{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001480369,0.0002936485,0.0002336036,0.0003997681,0.0004503341,0.001551989,0.0006959968,0.0007411202,0.009691529],"category_scores_gemma":[0.0009359742,0.0002150579,0.0001892147,0.0003872058,0.0005530767,0.001752057,0.001049691,0.0007996694,0.002560016],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005147066,"about_ca_system_score_gemma":0.000442428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002987102,"about_ca_topic_score_gemma":0.0004761808,"domain_scores_codex":[0.9998015,0.00002692998,0.000008674358,0.00004908574,0.0000873922,0.00002651214],"domain_scores_gemma":[0.9998098,0.00004482077,0.00001894693,0.00005168156,0.00005260141,0.00002218061],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006172014,0.00006398305,0.0003015753,0.0003423298,0.00004651866,0.0002135711,0.00008374533,0.01585887,0.0414896,0.7610251,0.02223542,0.1582776],"study_design_scores_gemma":[0.00003696796,0.0000723042,0.0004382338,0.0001345619,0.00002537049,0.0007060729,0.00007960755,0.1232509,0.0323701,0.5415713,0.3012772,0.0000373972],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04695485,0.01446589,0.5438375,0.007120511,0.003107086,0.000248776,0.0009228683,0.00242004,0.3809225],"genre_scores_gemma":[0.7288623,0.01268588,0.1388986,0.002632177,0.0008013338,0.0004941705,0.0007796582,0.0002118636,0.114634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009691529,"threshold_uncertainty_score":0.03242141,"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."}}