{"id":"W2058617895","doi":"10.5539/cis.v1n2p27","title":"A Prediction Model of China Population Growth","year":2008,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Notice; China; Index (typography); Population; Urbanization; sort; Limit (mathematics); Computer science; Computation; Econometrics; Population growth; Value (mathematics); Term (time); Statistics; Mathematics; Demography; Geography; Algorithm; Economics; Economic growth; Mathematical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002229159,0.00004088566,0.0001142301,0.0002752605,0.000139984,0.00002763441,0.00009517103,0.00001882575,0.00000862905],"category_scores_gemma":[0.00001511413,0.00004148674,0.00002867291,0.0002268626,0.0001192335,0.003456417,0.00003618084,0.00002505775,0.00002012547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002327462,"about_ca_system_score_gemma":0.0000153103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001467887,"about_ca_topic_score_gemma":4.15955e-7,"domain_scores_codex":[0.9994413,0.000001406817,0.0003589573,0.00009012106,0.00003868571,0.00006957517],"domain_scores_gemma":[0.99964,0.000004298286,0.0001944212,0.00007688905,0.0000467832,0.00003759442],"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":[0.000006402258,0.00001736454,0.1730269,0.00001990727,0.000008463973,4.797249e-8,0.002253676,0.04180432,0.000009079267,0.778401,0.0002821642,0.00417058],"study_design_scores_gemma":[0.00007553579,0.00001329182,0.2994333,0.000001617344,5.355728e-7,0.000002256809,0.000002916664,0.6940048,0.0000104552,0.006321056,0.0001019911,0.00003230249],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6053613,0.0000169407,0.388056,0.0001002143,0.00007008857,0.00003640946,0.00001831557,0.00000941178,0.006331303],"genre_scores_gemma":[0.996473,0.000145796,0.003198348,0.0001288614,0.00001947164,0.0000015373,0.00001098521,9.481909e-7,0.00002107798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.77208,"threshold_uncertainty_score":0.2505819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03223246758787768,"score_gpt":0.1867821553694252,"score_spread":0.1545496877815475,"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."}}