{"id":"W2143248015","doi":"10.1109/lpt.2007.902164","title":"Experimental Study of MLSE Receivers in the Presence of Narrowband and Vestigial Sideband Optical Filtering","year":2007,"lang":"en","type":"article","venue":"IEEE Photonics Technology Letters","topic":"Optical Network Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"TeraXion (Canada)","funders":"","keywords":"Narrowband; Sideband; Optical filter; Maximum likelihood sequence estimation; Keying; Compatible sideband transmission; Physics; Bit error rate; Filter (signal processing); Electronic engineering; Optics; Computer science; Telecommunications; Algorithm; Estimation theory; Engineering; Radio frequency","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":[0.001574319,0.0007584463,0.0008047087,0.0003626916,0.0004909559,0.000776273,0.001017773,0.001759796,0.002132299],"category_scores_gemma":[0.006780064,0.0004242593,0.000226009,0.0005081039,0.001038359,0.001456637,0.0008609913,0.0009251992,0.0005647347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003781316,"about_ca_system_score_gemma":0.0002472784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004103055,"about_ca_topic_score_gemma":0.0003963337,"domain_scores_codex":[0.9978217,0.0005538256,0.0001374434,0.0004311123,0.0007446503,0.0003114125],"domain_scores_gemma":[0.9919626,0.004916845,0.0009804212,0.0006924294,0.001208492,0.0002391921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001146404,0.0002500407,0.001663859,0.000164186,0.00005465677,0.0003102955,0.0004055503,0.002613828,0.9838662,0.0007644736,0.00008856622,0.008672045],"study_design_scores_gemma":[0.00007420056,0.001758424,0.001577312,0.00001831667,0.00003442521,0.000261602,0.00008436426,0.01603649,0.9792215,0.0001679615,0.0007404437,0.00002489889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784362,0.0002054258,0.01988158,0.00009397429,0.00003406053,0.00003413597,0.00006013901,0.0002169946,0.001037518],"genre_scores_gemma":[0.9845735,0.0001638698,0.01392745,0.00004476731,0.00003275434,0.00003861245,0.00006458985,0.0000491781,0.001105203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002132299,"threshold_uncertainty_score":0.008325934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01003689857935428,"score_gpt":0.2357249204582428,"score_spread":0.2256880218788885,"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."}}