{"id":"W2922103469","doi":"10.3390/rs11050574","title":"Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Urban and Regional Ecology; National Natural Science Foundation of China","keywords":"Population; Census; Remote sensing; Computer science; Point of interest; Ancillary data; Geography; Scale (ratio); Cartography","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":[0.0003899533,0.0001872616,0.00026972,0.00006306315,0.0001176811,0.00003914829,0.0001124091,0.0000709386,0.0001261762],"category_scores_gemma":[0.00005225703,0.0001626777,0.00002395253,0.0001215746,0.00009564171,0.0004086364,0.0002834686,0.0001627204,0.0001531659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279617,"about_ca_system_score_gemma":0.000007631385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002208153,"about_ca_topic_score_gemma":0.0001634244,"domain_scores_codex":[0.9985491,0.00007804763,0.0002806964,0.0004645194,0.0002390817,0.0003885691],"domain_scores_gemma":[0.9989369,0.00008125573,0.0002177546,0.0006479273,0.000006059227,0.0001101181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008658339,0.00001173842,0.01551001,0.0001166448,0.00002467939,0.00004264487,0.0006162698,0.00026075,0.1439032,0.000001900804,0.0001434604,0.8392821],"study_design_scores_gemma":[0.001142746,0.0001489445,0.3033051,0.0009220404,0.00004580413,0.0003520917,0.0004408249,0.683531,0.006391659,0.0002367741,0.002893619,0.0005893283],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889319,0.00004275427,0.007652279,0.0004938237,0.00008147471,0.000289131,0.00001099637,0.00004111638,0.002456537],"genre_scores_gemma":[0.8762496,0.00003168989,0.1231707,0.0001402119,0.00003626376,3.262888e-10,0.00005754102,0.00002937944,0.000284611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8386928,"threshold_uncertainty_score":0.6633803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05174120022082328,"score_gpt":0.2735671016154024,"score_spread":0.2218259013945791,"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."}}