{"id":"W4404048327","doi":"10.54254/2753-8818/51/2024ch0191","title":"Forecasting urban unemployment rate in China using ARIMA model","year":2024,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Autoregressive integrated moving average; Econometrics; Unemployment rate; China; Unemployment; Statistics; Economics; Mathematics; Time series; Geography; Macroeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000788222,0.0005144576,0.000476108,0.0009072855,0.000421424,0.0005364007,0.000689153,0.0004765252,0.0007631475],"category_scores_gemma":[0.001364955,0.0002171196,0.0007281625,0.0006348324,0.0001779223,0.0004336993,0.0004406609,0.0004087873,0.0001480696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006334881,"about_ca_system_score_gemma":0.0009912541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1083352,"about_ca_topic_score_gemma":0.05970172,"domain_scores_codex":[0.9997335,0.00005765742,0.00002257186,0.00007425492,0.000048795,0.00006327744],"domain_scores_gemma":[0.999705,0.0000987262,0.00005177614,0.00001832443,0.00009871029,0.00002754303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001761026,0.00009192224,0.1346977,0.0001127501,0.0001341871,0.0003226331,0.0002722218,0.8252693,0.002085468,0.002215672,0.00239803,0.03222404],"study_design_scores_gemma":[0.000004865163,0.00001794139,0.01397111,0.000005381112,0.00001729198,0.000009474791,0.00003446078,0.9853114,0.0001522704,0.0002176344,0.0002491562,0.000009081731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9746339,0.0004677644,0.02150488,0.0003555302,0.00004883318,0.00002479704,0.0007609169,0.0001908846,0.002012616],"genre_scores_gemma":[0.9956673,0.0002269689,0.002546566,0.00001731904,0.00002041766,0.0000179431,0.0005979045,0.000007104845,0.0008983302],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1083352,"threshold_uncertainty_score":0.2154092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0278698092179662,"score_gpt":0.2362979820076403,"score_spread":0.2084281727896741,"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."}}