{"id":"W2177079828","doi":"10.1109/whc.2005.86","title":"Learning and Identifying Haptic Icons under Workload","year":2005,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Haptic technology; Workload; Intrusiveness; Computer science; Task (project management); Set (abstract data type); Human–computer interaction; Variable (mathematics); Task analysis; Work (physics); Multimedia; Artificial intelligence; Psychology; Engineering","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":[0.00001687529,0.00005941416,0.00005532225,0.00005743698,0.0002249178,0.0001108506,0.00004334227,0.0000244358,0.0009067484],"category_scores_gemma":[0.0001289774,0.00005381515,0.00002416417,0.00008151239,0.00003889828,0.0002971948,0.00002817081,0.0001801275,0.0006041073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001571331,"about_ca_system_score_gemma":0.000006462824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001255182,"about_ca_topic_score_gemma":0.00004718786,"domain_scores_codex":[0.9994721,0.00003436708,0.00009012671,0.0001812643,0.00007881736,0.0001433049],"domain_scores_gemma":[0.9996209,0.0002254341,0.00002531987,0.00006744375,0.000007956334,0.00005294107],"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":[0.000005895383,0.0000336804,0.0008058776,0.000002892402,0.000003852051,0.000007899672,0.0004502849,0.0002911195,0.9727972,0.004928013,0.000316971,0.02035629],"study_design_scores_gemma":[0.0006708602,0.0000935182,0.003223565,0.00007456186,0.00003626045,0.0005024872,0.004691169,0.01956175,0.794897,0.001150133,0.1745705,0.00052822],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9165301,0.00001952859,0.001653822,0.001646088,0.000157746,0.00004948928,3.334039e-7,0.0001476424,0.07979523],"genre_scores_gemma":[0.9663968,0.00007003538,0.0002007538,0.001029082,0.00006192637,0.000002289576,8.931878e-8,0.00000701177,0.03223203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1779002,"threshold_uncertainty_score":0.9928259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06598491380645687,"score_gpt":0.329310350293389,"score_spread":0.2633254364869321,"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."}}