{"id":"W4410809823","doi":"10.1109/incacct65424.2025.11011394","title":"BioTwinNet: Dual-Stream Multilevel Feature Fusion for Classification of SARS-CoV-2 and Influenza Virus Variants via Genomic Image Processing","year":2025,"lang":"en","type":"article","venue":"","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Dual (grammatical number); Computer science; Feature (linguistics); Artificial intelligence; Virology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Virus; Coronavirus disease 2019 (COVID-19); Computational biology; Pattern recognition (psychology); Biology; 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":[0.0003594237,0.0008233665,0.0004845483,0.0009102733,0.0002157543,0.0004887534,0.0006596806,0.0005202779,0.002014235],"category_scores_gemma":[0.0007289585,0.0001585057,0.0005488819,0.0005358091,0.0001951491,0.0007555071,0.000764582,0.0005747892,0.0005889702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005382908,"about_ca_system_score_gemma":0.0006300884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005277798,"about_ca_topic_score_gemma":0.007279544,"domain_scores_codex":[0.9998423,0.00001882153,0.000007809785,0.00004244234,0.00005178275,0.00003679301],"domain_scores_gemma":[0.9998853,0.00002834753,0.00001549364,0.00001436328,0.00004097939,0.00001549662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001011638,0.0005287498,0.0101334,0.0001894002,0.0002743105,0.0004398289,0.00009832326,0.09175462,0.08593023,0.003648124,0.01761779,0.7883736],"study_design_scores_gemma":[0.00002532897,0.0002068497,0.003483407,0.00001246468,0.00004347186,0.0001216697,0.00003922519,0.9640132,0.02672828,0.002427861,0.002875724,0.00002260264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3525575,0.001588751,0.6241582,0.0006368944,0.0003842066,0.00027643,0.004032124,0.009443886,0.006922017],"genre_scores_gemma":[0.794713,0.0003444219,0.194462,0.0002467998,0.00006391339,0.0001604824,0.005093098,0.0001319,0.004784327],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005277798,"threshold_uncertainty_score":0.01049417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05192020973210533,"score_gpt":0.368437274114425,"score_spread":0.3165170643823197,"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."}}