004784688,0.0046406775,0.0033325285,0.0013792746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009677004,0.0012002867,0.0029877722,0.0010573474,0.0008444895,0.0026042874,0.0031395047,0.0019557283,0.013996485],"category_scores_gemma":[0.051952813,0.0007149343,0.0015128108,0.0010658315,0.0021473363,0.0066143214,0.0015441916,0.0035487064,0.0009400894],"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":[0.0042392514,0.0015055954,0.004253777,0.00050010334,0.00027200353,0.0008286312,0.0002803321,0.5188035,0.0058961036,0.39875928,0.0100533245,0.05460805],"study_design_scores_gemma":[0.0002766787,0.0004372413,0.000775965,0.000029899227,0.00004533205,0.00016306103,0.00006932673,0.8781742,0.0009846847,0.11782692,0.0011468648,0.0000697958],"about_ca_topic_score_codex":0.004047362,"about_ca_topic_score_gemma":0.0020846874,"teacher_disagreement_score":0.013996485,"about_ca_system_score_codex":0.0018321519,"about_ca_system_score_gemma":0.0013901931,"threshold_uncertainty_score":0.0511775},"labels":[],"label_agreement":null},{"id":"W3199266254","doi":"10.1093/jjfinec/nbad031","title":"Composite Likelihood for Stochastic Migration Model with Unobserved Factor","year":2023,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Credit Risk and Financial Regulations","field":"Economics, Econometrics and Finance","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 Toronto; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Econometrics; Mathematics; Probit; Probit model; Likelihood function; Ordered probit; Consistency (knowledge bases); Credit risk; Asymptotic distribution; Statistics; Economics; Applied mathematics; Actuarial science; Maximum likelihood","score_opus":0.06773781870515753,"score_gpt":0.2387599105451792,"score_spread":0.17102209184002165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199266254","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":[0.039158214,0.00016037085,0.9586023,0.0003007117,0.000019220171,0.00004784354,0.00016500159,0.00025155925,0.0012947419],"genre_scores_gemma":[0.822628,0.00031656038,0.17007211,0.00017789294,0.00008424628,0.00026725224,0.0007618672,0.00015815684,0.005533945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972408,0.001608179,0.0000853685,0.00044803365,0.00039975843,0.00021783939],"domain_scores_gemma":[0.9805213,0.014828652,0.0021744177,0.0009945682,0.001092606,0.00038838302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007877747,0.00064071955,0.0012067546,0.0012746213,0.00044801485,0.001718199,0.0022134793,0.0012177264,0.0057803094],"category_scores_gemma":[0.029280446,0.00044656004,0.0011850281,0.0015387344,0.0021403236,0.0026788064,0.0021648596,0.0021436042,0.00062431284],"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":[0.00020882954,0.000068847534,0.0071558445,0.0001606997,0.000065721164,0.00030127054,0.00025878876,0.7023317,0.0007789958,0.258984,0.0021004572,0.027584825],"study_design_scores_gemma":[0.000012635781,0.000015352576,0.0007964296,0.000012084543,0.0000061407522,0.00004342707,0.000017068092,0.95523953,0.00017497505,0.043227285,0.00044044634,0.000014725575],"about_ca_topic_score_codex":0.0059513906,"about_ca_topic_score_gemma":0.003363085,"teacher_disagreement_score":0.007877747,"about_ca_system_score_codex":0.0015493187,"about_ca_system_score_gemma":0.0013679477,"threshold_uncertainty_score":0.041662037},"labels":[],"label_agreement":null},{"id":"W3206747317","doi":"10.1093/jjfinec/nbab007","title":"Intraday Market Predictability: A Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Predictability; Sharpe ratio; Economics; Transaction cost; Econometrics; Volatility (finance); Equity (law); Financial economics; Market liquidity; Capital asset pricing model; Market timing; Monetary economics; Finance; Mathematics; Statistics","score_opus":0.0317867250835095,"score_gpt":0.19834934554048536,"score_spread":0.16656262045697587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206747317","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":[0.6513758,0.002199646,0.33794653,0.0016692679,0.00008664305,0.00004718313,0.0011894712,0.00079233974,0.0046930523],"genre_scores_gemma":[0.98969704,0.00025087455,0.008836897,0.00003665656,0.0000939839,0.00001699578,0.0004209733,0.000024381252,0.000622082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994585,0.0002020269,0.000044380733,0.00014205587,0.00008287397,0.00007012753],"domain_scores_gemma":[0.9961069,0.0027944485,0.00044535715,0.00032269178,0.00026280983,0.0000677513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017670055,0.00053912826,0.0006812519,0.0019720362,0.0003267325,0.0013110414,0.0006020482,0.000626958,0.0011617607],"category_scores_gemma":[0.006736529,0.00026916305,0.00054202584,0.0015289525,0.0003646069,0.0012206656,0.0006527699,0.0011839664,0.000252467],"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":[0.00020159713,0.00021658742,0.09213314,0.000070094626,0.0005309994,0.00033226403,0.00012188528,0.7235832,0.0015979258,0.01316653,0.002384876,0.1656609],"study_design_scores_gemma":[0.0000017529608,0.000013538592,0.0048938277,0.000004699445,0.000009760534,0.00001453137,0.000008549691,0.990017,0.000108037166,0.004771997,0.00015138916,0.0000048519196],"about_ca_topic_score_codex":0.0033006272,"about_ca_topic_score_gemma":0.0025370521,"teacher_disagreement_score":0.0033006272,"about_ca_system_score_codex":0.00052341813,"about_ca_system_score_gemma":0.00042740078,"threshold_uncertainty_score":0.009344935},"labels":[],"label_agreement":null},{"id":"W4241228865","doi":"10.1093/jjfinec/nbz037","title":"Does High-Frequency Social Media Data Improve Forecasts of Low-Frequency Consumer Confidence Measures?","year":2019,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":13,"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":"Estimator; Consumer confidence index; Discounting; Computer science; Econometrics; Index (typography); Social media; Measure (data warehouse); Sample (material); Sampling (signal processing); Sentiment analysis; Statistics; Data mining; Machine learning; Economics; Finance; Mathematics; Marketing; Business","score_opus":0.1485670598544888,"score_gpt":0.3442444046208788,"score_spread":0.19567734476639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4241228865","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":[0.91071135,0.0005335387,0.08037005,0.002403858,0.00024243514,0.000034586017,0.00090616936,0.00037848463,0.004419582],"genre_scores_gemma":[0.99303085,0.000057542242,0.0063234624,0.000057746132,0.00006830971,0.000004887891,0.00024287676,0.0000107955675,0.00020366262],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990854,0.0004979292,0.000054756423,0.00014704296,0.00013121161,0.00008350679],"domain_scores_gemma":[0.97899365,0.0156378,0.001696389,0.0015408495,0.0017469072,0.00038431736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005222417,0.0005945204,0.0004728659,0.0008064616,0.0002848708,0.0016808178,0.0006128504,0.000986314,0.0017898934],"category_scores_gemma":[0.033740938,0.00022885816,0.00045136354,0.00069992023,0.0002660354,0.0024687662,0.0006144034,0.0011375638,0.0005494067],"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":[0.0022744446,0.0006575686,0.4112561,0.00017714412,0.0006252054,0.00017137906,0.00025910238,0.35851967,0.0049838684,0.007666562,0.0064083487,0.20700072],"study_design_scores_gemma":[0.00003279256,0.0001293405,0.024963016,0.000031848907,0.00004778744,0.000016544658,0.00012550488,0.9673162,0.0021384445,0.0044634794,0.0007100944,0.000024908062],"about_ca_topic_score_codex":0.0070835724,"about_ca_topic_score_gemma":0.006787093,"teacher_disagreement_score":0.0070835724,"about_ca_system_score_codex":0.0003935195,"about_ca_system_score_gemma":0.0004581965,"threshold_uncertainty_score":0.027619064},"labels":[],"label_agreement":null},{"id":"W4281944475","doi":"10.1093/jjfinec/nbac016","title":"Time Variation in Cash Flows and Discount Rates","year":2022,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","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":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Conditional variance; Economics; Econometrics; Portfolio; Variance (accounting); Cash flow; Market portfolio; Variance decomposition of forecast errors; Benchmark (surveying); Conditional expectation; Asset (computer security); Financial economics; Capital asset pricing model; Autoregressive conditional heteroskedasticity; Finance; Computer science; Volatility (finance)","score_opus":0.022555540447478564,"score_gpt":0.20387999468911683,"score_spread":0.18132445424163826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281944475","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":[0.823015,0.0023832978,0.16511449,0.0009743469,0.00017002645,0.000040027233,0.0010060539,0.00015647909,0.0071402825],"genre_scores_gemma":[0.99474674,0.00043743395,0.002701567,0.000027670098,0.000063629785,0.000008588079,0.00026405323,0.000017440363,0.0017327084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994968,0.00012360595,0.000035080342,0.00014574968,0.00011769549,0.00008101306],"domain_scores_gemma":[0.990944,0.005152599,0.0025508613,0.00045953857,0.00059560087,0.0002972473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002909791,0.00044498697,0.00042796077,0.0010534935,0.00018733637,0.001703226,0.0005740268,0.0008486834,0.0023331493],"category_scores_gemma":[0.018960284,0.00030156825,0.00053551886,0.00092726527,0.00078433665,0.001717034,0.00052668236,0.0013364155,0.00017594459],"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":[0.0004634547,0.000121506906,0.110874474,0.00015538117,0.0004985418,0.0008689709,0.00044303676,0.4294732,0.0065172263,0.40257215,0.0021009734,0.045911018],"study_design_scores_gemma":[0.0000246793,0.00008539629,0.07609297,0.000055245942,0.00010022809,0.00022928897,0.00010852277,0.7626174,0.0018830701,0.15565492,0.0030387356,0.000109463675],"about_ca_topic_score_codex":0.003209714,"about_ca_topic_score_gemma":0.0016638372,"teacher_disagreement_score":0.003209714,"about_ca_system_score_codex":0.000781467,"about_ca_system_score_gemma":0.0003656965,"threshold_uncertainty_score":0.015388668},"labels":[],"label_agreement":null},{"id":"W4377822642","doi":"10.1093/jjfinec/nbad015","title":"Test for Trading Costs Effect in a Portfolio Selection Problem with Recursive Utility","year":2023,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","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":"Université de Montréal","funders":"","keywords":"Transaction cost; Portfolio; Selection (genetic algorithm); Econometrics; Test (biology); Economics; Trading strategy; Generalized method of moments; Sample (material); Empirical research; Financial economics; Microeconomics; Computer science; Panel data; Mathematics; Statistics","score_opus":0.03161666346759601,"score_gpt":0.23207636710651766,"score_spread":0.20045970363892165,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377822642","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.75239795,0.00023815795,0.24498303,0.0008203089,0.000035193254,0.00011463378,0.00021146763,0.00023056725,0.00096866826],"genre_scores_gemma":[0.97944754,0.000072712115,0.019378906,0.00007095671,0.000036127058,0.00006625698,0.00027308744,0.000027163433,0.0006272021],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98997515,0.0073821144,0.0004200604,0.0009964105,0.0007059588,0.0005203056],"domain_scores_gemma":[0.82990456,0.15800536,0.0059843822,0.0031722705,0.0018392585,0.0010941274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015592004,0.00088763115,0.0021738396,0.0015263763,0.0004986309,0.001992246,0.0018429004,0.001900447,0.002817078],"category_scores_gemma":[0.095044754,0.000552977,0.0009448769,0.0014167015,0.0014977398,0.0021725448,0.0015478146,0.0020695382,0.00017624382],"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":[0.0012795797,0.0007222441,0.23672228,0.00026770312,0.0015008243,0.0014452792,0.00034211125,0.58236206,0.0031705347,0.09026673,0.001864231,0.08005648],"study_design_scores_gemma":[0.00009696822,0.00024316556,0.015430578,0.000012705654,0.00009013874,0.00008178442,0.000067846675,0.96223634,0.00092286745,0.020607276,0.00018273883,0.00002764377],"about_ca_topic_score_codex":0.0042777555,"about_ca_topic_score_gemma":0.0021732769,"teacher_disagreement_score":0.015592004,"about_ca_system_score_codex":0.00093680294,"about_ca_system_score_gemma":0.0015242209,"threshold_uncertainty_score":0.08245939},"labels":[],"label_agreement":null},{"id":"W4388261895","doi":"10.1093/jjfinec/nbad027","title":"SGMM: Stochastic Approximation to Generalized Method of Moments","year":2023,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Statistical Methods and Inference","field":"Mathematics","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":"McMaster University","funders":"","keywords":"Computer science; Inference; Moment (physics); Scalability; Generalized method of moments; Convergence (economics); Estimator; Applied mathematics; Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Statistics","score_opus":0.16166920770939033,"score_gpt":0.4023868967911994,"score_spread":0.24071768908180904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388261895","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":[0.0010123686,0.00013704279,0.9981312,0.00011741336,0.000039341077,0.000022596678,0.000048840906,0.00022454264,0.00026670084],"genre_scores_gemma":[0.13209954,0.0006412109,0.8630046,0.000362791,0.00030868087,0.0004406161,0.0005570318,0.0004260787,0.002159445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99635726,0.0023416672,0.00012276761,0.00033384303,0.00068768766,0.00015670607],"domain_scores_gemma":[0.9924737,0.005153277,0.0006329822,0.00094593945,0.0006592097,0.00013485634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0052780984,0.0010588786,0.0014981912,0.0014526,0.00044688734,0.0013854501,0.00253877,0.0015689931,0.0035527656],"category_scores_gemma":[0.022169715,0.00066954887,0.0015897736,0.0018353132,0.0013794847,0.0015443579,0.0022999789,0.0027004608,0.0011357324],"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":[0.00012807237,0.00006452953,0.0016443884,0.0002400535,0.00020041865,0.00018526398,0.00011091353,0.61938494,0.001982812,0.24562672,0.0084161535,0.12201563],"study_design_scores_gemma":[0.000014309152,0.00001981592,0.0001881754,0.00002125634,0.000007678783,0.00003813911,0.00000798202,0.9422111,0.00041544728,0.05434387,0.0027171774,0.0000151106815],"about_ca_topic_score_codex":0.0038050925,"about_ca_topic_score_gemma":0.0029796795,"teacher_disagreement_score":0.0052780984,"about_ca_system_score_codex":0.0010537223,"about_ca_system_score_gemma":0.0024615417,"threshold_uncertainty_score":0.02791357},"labels":[],"label_agreement":null},{"id":"W4391743702","doi":"10.1093/jjfinec/nbag006","title":"Multi-Factor Timing with Deep Learning","year":2024,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","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":"Western University; University of Guelph; University of Waterloo","funders":"","keywords":"Leverage (statistics); Artificial neural network; Computer science; Artificial intelligence; Deep learning; Profitability index; Machine learning; Task (project management); Deep neural networks; Factor (programming language); Recurrent neural network; Economics; Finance","score_opus":0.055283575377798905,"score_gpt":0.232074640129933,"score_spread":0.1767910647521341,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391743702","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":[0.07547735,0.00087566563,0.91709274,0.00077425566,0.00013042895,0.000023072102,0.00049318426,0.0011737323,0.0039596097],"genre_scores_gemma":[0.9448411,0.00031694068,0.048272725,0.00016009777,0.00009297952,0.00003997001,0.00053341937,0.00009717684,0.0056455387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997563,0.00005159109,0.00001451454,0.00007608906,0.000046652338,0.000054917644],"domain_scores_gemma":[0.99904364,0.00046919548,0.0001654856,0.00011591737,0.00013343127,0.00007229269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008814155,0.0007938115,0.0006885953,0.00045033408,0.00023343056,0.00096678094,0.0009929399,0.0010066609,0.0041125105],"category_scores_gemma":[0.004251435,0.00046463072,0.00051739725,0.00059459155,0.00058554026,0.0017554967,0.0010437956,0.001575494,0.0006319492],"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":[0.00012331686,0.00004409091,0.0032661585,0.00004110618,0.0000588353,0.000090788635,0.000021248889,0.90946895,0.0011760924,0.021315875,0.00203196,0.06236149],"study_design_scores_gemma":[0.00000274392,0.000006833927,0.00012872509,0.0000036705808,0.0000028302172,0.000005110644,0.0000011444843,0.99221694,0.00021796992,0.0072044455,0.00020695987,0.000002643351],"about_ca_topic_score_codex":0.0075102686,"about_ca_topic_score_gemma":0.0076774037,"teacher_disagreement_score":0.0075102686,"about_ca_system_score_codex":0.0008340271,"about_ca_system_score_gemma":0.0009577186,"threshold_uncertainty_score":0.014933109},"labels":[],"label_agreement":null},{"id":"W4394618432","doi":"10.1093/jjfinec/nbae004","title":"Factor IV Estimation in Conditional Moment Models with an Application to Inflation Dynamics","year":2024,"lang":"en","type":"article","venue":"Journal of Financial 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":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council; Social Sciences and Humanities Research Council of Canada","keywords":"Estimation; Econometrics; Inflation (cosmology); Moment (physics); Economics; Dynamics (music); Dynamic factor; Mathematics; Physics","score_opus":0.06869273887562927,"score_gpt":0.24646382003162864,"score_spread":0.17777108115599938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394618432","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":[0.01037735,0.00015084627,0.98832184,0.00017286747,0.000023693312,0.000020342799,0.00009224153,0.00024247842,0.0005983094],"genre_scores_gemma":[0.6204123,0.00069604075,0.3739426,0.0001761806,0.00018375246,0.00019398266,0.00087449467,0.00024853766,0.0032721015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99826306,0.0010903426,0.000080287056,0.00021876549,0.00021020463,0.00013734444],"domain_scores_gemma":[0.9866253,0.010362408,0.0011582281,0.00091645936,0.0007544512,0.00018306417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004794268,0.0006167643,0.0010678987,0.0015085118,0.0003165588,0.0010575595,0.0011760612,0.00068399304,0.003962161],"category_scores_gemma":[0.02839402,0.0005709545,0.0011967933,0.0017177836,0.000981998,0.0011768064,0.001516547,0.0018781242,0.00036551143],"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":[0.00010199151,0.000066361696,0.0071702325,0.00014843316,0.00025191373,0.0001509499,0.00013912376,0.60028213,0.0010166537,0.3065924,0.0027334015,0.08134633],"study_design_scores_gemma":[0.000009981738,0.000020905398,0.0005883828,0.000020342557,0.00001759696,0.000020978054,0.000011820622,0.9414167,0.00033663507,0.05639676,0.001146733,0.000013242104],"about_ca_topic_score_codex":0.009667508,"about_ca_topic_score_gemma":0.0064491564,"teacher_disagreement_score":0.009667508,"about_ca_system_score_codex":0.00081843336,"about_ca_system_score_gemma":0.0018263151,"threshold_uncertainty_score":0.025354803},"labels":[],"label_agreement":null},{"id":"W4404205068","doi":"10.1093/jjfinec/nbae020","title":"Bootstrap Inference for Group Factor Models","year":2024,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; McGill University; Center for Interuniversity Research and Analysis on Organizations","funders":"Social Sciences and Humanities Research Council","keywords":"Inference; Factor (programming language); Group (periodic table); Econometrics; Factor analysis; Statistics; Mathematics; Computer science; Artificial intelligence","score_opus":0.29704886991005247,"score_gpt":0.4118689479299451,"score_spread":0.11482007801989264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404205068","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":[0.028075436,0.0003016693,0.9689182,0.00033637343,0.000085925414,0.00009219963,0.00014503904,0.00020456279,0.0018405525],"genre_scores_gemma":[0.6768402,0.00038329457,0.31910953,0.00037368803,0.00023053319,0.0006855053,0.00081179023,0.00016025013,0.0014051957],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9633521,0.03142287,0.00057444355,0.0020165753,0.0021136259,0.00052034744],"domain_scores_gemma":[0.8229961,0.14607373,0.005095334,0.019502781,0.0052200877,0.0011119731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.038181566,0.0009972308,0.0022236186,0.0024077098,0.0013134229,0.0018749037,0.0023033635,0.0017598331,0.007300998],"category_scores_gemma":[0.26816806,0.00059809303,0.0016274103,0.0025567976,0.0039355643,0.0030616347,0.0029648382,0.0031118887,0.001044271],"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":[0.0006485727,0.0003016297,0.020400064,0.0003379478,0.0007940896,0.00038798488,0.0008554672,0.08609315,0.0010654551,0.7272429,0.0064218994,0.15545094],"study_design_scores_gemma":[0.0001312244,0.00013152671,0.00212173,0.00008056178,0.000056958972,0.000073056115,0.00014497263,0.2891219,0.00060377736,0.7051953,0.0023096574,0.000029233137],"about_ca_topic_score_codex":0.0021694202,"about_ca_topic_score_gemma":0.0017389199,"teacher_disagreement_score":0.038181566,"about_ca_system_score_codex":0.000885856,"about_ca_system_score_gemma":0.0013905824,"threshold_uncertainty_score":0.20192581},"labels":[],"label_agreement":null},{"id":"W4405388634","doi":"10.1093/jjfinec/nbae029","title":"Identifying and Exploiting Alpha in Linear Asset Pricing Models with Strong, Semi-Strong, and Latent Factors","year":2024,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","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":"Trinity College","funders":"","keywords":"Estimator; Sharpe ratio; Econometrics; Factor analysis; Mathematics; Capital asset pricing model; Systematic risk; Sample size determination; Contrast (vision); Monte Carlo method; Statistics; Zero (linguistics); Applied mathematics; Economics; Computer science; Financial economics","score_opus":0.08111063773399309,"score_gpt":0.24822250351453382,"score_spread":0.1671118657805407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405388634","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":[0.24578927,0.00027115794,0.75249547,0.0002720568,0.000016670248,0.000037131336,0.00004388739,0.00014761399,0.0009267457],"genre_scores_gemma":[0.95564264,0.00017247675,0.04321388,0.000033856384,0.000036918635,0.000036271264,0.000052856278,0.000017484692,0.0007937013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707806,0.0020446002,0.00012882859,0.0002841722,0.0002801058,0.00018415622],"domain_scores_gemma":[0.93862575,0.05386925,0.003642103,0.002228549,0.001136598,0.00049770105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.015139492,0.0009063943,0.0010234996,0.0010767104,0.00040539095,0.0020582215,0.0010464854,0.0010817508,0.0010017377],"category_scores_gemma":[0.05566494,0.0009835871,0.0008847826,0.0009567906,0.0019020394,0.0024152948,0.0019828782,0.0016824917,0.00021926784],"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":[0.00028381374,0.0001798351,0.04139286,0.00012085435,0.00033041806,0.00045833486,0.00041650402,0.7904107,0.002327722,0.108578466,0.0005595995,0.054940876],"study_design_scores_gemma":[0.00001252431,0.000045582,0.0013689222,0.000008899412,0.000018868628,0.000036605637,0.000021504164,0.9699313,0.00029389106,0.028160444,0.000091077236,0.000010367977],"about_ca_topic_score_codex":0.002717318,"about_ca_topic_score_gemma":0.001997535,"teacher_disagreement_score":0.015139492,"about_ca_system_score_codex":0.0006686721,"about_ca_system_score_gemma":0.0010207279,"threshold_uncertainty_score":0.08006626},"labels":[],"label_agreement":null},{"id":"W4406380533","doi":"10.1093/jjfinec/nbae033","title":"An Information-Theoretic Asset Pricing Model","year":2025,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","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":"McGill University","funders":"Economic and Social Research Council","keywords":"Stochastic discount factor; Capital asset pricing model; Econometrics; Sharpe ratio; Economics; Risk premium; Equity (law); Kurtosis; Benchmark (surveying); Financial economics; Actuarial science; Statistics; Mathematics; Portfolio","score_opus":0.02143863437246926,"score_gpt":0.22549787299367935,"score_spread":0.2040592386212101,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406380533","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":[0.09685706,0.0008380789,0.8735379,0.0034483236,0.00012369854,0.00008271033,0.00097084366,0.0002821891,0.023859097],"genre_scores_gemma":[0.9532804,0.0006202039,0.030787617,0.00025927697,0.00019341268,0.00011358006,0.00035887596,0.000043295946,0.01434325],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.998833,0.00048448832,0.000049722057,0.0002063345,0.00027538184,0.00015105605],"domain_scores_gemma":[0.9946667,0.0037299427,0.00054430816,0.0003414707,0.0005017729,0.0002157979],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032594798,0.0007515187,0.0015218062,0.0012399867,0.0004578053,0.002829225,0.0022846446,0.0026607888,0.005157788],"category_scores_gemma":[0.009935086,0.00052503904,0.0011082481,0.0013947809,0.0019751848,0.0032699257,0.0011736858,0.0018419167,0.00082043116],"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":[0.00005271749,0.000054258704,0.00082613423,0.000053111147,0.000051138148,0.00024285488,0.00011369073,0.4221355,0.00044485577,0.5665187,0.0019411684,0.0075658164],"study_design_scores_gemma":[0.000015593278,0.000019867033,0.00020801151,0.000009412774,0.0000116132305,0.000066588225,0.0000109566545,0.85386986,0.000058259182,0.14518574,0.0005284571,0.000015597263],"about_ca_topic_score_codex":0.0038794444,"about_ca_topic_score_gemma":0.0016049034,"teacher_disagreement_score":0.005157788,"about_ca_system_score_codex":0.0014775462,"about_ca_system_score_gemma":0.0010064596,"threshold_uncertainty_score":0.017254531},"labels":[],"label_agreement":null},{"id":"W4408343059","doi":"10.1093/jjfinec/nbaf006","title":"Accounting for Changes in Long-Term Interest Rates: Evidence from Canada","year":2025,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Term (time); Economics; Accounting; Interest rate; Monetary economics","score_opus":0.15586601343745052,"score_gpt":0.27539560824375714,"score_spread":0.11952959480630662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408343059","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":[0.95064676,0.0047179954,0.0013236406,0.004520617,0.000074692005,0.000050066563,0.011881927,0.00011186441,0.026672492],"genre_scores_gemma":[0.99147826,0.0018672392,0.0005045673,0.00019139182,0.000014420425,0.0000059964805,0.0034992462,0.00001915053,0.0024197598],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9988431,0.000095764895,0.000048496862,0.00015252324,0.00057472097,0.00028535555],"domain_scores_gemma":[0.9899595,0.0011218335,0.0013126285,0.00050966215,0.0063341777,0.0007621689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002171216,0.00039373635,0.0004149078,0.0014741122,0.0018162557,0.0022155063,0.0015321027,0.00048820022,0.0022633593],"category_scores_gemma":[0.010174311,0.00023489652,0.00057782925,0.004452254,0.00075357145,0.00070122484,0.00082028646,0.0009854476,0.00028454317],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047689726,0.00009931925,0.9192437,0.0002415011,0.00037668535,0.00033035423,0.001862349,0.009778333,0.00071024994,0.008529871,0.013758088,0.044592638],"study_design_scores_gemma":[0.00003942961,0.00005438386,0.9627401,0.00012319282,0.0002764552,0.00007315887,0.002252179,0.010273624,0.00088347716,0.0009810933,0.022236316,0.00006657254],"about_ca_topic_score_codex":0.99804354,"about_ca_topic_score_gemma":0.99779177,"teacher_disagreement_score":0.033488914,"about_ca_system_score_codex":0.033488914,"about_ca_system_score_gemma":0.04771266,"threshold_uncertainty_score":0.24298024},"labels":[],"label_agreement":null},{"id":"W4416291642","doi":"10.1093/jjfinec/nbaf019","title":"Efficient Pricing and Model Calibration With Large Panels of Options","year":2025,"lang":"en","type":"article","venue":"Journal of Financial Econometrics","topic":"Stochastic processes and financial applications","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Valuation of options; Volatility (finance); Calibration; Finite difference methods for option pricing; Trinomial tree; Binomial options pricing model; Stochastic volatility","score_opus":0.0232005355723053,"score_gpt":0.224411515390857,"score_spread":0.20121097981855168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416291642","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":[0.07865988,0.00007981329,0.91847557,0.00019997127,0.000030853393,0.000043610584,0.00021638903,0.00053597934,0.001757929],"genre_scores_gemma":[0.8636264,0.00007420646,0.13405618,0.00012231275,0.000047783113,0.00016540205,0.0006420662,0.00017295116,0.0010927712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976368,0.0014912464,0.000079331556,0.00036797574,0.00030063133,0.00012395935],"domain_scores_gemma":[0.9883284,0.0075982297,0.00087867235,0.0023222768,0.000612287,0.00026016738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004765689,0.0005334342,0.001141999,0.0008729376,0.00050123135,0.0015837053,0.0015848133,0.0020694388,0.0032560907],"category_scores_gemma":[0.02393476,0.0009346032,0.0010402799,0.0009357375,0.00090518687,0.0019430555,0.0016559007,0.0028968803,0.00057833863],"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":[0.000041438383,0.00005600996,0.0024031692,0.000013398081,0.00006595239,0.00007114721,0.000042120537,0.9711549,0.00091578264,0.014213073,0.0004600714,0.010562863],"study_design_scores_gemma":[0.0000062294994,0.000006291994,0.0004234773,0.000003253723,0.000003825328,0.000008970898,0.000004440353,0.98709226,0.00023814329,0.012031271,0.00017427871,0.0000075802477],"about_ca_topic_score_codex":0.0034779396,"about_ca_topic_score_gemma":0.0022597408,"teacher_disagreement_score":0.004765689,"about_ca_system_score_codex":0.0007714068,"about_ca_system_score_gemma":0.0008249615,"threshold_uncertainty_score":0.025203705},"labels":[],"label_agreement":null}]}