{"id":"W2615868739","doi":"10.2105/ajph.2017.303767","title":"Use of a Digital Health Application for Influenza Surveillance in China","year":2017,"lang":"en","type":"article","venue":"American Journal of Public Health","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Institute on Drug Abuse; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"China; Medicine; Environmental health; Outbreak; Commission; Disease surveillance; People's Republic; Family medicine; Public health; Geography; Business; Virology; Nursing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.002495851,0.0001350473,0.0009524795,0.0002715967,0.0001109966,0.00008725274,0.0003340095,0.00002340292,0.00000415659],"category_scores_gemma":[0.002482528,0.0001181499,0.0001396234,0.0002609314,0.0003107429,0.0007113428,0.00005134911,0.0002169125,0.000001935021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003204824,"about_ca_system_score_gemma":0.002932951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001257367,"about_ca_topic_score_gemma":0.0002406427,"domain_scores_codex":[0.9976746,0.000177582,0.001098144,0.0002042592,0.0003797251,0.0004656686],"domain_scores_gemma":[0.9946138,0.00018444,0.003424498,0.0007099856,0.0003770499,0.0006901876],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001772401,0.0002297544,0.5959931,0.0001316852,0.0000353371,0.000002697448,0.0002348703,0.00000656124,0.00001062681,0.0001191643,0.001060728,0.4019982],"study_design_scores_gemma":[0.001409778,0.001530464,0.9410581,0.000118436,0.000002557794,0.00004909398,0.0001495428,0.0001902653,0.000001829186,0.00005590171,0.05535263,0.0000813845],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9460328,0.0003572608,0.01195189,0.04018568,0.0001048622,0.0007446105,0.0005306091,0.00001964487,0.00007264652],"genre_scores_gemma":[0.9947421,0.0002308701,0.002855467,0.001941587,0.0001290759,0.00001197357,0.00005178016,0.00002412716,0.00001300023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4019168,"threshold_uncertainty_score":0.5202929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07744805167144073,"score_gpt":0.3904357768784743,"score_spread":0.3129877252070336,"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."}}