{"id":"W2420110223","doi":"10.1109/lsp.2016.2572666","title":"Fold-based Kolmogorov–Smirnov Modulation Classifier","year":2016,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Wireless Signal Modulation Classification","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Fundamental Research Funds for the Central Universities; State Key Laboratory of Rail Traffic Control and Safety; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Classifier (UML); Computer science; Pattern recognition (psychology); Robustness (evolution); Artificial intelligence; Speech recognition; Phase-shift keying; Machine learning; Algorithm; Bit error rate; Decoding methods","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":[],"consensus_categories":[],"category_scores_codex":[0.0003695723,0.0002740521,0.0002088889,0.0002903141,0.0002759222,0.0003187201,0.0007763911,0.0001210371,0.00003557097],"category_scores_gemma":[0.00002908612,0.0002109493,0.0001027157,0.0005946872,0.0001381665,0.001687726,0.00004036479,0.0001406972,0.0001677717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002462823,"about_ca_system_score_gemma":0.0001774027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006707519,"about_ca_topic_score_gemma":0.000001670927,"domain_scores_codex":[0.9974819,0.0001385937,0.0004538194,0.0007576562,0.0007105141,0.0004574974],"domain_scores_gemma":[0.9985762,0.0002024672,0.0003436466,0.0005150574,0.0002172175,0.0001453884],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001468787,0.00004428017,0.0009266633,0.00002463522,0.000007959101,0.000006453535,0.00009165952,0.004580793,0.7337713,0.0006071875,0.001279883,0.2586445],"study_design_scores_gemma":[0.001196751,0.00006770626,0.02054593,0.0002964482,0.00001480801,0.000009463861,0.000005954939,0.8341608,0.1393932,0.002124389,0.001488491,0.0006960665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07228924,0.00001982138,0.9068348,0.01966308,0.0002998307,0.0001933224,0.000002478948,0.0004640838,0.0002333654],"genre_scores_gemma":[0.9778569,5.451974e-7,0.01868862,0.002933423,0.0002755161,0.00005158715,0.000003778794,0.00003441732,0.0001552294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9055676,"threshold_uncertainty_score":0.8602262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02862253564090129,"score_gpt":0.2433321534024782,"score_spread":0.2147096177615769,"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."}}