{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002004025,0.00009594641,0.0002115197,0.0002927308,0.00009274766,0.0001882856,0.0006282916,0.00006996885,0.0002217051],"category_scores_gemma":[0.0003297341,0.00009148924,0.00005827508,0.0001899721,0.00002934826,0.009994469,0.00007798347,0.0002008369,0.000006158906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002824439,"about_ca_system_score_gemma":0.0001940989,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07378383,"about_ca_topic_score_gemma":0.01432272,"domain_scores_codex":[0.9971648,0.000116495,0.001289684,0.0001064755,0.001181152,0.0001414049],"domain_scores_gemma":[0.9973692,0.0001115999,0.001748684,0.0001990594,0.0005210291,0.00005040993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001078188,0.0001457676,0.9632418,0.00001027479,0.00005498329,0.00003370971,0.01142189,0.000447596,0.00001135058,0.000111627,0.00001738466,0.02439582],"study_design_scores_gemma":[0.001443006,0.00008082471,0.968494,0.0001258959,0.00003821455,0.00001232826,0.02118187,0.007562386,0.00001609548,0.0006784514,0.0002553941,0.0001115225],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935153,0.00001116334,0.004029084,0.0001899963,0.001289219,0.0003390915,0.0001463724,0.000009806225,0.000469991],"genre_scores_gemma":[0.9985684,0.000009032851,0.0006656209,0.00003698119,0.0002028904,0.000001674601,0.0005043405,0.000003825102,0.000007221262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05946111,"threshold_uncertainty_score":0.932384,"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."}}