{"id":"W4386590612","doi":"10.1002/lpor.202200360","title":"Analog Programmable‐Photonic Computation","year":2023,"lang":"en","type":"article","venue":"Laser & Photonics Review","topic":"Neural Networks and Reservoir Computing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Horizon 2020 Framework Programme; Generalitat Valenciana","keywords":"Photonics; Electronics; Neuromorphic engineering; Computer science; Computation; Digital electronics; Metamaterial; Electronic engineering; Field-programmable analog array; Analogue electronics; Computer architecture; Electronic circuit; Computer hardware; Electrical engineering; Digital signal processing; Engineering; Analog signal; Artificial intelligence; Analog multiplier; Physics","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":[0.0004192169,0.0003561137,0.0003751057,0.0005867444,0.0003676169,0.001649586,0.0007846255,0.0009952238,0.004228997],"category_scores_gemma":[0.0007343338,0.0001976894,0.0004071013,0.0003850927,0.002804684,0.002023015,0.0008350763,0.001753631,0.0008328664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124811,"about_ca_system_score_gemma":0.0006930201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006223266,"about_ca_topic_score_gemma":0.0003621765,"domain_scores_codex":[0.9996934,0.00007008283,0.00001634262,0.00006495984,0.0001204869,0.00003480477],"domain_scores_gemma":[0.9996499,0.0001788405,0.00002447479,0.00006873041,0.00006265355,0.00001545587],"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":[0.000008414135,0.000009423688,0.00003604396,0.0001197116,0.000007292313,0.00002640827,0.00001755828,0.003975367,0.001804121,0.9782566,0.001868639,0.01387055],"study_design_scores_gemma":[0.00001786065,0.0000513708,0.000114265,0.0001604354,0.00001188519,0.0001539212,0.00002472074,0.04881321,0.005591843,0.8459048,0.09913003,0.00002570671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02181925,0.03904451,0.6342167,0.009762162,0.002800445,0.0001677153,0.0003288787,0.0008077698,0.2910526],"genre_scores_gemma":[0.7839471,0.04338527,0.1280408,0.00299975,0.001560924,0.000398071,0.0002860915,0.0001227842,0.03925932],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004228997,"threshold_uncertainty_score":0.01414746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02761253589305939,"score_gpt":0.2979694758003302,"score_spread":0.2703569399072708,"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."}}