{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009356932,0.0001369263,0.0004946231,0.0001358732,0.000208625,0.00007402174,0.0001416779,0.0000719644,0.00003018804],"category_scores_gemma":[0.0008281539,0.0001274791,0.0000290209,0.000130735,0.0002545922,0.0002457067,0.00008923275,0.0001531543,0.000003967405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005135627,"about_ca_system_score_gemma":0.0008599084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006035384,"about_ca_topic_score_gemma":0.0007648029,"domain_scores_codex":[0.9984813,0.0001675176,0.0004569292,0.0003266969,0.0002070524,0.0003604637],"domain_scores_gemma":[0.9981516,0.0001218723,0.000349334,0.000672698,0.0001378057,0.0005667183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003934796,0.0001073527,0.9796249,0.0002802175,0.00001310781,0.000004320897,0.0003717534,8.260906e-8,0.00001161448,0.000622288,0.002961109,0.01596394],"study_design_scores_gemma":[0.001359688,0.0001028643,0.9265366,0.0000404323,5.293289e-7,0.00001048137,0.0001133477,0.000213067,7.331023e-7,0.0000474666,0.07147639,0.00009842798],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.971154,0.001954863,0.0002254762,0.02378641,0.0001500375,0.0008804753,0.0002650553,0.00006223515,0.001521426],"genre_scores_gemma":[0.9973301,0.0005866964,0.0004015731,0.001078115,0.00007080494,0.00005538435,0.0002708417,0.00001358012,0.0001929456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06851529,"threshold_uncertainty_score":0.5198446,"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."}}