{"id":"W1505428460","doi":"10.1109/gem.2014.7048124","title":"Walking through sight: Seeing the ability to see, in a 3-D augmediated reality environment","year":2014,"lang":"en","type":"article","venue":"","topic":"Augmented Reality Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Sight; Space (punctuation); Computer science; Field (mathematics); Computer graphics (images); Mobile device; Flux (metallurgy); Human–computer interaction; Augmented reality; Field of view; Work (physics); Computer vision; Artificial intelligence; Physics; Optics; Mathematics; 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":[0.0003678276,0.0005058426,0.0001836581,0.0003293329,0.0003682307,0.001776101,0.0005758022,0.001072298,0.003053022],"category_scores_gemma":[0.002978285,0.0002310238,0.0003293068,0.0001738509,0.001959321,0.002951625,0.002702126,0.0007151378,0.0003948784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003080416,"about_ca_system_score_gemma":0.0003178674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001981117,"about_ca_topic_score_gemma":0.00211893,"domain_scores_codex":[0.999534,0.0001730758,0.00002340854,0.0001190461,0.00008127748,0.00006925575],"domain_scores_gemma":[0.9992777,0.0003094937,0.00012319,0.00008705004,0.0001071836,0.0000954416],"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":[0.00167804,0.0004363215,0.01649391,0.001545734,0.0001797325,0.003233383,0.0287277,0.02313635,0.3188745,0.1756269,0.01227789,0.4177897],"study_design_scores_gemma":[0.0003585442,0.004972439,0.09306718,0.001123142,0.0006475929,0.01343042,0.02652972,0.2074996,0.1544385,0.3209885,0.1757388,0.001205694],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5233143,0.001505382,0.4193126,0.001543057,0.0002066001,0.0002504788,0.0005462399,0.001176881,0.05214443],"genre_scores_gemma":[0.9445662,0.0006729815,0.04975866,0.0002181929,0.00002732454,0.0001186176,0.000175878,0.00005749805,0.004404484],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003053022,"threshold_uncertainty_score":0.01021332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023406330174209,"score_gpt":0.2574655668613558,"score_spread":0.2372315035596137,"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."}}