{"id":"W3108253735","doi":"10.1145/3384419.3430712","title":"Sensing finger input using an RFID transmission line","year":2020,"lang":"en","type":"article","venue":"","topic":"Hand Gesture Recognition Systems","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Waterloo","funders":"","keywords":"RSS; Computer science; Transmission (telecommunications); Key (lock); Radio-frequency identification; Radio frequency; Wireless; Transmission line; Antenna (radio); Line (geometry); Computer hardware; Chip; SIGNAL (programming language); Real-time computing; Embedded system; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003429868,0.0004587668,0.0004241505,0.0004511779,0.0002145438,0.0006774189,0.001012639,0.0005449293,0.001906035],"category_scores_gemma":[0.0014232,0.000295118,0.0002403517,0.0003532628,0.0002443586,0.000844471,0.0004406338,0.0002659735,0.001136139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000194467,"about_ca_system_score_gemma":0.0001484564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003405756,"about_ca_topic_score_gemma":0.0004352993,"domain_scores_codex":[0.9993799,0.00008399232,0.00004962021,0.0001934392,0.0002574917,0.00003556167],"domain_scores_gemma":[0.9991311,0.0003259787,0.0001396771,0.0001424878,0.0002267032,0.00003403998],"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":[0.0002688593,0.00008771224,0.003392211,0.000253727,0.00002885483,0.0003214225,0.0001730719,0.001493696,0.8742731,0.0004248759,0.0008593095,0.1184231],"study_design_scores_gemma":[0.00006756113,0.001282795,0.0100303,0.00004403546,0.0001351406,0.002855537,0.00009130636,0.05263329,0.9217797,0.0003305133,0.01064848,0.0001013213],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3065047,0.001138543,0.6807597,0.0002491951,0.000325914,0.0001578683,0.0002815007,0.004879258,0.005703301],"genre_scores_gemma":[0.7944845,0.0004686815,0.1994307,0.0003675239,0.00008894052,0.00009030108,0.0001546526,0.0001080101,0.004806711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001906035,"threshold_uncertainty_score":0.006376326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08789310079451829,"score_gpt":0.2866340521246106,"score_spread":0.1987409513300923,"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."}}