{"id":"W2890205989","doi":"10.23889/ijpds.v3i4.1028","title":"Using Linked Data and Advanced Analytics to Prioritize Health Concerns within Regions","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"","keywords":"Data science; Population; Computer science; Health indicator; Public health; Decision support system; Population health; Analytics; Disease surveillance; Data mining; Environmental health; Medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001668257,0.0001074155,0.0001950464,0.0002964645,0.0004315416,0.0002600647,0.001638333,0.00002400974,0.00001892824],"category_scores_gemma":[0.00281858,0.00009861868,0.00002023058,0.0003959425,0.0002769791,0.001996092,0.0009472956,0.000127931,0.000006209751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002583172,"about_ca_system_score_gemma":0.0007953776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001464643,"about_ca_topic_score_gemma":0.0002873262,"domain_scores_codex":[0.997757,0.00002957256,0.0005144633,0.0005713501,0.0008778695,0.0002497428],"domain_scores_gemma":[0.99713,0.00006284633,0.0003795179,0.001045434,0.0009392246,0.0004429562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003780009,0.0007638768,0.6051316,0.0002012808,0.0007168895,0.0001717345,0.002509645,0.003388618,0.0230042,0.02686791,0.07833446,0.2551298],"study_design_scores_gemma":[0.00266378,0.0004143366,0.2473095,0.0006890232,0.00007635304,0.000816208,0.0003204866,0.6698434,0.00007006797,0.001508705,0.07593457,0.0003535233],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6872541,0.0002429975,0.2749929,0.01709267,0.0061684,0.001313486,0.01267074,0.0001145334,0.0001501817],"genre_scores_gemma":[0.8674854,0.00005793845,0.1274596,0.001735992,0.001050737,0.000001540046,0.002112732,0.00001405951,0.00008203871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6664548,"threshold_uncertainty_score":0.4021553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2669284483601198,"score_gpt":0.5166225883596763,"score_spread":0.2496941399995565,"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."}}