{"id":"W7084918131","doi":"10.6084/m9.figshare.30286912.v1","title":"Comprehensive Global Dataset of SARS-CoV-2 Lineages and Spike Mutations for Therapeutic and Vaccine Research (734 Cities, 2019 - 2025 Q1, n=9.4 Million Approx.)","year":2025,"lang":"en","type":"dataset","venue":"Figshare","topic":"Electric Power System Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spike (software development); Spike train; Mutation; Quarter (Canadian coin); Genomics; Feature (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001198006,0.0003182181,0.0004901594,0.0003505945,0.0001115977,0.00009909127,0.0003291442,0.0003597446,0.000401602],"category_scores_gemma":[0.0002903914,0.0003322639,0.00004382498,0.0006092457,0.00001643042,0.0001322173,0.0001891036,0.0003258361,0.0000150607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001059741,"about_ca_system_score_gemma":0.0001325523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001013007,"about_ca_topic_score_gemma":0.0001102247,"domain_scores_codex":[0.9984196,0.00009724884,0.0004584833,0.000410096,0.0002693687,0.0003452494],"domain_scores_gemma":[0.9982729,0.0006216905,0.0001246244,0.000543453,0.0003888663,0.00004846916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001833373,0.00001714284,0.000001448422,0.00525807,0.0001420668,0.000003440231,0.00001289598,0.0003302551,0.00003920293,0.000003267534,0.9937314,0.000442455],"study_design_scores_gemma":[0.0005741687,0.00009699921,0.00003912027,0.001953281,0.00008094132,0.00001078367,0.0000212969,0.008774705,0.0003202032,0.00002805511,0.9878331,0.0002673664],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000008503275,0.01592014,0.0001584835,0.00002999886,0.0001269923,0.001347295,0.9823282,0.00005139357,0.0000289819],"genre_scores_gemma":[0.00004807161,0.0008823232,0.0002505272,0.00005108915,0.00008833206,0.0003598933,0.9982559,0.00002152614,0.0000423398],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01592769,"threshold_uncertainty_score":0.9999129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08907422692893774,"score_gpt":0.3673113940695796,"score_spread":0.2782371671406419,"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."}}