{"id":"W2973174887","doi":"10.18698/2308-6033-2019-8-1901","title":"Formation of the scale of time of devices of frequency-time supporting by a method of structural analysis","year":2019,"lang":"en","type":"article","venue":"Engineering Journal Science and Innovation","topic":"Advanced Signal Processing Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cascades (Canada)","funders":"","keywords":"GLONASS; Computer science; Reliability (semiconductor); Global Positioning System; Scale (ratio); Real-time computing; sync; Algorithm; GNSS applications; Electronic engineering; Telecommunications; Frame (networking); 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":[],"consensus_categories":[],"category_scores_codex":[0.001047199,0.00005781778,0.0002052472,0.0004910692,0.000020855,0.000006161664,0.0001704798,0.00002737765,0.00001121109],"category_scores_gemma":[0.0001286922,0.00004362761,0.00003075305,0.002982884,0.00006130296,0.000639403,0.00002109511,0.00008143809,4.622842e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002575028,"about_ca_system_score_gemma":0.00002759639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004923844,"about_ca_topic_score_gemma":1.358448e-7,"domain_scores_codex":[0.9989849,0.000008147755,0.0005652198,0.00005109737,0.0003058126,0.00008484947],"domain_scores_gemma":[0.9988481,0.00003196366,0.000531996,0.00008514273,0.0004916494,0.00001116641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000001262792,0.000001952629,0.001687792,0.00013194,0.00001413725,9.570103e-9,0.0002389943,0.07056544,0.9254549,0.0001113564,0.000002917874,0.001789279],"study_design_scores_gemma":[0.00003728455,0.0000246886,0.002870006,0.00009292495,0.00001804308,0.000002696946,0.00002963923,0.3124552,0.6842061,0.0002312461,0.000001161161,0.00003096479],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7499205,0.00004959493,0.2499079,0.000006188677,0.00001660724,0.00003989855,0.000004199791,0.000008295392,0.00004677789],"genre_scores_gemma":[0.945126,0.000003353172,0.05485753,0.000001353641,0.00000378503,3.295242e-7,0.000001171576,0.000003840313,0.000002651094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2418898,"threshold_uncertainty_score":0.1779082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004335129583039697,"score_gpt":0.2524389850215391,"score_spread":0.2481038554384994,"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."}}