{"id":"W4396671553","doi":"10.1080/21645515.2024.2344249","title":"Driving consistency: CEPI-Centralized Laboratory Network’s conversion factor initiative for SARS-CoV-2 clinical assays used for efficacy assessment of COVID vaccines","year":2024,"lang":"en","type":"article","venue":"Human Vaccines & Immunotherapeutics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"Translational Health Science and Technology Institute; Coalition for Epidemic Preparedness Innovations","keywords":"Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); 2019-20 coronavirus outbreak; Medicine; Virology; Environmental health; Outbreak; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09750586,0.001152739,0.001226238,0.00290158,0.001746214,0.009539541,0.006316166,0.002150838,0.004333328],"category_scores_gemma":[0.2631996,0.00101001,0.001417251,0.004452819,0.002345426,0.004627936,0.005135733,0.004787034,0.002674065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007067057,"about_ca_system_score_gemma":0.01858796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02855878,"about_ca_topic_score_gemma":0.01436048,"domain_scores_codex":[0.9044473,0.03330674,0.005899173,0.01593663,0.03789075,0.002519479],"domain_scores_gemma":[0.7888698,0.07411607,0.01855645,0.04285071,0.07244791,0.003158979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002133837,0.0008746639,0.1814042,0.001264302,0.0007878371,0.0003466556,0.001516534,0.06927529,0.009197448,0.09749223,0.2116344,0.4240727],"study_design_scores_gemma":[0.000739409,0.001147784,0.07270547,0.001085076,0.0003548207,0.0008663309,0.001222858,0.3716018,0.05456829,0.1213294,0.3738745,0.0005042442],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1496836,0.002811726,0.6871757,0.04385434,0.004585834,0.002932612,0.01944557,0.02482491,0.06468572],"genre_scores_gemma":[0.6316011,0.0006595268,0.3215699,0.00978094,0.000867571,0.001742153,0.02452891,0.002891784,0.006357993],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09750586,"threshold_uncertainty_score":0.5156664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1331229253878217,"score_gpt":0.4462325409175085,"score_spread":0.3131096155296867,"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."}}