{"id":"W2559504754","doi":"10.1145/3009939.3009942","title":"Use of Landmarks to Design Large and Efficient Command Interfaces","year":2016,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Selection (genetic algorithm); Human–computer interaction; Interface (matter); User interface; Artificial intelligence; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009050675,0.001020817,0.0004623682,0.0009277902,0.0005037198,0.001793927,0.001725852,0.0007145389,0.004471402],"category_scores_gemma":[0.005101859,0.0006983338,0.0003481625,0.000536202,0.0007668908,0.002763988,0.001859526,0.0008277234,0.001610148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004528592,"about_ca_system_score_gemma":0.0006692924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001048319,"about_ca_topic_score_gemma":0.00159332,"domain_scores_codex":[0.9994186,0.0001977253,0.00005313645,0.00009698916,0.0001705227,0.00006301065],"domain_scores_gemma":[0.9977899,0.0008837047,0.0002369548,0.0003733522,0.0005603641,0.0001557441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005917823,0.0002141081,0.002138686,0.0007949807,0.00007523481,0.000906452,0.002149761,0.1816427,0.2150821,0.09322004,0.008587256,0.4945968],"study_design_scores_gemma":[0.0001789639,0.0009928148,0.001059098,0.0001477717,0.0001198733,0.0007579104,0.0005991015,0.7544555,0.1387255,0.04054222,0.06228295,0.0001384502],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01413763,0.00026589,0.9794767,0.00009808956,0.00003434727,0.0001104564,0.00002402454,0.002975348,0.002877455],"genre_scores_gemma":[0.2577142,0.0003552156,0.7372944,0.00006737294,0.00002080578,0.0002698623,0.00009412033,0.0007877075,0.003396141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004471402,"threshold_uncertainty_score":0.01495832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03790905345154151,"score_gpt":0.2622465706449237,"score_spread":0.2243375171933822,"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."}}