{"id":"W2155134093","doi":"10.1186/1476-072x-5-56","title":"An unsupervised classification method for inferring original case locations from low-resolution disease maps.","year":2006,"lang":"en","type":"article","venue":"International Journal of Health Geographics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Geocoding; Health geography; Presentation (obstetrics); Identification (biology); Geographic information system; Health informatics; Computer science; Quality (philosophy); Metadata; Cartography; Geography; Information retrieval; Data science; Public health; Medicine; World Wide Web; Health policy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004454404,0.0008804107,0.0008121522,0.00761781,0.0007908116,0.002163685,0.00213271,0.001415106,0.003306125],"category_scores_gemma":[0.02108104,0.0004575894,0.001056365,0.003664121,0.0007686686,0.001634627,0.001305829,0.001243061,0.002684231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175097,"about_ca_system_score_gemma":0.001848121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006427956,"about_ca_topic_score_gemma":0.008135995,"domain_scores_codex":[0.996388,0.0007669211,0.0004852555,0.001001789,0.001150209,0.000207837],"domain_scores_gemma":[0.9859774,0.006766797,0.001999003,0.001752151,0.003300364,0.0002043209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003737683,0.000249012,0.02675948,0.0003737995,0.0001809934,0.0003937737,0.0006561012,0.02299076,0.009523669,0.003889058,0.01383855,0.9207711],"study_design_scores_gemma":[0.00008196551,0.00009151216,0.02195018,0.000123734,0.000139605,0.001256081,0.0005332372,0.9351068,0.01282715,0.01197346,0.01581682,0.00009951424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03684042,0.0002979191,0.9517439,0.0003153906,0.0001303,0.0005894019,0.00192614,0.005991055,0.002165541],"genre_scores_gemma":[0.153817,0.0001389168,0.8390913,0.0001043595,0.0001006706,0.0005258686,0.003933492,0.0002881084,0.002000245],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00761781,"threshold_uncertainty_score":0.02355742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349796327408408,"score_gpt":0.3930163964248658,"score_spread":0.358036763684025,"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."}}