{"id":"W2754982177","doi":"10.1145/3132026","title":"Passive Haptic Training to Improve Speed and Performance on a Keypad","year":2017,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Science Foundation","keywords":"Keypad; Haptic technology; Computer science; Text entry; Typing; Braille; Task (project management); Control (management); Words per minute; Learning effect; Human–computer interaction; Speech recognition; Multimedia; Simulation; Artificial intelligence; Reading (process); Computer hardware; Engineering; Operating system","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":[0.00006001484,0.00024553,0.0003003739,0.0002035977,0.0008371433,0.0002749514,0.001361971,0.0001161123,0.00000662815],"category_scores_gemma":[0.006307531,0.0001690937,0.0000676042,0.0001163309,0.0004435834,0.0006413008,0.001100163,0.0005754215,0.00001805256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005554262,"about_ca_system_score_gemma":0.00001551659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003076416,"about_ca_topic_score_gemma":0.000003164404,"domain_scores_codex":[0.9986637,0.000007233486,0.0002200314,0.0005654563,0.0002109926,0.0003326057],"domain_scores_gemma":[0.9984137,0.0003419104,0.000406394,0.0006743326,0.0001159801,0.0000476521],"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":[0.0004355161,0.00008388818,0.0004335265,0.00003793292,0.00001975764,0.000002216812,0.001423054,0.000006710894,0.9082324,0.001160105,0.0002821416,0.08788274],"study_design_scores_gemma":[0.0002587593,0.001893738,0.002684256,0.0004767286,0.00001636835,0.00004169835,0.006520286,0.0002348816,0.9837562,0.002786013,0.001138312,0.0001927943],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9882583,0.00001026674,5.657724e-7,0.003448514,0.0003299858,0.0006066377,0.00001466017,0.0001802928,0.00715075],"genre_scores_gemma":[0.9982979,0.0002191664,0.00008214214,0.0002469324,0.00003907737,0.0001441444,4.954193e-8,0.00002120645,0.0009494398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08768995,"threshold_uncertainty_score":0.7551159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03602311428892726,"score_gpt":0.2902666954669915,"score_spread":0.2542435811780643,"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."}}