{"id":"W3104812046","doi":"10.1051/photon/202010440","title":"Silicon photonics for artificial intelligence applications","year":2020,"lang":"en","type":"article","venue":"Photoniques","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Neuromorphic engineering; Computer science; Artificial neural network; Computer architecture; Artificial intelligence; Photonics; Implementation; Domain (mathematical analysis); Efficient energy use; Silicon photonics; Applications of artificial intelligence; Engineering; Software engineering; Electrical engineering; Materials science","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":[0.0001211895,0.0001106851,0.0001345092,0.00002277465,0.0001672117,0.0001692668,0.0007742047,0.00004366409,0.00000684652],"category_scores_gemma":[0.00002566474,0.00009973624,0.00008882019,0.0002765078,0.00002991969,0.0001609499,0.0002110649,0.0001152203,0.00001714905],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001473875,"about_ca_system_score_gemma":0.00003941058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006934007,"about_ca_topic_score_gemma":0.000003646224,"domain_scores_codex":[0.9990116,0.00002329805,0.0002275525,0.0003802789,0.0001133596,0.0002439106],"domain_scores_gemma":[0.9993113,0.000136724,0.0000729756,0.000269426,0.00007992864,0.0001296178],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003704426,0.0001299826,0.00004802665,0.0001245761,0.00002981315,0.000006983303,0.001473884,0.005323105,0.02201738,0.5897484,0.001985937,0.3790748],"study_design_scores_gemma":[0.0000192835,0.00009071578,0.000003830871,0.000007003754,0.000002572273,0.000001920333,0.00001452704,0.7993242,0.1322685,0.03232646,0.03581469,0.0001262862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002078315,0.0001922148,0.9914103,0.004532385,0.00009069521,0.0006528904,0.000002381437,0.0003722506,0.0006685904],"genre_scores_gemma":[0.7614181,0.00007703717,0.2341625,0.0036265,0.0003211546,0.0003435348,0.000005350781,0.00001768852,0.00002809389],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.794001,"threshold_uncertainty_score":0.4067126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06421414134398601,"score_gpt":0.2993387228229221,"score_spread":0.2351245814789361,"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."}}