{"id":"W2804591035","doi":"10.5210/ojphi.v10i1.8912","title":"Revitalizing the Global Public Health Intelligence Network (GPHIN)","year":2018,"lang":"en","type":"article","venue":"Online Journal of Public Health Informatics","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Relevance (law); Computer science; Situation awareness; Data science; Government (linguistics); Artificial intelligence; Knowledge management; Engineering; Political science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012731,0.0002692937,0.0008929359,0.0001885923,0.0004465635,0.0002244998,0.0007953511,0.00009135787,0.0001305436],"category_scores_gemma":[0.00347467,0.0001750979,0.0002190042,0.00152545,0.0003264275,0.001132134,0.0002275957,0.0007405718,0.00007685586],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078836,"about_ca_system_score_gemma":0.008436804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003493125,"about_ca_topic_score_gemma":0.0001359317,"domain_scores_codex":[0.9931956,0.0004849871,0.003675114,0.0001258765,0.001224695,0.001293707],"domain_scores_gemma":[0.9920723,0.0001821652,0.003427265,0.0007721137,0.001622512,0.001923634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006062507,0.0005365707,0.03137562,0.00108608,0.0002600579,0.00001509566,0.003225157,0.0000530115,1.300464e-7,0.006346568,0.1885406,0.7685004],"study_design_scores_gemma":[0.0006367937,0.001437313,0.02200233,0.0006627433,0.00001671117,0.001066151,0.002588659,0.005606617,2.345168e-7,0.0004223608,0.9653984,0.0001616367],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05644399,0.01219789,0.2971835,0.621717,0.004291003,0.001833476,0.0008478097,0.0002776704,0.0052076],"genre_scores_gemma":[0.5675421,0.01263952,0.1803194,0.2274097,0.01115481,0.00001007525,0.0006539287,0.00009411826,0.0001763983],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.7768578,"threshold_uncertainty_score":0.9971845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1393976700846671,"score_gpt":0.4089411339882786,"score_spread":0.2695434639036115,"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."}}