{"id":"W4240064625","doi":"10.17975/sfj-2017-008","title":"What can big data tell us about past, current, and future patterns of infection?","year":2017,"lang":"en","type":"article","venue":"STEM Fellowship Journal","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Current (fluid); Big data; Data science; History; Computer science; Data mining; Geology; Oceanography","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":[0.0005883228,0.0002107466,0.0004323662,0.0001260725,0.0003415761,0.0004184711,0.0004851935,0.00008084807,0.00008896276],"category_scores_gemma":[0.00006577092,0.0001700524,0.0000991835,0.0000581692,0.0001317764,0.0007657824,0.0003620905,0.0005458403,0.00001401927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004675103,"about_ca_system_score_gemma":0.0001957144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008682018,"about_ca_topic_score_gemma":0.0005163463,"domain_scores_codex":[0.9983704,0.0001157867,0.0004297598,0.0003448342,0.0004273339,0.0003118564],"domain_scores_gemma":[0.9970899,0.00004003969,0.000607059,0.001622259,0.0002038124,0.0004369036],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008089018,0.00008949127,0.8505577,0.0002532755,0.0001092344,0.00009397701,0.000150367,3.809007e-7,0.00006033304,0.000006196991,0.003733977,0.1448642],"study_design_scores_gemma":[0.001440384,0.00008805222,0.8697265,0.0008432465,0.000143608,0.0004541269,0.0001975544,0.00002781376,0.00007184246,0.00001796341,0.1268382,0.0001506417],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860393,0.005933752,0.00009728473,0.000904262,0.005688435,0.0002187889,0.0006951683,0.00002722158,0.0003957753],"genre_scores_gemma":[0.978133,0.01528453,0.00003410218,0.0001117389,0.005913885,0.000002515344,0.0001655563,0.00003192083,0.0003227127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1447135,"threshold_uncertainty_score":0.6934535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0669557529102045,"score_gpt":0.3329344087715539,"score_spread":0.2659786558613494,"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."}}