{"id":"W4404688589","doi":"10.1109/oceans55160.2024.10753871","title":"AQUARIA: Assistive Querying in Augmented Reality for Interactive Analytics","year":2024,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Augmented reality; Computer science; Human–computer interaction; Analytics; Visual analytics; Data science; Visualization; Artificial intelligence","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":[0.0009255771,0.0009015743,0.0004547756,0.0005851999,0.0004396566,0.001955187,0.00110387,0.0007895518,0.00619331],"category_scores_gemma":[0.002467916,0.0004241465,0.0007338589,0.0005178067,0.0005942013,0.001952431,0.002587934,0.0005356412,0.001202303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002586285,"about_ca_system_score_gemma":0.0003597555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001811054,"about_ca_topic_score_gemma":0.002277352,"domain_scores_codex":[0.9991125,0.0003050736,0.00006149057,0.0001597951,0.0003043439,0.00005681262],"domain_scores_gemma":[0.998982,0.0005537039,0.00006248995,0.0002423306,0.0001194287,0.0000399094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001889662,0.000444709,0.005487384,0.001529238,0.0002683352,0.001782861,0.01017087,0.02087907,0.2581028,0.03334284,0.02884501,0.6372572],"study_design_scores_gemma":[0.0004198202,0.00244163,0.01827136,0.0005969233,0.0004695888,0.005581321,0.004166781,0.293553,0.1496371,0.02595429,0.4983025,0.0006057317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06326465,0.001046032,0.9028678,0.0004293585,0.0001333511,0.0003343715,0.0009487547,0.01804122,0.0129344],"genre_scores_gemma":[0.4333301,0.0008666226,0.5542496,0.0003794398,0.00006695186,0.0003489278,0.001348473,0.0009671134,0.008442713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00619331,"threshold_uncertainty_score":0.02071869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04339140671089371,"score_gpt":0.3489771033829892,"score_spread":0.3055856966720955,"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."}}