{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":9,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":9,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"e193e4707640","filters":{"venue":"International Journal of Computational Economics and Econometrics"}},"results":[{"id":"W2029805418","doi":"10.1504/ijcee.2010.037940","title":"Revisiting deterministic extended-path: a simple and accurate solution method for macroeconomic models","year":2010,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Brock University","funders":"","keywords":"Simple (philosophy); Computer science; Path (computing); Mathematical optimization; Econometrics; Statistical physics; Mathematics; Physics","authors":[{"name":"David R.F. Love","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.104722689076992,"gpt":0.3034744013216806,"spread":0.1987517122446887,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001553679,0.0002304133,0.0006099781,0.001022142,0.0001464383,0.0003986402,0.0003628968,0.0001341207,0.00009282829],"category_scores_gemma":[0.0003104399,0.0002841481,0.0002101667,0.00007650516,0.00009040853,0.001136055,0.0001093173,0.0002531903,0.00001784487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001342605,"about_ca_system_score_gemma":0.00006474621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005724514,"about_ca_topic_score_gemma":0.00001034212,"domain_scores_codex":[0.9976957,0.00001416662,0.001561807,0.0004043233,0.00003240052,0.0002915451],"domain_scores_gemma":[0.9971835,0.0007079666,0.001619846,0.0001358479,0.0001263538,0.0002264693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002221165,0.00009574295,0.004742636,0.0000632878,0.00057145,0.000007408244,0.000402696,0.2193516,0.00001728344,0.6869561,0.0003567652,0.08721285],"study_design_scores_gemma":[0.000916568,0.0000722561,0.004573313,0.000007526482,0.00001344779,0.0002557054,0.00002730943,0.6110578,0.000005150051,0.3761701,0.006700428,0.000200401],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7339866,0.0007661517,0.259402,0.002165488,0.001511522,0.0002067632,0.0007958329,0.00001111856,0.001154506],"genre_scores_gemma":[0.9301818,0.00108411,0.0671955,0.0005815336,0.0008297546,0.000009639133,0.00004744641,0.00003210683,0.00003811534],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3917062,"threshold_uncertainty_score":0.9999611,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4233530069","doi":"10.1504/ijcee.2018.088324","title":"Testing for multi-fractality and efficiency in selected sovereign bond markets: a multi-fractal detrended moving average (MF-DMA) analysis","year":2017,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Multifractal system; Bond; Government bond; Bond market; Detrended fluctuation analysis; Financial market; Econometrics; Economics; Fractal; Financial economics; Monetary economics; Mathematics; Finance","authors":[{"name":"Selçuk Bayracı","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05828232548000708,"gpt":0.2748510108124339,"spread":0.2165686853324268,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001410852,0.0002262048,0.0008321536,0.002288941,0.000291015,0.0008496346,0.0005173545,0.0001028615,0.00006743825],"category_scores_gemma":[0.001537508,0.0002709076,0.0002781272,0.000446695,0.00009682977,0.0009311585,0.0001895748,0.0001869753,0.00000410613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002843777,"about_ca_system_score_gemma":0.00008981438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003821954,"about_ca_topic_score_gemma":0.0002338699,"domain_scores_codex":[0.9975709,0.00001781288,0.001626632,0.0004516797,0.00006943787,0.0002635501],"domain_scores_gemma":[0.9959838,0.0005909264,0.002621648,0.0001970647,0.0004500888,0.0001564064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002099577,0.0003886692,0.8753353,0.00004650266,0.002378759,0.00001943224,0.0003011721,0.07463323,0.000004970259,0.03453446,0.00002662518,0.01212089],"study_design_scores_gemma":[0.001571618,0.00004746603,0.4849464,0.00001200959,0.00003876051,0.00002632387,0.00005290061,0.5054075,0.000001772238,0.007352435,0.0003644619,0.0001783961],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9273608,0.0009437465,0.06951603,0.0003566229,0.0004083086,0.0001635136,0.0004567734,0.000006772232,0.0007873992],"genre_scores_gemma":[0.9656436,0.0002335074,0.03377826,0.00006957288,0.0001407254,0.000006944985,0.00003993724,0.00002026986,0.00006711452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4307742,"threshold_uncertainty_score":0.9999743,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4233565366","doi":"10.1504/ijcee.2017.080615","title":"The nature and propagation of shocks in the euro area: a comparative SVAR analysis","year":2016,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Economics; Quarter (Canadian coin); European union; European monetary union; Persistence (discontinuity); Sample (material); Order (exchange); Eu countries; Monetary economics; Financial crisis; International economics; Macroeconomics; Monetary policy; Geography; Finance","authors":[{"name":"Alberto Coco","is_ca":false},{"name":"Andrea Silvestrini","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07531721603887709,"gpt":0.2594747535906854,"spread":0.1841575375518083,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001468077,0.0001249365,0.000415826,0.001051169,0.00007555421,0.0001484517,0.0004102066,0.00007540646,0.00003451649],"category_scores_gemma":[0.0002139225,0.00008115272,0.0001502661,0.000295247,0.0001513797,0.0004582385,0.00005620475,0.0001689087,0.000005748517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109618,"about_ca_system_score_gemma":0.00003326612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003144794,"about_ca_topic_score_gemma":0.0000541609,"domain_scores_codex":[0.998451,0.00003338133,0.001131597,0.000190734,0.00005102588,0.0001422403],"domain_scores_gemma":[0.9971974,0.001299938,0.001234579,0.0001136587,0.00009677449,0.00005768971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003120898,0.000185009,0.3147731,0.00001396099,0.002546156,0.000007020129,0.001885259,0.1073401,0.000002878207,0.5596991,0.0007325031,0.0125028],"study_design_scores_gemma":[0.00173137,0.0001930291,0.657939,0.00002220937,0.00005834318,0.00009864804,0.0003793181,0.1112903,0.00001609722,0.2124602,0.01556851,0.0002429943],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871665,0.001870969,0.003545981,0.005762645,0.000319361,0.00009950469,0.0002443838,0.000001588947,0.0009890968],"genre_scores_gemma":[0.9962572,0.0028442,0.000406851,0.0003267126,0.0001109769,0.000003103234,0.00001049018,0.000005972724,0.00003451553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3472389,"threshold_uncertainty_score":0.3309312,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4403203409","doi":"10.1504/ijcee.2024.142062","title":"The trade led-growth hypothesis in China and G8 countries: pooled mean group estimation","year":2024,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Global trade and economics","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Estimation; China; Economics; Group (periodic table); Econometrics; Geography; Chemistry","authors":[{"name":"Khalid Usman","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02336677014433436,"gpt":0.2118663955907371,"spread":0.1884996254464028,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001255741,0.0001897631,0.0003976786,0.001108688,0.000109835,0.000914407,0.0003604433,0.00009489786,0.0000364089],"category_scores_gemma":[0.0001991075,0.0001892138,0.0001375595,0.0002237943,0.0001213149,0.0009200392,0.00006938317,0.0002246639,0.00003595265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978762,"about_ca_system_score_gemma":0.00007602786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005917716,"about_ca_topic_score_gemma":0.00004840501,"domain_scores_codex":[0.9980666,0.00001618504,0.001331444,0.0003091048,0.00005616191,0.0002204979],"domain_scores_gemma":[0.9983962,0.0007962649,0.0005470694,0.00008625991,0.00005438001,0.0001197966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006299483,0.00005599227,0.009349555,0.00003508801,0.0004034154,0.00001639454,0.0004924225,0.0295821,2.96117e-7,0.9392393,0.0004190401,0.02034339],"study_design_scores_gemma":[0.0008993779,0.0001004972,0.08227815,0.0000455582,0.00001687846,0.000213237,0.0001241705,0.2877668,0.000003366182,0.594738,0.03355173,0.0002622675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.953055,0.01361457,0.005854253,0.0214334,0.002574024,0.0001758529,0.0002848763,0.0000188936,0.002989093],"genre_scores_gemma":[0.9803271,0.01675794,0.002149097,0.0004480333,0.0002433426,0.00000498527,0.0000144058,0.00002374161,0.00003132978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3445013,"threshold_uncertainty_score":0.8817648,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4390085821","doi":"10.1504/ijcee.2024.135659","title":"American financial markets dependencies: a vine copula approach","year":2023,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"Sistema Nacional de Investigadores","keywords":"Vine copula; Copula (linguistics); Vine; Financial market; Volatility (finance); Economics; Pairwise comparison; Econometrics; Coronavirus disease 2019 (COVID-19); Financial economics; Finance; Statistics; Mathematics","authors":[{"name":"Arturo Lorenzo Valdés","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02678279371861012,"gpt":0.2357716811217491,"spread":0.208988887403139,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001631954,0.0001892872,0.0005608216,0.00182835,0.00008893143,0.000240024,0.0005145739,0.00007900984,0.0001183364],"category_scores_gemma":[0.000528686,0.000225827,0.0002195782,0.0006085322,0.0001280584,0.0004551186,0.0001672129,0.0002128992,0.00005045694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002367342,"about_ca_system_score_gemma":0.0001193729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004694403,"about_ca_topic_score_gemma":0.000008194884,"domain_scores_codex":[0.9979593,0.00002067901,0.001328806,0.0003447651,0.00009110755,0.0002553672],"domain_scores_gemma":[0.9977344,0.0003524492,0.001342742,0.0001364223,0.0002533153,0.0001806792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004377784,0.0004513462,0.384815,0.00006971476,0.0008655463,0.00005092991,0.0005575132,0.0616438,0.000001103887,0.4960254,0.004463379,0.05061852],"study_design_scores_gemma":[0.0009730573,0.00009956357,0.2562102,0.000008787812,0.000007819923,0.00009841134,0.00009858395,0.5405617,7.576182e-7,0.1692778,0.03238206,0.0002811872],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9699227,0.0004354132,0.01816656,0.001435829,0.001430521,0.0001117252,0.0004381184,0.00002236983,0.008036732],"genre_scores_gemma":[0.9905223,0.002188383,0.006219686,0.0003976995,0.0003554059,0.000005405356,0.00009092461,0.00002452027,0.000195662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4789179,"threshold_uncertainty_score":0.9208959,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2790528637","doi":"10.1504/ijcee.2018.10011265","title":"Assessment of R&amp;D and its impact on Indian manufacturing industries","year":2018,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Economic Growth and Productivity","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Business; Industrial organization; Manufacturing engineering; Computer science; Engineering","authors":[{"name":"Chandrima Sikdar","is_ca":false},{"name":"Kakali Mukhopadhyay","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04623203616849821,"gpt":0.2886137914636744,"spread":0.2423817552951762,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008739071,0.0001637019,0.0004648823,0.00138447,0.00006630385,0.0001360103,0.0002622229,0.00009037973,0.000198231],"category_scores_gemma":[0.0001539431,0.0001763896,0.0001097758,0.0001083103,0.0001109792,0.0005511941,0.00008939643,0.0001927908,0.00002427653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002067576,"about_ca_system_score_gemma":0.0001140792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002184766,"about_ca_topic_score_gemma":0.000006635166,"domain_scores_codex":[0.9985076,0.00001353968,0.001014116,0.0002596926,0.00004681688,0.0001582226],"domain_scores_gemma":[0.9979426,0.0002823911,0.001334899,0.00009684812,0.0001949947,0.0001482385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002869019,0.0003806206,0.4530353,0.00006129083,0.00160519,0.000008568384,0.0006186371,0.03762764,0.00001040222,0.4811741,0.0004829302,0.02470839],"study_design_scores_gemma":[0.00138123,0.0005913918,0.8388901,0.00003120182,0.00001130038,0.0001498355,0.00005582924,0.010877,0.0002851286,0.1388867,0.008525762,0.0003144738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934114,0.0004847293,0.0008991598,0.0007178836,0.0009076429,0.00007359381,0.0003014804,0.000003563684,0.003200556],"genre_scores_gemma":[0.996976,0.0008275828,0.001465369,0.0001762017,0.0004773386,0.000001329285,0.00001691765,0.00001492119,0.00004429309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3858548,"threshold_uncertainty_score":0.7192958,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4412822423","doi":"10.1504/ijcee.2025.147776","title":"Comparative analysis of automatic time-series forecasting approaches for potato wholesale price index in India","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Agricultural Economics and Practices","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Heritage College","funders":"","keywords":"Index (typography); Time series; Series (stratigraphy); Econometrics; Price index; Economics; Computer science; Machine learning","authors":[{"name":"Dipankar Das","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05333599013293917,"gpt":0.2574451940806424,"spread":0.2041092039477032,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004707291,0.00009480419,0.0004083122,0.000480753,0.00004462506,0.0001203236,0.0002371411,0.00005060327,0.00003966291],"category_scores_gemma":[0.00008888743,0.00005347945,0.0001461293,0.0005359668,0.0000409842,0.0004656576,0.00005544931,0.00007266634,7.045136e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008576691,"about_ca_system_score_gemma":0.00003806636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001463661,"about_ca_topic_score_gemma":0.0000511822,"domain_scores_codex":[0.9989849,0.00002145322,0.0007091787,0.0001357491,0.00005541375,0.00009327618],"domain_scores_gemma":[0.9975813,0.001308148,0.000849452,0.00001807308,0.0002024421,0.00004057483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004243476,0.0004018575,0.1955277,0.0000461989,0.004441843,0.000002980815,0.0006311788,0.6064744,0.00005012572,0.0698795,0.0001689024,0.121951],"study_design_scores_gemma":[0.0003105492,0.0000827599,0.5773711,0.00001787435,0.00007154375,0.000007844574,0.0003622005,0.4098172,0.00002020626,0.01024279,0.00160347,0.00009251873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971688,0.0002146412,0.0004261676,0.0008589075,0.000137886,0.0001042896,0.0001269383,0.000002244263,0.0009601442],"genre_scores_gemma":[0.9972917,0.0001420347,0.002299302,0.00008645388,0.00005765109,0.000004461578,0.00008683473,5.912279e-7,0.00003095941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3818434,"threshold_uncertainty_score":0.2180829,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2530929115","doi":"10.1504/ijcee.2017.10000627","title":"The nature and propagation of shocks in the euro area: a comparative SVAR analysis","year":2016,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Economics; Quarter (Canadian coin); European union; Persistence (discontinuity); European monetary union; Sample (material); Order (exchange); Financial crisis; Eu countries; Monetary economics; International economics; Macroeconomics; Monetary policy; Geography; Finance","authors":[{"name":"Andrea Silvestrini","is_ca":false},{"name":"Alberto Coco","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.07531721603887709,"gpt":0.2594747535906854,"spread":0.1841575375518083,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001468077,0.0001249365,0.000415826,0.001051169,0.00007555421,0.0001484517,0.0004102066,0.00007540646,0.00003451649],"category_scores_gemma":[0.0002139225,0.00008115272,0.0001502661,0.000295247,0.0001513797,0.0004582385,0.00005620475,0.0001689087,0.000005748517],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000109618,"about_ca_system_score_gemma":0.00003326612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003144794,"about_ca_topic_score_gemma":0.0000541609,"domain_scores_codex":[0.998451,0.00003338133,0.001131597,0.000190734,0.00005102588,0.0001422403],"domain_scores_gemma":[0.9971974,0.001299938,0.001234579,0.0001136587,0.00009677449,0.00005768971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003120898,0.000185009,0.3147731,0.00001396099,0.002546156,0.000007020129,0.001885259,0.1073401,0.000002878207,0.5596991,0.0007325031,0.0125028],"study_design_scores_gemma":[0.00173137,0.0001930291,0.657939,0.00002220937,0.00005834318,0.00009864804,0.0003793181,0.1112903,0.00001609722,0.2124602,0.01556851,0.0002429943],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871665,0.001870969,0.003545981,0.005762645,0.000319361,0.00009950469,0.0002443838,0.000001588947,0.0009890968],"genre_scores_gemma":[0.9962572,0.0028442,0.000406851,0.0003267126,0.0001109769,0.000003103234,0.00001049018,0.000005972724,0.00003451553],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3472389,"threshold_uncertainty_score":0.3309312,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4412914078","doi":"10.1504/ijcee.2025.10072592","title":"Comparative analysis of automatic time-series forecasting approaches for potato wholesale price index in India","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational Economics and Econometrics","topic":"Agricultural Economics and Practices","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Heritage College","funders":"","keywords":"Time series; Index (typography); Series (stratigraphy); Price index; Econometrics; Autoregressive integrated moving average; Economics; Computer science; Machine learning","authors":[{"name":"Dipankar Das","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.05333599013293917,"gpt":0.2574451940806424,"spread":0.2041092039477032,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004707291,0.00009480419,0.0004083122,0.000480753,0.00004462506,0.0001203236,0.0002371411,0.00005060327,0.00003966291],"category_scores_gemma":[0.00008888743,0.00005347945,0.0001461293,0.0005359668,0.0000409842,0.0004656576,0.00005544931,0.00007266634,7.045136e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008576691,"about_ca_system_score_gemma":0.00003806636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001463661,"about_ca_topic_score_gemma":0.0000511822,"domain_scores_codex":[0.9989849,0.00002145322,0.0007091787,0.0001357491,0.00005541375,0.00009327618],"domain_scores_gemma":[0.9975813,0.001308148,0.000849452,0.00001807308,0.0002024421,0.00004057483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004243476,0.0004018575,0.1955277,0.0000461989,0.004441843,0.000002980815,0.0006311788,0.6064744,0.00005012572,0.0698795,0.0001689024,0.121951],"study_design_scores_gemma":[0.0003105492,0.0000827599,0.5773711,0.00001787435,0.00007154375,0.000007844574,0.0003622005,0.4098172,0.00002020626,0.01024279,0.00160347,0.00009251873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971688,0.0002146412,0.0004261676,0.0008589075,0.000137886,0.0001042896,0.0001269383,0.000002244263,0.0009601442],"genre_scores_gemma":[0.9972917,0.0001420347,0.002299302,0.00008645388,0.00005765109,0.000004461578,0.00008683473,5.912279e-7,0.00003095941],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3818434,"threshold_uncertainty_score":0.2180829,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}