{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004431002,0.0001124978,0.0001441141,0.0001941317,0.0000665175,0.0002101205,0.0004182156,0.00005816273,0.00001340145],"category_scores_gemma":[0.00008333921,0.0001012749,0.00007634777,0.0007825511,0.00003271586,0.0004574621,0.0001684091,0.0001489414,0.00002828781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003488916,"about_ca_system_score_gemma":0.0001256623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003829833,"about_ca_topic_score_gemma":0.000610013,"domain_scores_codex":[0.9988185,0.00005401068,0.0002892995,0.0004708952,0.0001472585,0.0002200643],"domain_scores_gemma":[0.9988702,0.0005448511,0.00004856482,0.0004032219,0.00007478135,0.00005838623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001420836,0.0001713345,0.0001584156,0.00005984893,0.0001242642,0.00001325538,0.0009631032,0.0009385129,0.0005014697,0.9484964,0.011486,0.03707321],"study_design_scores_gemma":[0.0001979741,0.00003490869,0.001769356,0.00007132316,0.00001596897,0.000004464175,0.0001925879,0.9618329,0.001246277,0.02457497,0.009911635,0.0001476184],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002908962,0.00003318649,0.9812803,0.006951343,0.0002020817,0.0004502318,0.00002350153,0.00028245,0.01048601],"genre_scores_gemma":[0.9581441,0.00001015357,0.03990417,0.0003048902,0.00005456468,0.0002707711,0.00003242856,0.00001201974,0.001266946],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9608944,"threshold_uncertainty_score":0.4129869,"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."}}