{"meta":{"query_hash":"94bbfaffb4a2","filters":{"venue":"Journal of Forecasting"},"cohort_total":79,"direct_labels_cover":1,"predictions_cover":79,"exported":79,"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/94bbfaffb4a2","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Forecasting"},"results":[{"id":"W1484796914","doi":"10.1002/for.1242","title":"The Accuracy of Non‐traditional versus Traditional Methods of Forecasting Lumpy Demand","year":2011,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Demand forecasting; Computer science; Product (mathematics); Econometrics; Operations research; Economics; Mathematics","score_opus":0.6489373783846855,"score_gpt":0.45647002944725984,"score_spread":0.19246734893742562,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1484796914","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":[0.87464595,0.0031363533,0.11638755,0.0004594487,0.0001965346,0.00006577211,0.00048422927,0.00038475532,0.004239358],"genre_scores_gemma":[0.9704744,0.000576102,0.027689558,0.000039820432,0.000075347576,0.000022670483,0.00038180524,0.000021633483,0.00071871484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975485,0.001107996,0.0002343909,0.0003678684,0.0006473469,0.000093823044],"domain_scores_gemma":[0.9707245,0.023131551,0.0016727733,0.0016930572,0.0024921065,0.00028608006],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007503316,0.0006108042,0.00068228313,0.001564292,0.00024635732,0.0011915403,0.0010169647,0.0007710294,0.00091276324],"category_scores_gemma":[0.025448408,0.00023556936,0.00063104584,0.0010697856,0.00038782682,0.0019867397,0.00067639054,0.00068125356,0.00031218457],"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":[0.0017393434,0.00032147966,0.12890051,0.0005047009,0.0009640118,0.000110068555,0.0004237263,0.37418506,0.0039515775,0.0027717866,0.0023897337,0.48373795],"study_design_scores_gemma":[0.00005422117,0.0003023412,0.042474896,0.00005745102,0.000102615784,0.00007917511,0.00012741884,0.9512684,0.0028249621,0.0019521967,0.0007082202,0.000048157748],"about_ca_topic_score_codex":0.005899603,"about_ca_topic_score_gemma":0.0051558698,"teacher_disagreement_score":0.007503316,"about_ca_system_score_codex":0.0005771049,"about_ca_system_score_gemma":0.0004888559,"threshold_uncertainty_score":0.039681792},"labels":[],"label_agreement":null},{"id":"W1494215555","doi":"10.1002/for.2259","title":"Forecasting the Effects of a Canada–US Currency Union on Output and Prices: A Counterfactual Analysis","year":2013,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"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":"Counterfactual thinking; Economics; Econometrics; Robustness (evolution); Currency; Currency union; Dynamic stochastic general equilibrium; Monetary economics; Monetary policy","score_opus":0.061050506799335394,"score_gpt":0.203382955393045,"score_spread":0.14233244859370958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1494215555","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":[0.98054606,0.0006402065,0.012512128,0.0011857757,0.0002876191,0.00031382317,0.002077171,0.000107945154,0.002329134],"genre_scores_gemma":[0.9936469,0.0001585363,0.003685308,0.00016146731,0.00005349312,0.00013673944,0.001529967,0.000008656394,0.00061882666],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.991139,0.005384033,0.0005046535,0.0010873008,0.0012064321,0.00067853334],"domain_scores_gemma":[0.92841035,0.048696853,0.008885808,0.007285229,0.005925584,0.0007962483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.027743638,0.00082833914,0.0014344457,0.0011253469,0.0013728947,0.0034628524,0.0023743797,0.0027193516,0.0025566833],"category_scores_gemma":[0.049372535,0.0005606608,0.0022727242,0.001650575,0.002491163,0.0012620691,0.0014489968,0.0024323587,0.00020936882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010420811,0.0018539255,0.191801,0.0004220855,0.004082152,0.0011755107,0.0006102277,0.7155494,0.002900271,0.043548208,0.0065100756,0.021126313],"study_design_scores_gemma":[0.002611533,0.0025275291,0.1543478,0.00008431506,0.003121177,0.00020107008,0.0007146544,0.8086239,0.00765325,0.011634232,0.008135778,0.0003447015],"about_ca_topic_score_codex":0.29060248,"about_ca_topic_score_gemma":0.11206451,"teacher_disagreement_score":0.70939755,"about_ca_system_score_codex":0.0061755115,"about_ca_system_score_gemma":0.005699337,"threshold_uncertainty_score":0.57782197},"labels":[],"label_agreement":null},{"id":"W1528064681","doi":"10.1002/for.2317","title":"Bayesian Analysis of Asymmetric Stochastic Conditional Duration Model","year":2014,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Actua; University of Waterloo","funders":"","keywords":"Particle filter; Markov chain Monte Carlo; Computer science; Econometrics; Mathematics; Bayesian probability; Applied mathematics; Statistics; Kalman filter","score_opus":0.052479816384088836,"score_gpt":0.23942271670754267,"score_spread":0.18694290032345384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528064681","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":[0.079491615,0.00048207448,0.91562366,0.00056517107,0.00004186748,0.000042439486,0.00040129316,0.00019349433,0.003158371],"genre_scores_gemma":[0.9264061,0.000791199,0.066194795,0.00015310048,0.000140668,0.00016557076,0.0009981692,0.00012518848,0.0050253384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99835664,0.00073869433,0.00006421442,0.00038240434,0.00029699723,0.00016099193],"domain_scores_gemma":[0.9885147,0.008630114,0.0011591328,0.00063827826,0.00077943783,0.00027837497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005204315,0.00058116554,0.0014451288,0.0015399879,0.0005630871,0.0016051158,0.002060695,0.0015319852,0.0043830983],"category_scores_gemma":[0.02108078,0.0008433159,0.0010218979,0.0012020762,0.0012246368,0.0028051082,0.0013805473,0.002211397,0.00044916963],"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":[0.00010707636,0.000045077195,0.004431224,0.00007176778,0.00006773184,0.00014393056,0.00014894632,0.73723245,0.0006799074,0.23731486,0.0014595329,0.018297566],"study_design_scores_gemma":[0.0000067674346,0.0000058996957,0.0005900624,0.000012555195,0.000008283785,0.00002048107,0.000007915442,0.97009844,0.0000994251,0.028750945,0.00038648702,0.000012826422],"about_ca_topic_score_codex":0.011834787,"about_ca_topic_score_gemma":0.0073926887,"teacher_disagreement_score":0.011834787,"about_ca_system_score_codex":0.0016944605,"about_ca_system_score_gemma":0.0013234509,"threshold_uncertainty_score":0.027523398},"labels":[],"label_agreement":null},{"id":"W1542665681","doi":"10.1002/for.1267","title":"Global Capital Flows, Time‐Varying Fundamentals and Transitional Exchange Rate Dynamics","year":2011,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economics; Exchange rate; Sharpe ratio; Pound (networking); Monetary economics; Liberian dollar; Econometrics; Capital flows; Markov chain; Equity (law); Financial economics; Portfolio; Mathematics; Microeconomics; Statistics; Finance","score_opus":0.05583244939862192,"score_gpt":0.20428394287207138,"score_spread":0.14845149347344946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1542665681","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":[0.99562365,0.00013001772,0.002756434,0.00015743506,0.000007604801,0.000006127926,0.00007363431,0.000020572123,0.0012245028],"genre_scores_gemma":[0.9995505,0.00007052037,0.00015907674,0.000010996475,0.0000033485194,0.0000017273563,0.00004614112,0.0000020894415,0.00015563007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998274,0.0000614999,0.000011244849,0.000039485578,0.000017395249,0.00004305275],"domain_scores_gemma":[0.99622786,0.0019534146,0.0012757005,0.00023178787,0.000116285686,0.00019499536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016636931,0.0003377528,0.0003678456,0.0005617239,0.00023721151,0.00165997,0.00028957136,0.0004388037,0.0028348258],"category_scores_gemma":[0.006237814,0.00015596124,0.0005187792,0.0005305793,0.00085571676,0.0013732597,0.0008076533,0.0011013194,0.0001463319],"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":[0.0007911299,0.00018868485,0.43482217,0.00010658428,0.00045983345,0.0013397764,0.00072715187,0.45963645,0.0047584134,0.069800355,0.0011596888,0.026209729],"study_design_scores_gemma":[0.00011044084,0.0004904883,0.41206887,0.000046253397,0.0002535931,0.00029463306,0.0005611762,0.5316332,0.002949152,0.049801175,0.0017106115,0.00008045124],"about_ca_topic_score_codex":0.0050436384,"about_ca_topic_score_gemma":0.0027032555,"teacher_disagreement_score":0.0050436384,"about_ca_system_score_codex":0.0005097805,"about_ca_system_score_gemma":0.00026271606,"threshold_uncertainty_score":0.010028541},"labels":[],"label_agreement":null},{"id":"W1556548854","doi":"10.1002/for.1229","title":"Do Long‐Run Theory Restrictions Help in Forecasting?","year":2011,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"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":"Cointegration; Econometrics; Error correction model; Economics; Context (archaeology); Computer science","score_opus":0.23538890951135003,"score_gpt":0.2452937163915685,"score_spread":0.00990480688021847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1556548854","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":[0.61851823,0.0024668607,0.32922837,0.012249901,0.00046061032,0.00011186637,0.0010303298,0.001421546,0.03451233],"genre_scores_gemma":[0.98699456,0.0004086478,0.010524797,0.0003650997,0.000119603785,0.00001708032,0.00042473868,0.000057259203,0.0010881552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99676335,0.0014165813,0.000325195,0.0005299328,0.00065548706,0.00030946574],"domain_scores_gemma":[0.9630418,0.023972632,0.004468018,0.0049478956,0.0031152673,0.0004543166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.011940789,0.00092317583,0.0012400452,0.0011527758,0.0005623279,0.0049883435,0.001442484,0.0017728659,0.006210164],"category_scores_gemma":[0.05933742,0.0006867052,0.0008903417,0.001064277,0.0016645686,0.0077063497,0.0013871177,0.0030210887,0.001271878],"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":[0.00046527365,0.00021100843,0.060884595,0.00023467562,0.00046244668,0.00044382436,0.00050632027,0.7420429,0.0022122597,0.09137442,0.0042505064,0.096911736],"study_design_scores_gemma":[0.00009326317,0.00019604705,0.02452163,0.00017753958,0.00018726986,0.000149182,0.00055788265,0.8441568,0.003129325,0.12226063,0.0044210195,0.00014943861],"about_ca_topic_score_codex":0.017431865,"about_ca_topic_score_gemma":0.015288872,"teacher_disagreement_score":0.017431865,"about_ca_system_score_codex":0.0014177975,"about_ca_system_score_gemma":0.0033440287,"threshold_uncertainty_score":0.06314969},"labels":[],"label_agreement":null},{"id":"W1603664884","doi":"10.1002/for.1034","title":"Can panel data really improve the predictability of the monetary exchange rate model?","year":2007,"lang":"en","type":"preprint","venue":"Journal of Forecasting","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":"York University","funders":"","keywords":"Pooling; Predictability; Econometrics; Panel data; Context (archaeology); Predictive power; Computer science; Random walk; Time series; Exchange rate; Economics; Statistics; Machine learning; Artificial intelligence; Mathematics; Macroeconomics","score_opus":0.3060346092160098,"score_gpt":0.27077608943021547,"score_spread":0.03525851978579431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1603664884","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":[0.4949359,0.0046084546,0.46352726,0.014833928,0.00093617727,0.00017466766,0.0034967915,0.0013011729,0.016185654],"genre_scores_gemma":[0.97475815,0.0007960103,0.02113407,0.00066981517,0.00030045913,0.000056476565,0.001029611,0.000066183886,0.0011892003],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99589384,0.0033093267,0.00010658895,0.00035443317,0.00021567648,0.00012013549],"domain_scores_gemma":[0.92391086,0.06223027,0.002673632,0.008889045,0.0017027775,0.000593509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016123073,0.00090547244,0.0016116159,0.00078067725,0.0005584049,0.0024258292,0.00094286114,0.0018598603,0.006644341],"category_scores_gemma":[0.08205491,0.0004893424,0.0011077657,0.0014443769,0.0007692226,0.0037595585,0.0017996955,0.0024932495,0.0010159715],"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":[0.0013511516,0.00034665334,0.114199504,0.00025574415,0.0017829352,0.00067746843,0.00039750084,0.6238619,0.0011609374,0.04358521,0.016103523,0.19627745],"study_design_scores_gemma":[0.0001730635,0.00043199802,0.021675725,0.00010375262,0.00041920028,0.0000853436,0.00021984988,0.7683633,0.001345421,0.20137113,0.0057180254,0.000093143135],"about_ca_topic_score_codex":0.0052502723,"about_ca_topic_score_gemma":0.0032736047,"teacher_disagreement_score":0.016123073,"about_ca_system_score_codex":0.00045645903,"about_ca_system_score_gemma":0.00050947815,"threshold_uncertainty_score":0.08526796},"labels":[],"label_agreement":null},{"id":"W1606487396","doi":"10.1002/for.2264","title":"Forecasting Simultaneously High‐Dimensional Time Series: A Robust Model‐Based Clustering Approach","year":2013,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Booth University College","funders":"","keywords":"Outlier; Autoregressive model; Cluster analysis; Principal component analysis; Robustness (evolution); Computer science; Series (stratigraphy); Autoregressive integrated moving average; Econometrics; Time series; Data mining; Artificial intelligence; Mathematics; Machine learning","score_opus":0.1493626557280337,"score_gpt":0.33036353944583374,"score_spread":0.18100088371780004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1606487396","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":[0.016021824,0.00033753883,0.9825791,0.00018827403,0.000026899974,0.000019600293,0.00007802851,0.0002574218,0.0004913213],"genre_scores_gemma":[0.62029487,0.0005812963,0.37597686,0.00012075406,0.00019728197,0.00012934465,0.0008537643,0.00020230582,0.001643597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99879897,0.00053029286,0.00007251642,0.0002629891,0.00025853235,0.000076776094],"domain_scores_gemma":[0.9973002,0.0014408525,0.00038743013,0.00034137678,0.00045872777,0.00007137462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031381918,0.001133441,0.001986402,0.0018562176,0.00055846345,0.0013147715,0.0019674872,0.0015046161,0.00079150364],"category_scores_gemma":[0.0068841977,0.00061755016,0.0015942059,0.0017376993,0.0005621296,0.0011639046,0.0010218055,0.0014836807,0.00037946523],"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":[0.000055891283,0.000048703147,0.00076263555,0.000059691807,0.00020796295,0.000052281055,0.00006821669,0.9474935,0.0013863271,0.005679777,0.0011146471,0.043070395],"study_design_scores_gemma":[0.0000015484014,0.0000058216397,0.00015173758,0.0000027300966,0.0000065822533,0.0000053345393,0.0000054803327,0.99718386,0.00015776977,0.0023385754,0.00013504922,0.000005531586],"about_ca_topic_score_codex":0.0061256695,"about_ca_topic_score_gemma":0.0034273802,"teacher_disagreement_score":0.0061256695,"about_ca_system_score_codex":0.000797233,"about_ca_system_score_gemma":0.00086563325,"threshold_uncertainty_score":0.016596496},"labels":[],"label_agreement":null},{"id":"W1965402578","doi":"10.1002/for.1134","title":"Forecasting volatility with support vector machine‐based GARCH model","year":2009,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":86,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of British Columbia; Fudan University; Kyungpook National University; Deutsche Forschungsgemeinschaft","keywords":"Autoregressive conditional heteroskedasticity; Support vector machine; Volatility (finance); Econometrics; Artificial neural network; Computer science; Normality; Artificial intelligence; Machine learning; Economics; Statistics; Mathematics","score_opus":0.2387891771350272,"score_gpt":0.4069124056440709,"score_spread":0.16812322850904368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965402578","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":[0.47889417,0.001088153,0.51731163,0.00030801876,0.00008372934,0.000026525831,0.00017044139,0.00061074615,0.001506564],"genre_scores_gemma":[0.97310084,0.00017330975,0.02616911,0.000016055938,0.000023216115,0.000010112853,0.000088739485,0.000007250636,0.00041131946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949074,0.00023578329,0.000035015088,0.00005850484,0.00014058707,0.000039340335],"domain_scores_gemma":[0.9984408,0.0011088433,0.00015081913,0.0000824514,0.00018651885,0.00003059288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017958337,0.00032977146,0.00070300803,0.0006727473,0.00012790691,0.0005356276,0.00051631243,0.0005617044,0.0006604352],"category_scores_gemma":[0.003922719,0.0001555359,0.00043235588,0.00071275973,0.00018344984,0.0006032958,0.00024940225,0.00054912106,0.00016548086],"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":[0.00020506703,0.000083513085,0.007056885,0.00006290267,0.00011148258,0.00009133371,0.000030491774,0.89998895,0.002013133,0.0030155638,0.00066248834,0.08667825],"study_design_scores_gemma":[0.000003126352,0.0000135510045,0.0003216219,0.0000015651922,0.000003337889,0.0000042862403,0.0000013527148,0.9988356,0.00020998706,0.0005638358,0.000039743394,0.000002059709],"about_ca_topic_score_codex":0.0023946716,"about_ca_topic_score_gemma":0.0013600348,"teacher_disagreement_score":0.0023946716,"about_ca_system_score_codex":0.00029045422,"about_ca_system_score_gemma":0.00033352096,"threshold_uncertainty_score":0.0094973445},"labels":[],"label_agreement":null},{"id":"W2040735861","doi":"10.1002/for.1044","title":"On forecasting counts","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Series (stratigraphy); Econometrics; Negative binomial distribution; Bayesian probability; Poisson distribution; Computer science; Probabilistic forecasting; Time series; Statistics; Mathematics; Probabilistic logic","score_opus":0.09448738222617091,"score_gpt":0.2687520753828352,"score_spread":0.17426469315666426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040735861","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":[0.05555939,0.0014553689,0.92522615,0.0030537134,0.00027898402,0.00015874852,0.0007269059,0.0005803524,0.012960378],"genre_scores_gemma":[0.73576397,0.0041617267,0.24173331,0.00065455266,0.0008016841,0.00055005535,0.0021395495,0.00020917406,0.0139860865],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99730116,0.0013067017,0.000107066124,0.00050932216,0.0006165468,0.00015912343],"domain_scores_gemma":[0.97005033,0.024207255,0.0017476847,0.0016223707,0.002060202,0.0003122426],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0071518063,0.0010174894,0.001546717,0.0036288472,0.00065283175,0.002446976,0.0023936944,0.002487062,0.0070743286],"category_scores_gemma":[0.06932557,0.0006729487,0.0009850365,0.0041534365,0.0014132513,0.0056261634,0.001642044,0.0029283827,0.001871481],"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":[0.00017059698,0.000064573025,0.010678321,0.00020250374,0.000080353464,0.00011102619,0.00030050275,0.4536475,0.0004323224,0.3084698,0.010366263,0.2154763],"study_design_scores_gemma":[0.000014407981,0.000031580985,0.0015562929,0.00008993729,0.000020067224,0.00003714824,0.000058831927,0.83879334,0.00023025922,0.15600102,0.0031437322,0.000023377253],"about_ca_topic_score_codex":0.013303643,"about_ca_topic_score_gemma":0.008825751,"teacher_disagreement_score":0.013303643,"about_ca_system_score_codex":0.002064458,"about_ca_system_score_gemma":0.0011193847,"threshold_uncertainty_score":0.037822843},"labels":[],"label_agreement":null},{"id":"W2068799179","doi":"10.1002/for.1197","title":"Computationally efficient bootstrap prediction intervals for returns and volatilities in ARCH and GARCH processes","year":2010,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Autoregressive conditional heteroskedasticity; Arch; Econometrics; Sampling (signal processing); Prediction interval; Volatility (finance); Computer science; Representation (politics); Nonlinear system; Statistics; Mathematics; Engineering","score_opus":0.08742118243897747,"score_gpt":0.26977604008549533,"score_spread":0.18235485764651788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2068799179","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":[0.03722107,0.00023183659,0.9610145,0.00006337249,0.000019678013,0.000038791404,0.00005840498,0.00061867025,0.00073358783],"genre_scores_gemma":[0.5841769,0.00022649449,0.41403905,0.000053806878,0.0000614621,0.00015686727,0.0005014066,0.00015868293,0.00062530185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848396,0.0007456055,0.00008251152,0.00014435954,0.00047044575,0.000073222625],"domain_scores_gemma":[0.989219,0.007914539,0.0005760471,0.0012158372,0.00091532775,0.00015925865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005040524,0.00044249385,0.0007819471,0.0011563479,0.00028802725,0.00083557545,0.0013624636,0.00072486856,0.0022997514],"category_scores_gemma":[0.023655549,0.00040039708,0.0005917571,0.0006376438,0.00054198265,0.0011011543,0.0010028043,0.0011079363,0.00049784326],"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":[0.0005893723,0.00014973091,0.0057713646,0.00015772803,0.00013423253,0.00031075455,0.00017274878,0.66206586,0.010147759,0.033084676,0.001702551,0.28571323],"study_design_scores_gemma":[0.000021454362,0.000024930205,0.00064352655,0.000009212757,0.0000070360734,0.000042533553,0.000007529336,0.9895661,0.0022991656,0.0070304363,0.0003392811,0.000008823934],"about_ca_topic_score_codex":0.001247732,"about_ca_topic_score_gemma":0.0010663692,"teacher_disagreement_score":0.005040524,"about_ca_system_score_codex":0.0003682049,"about_ca_system_score_gemma":0.0005508221,"threshold_uncertainty_score":0.026657164},"labels":[],"label_agreement":null},{"id":"W2077421290","doi":"10.1002/for.929","title":"Unemployment variation over the business cycles: a comparison of forecasting models","year":2004,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial neural network; Unemployment; Business cycle; Econometrics; Linear model; Linear regression; Computer science; Series (stratigraphy); Aggregate (composite); Regression; Economics; Mathematics; Artificial intelligence; Statistics; Machine learning","score_opus":0.2191910576277073,"score_gpt":0.2730355441331966,"score_spread":0.053844486505489286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077421290","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":[0.9140709,0.000999892,0.07699357,0.0006472744,0.00009408478,0.00007648148,0.0003705245,0.00026558148,0.0064817294],"genre_scores_gemma":[0.992644,0.00032228103,0.005676004,0.000024960644,0.000025140895,0.000032899792,0.00020599565,0.000015303282,0.0010535606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956363,0.0002314802,0.000024647106,0.00007777277,0.00006370418,0.00003868684],"domain_scores_gemma":[0.9966306,0.0027654532,0.00019124075,0.000079966725,0.00026416074,0.00006853145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023526093,0.00059197715,0.00060602056,0.00091154437,0.00026667054,0.0008243533,0.00080357573,0.0009493026,0.0014536163],"category_scores_gemma":[0.006533075,0.00030986278,0.0008744523,0.00086199766,0.00027990073,0.0007556295,0.00035212495,0.000509148,0.00016691312],"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":[0.00013995587,0.000054910153,0.0064297942,0.000037819147,0.00007838895,0.000032963802,0.000043988657,0.974538,0.00012826035,0.0012388747,0.00024032609,0.017036755],"study_design_scores_gemma":[0.000004457858,0.000014837437,0.0013890075,0.000006147991,0.000010548062,0.0000029988817,0.000007033403,0.9980457,0.000039478222,0.00042394045,0.000051996136,0.0000037959992],"about_ca_topic_score_codex":0.03824826,"about_ca_topic_score_gemma":0.016180571,"teacher_disagreement_score":0.03824826,"about_ca_system_score_codex":0.0011995987,"about_ca_system_score_gemma":0.0006816611,"threshold_uncertainty_score":0.076051295},"labels":[],"label_agreement":null},{"id":"W2077565477","doi":"10.1002/for.1007","title":"The use of monthly indicators to forecast quarterly GDP in the short run: an application to the G7 countries","year":2007,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Ministero dell’Istruzione, dell’Università e della Ricerca","keywords":"Univariate; Benchmark (surveying); Econometrics; Real gross domestic product; Multivariate statistics; Economics; Computer science; Statistics; Geography; Mathematics","score_opus":0.11096821365127084,"score_gpt":0.2608456533671137,"score_spread":0.14987743971584283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077565477","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":[0.9954958,0.00013353638,0.002951246,0.00021512406,0.000015946853,0.000021329906,0.00033555133,0.00011037432,0.00072118966],"genre_scores_gemma":[0.9960849,0.000093238385,0.0032997155,0.0000151663535,0.000008646425,0.000016568576,0.0003408228,0.000011955039,0.0001289381],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924195,0.0005263972,0.000031806132,0.00007886897,0.000061164785,0.000059910006],"domain_scores_gemma":[0.9922775,0.005712054,0.0006174781,0.0005025177,0.00064931845,0.00024114604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005100121,0.00060455553,0.00080385566,0.0014375137,0.00034868802,0.0010230833,0.0007768924,0.0010432751,0.0010335658],"category_scores_gemma":[0.011984348,0.00030042583,0.0006447438,0.002185958,0.00039804305,0.0009667303,0.0008747682,0.001020687,0.00018275742],"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":[0.0014233188,0.00044054823,0.182793,0.00010883119,0.0004397595,0.00033426433,0.00031857408,0.7333598,0.0010865153,0.004221232,0.0032952032,0.072178885],"study_design_scores_gemma":[0.00009066685,0.00024639457,0.024405358,0.000016642336,0.000050485218,0.000023893648,0.00013009839,0.9725938,0.00081436866,0.0012285234,0.0003721008,0.000027650638],"about_ca_topic_score_codex":0.04177863,"about_ca_topic_score_gemma":0.014648774,"teacher_disagreement_score":0.04177863,"about_ca_system_score_codex":0.0007781546,"about_ca_system_score_gemma":0.0007649077,"threshold_uncertainty_score":0.083070874},"labels":[],"label_agreement":null},{"id":"W2083119721","doi":"10.1002/1099-131x(200103)20:2<145::aid-for787>3.0.co;2-5","title":"Cross-correlations and predictability of stock returns","year":2001,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Predictability; Econometrics; Stock (firearms); Economics; Financial economics; Mathematics; Statistics; Geography","score_opus":0.07026754148763727,"score_gpt":0.2520216582286761,"score_spread":0.18175411674103886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083119721","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":[0.98513377,0.001031762,0.010623079,0.000427279,0.000039238483,0.000010139126,0.00030278487,0.000084384046,0.002347517],"genre_scores_gemma":[0.99883634,0.00025559697,0.0004120019,0.000015921,0.000035202935,0.000005714771,0.00021244162,0.00000713611,0.00021965815],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99891615,0.00034775303,0.00012646403,0.00022261222,0.00024281333,0.0001442131],"domain_scores_gemma":[0.97452444,0.016298063,0.004877408,0.0021915224,0.0015468053,0.0005617506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035064346,0.00040920355,0.0005530358,0.0015044836,0.00030707574,0.0013607731,0.0003150306,0.00055339147,0.0018799943],"category_scores_gemma":[0.029632293,0.00033156804,0.0005820065,0.0011544379,0.00064564863,0.001546745,0.0010735517,0.00086484716,0.00040515064],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002763848,0.00008870258,0.89331317,0.000058183392,0.00063055166,0.00049164135,0.00033618454,0.050868675,0.0011008186,0.010811207,0.0011592362,0.040865224],"study_design_scores_gemma":[0.000021376341,0.00014326448,0.7416427,0.00003969223,0.00023300161,0.0003661088,0.00016918522,0.21423037,0.0013856969,0.040438306,0.0012809705,0.000049251154],"about_ca_topic_score_codex":0.0030137757,"about_ca_topic_score_gemma":0.002403315,"teacher_disagreement_score":0.0035064346,"about_ca_system_score_codex":0.00050664484,"about_ca_system_score_gemma":0.0004278643,"threshold_uncertainty_score":0.018544018},"labels":[],"label_agreement":null},{"id":"W2089502686","doi":"10.1002/for.1079","title":"Power transformation models and volatility forecasting","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Econometrics; Autoregressive model; Volatility (finance); Heteroscedasticity; Realized variance; Economics; Independent and identically distributed random variables; Forecast error; Computer science; Mathematics; Statistics; Random variable","score_opus":0.138524728483377,"score_gpt":0.23223592453955633,"score_spread":0.09371119605617934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2089502686","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":[0.14442506,0.0025744077,0.8249506,0.00308184,0.00030380356,0.000055504945,0.00039680747,0.00089744705,0.023314616],"genre_scores_gemma":[0.9739242,0.0011793177,0.017723296,0.00011038594,0.00020603977,0.00003839868,0.00031156564,0.000100513294,0.006406174],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906355,0.0004833461,0.000027764001,0.00011576502,0.00023813863,0.00007135614],"domain_scores_gemma":[0.9939235,0.00454305,0.00057856034,0.0003966328,0.0004446832,0.00011363404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022588077,0.0006035029,0.00081969774,0.0009284543,0.00030070092,0.0016955441,0.0009513516,0.001001814,0.004850968],"category_scores_gemma":[0.015530228,0.00024229311,0.0005531294,0.0015448837,0.0011788511,0.0023143906,0.0008411686,0.0016296183,0.0008529843],"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":[0.00006433401,0.000030439294,0.0020052774,0.000047747326,0.00005424358,0.00012537322,0.00009846882,0.78430396,0.000369716,0.17103119,0.0024959722,0.03937328],"study_design_scores_gemma":[0.000004357587,0.000007810148,0.0001987976,0.0000045293586,0.0000041498024,0.000013063271,0.00000780449,0.90116644,0.0000866672,0.097871214,0.0006306704,0.0000045239713],"about_ca_topic_score_codex":0.0073964684,"about_ca_topic_score_gemma":0.0021074554,"teacher_disagreement_score":0.0073964684,"about_ca_system_score_codex":0.00075899874,"about_ca_system_score_gemma":0.0005886683,"threshold_uncertainty_score":0.01622808},"labels":[],"label_agreement":null},{"id":"W2098806386","doi":"10.1002/for.986","title":"Non‐linear, non‐parametric, non‐fundamental exchange rate forecasting","year":2006,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bank of Canada; Lakehead University","funders":"University of British Columbia; Lakehead University","keywords":"Random walk; Mean squared error; Linear model; Exchange rate; Parametric statistics; Mathematics; Econometrics; Statistics; Computer science; Economics","score_opus":0.16672608192664456,"score_gpt":0.38640749290302645,"score_spread":0.21968141097638189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098806386","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":[0.52234614,0.0005676042,0.4689039,0.00034619824,0.00008745731,0.00009318274,0.0006294009,0.0006901056,0.0063360427],"genre_scores_gemma":[0.9648265,0.000116656294,0.032973837,0.000020433115,0.000020400406,0.000025238654,0.00023939194,0.000011332985,0.0017662206],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995338,0.00017597135,0.00002672477,0.00007243446,0.00016243878,0.000028709745],"domain_scores_gemma":[0.9981811,0.0011410275,0.00019330968,0.00018024303,0.0002718175,0.000032534732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015933492,0.0003663237,0.0005579963,0.00048791198,0.00020642523,0.0007569358,0.00062950596,0.0005350367,0.0010649728],"category_scores_gemma":[0.0070811505,0.0001912651,0.00036859684,0.00051488786,0.00028316161,0.0005712528,0.00040777828,0.00065827544,0.000212753],"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":[0.00013920155,0.00010269396,0.010625441,0.000079572106,0.000061417224,0.00008602114,0.000043991313,0.9018895,0.0025695902,0.002555034,0.00066601555,0.081181504],"study_design_scores_gemma":[0.0000034915076,0.000016601734,0.0027385203,0.000002267573,0.0000040013197,0.000013639816,0.000003560538,0.99591726,0.00053612835,0.00062823715,0.000130989,0.000005339427],"about_ca_topic_score_codex":0.010210452,"about_ca_topic_score_gemma":0.013617563,"teacher_disagreement_score":0.010210452,"about_ca_system_score_codex":0.0004603215,"about_ca_system_score_gemma":0.00062349765,"threshold_uncertainty_score":0.020302057},"labels":[],"label_agreement":null},{"id":"W2105292169","doi":"10.1002/for.2248","title":"Long‐Term Forecasting of Global Carbon Dioxide Emissions: Reducing Uncertainties Using a Per Capita Approach","year":2013,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Per capita; Econometrics; Range (aeronautics); Economics; Environmental science; Monte Carlo method; Term (time); Quartile; Yield (engineering); Statistics; Natural resource economics; Mathematics","score_opus":0.02727958173295202,"score_gpt":0.22980923249377566,"score_spread":0.20252965076082363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105292169","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":[0.47353587,0.0011269498,0.5085106,0.002982377,0.0001384222,0.00006237309,0.0019477042,0.0008089109,0.010886854],"genre_scores_gemma":[0.96317035,0.00033539743,0.035039287,0.000075907126,0.000070278846,0.00003199437,0.0006331071,0.00006633801,0.00057725795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989543,0.00066323037,0.000044015538,0.00017293298,0.00011322213,0.000052171086],"domain_scores_gemma":[0.9933484,0.0044206046,0.0007548229,0.00079028646,0.0005243978,0.00016158976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036871433,0.0006767023,0.0009572086,0.001560586,0.00033786215,0.0021118843,0.00097022136,0.0009980391,0.0014162294],"category_scores_gemma":[0.014999396,0.00051409245,0.00063466537,0.0015204657,0.0005051324,0.0024653513,0.0012902428,0.0010810466,0.00030695603],"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":[0.00008902369,0.000020427991,0.025217894,0.000038419632,0.00018856095,0.000070027396,0.00008122933,0.93173164,0.0005429864,0.006224892,0.0009731529,0.034821626],"study_design_scores_gemma":[0.0000054004513,0.000024874458,0.003936522,0.000013617086,0.000033218694,0.000021038166,0.000045697845,0.9865758,0.00033007385,0.008420521,0.0005754521,0.000017732305],"about_ca_topic_score_codex":0.006832945,"about_ca_topic_score_gemma":0.005477534,"teacher_disagreement_score":0.006832945,"about_ca_system_score_codex":0.00076175074,"about_ca_system_score_gemma":0.00078973424,"threshold_uncertainty_score":0.01949972},"labels":[],"label_agreement":null},{"id":"W2105499812","doi":"10.1002/for.2411","title":"Predicting Systemic Risk with Entropic Indicators","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Predictability; Systemic risk; Economics; Econometrics; Volatility (finance); Skewness; Risk management; Financial crisis; Actuarial science; Financial economics; Statistics; Mathematics; Finance","score_opus":0.018306303054569963,"score_gpt":0.18864357789917977,"score_spread":0.1703372748446098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105499812","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":[0.91937476,0.00027172812,0.078466356,0.00013042819,0.000014959219,0.000018373572,0.00021509183,0.000107986045,0.0014004451],"genre_scores_gemma":[0.99643916,0.00008794356,0.0032055203,0.0000066650314,0.000017609842,0.000004641729,0.000112630194,0.000003972178,0.000121916855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967456,0.00011711131,0.000026530726,0.000058209702,0.00009205787,0.000031510193],"domain_scores_gemma":[0.99670815,0.0018234368,0.0009760154,0.00017870338,0.00015473927,0.00015900166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012191026,0.0006752702,0.0004583013,0.0022717286,0.00014095497,0.0009986789,0.00023967611,0.0005160378,0.0005639225],"category_scores_gemma":[0.0058281706,0.00020684529,0.00033752684,0.0009897773,0.00039751313,0.0013425128,0.00091145793,0.00058208004,0.00011679599],"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":[0.0004157608,0.00024396881,0.39318627,0.0001019229,0.00039039756,0.00046757722,0.0002991336,0.5037238,0.010815868,0.017306656,0.0006731411,0.072375484],"study_design_scores_gemma":[0.000006301112,0.000081279606,0.05457582,0.000013581668,0.000031520285,0.00007182962,0.00004669507,0.9331222,0.0014580206,0.01038496,0.00017720366,0.000030713312],"about_ca_topic_score_codex":0.0009420543,"about_ca_topic_score_gemma":0.00081131875,"teacher_disagreement_score":0.0022717286,"about_ca_system_score_codex":0.00026379406,"about_ca_system_score_gemma":0.00023948312,"threshold_uncertainty_score":0.006447315},"labels":[],"label_agreement":null},{"id":"W2123871236","doi":"10.1002/for.1046","title":"Linear and threshold forecasts of output and inflation using stock and housing prices","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Economics; Econometrics; Inflation (cosmology); Stock (firearms); Equity (law); Real gross domestic product; Asset (computer security); Monetary economics; Financial economics; Macroeconomics; Computer science","score_opus":0.2604066037751568,"score_gpt":0.2580949808547658,"score_spread":0.002311622920390999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123871236","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":[0.98263747,0.00013927493,0.012342147,0.00019209697,0.000020278541,0.000022828823,0.0010946962,0.00020925216,0.0033419593],"genre_scores_gemma":[0.99755424,0.000041140196,0.0016378461,0.000007727588,0.000008042521,0.0000022320198,0.0005160375,0.0000036419094,0.00022918406],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996008,0.000082216124,0.000033021253,0.00007966069,0.00013596474,0.00006839005],"domain_scores_gemma":[0.9980842,0.0008017211,0.0003987823,0.00011247892,0.00047647857,0.00012633097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013160305,0.00029255528,0.0004209928,0.0010254786,0.00019163803,0.001245896,0.00036383903,0.00042449514,0.0015860422],"category_scores_gemma":[0.010095841,0.00021611978,0.00031269927,0.001240507,0.00020567024,0.0012217415,0.00047830504,0.00044732122,0.00043985908],"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":[0.0008645808,0.0001388358,0.6754204,0.00007861463,0.00018944743,0.00015835222,0.00023128137,0.23416951,0.002729999,0.003973669,0.002114561,0.079930805],"study_design_scores_gemma":[0.000027224933,0.00006172348,0.18058915,0.000017080136,0.000036429734,0.000019792076,0.00012247956,0.8145729,0.0018023462,0.0023561185,0.00036382308,0.000031014617],"about_ca_topic_score_codex":0.16739452,"about_ca_topic_score_gemma":0.12125235,"teacher_disagreement_score":0.16739452,"about_ca_system_score_codex":0.0011425556,"about_ca_system_score_gemma":0.0012628392,"threshold_uncertainty_score":0.33284032},"labels":[],"label_agreement":null},{"id":"W2129179837","doi":"10.1002/for.982","title":"Gamma stochastic volatility models","year":2006,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Econometrics; Autoregressive model; Stochastic volatility; Estimator; Autoregressive conditional heteroskedasticity; Volatility (finance); Autocorrelation; Forward volatility; Kurtosis; Economics; Financial models with long-tailed distributions and volatility clustering; STAR model; Mathematics; Statistics; Autoregressive integrated moving average; Time series","score_opus":0.07953029440778929,"score_gpt":0.21465130489533324,"score_spread":0.13512101048754394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129179837","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":[0.1187494,0.0056321165,0.8305642,0.0020012555,0.0004192998,0.00018319249,0.0012015369,0.00056812447,0.040680908],"genre_scores_gemma":[0.9433058,0.0039026788,0.029150294,0.00037172402,0.00031838173,0.00018474752,0.0007655133,0.000090535665,0.021910405],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99883825,0.00044103092,0.000045465138,0.00021710114,0.00027249573,0.00018562826],"domain_scores_gemma":[0.9982126,0.0009023302,0.00034458973,0.00017551247,0.00028757003,0.00007739702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020339582,0.0011761183,0.0014091858,0.0012426481,0.00047128645,0.0027678236,0.0018574542,0.0017126488,0.0048632384],"category_scores_gemma":[0.006995553,0.00044061532,0.0013208117,0.0015815467,0.0012449768,0.002192974,0.0012732429,0.0016322061,0.00090936833],"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":[0.00004863258,0.00003551486,0.0029723996,0.0001011294,0.000117334974,0.00034659437,0.00021065923,0.3806937,0.0009467654,0.5912411,0.0043240776,0.018962102],"study_design_scores_gemma":[0.000027008498,0.000039948496,0.0010027485,0.00005115029,0.000051078197,0.00025890023,0.00005995687,0.64066666,0.00040275932,0.34875134,0.008645976,0.000042479027],"about_ca_topic_score_codex":0.0063075647,"about_ca_topic_score_gemma":0.00296823,"teacher_disagreement_score":0.0063075647,"about_ca_system_score_codex":0.00135493,"about_ca_system_score_gemma":0.00091765966,"threshold_uncertainty_score":0.016269147},"labels":[],"label_agreement":null},{"id":"W2134453352","doi":"10.1002/for.1089","title":"Real‐time or current vintage: does the type of data matter for forecasting and model selection?","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"The King's University; Western University","funders":"","keywords":"Aggregate (composite); Econometrics; Model selection; Computer science; Real-time data; Selection (genetic algorithm); SETAR; Time series; Vintage; Economics; Autoregressive integrated moving average; Machine learning; STAR model","score_opus":0.2942822876067874,"score_gpt":0.310942431920834,"score_spread":0.01666014431404661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134453352","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":[0.894224,0.0031938127,0.08232204,0.008988474,0.0008779756,0.00022546914,0.0031475087,0.0002928998,0.006727861],"genre_scores_gemma":[0.987849,0.0007074911,0.0077156154,0.0004594916,0.00027689003,0.000043292028,0.0015282216,0.00014190958,0.0012782038],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98373044,0.011717198,0.0011306944,0.0013909616,0.001583563,0.0004470482],"domain_scores_gemma":[0.59735066,0.33750474,0.028157193,0.020387085,0.0146559365,0.0019443987],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.060111985,0.00044838828,0.0011821357,0.0010217421,0.00067779893,0.0044387793,0.002166295,0.0015804184,0.0040272223],"category_scores_gemma":[0.27908373,0.00046172642,0.0012183894,0.00221682,0.0012708143,0.0038701848,0.0008580989,0.0026464728,0.0011138121],"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":[0.0028898506,0.00034869337,0.7558766,0.0005274863,0.0028979063,0.0010098798,0.00086125097,0.06856297,0.002722705,0.009347092,0.008766669,0.14618891],"study_design_scores_gemma":[0.0007784246,0.0010094768,0.4437487,0.00070742395,0.002293489,0.00090669724,0.0018670141,0.48522642,0.012109496,0.034365725,0.016681194,0.0003058506],"about_ca_topic_score_codex":0.0089130765,"about_ca_topic_score_gemma":0.007834448,"teacher_disagreement_score":0.060111985,"about_ca_system_score_codex":0.0006674932,"about_ca_system_score_gemma":0.0011055034,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":true,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"split"},{"id":"W2138636187","doi":"10.1002/for.1061","title":"Forecasting commodity prices: GARCH, jumps, and mean reversion","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bank of Canada; Carleton University; Université Laval","funders":"Mitacs","keywords":"Mean reversion; Autoregressive conditional heteroskedasticity; Econometrics; Futures contract; Random walk; Economics; Convenience yield; Volatility (finance); Spot contract; Jump; Jump diffusion; Stochastic volatility; Financial economics; Mathematics; Statistics","score_opus":0.10357716725820716,"score_gpt":0.22777956016521778,"score_spread":0.12420239290701061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138636187","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":[0.81748354,0.0005908128,0.1785609,0.0007371643,0.000064288855,0.000027675116,0.0002108791,0.0004537979,0.001870918],"genre_scores_gemma":[0.9900107,0.00017797956,0.009181843,0.00002914764,0.00003581134,0.000007258543,0.0001318697,0.000018443403,0.00040707772],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905497,0.00051379844,0.00004839635,0.00012450939,0.0001977557,0.00006071553],"domain_scores_gemma":[0.99153215,0.006791988,0.00085837813,0.00040267492,0.0003001366,0.000114723494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00505589,0.0005562678,0.00092289306,0.0010988116,0.00022859189,0.001203506,0.0007284998,0.0009833323,0.00087323954],"category_scores_gemma":[0.018062074,0.00042289088,0.0007941732,0.0011882648,0.0005517535,0.0017581618,0.0005752696,0.0010607808,0.00016263852],"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":[0.00013019412,0.000047986265,0.01576786,0.000036612433,0.0001432149,0.00008672219,0.000050150906,0.9515275,0.0008486764,0.010360504,0.00045666774,0.020543935],"study_design_scores_gemma":[0.0000073111573,0.000024507404,0.0013048303,0.00000319801,0.0000117470245,0.000012015543,0.0000050772123,0.99348676,0.00022225142,0.0048463712,0.00006773317,0.0000081351645],"about_ca_topic_score_codex":0.0074264146,"about_ca_topic_score_gemma":0.0035738202,"teacher_disagreement_score":0.0074264146,"about_ca_system_score_codex":0.0005963828,"about_ca_system_score_gemma":0.00057944766,"threshold_uncertainty_score":0.026738405},"labels":[],"label_agreement":null},{"id":"W2142566359","doi":"10.1002/for.2363","title":"Efficient Multistep Forecast Procedures for Multivariate Time Series","year":2015,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Université de Montréal; University of Windsor","keywords":"Autoregressive model; Series (stratigraphy); Mathematics; Model selection; Truncation (statistics); Vector autoregression; Sample size determination; Selection (genetic algorithm); Autoregressive–moving-average model; Econometrics; Statistics; Applied mathematics; Computer science","score_opus":0.1481757110275164,"score_gpt":0.257513981932282,"score_spread":0.10933827090476558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2142566359","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":[0.002374754,0.000040825937,0.9971066,0.00002476471,0.000008899537,0.000018490722,0.000020027588,0.00014410433,0.00026156436],"genre_scores_gemma":[0.15576263,0.00025313708,0.840248,0.00007033387,0.00006688749,0.00026134666,0.00027911656,0.00016923883,0.0028893398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989021,0.0004912207,0.000061233266,0.00013494727,0.00034177123,0.00006874715],"domain_scores_gemma":[0.9956542,0.0032104938,0.0002803994,0.00034318285,0.00043154065,0.00008015693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026462437,0.0007534681,0.0007217946,0.0011731421,0.0003822411,0.0006570917,0.0009968946,0.00087024056,0.0041146334],"category_scores_gemma":[0.008625512,0.000524188,0.0011102109,0.00080487545,0.0006373853,0.0011612147,0.0014197532,0.0015031169,0.0008194303],"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":[0.00010640361,0.00006272039,0.00094666315,0.00012858947,0.00007844304,0.00010149493,0.00012338786,0.664443,0.007274839,0.07427736,0.0016723395,0.25078493],"study_design_scores_gemma":[0.000007048223,0.00002532394,0.0001798553,0.000006737325,0.000004766784,0.000011282283,0.0000050505296,0.98464,0.0014768112,0.0130367605,0.00059741497,0.000009018788],"about_ca_topic_score_codex":0.002764113,"about_ca_topic_score_gemma":0.0042391974,"teacher_disagreement_score":0.0041146334,"about_ca_system_score_codex":0.0006448501,"about_ca_system_score_gemma":0.0013730256,"threshold_uncertainty_score":0.013994813},"labels":[],"label_agreement":null},{"id":"W2148290296","doi":"10.1002/for.1116","title":"Risk factor beta conditional value‐at‐risk","year":2008,"lang":"en","type":"article","venue":"Journal of Forecasting","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":"York University","funders":"","keywords":"Portfolio; Econometrics; Economics; Value at risk; Index (typography); Benchmark (surveying); Value (mathematics); Percentile; Asset (computer security); Capital asset pricing model; Portfolio optimization; Estimation; Financial economics; Statistics; Computer science; Mathematics; Risk management; Finance","score_opus":0.06788871973516089,"score_gpt":0.21945121277255272,"score_spread":0.15156249303739183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148290296","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":[0.12841429,0.0011868755,0.85933375,0.0005226584,0.000113829854,0.000076958204,0.00049722655,0.00046475165,0.00938971],"genre_scores_gemma":[0.94920576,0.0009192404,0.04548266,0.000072399314,0.00018718802,0.00007006155,0.00056662736,0.000074213815,0.0034217527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9987055,0.00041981513,0.000047806163,0.00028750248,0.00042438236,0.00011487466],"domain_scores_gemma":[0.9932573,0.0041664047,0.0009666846,0.00071566226,0.0007562815,0.00013772503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029559783,0.0008370856,0.00096902734,0.0019245305,0.0002975088,0.002378747,0.0010554101,0.0007694909,0.004963653],"category_scores_gemma":[0.02149161,0.00039069046,0.0006762819,0.0011542988,0.00089798815,0.0026677763,0.0008184493,0.0016824851,0.0007910078],"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":[0.00012996185,0.00006491913,0.024356168,0.0000834875,0.00029013082,0.0003004477,0.00018727127,0.63422495,0.0015484102,0.2530439,0.0034242035,0.0823462],"study_design_scores_gemma":[0.000012820715,0.000036943187,0.0070138834,0.00004173922,0.00004011377,0.00013482929,0.00002416503,0.82696337,0.001010171,0.162982,0.0016851107,0.000054845128],"about_ca_topic_score_codex":0.0040121907,"about_ca_topic_score_gemma":0.0016167861,"teacher_disagreement_score":0.004963653,"about_ca_system_score_codex":0.0009544151,"about_ca_system_score_gemma":0.0005737257,"threshold_uncertainty_score":0.016605139},"labels":[],"label_agreement":null},{"id":"W2151212654","doi":"10.1002/for.872","title":"Forecasting some low‐predictability time series using diffusion indices","year":2003,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; McGill University; Center for Interuniversity Research and Analysis on Organizations","funders":"McGill University","keywords":"Predictability; Index (typography); Econometrics; Diffusion; Horizon; Series (stratigraphy); Economics; Time horizon; Consensus forecast; Investment (military); Value (mathematics); Time series; Computer science; Statistics; Mathematics; Finance","score_opus":0.04451176708216419,"score_gpt":0.22119337338306871,"score_spread":0.17668160630090451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2151212654","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":[0.885389,0.00072137493,0.10820416,0.0009787402,0.00007294061,0.00002893615,0.0004206094,0.00024862145,0.0039357315],"genre_scores_gemma":[0.993411,0.0002135344,0.0058552804,0.00001571804,0.00003261967,0.0000065760237,0.00018844721,0.000009816645,0.00026699208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995197,0.00015002319,0.00004592962,0.000077030396,0.000166856,0.000040573086],"domain_scores_gemma":[0.9915788,0.0055545815,0.0015443342,0.00041481238,0.0007225631,0.0001849011],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035681992,0.00047515947,0.0006422902,0.0011828126,0.00023338805,0.0015338929,0.0004898436,0.0008113126,0.0008529441],"category_scores_gemma":[0.02143611,0.0002452577,0.0003619903,0.00091618806,0.00039828237,0.0023080353,0.0005671135,0.0011254541,0.00014555191],"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":[0.00040395116,0.000081788174,0.055485517,0.00012519577,0.00015200033,0.00023858428,0.00026914492,0.8445149,0.0053936383,0.038129844,0.001887621,0.05331781],"study_design_scores_gemma":[0.000008485944,0.00001511269,0.0032640873,0.000007867203,0.000009234167,0.000014055778,0.000016938586,0.9850936,0.0009198211,0.010395267,0.00024375133,0.00001171211],"about_ca_topic_score_codex":0.004736893,"about_ca_topic_score_gemma":0.0026398806,"teacher_disagreement_score":0.004736893,"about_ca_system_score_codex":0.0007714189,"about_ca_system_score_gemma":0.00032159776,"threshold_uncertainty_score":0.018870711},"labels":[],"label_agreement":null},{"id":"W2160439741","doi":"10.1002/for.2353","title":"A Simple Linear Regression Approach to Modeling and Forecasting Mortality Rates","year":2015,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; National Taiwan University; University of Waterloo; National Science Council","keywords":"Econometrics; Statistics; Linear regression; Regression; Mortality rate; Regression analysis; Lag; Logarithm; Linear model; Mathematics; Simple linear regression; Computer science; Demography","score_opus":0.21625717313170992,"score_gpt":0.375555006464455,"score_spread":0.15929783333274508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160439741","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":[0.025406161,0.00091225666,0.96747106,0.0006734133,0.00014816689,0.00014224142,0.00096124277,0.00091683253,0.0033685085],"genre_scores_gemma":[0.5642147,0.0039063394,0.40242362,0.00037318937,0.0006279546,0.00083327916,0.002007704,0.00021402768,0.025399249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986755,0.00063974096,0.000094888805,0.00031263105,0.00020186363,0.000075333795],"domain_scores_gemma":[0.9987632,0.0007190648,0.00021691594,0.000077100885,0.00019182934,0.000031941236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023422574,0.0012400475,0.0010029151,0.0018107315,0.00032041036,0.00096718356,0.002045984,0.0014806321,0.0041955705],"category_scores_gemma":[0.00583519,0.0005407095,0.0016857473,0.0025318516,0.00035739594,0.0012782734,0.00066226587,0.0015268801,0.001897004],"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":[0.00005930194,0.00011777021,0.0059307828,0.00022696365,0.00021644843,0.00022421955,0.00017901916,0.86898476,0.0020510666,0.03071479,0.0026977276,0.08859707],"study_design_scores_gemma":[0.000008307289,0.00005209122,0.0009774131,0.000015111691,0.000029095314,0.000042436466,0.000017023553,0.9865101,0.00028028482,0.009887455,0.0021539673,0.000026759239],"about_ca_topic_score_codex":0.013713714,"about_ca_topic_score_gemma":0.0100766495,"teacher_disagreement_score":0.013713714,"about_ca_system_score_codex":0.000796624,"about_ca_system_score_gemma":0.00091454526,"threshold_uncertainty_score":0.027267814},"labels":[],"label_agreement":null},{"id":"W2194956916","doi":"10.1002/for.2397","title":"Bayesian Analysis of a Threshold Stochastic Volatility Model","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Stochastic volatility; Econometrics; Threshold model; Volatility (finance); Particle filter; Threshold limit value; Markov chain Monte Carlo; Mathematics; Bayesian probability; Financial models with long-tailed distributions and volatility clustering; Statistics; Forward volatility; Kalman filter","score_opus":0.08219115624416455,"score_gpt":0.24869530454509473,"score_spread":0.1665041483009302,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2194956916","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":[0.090454124,0.00053167995,0.90293807,0.0008022349,0.00004078676,0.000050530718,0.00031766866,0.00022198725,0.0046429443],"genre_scores_gemma":[0.9513507,0.0007344946,0.04189896,0.00015095771,0.00008264192,0.000104768886,0.0004324966,0.000102091384,0.005142758],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990007,0.00036880348,0.000052705916,0.00021695741,0.00021336271,0.00014732666],"domain_scores_gemma":[0.9955448,0.0031610928,0.00055428664,0.00016085082,0.00040740945,0.0001714243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00333571,0.0006647884,0.0016137224,0.0013875557,0.00054240774,0.0018619009,0.0017446547,0.001785995,0.0030377312],"category_scores_gemma":[0.011520958,0.00083856017,0.0010029051,0.001052176,0.0011494003,0.0028282313,0.0012106784,0.00179269,0.00031445295],"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":[0.000073209514,0.00003070543,0.002359827,0.00006350316,0.00006982324,0.0001697293,0.00012219339,0.7825832,0.0008842509,0.20261899,0.0010563354,0.009968157],"study_design_scores_gemma":[0.0000070309493,0.000005792797,0.00028053147,0.000006159571,0.0000075555995,0.000013953365,0.000007448014,0.97567016,0.000054172157,0.02374558,0.00019273089,0.000008939304],"about_ca_topic_score_codex":0.012930968,"about_ca_topic_score_gemma":0.0057614595,"teacher_disagreement_score":0.012930968,"about_ca_system_score_codex":0.0014605548,"about_ca_system_score_gemma":0.00143327,"threshold_uncertainty_score":0.025711358},"labels":[],"label_agreement":null},{"id":"W2300215694","doi":"10.1002/for.2398","title":"Forecasting Errors, Directional Accuracy and Profitability of Currency Trading: The Case of EUR/USD Exchange Rate","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"","keywords":"Econometrics; Exchange rate; Trading strategy; Profitability index; Currency; Economics; Multivariate statistics; Benchmark (surveying); Computer science; Statistics; Finance; Mathematics; Monetary economics","score_opus":0.1923253549047189,"score_gpt":0.2702042780884004,"score_spread":0.07787892318368153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2300215694","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":[0.98866534,0.00050592766,0.008941583,0.0002968221,0.000023863624,0.000007738117,0.00029655814,0.000046012203,0.0012161785],"genre_scores_gemma":[0.99804014,0.00016737408,0.0013163646,0.000009266147,0.00001536039,0.000002478098,0.00026103263,0.000007736361,0.0001803037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9992436,0.0003167344,0.000064778644,0.00010401016,0.00017749729,0.00009335449],"domain_scores_gemma":[0.982154,0.013499401,0.0020508522,0.0010385782,0.0010000862,0.00025712696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061412547,0.00066482456,0.00071719306,0.0008147349,0.0002509874,0.0014516162,0.00046992206,0.00071857293,0.0008246365],"category_scores_gemma":[0.022668688,0.00023221145,0.0005368108,0.00093875214,0.0005994741,0.0014816265,0.0008359708,0.001129118,0.00015819017],"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":[0.00080902356,0.000100593774,0.17834093,0.00008022078,0.00027510556,0.0004590253,0.00017527094,0.7782128,0.0010346633,0.0059253597,0.0013877082,0.033199262],"study_design_scores_gemma":[0.000017762814,0.00010758142,0.049213577,0.00002233615,0.000084526946,0.0001063311,0.00012573691,0.94348997,0.0011553232,0.0053095026,0.00032979524,0.00003745606],"about_ca_topic_score_codex":0.009749956,"about_ca_topic_score_gemma":0.0054304167,"teacher_disagreement_score":0.009749956,"about_ca_system_score_codex":0.00038426588,"about_ca_system_score_gemma":0.0004631421,"threshold_uncertainty_score":0.03247845},"labels":[],"label_agreement":null},{"id":"W2343721382","doi":"10.1002/for.2416","title":"Yield Curve Forecasting with the Burg Model","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Autoregressive model; Yield curve; Econometrics; Yield (engineering); Impulse response; Economics; Mathematics; Recursion (computer science); Bond; Applied mathematics; Algorithm; Finance; Mathematical analysis","score_opus":0.21279049440670839,"score_gpt":0.22370225036014038,"score_spread":0.010911755953431995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343721382","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":[0.2027934,0.0008941911,0.78259826,0.0012643303,0.00011581448,0.00004107893,0.0007545598,0.0010740968,0.010464309],"genre_scores_gemma":[0.9355388,0.00047198604,0.058074642,0.00012516264,0.00007606333,0.000058012964,0.0007200542,0.000079769045,0.004855471],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972206,0.00009684592,0.00001630996,0.00006667076,0.00006894546,0.000029033587],"domain_scores_gemma":[0.9989881,0.00049775327,0.00016180032,0.0000994615,0.00021419283,0.000038586175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013268283,0.0005277495,0.0006924285,0.0007146165,0.00021215645,0.0011307261,0.0007599649,0.001226139,0.0019995046],"category_scores_gemma":[0.0044664713,0.00040540737,0.0004677173,0.0007471154,0.0004617361,0.0015219417,0.0005347976,0.00084577076,0.00064692827],"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":[0.000037526577,0.00001400627,0.0021778804,0.000016102866,0.000023196399,0.00004542656,0.000046411446,0.95847046,0.0006879689,0.017508484,0.0015224775,0.019450035],"study_design_scores_gemma":[0.0000027431222,0.0000050055664,0.00013295854,0.0000021577798,0.0000021866344,0.0000043991513,0.0000024215776,0.9966582,0.000090238085,0.0027871167,0.000307984,0.0000044562657],"about_ca_topic_score_codex":0.009350769,"about_ca_topic_score_gemma":0.0039205267,"teacher_disagreement_score":0.009350769,"about_ca_system_score_codex":0.00054834643,"about_ca_system_score_gemma":0.0006123634,"threshold_uncertainty_score":0.018592715},"labels":[],"label_agreement":null},{"id":"W2346528261","doi":"10.1002/for.2426","title":"Bayesian Forecasting for Time Series of Categorical Data","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"Indian Statistical Institute","keywords":"Categorical variable; Bayesian probability; Frequentist inference; Computer science; Time series; Series (stratigraphy); Econometrics; Autoregressive model; Bayesian average; Data mining; Variable-order Bayesian network; Artificial intelligence; Machine learning; Bayesian inference; Mathematics","score_opus":0.3132649776571354,"score_gpt":0.3992068342915667,"score_spread":0.08594185663443132,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2346528261","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":[0.025368784,0.0011099629,0.9707444,0.0010622725,0.00008664861,0.00003333886,0.00026127786,0.00017399398,0.0011593421],"genre_scores_gemma":[0.7611405,0.0040201223,0.2268139,0.0003342845,0.0005417142,0.0002945791,0.0013810741,0.00009555774,0.0053782878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9969337,0.0016220494,0.00014754645,0.0005703241,0.0005344117,0.00019184443],"domain_scores_gemma":[0.9823893,0.0147786625,0.0013818485,0.0005963525,0.00064594083,0.00020782623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010364346,0.0008235061,0.0018411918,0.0020141168,0.0005459962,0.0014966085,0.0018579855,0.0021689062,0.002228642],"category_scores_gemma":[0.035311982,0.00054583536,0.0013395362,0.0025106478,0.001140816,0.0033746809,0.001169923,0.0030013565,0.00047796388],"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":[0.0001252832,0.000050586994,0.005028114,0.00023409656,0.00015199208,0.00016994405,0.0003207704,0.6768734,0.00075383094,0.22596271,0.002193582,0.088135704],"study_design_scores_gemma":[0.000005619643,0.000019038931,0.0006701944,0.000022873008,0.000012537619,0.000025082401,0.000025330477,0.8918014,0.000095031384,0.10642646,0.00088085816,0.000015462962],"about_ca_topic_score_codex":0.007660638,"about_ca_topic_score_gemma":0.005201417,"teacher_disagreement_score":0.010364346,"about_ca_system_score_codex":0.0014075871,"about_ca_system_score_gemma":0.0009911876,"threshold_uncertainty_score":0.05481249},"labels":[],"label_agreement":null},{"id":"W2511204714","doi":"10.1002/for.2432","title":"Integrating Quarterly Data into a Dynamic Factor Model of US Monthly GDP","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Econometrics; Dynamic factor; Markov chain Monte Carlo; Gross domestic product; Bayesian probability; Markov chain; Statistics; Monte Carlo method; Economics; Mathematics; Macroeconomics","score_opus":0.172740767795882,"score_gpt":0.2626324939947823,"score_spread":0.08989172619890029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511204714","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":[0.13996474,0.000683059,0.84611243,0.0019280047,0.00015958642,0.00008444838,0.0021935236,0.0004455502,0.008428727],"genre_scores_gemma":[0.86821264,0.0015916941,0.11614282,0.00029823394,0.00016970882,0.00024341866,0.0031759983,0.00017681056,0.009988606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992155,0.00031204178,0.00003942139,0.00023345277,0.00013042719,0.0000691183],"domain_scores_gemma":[0.997934,0.0012539787,0.00037504744,0.00018198986,0.00019578174,0.00005915568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019716104,0.00046870878,0.000690402,0.0009816113,0.00032860183,0.0015353367,0.0009143849,0.0009721951,0.0029766455],"category_scores_gemma":[0.009797689,0.0005655112,0.00092039595,0.0017767181,0.00053558295,0.0022172576,0.00074367947,0.0015109429,0.00068675826],"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":[0.00006052716,0.000057291323,0.014728796,0.00005938324,0.00007938925,0.00014483988,0.00018986207,0.7977877,0.0004657816,0.15004548,0.0031081794,0.033272773],"study_design_scores_gemma":[0.000017678467,0.000028951474,0.002768263,0.000024623154,0.000026705147,0.00005017646,0.00003013108,0.947582,0.00016108644,0.044169776,0.005111896,0.00002871612],"about_ca_topic_score_codex":0.032151125,"about_ca_topic_score_gemma":0.023200216,"teacher_disagreement_score":0.032151125,"about_ca_system_score_codex":0.0013750638,"about_ca_system_score_gemma":0.0013192313,"threshold_uncertainty_score":0.06392801},"labels":[],"label_agreement":null},{"id":"W2521147242","doi":"10.1002/for.2439","title":"Benchmark Forecast and Error Modeling","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Benchmarking; Benchmark (surveying); Computer science; Autoregressive model; Process (computing); Regression; Regression analysis; Software; Data mining; Econometrics; Machine learning; Statistics; Mathematics; Economics","score_opus":0.19540538017296816,"score_gpt":0.23596803792719317,"score_spread":0.04056265775422502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521147242","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":[0.02377605,0.00037350386,0.9654249,0.00060753914,0.00021080601,0.00017990674,0.0014928924,0.0012609592,0.0066734455],"genre_scores_gemma":[0.6275318,0.00078034744,0.35406116,0.00025503803,0.00030577357,0.00081476424,0.0058638523,0.00048532858,0.009901892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99372524,0.0030382753,0.00038545256,0.00096509006,0.0014794413,0.00040643802],"domain_scores_gemma":[0.98583233,0.0069212196,0.0019391906,0.0019317925,0.0031441278,0.00023135869],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010894947,0.0013980496,0.0015009744,0.0022486118,0.0006085905,0.0028221363,0.0024202631,0.0018302953,0.0048716827],"category_scores_gemma":[0.056990694,0.00052179146,0.0013207827,0.0034315,0.0007688122,0.0028334213,0.0016584757,0.0024239998,0.0015023978],"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":[0.00015591021,0.00006921471,0.0074860933,0.0001744246,0.00011274088,0.00008713602,0.00020154513,0.76191396,0.00045773265,0.13321848,0.009175289,0.08694743],"study_design_scores_gemma":[0.000013109318,0.00003348447,0.0015164461,0.00005042992,0.000018114806,0.000021507145,0.00004663521,0.95661426,0.00082749134,0.037279226,0.0035483527,0.000030841602],"about_ca_topic_score_codex":0.014519912,"about_ca_topic_score_gemma":0.006241895,"teacher_disagreement_score":0.014519912,"about_ca_system_score_codex":0.0018073115,"about_ca_system_score_gemma":0.0015089228,"threshold_uncertainty_score":0.057618618},"labels":[],"label_agreement":null},{"id":"W2563942437","doi":"10.1002/for.2501","title":"Extracting information shocks from the Bank of England inflation density forecasts","year":2017,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"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":"Universidad de Cantabria; University of Leicester","keywords":"Inflation (cosmology); Shock (circulatory); Econometrics; Variable (mathematics); Variance (accounting); Ex-ante; Set (abstract data type); Economics; Measure (data warehouse); Point (geometry); Quarter (Canadian coin); Computer science; Macroeconomics; Mathematics; Data mining; Accounting","score_opus":0.11782065606469777,"score_gpt":0.24147867144410867,"score_spread":0.1236580153794109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2563942437","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":[0.86709696,0.0009844097,0.108038634,0.0009041087,0.00026168828,0.00013021419,0.014924285,0.0013446063,0.0063151647],"genre_scores_gemma":[0.9823552,0.00036854146,0.011447196,0.00002808215,0.00010008517,0.00002306479,0.004758742,0.00003614564,0.00088295207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989862,0.0001729151,0.00011495627,0.00015846668,0.00048192238,0.00008556955],"domain_scores_gemma":[0.9888508,0.0064718593,0.0016406387,0.00092980376,0.0019660082,0.000140912],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014293378,0.0004269728,0.00057723833,0.003980411,0.00017737504,0.0015094753,0.00038764573,0.00066432793,0.0012297655],"category_scores_gemma":[0.026490249,0.00053136883,0.00023934706,0.0029438692,0.0002548798,0.0013081421,0.0007858363,0.0009880884,0.000689556],"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":[0.0014812484,0.00015923477,0.35316467,0.000465328,0.00029737144,0.0011800535,0.0015260822,0.21627814,0.01019578,0.013374427,0.01561908,0.3862585],"study_design_scores_gemma":[0.00006005167,0.00014716455,0.3877113,0.00012759044,0.0001035755,0.0002801295,0.00044289313,0.5770412,0.00859585,0.01380661,0.011502971,0.00018066488],"about_ca_topic_score_codex":0.015034422,"about_ca_topic_score_gemma":0.008774607,"teacher_disagreement_score":0.015034422,"about_ca_system_score_codex":0.0008053319,"about_ca_system_score_gemma":0.00054362614,"threshold_uncertainty_score":0.029893875},"labels":[],"label_agreement":null},{"id":"W2580857550","doi":"10.1002/for.2518","title":"Exchange rate forecasting and the performance of currency portfolios","year":2018,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Thompson Rivers University","funders":"Oesterreichische Nationalbank","keywords":"Currency; Exchange rate; Portfolio; Economics; Econometrics; Profitability index; Liberian dollar; Pound (networking); Trading strategy; Financial economics; Monetary economics; Computer science; Finance","score_opus":0.12416251519931788,"score_gpt":0.2340993400482817,"score_spread":0.10993682484896382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580857550","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":[0.9759177,0.0005120972,0.021098744,0.00030975102,0.000023639896,0.000012441369,0.00011094869,0.00008651033,0.0019281638],"genre_scores_gemma":[0.99736315,0.00008995106,0.0022838858,0.000010616585,0.0000100871775,0.0000019493534,0.00009032259,0.000005119246,0.00014498431],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906534,0.0005075375,0.00007164975,0.00012120169,0.00016804783,0.00006613942],"domain_scores_gemma":[0.98857623,0.00830034,0.0015971226,0.0005866514,0.0007266291,0.00021308631],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006617841,0.000534739,0.0006263201,0.0009949626,0.00016272647,0.002139911,0.00028779203,0.0006301308,0.0009743483],"category_scores_gemma":[0.025281934,0.00024472395,0.0003105898,0.0007604829,0.00026115272,0.0018303161,0.00053663633,0.00062295794,0.00019932257],"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":[0.0009373732,0.00021961861,0.1835625,0.000061720595,0.0005893882,0.00011464696,0.00010337655,0.7341859,0.0023996534,0.0033339392,0.00064098806,0.07385098],"study_design_scores_gemma":[0.000038054208,0.00016626263,0.03690453,0.000024827232,0.00008428689,0.00004342571,0.0000623922,0.9574113,0.0014924908,0.0035302297,0.00021515613,0.000027125248],"about_ca_topic_score_codex":0.0022209869,"about_ca_topic_score_gemma":0.001420079,"teacher_disagreement_score":0.006617841,"about_ca_system_score_codex":0.0003872695,"about_ca_system_score_gemma":0.00036270847,"threshold_uncertainty_score":0.034998894},"labels":[],"label_agreement":null},{"id":"W2610239431","doi":"10.1002/for.2472","title":"The impact of parameter and model uncertainty on market risk predictions from GARCH‐type models","year":2017,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung","keywords":"Econometrics; Pooling; Bayesian probability; Mathematics; Linear model; Autoregressive conditional heteroskedasticity; Bayesian vector autoregression; Statistics; Computer science; Volatility (finance); Artificial intelligence","score_opus":0.10884189097326721,"score_gpt":0.2853854384292182,"score_spread":0.176543547455951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2610239431","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":[0.94538075,0.00072239066,0.051070143,0.00048432086,0.000029276693,0.000018927436,0.00021013686,0.00035486798,0.0017292296],"genre_scores_gemma":[0.9965113,0.00009822524,0.0030570093,0.000027293077,0.00001700309,0.000006134349,0.00014936992,0.000024776728,0.000108919405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965565,0.0020048653,0.00017217208,0.0002975,0.00073029415,0.00023876934],"domain_scores_gemma":[0.9199425,0.07163765,0.0028931291,0.0031407555,0.0019388312,0.00044717427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.017549789,0.0008420263,0.0010738315,0.0011745723,0.00049100805,0.0017632558,0.0008256111,0.0011675996,0.00091225037],"category_scores_gemma":[0.054980528,0.00060969504,0.00097228226,0.00088170485,0.0007591752,0.0029990275,0.0011291084,0.001587012,0.00017245692],"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":[0.00036816226,0.000048740396,0.011686066,0.000038022717,0.00022805452,0.000062232815,0.00007276302,0.96916807,0.00091205275,0.001311383,0.0002714798,0.015833005],"study_design_scores_gemma":[0.000015295967,0.00009921967,0.004318639,0.000018568477,0.00005280237,0.000026457203,0.000026939071,0.991531,0.0018267862,0.0019930333,0.00006285236,0.000028403228],"about_ca_topic_score_codex":0.00809875,"about_ca_topic_score_gemma":0.004985906,"teacher_disagreement_score":0.017549789,"about_ca_system_score_codex":0.00074384,"about_ca_system_score_gemma":0.0007813102,"threshold_uncertainty_score":0.09281325},"labels":[],"label_agreement":null},{"id":"W2887488964","doi":"10.1002/for.2541","title":"Workforce forecasting models: A systematic review","year":2018,"lang":"en","type":"review","venue":"Journal of Forecasting","topic":"Complex Systems and Decision Making","field":"Decision Sciences","cited_by":68,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Workforce; Scope (computer science); Workforce planning; Reliability (semiconductor); Computer science; Workforce management; Relevance (law); Analytics; Management science; Data science; Economics; Political science; Economic growth","score_opus":0.6639334249707961,"score_gpt":0.48006218734989964,"score_spread":0.18387123762089647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887488964","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038854813,0.9971233,0.0008284794,0.0006290915,0.00016033275,0.00006193641,0.00032551013,0.00001864982,0.000464186],"genre_scores_gemma":[0.0046808315,0.99371064,0.0009907836,0.00017209678,0.000086021355,0.000072712915,0.00018612412,0.000005397712,0.00009548403],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9975858,0.00092903123,0.00057249516,0.00024343113,0.0005925616,0.00007663386],"domain_scores_gemma":[0.9757858,0.020139318,0.0017342428,0.0003437908,0.0018463243,0.0001506168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006205455,0.0015666686,0.0028529032,0.008140787,0.00036379707,0.0017744082,0.0026045698,0.001574625,0.007320495],"category_scores_gemma":[0.03182168,0.0007133567,0.0044821827,0.008365701,0.00040667018,0.0024125574,0.0009677274,0.0012169906,0.00082878524],"study_design_candidate":"systematic_review","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":[0.00014251898,0.00006876039,0.0013890815,0.35018966,0.0028149819,0.00013743366,0.00020165711,0.003739006,0.0001125114,0.0029425384,0.019062662,0.6191993],"study_design_scores_gemma":[0.00010918773,0.00024255866,0.0049204305,0.72688615,0.013342143,0.00056690845,0.00045074752,0.0035043575,0.0003462419,0.006171479,0.2433366,0.0001232157],"about_ca_topic_score_codex":0.009523543,"about_ca_topic_score_gemma":0.014391639,"teacher_disagreement_score":0.009523543,"about_ca_system_score_codex":0.0017892426,"about_ca_system_score_gemma":0.008790681,"threshold_uncertainty_score":0.03281796},"labels":[],"label_agreement":null},{"id":"W2894383657","doi":"10.1002/for.2556","title":"Does geographic location matter to stock return predictability?","year":2018,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Predictability; Random walk; Portfolio; Econometrics; Stock (firearms); Variance (accounting); Modern portfolio theory; Random walk hypothesis; Economics; Financial economics; Stock market; Statistics; Geography; Mathematics","score_opus":0.034579764358434135,"score_gpt":0.22344097409364155,"score_spread":0.18886120973520742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894383657","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":[0.9969201,0.00037406874,0.0010995956,0.00041572412,0.000009891387,0.000004143321,0.00013416879,0.000012259371,0.0010300371],"genre_scores_gemma":[0.99970764,0.00007055244,0.000063768915,0.000010557318,0.000014147557,7.585099e-7,0.000034009827,0.0000013836782,0.0000971371],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99953914,0.00018922133,0.000038695413,0.000112046655,0.00006012303,0.000060755792],"domain_scores_gemma":[0.98189205,0.00911438,0.00661224,0.00091937,0.0008031179,0.00065896154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010345717,0.00017997205,0.00039226832,0.00077958556,0.00022930656,0.0012788555,0.0003511506,0.00063020113,0.0030136353],"category_scores_gemma":[0.012792975,0.00014672913,0.0003624584,0.0013463213,0.00068361737,0.0011370514,0.00048649622,0.000381263,0.000405504],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015021456,0.00004123796,0.9850365,0.000028534776,0.0002272611,0.00025120078,0.00012309654,0.004243074,0.00053774053,0.0010467957,0.00025409643,0.008060306],"study_design_scores_gemma":[0.000023266453,0.00013584792,0.97934157,0.000023994984,0.00017703841,0.00025596432,0.0005635946,0.014378812,0.0004859805,0.0039951717,0.00059865456,0.000020023892],"about_ca_topic_score_codex":0.0061983163,"about_ca_topic_score_gemma":0.004715712,"teacher_disagreement_score":0.0061983163,"about_ca_system_score_codex":0.00025440974,"about_ca_system_score_gemma":0.00022439881,"threshold_uncertainty_score":0.012324452},"labels":[],"label_agreement":null},{"id":"W2909775723","doi":"10.1002/for.2569","title":"The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Futures contract; Volatility (finance); Economics; Futures market; Econometrics; Stock market; Jump; Stock market volatility; Financial economics; Stock (firearms)","score_opus":0.04662875508698446,"score_gpt":0.23033332502773607,"score_spread":0.1837045699407516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909775723","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":[0.9868138,0.0027074972,0.0051890505,0.0017301727,0.0000663193,0.000010516809,0.0001624489,0.000050634346,0.0032695706],"genre_scores_gemma":[0.99854714,0.0006537358,0.0003303882,0.00006320669,0.00011953105,0.0000014439759,0.000085003376,0.0000075365183,0.00019203417],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99921393,0.00018742513,0.00007000668,0.00020185579,0.00024111143,0.00008566108],"domain_scores_gemma":[0.9525865,0.03571757,0.006568904,0.0018567934,0.0023782067,0.0008919517],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0032519319,0.0004217821,0.00055782025,0.0011922687,0.0004476257,0.001734957,0.0012120462,0.0014337165,0.0029750892],"category_scores_gemma":[0.027893838,0.00029983578,0.0006188959,0.0008709448,0.0009723142,0.0030706131,0.0010345185,0.0022906666,0.00033947927],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002916004,0.00077184965,0.79751945,0.00037434985,0.0010762722,0.0015826757,0.00073479227,0.060441542,0.0050184946,0.016184606,0.0030104825,0.11036942],"study_design_scores_gemma":[0.00020534616,0.0005283842,0.50264263,0.00023285062,0.0008847399,0.00027539188,0.0010418219,0.45627472,0.004618311,0.029721726,0.0034318492,0.00014226708],"about_ca_topic_score_codex":0.007030436,"about_ca_topic_score_gemma":0.003167257,"teacher_disagreement_score":0.007030436,"about_ca_system_score_codex":0.00039974836,"about_ca_system_score_gemma":0.0003617809,"threshold_uncertainty_score":0.017198026},"labels":[],"label_agreement":null},{"id":"W2947491227","doi":"10.1002/for.2615","title":"The dynamic effect of macroeconomic news on the euro/US dollar exchange rate","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; Brock University","funders":"","keywords":"Economics; Recession; Volatility (finance); Monetary economics; Liberian dollar; Us dollar; Sovereign debt; Debt; European debt crisis; Great recession; Exchange rate; Sovereignty; International economics; Econometrics; Macroeconomics; Keynesian economics; European union; Finance; European integration; Political science","score_opus":0.04667145162187038,"score_gpt":0.22362619186065233,"score_spread":0.17695474023878194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947491227","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":[0.9945478,0.00046141958,0.0001765712,0.0005278905,0.000072833056,0.0000055535825,0.0010441105,0.00001852252,0.0031452612],"genre_scores_gemma":[0.9979791,0.0002707848,0.000040709303,0.000053685468,0.00006991362,0.0000022494928,0.0008978179,0.0000067811384,0.000678981],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996076,0.00010309735,0.00003772916,0.00006819201,0.00010038567,0.000083028026],"domain_scores_gemma":[0.991488,0.0038497557,0.0032545165,0.0002101327,0.000737868,0.00045971342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013018845,0.000259826,0.0003401004,0.00096872455,0.00018553348,0.0018723801,0.00016983289,0.0004867277,0.0031617503],"category_scores_gemma":[0.009200707,0.00014108393,0.00028088663,0.0010425106,0.00027351506,0.00074345554,0.0006297751,0.0009341016,0.000618028],"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":[0.0013893648,0.0004177779,0.9584854,0.00008079387,0.00036982144,0.0003989404,0.0001915577,0.008844597,0.0022091756,0.0015628581,0.0052455086,0.020804105],"study_design_scores_gemma":[0.00001887759,0.00019302373,0.9893227,0.000025911995,0.00010458979,0.00005088566,0.0003523339,0.0071233977,0.0008309955,0.00030406888,0.0016553442,0.000017858358],"about_ca_topic_score_codex":0.0057747136,"about_ca_topic_score_gemma":0.0049112984,"teacher_disagreement_score":0.0057747136,"about_ca_system_score_codex":0.00039827477,"about_ca_system_score_gemma":0.00029321542,"threshold_uncertainty_score":0.011482179},"labels":[],"label_agreement":null},{"id":"W2994694912","doi":"10.1002/for.2641","title":"Forecasting of dependence, market, and investment risks of a global index portfolio","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Vine copula; Portfolio; Economics; Value at risk; Portfolio optimization; Downside risk; Econometrics; CVAR; Financial economics; Diversification (marketing strategy); Index (typography); Expected shortfall; Risk management; Copula (linguistics); Business; Finance; Computer science","score_opus":0.06847074415771963,"score_gpt":0.2600649460413053,"score_spread":0.19159420188358567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994694912","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":[0.9937185,0.00005361792,0.0054572714,0.000042556592,0.0000037465893,0.0000061616543,0.000111132395,0.000022687673,0.00058441365],"genre_scores_gemma":[0.9984603,0.000034872937,0.001068513,0.000004588893,0.000003984516,0.0000027720512,0.00024218691,0.000004135121,0.00017871629],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998047,0.00005292177,0.0000101542655,0.000054056003,0.000049973467,0.000028356495],"domain_scores_gemma":[0.9984971,0.0007443265,0.00034792122,0.00014220657,0.00017458094,0.000093764334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015499331,0.0004229812,0.0003758807,0.0008444356,0.00013686788,0.00074442365,0.00030703182,0.0003249008,0.0006665581],"category_scores_gemma":[0.0046943603,0.0001713871,0.00039250788,0.000590088,0.00020126249,0.00090275327,0.00043763072,0.00054372224,0.000120629506],"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":[0.00046969388,0.00016831639,0.39021465,0.00004123233,0.00036004727,0.00043713112,0.00014143348,0.5466415,0.0092257885,0.005796779,0.0011683295,0.04533505],"study_design_scores_gemma":[0.000009370123,0.0001614021,0.15397774,0.000007696458,0.00005399976,0.00007511452,0.00006683168,0.8410856,0.0023614552,0.001697401,0.00048437653,0.000018947529],"about_ca_topic_score_codex":0.0038267006,"about_ca_topic_score_gemma":0.0029802988,"teacher_disagreement_score":0.0038267006,"about_ca_system_score_codex":0.0004591098,"about_ca_system_score_gemma":0.00026182138,"threshold_uncertainty_score":0.00819689},"labels":[],"label_agreement":null},{"id":"W2994808218","doi":"10.1002/for.2639","title":"A predictive model of train delays on a railway line","year":2019,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Department of Science and Technology of Sichuan Province; China Scholarship Council","keywords":"Computer science; Artificial neural network; Python (programming language); Random forest; Predictive modelling; Artificial intelligence; Machine learning","score_opus":0.02369394031512286,"score_gpt":0.201390566858223,"score_spread":0.17769662654310014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994808218","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":[0.91716754,0.00015508308,0.078583464,0.00027203836,0.00004648262,0.000024268747,0.001495351,0.00043950998,0.0018162174],"genre_scores_gemma":[0.99548244,0.000053591884,0.0032306267,0.000006063164,0.000007336527,0.000012826981,0.00037680395,0.000007920673,0.0008222803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999889,0.000019544403,0.0000057781854,0.000048015925,0.000018448358,0.000019213598],"domain_scores_gemma":[0.9996866,0.00015548953,0.000052405077,0.000017671075,0.000073781244,0.000014110097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034944192,0.00041928721,0.00025044093,0.00050344475,0.00019984323,0.00042897,0.0005127234,0.00041135214,0.0015214006],"category_scores_gemma":[0.0011723422,0.00019683469,0.00037083478,0.00071807764,0.00013610485,0.00046419888,0.00019301192,0.00061095704,0.00023258991],"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":[0.00007183722,0.000058303405,0.00927823,0.000017019216,0.000018664696,0.000072569725,0.0000324024,0.9763259,0.001449031,0.0007588163,0.0004377554,0.011479523],"study_design_scores_gemma":[0.0000013604978,0.0000064869896,0.0011422347,0.000001371851,0.0000028643224,0.0000031745851,0.000003647077,0.99853826,0.00012553245,0.00011744723,0.00005577166,0.0000018987416],"about_ca_topic_score_codex":0.05404642,"about_ca_topic_score_gemma":0.028810345,"teacher_disagreement_score":0.05404642,"about_ca_system_score_codex":0.0008088844,"about_ca_system_score_gemma":0.000581663,"threshold_uncertainty_score":0.10746366},"labels":[],"label_agreement":null},{"id":"W2999793792","doi":"10.1002/for.2650","title":"Short‐run wavelet‐based covariance regimes for applied portfolio management","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"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 of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Covariance; Portfolio; Wavelet; Econometrics; Computer science; Project portfolio management; Portfolio optimization; Economics; Financial economics; Mathematics; Statistics; Artificial intelligence","score_opus":0.08140277547360625,"score_gpt":0.2344701920577541,"score_spread":0.15306741658414785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999793792","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":[0.030280335,0.0003253974,0.96756446,0.00033012443,0.000037909118,0.000023110966,0.00006850074,0.00014609212,0.001224103],"genre_scores_gemma":[0.61229837,0.0011177195,0.3840756,0.0000912026,0.00017116003,0.00011438975,0.0003908452,0.0001321156,0.0016085131],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927837,0.0003510787,0.00005649315,0.000095193696,0.0001828431,0.000036046098],"domain_scores_gemma":[0.9955258,0.0030326035,0.0004847297,0.00040672772,0.0004580715,0.00009209381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0041921553,0.00045356495,0.0005212402,0.0013353678,0.00024708817,0.0015010429,0.0005989696,0.00085713924,0.0015120389],"category_scores_gemma":[0.016579613,0.00036554455,0.0004949796,0.0012490685,0.0005591833,0.0020760638,0.0008863566,0.0014619074,0.00034135312],"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":[0.000109123815,0.00012427509,0.0065187383,0.00009725658,0.00014289195,0.0000736217,0.00010534625,0.56180066,0.006096587,0.13657801,0.0018409465,0.2865125],"study_design_scores_gemma":[0.0000037049151,0.000010176811,0.0006770848,0.000009744075,0.0000036500576,0.000006843467,0.0000055779,0.97994184,0.0004386367,0.01850741,0.00038987005,0.000005391247],"about_ca_topic_score_codex":0.0010961551,"about_ca_topic_score_gemma":0.0007270669,"teacher_disagreement_score":0.0041921553,"about_ca_system_score_codex":0.0005112189,"about_ca_system_score_gemma":0.0006914348,"threshold_uncertainty_score":0.022170544},"labels":[],"label_agreement":null},{"id":"W3003465369","doi":"10.1002/for.2676","title":"Using the yield curve to forecast economic growth","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Econometrics; Yield (engineering); Yield curve; Quarter (Canadian coin); Economics; Interest rate; Gross domestic product; Product (mathematics); Time series; Sample (material); Nowcasting; Series (stratigraphy); Computer science; Mathematics; Machine learning; Macroeconomics","score_opus":0.368215246217224,"score_gpt":0.2699758576157541,"score_spread":0.09823938860146991,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003465369","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":[0.84356093,0.0011208662,0.13859165,0.0010608408,0.00020765993,0.000049119742,0.0015719539,0.0009885045,0.012848538],"genre_scores_gemma":[0.99039686,0.0002766546,0.007843112,0.000021117834,0.000031028092,0.000008388798,0.0006665347,0.000023879054,0.00073248585],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996093,0.00014760758,0.00002543196,0.000065854605,0.00011574323,0.000036158654],"domain_scores_gemma":[0.9960977,0.0022877338,0.00039637252,0.00025685463,0.000858915,0.00010243314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021933329,0.0005118394,0.0003688517,0.001701533,0.00014666156,0.0011761222,0.00028318487,0.0005626265,0.0014263319],"category_scores_gemma":[0.014641954,0.00014528734,0.00027450465,0.0011632356,0.0002330943,0.0016448676,0.00050665357,0.0006645622,0.0006489157],"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":[0.00030060194,0.00004558751,0.07412094,0.000052617423,0.00007859946,0.000084124105,0.000073342104,0.8055309,0.0018690585,0.005962074,0.0027691165,0.109113105],"study_design_scores_gemma":[0.000007045586,0.000033447035,0.009890873,0.0000106901825,0.000008455573,0.00001283834,0.000028988263,0.9837685,0.0014555211,0.0038449958,0.0009249209,0.000013669665],"about_ca_topic_score_codex":0.011043959,"about_ca_topic_score_gemma":0.0029170103,"teacher_disagreement_score":0.011043959,"about_ca_system_score_codex":0.0004987935,"about_ca_system_score_gemma":0.00046795278,"threshold_uncertainty_score":0.021959364},"labels":[],"label_agreement":null},{"id":"W3014694269","doi":"10.1002/for.2689","title":"Predictive modeling of consumer color preference: Using retail data and merchandise images","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Color perception and design","field":"Psychology","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Multinomial logistic regression; Popularity; Sample (material); Preference; Consumer behaviour; Product (mathematics); Computer science; Order (exchange); Function (biology); Advertising; Econometrics; Business; Economics; Mathematics; Machine learning; Microeconomics; Psychology","score_opus":0.49151258115810076,"score_gpt":0.3769196897903212,"score_spread":0.11459289136777956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014694269","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":[0.9744056,0.00018803733,0.024158902,0.00014335963,0.000020094803,0.000025201452,0.0002712659,0.000118915304,0.000668654],"genre_scores_gemma":[0.9953526,0.00006300395,0.003962294,0.000012327499,0.000008460229,0.000008798185,0.00023958969,0.000003800544,0.0003491223],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984825,0.000057315723,0.0000066276493,0.000038966493,0.00002742072,0.000021355136],"domain_scores_gemma":[0.99841297,0.0011288995,0.000114349656,0.00011726027,0.00018272779,0.000043767595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085590594,0.0005330759,0.00038682012,0.0009588795,0.0001501986,0.0005649646,0.0005036931,0.00050095865,0.0010203316],"category_scores_gemma":[0.0029326866,0.00021450882,0.00046953675,0.0007273112,0.00025375857,0.00059612334,0.00022123703,0.00063363276,0.00025382295],"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":[0.0007133807,0.000758594,0.108810194,0.000056066594,0.000119453114,0.0001985738,0.00010082954,0.79103386,0.0025509654,0.0010941149,0.0010492734,0.09351473],"study_design_scores_gemma":[0.0000021435446,0.000011977528,0.0034933689,0.0000015931778,0.0000041233425,0.0000045333504,0.000007614077,0.9960855,0.00018677206,0.00017187503,0.00002797396,0.0000024998033],"about_ca_topic_score_codex":0.020242034,"about_ca_topic_score_gemma":0.013809327,"teacher_disagreement_score":0.020242034,"about_ca_system_score_codex":0.0006168087,"about_ca_system_score_gemma":0.00025334253,"threshold_uncertainty_score":0.040248454},"labels":[],"label_agreement":null},{"id":"W3015884958","doi":"10.1002/for.2691","title":"Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Volatility (finance); Cryptocurrency; Econometrics; Markov chain; Jump; Realized variance; Stochastic volatility; Computer science; Robustness (evolution); Economics; Machine learning","score_opus":0.05797193086110943,"score_gpt":0.24726486547757684,"score_spread":0.1892929346164674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015884958","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":[0.27557915,0.0007270419,0.7167734,0.0014823566,0.00012094713,0.0000874615,0.0005943945,0.00052885176,0.0041064373],"genre_scores_gemma":[0.9847088,0.00015660176,0.013481943,0.00008607567,0.000059207818,0.000040573315,0.00018208651,0.000017193017,0.0012676738],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992119,0.0003618872,0.000041256815,0.00015557541,0.00012115936,0.000108279746],"domain_scores_gemma":[0.99621946,0.0026099153,0.0004559687,0.00022836504,0.00031796066,0.0001685079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025171738,0.00056882744,0.0011479683,0.0008633339,0.00046344593,0.0011225599,0.0013988181,0.001030352,0.0026409156],"category_scores_gemma":[0.005953227,0.0004749627,0.0008856159,0.000609467,0.00060846057,0.001407894,0.00083212624,0.0015152083,0.00027696235],"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":[0.0002098362,0.000068569534,0.0056573898,0.000044741682,0.00008277236,0.00010698288,0.00005738424,0.945494,0.0010038746,0.031369954,0.00058893836,0.015315554],"study_design_scores_gemma":[0.0000027837434,0.0000061082915,0.00010289602,0.0000018211267,0.0000025013956,0.0000032870284,0.0000018624334,0.997335,0.000053334887,0.002436671,0.000051379247,0.0000022831962],"about_ca_topic_score_codex":0.009342306,"about_ca_topic_score_gemma":0.006893074,"teacher_disagreement_score":0.009342306,"about_ca_system_score_codex":0.001057558,"about_ca_system_score_gemma":0.0008838725,"threshold_uncertainty_score":0.018575847},"labels":[],"label_agreement":null},{"id":"W3037145279","doi":"10.1002/for.2717","title":"A causal model for short‐term time series analysis to predict incoming Medicare workload","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Workload; Computer science; Term (time); Time series; Ensemble forecasting; Ensemble learning; Series (stratigraphy); Machine learning; Interval (graph theory); Artificial intelligence; Mathematics","score_opus":0.18851718813666118,"score_gpt":0.38659307591517056,"score_spread":0.19807588777850937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037145279","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":[0.1717364,0.00083545974,0.82227355,0.001040462,0.00020476409,0.00008537815,0.0005308097,0.00061942503,0.0026737899],"genre_scores_gemma":[0.9690693,0.0005051954,0.027080942,0.00008736777,0.00012762165,0.00010423458,0.000344849,0.000028627439,0.002651756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994906,0.00016334113,0.00003272991,0.0001376503,0.0001002117,0.00007534268],"domain_scores_gemma":[0.9974269,0.0018615158,0.00025607942,0.000077696044,0.00029995642,0.00007779058],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020613512,0.0007263183,0.00072187505,0.0011311297,0.00047020926,0.00090819976,0.0013158683,0.0010631294,0.0026438523],"category_scores_gemma":[0.005238988,0.00044739904,0.0010447885,0.00081201986,0.00035791556,0.0009997345,0.00043646796,0.0014957557,0.00030971217],"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":[0.000057134457,0.00007551702,0.0046700947,0.000035222754,0.00008945609,0.00009931923,0.000052024592,0.9708581,0.0005771218,0.006183087,0.0006287129,0.016674206],"study_design_scores_gemma":[0.0000010566264,0.000004997125,0.00023297528,0.0000016479853,0.000005002141,0.0000036438405,0.0000023912096,0.9990336,0.000044333865,0.00061834685,0.000050105176,0.0000018876198],"about_ca_topic_score_codex":0.023099028,"about_ca_topic_score_gemma":0.01729122,"teacher_disagreement_score":0.023099028,"about_ca_system_score_codex":0.0009549078,"about_ca_system_score_gemma":0.0012205662,"threshold_uncertainty_score":0.045929193},"labels":[],"label_agreement":null},{"id":"W3037592550","doi":"10.1002/for.2716","title":"Forecast performance and bubble analysis in noncausal MAR(1, 1) processes","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Futures contract; Econometrics; Estimator; Nonlinear system; Bubble; Term (time); Gaussian; Variance (accounting); Mathematics; Series (stratigraphy); Lévy process; Applied mathematics; Economics; Statistical physics; Computer science; Statistics; Financial economics; Physics; Geology","score_opus":0.05588153369421907,"score_gpt":0.21931550334654565,"score_spread":0.1634339696523266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037592550","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":[0.692269,0.0006585146,0.30396467,0.0005030927,0.00005397636,0.000035443183,0.00015058677,0.0004899224,0.0018748398],"genre_scores_gemma":[0.99169296,0.000116175834,0.007737005,0.000017733875,0.000015169311,0.000007092822,0.00013526592,0.000014889961,0.00026360384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992919,0.00040061426,0.000031301155,0.00009247695,0.00013014018,0.000053625216],"domain_scores_gemma":[0.9839355,0.013877274,0.00068608933,0.0004056638,0.00093807525,0.00015744293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0061025,0.0006374284,0.00070257473,0.0006794633,0.00020874671,0.0011059195,0.0006300909,0.0009413357,0.00095215184],"category_scores_gemma":[0.019708853,0.0003228108,0.00042396982,0.000325868,0.00075012055,0.001313014,0.00064820214,0.0011904921,0.00014316278],"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":[0.00024144624,0.000032295193,0.003733438,0.000039935647,0.00004141011,0.000050521434,0.000044840002,0.97675246,0.00107779,0.0049682753,0.00018352167,0.012834001],"study_design_scores_gemma":[0.000002365373,0.000009961091,0.00026581166,0.0000012244693,0.0000017202628,0.000002837465,0.0000017743588,0.99907327,0.00018110641,0.00044648448,0.0000112192465,0.000002216889],"about_ca_topic_score_codex":0.010954038,"about_ca_topic_score_gemma":0.0035226317,"teacher_disagreement_score":0.010954038,"about_ca_system_score_codex":0.0005672397,"about_ca_system_score_gemma":0.00066966057,"threshold_uncertainty_score":0.03227353},"labels":[],"label_agreement":null},{"id":"W3112242143","doi":"10.1002/for.2752","title":"Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect","year":2020,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Jump; Futures contract; Volatility (finance); Leverage effect; Leverage (statistics); Econometrics; Futures market; Economics; R&D intensity; Financial economics; Mathematics; Statistics; Autoregressive conditional heteroskedasticity; Physics","score_opus":0.02852553310382301,"score_gpt":0.19957350156064024,"score_spread":0.17104796845681725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112242143","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":[0.9911539,0.00017416502,0.007168926,0.0002730021,0.000018964005,0.00000810752,0.00014574033,0.00003681608,0.0010202847],"genre_scores_gemma":[0.9991266,0.00007300666,0.0004475411,0.000008477013,0.000017362328,0.000001691675,0.00011964057,0.0000025046488,0.00020325014],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997521,0.000058658767,0.000016714399,0.000057860758,0.000060474584,0.000054314012],"domain_scores_gemma":[0.99799746,0.0011469944,0.00031239702,0.00012663589,0.00024590082,0.00017060187],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016679912,0.00051639706,0.00049303105,0.0010235319,0.0002886755,0.00097051216,0.00045950824,0.0005402587,0.001129109],"category_scores_gemma":[0.0054386975,0.00020921257,0.00064025476,0.0005846658,0.0002649803,0.0014343385,0.00050303317,0.00075257214,0.00011441212],"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":[0.0004661616,0.00019652731,0.6064011,0.00008361249,0.00033635777,0.0004842536,0.00019441525,0.3243464,0.0049871146,0.0060959645,0.0014113147,0.05499682],"study_design_scores_gemma":[0.0000135099035,0.000046011377,0.056540217,0.000010726146,0.00006316455,0.000026665406,0.000076854354,0.94014096,0.0010376185,0.0017603727,0.0002651383,0.000018779627],"about_ca_topic_score_codex":0.015393791,"about_ca_topic_score_gemma":0.011412102,"teacher_disagreement_score":0.015393791,"about_ca_system_score_codex":0.00046661694,"about_ca_system_score_gemma":0.0006019234,"threshold_uncertainty_score":0.030608356},"labels":[],"label_agreement":null},{"id":"W3113166639","doi":"10.1002/for.3019","title":"A multivariate GARCH–jump mixture model","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Jump; Multivariate statistics; Autoregressive conditional heteroskedasticity; Econometrics; Stock (firearms); Mathematics; Benchmark (surveying); Economics; Statistics; Volatility (finance); Geography; Physics","score_opus":0.1435665755762209,"score_gpt":0.2696766128286389,"score_spread":0.126110037252418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3113166639","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":[0.11932189,0.0008863881,0.86886936,0.0010600747,0.00020520514,0.000067170724,0.00080439076,0.0007883771,0.007997146],"genre_scores_gemma":[0.93666303,0.0005523811,0.04859247,0.0001564065,0.0002635993,0.00008504696,0.0009656905,0.0000996255,0.012621673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9990694,0.00028331255,0.000036457594,0.00021484388,0.0002855855,0.000110400986],"domain_scores_gemma":[0.99871564,0.0007048604,0.00018084486,0.00013103227,0.00019075519,0.00007696131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017529624,0.00065711234,0.0012038822,0.0011315321,0.00042554553,0.0015518662,0.0021118452,0.0014795349,0.0041280906],"category_scores_gemma":[0.0036677888,0.0006077764,0.0012838948,0.0013463458,0.00066978694,0.0020403827,0.0010419923,0.0018035363,0.00074347225],"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":[0.00024816082,0.00013890894,0.007922553,0.000101951315,0.00032756795,0.00030371928,0.00012508025,0.74731165,0.0030851634,0.18965706,0.0035191423,0.04725903],"study_design_scores_gemma":[0.000011434208,0.000014455171,0.0006874075,0.0000041318226,0.000023327984,0.000031923668,0.000004723142,0.9855773,0.00013844982,0.012839525,0.0006553274,0.000011999587],"about_ca_topic_score_codex":0.0077868593,"about_ca_topic_score_gemma":0.004622334,"teacher_disagreement_score":0.0077868593,"about_ca_system_score_codex":0.0007330847,"about_ca_system_score_gemma":0.00072116684,"threshold_uncertainty_score":0.015483081},"labels":[],"label_agreement":null},{"id":"W3121356139","doi":"10.1002/(sici)1099-131x(200004)19:3<201::aid-for753>3.0.co;2-4","title":"Neural network versus econometric models in forecasting inflation","year":2000,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Econometric model; Autoregressive integrated moving average; Econometrics; Autoregressive model; Bayesian vector autoregression; Artificial neural network; Inflation (cosmology); Computer science; Mean squared error; Bayesian probability; Economics; Time series; Statistics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.3101054711190852,"score_gpt":0.3916595949918031,"score_spread":0.08155412387271788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3121356139","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":[0.2135852,0.054210123,0.6886191,0.009871285,0.0012297255,0.00013083703,0.0006995815,0.0006419231,0.03101231],"genre_scores_gemma":[0.8678067,0.02249473,0.09893933,0.00078343466,0.0009833878,0.00018292198,0.00046363345,0.00008655699,0.008259282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892884,0.00054893055,0.000057626836,0.00010981574,0.00029348276,0.00006121973],"domain_scores_gemma":[0.9969242,0.0024219663,0.00023386473,0.00013894045,0.00022734667,0.000053643405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002689668,0.0006056314,0.00074329233,0.0010309686,0.0001605896,0.0012394417,0.0007591871,0.0019186245,0.0017235301],"category_scores_gemma":[0.014484277,0.00029197015,0.00032956875,0.0018634426,0.0006004886,0.002535637,0.0006313653,0.0011929106,0.0004968309],"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":[0.00032385087,0.000118207885,0.0062567224,0.00029296672,0.00021271862,0.00010921125,0.000055974586,0.8423916,0.0005555893,0.03705768,0.0017186424,0.11090685],"study_design_scores_gemma":[0.00003925535,0.000056566205,0.0017582007,0.00006600368,0.000040585892,0.000035258203,0.000033711316,0.9650414,0.00036312136,0.030488225,0.0020597111,0.000017819217],"about_ca_topic_score_codex":0.0063878247,"about_ca_topic_score_gemma":0.0051134476,"teacher_disagreement_score":0.0063878247,"about_ca_system_score_codex":0.0006398353,"about_ca_system_score_gemma":0.00048539403,"threshold_uncertainty_score":0.014224529},"labels":[],"label_agreement":null},{"id":"W3124307699","doi":"10.1002/for.2396","title":"Monthly Beta Forecasting with Low‐, Medium‐ and High‐Frequency Stock Returns","year":2016,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"BETA (programming language); Estimator; Econometrics; Portfolio; Stock (firearms); Economics; Expected return; Statistics; Mathematics; Computer science; Financial economics; Geography","score_opus":0.049605593866940814,"score_gpt":0.21238349642200605,"score_spread":0.16277790255506525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124307699","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":[0.9319465,0.0005583647,0.063961364,0.00014828317,0.00006740077,0.000020071791,0.00037491554,0.00049614697,0.0024269158],"genre_scores_gemma":[0.9919778,0.00011920454,0.0072919093,0.00001325469,0.000023644052,0.0000055796213,0.0002880836,0.000018080078,0.00026234964],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917823,0.00027265112,0.000053180436,0.0001517745,0.00027248895,0.00007167111],"domain_scores_gemma":[0.99582016,0.002338078,0.00067921105,0.00044904774,0.00055407314,0.00015941],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026166292,0.0005615507,0.00055743544,0.0006854064,0.00015098449,0.0009182902,0.00041856559,0.00051980285,0.0008296453],"category_scores_gemma":[0.013907695,0.00020199247,0.00032795867,0.00060070097,0.00013784431,0.00090558204,0.00045753396,0.00064448727,0.00038098142],"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":[0.0019747699,0.0002816666,0.18865067,0.0001819171,0.00034969262,0.00019895645,0.00027776344,0.510455,0.011392703,0.0027809546,0.003278367,0.28017765],"study_design_scores_gemma":[0.000035979912,0.00027570486,0.075754,0.000024951332,0.000057813817,0.00010633192,0.000046055666,0.9156418,0.0057662637,0.0015322147,0.0007136682,0.00004527406],"about_ca_topic_score_codex":0.002764852,"about_ca_topic_score_gemma":0.0023628,"teacher_disagreement_score":0.002764852,"about_ca_system_score_codex":0.00026081703,"about_ca_system_score_gemma":0.00030798436,"threshold_uncertainty_score":0.013838232},"labels":[],"label_agreement":null},{"id":"W3124669735","doi":"10.1002/for.2757","title":"Convolution‐based filtering and forecasting: An application to WTI crude oil prices","year":2021,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive model; West Texas Intermediate; Econometrics; Convolution (computer science); Series (stratigraphy); Component (thermodynamics); Hodrick–Prescott filter; Time series; Mathematics; Autoregressive–moving-average model; Commodity; Computer science; Applied mathematics; Economics; Statistics; Artificial intelligence; Finance; Artificial neural network; Business cycle","score_opus":0.06610561391593361,"score_gpt":0.24719499826173777,"score_spread":0.18108938434580416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3124669735","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":[0.079188,0.00023393739,0.91709906,0.00044897283,0.000069956885,0.000028665352,0.000052655818,0.00045620016,0.0024224115],"genre_scores_gemma":[0.7680683,0.00028328405,0.22977543,0.000062652514,0.0000649213,0.000033591426,0.00006017923,0.00004267892,0.0016088679],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996593,0.00012536476,0.00002372798,0.00004058293,0.00012206218,0.000029009538],"domain_scores_gemma":[0.99784017,0.0015486968,0.0001276107,0.00016978297,0.00026966073,0.000043999706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015483239,0.00040448783,0.0005798576,0.0005494279,0.00043979345,0.0006520825,0.0005213979,0.0009286312,0.0011224636],"category_scores_gemma":[0.005733733,0.0002530509,0.0005962177,0.0008385681,0.00044363737,0.00074678584,0.00047429244,0.0008955346,0.00012259612],"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":[0.00015761504,0.00007454422,0.0024662924,0.000045254426,0.00005863606,0.00015178854,0.0001021554,0.84741116,0.005899617,0.03300586,0.00067304616,0.10995408],"study_design_scores_gemma":[0.0000030010174,0.0000046839773,0.00014308422,0.0000011826606,0.0000022950298,0.00000832838,0.0000016554742,0.9976447,0.00044910255,0.0016085857,0.00013039053,0.000002992698],"about_ca_topic_score_codex":0.019688204,"about_ca_topic_score_gemma":0.012343231,"teacher_disagreement_score":0.019688204,"about_ca_system_score_codex":0.0008821939,"about_ca_system_score_gemma":0.0008923128,"threshold_uncertainty_score":0.0391472},"labels":[],"label_agreement":null},{"id":"W3125713255","doi":"10.1002/for.2552","title":"An analysis on the predictability of CAPM beta for momentum returns","year":2018,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Capital asset pricing model; Predictability; Momentum (technical analysis); BETA (programming language); Economics; Econometrics; Financial economics; Stock (firearms); Estimator; Trading strategy; Mathematics; Statistics; Computer science; Geography","score_opus":0.07226554214504533,"score_gpt":0.25336546121809517,"score_spread":0.18109991907304984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3125713255","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":[0.98674715,0.00017348662,0.0102990735,0.00027826437,0.00002953521,0.00000917073,0.00021014315,0.00013571602,0.0021174953],"genre_scores_gemma":[0.9994281,0.000031305062,0.00027918455,0.0000071449563,0.000022302802,0.0000022001832,0.00012361028,0.00001007779,0.000096080956],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935967,0.00017445518,0.000027622515,0.00012962751,0.00020422367,0.00010440577],"domain_scores_gemma":[0.9647209,0.026956165,0.0035884748,0.0022024892,0.001853004,0.0006789597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003910943,0.00042526025,0.00049119577,0.0016286876,0.00035647926,0.0014937425,0.00041977197,0.0005027288,0.0013657225],"category_scores_gemma":[0.0343869,0.00029964748,0.00036324898,0.000940446,0.0005742772,0.0010158248,0.00045426528,0.001028568,0.00023386073],"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":[0.0006510421,0.0001167002,0.7709803,0.000062337764,0.00026040687,0.0013577898,0.0004377487,0.16028221,0.004753385,0.015025193,0.0033212074,0.042751633],"study_design_scores_gemma":[0.000014625297,0.00010195658,0.16941781,0.000024653289,0.000047484762,0.00026416185,0.00007031835,0.82179296,0.0013920298,0.006319879,0.0005164738,0.00003754069],"about_ca_topic_score_codex":0.002824401,"about_ca_topic_score_gemma":0.001263551,"teacher_disagreement_score":0.003910943,"about_ca_system_score_codex":0.0005035947,"about_ca_system_score_gemma":0.00034804587,"threshold_uncertainty_score":0.020683289},"labels":[],"label_agreement":null},{"id":"W3165450722","doi":"10.1002/for.2799","title":"A new Markov regime‐switching count time series approach for forecasting initial public offering volumes and detecting issue cycles","year":2021,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","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":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Initial public offering; Econometrics; Autoregressive model; Autoregressive conditional heteroskedasticity; Economics; Markov chain; Quantile; Conditional variance; Heteroscedasticity; Financial economics; Statistics; Mathematics; Finance; Volatility (finance)","score_opus":0.05306151173261885,"score_gpt":0.23583432525590625,"score_spread":0.1827728135232874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165450722","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":[0.079552904,0.00024665426,0.91739255,0.00034246416,0.000074836695,0.000054001837,0.00029212728,0.0003728818,0.0016715977],"genre_scores_gemma":[0.90614194,0.0003848211,0.088624254,0.00013907463,0.00020267724,0.00018705864,0.00079141173,0.0000666535,0.0034621684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990901,0.00033150104,0.00006205254,0.00022641639,0.00019556939,0.000094360286],"domain_scores_gemma":[0.99581295,0.0029929392,0.0004920413,0.00020334077,0.00036347238,0.00013514888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024874653,0.00052670116,0.0011326002,0.0018736004,0.0004295306,0.0010356555,0.001717737,0.0008929745,0.0025124694],"category_scores_gemma":[0.0069799256,0.00068605016,0.0012652514,0.00112986,0.0005536484,0.0014260345,0.0007792925,0.0014476073,0.00033415083],"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":[0.00019994323,0.00016611307,0.01711388,0.00010948401,0.00024319875,0.0002075398,0.00015047866,0.8594255,0.0024970425,0.043073636,0.0016456022,0.075167544],"study_design_scores_gemma":[0.0000021406486,0.0000055174132,0.00032921263,0.0000023524824,0.000004006832,0.0000052661085,0.0000025625611,0.99778974,0.000055239023,0.0017164701,0.000083825966,0.000003656835],"about_ca_topic_score_codex":0.007647035,"about_ca_topic_score_gemma":0.0061758715,"teacher_disagreement_score":0.007647035,"about_ca_system_score_codex":0.00073589425,"about_ca_system_score_gemma":0.00071438844,"threshold_uncertainty_score":0.015205026},"labels":[],"label_agreement":null},{"id":"W3193155117","doi":"10.1002/for.993","title":"Forecasting volatility","year":2006,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Volatility (finance); Econometrics; Forward volatility; Implied volatility; Stochastic volatility; Volatility smile; Economics; Realized variance; Volatility swap; Volatility risk premium","score_opus":0.08245622204750369,"score_gpt":0.22418660216938346,"score_spread":0.14173038012187977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193155117","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":[0.4804112,0.0020224852,0.4846916,0.0021771842,0.00038159368,0.0000624484,0.0019268114,0.0012320268,0.027094513],"genre_scores_gemma":[0.97987205,0.00051772426,0.017326599,0.000056926183,0.00008673099,0.000015032784,0.00061268575,0.000044105338,0.0014681309],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996182,0.00011558994,0.000017795312,0.000073117844,0.00015059389,0.000024756157],"domain_scores_gemma":[0.99794585,0.0013186488,0.00021244906,0.00018529625,0.00029542137,0.00004234616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012521256,0.00036143322,0.0003572946,0.0009040185,0.00015093294,0.0014035078,0.00041182688,0.00067897525,0.002379767],"category_scores_gemma":[0.010322004,0.00013545851,0.0002627689,0.00082868815,0.00020590411,0.0010947431,0.00034189457,0.0006676198,0.00071117573],"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":[0.00008625171,0.00004180509,0.024874935,0.00007197795,0.000091072514,0.00011241119,0.00010010585,0.78708845,0.003391605,0.036539037,0.006970068,0.14063227],"study_design_scores_gemma":[0.000005569558,0.000015565221,0.0041074185,0.00001784854,0.000010223899,0.000020956883,0.000021588707,0.97394335,0.0015177773,0.018497549,0.0018301893,0.0000120159475],"about_ca_topic_score_codex":0.0027199076,"about_ca_topic_score_gemma":0.001416025,"teacher_disagreement_score":0.0027199076,"about_ca_system_score_codex":0.0004816672,"about_ca_system_score_gemma":0.00031848834,"threshold_uncertainty_score":0.007961154},"labels":[],"label_agreement":null},{"id":"W3195601859","doi":"10.1002/for.2942","title":"Worse than you think: Public debt forecast errors in advanced and developing economies","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Fiscal Policies and Political Economy","field":"Economics, Econometrics and Finance","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Balliol College, University of Oxford; University of Oxford; Georgetown University; Queen's University; London School of Economics and Political Science; University of Illinois at Urbana-Champaign","keywords":"Economics; Recession; Debt; Emerging markets; Monetary economics; Gross domestic product; Real gross domestic product; Macroeconomics","score_opus":0.08271555542471594,"score_gpt":0.25100267698352785,"score_spread":0.16828712155881193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195601859","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":[0.9844044,0.00047651207,0.00061986374,0.00068517367,0.000025921932,0.0000062630475,0.012174477,0.000033984295,0.0015734793],"genre_scores_gemma":[0.9889882,0.00028255553,0.00015975765,0.000045585497,0.000030952302,0.000005336658,0.010249516,0.0000057611483,0.00023222531],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951243,0.000120262055,0.000063861204,0.000117531206,0.00010979441,0.000076118835],"domain_scores_gemma":[0.992222,0.0028856334,0.0032315098,0.00046656403,0.0008886851,0.0003056617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017265126,0.0002726725,0.00042267807,0.0014892557,0.00017185643,0.0012147386,0.00023878746,0.00044625284,0.0012666845],"category_scores_gemma":[0.011521501,0.00020745184,0.00027531717,0.0022772062,0.00023305317,0.0008178276,0.000822605,0.00082530006,0.00046402967],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013372305,0.000030554285,0.97260654,0.000033778055,0.00017417154,0.000104529885,0.0003026987,0.012305625,0.000089313275,0.00062759663,0.0058283354,0.007762981],"study_design_scores_gemma":[0.000017642531,0.000030310412,0.9789661,0.00008330576,0.000054533506,0.000072990886,0.0007790997,0.013688128,0.0003994512,0.0009943194,0.004885104,0.00002893652],"about_ca_topic_score_codex":0.024842612,"about_ca_topic_score_gemma":0.014703157,"teacher_disagreement_score":0.024842612,"about_ca_system_score_codex":0.000444656,"about_ca_system_score_gemma":0.0003998497,"threshold_uncertainty_score":0.049396038},"labels":[],"label_agreement":null},{"id":"W4210649882","doi":"10.1002/for.2787","title":"Issue Information","year":2022,"lang":"en","type":"paratext","venue":"Journal of Forecasting","topic":"Diverse Scientific and Economic Studies","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":"McGill University; University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.0631382867450737,"score_gpt":0.2224138600775679,"score_spread":0.1592755733324942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210649882","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038863753,0.0007074251,0.00093677774,0.0056778006,0.018835653,0.0005917987,0.06319949,0.002490767,0.90717167],"genre_scores_gemma":[0.0013158359,0.0004732221,0.0003598416,0.0013797828,0.0016640293,0.00015834939,0.016584307,0.00063209713,0.9774325],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987684,0.00014275442,0.0001063461,0.000179124,0.00063236884,0.00017104151],"domain_scores_gemma":[0.9934176,0.0010500592,0.00024864596,0.00077299244,0.0031295673,0.0013810366],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013950414,0.0011461889,0.0014616395,0.003287644,0.0014094911,0.0052491296,0.0018843119,0.0022888265,0.94966334],"category_scores_gemma":[0.011582105,0.00052663265,0.0007944733,0.0031878187,0.00036544903,0.0031425955,0.0018473347,0.0017982681,0.8944495],"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":[0.000029713923,0.000018550714,0.000042762513,0.0001171155,0.0000014467654,0.000013430014,0.0000075509543,0.000020062753,0.00006114468,0.00074473995,0.97920895,0.019734474],"study_design_scores_gemma":[0.000016527865,0.000017267008,0.00020560132,0.00010464029,0.0000018875621,0.000022163857,0.000019486697,0.000049094597,0.00008161496,0.0005897516,0.9988877,0.000004198385],"about_ca_topic_score_codex":0.0019226493,"about_ca_topic_score_gemma":0.0037030724,"teacher_disagreement_score":0.05033666,"about_ca_system_score_codex":0.00117598,"about_ca_system_score_gemma":0.002090964,"threshold_uncertainty_score":0.07179916},"labels":[],"label_agreement":null},{"id":"W4252446806","doi":"10.1002/for.2493","title":"Issue Information","year":2018,"lang":"en","type":"paratext","venue":"Journal of Forecasting","topic":"Diverse Scientific and Economic Studies","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":"York University; University of Waterloo","funders":"Russian Academy of Sciences","keywords":"Computer science","score_opus":0.06501866908122084,"score_gpt":0.22852760882267834,"score_spread":0.1635089397414575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252446806","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005216715,0.0010534778,0.0011983538,0.007517251,0.024301805,0.00067364925,0.08288269,0.003224477,0.87862664],"genre_scores_gemma":[0.0018793694,0.0006834477,0.00043757717,0.0016456692,0.0020752233,0.00019837936,0.022815837,0.00083145325,0.9694332],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986351,0.00016946543,0.000126253,0.000208892,0.00066739705,0.00019286842],"domain_scores_gemma":[0.9923145,0.001281254,0.00030162785,0.0009595655,0.0035419397,0.0016011591],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0015766864,0.00111171,0.0015037188,0.003305998,0.0014590973,0.005540296,0.002002161,0.0022491864,0.95541906],"category_scores_gemma":[0.014522743,0.0004860216,0.0008466415,0.0032537654,0.0003716121,0.0035024914,0.0020676798,0.0018169571,0.8918945],"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":[0.00003201415,0.00001916486,0.000046532317,0.00012992864,0.0000017662896,0.000013364116,0.00000849718,0.000021055686,0.000054076176,0.0007488602,0.9783886,0.020536203],"study_design_scores_gemma":[0.000016432556,0.00001756632,0.00019145386,0.0001220005,0.0000020967977,0.000023103998,0.000021539174,0.00004850647,0.00007797391,0.0006818862,0.99879324,0.0000040986765],"about_ca_topic_score_codex":0.0016090071,"about_ca_topic_score_gemma":0.0030597171,"teacher_disagreement_score":0.044580936,"about_ca_system_score_codex":0.0011912822,"about_ca_system_score_gemma":0.0021495128,"threshold_uncertainty_score":0.063589215},"labels":[],"label_agreement":null},{"id":"W4283741364","doi":"10.1002/for.2885","title":"Predicting earnings management through machine learning ensemble classifiers","year":2022,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University; Concordia University","funders":"Social Sciences and Humanities Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Artificial intelligence; Computer science; Ensemble forecasting; Support vector machine; Ensemble learning; Machine learning; Principal component analysis; Random subspace method; Context (archaeology); Feature selection; Pattern recognition (psychology); Classifier (UML)","score_opus":0.04131222352818766,"score_gpt":0.2561634504400872,"score_spread":0.21485122691189953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283741364","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":[0.6556103,0.002318952,0.33325338,0.000880305,0.00039915895,0.00012450542,0.0008777419,0.0011977309,0.0053379377],"genre_scores_gemma":[0.9680618,0.00025270873,0.029944228,0.000074409545,0.00012855689,0.000036245612,0.0005868027,0.000016050322,0.00089916587],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990243,0.0002552443,0.00007914033,0.00017632435,0.00033959627,0.00012525514],"domain_scores_gemma":[0.99551636,0.0022200572,0.0005150848,0.00031523022,0.0012842842,0.00014903562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030768875,0.000865639,0.001190266,0.0027373848,0.00044417306,0.0010377305,0.0008700532,0.0008018505,0.0008313642],"category_scores_gemma":[0.0070161195,0.00020375737,0.0007669674,0.0014426069,0.00017185512,0.0014207197,0.0006048861,0.0011353608,0.0004267636],"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":[0.00030295344,0.00044018135,0.12472141,0.000060635546,0.0005370122,0.00017802819,0.00009729674,0.39633134,0.0022925064,0.001551672,0.0062819347,0.46720505],"study_design_scores_gemma":[0.0000044924814,0.00004772734,0.0057012322,0.000014313982,0.00003855171,0.000017184446,0.000023693085,0.99218047,0.00072468864,0.00084886356,0.0003907393,0.000008010946],"about_ca_topic_score_codex":0.0053100996,"about_ca_topic_score_gemma":0.0044193035,"teacher_disagreement_score":0.0053100996,"about_ca_system_score_codex":0.00057343947,"about_ca_system_score_gemma":0.00053907715,"threshold_uncertainty_score":0.016272306},"labels":[],"label_agreement":null},{"id":"W4293226250","doi":"10.1002/for.2903","title":"A tug of war of forecasting the US stock market volatility: Oil futures overnight versus intraday information","year":2022,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Volatility (finance); Economics; Futures contract; Econometrics; Stock market; Leverage effect; Futures market; Financial economics; Stock (firearms); Oil price; Leverage (statistics); Monetary economics; Autoregressive conditional heteroskedasticity; Statistics; Mathematics","score_opus":0.03749744999776277,"score_gpt":0.21423874163979098,"score_spread":0.1767412916420282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293226250","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":[0.9706158,0.0015504899,0.017908404,0.0024794913,0.00025571976,0.000019961612,0.00055483496,0.0001740808,0.006441233],"genre_scores_gemma":[0.99561286,0.000377099,0.002569926,0.00009722646,0.00012298174,0.000004372381,0.00024565015,0.000018008153,0.00095188717],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960595,0.00014704384,0.000027310296,0.00007920491,0.00008962373,0.000050863197],"domain_scores_gemma":[0.996845,0.0020635012,0.00031417157,0.00029892157,0.00032724082,0.00015108298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021123607,0.000641307,0.00061820826,0.001092459,0.00042852346,0.0017521883,0.00033429227,0.0006584046,0.0024971326],"category_scores_gemma":[0.00912284,0.0002131144,0.00041458913,0.0007159137,0.00037368338,0.0020253372,0.00087887904,0.001165021,0.00045536627],"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":[0.0019233099,0.00033331112,0.3668373,0.00013160345,0.0006322938,0.00066335336,0.00038094196,0.28457278,0.010582387,0.00711022,0.008459209,0.31837335],"study_design_scores_gemma":[0.00003765028,0.0004594764,0.13044569,0.00013078701,0.00016626631,0.00015747154,0.0006811762,0.8493691,0.0072685033,0.007181768,0.004022573,0.000079547004],"about_ca_topic_score_codex":0.009735014,"about_ca_topic_score_gemma":0.0062154224,"teacher_disagreement_score":0.009735014,"about_ca_system_score_codex":0.00043362944,"about_ca_system_score_gemma":0.00051819306,"threshold_uncertainty_score":0.019356728},"labels":[],"label_agreement":null},{"id":"W4319440180","doi":"10.1002/for.2956","title":"Using a machine learning approach and big data to augment WASDE forecasts: Empirical evidence from US corn yield","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Yield (engineering); Computer science; Machine learning; Agriculture; Econometrics; Economics; Geography","score_opus":0.6783027688674481,"score_gpt":0.3642247543382261,"score_spread":0.314078014529222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319440180","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":[0.98237073,0.000949373,0.008664318,0.0019513096,0.00011755619,0.000036082223,0.0013736039,0.00016148709,0.0043755644],"genre_scores_gemma":[0.9961139,0.00022213046,0.0024641687,0.000064841974,0.00003398583,0.0000087697,0.00089400547,0.000008376571,0.0001898921],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871016,0.0006726948,0.000069364905,0.0001534088,0.0003341578,0.000060128765],"domain_scores_gemma":[0.9706945,0.022291854,0.0022580482,0.0016340244,0.0028221377,0.00029934838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0049565905,0.00044470478,0.00027639547,0.0010518418,0.0002944348,0.0012553163,0.0006443356,0.00069766596,0.0010428398],"category_scores_gemma":[0.025269529,0.00021765252,0.0004256447,0.0016838738,0.00039464916,0.0020259435,0.00054397446,0.0013015232,0.00030599383],"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":[0.0006757287,0.0005654649,0.61528367,0.00019924525,0.00047426415,0.00029094703,0.00024041989,0.26526982,0.0008622779,0.002548202,0.006220165,0.107369676],"study_design_scores_gemma":[0.00005510951,0.0003180655,0.22105528,0.0001280869,0.0001247898,0.00007629994,0.00051219057,0.7653681,0.0020249235,0.005888529,0.0043886737,0.000059945763],"about_ca_topic_score_codex":0.015276578,"about_ca_topic_score_gemma":0.014668932,"teacher_disagreement_score":0.015276578,"about_ca_system_score_codex":0.0007226291,"about_ca_system_score_gemma":0.0005240529,"threshold_uncertainty_score":0.030375302},"labels":[],"label_agreement":null},{"id":"W4319788576","doi":"10.1002/for.2957","title":"Forecasting the exchange rate with the Taylor rule model during times of alternative monetary policies","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","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":"Centre for International Governance Innovation","funders":"","keywords":"Taylor rule; Economics; Government bond; Econometrics; Interest rate; Shadow (psychology); Sample (material); Monetary policy; Exchange rate; Bond; Random walk; Monetary economics; Central bank; Statistics; Finance","score_opus":0.13233619383576525,"score_gpt":0.23842481353341233,"score_spread":0.10608861969764707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319788576","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":[0.9848326,0.000118259704,0.013936925,0.0002046254,0.000036443595,0.000009070949,0.00014430653,0.000051827774,0.00066601037],"genre_scores_gemma":[0.9968736,0.00007482289,0.002653504,0.000014507209,0.000017034663,0.000005389535,0.00014319168,0.0000067646597,0.00021121126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965346,0.00017974069,0.000020008478,0.00006130603,0.000047011334,0.00003848534],"domain_scores_gemma":[0.9955615,0.003254294,0.0005826229,0.00018132533,0.00030094068,0.00011938227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028246897,0.0004943572,0.0007102306,0.0004397723,0.00024302886,0.0010629325,0.0005479097,0.0009047553,0.0006557282],"category_scores_gemma":[0.009314795,0.0003156156,0.00043234247,0.00040191243,0.0002690412,0.0013492306,0.0002991012,0.0010762013,0.0001707394],"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":[0.00028014043,0.000064088184,0.017858637,0.000025495487,0.00005941269,0.00012113416,0.00007819332,0.97080976,0.0009319861,0.0021788508,0.0005465991,0.0070457025],"study_design_scores_gemma":[0.0000094993675,0.000030254927,0.0022232372,0.0000035507408,0.000007941265,0.0000065316585,0.000019216443,0.99675983,0.00027076574,0.000596511,0.000066345216,0.0000063113202],"about_ca_topic_score_codex":0.011069811,"about_ca_topic_score_gemma":0.006973933,"teacher_disagreement_score":0.011069811,"about_ca_system_score_codex":0.00048965967,"about_ca_system_score_gemma":0.0004742775,"threshold_uncertainty_score":0.022010744},"labels":[],"label_agreement":null},{"id":"W4385443632","doi":"10.1002/for.2866","title":"Issue Information","year":2023,"lang":"en","type":"paratext","venue":"Journal of Forecasting","topic":"Diverse Scientific and Economic Studies","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":"McGill University; University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.0849245082485779,"score_gpt":0.23555508692692928,"score_spread":0.1506305786783514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385443632","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037846563,0.000704913,0.00094135967,0.0056009153,0.018749725,0.0005803834,0.06097629,0.0024681354,0.9095997],"genre_scores_gemma":[0.0012723343,0.0004596866,0.00034941724,0.0013223649,0.0016206936,0.00015362026,0.015979746,0.0006199165,0.97822225],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987489,0.0001443078,0.00010757439,0.00018061596,0.0006449157,0.00017360426],"domain_scores_gemma":[0.9933322,0.0010526193,0.00025170192,0.0007805401,0.0031843316,0.0013985661],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014080368,0.0011501849,0.0014725422,0.0033079076,0.0014168837,0.0053751627,0.0019063015,0.0023092567,0.94902647],"category_scores_gemma":[0.011692757,0.00052606576,0.00079882244,0.0032306013,0.00037400343,0.0031813718,0.0018653895,0.001812765,0.89460784],"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":[0.000029642308,0.000018042954,0.000042115313,0.00011617116,0.0000014252196,0.000013263209,0.000007462619,0.000020538913,0.000059602564,0.00076620444,0.9792087,0.019716697],"study_design_scores_gemma":[0.00001623632,0.000016835462,0.00020091185,0.000104152,0.0000018251949,0.000021657006,0.000019077206,0.000049354032,0.00007974948,0.0005980111,0.9988882,0.0000041541534],"about_ca_topic_score_codex":0.0019712285,"about_ca_topic_score_gemma":0.0038120833,"teacher_disagreement_score":0.050973535,"about_ca_system_score_codex":0.0012298246,"about_ca_system_score_gemma":0.0021342188,"threshold_uncertainty_score":0.072707474},"labels":[],"label_agreement":null},{"id":"W4388247556","doi":"10.1002/for.2868","title":"Issue Information","year":2023,"lang":"en","type":"paratext","venue":"Journal of Forecasting","topic":"Diverse Scientific and Economic Studies","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":"McGill University; University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.0849245082485779,"score_gpt":0.23555508692692928,"score_spread":0.1506305786783514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388247556","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037846563,0.000704913,0.00094135967,0.0056009153,0.018749725,0.0005803834,0.06097629,0.0024681354,0.9095997],"genre_scores_gemma":[0.0012723343,0.0004596866,0.00034941724,0.0013223649,0.0016206936,0.00015362026,0.015979746,0.0006199165,0.97822225],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987489,0.0001443078,0.00010757439,0.00018061596,0.0006449157,0.00017360426],"domain_scores_gemma":[0.9933322,0.0010526193,0.00025170192,0.0007805401,0.0031843316,0.0013985661],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014080368,0.0011501849,0.0014725422,0.0033079076,0.0014168837,0.0053751627,0.0019063015,0.0023092567,0.94902647],"category_scores_gemma":[0.011692757,0.00052606576,0.00079882244,0.0032306013,0.00037400343,0.0031813718,0.0018653895,0.001812765,0.89460784],"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":[0.000029642308,0.000018042954,0.000042115313,0.00011617116,0.0000014252196,0.000013263209,0.000007462619,0.000020538913,0.000059602564,0.00076620444,0.9792087,0.019716697],"study_design_scores_gemma":[0.00001623632,0.000016835462,0.00020091185,0.000104152,0.0000018251949,0.000021657006,0.000019077206,0.000049354032,0.00007974948,0.0005980111,0.9988882,0.0000041541534],"about_ca_topic_score_codex":0.0019712285,"about_ca_topic_score_gemma":0.0038120833,"teacher_disagreement_score":0.050973535,"about_ca_system_score_codex":0.0012298246,"about_ca_system_score_gemma":0.0021342188,"threshold_uncertainty_score":0.072707474},"labels":[],"label_agreement":null},{"id":"W4388851143","doi":"10.1002/for.3043","title":"A comparison of Range Value at Risk (RVaR) forecasting models","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Market Dynamics and Volatility","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":"University of Waterloo","funders":"Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Univariate; Econometrics; Multivariate statistics; Value at risk; Expected shortfall; Context (archaeology); Range (aeronautics); Autoregressive conditional heteroskedasticity; Asset (computer security); Empirical research; Computer science; Economics; Statistics; Risk management; Mathematics; Finance","score_opus":0.15963323013923966,"score_gpt":0.28657658258450613,"score_spread":0.12694335244526647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388851143","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":[0.49676132,0.0025417432,0.4910421,0.0009430922,0.0002487437,0.00009472002,0.00063740864,0.0011952154,0.0065356037],"genre_scores_gemma":[0.96603906,0.00048882776,0.031658772,0.00006124212,0.00007746573,0.00004039072,0.0004297338,0.000069013746,0.0011353898],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853134,0.00072572986,0.00009070563,0.00025209805,0.00026167117,0.00013843123],"domain_scores_gemma":[0.9929698,0.0050859195,0.0005950918,0.0003448091,0.0008463903,0.0001580644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004703702,0.0008885653,0.0011962303,0.0014275707,0.00027530536,0.0015145304,0.0011616594,0.0009759515,0.001273303],"category_scores_gemma":[0.009907255,0.00029052625,0.0012748484,0.0009397571,0.00029156456,0.0014535617,0.00059036206,0.0010666227,0.00034061586],"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":[0.00018548913,0.000062872394,0.0063684355,0.00005717651,0.00015098191,0.00006744767,0.000079027996,0.94882005,0.00065352605,0.0048689777,0.000726458,0.037959613],"study_design_scores_gemma":[0.0000037109855,0.00003619233,0.000731243,0.000007802681,0.000015421516,0.00001253972,0.000009393098,0.997914,0.00013817346,0.0010102693,0.000112219546,0.000008991594],"about_ca_topic_score_codex":0.01017202,"about_ca_topic_score_gemma":0.003955597,"teacher_disagreement_score":0.01017202,"about_ca_system_score_codex":0.00055779767,"about_ca_system_score_gemma":0.00078692957,"threshold_uncertainty_score":0.02487582},"labels":[],"label_agreement":null},{"id":"W4391481314","doi":"10.1002/for.2991","title":"Issue Information","year":2024,"lang":"en","type":"paratext","venue":"Journal of Forecasting","topic":"Diverse Scientific and Economic Studies","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":"McGill University; University of Waterloo","funders":"","keywords":"Computer science","score_opus":0.060996859374950536,"score_gpt":0.22986909306665043,"score_spread":0.1688722336916999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391481314","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00038729934,0.0006950596,0.00093322934,0.005640815,0.018352458,0.00059142837,0.06377288,0.00247249,0.9071545],"genre_scores_gemma":[0.001295923,0.00046348784,0.0003557669,0.0013828301,0.0016380157,0.00016029632,0.016784042,0.00062719506,0.9772924],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99876547,0.00014258883,0.00010535129,0.0001792079,0.000635372,0.000172109],"domain_scores_gemma":[0.9934803,0.0010430082,0.00024502305,0.0007595569,0.0030959833,0.0013761014],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0013989593,0.0011305877,0.0014650989,0.0032676868,0.0013987587,0.0051764436,0.0018897465,0.002271961,0.9505294],"category_scores_gemma":[0.01148435,0.00052136515,0.0007939939,0.0031528252,0.00036470513,0.0031229258,0.001854247,0.0017915778,0.89483786],"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":[0.00002959691,0.000018373652,0.00004242449,0.000116441995,0.0000014320274,0.000013226764,0.000007476023,0.000019784333,0.000061218234,0.0007407644,0.9791422,0.019807106],"study_design_scores_gemma":[0.000016650672,0.000017071186,0.00020310978,0.00010429194,0.0000018751024,0.00002175073,0.000019200645,0.000048134807,0.00008095763,0.00058606843,0.9988967,0.0000041860844],"about_ca_topic_score_codex":0.0019359833,"about_ca_topic_score_gemma":0.0037741151,"teacher_disagreement_score":0.049470603,"about_ca_system_score_codex":0.0011851873,"about_ca_system_score_gemma":0.002103214,"threshold_uncertainty_score":0.07056379},"labels":[],"label_agreement":null},{"id":"W4391611445","doi":"10.1002/for.3074","title":"Two‐stage credit risk prediction framework based on three‐way decisions with automatic threshold learning","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Natural Science Foundation of Hebei Province; National Natural Science Foundation of China","keywords":"Computer science; Particle swarm optimization; Machine learning; Optimal decision; Binary decision diagram; Credit risk; Data mining; Artificial intelligence; Decision tree; Finance; Algorithm; Business","score_opus":0.026295095328682815,"score_gpt":0.23882711377737217,"score_spread":0.21253201844868935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391611445","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":[0.07870647,0.00044334185,0.9167604,0.00049950724,0.000088185654,0.00012817778,0.00017342503,0.0006847028,0.0025158369],"genre_scores_gemma":[0.90574586,0.00020594818,0.09132262,0.00011429265,0.00006305805,0.00014659297,0.0002203601,0.00002486991,0.0021563652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890447,0.00018808033,0.00010899964,0.00035825107,0.00027781207,0.00016247219],"domain_scores_gemma":[0.9987129,0.0005838015,0.000117249416,0.000062697356,0.00042244242,0.00010094411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016782552,0.00087464973,0.001185207,0.0012495975,0.0006534306,0.0015102978,0.0016734607,0.0012955386,0.0019763976],"category_scores_gemma":[0.0032705783,0.0005207058,0.000976408,0.00089076255,0.0005669659,0.0019254732,0.0009787625,0.0013854674,0.00026761266],"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":[0.0002616613,0.00020049108,0.0071936618,0.00009875582,0.00009345396,0.0002640578,0.00018073019,0.8201323,0.0034564107,0.0114848325,0.0018572845,0.15477626],"study_design_scores_gemma":[0.000004841695,0.000015635786,0.00022848268,0.0000035880705,0.000007587098,0.000008414033,0.000005309256,0.9978102,0.0003099456,0.0014878545,0.00011301149,0.000005087382],"about_ca_topic_score_codex":0.015704107,"about_ca_topic_score_gemma":0.007325122,"teacher_disagreement_score":0.015704107,"about_ca_system_score_codex":0.001186301,"about_ca_system_score_gemma":0.0017549961,"threshold_uncertainty_score":0.031225383},"labels":[],"label_agreement":null},{"id":"W4391824537","doi":"10.1002/for.3086","title":"Space, mortality, and economic growth","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social 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":"St. Francis Xavier University","funders":"","keywords":"Gross domestic product; Econometrics; Model selection; Space (punctuation); Economics; Economic model; Growth model; Selection (genetic algorithm); Lag; Computer science; Statistics; Mathematics; Macroeconomics","score_opus":0.05966678313283622,"score_gpt":0.3299953345094367,"score_spread":0.27032855137660045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391824537","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":[0.9505174,0.0015733939,0.037242685,0.001558863,0.00007492589,0.000014439429,0.00045165306,0.00007768757,0.0084890295],"genre_scores_gemma":[0.9984458,0.0002719884,0.0006326957,0.000011987398,0.000013683461,0.0000040826085,0.00008508477,0.0000031801453,0.000531446],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9997427,0.00013511018,0.0000083076975,0.000037476333,0.000037471218,0.00003892925],"domain_scores_gemma":[0.9981483,0.0012283663,0.0002478355,0.00008850197,0.00019404822,0.0000929298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011127706,0.0003839744,0.0002513351,0.0009169417,0.00022912912,0.00089417084,0.0002999862,0.00041645658,0.0027982914],"category_scores_gemma":[0.0047674645,0.000091035276,0.00037443562,0.0008815205,0.0007292913,0.0008167338,0.0007544585,0.00061179436,0.00017406154],"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":[0.000114014314,0.000062712505,0.12114796,0.00005018939,0.000097482836,0.00016856893,0.00014983512,0.790412,0.00039161468,0.06425687,0.0015242716,0.021624481],"study_design_scores_gemma":[0.00000567904,0.00007733145,0.035344005,0.000026601861,0.000021770546,0.000046838857,0.0002426431,0.91591907,0.00024571878,0.046443623,0.0016112803,0.000015405625],"about_ca_topic_score_codex":0.009642974,"about_ca_topic_score_gemma":0.0048597436,"teacher_disagreement_score":0.009642974,"about_ca_system_score_codex":0.0008339091,"about_ca_system_score_gemma":0.0005995181,"threshold_uncertainty_score":0.019173682},"labels":[],"label_agreement":null},{"id":"W4391921422","doi":"10.1002/for.3092","title":"Conservatism and information rigidity of the European Bank for Reconstruction and Development's growth forecast: Quarter‐century assessment","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","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":"Japan Society for the Promotion of Science","keywords":"Quarter (Canadian coin); Conservatism; Rigidity (electromagnetism); Economics; Econometrics; Political science; History; Engineering; Law; Archaeology","score_opus":0.06654008138225227,"score_gpt":0.22168581954284455,"score_spread":0.1551457381605923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391921422","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":[0.9845554,0.00055401254,0.0044065556,0.0015164282,0.00005231148,0.000020391091,0.0006946098,0.00006071352,0.008139513],"genre_scores_gemma":[0.99865144,0.00008923732,0.00057491125,0.000035356046,0.000023602392,0.0000034557434,0.0003809304,0.000006465347,0.00023470153],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9927812,0.0028695473,0.0010408002,0.000624817,0.002178134,0.0005055683],"domain_scores_gemma":[0.8732229,0.065743566,0.034462586,0.008933873,0.016125293,0.0015117852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02449749,0.00027706457,0.0005898769,0.0029945183,0.0004822311,0.0038037803,0.0005952133,0.0008115871,0.001246829],"category_scores_gemma":[0.079701215,0.00030264343,0.0005094728,0.0029430422,0.0010601206,0.002843798,0.0014034068,0.0016074498,0.00026980977],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003092271,0.00014183998,0.7283952,0.00019329348,0.0006112787,0.00045372822,0.0026086585,0.14963049,0.001852827,0.021026876,0.0042833984,0.087710105],"study_design_scores_gemma":[0.0000857208,0.0005310995,0.7663042,0.00021874633,0.00022139256,0.0003194301,0.0019383249,0.20396101,0.004943273,0.01307891,0.0082023265,0.00019549421],"about_ca_topic_score_codex":0.00906613,"about_ca_topic_score_gemma":0.0035209917,"teacher_disagreement_score":0.02449749,"about_ca_system_score_codex":0.0024057638,"about_ca_system_score_gemma":0.0015151491,"threshold_uncertainty_score":0.12955666},"labels":[],"label_agreement":null},{"id":"W4392771668","doi":"10.1002/for.3095","title":"Improving demand forecasting for customers with missing downstream data in intermittent demand supply chains with supervised multivariate clustering","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Demand forecasting; Supply chain; Cluster analysis; Vendor; Downstream (manufacturing); Computer science; Missing data; Demand management; Demand patterns; Supply and demand; Supply chain management; Operations research; Business; Marketing; Economics; Artificial intelligence; Machine learning; Microeconomics","score_opus":0.16413391350985915,"score_gpt":0.3699132245148424,"score_spread":0.20577931100498323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392771668","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":[0.44659933,0.00024103963,0.5502162,0.00038963216,0.000076771976,0.00003760627,0.00036660524,0.000873529,0.0011992963],"genre_scores_gemma":[0.95523137,0.0000617653,0.043319345,0.000042824315,0.000046139743,0.00001901882,0.00052555354,0.00003213948,0.0007218297],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950457,0.00014607489,0.000026601125,0.00013280143,0.000118333424,0.000071635535],"domain_scores_gemma":[0.99762493,0.0010573865,0.00035354708,0.00029892044,0.00054933183,0.000115862094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001097353,0.0006176341,0.00087212154,0.00073181256,0.0003598426,0.00052665,0.0011576969,0.0007295092,0.0007520807],"category_scores_gemma":[0.003391222,0.00031205063,0.00060898997,0.0008869683,0.00029140507,0.0010120354,0.00057696935,0.0009749649,0.0003530956],"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":[0.00026815245,0.00021399255,0.012567712,0.000052334064,0.000089185545,0.00006866362,0.00013962951,0.88287354,0.0025090126,0.00097612146,0.0018735428,0.09836804],"study_design_scores_gemma":[0.0000013775325,0.000007509351,0.0004442294,0.0000011801553,0.0000020708537,0.0000026433177,0.000009115782,0.99894553,0.00023885626,0.0002920411,0.00005305647,0.0000024153517],"about_ca_topic_score_codex":0.0122986715,"about_ca_topic_score_gemma":0.009266074,"teacher_disagreement_score":0.0122986715,"about_ca_system_score_codex":0.00066815194,"about_ca_system_score_gemma":0.00059185555,"threshold_uncertainty_score":0.024454176},"labels":[],"label_agreement":null},{"id":"W4393868764","doi":"10.1002/for.3123","title":"An infinite hidden Markov model with stochastic volatility","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Philosophy and Social Science Foundation of Hunan Province; Social Sciences and Humanities Research Council of Canada; National Natural Science Foundation of China","keywords":"Stochastic volatility; Econometrics; Volatility (finance); Markov chain; Hidden Markov model; Economics; Mathematics; Financial economics; Statistical physics; Computer science; Statistics; Artificial intelligence; Physics","score_opus":0.05895035572317358,"score_gpt":0.2527799162886512,"score_spread":0.19382956056547762,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393868764","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":[0.1001953,0.00075188174,0.88680613,0.0024053585,0.00021875395,0.000066223634,0.0010996157,0.00049540715,0.007961314],"genre_scores_gemma":[0.948786,0.0005547202,0.03418541,0.00025876827,0.00023071123,0.000211978,0.0008130889,0.00008790262,0.014871325],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998145,0.0008102104,0.00007431588,0.00042897853,0.000309493,0.00023196697],"domain_scores_gemma":[0.9925074,0.0059606056,0.00061890593,0.0002654603,0.0004470578,0.00020046119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033424033,0.0006901175,0.001612259,0.001191343,0.0006710709,0.0024154545,0.0025062033,0.0023071175,0.0062318556],"category_scores_gemma":[0.010243922,0.00086134585,0.0012590392,0.0010726097,0.0016447119,0.0027412195,0.0015383345,0.0024546948,0.00074311625],"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":[0.000108749555,0.00006770243,0.0027703396,0.00007942581,0.00010195441,0.0003448524,0.0002307553,0.6740358,0.0006652607,0.30924642,0.0019283234,0.010420506],"study_design_scores_gemma":[0.000013136934,0.000008281174,0.00021754959,0.000010356781,0.00001160597,0.00002636771,0.00001085902,0.9559992,0.00006937558,0.043154903,0.00046623964,0.000012185197],"about_ca_topic_score_codex":0.011999331,"about_ca_topic_score_gemma":0.007678958,"teacher_disagreement_score":0.011999331,"about_ca_system_score_codex":0.0015889822,"about_ca_system_score_gemma":0.0012205923,"threshold_uncertainty_score":0.023858964},"labels":[],"label_agreement":null},{"id":"W4399615628","doi":"10.1002/for.3164","title":"Reducing transaction costs using intraday forecasts of limit order book slopes","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","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":"HEC Montréal; Université du Québec à Montréal; Université du Québec en Abitibi-Témiscamingue","funders":"Federación Nacional de Cultivadores de Palma de Aceite","keywords":"Transaction cost; Limit (mathematics); Order (exchange); Econometrics; Order book; Database transaction; Economics; Computer science; Mathematics; Finance; Database","score_opus":0.06747201671472121,"score_gpt":0.24250206525547013,"score_spread":0.17503004854074894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399615628","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":[0.92892283,0.00060890295,0.06572027,0.00055878796,0.00004955445,0.000034192093,0.00090940064,0.00036941905,0.0028266283],"genre_scores_gemma":[0.98856926,0.000086327374,0.010766243,0.000015478443,0.00001847141,0.0000070841284,0.00028173422,0.000009785415,0.0002455863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992993,0.00025270059,0.00005977613,0.00012498745,0.00020471383,0.000058568377],"domain_scores_gemma":[0.99057966,0.0053975494,0.0018805014,0.00083813904,0.001110052,0.00019408813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018307196,0.0005355485,0.0005361072,0.0013029055,0.00019525089,0.0014358308,0.00053704856,0.00044514882,0.0012425352],"category_scores_gemma":[0.012994571,0.00022472726,0.00024931048,0.0012026094,0.00017250674,0.0018099163,0.0005140606,0.00091555965,0.00027766434],"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":[0.0005559166,0.00032725232,0.2661896,0.00012332143,0.0002492428,0.00015461174,0.00020081316,0.55799854,0.0046657166,0.003401395,0.003683661,0.16244987],"study_design_scores_gemma":[0.000016073323,0.0000871371,0.03323245,0.000017832059,0.00003489843,0.000034165354,0.000077544304,0.96205527,0.0017875205,0.0020570164,0.0005740559,0.000026089976],"about_ca_topic_score_codex":0.011039737,"about_ca_topic_score_gemma":0.011521974,"teacher_disagreement_score":0.011039737,"about_ca_system_score_codex":0.00051328674,"about_ca_system_score_gemma":0.0006547955,"threshold_uncertainty_score":0.02195096},"labels":[],"label_agreement":null},{"id":"W4404080134","doi":"10.1002/for.3204","title":"Forecasting Beta Using Ultra High Frequency Data","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"BETA (programming language); Computer science; Econometrics; Economics","score_opus":0.2375268939497229,"score_gpt":0.2883996406675512,"score_spread":0.05087274671782829,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404080134","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":[0.94081306,0.00022832981,0.05462952,0.00018356809,0.00008033427,0.00003731856,0.0010683498,0.00025850735,0.002701129],"genre_scores_gemma":[0.98625886,0.00006042382,0.012397744,0.000034306435,0.000048627062,0.000018022996,0.00089542934,0.000012625001,0.0002738941],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854654,0.0007058414,0.00007205592,0.00018568647,0.0003653548,0.0001245732],"domain_scores_gemma":[0.9842918,0.011323781,0.0013821103,0.0015013644,0.0012188691,0.00028206033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004299523,0.00037637012,0.0005916336,0.0023210933,0.00019574822,0.0010012377,0.00059344224,0.0009738829,0.001688654],"category_scores_gemma":[0.022000406,0.00020003463,0.00032021094,0.0016653094,0.0002469198,0.0011764707,0.00061099837,0.0009172964,0.00050363527],"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":[0.0012316729,0.0005090705,0.515749,0.00018600038,0.00030886437,0.00031614426,0.0003121144,0.17260443,0.009176655,0.003956417,0.004124122,0.29152554],"study_design_scores_gemma":[0.00006590647,0.00047023743,0.32060882,0.000065038636,0.00006442557,0.00026714354,0.00020452352,0.66397685,0.0068398435,0.005200527,0.0021454361,0.000091191076],"about_ca_topic_score_codex":0.0016579175,"about_ca_topic_score_gemma":0.0013464931,"teacher_disagreement_score":0.004299523,"about_ca_system_score_codex":0.00026489797,"about_ca_system_score_gemma":0.00017241696,"threshold_uncertainty_score":0.022738338},"labels":[],"label_agreement":null},{"id":"W4404641964","doi":"10.1002/for.3210","title":"Using a Wage–Price‐Setting Model to Forecast US Inflation","year":2024,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"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":"Inflation (cosmology); Univariate; Economics; Econometrics; Quarter (Canadian coin); Wage; Phillips curve; Forecast period; Productivity; Multivariate statistics; Monetary policy; Macroeconomics; Statistics; Mathematics; Labour economics","score_opus":0.24806587660693674,"score_gpt":0.28966216085998564,"score_spread":0.0415962842530489,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404641964","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":[0.9261565,0.00018449407,0.06652884,0.00093917374,0.0001105314,0.0000290435,0.0005824943,0.00024924558,0.005219655],"genre_scores_gemma":[0.9962806,0.00004366121,0.0026846174,0.000023935096,0.00001829406,0.00000820258,0.00020007268,0.000006518875,0.0007340586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997615,0.00008828831,0.0000135492655,0.00005971385,0.00004074376,0.000036132446],"domain_scores_gemma":[0.99915206,0.0004890692,0.000119861754,0.000049854287,0.0001378602,0.00005141447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010590466,0.00035707647,0.00048901455,0.00041234327,0.00022851647,0.001161899,0.0007316714,0.0009145618,0.0010569779],"category_scores_gemma":[0.0039735134,0.00028615535,0.00045772412,0.00048404466,0.00024872666,0.0006739792,0.00033374014,0.0010311868,0.00024448373],"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":[0.00009728909,0.00003767668,0.0068024406,0.000010262502,0.000033218934,0.00005207838,0.000027179558,0.9822859,0.0003792114,0.0020699648,0.0005930317,0.0076116035],"study_design_scores_gemma":[0.000004062726,0.000009162849,0.00088233384,0.0000010634113,0.000003089427,0.0000028750333,0.0000030303047,0.99858093,0.000060317965,0.00039166588,0.000058599664,0.0000029098499],"about_ca_topic_score_codex":0.035919502,"about_ca_topic_score_gemma":0.012924103,"teacher_disagreement_score":0.035919502,"about_ca_system_score_codex":0.0007917828,"about_ca_system_score_gemma":0.0007665634,"threshold_uncertainty_score":0.07142085},"labels":[],"label_agreement":null},{"id":"W4407601723","doi":"10.1002/for.3261","title":"Forecasting the Confirmed COVID‐19 Cases Using Modal Regression","year":2025,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":2,"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":"Yonsei University","keywords":"Coronavirus disease 2019 (COVID-19); Modal; Econometrics; Regression; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Statistics; Computer science; Mathematics; Virology; Medicine; Internal medicine; Chemistry","score_opus":0.6020515917172558,"score_gpt":0.4913457903066035,"score_spread":0.11070580141065223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407601723","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":[0.8416054,0.00057332945,0.14970185,0.0014272504,0.00012451425,0.00007135483,0.0024694519,0.00046677905,0.003559999],"genre_scores_gemma":[0.9900407,0.00009766183,0.008456874,0.000050105296,0.000034420613,0.000016362277,0.00085997715,0.000013538593,0.00043036416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916375,0.00040161287,0.000037912327,0.00015772236,0.0001528702,0.000086263],"domain_scores_gemma":[0.9902497,0.0070428355,0.0009701503,0.0004207282,0.0010978706,0.00021885661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046505034,0.00050785823,0.0006200963,0.0012733508,0.00023452748,0.00089916924,0.00090971624,0.00069626217,0.0017125461],"category_scores_gemma":[0.022235291,0.00022003245,0.0006428166,0.0008646383,0.00030013066,0.0009872989,0.00062113284,0.0012543626,0.0002912795],"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":[0.00018873687,0.000054529628,0.061608367,0.000033798988,0.000080765065,0.00008403142,0.000057762092,0.9125624,0.00054179714,0.0031895032,0.0017991373,0.019799134],"study_design_scores_gemma":[0.0000028221798,0.00001755887,0.003137461,0.0000059526587,0.000006418148,0.000007930783,0.000024759189,0.9955942,0.00013056856,0.00092989416,0.0001354084,0.000006989854],"about_ca_topic_score_codex":0.03670983,"about_ca_topic_score_gemma":0.02031496,"teacher_disagreement_score":0.03670983,"about_ca_system_score_codex":0.0008965001,"about_ca_system_score_gemma":0.00066492555,"threshold_uncertainty_score":0.072992325},"labels":[],"label_agreement":null},{"id":"W4410734606","doi":"10.1002/for.3284","title":"Default Prediction Framework With Optimal Feature Set and Matching Ratio","year":2025,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","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":"National Natural Science Foundation of China","keywords":"Feature (linguistics); Matching (statistics); Computer science; Set (abstract data type); Pattern recognition (psychology); Artificial intelligence; Econometrics; Mathematics; Statistics","score_opus":0.01887874567049567,"score_gpt":0.2666547613807316,"score_spread":0.24777601571023591,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410734606","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":[0.19350918,0.00078856613,0.79880154,0.0011043603,0.00009481379,0.00014967858,0.00076572166,0.0021946141,0.0025915373],"genre_scores_gemma":[0.94341487,0.00010315349,0.05408428,0.00017368607,0.00008212334,0.000119579956,0.0008383405,0.000049639133,0.001134411],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872357,0.00038004582,0.00007643586,0.00035933455,0.0002852458,0.00017527623],"domain_scores_gemma":[0.9980034,0.00095254026,0.00023075966,0.00022166852,0.0004874293,0.00010418139],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035660502,0.000984504,0.0016378172,0.0018620365,0.0005796344,0.0012576364,0.0023519748,0.0011777966,0.0017352857],"category_scores_gemma":[0.0077710813,0.00036688655,0.0007233505,0.0012810737,0.00051384553,0.0024127765,0.0010604116,0.0014292146,0.00048782318],"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":[0.0008444559,0.00062977243,0.020048404,0.00009884895,0.0001884572,0.00044661004,0.00013281032,0.61030424,0.0032702794,0.011154843,0.010433474,0.34244776],"study_design_scores_gemma":[0.000009860329,0.000017781987,0.00041168678,0.0000036499403,0.000008423994,0.0000128225365,0.000005213003,0.9953929,0.0002531106,0.0037704413,0.00010993192,0.000004122716],"about_ca_topic_score_codex":0.0058171516,"about_ca_topic_score_gemma":0.003872156,"teacher_disagreement_score":0.0058171516,"about_ca_system_score_codex":0.0011443687,"about_ca_system_score_gemma":0.001215891,"threshold_uncertainty_score":0.018859327},"labels":[],"label_agreement":null},{"id":"W4411237125","doi":"10.1002/for.3159","title":"Issue Information","year":2025,"lang":"en","type":"paratext","venue":"Journal of Forecasting","topic":"Diverse Scientific and Economic Studies","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 Waterloo","funders":"","keywords":"Computer science","score_opus":0.051683287449072295,"score_gpt":0.22748790246936912,"score_spread":0.17580461502029682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411237125","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00037747674,0.0007167069,0.0009429896,0.005520027,0.018740207,0.00057307445,0.06121263,0.0024397438,0.90947706],"genre_scores_gemma":[0.0012657521,0.00046724093,0.00035251072,0.0013244252,0.0015888578,0.00015315297,0.016154239,0.00061662105,0.9780772],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987556,0.00014501154,0.000106434025,0.00018160477,0.00063766574,0.00017367779],"domain_scores_gemma":[0.9934603,0.001031675,0.00024600103,0.00075554353,0.0031158966,0.0013906052],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0014090951,0.0011368672,0.0014435119,0.0032659322,0.0014008151,0.005316334,0.0018904229,0.0022775775,0.94995207],"category_scores_gemma":[0.011592542,0.00051377405,0.00079230464,0.0031439713,0.00036954542,0.0031671708,0.0018700466,0.0017948603,0.894198],"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":[0.000029543484,0.000017751187,0.000040890016,0.00011731951,0.0000014151799,0.000013157864,0.000007456816,0.000020190377,0.00005995084,0.00077580486,0.9788271,0.020089433],"study_design_scores_gemma":[0.000015745194,0.00001659491,0.00019003135,0.0001041566,0.0000017684531,0.00002136738,0.000018732148,0.000047007765,0.00007786431,0.0005912982,0.9989114,0.000004022561],"about_ca_topic_score_codex":0.0019230408,"about_ca_topic_score_gemma":0.0037865276,"teacher_disagreement_score":0.050047934,"about_ca_system_score_codex":0.0012238362,"about_ca_system_score_gemma":0.002123632,"threshold_uncertainty_score":0.07138729},"labels":[],"label_agreement":null},{"id":"W4415643114","doi":"10.1002/for.70040","title":"Threshold MIDAS Forecasting of Canadian Inflation Rate","year":2025,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Inflation (cosmology); Autoregressive model; Index (typography); Benchmark (surveying); Threshold model","score_opus":0.215242736414234,"score_gpt":0.3778149873711728,"score_spread":0.1625722509569388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415643114","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":[0.8194968,0.0009041135,0.16420048,0.0017503515,0.00019581949,0.00010848122,0.0028687096,0.0010896445,0.009385599],"genre_scores_gemma":[0.98747873,0.00012324768,0.01041543,0.000046248784,0.000020411691,0.000020101716,0.00083830557,0.000013934147,0.0010436783],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990988,0.0002509124,0.000035863115,0.00015112448,0.00032817,0.00013518124],"domain_scores_gemma":[0.99843127,0.00042470888,0.00018746524,0.00012990466,0.00069990446,0.00012680324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023755876,0.0005265511,0.0006876011,0.0009386373,0.00054840185,0.001327502,0.0015734658,0.0004683546,0.0016220463],"category_scores_gemma":[0.0073353956,0.00028011613,0.0006258702,0.0012441599,0.00036415757,0.00075635646,0.0006219803,0.0009816899,0.0002065412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084035564,0.00015381225,0.10308779,0.00011105633,0.00022300336,0.00009776911,0.00020587673,0.7620173,0.0014602363,0.019321576,0.006671838,0.10580941],"study_design_scores_gemma":[0.000010952931,0.000022828346,0.005619917,0.000007184683,0.000012715901,0.0000044486314,0.00003803841,0.9919059,0.00028572645,0.0015367201,0.0005453705,0.000010204221],"about_ca_topic_score_codex":0.51357085,"about_ca_topic_score_gemma":0.47366178,"teacher_disagreement_score":0.48642915,"about_ca_system_score_codex":0.0038566454,"about_ca_system_score_gemma":0.004723621,"threshold_uncertainty_score":0.9785877},"labels":[],"label_agreement":null},{"id":"W4415673425","doi":"10.1002/for.70059","title":"A Two‐Stage NLP‐Driven Framework for Interval‐Valued Carbon Price Prediction Using Sentiment Analysis and Error Correction","year":2025,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Mean squared prediction error; Point (geometry); Interval (graph theory); Sentiment analysis; Carbon price; Error detection and correction; Convolutional neural network; Prediction interval","score_opus":0.14522452395505978,"score_gpt":0.4397545984821439,"score_spread":0.29453007452708413,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415673425","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":[0.017686717,0.00017083279,0.97924095,0.0002829234,0.000048338363,0.00007344085,0.00020696352,0.0012875027,0.0010023002],"genre_scores_gemma":[0.67250603,0.00021223394,0.32272837,0.00028307142,0.00012029543,0.0002945634,0.0008319888,0.00015085535,0.0028726193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995009,0.00010233856,0.00004111946,0.00016248737,0.00013777241,0.000055395423],"domain_scores_gemma":[0.9990212,0.0005245094,0.00009615411,0.000051581384,0.00026813627,0.000038604976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013480984,0.000916477,0.0007453422,0.0007924318,0.00035700688,0.0009973643,0.001535532,0.0010410051,0.0027067428],"category_scores_gemma":[0.00282961,0.0005041802,0.00089296437,0.00054757687,0.00041571268,0.0012000004,0.00091748475,0.0014057297,0.0005767769],"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":[0.0002416691,0.00021293931,0.0026607225,0.00015175507,0.0001202599,0.00038331765,0.00014744925,0.74507904,0.012266978,0.009222159,0.002921913,0.22659187],"study_design_scores_gemma":[0.0000023311047,0.000005943371,0.000076659526,0.0000019011885,0.0000035100986,0.0000041117987,0.0000022880479,0.99858344,0.00035747187,0.0008566387,0.00010362414,0.0000020113766],"about_ca_topic_score_codex":0.01404716,"about_ca_topic_score_gemma":0.013266639,"teacher_disagreement_score":0.01404716,"about_ca_system_score_codex":0.0008155544,"about_ca_system_score_gemma":0.001331081,"threshold_uncertainty_score":0.027930737},"labels":[],"label_agreement":null},{"id":"W4417448684","doi":"10.1002/for.70077","title":"When Are Statistical Forecast Gains Economically Relevant? Evidence From Bitcoin Returns","year":2025,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Blockchain Technology Applications and Security","field":"Computer Science","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":"Wilfrid Laurier University","funders":"Wilfrid Laurier University","keywords":"Bivariate analysis; Index (typography); Forecast error; Trading strategy; Consensus forecast; Stock market index; Stock market; Yield (engineering)","score_opus":0.04572255639616593,"score_gpt":0.2808088333070624,"score_spread":0.23508627691089648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417448684","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":[0.9861924,0.000775292,0.001511493,0.0034822277,0.000047595586,0.0000061840615,0.00052577385,0.000051523355,0.0074074334],"genre_scores_gemma":[0.9994868,0.00011965095,0.000048419948,0.000046711157,0.000035329074,9.0141646e-7,0.00011176865,0.0000054569,0.00014493501],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988342,0.0003743544,0.00008491898,0.00019839082,0.00036793842,0.00014030193],"domain_scores_gemma":[0.9158804,0.05764131,0.015146228,0.0046688626,0.0051641217,0.0014990333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039708023,0.00030532098,0.0003670646,0.00088173425,0.0003348527,0.0023186565,0.0004540371,0.0008546419,0.0040838225],"category_scores_gemma":[0.051584538,0.00017725416,0.00025304512,0.0009183349,0.0015569949,0.0033159447,0.000909367,0.0017056527,0.0008011314],"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":[0.0027785602,0.00030291104,0.90319425,0.00012769747,0.00029485804,0.00051866093,0.00048601447,0.01744002,0.002516424,0.014394801,0.00485545,0.053090297],"study_design_scores_gemma":[0.00015177135,0.0004906348,0.8925532,0.00009047914,0.00027196668,0.00027054953,0.0013778703,0.05487512,0.005431392,0.039439242,0.004948219,0.000099505225],"about_ca_topic_score_codex":0.0030528854,"about_ca_topic_score_gemma":0.0018628644,"teacher_disagreement_score":0.0040838225,"about_ca_system_score_codex":0.0005182499,"about_ca_system_score_gemma":0.000295692,"threshold_uncertainty_score":0.020999849},"labels":[],"label_agreement":null}]}