{"id":"W7164296112","doi":"10.4050/sm-2022-helmot-5286","title":"ARINC 661 Common Graphics User Application","year":2022,"lang":"","type":"article","venue":"","topic":"Mathematics, Computing, and Information Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"","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","sts"],"consensus_categories":[],"category_scores_codex":[0.00163797,0.0002871435,0.0003301824,0.00032787,0.002064721,0.0007634531,0.002005551,0.00007625727,0.0007981297],"category_scores_gemma":[0.00003205585,0.0003111914,0.0001490675,0.001412115,0.00009822814,0.001282679,0.002142872,0.0005870577,0.00019715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001289814,"about_ca_system_score_gemma":0.0002157754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000806968,"about_ca_topic_score_gemma":0.00000841341,"domain_scores_codex":[0.9967453,0.0001513772,0.001121382,0.0005004674,0.0009924092,0.0004890387],"domain_scores_gemma":[0.997527,0.0001309591,0.000828563,0.00113842,0.0002122526,0.000162817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003816312,0.0002343569,0.0005421609,0.0002069423,0.00002107651,0.000001324641,0.007055742,0.002401302,0.00001489587,0.8909716,0.005411754,0.09313505],"study_design_scores_gemma":[0.000260316,0.00007512708,0.000253023,0.00001644862,0.00001224277,0.00004123987,0.000395895,0.8328114,0.000149426,0.03250817,0.1331288,0.0003480108],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004209602,0.0001555451,0.9303572,0.001250257,0.0007952774,0.0004461876,0.000003768804,0.0003070304,0.06247515],"genre_scores_gemma":[0.96832,0.0000302164,0.02336984,0.00467586,0.0001171332,0.00008268104,0.00001750963,0.00002199142,0.003364829],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9641103,"threshold_uncertainty_score":0.999934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641542834120861,"score_gpt":0.2495107381896755,"score_spread":0.2330953098484669,"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."}}