{"id":"W4280594276","doi":"10.18280/isi.270213","title":"Impact of Vaccination on COVID-19 Spread in Real Time: Visualization and Analysis Tool","year":2022,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Data science; Vaccination; Big data; Variety (cybernetics); Computer science; Pandemic; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Disease; Visualization; Infectious disease (medical specialty); Virology; Data mining; Medicine; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001940266,0.0001330454,0.0003073041,0.001165546,0.0007783019,0.00001926769,0.0001213055,0.0001241687,0.001357763],"category_scores_gemma":[0.001869367,0.0001328905,0.00008185187,0.001772281,0.00003854893,0.0009134102,0.0001039402,0.0002585298,0.00004781932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002649143,"about_ca_system_score_gemma":0.0005692157,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009259004,"about_ca_topic_score_gemma":0.0005714864,"domain_scores_codex":[0.997095,0.0009240459,0.001183637,0.0001452413,0.0003650365,0.0002870345],"domain_scores_gemma":[0.9978724,0.0007880321,0.0007466074,0.0002256345,0.0002654992,0.0001017987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005519142,0.00006289574,0.8652456,0.000595562,0.0000912984,0.000001473194,0.06975496,0.03718225,0.0001258532,0.007946273,0.0005988833,0.01784308],"study_design_scores_gemma":[0.0004776445,0.0006636335,0.7341076,0.0000814243,0.00007425113,0.000003511248,0.01439213,0.2455939,0.00007218307,0.00389281,0.0003886193,0.0002522462],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920141,0.00001730138,0.003500096,0.00007603187,0.0001179394,0.0009785116,0.0001673542,0.00008804684,0.003040656],"genre_scores_gemma":[0.9986483,0.00004347576,0.00008118936,0.0002449144,0.00002530233,0.0002331239,0.0006563033,0.00001016583,0.00005725556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2084117,"threshold_uncertainty_score":0.9995551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06871721484604115,"score_gpt":0.4430142869905085,"score_spread":0.3742970721444673,"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."}}