{"id":"W6888442983","doi":"10.20380/gi2022.24","title":"A Design Framework for Contextual and Embedded Information Visualizations in Spatial Augmented Reality","year":2022,"lang":"en","type":"article","venue":"Canada Human-Computer Communications Society","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Augmented reality; Visualization; Context (archaeology); Process (computing); Spatial contextual awareness; Data visualization; Information visualization; Design process","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005917945,0.000140782,0.0001845776,0.00008455351,0.00132844,0.000239757,0.001541863,0.00004908977,0.00001684267],"category_scores_gemma":[0.00004799072,0.0001715875,0.00005668141,0.0005842505,0.00008689514,0.0005428857,0.001234507,0.0002548007,5.090092e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004212001,"about_ca_system_score_gemma":0.0006523194,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02591213,"about_ca_topic_score_gemma":0.03303241,"domain_scores_codex":[0.9984398,0.0003111604,0.0004905753,0.0002295642,0.0002992286,0.0002296303],"domain_scores_gemma":[0.9978852,0.0004228867,0.0002228881,0.001204199,0.000176909,0.00008791484],"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":[0.000006038932,0.0002175196,0.0002257425,0.00004777536,0.0000637963,6.739974e-7,0.01165649,0.007983924,0.00001521236,0.886265,0.08349438,0.01002345],"study_design_scores_gemma":[0.0006255108,0.00004881046,0.0005678977,0.00001419792,0.0000101517,0.000003092124,0.0007529607,0.9462858,0.0000104122,0.004993037,0.04647918,0.0002089369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002087707,0.00005869243,0.9956436,0.003166928,0.0001355103,0.0005351024,0.0001104006,0.00008636024,0.00005460738],"genre_scores_gemma":[0.4608045,0.00005489313,0.5288164,0.008217518,0.00004333706,0.000399958,0.001584396,0.00001748314,0.00006145608],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9383019,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06179005231513975,"score_gpt":0.3341619149715408,"score_spread":0.2723718626564011,"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."}}