{"id":"W4400119922","doi":"10.3390/ijgi13070226","title":"Automated Geospatial Approach for Assessing SDG Indicator 11.3.1: A Multi-Level Evaluation of Urban Land Use Expansion across Africa","year":2024,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Nuclear Safety and Security Commission; Colorado State University; National Aeronautics and Space Administration","keywords":"Urban agglomeration; Geospatial analysis; Geography; Urbanization; Environmental planning; Land use; Population; Urban planning; Sustainable development; Environmental resource management; Cartography; Economic growth; Economic geography; Political science; Civil engineering; Environmental science; Environmental health; Engineering","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":[0.0008904567,0.0007678574,0.0004764143,0.007214943,0.0005009162,0.001585122,0.000599102,0.0004961757,0.001700881],"category_scores_gemma":[0.002586432,0.0002636824,0.000567677,0.00567975,0.0002881654,0.00109724,0.001353968,0.0003798298,0.0004916888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007930248,"about_ca_system_score_gemma":0.0008476736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02005311,"about_ca_topic_score_gemma":0.02818846,"domain_scores_codex":[0.9992359,0.0001626179,0.00006886975,0.0001500873,0.0002682737,0.0001143865],"domain_scores_gemma":[0.9988168,0.0002577598,0.0002423802,0.0001297648,0.0004793058,0.00007400945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000303841,0.0004302809,0.4653339,0.000881454,0.0003531537,0.001291648,0.003187012,0.1589192,0.03025435,0.007243024,0.01118533,0.3206167],"study_design_scores_gemma":[0.00003728909,0.0001161162,0.3071885,0.0001543775,0.0001044141,0.0004265057,0.005333711,0.6424699,0.02070115,0.003360096,0.01998836,0.0001195318],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.849672,0.0004239629,0.1118352,0.0003361567,0.00005172294,0.0007924629,0.02070429,0.003972206,0.01221199],"genre_scores_gemma":[0.8489212,0.0001837282,0.1421619,0.00003540558,0.00001413379,0.0003947164,0.006966784,0.0001139191,0.001208196],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02005311,"threshold_uncertainty_score":0.03987277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04682912890859996,"score_gpt":0.3247491612759768,"score_spread":0.2779200323673768,"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."}}