{"id":"W7083427451","doi":"10.1007/978-3-031-91141-5_9","title":"Mapping the Threat: Using a Geospatial Lens on Malakand Division, Pakistan, to Gain Early Insights into Antimicrobial Resistance (AMR)","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Eastern European Communism and Reforms","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Rural Health Research Society; University of Saskatchewan; University of Calgary","funders":"","keywords":"Foothills; Geospatial analysis; Amoxicillin; Population; Geographic information system; Functional illiteracy; Psychological intervention; Spatial analysis; Spatial epidemiology","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":[0.0001799305,0.0003305212,0.0000920196,0.001488858,0.001737918,0.003271603,0.0003805977,0.0005569946,0.009341454],"category_scores_gemma":[0.0007141094,0.0001318829,0.0001271663,0.003974465,0.001403276,0.002305018,0.001264341,0.0007369716,0.0006853513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002367376,"about_ca_system_score_gemma":0.002494782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09642618,"about_ca_topic_score_gemma":0.2569071,"domain_scores_codex":[0.9999346,0.00001929797,0.000002072615,0.000008230551,0.00001999886,0.00001585095],"domain_scores_gemma":[0.999833,0.00009648173,0.00001865191,0.00000845561,0.00002901865,0.00001449972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0000479794,0.00005459859,0.02975438,0.0008198927,0.00002614842,0.003895033,0.0905041,0.005582001,0.002451661,0.3797743,0.1220564,0.3650337],"study_design_scores_gemma":[0.000004647584,0.00003294953,0.03927803,0.0004274073,0.0000209785,0.0008226903,0.1533228,0.002653512,0.0006201807,0.05290895,0.7498776,0.00003027162],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2465709,0.01456433,0.01677569,0.02874176,0.0007078295,0.0001131623,0.004468917,0.0002991477,0.6877583],"genre_scores_gemma":[0.862576,0.01975355,0.02405101,0.001246445,0.0002133959,0.0000700971,0.001278365,0.0001127103,0.0906984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09642618,"threshold_uncertainty_score":0.1917298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05045977332699934,"score_gpt":0.3149931878232149,"score_spread":0.2645334144962156,"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."}}