{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008838317,0.0001932715,0.0005488718,0.0007047066,0.0001519254,0.0001551035,0.004566672,0.00008180959,0.0002447385],"category_scores_gemma":[0.00006380956,0.000169737,0.0004099547,0.001781051,0.00008645205,0.002162842,0.001016432,0.0003486379,0.00004022472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005745903,"about_ca_system_score_gemma":0.0002116432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006203312,"about_ca_topic_score_gemma":0.0000337352,"domain_scores_codex":[0.9975407,0.00009301804,0.001037868,0.0004539983,0.0006236431,0.0002508296],"domain_scores_gemma":[0.9957939,0.0001225577,0.00138145,0.001953341,0.0005264446,0.0002222739],"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":[0.0001177677,0.002773196,0.01062164,0.00006232271,0.01617034,0.0005232719,0.003274621,0.003709392,0.0003625245,0.4848575,0.02780365,0.4497238],"study_design_scores_gemma":[0.0009229421,0.0007459162,0.04039383,0.0001313783,0.01303704,0.0004105608,0.0002544803,0.5331237,0.0003755522,0.3326739,0.07682262,0.001108071],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002287528,0.0004829299,0.9928872,0.0008107325,0.0001115235,0.00006269087,0.0001123282,0.00002559154,0.005278258],"genre_scores_gemma":[0.3086821,0.0005211044,0.6900035,0.0003020508,0.0002160763,0.000001229679,0.00005023529,0.00001029556,0.0002133435],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5294143,"threshold_uncertainty_score":0.8486089,"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03638481,0.0002249499,0.0008126551,0.0002332796,0.0005766765,0.0006651163,0.002606683,0.00006724753,0.0001693347],"category_scores_gemma":[0.01867002,0.00006814406,0.0002947208,0.001582387,0.0009397218,0.0007393734,0.0008216961,0.0006767172,8.648333e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006515563,"about_ca_system_score_gemma":0.0004544574,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09351984,"about_ca_topic_score_gemma":0.1013947,"domain_scores_codex":[0.991616,0.004747116,0.001156784,0.0004234425,0.001800376,0.0002562919],"domain_scores_gemma":[0.9776239,0.01683073,0.001896839,0.00279579,0.0006707325,0.0001819944],"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":[0.003905576,0.00009895606,0.8511091,0.0000203273,0.005540147,0.00001012341,0.005771005,0.004476202,0.0002849299,0.0003004568,0.04094136,0.08754181],"study_design_scores_gemma":[0.0005583351,0.00003636913,0.3462058,0.0000207958,0.0007212583,0.00003000861,0.0006493223,0.6410432,0.000003776814,0.01065011,0.00001958814,0.00006132713],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887687,0.0003213919,0.003611015,0.00644245,0.00006213944,0.0003108326,0.0002034243,0.000002037274,0.0002780352],"genre_scores_gemma":[0.9973958,0.00008193989,0.00207713,0.0002429493,0.00005961425,0.000002315851,0.000002363162,0.000009479937,0.0001283817],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6365671,"threshold_uncertainty_score":0.9922446,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}