{"id":"W3195929405","doi":"10.1109/whc49131.2021.9517229","title":"Analog Position using Sensor Fusion for Haptic Systems Control","year":2021,"lang":"en","type":"article","venue":"","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Encoder; Rotary encoder; Computer science; Haptic technology; Latency (audio); Position sensor; Position (finance); Integrator; Sensor fusion; SIGNAL (programming language); Fusion; Real-time computing; Computer hardware; Control theory (sociology); Computer vision; Artificial intelligence; Engineering; Control (management); Electrical engineering; Bandwidth (computing); 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":[0.00007983898,0.00007899932,0.0001458401,0.00003937455,0.00006118275,0.00008244145,0.00002380956,0.00005824331,0.00007302439],"category_scores_gemma":[0.00001463633,0.00007431347,0.00004726895,0.00006708362,0.000004372756,0.00006451399,0.000003256212,0.00003060944,0.00002780692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000573494,"about_ca_system_score_gemma":0.00001576033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002753257,"about_ca_topic_score_gemma":0.00001745661,"domain_scores_codex":[0.999478,0.00002156771,0.0001911164,0.0001017679,0.00007937168,0.0001282094],"domain_scores_gemma":[0.9996831,0.00004285185,0.00001452284,0.0001091137,0.0001045055,0.0000458572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008594574,0.00001752813,0.0001887067,0.0002335148,0.00008778174,0.00001646217,0.0001295758,0.2023955,0.7862642,0.00898237,0.001216141,0.0004596018],"study_design_scores_gemma":[0.0005820325,0.00001301819,0.0000991558,0.00003913109,0.00002541685,0.00008383413,0.0003045592,0.9937288,0.00262632,0.00000741344,0.00238373,0.0001065777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1230217,0.0004703885,0.8708392,0.00007810204,0.001179587,0.0003621464,0.00002157922,0.0002882041,0.003739043],"genre_scores_gemma":[0.9971056,0.000005025106,0.001815078,0.00007670864,0.000230138,0.00001851566,0.00002013459,0.00001863718,0.0007101694],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8740839,"threshold_uncertainty_score":0.3030415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01791959029115894,"score_gpt":0.2283969077428246,"score_spread":0.2104773174516657,"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."}}