{"id":"W2330383126","doi":"10.1109/mcg.2016.38","title":"Spatial Analytic Interfaces: Spatial User Interfaces for In Situ Visual Analytics","year":2016,"lang":"en","type":"article","venue":"IEEE Computer Graphics and Applications","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Human–computer interaction; Computer science; Leverage (statistics); Analytics; Visual analytics; Wearable computer; User interface; Mobile device; Spatial contextual awareness; Context (archaeology); Wearable technology; Data science; Visualization; Multimedia; World Wide Web; 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":[0.001727225,0.001395619,0.0004996558,0.0009509189,0.0004346918,0.003746074,0.001220352,0.001416285,0.02938832],"category_scores_gemma":[0.008654932,0.0004246283,0.0005028471,0.001124336,0.0008037576,0.005140243,0.00249749,0.001186643,0.006659408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003074885,"about_ca_system_score_gemma":0.0004076252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007889535,"about_ca_topic_score_gemma":0.001066435,"domain_scores_codex":[0.9989716,0.0003774018,0.00007589359,0.00009905963,0.0004185783,0.00005746598],"domain_scores_gemma":[0.9968941,0.001742973,0.000153862,0.0003725332,0.000713556,0.0001228891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005683129,0.0001777628,0.001555929,0.001937972,0.0001222744,0.0004313739,0.005470441,0.001683937,0.03900465,0.09269842,0.1749705,0.6813785],"study_design_scores_gemma":[0.0001611945,0.0004783555,0.002189285,0.000993726,0.0001501831,0.001356413,0.002539231,0.03547264,0.01902099,0.1002527,0.8371944,0.0001909594],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01113108,0.005567747,0.9004264,0.00327679,0.001120084,0.0004508402,0.001541839,0.02675445,0.04973068],"genre_scores_gemma":[0.2374123,0.008245946,0.6932343,0.004010239,0.001271175,0.001603678,0.002782162,0.002953095,0.04848708],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02938832,"threshold_uncertainty_score":0.09831381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02390453560667586,"score_gpt":0.3035168618861442,"score_spread":0.2796123262794684,"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."}}