{"id":"W2884011204","doi":"10.14745/ccdr.v41i09a03","title":"Big Data is changing the battle against infectious diseases","year":2015,"lang":"en","type":"article","venue":"Canada Communicable Disease Report","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Canadian Institutes of Health Research; Agriculture and Agri-Food Canada; Government of Canada","keywords":"Big data; Bespoke; Battle; Multidisciplinary approach; Data science; Computer science; Infectious disease (medical specialty); Computer security; Business; Disease; Medicine; Political science; Geography; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0006336605,0.0002889226,0.0004774725,0.00009436337,0.0003721523,0.00007966318,0.001246346,0.00004110766,0.00007523981],"category_scores_gemma":[0.004319392,0.0002256894,0.00009731311,0.0006970956,0.0002140204,0.0001937225,0.001767159,0.0003052408,0.00002588362],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005059349,"about_ca_system_score_gemma":0.0100352,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.0868056,"about_ca_topic_score_gemma":0.1341802,"domain_scores_codex":[0.9971851,0.0001942433,0.0005078281,0.0005477896,0.0009788587,0.0005862215],"domain_scores_gemma":[0.9886398,0.0003129772,0.0002587725,0.00870673,0.0004502447,0.001631442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001532139,0.0002795231,0.3477417,0.0001488367,0.0003422271,0.0081771,0.0000772644,0.00009134018,0.000006921556,0.0000325225,0.6350123,0.007937024],"study_design_scores_gemma":[0.001096423,0.00001974383,0.06470281,0.00017186,0.0004167787,0.0002724148,0.0005751404,0.002317156,0.000008090758,0.00004846737,0.9300067,0.0003644413],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8162141,0.05229804,0.0003055541,0.02894898,0.007987187,0.003271314,0.01883406,0.001520905,0.07061984],"genre_scores_gemma":[0.9845471,0.0002414916,0.00003007279,0.005121391,0.0004199431,0.00006577947,0.008090774,0.0000654737,0.001417975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2949943,"threshold_uncertainty_score":0.995577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0800135105782911,"score_gpt":0.293943672934306,"score_spread":0.2139301623560149,"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."}}