{"id":"W2605545985","doi":"10.2196/publichealth.6925","title":"Zika in Twitter: Temporal Variations of Locations, Actors, and Concepts","year":2017,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Zika virus; Geography; Computer science; Data science; Biology; Virology","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":[0.001097435,0.0002249163,0.0002496064,0.002905295,0.00123961,0.002738111,0.0003706052,0.0004168706,0.00288389],"category_scores_gemma":[0.01017121,0.0002562589,0.0001847143,0.00516224,0.0009465281,0.003893069,0.002588509,0.0007163235,0.0008006701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001134993,"about_ca_system_score_gemma":0.000426821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01420465,"about_ca_topic_score_gemma":0.02500353,"domain_scores_codex":[0.9988043,0.0003996489,0.0001196229,0.0002649016,0.0002585794,0.0001528827],"domain_scores_gemma":[0.9947246,0.002969748,0.001151992,0.0002464282,0.0006996384,0.0002075807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006059714,0.000072054,0.6638035,0.001240667,0.0001565268,0.001558802,0.2011925,0.0011619,0.009916988,0.01665481,0.02031107,0.08332523],"study_design_scores_gemma":[0.00002078633,0.00005520344,0.7628857,0.0003594306,0.00008514127,0.0006827029,0.1326825,0.005996927,0.00151038,0.004279509,0.09133378,0.0001079203],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.968832,0.001053676,0.003223785,0.002124039,0.0001002046,0.0001585157,0.01048348,0.0001032184,0.01392119],"genre_scores_gemma":[0.991867,0.0004738814,0.00240923,0.0001100493,0.00005212907,0.0001851405,0.003463438,0.00003955561,0.001399564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01420465,"threshold_uncertainty_score":0.02824396,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04625401294774238,"score_gpt":0.3721192092418688,"score_spread":0.3258651962941264,"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."}}