{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002723113,0.000239204,0.0008573453,0.0001792853,0.0002768067,0.00003137939,0.001634548,0.00009215876,0.0004850239],"category_scores_gemma":[0.001640794,0.0002176469,0.00005238202,0.0004758232,0.0001715372,0.0003301597,0.005228559,0.0006610632,0.00002393144],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003204479,"about_ca_system_score_gemma":0.007031704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002509498,"about_ca_topic_score_gemma":0.0006054775,"domain_scores_codex":[0.9940166,0.0002072819,0.002335265,0.0003385404,0.002589446,0.0005128423],"domain_scores_gemma":[0.9957213,0.0002031146,0.0008435379,0.002338972,0.0001185577,0.0007745565],"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":[0.0002329971,0.0008940155,0.01888923,0.004871269,0.0005755968,0.0000874992,0.01131151,0.00006348917,0.00002007205,0.0001556842,0.03522125,0.9276774],"study_design_scores_gemma":[0.002566376,0.0001804319,0.003592489,0.0006750866,0.00006543944,0.000169297,0.004060406,0.8869055,0.000009015323,0.000006052315,0.1015044,0.0002654817],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9533398,0.0001660316,0.03489966,0.001021345,0.0007635423,0.001637021,0.007696462,0.0001879217,0.0002882141],"genre_scores_gemma":[0.9523488,0.00000911951,0.02763233,0.003388894,0.0003873947,0.00004066552,0.01614335,0.00003588265,0.00001355053],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9274119,"threshold_uncertainty_score":0.9985975,"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."}}