{"id":"W2151620395","doi":"10.1109/ccece.1995.528118","title":"The general unknown parameter receiver","year":2002,"lang":"en","type":"article","venue":"","topic":"Distributed Sensor Networks and Detection Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Intersymbol interference; Demodulation; Computer science; Offset (computer science); Frequency offset; Channel (broadcasting); Detection theory; Interference (communication); Electronic engineering; Adjacent-channel interference; Noise (video); Radio receiver design; Algorithm; Telecommunications; Orthogonal frequency-division multiplexing; Engineering; Detector; Artificial intelligence; Transmitter","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":[0.00008895357,0.00006927591,0.00005287343,0.00001558695,0.0002464504,0.0003185575,0.0004289991,0.00003321594,0.0001963672],"category_scores_gemma":[0.00001936201,0.00004034033,0.00005195442,0.0002508192,0.00003462669,0.0001562564,0.00007414948,0.00008759039,0.0004463143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001355756,"about_ca_system_score_gemma":0.000002545783,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008495631,"about_ca_topic_score_gemma":0.000006583919,"domain_scores_codex":[0.9993073,0.00003680598,0.0001106118,0.0001792606,0.0001471337,0.0002188825],"domain_scores_gemma":[0.999371,0.0001056262,0.00002612331,0.0003907976,0.00004204748,0.0000644353],"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":[0.000001088844,0.00002651446,0.00001650429,3.449136e-7,0.00001386315,0.000007854994,0.00003931671,0.0004043845,0.00001691637,0.08780627,0.201091,0.7105759],"study_design_scores_gemma":[0.00006416358,0.00001799462,0.00009836094,5.006218e-7,7.618378e-7,0.00001031976,0.000001867367,0.5286527,0.0001372078,0.001236896,0.4697228,0.00005638719],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002208307,0.0002987257,0.911746,0.004366311,0.001516994,0.00009410379,8.478281e-7,0.0003583222,0.07941034],"genre_scores_gemma":[0.5457695,0.0008900475,0.1336782,0.006518739,0.001005631,0.00004111693,0.00000293382,0.00002742247,0.3120664],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7780679,"threshold_uncertainty_score":0.5736616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01835239344338045,"score_gpt":0.2203558618132216,"score_spread":0.2020034683698411,"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."}}