{"id":"W192833019","doi":"10.1007/978-3-642-40477-1_12","title":"When Paper Meets Multi-touch: A Study of Multi-modal Interactions in Air Traffic Control","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Modalities; Air traffic control; Human–computer interaction; Modal; Set (abstract data type); Context (archaeology); Control (management); Modality (human–computer interaction); Range (aeronautics); Artificial intelligence; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004503777,0.0006637669,0.0008912738,0.00137734,0.000171757,0.0001670384,0.003051471,0.0002041162,0.0001006398],"category_scores_gemma":[0.0001234046,0.0005907364,0.0002126338,0.0004877638,0.0004130949,0.001653998,0.0008232964,0.001192013,0.000100663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003704785,"about_ca_system_score_gemma":0.0003180964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004225812,"about_ca_topic_score_gemma":0.002455498,"domain_scores_codex":[0.9957849,0.0001324172,0.0009132029,0.001651166,0.0007970502,0.0007213004],"domain_scores_gemma":[0.9968086,0.000697098,0.0004969292,0.001285871,0.0005511749,0.0001603028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002280805,0.007684565,0.002524018,0.000198583,0.0004738189,0.0007607524,0.08342745,0.4553519,0.02440913,0.005451423,0.0002744683,0.4192158],"study_design_scores_gemma":[0.003692319,0.0007972094,0.008699317,0.0005626356,0.00002952077,0.00005306725,0.00002426656,0.9822078,0.001200915,0.001173385,0.00056359,0.0009959702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01032345,0.0001409718,0.9848953,0.0006198511,0.001894861,0.001478953,0.00001181436,0.00004244003,0.0005923822],"genre_scores_gemma":[0.9466587,0.000006651976,0.05154704,0.001230237,0.0001189659,0.00005623684,0.000003179943,0.00003397594,0.0003450544],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9363352,"threshold_uncertainty_score":0.9996544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02479052289355645,"score_gpt":0.2778945282121512,"score_spread":0.2531040053185947,"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."}}