{"id":"W4412754181","doi":"10.2139/ssrn.5372225","title":"Stacked One-vs-One (Sovo): A New Approach for Multi-Class Classification for Semg Recognition","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Class (philosophy); Pattern recognition (psychology); Artificial intelligence; Computer science; Speech recognition","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":[0.001784576,0.00211101,0.002936588,0.002853622,0.001086661,0.002561303,0.00291986,0.002415082,0.008806184],"category_scores_gemma":[0.003323611,0.0007462185,0.002073024,0.002231473,0.0007117541,0.00223209,0.003044522,0.002358132,0.004695983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004939655,"about_ca_system_score_gemma":0.001292232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004651492,"about_ca_topic_score_gemma":0.01028569,"domain_scores_codex":[0.9981614,0.0003458046,0.0001132006,0.0004463514,0.0006541935,0.0002791026],"domain_scores_gemma":[0.9983732,0.0005008493,0.00009386147,0.0004560463,0.0004469147,0.0001290743],"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":[0.0004553318,0.00020521,0.0007952651,0.0001113742,0.0001535642,0.00006900029,0.00008162369,0.00923725,0.02625169,0.002739378,0.006318294,0.9535819],"study_design_scores_gemma":[0.00004042074,0.0002452249,0.001800263,0.00004357726,0.0001131216,0.0003195115,0.0001333655,0.9352739,0.03544241,0.01604462,0.01046613,0.00007748945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01184662,0.0006318913,0.9766549,0.000154975,0.0004184381,0.0001456161,0.0004107449,0.007587673,0.002149136],"genre_scores_gemma":[0.2287327,0.0006295888,0.7576008,0.000516282,0.0003840599,0.0003456867,0.001958654,0.00170694,0.008125274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008806184,"threshold_uncertainty_score":0.02945966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1425911362016635,"score_gpt":0.3227645324264219,"score_spread":0.1801733962247584,"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."}}