{"id":"W4386159607","doi":"10.1109/whc56415.2023.10224492","title":"Training to Understand Complex Haptic Phrases: A Longitudinal Investigation","year":2023,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Vocabulary; Haptic technology; Computer science; Wearable computer; Human–computer interaction; Rendering (computer graphics); Artificial intelligence; Multimedia; Natural language processing; Linguistics","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":[0.003360433,0.0005278594,0.0007653648,0.0009288542,0.001826571,0.001337247,0.0005853404,0.00114067,0.002127191],"category_scores_gemma":[0.008995319,0.000437329,0.0004525431,0.0004142016,0.0008961444,0.001112432,0.001260379,0.001731216,0.001407464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004602232,"about_ca_system_score_gemma":0.0008105855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003846435,"about_ca_topic_score_gemma":0.006460086,"domain_scores_codex":[0.9989178,0.0002724002,0.00008368458,0.0002035533,0.0003087597,0.0002137627],"domain_scores_gemma":[0.9948509,0.001124469,0.0009060582,0.0006261279,0.001494844,0.000997495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001247395,0.02067316,0.7578802,0.0002911749,0.00016492,0.003459034,0.08627679,0.0003637856,0.01313852,0.0003049821,0.001584714,0.1146153],"study_design_scores_gemma":[0.00006234579,0.02337403,0.9128947,0.0002125598,0.0001440732,0.003331327,0.04292748,0.001111081,0.006020681,0.0005645161,0.009185006,0.0001721868],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986386,0.0001509379,0.0004321196,0.00005580322,0.000008274988,0.00008568339,0.00008678596,0.00001288341,0.0005288503],"genre_scores_gemma":[0.9944787,0.0002721444,0.0008814629,0.00007844721,0.00001318993,0.0001724352,0.0002701695,0.00001366035,0.003819865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003846435,"threshold_uncertainty_score":0.0177719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4387714050549789,"score_gpt":0.3750789952755682,"score_spread":0.06369240977941071,"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."}}