{"id":"W1992795772","doi":"10.1145/1166253.1166296","title":"Multi-user, multi-display interaction with a single-user, single-display geospatial application","year":2006,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Geospatial-Intelligence Agency; University of Calgary; Google; U.S. Department of Defense","keywords":"Geospatial analysis; Computer science; Exploit; Human–computer interaction; Adaptation (eye); Set (abstract data type); Computer graphics (images); Remote sensing","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001365958,0.0004075502,0.0002918834,0.0002346049,0.0002643679,0.0003332595,0.0007676131,0.0001370168,0.00005208365],"category_scores_gemma":[0.00003015887,0.0003339822,0.0001402133,0.0004796423,0.0000795599,0.002006461,0.0002031667,0.0002923966,0.000453055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002590019,"about_ca_system_score_gemma":0.00005370618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00175182,"about_ca_topic_score_gemma":0.002485866,"domain_scores_codex":[0.9975984,0.00009360656,0.0004537414,0.0009056557,0.00039171,0.0005568538],"domain_scores_gemma":[0.9982553,0.0001320033,0.0003290426,0.0007535246,0.0004104883,0.0001196048],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002468192,0.003628151,0.0049005,0.00003615132,0.00009920304,0.00003882498,0.0007367251,0.00136978,0.9560561,0.02371626,0.003375229,0.00579621],"study_design_scores_gemma":[0.005589052,0.00127156,0.05343622,0.0001762468,0.00008804678,0.0002257001,0.0006227046,0.4346738,0.43948,0.00009306924,0.06266434,0.001679171],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04760627,0.0000352368,0.946402,0.0003125213,0.0005215101,0.0005450851,0.00000972556,0.000161295,0.004406397],"genre_scores_gemma":[0.908093,0.000002487975,0.08636892,0.0005531983,0.0001817975,0.0001061087,0.00009091992,0.00004016803,0.004563373],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8604867,"threshold_uncertainty_score":0.9999112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01629006678067219,"score_gpt":0.2547959413687421,"score_spread":0.2385058745880699,"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."}}