{"id":"W3189476543","doi":"10.1364/ofc.2021.th5a.2","title":"Neuromorphic Photonic Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Queen's University","funders":"","keywords":"Neuromorphic engineering; Photonics; Computer science; Exploit; Bandwidth (computing); Low latency (capital markets); Computer architecture; Latency (audio); Electronic engineering; Artificial neural network; Optoelectronics; Artificial intelligence; Telecommunications; Physics; Computer network; 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":[0.00009449943,0.00008857387,0.0001051167,0.00001974037,0.0001241503,0.0002105829,0.0005479931,0.00003742291,0.00007361342],"category_scores_gemma":[0.00001128688,0.00007224701,0.00006946462,0.0005067146,0.00001425647,0.0001606339,0.0005821914,0.0001727935,0.0000379452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007682268,"about_ca_system_score_gemma":0.00004333232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005282155,"about_ca_topic_score_gemma":0.000007743075,"domain_scores_codex":[0.9990256,0.0000614,0.0001361512,0.0003367353,0.0001398593,0.0003002193],"domain_scores_gemma":[0.9992289,0.00008333958,0.00003051529,0.0005048345,0.00006174017,0.00009069811],"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":[0.000006048868,0.000288663,0.002631874,0.00002985447,0.00007276468,0.004433021,0.0001287685,0.4107062,0.005504646,0.3322541,0.045319,0.1986251],"study_design_scores_gemma":[0.0001058129,0.00001729388,0.0006120057,0.000008626324,0.000001258682,0.0001574326,0.000002125369,0.9874421,0.001353997,0.0008344534,0.009353924,0.0001109302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02595309,0.000731225,0.9558019,0.002937638,0.001214825,0.00005503357,6.969676e-8,0.0003359075,0.0129703],"genre_scores_gemma":[0.9791381,0.00007237242,0.0159343,0.002671271,0.0001858095,0.000001976528,0.000001064559,0.00000726456,0.001987854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.953185,"threshold_uncertainty_score":0.2946147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02244756377372908,"score_gpt":0.2185331345173805,"score_spread":0.1960855707436514,"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."}}