{"id":"W6904737977","doi":"10.1371/journal.pone.0259712.g004","title":"Study samples (marked as P1, P2, etc.), their nearest three neighbors, and a random selection of 25 Canadian and 25 global SARS-CoV-2 sequences for context.","year":2021,"lang":"en","type":"other","venue":"Figshare","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Feature selection; Sequence (biology); Random sequence; k-nearest neighbors algorithm","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001071473,0.000352789,0.0005242253,0.0001661262,0.0001582543,0.0003569618,0.0006083732,0.0002195371,0.00100868],"category_scores_gemma":[0.0007328857,0.0003205038,0.00006200064,0.0003717243,0.00004787766,0.0002557641,0.000248744,0.0001569673,0.000002822659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000106129,"about_ca_system_score_gemma":0.0007689444,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02522969,"about_ca_topic_score_gemma":0.4087818,"domain_scores_codex":[0.9984509,0.00007072704,0.0002500325,0.0007064654,0.0001890339,0.0003327967],"domain_scores_gemma":[0.998796,0.0001756341,0.0003085314,0.0003752419,0.0002409985,0.0001035873],"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":[0.00007714064,0.0001298765,0.002730606,0.001533457,0.0003544294,0.0000819068,0.0008218751,4.759857e-7,0.0004616801,0.0007400445,0.8892956,0.1037729],"study_design_scores_gemma":[0.004462945,0.0009209643,0.003921934,0.01198867,0.00009010635,0.0002321875,0.0003818268,0.005351079,0.002715318,0.01389662,0.9542406,0.001797711],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.0009297873,0.07812435,0.1228205,0.001884592,0.0009228948,0.02180842,0.6574181,0.004255576,0.1118358],"genre_scores_gemma":[0.3938014,0.0005158951,0.5141714,0.004512936,0.001452651,0.007120313,0.05927717,0.001541306,0.01760693],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.5981409,"threshold_uncertainty_score":0.9999247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06204877688954163,"score_gpt":0.3202878245536225,"score_spread":0.2582390476640808,"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."}}