{"id":"W2113087898","doi":"10.1109/milcom.1999.821352","title":"An algorithmic approach to preamble sequence optimization","year":2003,"lang":"en","type":"article","venue":"","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Algorithm; Pseudorandom number generator; Computer science; Sequence (biology); Preamble; Synchronization (alternating current); Set (abstract data type); Complementary sequences; Channel (broadcasting); Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008872531,0.0009712883,0.0006963647,0.0007330456,0.0004561989,0.000935233,0.0009444521,0.0009394179,0.004108842],"category_scores_gemma":[0.003300736,0.0004744661,0.000550418,0.0006828113,0.0006556829,0.0008155785,0.00095722,0.001010714,0.000770131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005714499,"about_ca_system_score_gemma":0.00151406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280767,"about_ca_topic_score_gemma":0.0019508,"domain_scores_codex":[0.9994683,0.000160599,0.00003099422,0.00007229404,0.0002261671,0.00004168911],"domain_scores_gemma":[0.9991828,0.0005304078,0.00004699476,0.00006450248,0.0001586238,0.00001665431],"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":[0.00003319984,0.00005111987,0.000176637,0.0001103258,0.00002394659,0.00005143098,0.00005112529,0.8622036,0.002876374,0.04946171,0.001223168,0.08373746],"study_design_scores_gemma":[0.00002065699,0.00005193801,0.00005039961,0.00001441619,0.000005667967,0.00003372089,0.00001045512,0.980415,0.000928151,0.01642376,0.002038527,0.000007364705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001240154,0.00008519328,0.9965635,0.00006501139,0.0000122197,0.00003633828,0.00001129219,0.00007373292,0.00191253],"genre_scores_gemma":[0.06342167,0.0002978604,0.9331437,0.00009953145,0.00005209168,0.0004357774,0.0001014238,0.00008239393,0.002365541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004108842,"threshold_uncertainty_score":0.01374549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01865122834107198,"score_gpt":0.2508879902975211,"score_spread":0.2322367619564491,"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."}}