{"id":"W4313855543","doi":"10.1117/12.2656199","title":"Deep learning-based image-like channelization for broadband receiver","year":2023,"lang":"en","type":"article","venue":"Advanced Optical Manufacturing Technologies and Applications 2022; and 2nd International Forum of Young Scientists on Advanced Optical Manufacturing (AOMTA and YSAOM 2022)","topic":"Optical Network Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Baseband; Bandwidth (computing); Broadband; Channelized; Electronic engineering; SIGNAL (programming language); Channel (broadcasting); Telecommunications; Engineering","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":[0.0002192908,0.0004475065,0.000280196,0.000185295,0.0001606038,0.0002716128,0.00059278,0.0003859971,0.001405091],"category_scores_gemma":[0.000322797,0.0001870877,0.0003219306,0.0001837036,0.0003262261,0.0005700763,0.0003946872,0.000620231,0.0003603696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003207285,"about_ca_system_score_gemma":0.0006488984,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00157011,"about_ca_topic_score_gemma":0.002906088,"domain_scores_codex":[0.9998806,0.00001874027,0.000004581592,0.0000255666,0.00004207905,0.00002847833],"domain_scores_gemma":[0.9998646,0.00002938895,0.00002625627,0.0000252586,0.00004270712,0.00001185964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002554267,0.0001868825,0.001170894,0.0001381806,0.00009883149,0.000121285,0.00007001635,0.3859127,0.370095,0.01320285,0.001743108,0.2270049],"study_design_scores_gemma":[0.000009203325,0.00007549358,0.0002141055,0.000003907204,0.00001595483,0.00003737271,0.000006574354,0.9239644,0.07307066,0.001108574,0.001480835,0.00001294015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04205773,0.0002018986,0.954342,0.00009408902,0.00004254523,0.00002368755,0.00005275012,0.00110253,0.002082758],"genre_scores_gemma":[0.6233422,0.0002410719,0.3704706,0.0001468137,0.00005997664,0.0000535714,0.000246905,0.0001443325,0.005294636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00157011,"threshold_uncertainty_score":0.004700482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005086617712235952,"score_gpt":0.2340629648363986,"score_spread":0.2289763471241626,"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."}}