{"id":"W4407416130","doi":"10.1038/s41591-025-03501-4","title":"Pandemic monitoring with global aircraft-based wastewater surveillance networks","year":2025,"lang":"en","type":"article","venue":"Nature Medicine","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Interdisciplinary Research in Rehabilitation; Université Laval","funders":"National Institute of General Medical Sciences; Fonds de recherche du Québec – Nature et technologies; Centers for Disease Control and Prevention; U.S. Department of Health and Human Services; National Institutes of Health; Government of Canada; Natural Sciences and Engineering Research Council of Canada; Bill and Melinda Gates Foundation","keywords":"Situation awareness; Wastewater; Computer science; Warning system; Function (biology); Drone; Resource (disambiguation); Transmission (telecommunications); Coronavirus disease 2019 (COVID-19); Pandemic; Operations research; Risk analysis (engineering); Environmental science; Engineering; Telecommunications; Business; Environmental engineering; Medicine; Biology; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001303213,0.0007037212,0.000514138,0.0005394182,0.0002930354,0.0009443257,0.0008013399,0.0009059444,0.0009908826],"category_scores_gemma":[0.004366155,0.0003886249,0.0006451569,0.0004349378,0.0007224593,0.001290295,0.001148331,0.0006718202,0.00005657406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200913,"about_ca_system_score_gemma":0.001002754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01786187,"about_ca_topic_score_gemma":0.01091536,"domain_scores_codex":[0.9994456,0.0003214008,0.00001564832,0.0001140258,0.00004391359,0.00005943141],"domain_scores_gemma":[0.9984905,0.001002467,0.0002771818,0.0000739804,0.00009232377,0.00006346161],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00001491744,0.000009649995,0.002822514,0.000007713564,0.00001598378,0.00002626162,0.00001383246,0.9927163,0.0001653636,0.002374782,0.0001031716,0.001729507],"study_design_scores_gemma":[0.000006278617,0.00002692637,0.0007984574,0.000003853636,0.000007386964,0.000008647419,0.00004043018,0.9963715,0.00008628526,0.002482315,0.0001641908,0.000003642215],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5185987,0.0003910026,0.4716747,0.001809279,0.00006130617,0.0001254532,0.0008779313,0.000196128,0.00626558],"genre_scores_gemma":[0.9768445,0.0001661538,0.0220426,0.00005602883,0.0000211581,0.00004514864,0.0001696819,0.00001041551,0.0006442953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01786187,"threshold_uncertainty_score":0.03551579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0657737011855956,"score_gpt":0.403845128774688,"score_spread":0.3380714275890924,"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."}}