{"id":"W1599040639","doi":"10.1109/iscas.2001.921288","title":"Design of piece-wise maps for spread spectrum communication using genetic programming","year":2002,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Genetic programming; Spread spectrum; Computer science; Construct (python library); Code (set theory); Sequence (biology); Genetic algorithm; Spectrum (functional analysis); Interval (graph theory); Algorithm; Theoretical computer science; Artificial intelligence; Programming language; Mathematics; Machine learning; Code division multiple access; Telecommunications","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":[0.0001284161,0.00006843588,0.00008340707,0.00005053467,0.0001620756,0.0000395306,0.0005261225,0.0000292029,0.00001725872],"category_scores_gemma":[0.000007816659,0.00006651712,0.00003694073,0.0002550661,0.00004309669,0.0001811721,0.00009511287,0.00003778289,0.000009278043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002684563,"about_ca_system_score_gemma":0.00001611253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003621747,"about_ca_topic_score_gemma":0.000002116017,"domain_scores_codex":[0.9993513,0.00003159084,0.0001939131,0.0001702833,0.00009795617,0.0001549247],"domain_scores_gemma":[0.9991623,0.000100402,0.00008828181,0.0005520663,0.00005967298,0.00003734261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009434325,0.001518141,0.0007292614,0.00009848917,0.00007996371,0.000001804066,0.001870932,0.06245314,0.01030662,0.4582464,0.00536602,0.4593198],"study_design_scores_gemma":[0.0001611957,0.00005491691,0.0002520062,0.00001115108,0.000006852949,0.000009465249,0.00001571421,0.9832944,0.001483544,0.01243933,0.002181962,0.00008952014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008673524,0.0004339883,0.9969794,0.0008672067,0.00002278801,0.0004984917,0.000001642926,0.00007356286,0.0002555769],"genre_scores_gemma":[0.1282035,0.00004279464,0.8714531,0.00002911132,0.00001899795,0.00006430432,0.000002160115,0.000005296429,0.0001806775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9208412,"threshold_uncertainty_score":0.2712489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06026838104876835,"score_gpt":0.2691165363102545,"score_spread":0.2088481552614861,"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."}}