{"id":"W4400316578","doi":"10.1007/978-3-031-62849-8_10","title":"Automatic Bars with Single-Switch Scanning for Target Selection","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Selection (genetic algorithm); Artificial intelligence","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":[0.0003364771,0.001178811,0.0009573153,0.0008816863,0.0004609186,0.0007593336,0.002788797,0.001080649,0.02286735],"category_scores_gemma":[0.0006905859,0.0007586505,0.0003918379,0.0009721632,0.0002728132,0.001136266,0.001079266,0.0008520327,0.007622306],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002740831,"about_ca_system_score_gemma":0.0003576252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004599086,"about_ca_topic_score_gemma":0.001070738,"domain_scores_codex":[0.9995025,0.00007524053,0.00001899639,0.0001478388,0.0001852535,0.00007016648],"domain_scores_gemma":[0.9993312,0.0003017013,0.0000346794,0.0001720788,0.0001235924,0.00003681821],"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":[0.001202684,0.0001975017,0.0004538468,0.0003504962,0.00004938174,0.0001537954,0.00007793718,0.00180699,0.5379096,0.004248068,0.01124032,0.4423094],"study_design_scores_gemma":[0.0001632581,0.0009753938,0.004021992,0.00008341191,0.0001623302,0.002289518,0.00006157111,0.1910602,0.7509598,0.00519217,0.04486512,0.0001653456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04417852,0.001284924,0.9168274,0.00009381746,0.0004882166,0.0001596612,0.0003978231,0.01898693,0.01758264],"genre_scores_gemma":[0.3537657,0.0006093876,0.620724,0.0003157269,0.0002454086,0.00024155,0.0008082986,0.001695779,0.02159414],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02286735,"threshold_uncertainty_score":0.07649893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403810320596017,"score_gpt":0.2474991320369,"score_spread":0.2334610288309399,"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."}}