{"id":"W2793461383","doi":"10.1111/maq.12440","title":"Cell Phones ≠ Self and Other Problems with Big Data Detection and Containment during Epidemics","year":2018,"lang":"en","type":"article","venue":"Medical Anthropology Quarterly","topic":"Viral Infections and Outbreaks Research","field":"Medicine","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Big data; Containment (computer programming); Sierra leone; Outbreak; TRACE (psycholinguistics); Coronavirus disease 2019 (COVID-19); Computer science; Phone; Internet privacy; Computer security; Disease; Data science; History; Virology; Medicine; Infectious disease (medical specialty); Ethnology; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003961283,0.0001282246,0.0002519485,0.0000896999,0.0002651239,0.00001609759,0.00007541251,0.0001692641,0.0004021764],"category_scores_gemma":[0.00003207855,0.00008406249,0.00001302117,0.0001135774,0.001802265,0.00006560913,0.00006591703,0.0002889537,0.00002622809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002876248,"about_ca_system_score_gemma":0.00006778649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001418116,"about_ca_topic_score_gemma":0.002008929,"domain_scores_codex":[0.9987589,0.00009141386,0.0002074493,0.000386023,0.000248757,0.000307502],"domain_scores_gemma":[0.9992781,0.00006332764,0.00004705964,0.0002808167,0.00005579347,0.0002749522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001998714,0.002179528,0.2972621,0.001489735,0.000733176,0.0005998736,0.01077669,1.425646e-7,0.05685942,0.0002040281,0.002392119,0.6255045],"study_design_scores_gemma":[0.05737374,0.141511,0.296214,0.001829145,0.001434049,0.02992485,0.03384313,0.04138625,0.06628608,0.001434422,0.3260082,0.002755051],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876943,0.0004417722,0.005915322,0.004984652,0.0002324814,0.0002930553,0.000008561199,0.000071699,0.0003581831],"genre_scores_gemma":[0.998279,0.0002305453,0.0002063952,0.0005150798,0.0005683123,0.00001613077,0.000005409295,0.00001747634,0.0001616266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6227494,"threshold_uncertainty_score":0.6640521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03488319904129604,"score_gpt":0.3214542023881605,"score_spread":0.2865710033468645,"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."}}