{"id":"W3026612013","doi":"10.1061/(asce)cp.1943-5487.0000906","title":"Quantifying Remoteness for Risk and Resilience Assessment Using Nighttime Satellite Imagery","year":2020,"lang":"en","type":"article","venue":"Journal of Computing in Civil Engineering","topic":"Impact of Light on Environment and Health","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Geography; Satellite imagery; Psychological resilience; Urbanization; Index (typography); Resilience (materials science); Multivariate statistics; Census; Environmental resource management; Population; Satellite; Physical geography; Cartography; Remote sensing; Environmental science; Regional science; Computer science; Engineering; Demography; Machine learning; Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001220213,0.0001333832,0.000272395,0.00007197625,0.00009186256,0.00004224197,0.0001482408,0.0000447412,0.00001834149],"category_scores_gemma":[0.0001406654,0.0001246894,0.00005973145,0.0001690584,0.0000319618,0.0002697085,0.00009418235,0.0003064533,0.000001443242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002080107,"about_ca_system_score_gemma":0.00002297051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001874639,"about_ca_topic_score_gemma":0.000006262181,"domain_scores_codex":[0.9987036,0.00004048845,0.0004928833,0.0001638606,0.0002646128,0.000334565],"domain_scores_gemma":[0.9991862,0.0002386705,0.000309569,0.00007271238,0.000007283498,0.0001855685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002684443,0.0000257981,0.4586412,0.00009807313,0.0000100976,0.00002401213,0.0009252541,0.5033371,0.03224512,0.00001584144,0.00001261998,0.004637998],"study_design_scores_gemma":[0.0003802785,0.00009795173,0.497945,0.0001345162,0.00001279051,0.00002870607,0.0000556111,0.5003076,0.0003761815,0.00001541364,0.0005208486,0.0001250616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7977822,0.0002077013,0.2015176,0.0001993725,0.000103704,0.00009124228,6.349205e-7,0.00001057662,0.00008704671],"genre_scores_gemma":[0.9188953,0.0001440072,0.08073915,0.00008292943,0.0001195816,1.760118e-7,2.073278e-7,0.0000169924,0.000001655461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1211131,"threshold_uncertainty_score":0.5084684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02947444792285583,"score_gpt":0.2886600673488556,"score_spread":0.2591856194259998,"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."}}