{"id":"W3206191641","doi":"10.1145/3476090","title":"Naturally Together: A Systematic Approach for Multi-User Interaction With Natural Interfaces","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Human-Computer Interaction","topic":"Innovative Human-Technology Interaction","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Human–computer interaction; User interface; Set (abstract data type); User interface design; Natural (archaeology); Space (punctuation); Post-WIMP; User modeling; User group; User experience design; Computer user satisfaction; User requirements document; Natural user interface; World Wide Web","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":[0.05142862,0.003687371,0.002391544,0.01737252,0.005007817,0.01250092,0.004810686,0.003200009,0.004805699],"category_scores_gemma":[0.07766292,0.003637832,0.005343608,0.009324709,0.01264632,0.01521155,0.01125741,0.004876892,0.001974243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006201442,"about_ca_system_score_gemma":0.01442798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007712207,"about_ca_topic_score_gemma":0.01245124,"domain_scores_codex":[0.9124048,0.06380159,0.006051869,0.007135995,0.009505702,0.001100006],"domain_scores_gemma":[0.916362,0.05574362,0.004391778,0.01254163,0.00962178,0.001339253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0002237984,0.0008440241,0.01477573,0.007454221,0.0006783017,0.0008941108,0.1145911,0.007682831,0.007273614,0.4271818,0.007043901,0.4113567],"study_design_scores_gemma":[0.0003514143,0.001000099,0.01006265,0.00916863,0.0009150207,0.002062061,0.05711312,0.09646631,0.0087165,0.5833639,0.2301791,0.0006012119],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004307657,0.000972482,0.9864917,0.0006032945,0.00005065168,0.002478175,0.0002071498,0.0007237325,0.004165133],"genre_scores_gemma":[0.03490318,0.0005765104,0.9586312,0.0001453216,0.00002153599,0.003986976,0.0003052351,0.0001598368,0.001270146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05142862,"threshold_uncertainty_score":0.2719837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05898693903945092,"score_gpt":0.3270263493376211,"score_spread":0.2680394102981702,"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."}}