{"id":"W2163475593","doi":"10.1109/pccc.1991.113886","title":"Keyboard optimization using genetic techniques","year":2002,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Alphabet; Set (abstract data type); Power set; Computer science; Genetic algorithm; Character (mathematics); Finite set; Optimization problem; Power (physics); Theoretical computer science; Combinatorics; Artificial intelligence; Algorithm; Discrete mathematics; Mathematics; Machine learning; Programming language","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":[0.0005059695,0.0009343316,0.001116915,0.0008351619,0.0004652035,0.00100497,0.0008626308,0.001471375,0.002889581],"category_scores_gemma":[0.001819667,0.0004341678,0.0007490219,0.0009825502,0.0007632065,0.0008119766,0.0007124871,0.000860972,0.0004415521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008154169,"about_ca_system_score_gemma":0.0008514946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002847438,"about_ca_topic_score_gemma":0.002059839,"domain_scores_codex":[0.9996196,0.0001094302,0.00002040078,0.00008671999,0.0001008896,0.00006293545],"domain_scores_gemma":[0.9995083,0.0002894728,0.00005410217,0.00004360531,0.00008332619,0.00002128096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005922579,0.00006240339,0.0004337519,0.00008284483,0.00004376993,0.00007620787,0.00006578243,0.9122108,0.005071627,0.01237332,0.001283908,0.06823636],"study_design_scores_gemma":[0.00002417023,0.00004368111,0.00009670517,0.00001142164,0.00001372634,0.00003334519,0.00002478963,0.9910987,0.001313128,0.005734657,0.001598591,0.00000700935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05318668,0.000694741,0.9363651,0.0002978978,0.00007184385,0.00009388184,0.00007341276,0.0007037044,0.008512757],"genre_scores_gemma":[0.4588214,0.0007911433,0.5303904,0.0002376027,0.00006151209,0.0003281075,0.0002403338,0.0002417463,0.008887798],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002889581,"threshold_uncertainty_score":0.009666562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03063806085196288,"score_gpt":0.2425979216463665,"score_spread":0.2119598607944037,"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."}}