{"id":"W2108899063","doi":"10.1109/ccece.2003.1226307","title":"Estimation of high-speed data radio transmission line parameters","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Distortion (music); Computer science; Algorithm; Maximum likelihood; Line (geometry); Transmission (telecommunications); Estimation theory; Cramér–Rao bound; Transmission line; Data transmission; Joint (building); Statistics; Mathematical optimization; Mathematics; Telecommunications; Engineering; Computer network","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":[0.0000560165,0.00009030782,0.0001235914,0.00005321266,0.00001094123,0.000004975116,0.0002093951,0.0000409763,0.00001803316],"category_scores_gemma":[0.00001624007,0.00008293448,0.00001616783,0.00009024676,0.00002220751,0.0002486224,0.00002866533,0.00006633322,0.000004682145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004090351,"about_ca_system_score_gemma":0.000006025553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003924203,"about_ca_topic_score_gemma":0.000001665876,"domain_scores_codex":[0.9995219,0.000004249841,0.0001665807,0.000123757,0.00008635685,0.00009712335],"domain_scores_gemma":[0.999525,0.00001902957,0.00001913003,0.0003924817,0.00001082251,0.00003352699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006530621,0.00001304777,9.383019e-7,0.00003798902,0.00001019792,0.000001523953,0.00003292596,0.8863748,0.04817267,0.001151789,0.00007644812,0.06412109],"study_design_scores_gemma":[0.0002754122,0.00004969511,0.00009483861,0.00007809097,0.000008322926,0.000003225274,0.000003462962,0.3949428,0.5980812,0.006033942,0.0003146511,0.0001143616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0691447,0.00004906606,0.9296723,0.00005047967,0.00004176299,0.0001060321,0.00001778889,0.0006998269,0.0002180575],"genre_scores_gemma":[0.5441352,0.00002972243,0.4557532,0.000004007443,0.000005666954,0.00000109053,0.00004706377,0.00001279669,0.00001130502],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5499086,"threshold_uncertainty_score":0.338197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03543032658281087,"score_gpt":0.2705634127284183,"score_spread":0.2351330861456075,"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."}}