{"id":"W4220845292","doi":"10.1145/3517262","title":"Investigating Cross-Modal Approaches for Evaluating Error Acceptability of a Recognition-Based Input Technique","year":2022,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Touchscreen; Modality (human–computer interaction); Computer science; Modalities; Human–computer interaction; Gesture; Modal; Virtual reality; Artificial intelligence; Multimedia; Speech recognition; Machine learning","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":[0.009041057,0.001000014,0.0004232526,0.001827939,0.0004313452,0.001865923,0.0008506646,0.001336452,0.002333607],"category_scores_gemma":[0.07144675,0.0002962355,0.0006224568,0.0006566604,0.0009850496,0.001633351,0.002360485,0.0008211449,0.0003430643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005642244,"about_ca_system_score_gemma":0.0004074367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001238334,"about_ca_topic_score_gemma":0.001957221,"domain_scores_codex":[0.9862352,0.006696885,0.001213096,0.001262803,0.004243884,0.0003481896],"domain_scores_gemma":[0.9257832,0.05500909,0.007175283,0.002702994,0.008695459,0.0006340631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01129176,0.00211932,0.09136762,0.003882937,0.0007177322,0.0005142837,0.01114447,0.0139825,0.4313414,0.0031693,0.0005042244,0.4299644],"study_design_scores_gemma":[0.0005255263,0.02353975,0.5440018,0.0009034008,0.001206415,0.002995323,0.01207854,0.1491176,0.2546151,0.006005661,0.004092149,0.0009187661],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7923449,0.0004245334,0.1989043,0.00009679315,0.00006276582,0.001005184,0.000168459,0.0003546961,0.006638452],"genre_scores_gemma":[0.9015576,0.000171519,0.09600213,0.0000913251,0.00002528708,0.0007691015,0.000138835,0.00007846908,0.001165653],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009041057,"threshold_uncertainty_score":0.04781425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06677879349530273,"score_gpt":0.3302559672910291,"score_spread":0.2634771737957263,"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."}}