{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":2,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":2,"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":"8eec9f1d3431","filters":{"venue":"Journal of Data Analysis"}},"results":[{"id":"W2267200606","doi":"10.6338/jda.201110_6(5).0008","title":"X-12-ARIMA对中国季度GDP的探索","year":2011,"lang":"zh","type":"article","venue":"Journal of Data Analysis","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","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":"Autoregressive integrated moving average; China; Real gross domestic product; Quarter (Canadian coin); Index (typography); Economics; Seasonal adjustment; Econometrics; GDP deflator; Gross domestic product; Accounting; Statistics; Geography; Time series; Mathematics; Macroeconomics; Computer science","authors":[{"name":"王军伟","is_ca":false},{"name":"曾林蕊","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.1643455733332151,"gpt":0.3661070833135371,"spread":0.201761509980322,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003074521,0.001271147,0.001219272,0.000838485,0.0007963098,0.001337733,0.001580451,0.0006985735,0.01625039],"category_scores_gemma":[0.006835192,0.0004735096,0.001429193,0.001924941,0.0005005763,0.001011886,0.0006764599,0.001750304,0.004824572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007311658,"about_ca_system_score_gemma":0.001808143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02665622,"about_ca_topic_score_gemma":0.01895001,"domain_scores_codex":[0.9974011,0.0008601082,0.0001555656,0.0009184405,0.0004203454,0.0002445333],"domain_scores_gemma":[0.9976541,0.0009581237,0.0003484184,0.0003221105,0.0006427559,0.000074369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002169527,0.0009343722,0.2288069,0.001312709,0.002304584,0.0008539766,0.001175101,0.09281678,0.009510782,0.06837062,0.1158578,0.4758869],"study_design_scores_gemma":[0.0004188811,0.001728186,0.1954729,0.0002687565,0.001539729,0.0006046646,0.0008995469,0.5571024,0.01274285,0.03241636,0.1963471,0.0004585484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2779736,0.001860761,0.6441553,0.001654327,0.001780209,0.001689948,0.03566877,0.008783909,0.02643323],"genre_scores_gemma":[0.6853151,0.001621693,0.2044421,0.0005734934,0.0005822429,0.002575224,0.03410606,0.0006021123,0.07018191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02665622,"threshold_uncertainty_score":0.05436301,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2301336737","doi":"10.6338/jda.201302_8(1).0004","title":"An Impact of the Canada and the U.K. Return Volatility on the Hong Kong and the Singapore Stock Market Returns: A DCC and Bivariate AIGARCH Model","year":2013,"lang":"en","type":"article","venue":"Journal of Data Analysis","topic":"Stock Market Forecasting Methods","field":"Decision Sciences","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":"Stock market; Volatility (finance); Stock (firearms); Stock market bubble; Bivariate analysis; Financial economics; Restricted stock; Economics; Stock exchange; Stock market index; Business; Monetary economics; Econometrics; Finance","authors":[{"name":"Cheng-Yen Hsu","is_ca":false},{"name":"Wann-Jyi Horng","is_ca":false},{"name":"Liu-Hsiang Hsu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.134875916336006,"gpt":0.404852650732405,"spread":0.269976734396399,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002193233,0.0004097332,0.0005685342,0.0007770253,0.0004174323,0.002316318,0.0006243989,0.0006368922,0.001978106],"category_scores_gemma":[0.005323164,0.0002560567,0.0009329764,0.001168294,0.0006660009,0.001059449,0.0008340311,0.00100652,0.000228935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001544717,"about_ca_system_score_gemma":0.00233143,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1312317,"about_ca_topic_score_gemma":0.0671888,"domain_scores_codex":[0.9989966,0.0002612133,0.00004684631,0.000242446,0.0001951321,0.0002577703],"domain_scores_gemma":[0.9968348,0.001586843,0.0006424473,0.0001846937,0.0004866045,0.0002645901],"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":[0.0005584824,0.0002094801,0.7186681,0.000119896,0.0006858967,0.00178557,0.0006238613,0.1953008,0.002085203,0.04414462,0.00348392,0.03233421],"study_design_scores_gemma":[0.00006120387,0.0001601737,0.2222263,0.00003166146,0.0005683445,0.0001871781,0.0006108742,0.7654806,0.001133751,0.0070606,0.002394418,0.00008474922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98587,0.0006717424,0.007868215,0.0007879764,0.00006009708,0.0000270729,0.0003682607,0.00004811463,0.004298555],"genre_scores_gemma":[0.998038,0.0002260176,0.0003692459,0.00002882268,0.00001554299,0.00000588141,0.0002139577,0.000004842153,0.001097689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8687683,"threshold_uncertainty_score":0.2609356,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}