{"id":"W4414913174","doi":"10.1038/s41598-025-18706-3","title":"Clustering and time series analyses of hybrid immunity to SARS-COV-2 using data from the BQC19 biobank","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Quebec Population Health Research Network; McGill University; Université de Montréal; McGill University Health Centre; HEC Montréal","funders":"Fonds de Recherche du Québec - Santé; Ministère de la Santé et des Services sociaux; Ministère de la Santé; Génome Québec; Public Health Agency; Public Health Agency of Canada","keywords":"Dynamic time warping; Pandemic; Cluster analysis; Vaccination; Hierarchical clustering; Coronavirus disease 2019 (COVID-19); Series (stratigraphy); Temporality; Sequence (biology)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001859704,0.0003793459,0.0003863815,0.002138036,0.0004874713,0.0006115154,0.0003592476,0.0004639142,0.0007454551],"category_scores_gemma":[0.003535629,0.00008601189,0.000634669,0.00242989,0.0002530331,0.0002430082,0.0004064545,0.0003909134,0.0003976909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001143406,"about_ca_system_score_gemma":0.0009407909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1077932,"about_ca_topic_score_gemma":0.09579533,"domain_scores_codex":[0.999241,0.0002857882,0.00004843639,0.000201629,0.0001097705,0.0001133505],"domain_scores_gemma":[0.998365,0.0005750776,0.0002859912,0.0002181973,0.000419865,0.0001359125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009597311,0.0004596122,0.8320706,0.0002030827,0.0008633169,0.0005619805,0.001493219,0.0554238,0.01561442,0.001558375,0.006013159,0.08477866],"study_design_scores_gemma":[0.00001850976,0.00009421472,0.8793849,0.00002615545,0.00006570954,0.0001186139,0.0008347811,0.1143936,0.001184145,0.0005238604,0.003315141,0.00004035527],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925271,0.0001655735,0.003932124,0.0001037783,0.00001833324,0.00003674554,0.002701243,0.00004977007,0.000465226],"genre_scores_gemma":[0.9800495,0.000124426,0.006624187,0.00002975657,0.00001718108,0.00005476986,0.01240646,0.00002208789,0.0006716527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1077932,"threshold_uncertainty_score":0.2143316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1546144252126865,"score_gpt":0.427553997148846,"score_spread":0.2729395719361594,"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."}}