{"id":"W4308271050","doi":"10.32920/21505098","title":"Disaggregating population data for assessing progress of SDGs: methods and applications","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Natural Science Foundation of China","keywords":"Population; Geospatial analysis; Sustainable development; Distribution (mathematics); Computer science; Population growth; Spatial analysis; Geography; Statistics; Data mining; Cartography; Mathematics; Remote sensing; Ecology; 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":[0.001469671,0.0001450913,0.000240493,0.00003360083,0.0002533621,0.00005184128,0.0004600028,0.00008807488,0.0008199333],"category_scores_gemma":[0.0000422509,0.0001347232,0.00003450616,0.00007016888,0.0001215081,0.0002577528,0.00283203,0.0001965992,0.000001318998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001621071,"about_ca_system_score_gemma":0.00001673944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004862026,"about_ca_topic_score_gemma":0.00002990575,"domain_scores_codex":[0.9985351,0.0001160618,0.0003607675,0.0005272071,0.0002422221,0.0002186461],"domain_scores_gemma":[0.9985218,0.000160797,0.0004245398,0.000809057,0.000002331676,0.00008147815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000007164659,0.0001214279,0.6028422,0.0003547818,0.00001887523,9.017635e-8,0.000199234,0.0003220024,0.0002028665,0.000502038,0.0001490122,0.3952803],"study_design_scores_gemma":[0.0002807482,0.00005179889,0.9415519,0.00005928285,0.0001175764,0.000001465297,0.0004599274,0.02268491,0.000156909,0.008337104,0.02589784,0.0004005326],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3420895,0.001122939,0.6369639,0.00176065,0.000320896,0.006099598,0.0005479696,0.0001469777,0.01094755],"genre_scores_gemma":[0.3624371,0.00006498996,0.6355305,0.00006134192,0.00007339847,0.000365174,0.001225673,0.00002543124,0.0002164018],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3948798,"threshold_uncertainty_score":0.8977694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09703337464610982,"score_gpt":0.4595012838556843,"score_spread":0.3624679092095745,"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."}}