{"id":"W4401341977","doi":"10.1101/2024.08.02.24311418","title":"Optimization and performance analytics of global aircraft-based wastewater surveillance networks","year":2024,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Water Systems and Optimization","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Centers for Disease Control and Prevention; National Institutes of Health; Bill and Melinda Gates Foundation; U.S. Department of Health and Human Services","keywords":"Analytics; Wastewater; Computer science; Environmental science; Business; Data science; Environmental engineering","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":[0.001120581,0.001140151,0.0007424746,0.0005168124,0.000268594,0.0009330324,0.0006134316,0.0007679965,0.001306122],"category_scores_gemma":[0.004091455,0.0003428746,0.0005390078,0.0004207222,0.0008907328,0.0007512706,0.0008899414,0.0007307585,0.0001001305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001070022,"about_ca_system_score_gemma":0.0009474132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01002955,"about_ca_topic_score_gemma":0.003399028,"domain_scores_codex":[0.9995978,0.0001717112,0.00001366034,0.00008256757,0.00005376209,0.00008049645],"domain_scores_gemma":[0.9983377,0.001194171,0.0002208454,0.00005319006,0.0001196136,0.00007438157],"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":[0.000008404277,0.000004767433,0.0003447554,0.000005309304,0.000004260197,0.000007654487,0.000003791299,0.9980714,0.00008171681,0.0008987135,0.00006463697,0.000504682],"study_design_scores_gemma":[0.00000124001,0.000005200062,0.0001350737,9.92099e-7,0.000001120668,0.000001274032,0.000005315581,0.9988493,0.00004757167,0.0009197043,0.00003228686,9.322567e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4939463,0.0006048588,0.4925568,0.00183423,0.0000510289,0.00009952573,0.001066891,0.0003587888,0.00948161],"genre_scores_gemma":[0.9857587,0.0002031935,0.01242261,0.00004897537,0.00001735539,0.00005633806,0.0002762793,0.00003415656,0.001182367],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01002955,"threshold_uncertainty_score":0.01994234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00804219038009448,"score_gpt":0.1960946968997299,"score_spread":0.1880525065196354,"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."}}