{"id":"W4412055824","doi":"10.1145/3719160.3736638","title":"TacTalk: Personalizing Haptics Through Conversation","year":2025,"lang":"en","type":"article","venue":"","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conversation; Haptic technology; Computer science; Human–computer interaction; Multimedia; Artificial intelligence; Communication; Psychology","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":[0.0007529975,0.000876157,0.0003131635,0.0003393508,0.0005563693,0.00166511,0.001034823,0.001043407,0.02059211],"category_scores_gemma":[0.003968549,0.0003063543,0.0004624893,0.0002049725,0.0005931163,0.002488283,0.003594257,0.0008148234,0.00410275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001323881,"about_ca_system_score_gemma":0.0002049053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002131266,"about_ca_topic_score_gemma":0.0003240649,"domain_scores_codex":[0.9994895,0.0001728019,0.00001933655,0.0001002024,0.0001482649,0.00006986309],"domain_scores_gemma":[0.998572,0.0009119102,0.0000603523,0.0002169733,0.00007618484,0.0001626055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002465213,0.0005233511,0.002598753,0.001389809,0.0001872424,0.001483079,0.01222597,0.003152857,0.3950221,0.02081536,0.03399331,0.5261429],"study_design_scores_gemma":[0.0007274548,0.003079433,0.02606363,0.0008936927,0.0009469643,0.008823486,0.01058407,0.08577368,0.2873023,0.1072836,0.4679051,0.0006166358],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1893523,0.002461649,0.7087979,0.001028414,0.001101258,0.0004384493,0.0009422873,0.02025896,0.07561875],"genre_scores_gemma":[0.7763191,0.001122542,0.1810289,0.0007686698,0.0004742147,0.0005659582,0.001019575,0.002146985,0.03655406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02059211,"threshold_uncertainty_score":0.06888753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05635908237254642,"score_gpt":0.325358780013706,"score_spread":0.2689996976411596,"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."}}