{"id":"W2379459575","doi":"","title":"Developing model of Wuwei City under the control of water resources with the STIRPAT model","year":2013,"lang":"en","type":"article","venue":"Ganhanqu dili","topic":"Water Resources and Sustainability","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Urbanization; Water resources; Per capita; Population; Consumption (sociology); Environmental science; Driving factors; China; Population growth; Water resource management; Natural resource economics; Agricultural economics; Geography; Economics; Economic growth; Demography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003732848,0.0001347792,0.0001797768,0.00001290324,0.0001700045,0.00002605437,0.0004505691,0.00004616891,0.0002130668],"category_scores_gemma":[0.000009854075,0.00005139349,0.00005801997,0.00008309689,0.0006171024,0.0001160598,0.0002271641,0.0001105361,0.00001659823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006984198,"about_ca_system_score_gemma":0.00001305291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001479079,"about_ca_topic_score_gemma":0.0001730181,"domain_scores_codex":[0.9988332,0.00007777665,0.00021837,0.0002142517,0.0003520198,0.000304356],"domain_scores_gemma":[0.9993336,0.000060399,0.00007931807,0.0004453242,0.00003653027,0.00004484869],"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":[0.00007027093,0.00006489405,0.02470147,0.00003539401,0.00003715141,3.564824e-7,0.01416902,0.948387,0.01130325,0.0003082046,0.0002079612,0.0007150075],"study_design_scores_gemma":[0.0007234974,0.00009590637,0.04990199,0.00001834384,0.00004579315,0.000002244713,0.004890613,0.9024119,0.02262739,0.01831741,0.0006698069,0.0002951464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9389217,0.00001519728,0.05679628,0.002838407,0.000006400157,0.0004186723,0.00000468259,0.00001188836,0.0009867087],"genre_scores_gemma":[0.99806,0.000001956526,0.0004119309,0.0003457331,0.000007667844,0.00003936754,8.645548e-7,0.00001064794,0.001121774],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0591383,"threshold_uncertainty_score":0.2332933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01598179976625494,"score_gpt":0.2010889816571741,"score_spread":0.1851071818909192,"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."}}