{"id":"W2349572040","doi":"","title":"Population prediction based on multi-models-Case of Gannan,Tibetan Autonomous Prefecture","year":2011,"lang":"en","type":"article","venue":"Ganhanqu ziyuan yu huanjing","topic":"Regional Economic and Spatial Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Population; Logistic regression; Geography; Predictive modelling; Computer science; Demography; Machine learning","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":[0.0007988834,0.0005671381,0.0005897869,0.0008436788,0.0006876299,0.001082677,0.001342979,0.001079466,0.003298054],"category_scores_gemma":[0.001850546,0.0002953772,0.00112642,0.0008431162,0.0005872902,0.0009188128,0.0007950561,0.0007177725,0.0001913322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001784301,"about_ca_system_score_gemma":0.0008593631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1505188,"about_ca_topic_score_gemma":0.06480802,"domain_scores_codex":[0.9997007,0.0001320055,0.00001030052,0.00005631414,0.00002517031,0.00007566059],"domain_scores_gemma":[0.9993225,0.0003848405,0.00008034154,0.00003276375,0.0001119713,0.00006766204],"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":[0.0000612291,0.00002870429,0.01237706,0.00002439593,0.00003426456,0.0007193051,0.00008137184,0.9777277,0.0001229,0.005646806,0.000669827,0.002506467],"study_design_scores_gemma":[0.00000832658,0.00001749683,0.002736212,0.000005009671,0.00001477848,0.00004931536,0.00008955794,0.9954543,0.00004009127,0.001359871,0.0002164589,0.000008548836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9562968,0.0004477891,0.02898758,0.001063259,0.00004264548,0.00004356308,0.001007343,0.0001062735,0.01200477],"genre_scores_gemma":[0.995595,0.0001197977,0.001770039,0.00001770649,0.00001071535,0.00002346477,0.0002397443,0.000007853673,0.00221576],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1505188,"threshold_uncertainty_score":0.2992854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08891797102858422,"score_gpt":0.2208541142556474,"score_spread":0.1319361432270632,"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."}}