{"id":"W4409736022","doi":"10.1145/3706598.3714021","title":"Everything to Gain: Combining Area Cursors with increased Control-Display Gain for Fast and Accurate Touchless Input","year":2025,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; UK Research and Innovation; HORIZON EUROPE Framework Programme; Government of the United Kingdom; McGill University","keywords":"Computer science; Automatic gain control; Control (management); Computer graphics (images); Artificial intelligence; Telecommunications; Bandwidth (computing)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003802557,0.001050524,0.0004394572,0.0005069347,0.0002118799,0.0009307758,0.0009914398,0.0007391156,0.009161538],"category_scores_gemma":[0.003415115,0.0003446141,0.0003369854,0.0003978824,0.0004705301,0.001690524,0.001865796,0.0006445192,0.001500961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002362795,"about_ca_system_score_gemma":0.0002568881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008594512,"about_ca_topic_score_gemma":0.001377547,"domain_scores_codex":[0.9995427,0.00007789629,0.00002592026,0.0001322681,0.0001407684,0.00008049224],"domain_scores_gemma":[0.9987565,0.0005827736,0.00008052165,0.0002561832,0.000182342,0.0001416952],"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":[0.0009754779,0.0003280118,0.001245919,0.0005444549,0.00005237378,0.0003146523,0.0004231639,0.002996309,0.7433643,0.002432274,0.002400056,0.2449229],"study_design_scores_gemma":[0.00116015,0.006024804,0.0360521,0.0002998161,0.0006444139,0.005491211,0.0004990555,0.1381414,0.7306371,0.0105467,0.07009197,0.0004112968],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4217419,0.001876502,0.5477784,0.0006091279,0.0004319391,0.0003961002,0.0002885409,0.01281053,0.01406706],"genre_scores_gemma":[0.7969815,0.0004233279,0.1961168,0.0005041407,0.00009564013,0.0001892344,0.0001502788,0.0007581362,0.004780899],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009161538,"threshold_uncertainty_score":0.03064835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185378478197488,"score_gpt":0.264178935278567,"score_spread":0.2523251504965921,"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."}}