{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007220158,0.0004736493,0.0003181836,0.001090758,0.0003434918,0.0006078511,0.0009451128,0.000616677,0.000572893],"category_scores_gemma":[0.01267575,0.0002996201,0.0005556931,0.001478648,0.0003333531,0.001595836,0.0008160096,0.0004586905,0.0001538982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000692002,"about_ca_system_score_gemma":0.001287451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02005628,"about_ca_topic_score_gemma":0.02391854,"domain_scores_codex":[0.9962748,0.002686609,0.0001690288,0.0002700438,0.0004528534,0.0001466764],"domain_scores_gemma":[0.9909816,0.004935653,0.001009784,0.001059872,0.001671093,0.0003419122],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006465177,0.002709171,0.885007,0.0002630384,0.0002886078,0.0005114281,0.0005311628,0.04359135,0.005735963,0.0008203367,0.0006971605,0.05919832],"study_design_scores_gemma":[0.000328491,0.00498793,0.5583829,0.0001114023,0.000248631,0.000397597,0.002990457,0.4217665,0.006741314,0.0009812136,0.002956462,0.0001070336],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870203,0.00005295607,0.0105773,0.0003112576,0.000009039107,0.00043757,0.0009377633,0.0000472849,0.0006065682],"genre_scores_gemma":[0.9583373,0.0001129802,0.03904461,0.00004723842,0.00002052535,0.0004561296,0.001790869,0.000009017691,0.0001812482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02005628,"threshold_uncertainty_score":0.03987908,"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."}}