{"id":"W2948712564","doi":"10.1155/2019/9195845","title":"Avionics Graphics Hardware Performance Prediction with Machine Learning","year":2019,"lang":"en","type":"article","venue":"Scientific Programming","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Consortium de Recherche et d’innovation en Aérospatiale au Québec","keywords":"Avionics; Computer science; Software; Rendering (computer graphics); Graphical user interface; Hardware architecture; Emulation; Graphics; Computer hardware; Embedded system; Artificial intelligence; Operating system","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002636181,0.0001210392,0.0001435089,0.0003340479,0.0007843323,0.001254442,0.0005102345,0.00006230954,0.0002659142],"category_scores_gemma":[0.0001622033,0.00008553122,0.00006789719,0.002311043,0.0001698982,0.0006444962,0.0001015554,0.0002587574,0.0003761896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003200992,"about_ca_system_score_gemma":0.00006449473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001006764,"about_ca_topic_score_gemma":0.00001403267,"domain_scores_codex":[0.9973264,0.00005382259,0.0003855654,0.0006434987,0.00130261,0.0002881582],"domain_scores_gemma":[0.9983807,0.000136477,0.0002148841,0.0006791473,0.0004947669,0.00009407738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002107028,0.00007667282,0.5336489,0.00001425446,0.000008632192,8.036977e-7,0.0004687825,0.004184347,0.000514579,0.003994519,0.0006386049,0.4564289],"study_design_scores_gemma":[0.0002497777,0.0001559558,0.009896299,0.00003756218,0.000009399668,0.000009607441,0.0002730754,0.1943006,0.001052425,0.001047812,0.7927975,0.0001700106],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636934,0.0001200576,0.03244874,0.0002084502,0.0004778092,0.0007822443,0.00001491062,0.0005002466,0.001754122],"genre_scores_gemma":[0.980698,0.000006346628,0.006990953,0.00002368417,0.00002761277,0.00005498298,0.00005935323,0.00001357716,0.01212549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7921589,"threshold_uncertainty_score":0.9997823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05197543250107475,"score_gpt":0.3276633152458627,"score_spread":0.2756878827447879,"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."}}