{"id":"W2077107286","doi":"10.1109/tbcas.2012.2227962","title":"A Wireless Magnetoresistive Sensing System for an Intraoral Tongue-Computer Interface","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Circuits and Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Electrical engineering; Wireless; Chip; CMOS; Printed circuit board; System on a chip; Computer science; Interface (matter); Embedded system; Engineering; Transmitter; Computer hardware; Telecommunications","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":[0.0005476337,0.0002388998,0.0003735602,0.0002550581,0.000307931,0.000167378,0.0003439517,0.000244352,0.000001513655],"category_scores_gemma":[0.000003695396,0.0001991544,0.00008094736,0.0002921286,0.0001969028,0.0003336417,0.000004998512,0.000251384,0.00001442009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001085184,"about_ca_system_score_gemma":0.00003779739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009325926,"about_ca_topic_score_gemma":0.00000708442,"domain_scores_codex":[0.9981521,0.000150535,0.0003781075,0.0004920393,0.0002826362,0.0005445678],"domain_scores_gemma":[0.998868,0.0001838189,0.000103171,0.0003785561,0.0001010143,0.0003654083],"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":[0.00003410393,0.0006027715,0.00005847505,0.0005388923,0.000162622,0.00001864659,0.001958099,0.0001389563,0.01548483,0.01282089,0.0004238483,0.9677579],"study_design_scores_gemma":[0.004508699,0.00497223,0.002230236,0.0016221,0.0002347629,0.001991579,0.002739776,0.9381956,0.02777334,0.0001426868,0.01344087,0.002148048],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08413447,0.0001305887,0.9111011,0.0001283773,0.003587036,0.0003551353,0.00003520078,0.0004763606,0.00005174685],"genre_scores_gemma":[0.9976526,0.000003586551,0.001831931,0.00004198524,0.00034821,0.00003983966,0.000002637431,0.00001983793,0.00005932708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9656098,"threshold_uncertainty_score":0.8121282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02995816701455403,"score_gpt":0.267219377575142,"score_spread":0.237261210560588,"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."}}