{"id":"W2944461851","doi":"10.23919/cleo.2019.8748940","title":"Multiwavelength Neuromorphic Photonics","year":2019,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Neuromorphic engineering; Photonics; Computer science; Electronics; Computer architecture; Efficient energy use; Electronic engineering; Artificial intelligence; Electrical engineering; Optoelectronics; Artificial neural network; Physics; 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":[0.0001320527,0.0002743534,0.0002331748,0.0003403445,0.0003453009,0.00108181,0.000689193,0.0007190648,0.00795806],"category_scores_gemma":[0.0004149689,0.0002444821,0.0002034012,0.0003065372,0.0004895374,0.001357032,0.0009578353,0.000663268,0.002263177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004828182,"about_ca_system_score_gemma":0.0002301608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000184541,"about_ca_topic_score_gemma":0.000273664,"domain_scores_codex":[0.9998142,0.00001782255,0.000008566298,0.00005060475,0.00008545861,0.00002336447],"domain_scores_gemma":[0.9998177,0.00004959917,0.00002575177,0.00004593044,0.00003730868,0.0000237739],"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":[0.0001422987,0.0001125088,0.0004849549,0.000629593,0.0000590427,0.000421781,0.00009668041,0.01400557,0.4957038,0.2277781,0.01252547,0.2480402],"study_design_scores_gemma":[0.00004250735,0.0002307947,0.001164465,0.0002207907,0.00004181929,0.0021194,0.0001056486,0.1970755,0.3486368,0.1794711,0.2707886,0.0001026628],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1298848,0.02713162,0.5776624,0.005652368,0.002962362,0.0001466173,0.0007204749,0.005066825,0.2507726],"genre_scores_gemma":[0.7499307,0.007801902,0.1879394,0.001322304,0.0004136449,0.0001546104,0.0002153614,0.0002523577,0.05196969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00795806,"threshold_uncertainty_score":0.02662235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02230223668043163,"score_gpt":0.2221634499000382,"score_spread":0.1998612132196066,"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."}}