{"id":"W3096998056","doi":"10.1364/ol.409474","title":"Blind source separation with integrated photonics and reduced dimensional statistics","year":2020,"lang":"en","type":"article","venue":"Optics Letters","topic":"Advanced Photonic Communication Systems","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Defense Advanced Research Projects Agency; National Science Foundation","keywords":"Photonics; Radio frequency; Computer science; Blind signal separation; Robustness (evolution); Wireless; Electronic engineering; Interference (communication); Telecommunications; Electronic warfare; Frequency band; Channel (broadcasting); Antenna (radio); Physics; Radar; Optics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004472593,0.0001275107,0.0001329356,0.00002740645,0.00005024803,0.00004001037,0.00009435415,0.00003566413,0.00001422743],"category_scores_gemma":[0.00002026666,0.0001215191,0.000010397,0.0001430325,0.00005040655,0.00007722187,0.00002880886,0.0002010798,0.00001403673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004376272,"about_ca_system_score_gemma":0.0000183743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002693385,"about_ca_topic_score_gemma":0.000003577059,"domain_scores_codex":[0.9994017,0.00002088718,0.0001756834,0.0001359021,0.0001371417,0.0001287256],"domain_scores_gemma":[0.9995384,0.00007662273,0.00004214058,0.0002045812,0.00004484526,0.00009347442],"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":[0.0000900977,0.00001175242,0.00007678253,0.00006684832,0.00009450618,0.00001082718,0.001841751,0.7056926,0.2868399,0.000469282,0.003955144,0.0008504813],"study_design_scores_gemma":[0.0006433785,0.0000422082,0.00005247571,0.00002706205,0.00001805863,0.00001620006,0.0001132282,0.9778816,0.004962102,0.00001051223,0.0160225,0.0002106376],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6471922,0.00009799969,0.3504769,0.001175338,0.00007621668,0.0002571028,0.00004052123,0.0002228149,0.0004609024],"genre_scores_gemma":[0.8484074,0.00003496862,0.1503814,0.0009497191,0.00002479926,0.0000186361,0.00009939176,0.00004802654,0.00003567128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2818778,"threshold_uncertainty_score":0.4955406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01502827939109015,"score_gpt":0.23649636027206,"score_spread":0.2214680808809699,"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."}}