{"id":"W2968021232","doi":"10.3390/ijgi8080356","title":"Geospatial Disaggregation of Population Data in Supporting SDG Assessments: A Case Study from Deqing County, China","year":2019,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Weighting; Geospatial analysis; Population; China; Geography; Sustainable development; Ancillary data; Scale (ratio); Computer science; Environmental resource management; Statistics; Cartography; Remote sensing; Environmental science; Mathematics; Environmental health; Political science","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":[0.001903092,0.0005160312,0.0003595848,0.001918569,0.0009321953,0.0008730245,0.0008944346,0.0005207088,0.0004791695],"category_scores_gemma":[0.002742975,0.0002354589,0.000442296,0.004334981,0.0006371931,0.0007383293,0.001268791,0.0003621867,0.00007250592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003327442,"about_ca_system_score_gemma":0.003446635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2492775,"about_ca_topic_score_gemma":0.2753947,"domain_scores_codex":[0.9990205,0.0003579838,0.00007664409,0.0001273212,0.0002454681,0.0001720274],"domain_scores_gemma":[0.998645,0.0003589754,0.00015816,0.0001736051,0.0005079526,0.000156284],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002131106,0.000512565,0.7870986,0.0004315077,0.000196688,0.01137442,0.01160241,0.07007563,0.004689315,0.004009058,0.003337359,0.1064594],"study_design_scores_gemma":[0.00008163451,0.0002772111,0.7401374,0.0001716126,0.0002530543,0.000905902,0.03842065,0.2016485,0.004222815,0.002162126,0.01158871,0.0001303261],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961057,0.0001088503,0.001851944,0.0002441624,0.000006752942,0.0000863364,0.0002852493,0.00002937734,0.001281668],"genre_scores_gemma":[0.9943779,0.0001789219,0.004442654,0.000035683,0.000005753806,0.00005960139,0.0003842375,0.000006455546,0.0005086975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2492775,"threshold_uncertainty_score":0.4956532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02054797574684888,"score_gpt":0.3842124124316288,"score_spread":0.36366443668478,"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."}}