{"id":"W6888502504","doi":"10.21227/3n4y-7y55","title":"EMGNet: An EMG Dataset for Locomotor Intent Recognition","year":2024,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Electromyography; Preprocessor; Pipeline (software); Pattern recognition (psychology); Generalization; Biceps; Data pre-processing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001737386,0.001205247,0.001094865,0.0009097881,0.0002000369,0.0005888852,0.002475406,0.0008165012,0.002848994],"category_scores_gemma":[0.0003198796,0.001204963,0.0002872524,0.000535709,0.0002302815,0.001219432,0.0005198776,0.001263887,0.2519569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004045807,"about_ca_system_score_gemma":0.0005093154,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008547705,"about_ca_topic_score_gemma":0.002671713,"domain_scores_codex":[0.993818,0.000162183,0.001385346,0.002516696,0.0010266,0.001091102],"domain_scores_gemma":[0.9930483,0.0001388609,0.0007158447,0.005192737,0.0003125429,0.0005917917],"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":[0.0003106885,0.0005514671,9.530338e-7,0.001281384,0.0003846485,0.0004607816,0.00001209845,0.000001203495,0.0002551233,4.731581e-7,0.9953045,0.001436691],"study_design_scores_gemma":[0.0007092796,0.0005056871,0.000003089606,0.0005218845,0.001689649,0.0002391876,0.00004629523,0.00006279207,0.0002727984,0.0001624844,0.9943153,0.001471569],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004428448,0.0001282187,0.00005476571,0.00004285836,0.005616336,0.003115308,0.9906394,0.000343196,0.00001561257],"genre_scores_gemma":[0.000001470373,0.0001294597,0.0005140624,0.0006987871,0.002654317,0.001671969,0.9938672,0.0003494391,0.0001133186],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.249108,"threshold_uncertainty_score":0.99904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103443018580615,"score_gpt":0.3654515367875087,"score_spread":0.2620085182068937,"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."}}