{"id":"W3080230046","doi":"10.5194/isprs-archives-xliii-b4-2020-109-2020","title":"SOCIOECONOMIC STATUS FROM SPACE: EXAMPLE OF ESTIMATING THAILAND’s SUB-DISTRICT HOUSEHOLD INCOME BASED ON REMOTELY SENSED AND GEOSPATIAL DATA","year":2020,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Geo-Informatics and Space Technology Development Agency","keywords":"Socioeconomic status; Household income; Geospatial analysis; Geography; Imputation (statistics); Proxy (statistics); Estimation; Missing data; Socioeconomics; Statistics; Remote sensing; Population; Demography; Economics; Mathematics","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00115929,0.0003893502,0.0004569021,0.000360608,0.0009596897,0.0004099124,0.001488987,0.0000876829,0.00002377759],"category_scores_gemma":[0.0006489423,0.0002583504,0.0001985055,0.0004643014,0.002817941,0.0005370593,0.001387413,0.0004125139,0.000004643236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006786193,"about_ca_system_score_gemma":0.0001701806,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8509085,"about_ca_topic_score_gemma":0.1150509,"domain_scores_codex":[0.9958043,0.00028048,0.001283132,0.0005012263,0.00160257,0.0005282984],"domain_scores_gemma":[0.9960504,0.001277966,0.001790704,0.0005733068,0.0000510017,0.0002566069],"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":[0.0002556833,0.00002138498,0.005781919,0.00003594008,0.00004693868,3.143811e-7,0.003145115,0.0113169,0.00277322,0.000001677867,0.00005327064,0.9765676],"study_design_scores_gemma":[0.0009225886,0.0002097979,0.06044797,0.0001781247,0.00003997124,0.00001745088,0.0007898858,0.9306689,0.004078437,0.001033535,0.001352526,0.0002608398],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2187156,0.00001449128,0.7726449,0.004319222,0.0006957001,0.0005382248,0.000436363,0.00003253929,0.002602931],"genre_scores_gemma":[0.9899719,0.0001063989,0.008069161,0.001595409,0.0001249007,1.713726e-7,0.0001080817,0.00001291798,0.00001110279],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9763068,"threshold_uncertainty_score":0.9999869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03381336688762875,"score_gpt":0.2554046586235607,"score_spread":0.221591291735932,"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."}}