{"id":"W2075326247","doi":"10.1145/2669485.2669549","title":"Overcoming Interaction Barriers in Large Public Displays Using Personal Devices","year":2014,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Work (physics); Human–computer interaction; Space (punctuation); Internet privacy; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003554392,0.001012438,0.0005461554,0.0004647603,0.002465018,0.003829506,0.001808992,0.001489976,0.006712757],"category_scores_gemma":[0.01210845,0.0006299969,0.0005437774,0.000289381,0.001583029,0.004063774,0.005515579,0.001359734,0.0010514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005894315,"about_ca_system_score_gemma":0.0006525129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001042038,"about_ca_topic_score_gemma":0.001225873,"domain_scores_codex":[0.995382,0.002841346,0.0002046362,0.000322929,0.0008499605,0.0003990985],"domain_scores_gemma":[0.9912308,0.005630041,0.0006624029,0.001383856,0.0006961586,0.0003967328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001892023,0.0007217636,0.0134274,0.002863028,0.0002033732,0.004504923,0.1820966,0.0123538,0.3326077,0.1248394,0.01587197,0.3086181],"study_design_scores_gemma":[0.000511042,0.003946873,0.02668709,0.00231461,0.0006079067,0.008468366,0.07574036,0.07255423,0.1414275,0.06604373,0.6010329,0.0006655228],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4744121,0.001086144,0.4873011,0.002086064,0.0001991943,0.0003556437,0.00005806566,0.002679124,0.03182249],"genre_scores_gemma":[0.9071035,0.0002819367,0.0831733,0.0002855706,0.00004916273,0.0003736319,0.00004960236,0.0002941336,0.008389218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006712757,"threshold_uncertainty_score":0.02245641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02284052570717287,"score_gpt":0.2853456580810454,"score_spread":0.2625051323738725,"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."}}