{"id":"W1902594084","doi":"10.1109/dasc.2015.7311667","title":"Mandates and avionics R&D an airborne perspective","year":2015,"lang":"en","type":"article","venue":"2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC)","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Avionics; Upgrade; Rulemaking; Perspective (graphical); Business; Service (business); Plan (archaeology); Aeronautics; Engineering; Telecommunications; Computer science; Marketing; Political science; Geography; Aerospace engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000302533,0.0004298835,0.0004303966,0.0001853913,0.0001207591,0.001081851,0.0003391221,0.0002130629,0.00002545905],"category_scores_gemma":[0.00005394408,0.0004199711,0.00006206737,0.0002668056,0.0001218644,0.001770169,0.00008480512,0.0002577319,0.0002511094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002668786,"about_ca_system_score_gemma":0.0001181253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009398771,"about_ca_topic_score_gemma":0.0001524383,"domain_scores_codex":[0.9980075,0.00004821816,0.0004669157,0.0005147535,0.0004649296,0.0004977341],"domain_scores_gemma":[0.998362,0.00003783464,0.0001167366,0.0005068057,0.0005360705,0.0004405722],"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":[0.0002471349,0.0008346623,0.007189596,0.001159813,0.0008130471,0.0001082282,0.01730459,0.6317874,0.0001461528,0.2361307,0.08408914,0.02018954],"study_design_scores_gemma":[0.002298153,0.0004338754,0.0007831687,0.0003868691,0.000149992,0.00008678077,0.01046079,0.9313183,0.00006543831,0.002708076,0.0494946,0.001813903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6461332,0.006881619,0.2013995,0.0005591219,0.006227235,0.002915005,0.0004709127,0.003967163,0.1314462],"genre_scores_gemma":[0.9954722,0.0002668734,0.0004169613,0.0000175009,0.0002112592,0.00005148216,0.0002677556,0.00007656239,0.0032194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.349339,"threshold_uncertainty_score":0.9999551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04211230477760494,"score_gpt":0.255992925518859,"score_spread":0.2138806207412541,"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."}}