{"id":"W4403576064","doi":"10.1145/3643834.3661630","title":"Understanding Gesture and Microgesture Inputs for Augmented Reality Maps","year":2024,"lang":"en","type":"article","venue":"Designing Interactive Systems Conference","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Universitas Brawijaya; Natural Sciences and Engineering Research Council of Canada; Mitacs; Canada Research Chairs","keywords":"Gesture; Augmented reality; Computer science; Human–computer interaction; Set (abstract data type); Space (punctuation); Gesture recognition; Artificial intelligence","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":[0.0004041071,0.0006320067,0.0002112344,0.0003034209,0.0004562594,0.002258552,0.0005319661,0.0009142525,0.00551695],"category_scores_gemma":[0.002383912,0.0003112425,0.0003451543,0.0002034021,0.0009356104,0.002277618,0.00146852,0.0004883894,0.0005629779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000255721,"about_ca_system_score_gemma":0.0002756586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001861813,"about_ca_topic_score_gemma":0.002704713,"domain_scores_codex":[0.9995961,0.0001315002,0.00002006383,0.00008183227,0.0001186589,0.00005188472],"domain_scores_gemma":[0.9993553,0.0004175354,0.00005412392,0.00007916745,0.00007415825,0.00001980415],"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":[0.0005502928,0.0001000259,0.005421747,0.001892217,0.00005368666,0.003380252,0.04525847,0.02266309,0.4497268,0.08655111,0.002931757,0.3814706],"study_design_scores_gemma":[0.0001240482,0.001156186,0.06400405,0.001698639,0.0001882956,0.009345113,0.04940332,0.3405218,0.2607544,0.08466771,0.1877224,0.0004140358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.274273,0.00149346,0.6827917,0.000665925,0.00009183802,0.0001597108,0.0001737653,0.0008854191,0.03946518],"genre_scores_gemma":[0.8856906,0.0007739797,0.106039,0.0001123922,0.0000249654,0.00010419,0.00009282221,0.000116627,0.007045486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00551695,"threshold_uncertainty_score":0.01845604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1366221751138379,"score_gpt":0.3149233481390738,"score_spread":0.1783011730252359,"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."}}