{"id":"W4391188100","doi":"10.1186/s12889-024-17684-x","title":"Spatial multi-criteria decision analysis for the selection of sentinel regions in tick-borne disease surveillance","year":2024,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Vector-borne infectious diseases","field":"Immunology and Microbiology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; Institut National de Santé Publique du Québec; Université de Montréal; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Université de Sherbrooke","funders":"Canadian Institutes of Health Research; Université de Montréal; Public Health Agency; Public Health Agency of Canada","keywords":"Medicine; Biostatistics; Selection (genetic algorithm); Disease surveillance; Public health; Epidemiology; Environmental health; Disease; Pathology; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001100677,0.0001370696,0.0003165721,0.0004762371,0.0001773788,0.0000492842,0.0001584693,0.00008863346,0.0002236761],"category_scores_gemma":[0.001150368,0.0001027845,0.000234084,0.001247516,0.00009498624,0.00009903519,0.00004530369,0.0001493297,0.00002686041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000179111,"about_ca_system_score_gemma":0.001078898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002467915,"about_ca_topic_score_gemma":0.01113361,"domain_scores_codex":[0.9982418,0.000462598,0.0004741383,0.0003668629,0.00005143413,0.0004031502],"domain_scores_gemma":[0.9982399,0.00110094,0.0001221667,0.0003102258,0.0001505767,0.00007621631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002737509,0.000680444,0.9768004,0.0003050711,0.0007020066,0.000001735543,0.0002505755,0.0002786843,0.0007349752,0.002302262,0.006985134,0.01068489],"study_design_scores_gemma":[0.000705975,0.00005090117,0.9740394,0.0000297898,0.00008388794,0.0000046613,0.00003971096,0.01505904,0.00002218676,0.00006972131,0.009797036,0.00009763191],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4776157,0.009260036,0.5023722,0.007566237,0.001348421,0.00108418,0.0004951619,0.0002391614,0.00001890449],"genre_scores_gemma":[0.9988399,0.000172031,0.0001781423,0.0001566024,0.00006596994,0.0001172461,0.0002840469,0.00001772213,0.000168404],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5212241,"threshold_uncertainty_score":0.6212814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04186031142436376,"score_gpt":0.3373604827980429,"score_spread":0.2955001713736791,"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."}}