{"id":"W2110102136","doi":"10.1038/srep09112","title":"Monitoring Disease Trends using Hospital Traffic Data from High Resolution Satellite Imagery: A Feasibility Study","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"U.S. National Library of Medicine; National Institute of Environmental Health Sciences; Intelligence Advanced Research Projects Activity; National Institutes of Health","keywords":"Satellite imagery; Remote sensing; Computer science; High resolution; Satellite; Geography; Engineering","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"],"consensus_categories":[],"category_scores_codex":[0.002880971,0.0002764769,0.0004637232,0.0002497888,0.0002489598,0.0003733195,0.0003666093,0.00005404713,0.00006957457],"category_scores_gemma":[0.001362034,0.0002552262,0.0001091908,0.0009525883,0.0003105835,0.0008134707,0.0005421935,0.000186033,0.00004482419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003896601,"about_ca_system_score_gemma":0.0007291377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003711345,"about_ca_topic_score_gemma":0.00005344904,"domain_scores_codex":[0.9948617,0.0002122691,0.0007946722,0.002141818,0.001507746,0.0004817437],"domain_scores_gemma":[0.9926359,0.00003730909,0.0004005289,0.005523743,0.000391005,0.0010115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003284348,0.002178594,0.975604,0.00002741493,0.00008888337,0.005433466,0.0005345336,0.0001938741,0.0009064791,2.631268e-7,0.006095136,0.008608953],"study_design_scores_gemma":[0.001100058,0.0001103325,0.9909515,0.0001338539,0.0003647348,0.00003491471,0.0006526127,0.003316047,0.0001171786,0.0001658789,0.002724597,0.0003282923],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868785,0.001374489,0.00004403194,0.0001038055,0.009961836,0.000771536,0.0004686511,0.0002966968,0.0001005119],"genre_scores_gemma":[0.9937786,0.000004097803,0.00137362,0.0000062877,0.0005114657,0.00001431147,0.003723713,0.00003713125,0.0005508086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01534753,"threshold_uncertainty_score":0.99999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.116786060241078,"score_gpt":0.35944545458071,"score_spread":0.242659394339632,"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."}}