{"id":"W2121888586","doi":"10.1109/tbme.2006.877800","title":"Detection and classification of sensory information from acute spinal cord recordings","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Collaboration On Repair Discoveries; TRIUMF; Neil Squire Society; University of British Columbia","funders":"","keywords":"Sensory system; Spinal cord; Neurophysiology; Neuroscience; GRASP; Computer science; Spinal cord injury; Sensory stimulation therapy; Peripheral; Stimulus (psychology); Functional electrical stimulation; Pattern recognition (psychology); Artificial neural network; Artificial intelligence; Stimulation; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.00005541944,0.00009805793,0.0001052544,0.0002132979,0.00005638544,0.0000297202,0.00007024981,0.00008466457,0.00001091546],"category_scores_gemma":[0.00001054267,0.00009436965,0.00003806774,0.0002157723,0.00006444794,0.0002960238,0.000001150646,0.000168253,0.00001215429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003265894,"about_ca_system_score_gemma":0.000007692809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006083549,"about_ca_topic_score_gemma":0.000002808263,"domain_scores_codex":[0.9992714,0.00001423975,0.0002542101,0.0001513041,0.0001895266,0.0001193135],"domain_scores_gemma":[0.9996777,0.00009665653,0.00006196942,0.0000912482,0.00001872125,0.00005367993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006999182,0.00002964707,9.688447e-7,0.00001476632,0.000005815036,9.941426e-7,0.00002917596,0.0007308088,0.8650811,0.00002351632,0.00001170027,0.1340015],"study_design_scores_gemma":[0.0002803275,0.0003020714,0.001017405,0.00004982337,0.0000170333,0.0000205669,0.0000147581,0.1687379,0.8275452,0.00003832919,0.001871288,0.000105294],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4527611,0.000003149454,0.5465718,0.00007506036,0.0004079856,0.0000494097,0.00002772248,0.00007366663,0.00003019853],"genre_scores_gemma":[0.9984764,0.00002060516,0.00137309,0.00004074353,0.00005297103,0.000008986413,0.000002698875,0.000007078675,0.00001735738],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5457155,"threshold_uncertainty_score":0.3848283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0167792286989845,"score_gpt":0.2393342930987239,"score_spread":0.2225550643997394,"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."}}