{"id":"W4417106817","doi":"10.1038/s41598-025-29316-4","title":"Contrasting pre-vaccine COVID-19 waves in Italy through functional data analysis","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Huck Institutes of the Life Sciences","keywords":"Functional data analysis; Regression analysis; Government (linguistics); Differential (mechanical device); Smoothing; Quality (philosophy)","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":[0.003543771,0.000387996,0.0004846208,0.002733603,0.0003481307,0.000900593,0.0006413966,0.0004649861,0.002396939],"category_scores_gemma":[0.01000827,0.0002015865,0.0009286945,0.003598378,0.0004050147,0.0006615622,0.001461346,0.0005845986,0.0006020565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007573471,"about_ca_system_score_gemma":0.0009646529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06810998,"about_ca_topic_score_gemma":0.05239792,"domain_scores_codex":[0.9978389,0.001112503,0.0001284208,0.0003219048,0.0001824652,0.000415742],"domain_scores_gemma":[0.9959632,0.001666616,0.001066677,0.0006190268,0.0004385177,0.0002459896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002377261,0.00005302028,0.9835067,0.00009138814,0.0002510631,0.0001115773,0.0007281118,0.00227229,0.0001725459,0.0008269142,0.002730184,0.009018413],"study_design_scores_gemma":[0.000006838418,0.00004641298,0.9940646,0.00003750385,0.00004787994,0.00004211687,0.0009111397,0.003012785,0.00006549183,0.0002251828,0.001528303,0.00001170974],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982188,0.0003873674,0.003653859,0.0005796702,0.00002514781,0.00005646829,0.01077539,0.00006289983,0.002271159],"genre_scores_gemma":[0.9804048,0.0001664272,0.001768344,0.00006783859,0.00002936937,0.0001094939,0.0167633,0.00002782389,0.0006627091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06810998,"threshold_uncertainty_score":0.1354271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2965322396902877,"score_gpt":0.4595786921315722,"score_spread":0.1630464524412844,"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."}}