{"id":"W4221004914","doi":"10.2196/35073","title":"Investigating Health Context Using a Spatial Data Analytical Tool: Development of a Geospatial Big Data Ecosystem","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine","keywords":"Geospatial analysis; Data science; Computer science; Geographic information system; Big data; Context (archaeology); Spatial data infrastructure; Spatial contextual awareness; Spatial epidemiology; Health geography; Spatial analysis; Knowledge management; Geography; Data mining; Health care; Cartography; Remote sensing; Health policy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01946783,0.001109433,0.00086435,0.009454787,0.002428817,0.01193444,0.003588453,0.001599725,0.003026882],"category_scores_gemma":[0.03928524,0.0009660232,0.002282119,0.009028522,0.00296378,0.0190432,0.01147127,0.003128564,0.001235385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002278429,"about_ca_system_score_gemma":0.007663035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006267918,"about_ca_topic_score_gemma":0.00677668,"domain_scores_codex":[0.9909627,0.004041205,0.001114781,0.001193374,0.002369582,0.0003183254],"domain_scores_gemma":[0.9714669,0.0124178,0.001620022,0.0048391,0.007431777,0.002224401],"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":[0.0001727973,0.0003312442,0.04193437,0.002549898,0.0005537889,0.001655856,0.01629175,0.01781852,0.005090338,0.3862942,0.06151094,0.4657963],"study_design_scores_gemma":[0.00006842368,0.0001923539,0.01007362,0.003137371,0.0003562558,0.001025821,0.01808054,0.1329347,0.005609463,0.3866264,0.4416414,0.0002537394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01361126,0.001352091,0.9417568,0.01713776,0.000523429,0.001427819,0.005003436,0.005259682,0.01392764],"genre_scores_gemma":[0.04172879,0.0006956441,0.9524561,0.0006337258,0.0001029458,0.0005119376,0.002802356,0.000302173,0.0007664618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01946783,"threshold_uncertainty_score":0.102957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1512190503759598,"score_gpt":0.3730215576694672,"score_spread":0.2218025072935074,"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."}}