{"id":"W2025761673","doi":"10.4018/ijghpc.2014070102","title":"Accelerating a Cloud-Based Software GNSS Receiver","year":2014,"lang":"en","type":"article","venue":"International Journal of Grid and High Performance Computing","topic":"GNSS positioning and interference","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Cloud computing; GNSS applications; Software; Exploit; Operating system; Throughput; Real-time computing; Process (computing); Multi-core processor; Embedded system; Global Positioning System; Computer security","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":[],"consensus_categories":[],"category_scores_codex":[0.0002193421,0.00009519583,0.0001315513,0.0001173402,0.00008441906,0.0001129058,0.0002117784,0.0000344558,0.00001593808],"category_scores_gemma":[0.00004383475,0.00008665537,0.00003744776,0.00004368487,0.00002577334,0.0002181768,0.00002837394,0.0002316206,0.000005544285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004218327,"about_ca_system_score_gemma":0.0000148973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003533754,"about_ca_topic_score_gemma":5.524488e-7,"domain_scores_codex":[0.99928,0.00001543032,0.0003043163,0.00007079889,0.000208091,0.0001213936],"domain_scores_gemma":[0.9994531,0.00008474961,0.0001192755,0.00004781591,0.0002456866,0.00004942219],"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":[0.00007073,0.00005257826,0.03365537,0.000121582,0.0001587836,0.00001218404,0.0007294253,0.7375395,0.002343154,0.000502467,0.003180665,0.2216336],"study_design_scores_gemma":[0.0007744918,0.0001753076,0.03820595,0.0008378719,0.00001212628,0.0001553773,0.00002424513,0.9510295,0.005939332,0.00005599343,0.002608794,0.0001809719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9419209,0.00008043836,0.05371561,0.00008646009,0.003600734,0.00001627224,0.000002403186,0.00004304897,0.0005340831],"genre_scores_gemma":[0.9893306,0.00004412601,0.008230543,0.0001247089,0.002245387,3.376606e-7,0.000004470729,0.00001132943,0.000008422666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2214526,"threshold_uncertainty_score":0.3533704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009372090477687967,"score_gpt":0.2134338051889909,"score_spread":0.2040617147113029,"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."}}