{"id":"W2516913841","doi":"10.1111/cag.12295","title":"Guidelines for creating framework data for GIS analysis in low‐ and middle‐income countries","year":2016,"lang":"en","type":"article","venue":"Canadian Geographies / Géographies canadiennes","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Grand Challenges Canada; Bill and Melinda Gates Foundation","keywords":"Mandate; Key (lock); Computer science; Data science; Open data; Scale (ratio); Low and middle income countries; World Wide Web; Geography; Developing country; Computer security; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008137972,0.0005034441,0.001074373,0.005902349,0.0004494792,0.0001252798,0.000674248,0.0002606348,0.00006684631],"category_scores_gemma":[0.004429011,0.0004133368,0.0003887171,0.004991887,0.001252241,0.0004161073,0.0001206826,0.0001291945,0.000001873799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001305269,"about_ca_system_score_gemma":0.0004285025,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2351264,"about_ca_topic_score_gemma":0.9843807,"domain_scores_codex":[0.9961783,0.0000515031,0.0008886,0.001193601,0.0002569515,0.001431007],"domain_scores_gemma":[0.9943521,0.001294747,0.0002360469,0.001802979,0.0008930034,0.001421117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002402697,0.00002095276,0.9821017,0.0008245603,0.001550009,0.00006232724,0.0002114133,0.000006019282,0.00000711413,0.004136075,0.006968217,0.003871336],"study_design_scores_gemma":[0.002835053,0.0003242225,0.8182368,0.003285088,0.001932543,0.00003487773,0.002569576,0.0005641232,0.00001421758,0.003893608,0.1651078,0.001202062],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9317429,0.01060412,0.0009078952,0.01288809,0.0005127423,0.002462536,0.04050504,0.0001974152,0.0001792903],"genre_scores_gemma":[0.9680377,0.006350899,0.01839101,0.002686433,0.0004945114,0.0006588868,0.002866611,0.0001397093,0.0003742712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7492543,"threshold_uncertainty_score":0.9998319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02906067230038667,"score_gpt":0.2824755105664312,"score_spread":0.2534148382660445,"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."}}