{"id":"W2277127407","doi":"","title":"臺灣主要貿易預測之績效評析－以中華經濟研究院、行政院主計總處與中央研究院經濟研究所為例","year":2013,"lang":"zh","type":"article","venue":"","topic":"Technology and Data Analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Consensus forecast; Economics; Actuarial science; Statistics; Accounting; Econometrics; Geography; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003160994,0.0003400983,0.0003202854,0.002216964,0.0002883325,0.001351994,0.0003245788,0.0003804933,0.001874352],"category_scores_gemma":[0.01214965,0.0001224765,0.0003925139,0.003228707,0.000331295,0.001287093,0.0004837796,0.0006813548,0.0006788863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007440159,"about_ca_system_score_gemma":0.001270527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01775695,"about_ca_topic_score_gemma":0.01906837,"domain_scores_codex":[0.9990164,0.0001848527,0.0001168681,0.0001657117,0.0004101906,0.0001060144],"domain_scores_gemma":[0.9895054,0.003992125,0.003381943,0.0007512307,0.002066294,0.0003029711],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002908352,0.0001628085,0.8966208,0.00007611581,0.0001451608,0.0002475196,0.0004129626,0.04886117,0.001139249,0.002217164,0.003279265,0.04654696],"study_design_scores_gemma":[0.00003980127,0.0003657491,0.8743203,0.00004732842,0.0001415034,0.0001901288,0.001350184,0.1123881,0.00498743,0.0018155,0.004276185,0.00007788595],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898918,0.0001882893,0.003366284,0.0002310227,0.00002276184,0.00003888757,0.002772557,0.00006360404,0.003424886],"genre_scores_gemma":[0.9894874,0.0001922642,0.002397651,0.00002297898,0.00002630839,0.00002592402,0.006769154,0.00001060603,0.001067789],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01775695,"threshold_uncertainty_score":0.03530717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00978972766329188,"score_gpt":0.2168835646140915,"score_spread":0.2070938369507996,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}